From 413a3e5cee577a09efe984774bd581551cc9d2ab Mon Sep 17 00:00:00 2001 From: Tzu-Mao Li Date: Thu, 3 Sep 2020 22:30:30 -0400 Subject: [PATCH] initial commit --- .gitignore | 10 + .gitmodules | 6 + CMakeLists.txt | 140 + README.md | 115 + aabb.h | 67 + apps/.gitignore | 3 + apps/Makefile | 10 + apps/curve_subdivision.py | 85 + apps/finite_difference_comp.py | 197 + apps/gaussian_blur.py | 93 + apps/generative_models/.gitignore | 1 + apps/generative_models/README.md | 5 + apps/generative_models/__init__.py | 0 apps/generative_models/data.py | 229 + apps/generative_models/eval_gan.py | 182 + apps/generative_models/losses.py | 99 + apps/generative_models/mnist_vae.py | 1026 + apps/generative_models/models.py | 484 + apps/generative_models/modules.py | 11 + apps/generative_models/rendering.py | 307 + apps/generative_models/sketch_rnn.py | 461 + apps/generative_models/sketch_vae.py | 524 + apps/generative_models/train_gan.py | 489 + apps/geometry.py | 226 + apps/image_compare.py | 45 + apps/imgs/baboon.png | Bin 0 -> 637192 bytes apps/imgs/baboon.svg | 8694 +++ apps/imgs/boston.svg | 1936 + apps/imgs/circle.svg | 12 + apps/imgs/contour.svg | 53256 ++++++++++++++++ apps/imgs/eleven_below_single.svg | 247 + apps/imgs/fallingwater.jpg | Bin 0 -> 332935 bytes apps/imgs/fallingwater.svg | 3457 + apps/imgs/flower.jpg | Bin 0 -> 335623 bytes apps/imgs/flower.svg | 3771 ++ apps/imgs/hawaii.svg | 1151 + apps/imgs/hokusai.png | Bin 0 -> 771662 bytes apps/imgs/johnny_automatic_flower_pot.svg | 376 + apps/imgs/kitty.jpg | Bin 0 -> 639810 bytes apps/imgs/kitty.svg | 3676 ++ apps/imgs/linux.svg | 711 + apps/imgs/male-face1.svg | 997 + apps/imgs/mcseem2.svg | 2191 + apps/imgs/note_small.svg | 25 + apps/imgs/peppers.svg | 1 + apps/imgs/peppers.tiff | Bin 0 -> 786572 bytes apps/imgs/reschart.svg | 761 + apps/imgs/scream.jpg | Bin 0 -> 98216 bytes apps/imgs/seamcarving/cat.svg | 355 + apps/imgs/seamcarving/hokusai.svg | 9840 +++ apps/imgs/seamcarving/ice_cream.svg | 353 + apps/imgs/seamcarving/license.txt | 7 + apps/imgs/seamcarving/seaside2.svg | 224 + apps/imgs/seamcarving/sunset2.svg | 899 + apps/imgs/seurat.jpg | Bin 0 -> 309542 bytes apps/imgs/shared_edge.svg | 13 + apps/imgs/starry_night.jpg | Bin 0 -> 1363698 bytes apps/imgs/tiger.svg | 317 + apps/optimize_pixel_filter.py | 115 + apps/painterly_rendering.py | 223 + apps/quadratic_distance_approx.py | 76 + apps/refine_svg.py | 115 + apps/render_svg.py | 41 + apps/seam_carving.py | 284 + apps/shared_edge_compare.py | 127 + apps/simple_transform_svg.py | 237 + apps/single_circle.py | 106 + apps/single_circle_outline.py | 118 + apps/single_circle_sdf.py | 114 + apps/single_circle_tf.py | 94 + apps/single_curve.py | 121 + apps/single_curve_outline.py | 132 + apps/single_curve_sdf.py | 125 + apps/single_curve_sdf_trans.py | 172 + apps/single_ellipse.py | 105 + apps/single_ellipse_transform.py | 108 + apps/single_gradient.py | 127 + apps/single_open_curve.py | 117 + apps/single_open_curve_thickness.py | 120 + apps/single_path.py | 99 + apps/single_path_sdf.py | 105 + apps/single_polygon.py | 108 + apps/single_rect.py | 102 + apps/single_stroke.py | 117 + apps/sketch_gan.py | 213 + apps/style_transfer.py | 291 + apps/svg_brush.py | 167 + apps/svg_parse_test.py | 63 + apps/test_eval_positions.py | 109 + apps/textureSyn/1.jpg | Bin 0 -> 16694 bytes apps/textureSyn/2.jpg | Bin 0 -> 26607 bytes apps/textureSyn/3.jpg | Bin 0 -> 30374 bytes apps/textureSyn/make_gif.py | 26 + apps/textureSyn/patchBasedTextureSynthesis.py | 388 + apps/textureSyn/traced_1.png | Bin 0 -> 74575 bytes apps/textureSyn/traced_1.svg | 1969 + apps/textureSyn/traced_2.png | Bin 0 -> 79575 bytes apps/textureSyn/traced_2.svg | 8204 +++ apps/textureSyn/traced_3.png | Bin 0 -> 125719 bytes apps/textureSyn/traced_3.svg | 13302 ++++ apps/texture_synthesis.py | 180 + atomic.cpp | 27 + atomic.h | 139 + cdf.h | 29 + cmake/FindTensorFlow.cmake | 34 + cmake/FindThrust.cmake | 40 + color.cpp | 25 + color.h | 63 + compute_distance.h | 949 + cuda_utils.h | 53 + diffvg.cpp | 1792 + diffvg.h | 156 + edge_query.h | 7 + filter.h | 106 + matrix.h | 544 + parallel.cpp | 273 + parallel.h | 91 + pcg.h | 40 + ptr.h | 23 + pydiffvg/__init__.py | 9 + pydiffvg/color.py | 24 + pydiffvg/device.py | 25 + pydiffvg/image.py | 22 + pydiffvg/optimize_svg.py | 1606 + pydiffvg/parse_svg.py | 578 + pydiffvg/pixel_filter.py | 9 + pydiffvg/render_pytorch.py | 870 + pydiffvg/save_svg.py | 150 + pydiffvg/shape.py | 172 + pydiffvg_tensorflow/__init__.py | 24 + pydiffvg_tensorflow/color.py | 23 + pydiffvg_tensorflow/custom_ops/CMakeLists.txt | 29 + pydiffvg_tensorflow/custom_ops/data_ptr.cc | 88 + pydiffvg_tensorflow/device.py | 59 + pydiffvg_tensorflow/image.py | 22 + pydiffvg_tensorflow/pixel_filter.py | 8 + pydiffvg_tensorflow/render_tensorflow.py | 649 + pydiffvg_tensorflow/shape.py | 53 + sample_boundary.h | 454 + scene.cpp | 1035 + scene.h | 120 + setup.py | 96 + shape.cpp | 22 + shape.h | 169 + solve.h | 59 + vector.h | 817 + winding_number.h | 202 + within_distance.h | 446 + 148 files changed, 138536 insertions(+) create mode 100644 .gitignore create mode 100644 .gitmodules create mode 100644 CMakeLists.txt create mode 100644 README.md create mode 100644 aabb.h create mode 100644 apps/.gitignore create mode 100644 apps/Makefile create mode 100644 apps/curve_subdivision.py create mode 100644 apps/finite_difference_comp.py create mode 100644 apps/gaussian_blur.py create mode 100644 apps/generative_models/.gitignore create mode 100644 apps/generative_models/README.md create mode 100644 apps/generative_models/__init__.py create mode 100644 apps/generative_models/data.py create mode 100644 apps/generative_models/eval_gan.py create mode 100644 apps/generative_models/losses.py create mode 100644 apps/generative_models/mnist_vae.py create mode 100644 apps/generative_models/models.py create mode 100644 apps/generative_models/modules.py create mode 100644 apps/generative_models/rendering.py create mode 100755 apps/generative_models/sketch_rnn.py create mode 100755 apps/generative_models/sketch_vae.py create mode 100755 apps/generative_models/train_gan.py create mode 100644 apps/geometry.py create mode 100644 apps/image_compare.py create mode 100644 apps/imgs/baboon.png create mode 100644 apps/imgs/baboon.svg create mode 100644 apps/imgs/boston.svg create mode 100644 apps/imgs/circle.svg create mode 100644 apps/imgs/contour.svg create mode 100644 apps/imgs/eleven_below_single.svg create mode 100644 apps/imgs/fallingwater.jpg create mode 100644 apps/imgs/fallingwater.svg create mode 100644 apps/imgs/flower.jpg create mode 100644 apps/imgs/flower.svg create mode 100644 apps/imgs/hawaii.svg create mode 100644 apps/imgs/hokusai.png create mode 100644 apps/imgs/johnny_automatic_flower_pot.svg create mode 100644 apps/imgs/kitty.jpg create mode 100644 apps/imgs/kitty.svg create mode 100644 apps/imgs/linux.svg create mode 100644 apps/imgs/male-face1.svg create mode 100644 apps/imgs/mcseem2.svg create mode 100644 apps/imgs/note_small.svg create mode 100644 apps/imgs/peppers.svg create mode 100644 apps/imgs/peppers.tiff create mode 100644 apps/imgs/reschart.svg create mode 100644 apps/imgs/scream.jpg create mode 100644 apps/imgs/seamcarving/cat.svg create mode 100644 apps/imgs/seamcarving/hokusai.svg create mode 100644 apps/imgs/seamcarving/ice_cream.svg create mode 100644 apps/imgs/seamcarving/license.txt create mode 100644 apps/imgs/seamcarving/seaside2.svg create mode 100644 apps/imgs/seamcarving/sunset2.svg create mode 100644 apps/imgs/seurat.jpg create mode 100644 apps/imgs/shared_edge.svg create mode 100644 apps/imgs/starry_night.jpg create mode 100644 apps/imgs/tiger.svg create mode 100644 apps/optimize_pixel_filter.py create mode 100644 apps/painterly_rendering.py create mode 100644 apps/quadratic_distance_approx.py create mode 100644 apps/refine_svg.py create mode 100644 apps/render_svg.py create mode 100644 apps/seam_carving.py create mode 100644 apps/shared_edge_compare.py create mode 100644 apps/simple_transform_svg.py create mode 100644 apps/single_circle.py create mode 100644 apps/single_circle_outline.py create mode 100644 apps/single_circle_sdf.py create mode 100644 apps/single_circle_tf.py create mode 100644 apps/single_curve.py create mode 100644 apps/single_curve_outline.py create mode 100644 apps/single_curve_sdf.py create mode 100644 apps/single_curve_sdf_trans.py create mode 100644 apps/single_ellipse.py create mode 100644 apps/single_ellipse_transform.py create mode 100644 apps/single_gradient.py create mode 100644 apps/single_open_curve.py create mode 100644 apps/single_open_curve_thickness.py create mode 100644 apps/single_path.py create mode 100644 apps/single_path_sdf.py create mode 100644 apps/single_polygon.py create mode 100644 apps/single_rect.py create mode 100644 apps/single_stroke.py create mode 100644 apps/sketch_gan.py create mode 100644 apps/style_transfer.py create mode 100644 apps/svg_brush.py create mode 100644 apps/svg_parse_test.py create mode 100644 apps/test_eval_positions.py create mode 100755 apps/textureSyn/1.jpg create mode 100755 apps/textureSyn/2.jpg create mode 100755 apps/textureSyn/3.jpg create mode 100644 apps/textureSyn/make_gif.py create mode 100755 apps/textureSyn/patchBasedTextureSynthesis.py create mode 100644 apps/textureSyn/traced_1.png create mode 100644 apps/textureSyn/traced_1.svg create mode 100644 apps/textureSyn/traced_2.png create mode 100644 apps/textureSyn/traced_2.svg create mode 100644 apps/textureSyn/traced_3.png create mode 100644 apps/textureSyn/traced_3.svg create mode 100644 apps/texture_synthesis.py create mode 100644 atomic.cpp create mode 100644 atomic.h create mode 100644 cdf.h create mode 100644 cmake/FindTensorFlow.cmake create mode 100644 cmake/FindThrust.cmake create mode 100644 color.cpp create mode 100644 color.h create mode 100644 compute_distance.h create mode 100644 cuda_utils.h create mode 100644 diffvg.cpp create mode 100644 diffvg.h create mode 100644 edge_query.h create mode 100644 filter.h create mode 100644 matrix.h create mode 100644 parallel.cpp create mode 100644 parallel.h create mode 100644 pcg.h create mode 100644 ptr.h create mode 100644 pydiffvg/__init__.py create mode 100644 pydiffvg/color.py create mode 100644 pydiffvg/device.py create mode 100644 pydiffvg/image.py create mode 100644 pydiffvg/optimize_svg.py create mode 100644 pydiffvg/parse_svg.py create mode 100644 pydiffvg/pixel_filter.py create mode 100644 pydiffvg/render_pytorch.py create mode 100644 pydiffvg/save_svg.py create mode 100644 pydiffvg/shape.py create mode 100644 pydiffvg_tensorflow/__init__.py create mode 100644 pydiffvg_tensorflow/color.py create mode 100644 pydiffvg_tensorflow/custom_ops/CMakeLists.txt create mode 100644 pydiffvg_tensorflow/custom_ops/data_ptr.cc create mode 100644 pydiffvg_tensorflow/device.py create mode 100644 pydiffvg_tensorflow/image.py create mode 100644 pydiffvg_tensorflow/pixel_filter.py create mode 100644 pydiffvg_tensorflow/render_tensorflow.py create mode 100644 pydiffvg_tensorflow/shape.py create mode 100644 sample_boundary.h create mode 100644 scene.cpp create mode 100644 scene.h create mode 100644 setup.py create mode 100644 shape.cpp create mode 100644 shape.h create mode 100644 solve.h create mode 100644 vector.h create mode 100644 winding_number.h create mode 100644 within_distance.h diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..bb2ce51 --- /dev/null +++ b/.gitignore @@ -0,0 +1,10 @@ +build +apps/results +apps/files +apps/__pycache__ +compile_commands.json +.vimrc +diffvg.egg-info +dist +__pycache__ +.DS_Store diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..30869ff --- /dev/null +++ b/.gitmodules @@ -0,0 +1,6 @@ +[submodule "pybind11"] + path = pybind11 + url = https://github.com/pybind/pybind11 +[submodule "thrust"] + path = thrust + url = https://github.com/thrust/thrust.git diff --git a/CMakeLists.txt b/CMakeLists.txt new file mode 100644 index 0000000..233e4be --- /dev/null +++ b/CMakeLists.txt @@ -0,0 +1,140 @@ +cmake_minimum_required(VERSION 3.12) + +project(diffvg VERSION 0.0.1 DESCRIPTION "Differentiable Vector Graphics") + +set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_SOURCE_DIR}/cmake/") +set(CMAKE_EXPORT_COMPILE_COMMANDS ON) + +if(WIN32) + find_package(Python 3.6 COMPONENTS Development REQUIRED) +else() + find_package(Python 3.7 COMPONENTS Development REQUIRED) +endif() +add_subdirectory(pybind11) + +option(DIFFVG_CUDA "Build diffvg with GPU code path?" ON) + +if(DIFFVG_CUDA) + message(STATUS "Build with CUDA support") + find_package(CUDA 10 REQUIRED) + set(CMAKE_CUDA_STANDARD 11) + if(NOT WIN32) + # Hack: for some reason the line above doesn't work on some Linux systems. + set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -std=c++11") + #set(CUDA_NVCC_FLAGS_DEBUG "-g -G") + endif() +else() + message(STATUS "Build without CUDA support") + find_package(Thrust REQUIRED) +endif() + +# include_directories(${CMAKE_SOURCE_DIR}/pybind11/include) +include_directories(${PYTHON_INCLUDE_PATH}) +find_package(PythonLibs REQUIRED) +include_directories(${PYTHON_INCLUDE_PATH}) +include_directories(${PYTHON_INCLUDE_DIRS}) +include_directories(pybind11/include) +if(DIFFVG_CUDA) + link_directories(${CUDA_LIBRARIES}) +else() + include_directories(${THRUST_INCLUDE_DIR}) +endif() + +if(NOT MSVC) + # These compile definitions are not meaningful for MSVC + add_compile_options(-Wall -g -O3 -fvisibility=hidden -Wno-unknown-pragmas) +else() + add_compile_options(/Wall /Zi) + add_link_options(/DEBUG) +endif() + +if(NOT DIFFVG_CUDA) + add_compile_options("-DTHRUST_DEVICE_SYSTEM=THRUST_DEVICE_SYSTEM_CPP") +endif() + +set(SRCS atomic.h + color.h + cdf.h + cuda_utils.h + diffvg.h + edge_query.h + filter.h + matrix.h + parallel.h + pcg.h + ptr.h + sample_boundary.h + scene.h + shape.h + solve.h + vector.h + within_distance.h + winding_number.h + atomic.cpp + color.cpp + diffvg.cpp + parallel.cpp + scene.cpp + shape.cpp) + +if(DIFFVG_CUDA) + add_compile_definitions(COMPILE_WITH_CUDA) + set_source_files_properties( + diffvg.cpp + scene.cpp + PROPERTIES CUDA_SOURCE_PROPERTY_FORMAT OBJ) + + cuda_add_library(diffvg MODULE ${SRCS}) +else() + add_library(diffvg MODULE ${SRCS}) +endif() + +if(APPLE) + # The "-undefined dynamic_lookup" is a hack for systems with + # multiple Python installed. If we link a particular Python version + # here, and we import it with a different Python version later. + # likely a segmentation fault. + # The solution for Linux Mac OS machines, as mentioned in + # https://github.com/pybind/pybind11/blob/master/tools/pybind11Tools.cmake + # is to not link against Python library at all and resolve the symbols + # at compile time. + set(DYNAMIC_LOOKUP "-undefined dynamic_lookup") +endif() + +target_link_libraries(diffvg ${DYNAMIC_LOOKUP}) + +if(WIN32) + # See: https://pybind11.readthedocs.io/en/master/compiling.html#advanced-interface-library-target + target_link_libraries(diffvg pybind11::module) + set_target_properties(diffvg PROPERTIES PREFIX "${PYTHON_MODULE_PREFIX}" + SUFFIX "${PYTHON_MODULE_EXTENSION}") +endif() + +set_target_properties(diffvg PROPERTIES SKIP_BUILD_RPATH FALSE) +set_target_properties(diffvg PROPERTIES BUILD_WITH_INSTALL_RPATH TRUE) +if(UNIX AND NOT APPLE) + set_target_properties(diffvg PROPERTIES INSTALL_RPATH "$ORIGIN") +elseif(APPLE) + set_target_properties(diffvg PROPERTIES INSTALL_RPATH "@loader_path") +endif() + +set_property(TARGET diffvg PROPERTY CXX_STANDARD 11) +set_target_properties(diffvg PROPERTIES PREFIX "") +# Still enable assertion in release mode +string( REPLACE "/DNDEBUG" "" CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE}") +string( REPLACE "-DNDEBUG" "" CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE}") +string( REPLACE "/DNDEBUG" "" CMAKE_CXX_FLAGS_RELWITHDEBINFO "${CMAKE_CXX_FLAGS_RELWITHDEBINFO}") +string( REPLACE "-DNDEBUG" "" CMAKE_CXX_FLAGS_RELWITHDEBINFO "${CMAKE_CXX_FLAGS_RELWITHDEBINFO}") +string( REPLACE "/DNDEBUG" "" CMAKE_C_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE}") +string( REPLACE "-DNDEBUG" "" CMAKE_C_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE}") +string( REPLACE "/DNDEBUG" "" CMAKE_C_FLAGS_RELWITHDEBINFO "${CMAKE_CXX_FLAGS_RELWITHDEBINFO}") +string( REPLACE "-DNDEBUG" "" CMAKE_C_FLAGS_RELWITHDEBINFO "${CMAKE_CXX_FLAGS_RELWITHDEBINFO}") + +if(NOT WIN32) + find_package(TensorFlow) + if(TensorFlow_FOUND) + add_subdirectory(pydiffvg_tensorflow/custom_ops) + else() + message(INFO " Building without TensorFlow support (not found)") + endif() +endif() diff --git a/README.md b/README.md new file mode 100644 index 0000000..1cbe3e9 --- /dev/null +++ b/README.md @@ -0,0 +1,115 @@ +# diffvg +Differentiable Rasterizer for Vector Graphics +https://people.csail.mit.edu/tzumao/diffvg + +diffvg is a differentiable rasterizer for 2D vector graphics. See the webpage for more info. + +![teaser](https://user-images.githubusercontent.com/951021/92184822-2a0bc500-ee20-11ea-81a6-f26af2d120f4.jpg) + +![circle](https://user-images.githubusercontent.com/951021/63556018-0b2ddf80-c4f8-11e9-849c-b4ecfcb9a865.gif) +![ellipse](https://user-images.githubusercontent.com/951021/63556021-0ec16680-c4f8-11e9-8fc6-8b34de45b8be.gif) +![rect](https://user-images.githubusercontent.com/951021/63556028-12ed8400-c4f8-11e9-8072-81702c9193e1.gif) +![polygon](https://user-images.githubusercontent.com/951021/63980999-1e99f700-ca72-11e9-9786-1cba14d2d862.gif) +![curve](https://user-images.githubusercontent.com/951021/64042667-3d9e9480-cb17-11e9-88d8-2f7b9da8b8ab.gif) +![path](https://user-images.githubusercontent.com/951021/64070625-7a52b480-cc19-11e9-9380-eac02f56f693.gif) +![gradient](https://user-images.githubusercontent.com/951021/64898668-da475300-d63c-11e9-917a-825b94be0710.gif) +![circle_outline](https://user-images.githubusercontent.com/951021/65125594-84f7a280-d9aa-11e9-8bc4-669fd2eff2f4.gif) +![ellipse_transform](https://user-images.githubusercontent.com/951021/67149013-06b54700-f25b-11e9-91eb-a61171c6d4a4.gif) + +# Install +``` +git submodule update --init --recursive +conda install pytorch torchvision -c pytorch +conda install numpy +conda install scikit-image +pip install svgwrite +pip install svgpathtools +pip install cssutils +pip install numba +pip install torch-tools +pip install visdom +python setup.py install +``` + +# Building in debug mode + +``` +python setup.py build --debug install +``` + +# Run +``` +cd apps +``` + +Optimizing a single circle to a target. +``` +python single_circle.py +``` + +Finite difference comparison. +``` +finite_difference_comp.py [-h] [--size_scale SIZE_SCALE] + [--clamping_factor CLAMPING_FACTOR] + [--use_prefiltering USE_PREFILTERING] + svg_file +``` +e.g., +``` +python finite_difference_comp.py imgs/tiger.svg +``` + +Interactive editor +``` +python svg_brush.py +``` + +Painterly rendering +``` +painterly_rendering.py [-h] [--num_paths NUM_PATHS] + [--max_width MAX_WIDTH] [--use_lpips_loss] + [--num_iter NUM_ITER] [--use_blob] + target +``` +e.g., +``` +python painterly_rendering.py imgs/fallingwater.jpg --num_paths 2048 --max_width 4.0 --use_lpips_loss +``` + +Image vectorization +``` +python refine_svg.py [-h] [--use_lpips_loss] [--num_iter NUM_ITER] svg target +``` +e.g., +``` +python refine_svg.py imgs/flower.svg imgs/flower.jpg +``` + +Seam carving +``` +python seam_carving.py [-h] [--svg SVG] [--optim_steps OPTIM_STEPS] +``` +e.g., +``` +python seam_carving.py imgs/hokusai.svg +``` + +Vector variational autoencoder & vector GAN: + +For the GAN models, see `apps/generative_models/train_gan.py`. Generate samples from a pretrained using `apps/generative_models/eval_gan.py`. + +For the VAE models, see `apps/generative_models/mnist_vae.py`. + +If you use diffvg in your academic work, please cite + +``` +@article{Li:2020:DVG, + title = {Differentiable Vector Graphics Rasterization for Editing and Learning}, + author = {Li, Tzu-Mao and Luk\'{a}\v{c}, Michal and Gharbi Micha\"{e}l and Jonathan Ragan-Kelley}, + journal = {ACM Trans. Graph. (Proc. SIGGRAPH Asia)}, + volume = {39}, + number = {6}, + pages = {193:1--193:15}, + year = {2020} +} +``` diff --git a/aabb.h b/aabb.h new file mode 100644 index 0000000..c35968e --- /dev/null +++ b/aabb.h @@ -0,0 +1,67 @@ +#pragma once + +#include "diffvg.h" +#include "cuda_utils.h" +#include "vector.h" +#include "matrix.h" + +struct AABB { + DEVICE + inline AABB(const Vector2f &p_min = Vector2f{infinity(), infinity()}, + const Vector2f &p_max = Vector2f{-infinity(), -infinity()}) + : p_min(p_min), p_max(p_max) {} + Vector2f p_min, p_max; +}; + +DEVICE +inline +AABB merge(const AABB &box, const Vector2f &p) { + return AABB{Vector2f{min(p.x, box.p_min.x), min(p.y, box.p_min.y)}, + Vector2f{max(p.x, box.p_max.x), max(p.y, box.p_max.y)}}; +} + +DEVICE +inline +AABB merge(const AABB &box0, const AABB &box1) { + return AABB{Vector2f{min(box0.p_min.x, box1.p_min.x), min(box0.p_min.y, box1.p_min.y)}, + Vector2f{max(box0.p_max.x, box1.p_max.x), max(box0.p_max.y, box1.p_max.y)}}; +} + +DEVICE +inline +bool inside(const AABB &box, const Vector2f &p) { + return p.x >= box.p_min.x && p.x <= box.p_max.x && + p.y >= box.p_min.y && p.y <= box.p_max.y; +} + +DEVICE +inline +bool inside(const AABB &box, const Vector2f &p, float radius) { + return p.x >= box.p_min.x - radius && p.x <= box.p_max.x + radius && + p.y >= box.p_min.y - radius && p.y <= box.p_max.y + radius; +} + +DEVICE +inline +AABB enlarge(const AABB &box, float width) { + return AABB{Vector2f{box.p_min.x - width, box.p_min.y - width}, + Vector2f{box.p_max.x + width, box.p_max.y + width}}; +} + +DEVICE +inline +AABB transform(const Matrix3x3f &xform, const AABB &box) { + auto ret = AABB(); + ret = merge(ret, xform_pt(xform, Vector2f{box.p_min.x, box.p_min.y})); + ret = merge(ret, xform_pt(xform, Vector2f{box.p_min.x, box.p_max.y})); + ret = merge(ret, xform_pt(xform, Vector2f{box.p_max.x, box.p_min.y})); + ret = merge(ret, xform_pt(xform, Vector2f{box.p_max.x, box.p_max.y})); + return ret; +} + +DEVICE +inline +bool within_distance(const AABB &box, const Vector2f &pt, float r) { + return pt.x >= box.p_min.x - r && pt.x <= box.p_max.x + r && + pt.y >= box.p_min.y - r && pt.y <= box.p_max.y + r; +} diff --git a/apps/.gitignore b/apps/.gitignore new file mode 100644 index 0000000..fe18dd3 --- /dev/null +++ b/apps/.gitignore @@ -0,0 +1,3 @@ +mnist +data/sketchrnn_cat.npz +data diff --git a/apps/Makefile b/apps/Makefile new file mode 100644 index 0000000..ecc25e4 --- /dev/null +++ b/apps/Makefile @@ -0,0 +1,10 @@ +SEAM_IMAGES=seaside2 sunset2 hokusai cat ice_cream +SEAM_OUT=results/seam_carving +SEAM_RESULTS=$(addsuffix /out.mp4,$(addprefix $(SEAM_OUT)/,$(SEAM_IMAGES))) + +all: $(SEAM_RESULTS) + echo $(SEAM_RESULTS) + +$(SEAM_OUT)/%/out.mp4: imgs/seamcarving/%.svg + python seam_carving.py --svg $^ + diff --git a/apps/curve_subdivision.py b/apps/curve_subdivision.py new file mode 100644 index 0000000..7f03df1 --- /dev/null +++ b/apps/curve_subdivision.py @@ -0,0 +1,85 @@ +import svgpathtools +import numpy as np +import math + +def split_cubic(c, t): + c0, c1 = svgpathtools.split_bezier(c, t) + return svgpathtools.CubicBezier(c0[0], c0[1], c0[2], c0[3]), svgpathtools.CubicBezier(c1[0], c1[1], c1[2], c1[3]) + +def cubic_to_quadratic(curve): + # Best L2 approximation + m = (-curve.start + 3 * curve.control1 + 3 * curve.control2 - curve.end) / 4.0 + return svgpathtools.QuadraticBezier(curve.start, m, curve.end) + +def convert_and_write_svg(cubic, filename): + cubic_path = svgpathtools.Path(cubic) + cubic_ctrl = svgpathtools.Path(svgpathtools.Line(cubic.start, cubic.control1), + svgpathtools.Line(cubic.control1, cubic.control2), + svgpathtools.Line(cubic.control2, cubic.end)) + cubic_color = (50, 50, 200) + cubic_ctrl_color = (150, 150, 150) + + r = 4.0 + + paths = [cubic_path, cubic_ctrl] + colors = [cubic_color, cubic_ctrl_color] + dots = [cubic_path[0].start, cubic_path[0].control1, cubic_path[0].control2, cubic_path[0].end] + ncols = ['green', 'green', 'green', 'green'] + nradii = [r, r, r, r] + stroke_widths = [3.0, 1.5] + + def add_quadratic(q): + paths.append(q) + q_ctrl = svgpathtools.Path(svgpathtools.Line(q.start, q.control), + svgpathtools.Line(q.control, q.end)) + paths.append(q_ctrl) + colors.append((200, 50, 50)) # q_color + colors.append((150, 150, 150)) # q_ctrl_color + dots.append(q.start) + dots.append(q.control) + dots.append(q.end) + ncols.append('purple') + ncols.append('purple') + ncols.append('purple') + nradii.append(r) + nradii.append(r) + nradii.append(r) + stroke_widths.append(3.0) + stroke_widths.append(1.5) + + prec = 1.0 + queue = [cubic] + num_quadratics = 0 + while len(queue) > 0: + c = queue[-1] + queue = queue[:-1] + + # Criteria for conversion + # http://caffeineowl.com/graphics/2d/vectorial/cubic2quad01.html + p = c.end - 3 * c.control2 + 3 * c.control1 - c.start + d = math.sqrt(p.real * p.real + p.imag * p.imag) * math.sqrt(3.0) / 36 + t = math.pow(1.0 / d, 1.0 / 3.0) + + if t < 1.0: + c0, c1 = split_cubic(c, 0.5) + queue.append(c0) + queue.append(c1) + else: + quadratic = cubic_to_quadratic(c) + print(quadratic) + add_quadratic(quadratic) + num_quadratics += 1 + print('num_quadratics:', num_quadratics) + + svgpathtools.wsvg(paths, + colors = colors, + stroke_widths = stroke_widths, + nodes = dots, + node_colors = ncols, + node_radii = nradii, + filename = filename) + +convert_and_write_svg(svgpathtools.CubicBezier(100+200j, 426+50j, 50+50j, 300+200j), + 'results/curve_subdivision/subdiv_curve0.svg') +convert_and_write_svg(svgpathtools.CubicBezier(100+200j, 427+50j, 50+50j, 300+200j), + 'results/curve_subdivision/subdiv_curve1.svg') diff --git a/apps/finite_difference_comp.py b/apps/finite_difference_comp.py new file mode 100644 index 0000000..331f6d4 --- /dev/null +++ b/apps/finite_difference_comp.py @@ -0,0 +1,197 @@ +# python finite_difference_comp.py imgs/tiger.svg +# python finite_difference_comp.py --use_prefiltering True imgs/tiger.svg +# python finite_difference_comp.py imgs/boston.svg +# python finite_difference_comp.py --use_prefiltering True imgs/boston.svg +# python finite_difference_comp.py imgs/contour.svg +# python finite_difference_comp.py --use_prefiltering True imgs/contour.svg +# python finite_difference_comp.py --size_scale 0.5 --clamping_factor 0.05 imgs/hawaii.svg +# python finite_difference_comp.py --size_scale 0.5 --clamping_factor 0.05 --use_prefiltering True imgs/hawaii.svg +# python finite_difference_comp.py imgs/mcseem2.svg +# python finite_difference_comp.py --use_prefiltering True imgs/mcseem2.svg +# python finite_difference_comp.py imgs/reschart.svg +# python finite_difference_comp.py --use_prefiltering True imgs/reschart.svg + +import pydiffvg +import diffvg +from matplotlib import cm +import matplotlib.pyplot as plt +import argparse +import torch + +pydiffvg.set_print_timing(True) +#pydiffvg.set_use_gpu(False) + +def normalize(x, min_, max_): + range = max(abs(min_), abs(max_)) + return (x + range) / (2 * range) + +def main(args): + canvas_width, canvas_height, shapes, shape_groups = \ + pydiffvg.svg_to_scene(args.svg_file) + + w = int(canvas_width * args.size_scale) + h = int(canvas_height * args.size_scale) + + print(w, h) + curve_counts = 0 + for s in shapes: + if isinstance(s, pydiffvg.Circle): + curve_counts += 1 + elif isinstance(s, pydiffvg.Ellipse): + curve_counts += 1 + elif isinstance(s, pydiffvg.Path): + curve_counts += len(s.num_control_points) + elif isinstance(s, pydiffvg.Polygon): + curve_counts += len(s.points) - 1 + if s.is_closed: + curve_counts += 1 + elif isinstance(s, pydiffvg.Rect): + curve_counts += 1 + print('curve_counts:', curve_counts) + + pfilter = pydiffvg.PixelFilter(type = diffvg.FilterType.box, + radius = torch.tensor(0.5)) + + use_prefiltering = args.use_prefiltering + print('use_prefiltering:', use_prefiltering) + + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + filter = pfilter, + use_prefiltering = use_prefiltering) + + num_samples_x = args.num_spp + num_samples_y = args.num_spp + if (use_prefiltering): + num_samples_x = 1 + num_samples_y = 1 + + render = pydiffvg.RenderFunction.apply + img = render(w, # width + h, # height + num_samples_x, # num_samples_x + num_samples_y, # num_samples_y + 0, # seed + None, # background_image + *scene_args) + pydiffvg.imwrite(img.cpu(), 'results/finite_difference_comp/img.png', gamma=1.0) + + epsilon = 0.1 + def perturb_scene(axis, epsilon): + for s in shapes: + if isinstance(s, pydiffvg.Circle): + s.center[axis] += epsilon + elif isinstance(s, pydiffvg.Ellipse): + s.center[axis] += epsilon + elif isinstance(s, pydiffvg.Path): + s.points[:, axis] += epsilon + elif isinstance(s, pydiffvg.Polygon): + s.points[:, axis] += epsilon + elif isinstance(s, pydiffvg.Rect): + s.p_min[axis] += epsilon + s.p_max[axis] += epsilon + for s in shape_groups: + if isinstance(s.fill_color, pydiffvg.LinearGradient): + s.fill_color.begin[axis] += epsilon + s.fill_color.end[axis] += epsilon + + perturb_scene(0, epsilon) + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + filter = pfilter, + use_prefiltering = use_prefiltering) + render = pydiffvg.RenderFunction.apply + img0 = render(w, # width + h, # height + num_samples_x, # num_samples_x + num_samples_y, # num_samples_y + 0, # seed + None, # background_image + *scene_args) + + perturb_scene(0, -2 * epsilon) + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + filter = pfilter, + use_prefiltering = use_prefiltering) + img1 = render(w, # width + h, # height + num_samples_x, # num_samples_x + num_samples_y, # num_samples_y + 0, # seed + None, # background_image + *scene_args) + x_diff = (img0 - img1) / (2 * epsilon) + x_diff = x_diff.sum(axis = 2) + x_diff_max = x_diff.max() * args.clamping_factor + x_diff_min = x_diff.min() * args.clamping_factor + print(x_diff.max()) + print(x_diff.min()) + x_diff = cm.viridis(normalize(x_diff, x_diff_min, x_diff_max).cpu().numpy()) + pydiffvg.imwrite(x_diff, 'results/finite_difference_comp/finite_x_diff.png', gamma=1.0) + + perturb_scene(0, epsilon) + + perturb_scene(1, epsilon) + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + filter = pfilter, + use_prefiltering = use_prefiltering) + render = pydiffvg.RenderFunction.apply + img0 = render(w, # width + h, # height + num_samples_x, # num_samples_x + num_samples_y, # num_samples_y + 0, # seed + None, # background_image + *scene_args) + + perturb_scene(1, -2 * epsilon) + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + filter = pfilter, + use_prefiltering = use_prefiltering) + img1 = render(w, # width + h, # height + num_samples_x, # num_samples_x + num_samples_y, # num_samples_y + 0, # seed + None, # background_image + *scene_args) + y_diff = (img0 - img1) / (2 * epsilon) + y_diff = y_diff.sum(axis = 2) + y_diff_max = y_diff.max() * args.clamping_factor + y_diff_min = y_diff.min() * args.clamping_factor + y_diff = cm.viridis(normalize(y_diff, y_diff_min, y_diff_max).cpu().numpy()) + pydiffvg.imwrite(y_diff, 'results/finite_difference_comp/finite_y_diff.png', gamma=1.0) + perturb_scene(1, epsilon) + + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + filter = pfilter, + use_prefiltering = use_prefiltering) + render_grad = pydiffvg.RenderFunction.render_grad + img_grad = render_grad(torch.ones(h, w, 4, device = pydiffvg.get_device()), + w, # width + h, # height + num_samples_x, # num_samples_x + num_samples_y, # num_samples_y + 0, # seed + None, # background_image + *scene_args) + print(img_grad[:, :, 0].max()) + print(img_grad[:, :, 0].min()) + x_diff = cm.viridis(normalize(img_grad[:, :, 0], x_diff_min, x_diff_max).cpu().numpy()) + y_diff = cm.viridis(normalize(img_grad[:, :, 1], y_diff_min, y_diff_max).cpu().numpy()) + pydiffvg.imwrite(x_diff, 'results/finite_difference_comp/ours_x_diff.png', gamma=1.0) + pydiffvg.imwrite(y_diff, 'results/finite_difference_comp/ours_y_diff.png', gamma=1.0) + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("svg_file", help="source SVG path") + parser.add_argument("--size_scale", type=float, default=1.0) + parser.add_argument("--clamping_factor", type=float, default=0.1) + parser.add_argument("--num_spp", type=int, default=4) + parser.add_argument("--use_prefiltering", type=bool, default=False) + args = parser.parse_args() + main(args) diff --git a/apps/gaussian_blur.py b/apps/gaussian_blur.py new file mode 100644 index 0000000..8d14802 --- /dev/null +++ b/apps/gaussian_blur.py @@ -0,0 +1,93 @@ +""" +""" +import os +import pydiffvg +import torch as th +import scipy.ndimage.filters as F + + +def render(canvas_width, canvas_height, shapes, shape_groups): + _render = pydiffvg.RenderFunction.apply + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + img = _render(canvas_width, # width + canvas_height, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + None, + *scene_args) + return img + + +def main(): + pydiffvg.set_device(th.device('cuda:1')) + + # Load SVG + svg = os.path.join("imgs", "peppers.svg") + canvas_width, canvas_height, shapes, shape_groups = \ + pydiffvg.svg_to_scene(svg) + + # Save initial state + ref = render(canvas_width, canvas_height, shapes, shape_groups) + pydiffvg.imwrite(ref.cpu(), 'results/gaussian_blur/init.png', gamma=2.2) + + target = F.gaussian_filter(ref.cpu().numpy(), [10, 10, 0]) + target = th.from_numpy(target).to(ref.device) + pydiffvg.imwrite(target.cpu(), 'results/gaussian_blur/target.png', gamma=2.2) + + # Collect variables to optimize + points_vars = [] + width_vars = [] + for path in shapes: + path.points.requires_grad = True + points_vars.append(path.points) + path.stroke_width.requires_grad = True + width_vars.append(path.stroke_width) + color_vars = [] + for group in shape_groups: + # do not optimize alpha + group.fill_color[..., :3].requires_grad = True + color_vars.append(group.fill_color) + + # Optimize + points_optim = th.optim.Adam(points_vars, lr=1.0) + width_optim = th.optim.Adam(width_vars, lr=1.0) + color_optim = th.optim.Adam(color_vars, lr=0.01) + + for t in range(20): + print('\niteration:', t) + points_optim.zero_grad() + width_optim.zero_grad() + color_optim.zero_grad() + # Forward pass: render the image. + img = render(canvas_width, canvas_height, shapes, shape_groups) + # Save the intermediate render. + pydiffvg.imwrite(img.cpu(), 'results/gaussian_blur/iter_{}.png'.format(t), gamma=2.2) + loss = (img - target)[..., :3].pow(2).mean() + + print('alpha:', img[..., 3].mean().item()) + print('render loss:', loss.item()) + + # Backpropagate the gradients. + loss.backward() + + # Take a gradient descent step. + points_optim.step() + width_optim.step() + color_optim.step() + for group in shape_groups: + group.fill_color.data.clamp_(0.0, 1.0) + + # Final render + img = render(canvas_width, canvas_height, shapes, shape_groups) + pydiffvg.imwrite(img.cpu(), 'results/gaussian_blur/final.png', gamma=2.2) + + # Convert the intermediate renderings to a video. + from subprocess import call + call(["ffmpeg", "-framerate", "24", "-i", + "results/gaussian_blur/iter_%d.png", "-vb", "20M", + "results/gaussian_blur/out.mp4"]) + +if __name__ == "__main__": + main() diff --git a/apps/generative_models/.gitignore b/apps/generative_models/.gitignore new file mode 100644 index 0000000..78fb1fc --- /dev/null +++ b/apps/generative_models/.gitignore @@ -0,0 +1 @@ +.gdb_history diff --git a/apps/generative_models/README.md b/apps/generative_models/README.md new file mode 100644 index 0000000..6b987ee --- /dev/null +++ b/apps/generative_models/README.md @@ -0,0 +1,5 @@ +# Usage + +For the GAN models, see `train_gan.py`. Generate samples from a pretrained using `eval_gan.py` + +For the VAE models, see `mnist_vae.py`. diff --git a/apps/generative_models/__init__.py b/apps/generative_models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/apps/generative_models/data.py b/apps/generative_models/data.py new file mode 100644 index 0000000..bdbac65 --- /dev/null +++ b/apps/generative_models/data.py @@ -0,0 +1,229 @@ +import os +import time +import torch as th +import numpy as np +import torchvision.datasets as dset +import torchvision.transforms as transforms +import imageio + +import ttools +import rendering + +BASE_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), os.pardir) +DATA = os.path.join(BASE_DIR, "data") + +LOG = ttools.get_logger(__name__) + + +class QuickDrawImageDataset(th.utils.data.Dataset): + BASE_DATA_URL = \ + "https://console.cloud.google.com/storage/browser/_details/quickdraw_dataset/full/numpy_bitmap/cat.npy" + """ + Args: + spatial_limit(int): maximum spatial extent in pixels. + """ + def __init__(self, imsize, train=True): + super(QuickDrawImageDataset, self).__init__() + file = os.path.join(DATA, "cat.npy") + + self.imsize = imsize + + if not os.path.exists(file): + msg = "Dataset file %s does not exist, please download" + " it from %s" % (file, QuickDrawImageDataset.BASE_DATA_URL) + LOG.error(msg) + raise RuntimeError(msg) + + self.data = np.load(file, allow_pickle=True, encoding="latin1") + + def __len__(self): + return self.data.shape[0] + + def __getitem__(self, idx): + im = np.reshape(self.data[idx], (1, 1, 28, 28)) + im = th.from_numpy(im).float() / 255.0 + im = th.nn.functional.interpolate(im, size=(self.imsize, self.imsize)) + + # Bring it to [-1, 1] + im = th.clamp(im, 0, 1) + im -= 0.5 + im /= 0.5 + + return im.squeeze(0) + + +class QuickDrawDataset(th.utils.data.Dataset): + BASE_DATA_URL = \ + "https://storage.cloud.google.com/quickdraw_dataset/sketchrnn" + + """ + Args: + spatial_limit(int): maximum spatial extent in pixels. + """ + def __init__(self, dataset, mode="train", + max_seq_length=250, + spatial_limit=1000): + super(QuickDrawDataset, self).__init__() + file = os.path.join(DATA, "sketchrnn_"+dataset) + remote = os.path.join(QuickDrawDataset.BASE_DATA_URL, dataset) + + self.max_seq_length = max_seq_length + self.spatial_limit = spatial_limit + + if mode not in ["train", "test", "valid"]: + return ValueError("Only allowed data mode are 'train' and 'test'," + " 'valid'.") + + if not os.path.exists(file): + msg = "Dataset file %s does not exist, please download" + " it from %s" % (file, remote) + LOG.error(msg) + raise RuntimeError(msg) + + data = np.load(file, allow_pickle=True, encoding="latin1")[mode] + data = self.purify(data) + data = self.normalize(data) + + # Length of longest sequence in the dataset + self.nmax = max([len(seq) for seq in data]) + self.sketches = data + + def __repr__(self): + return "Dataset with %d sequences of max length %d" % \ + (len(self.sketches), self.nmax) + + def __len__(self): + return len(self.sketches) + + def __getitem__(self, idx): + """Return the idx-th stroke in 5-D format, padded to length (Nmax+2). + + The first and last element of the sequence are fixed to "start-" and + "end-of-sequence" token. + + dx, dy, + 3 numbers for one-hot encoding of state: + 1 0 0: pen touching paper till next point + 0 1 0: pen lifted from paper after current point + 0 0 1: drawing has ended, next points (including current will not be + drawn) + """ + sample_data = self.sketches[idx] + + # Allow two extra slots for start/end of sequence tokens + sample = np.zeros((self.nmax+2, 5), dtype=np.float32) + + n = sample_data.shape[0] + + # normalize dx, dy + deltas = sample_data[:, :2] + # Absolute coordinates + positions = deltas[..., :2].cumsum(0) + maxi = np.abs(positions).max() + 1e-8 + deltas = deltas / (1.1 * maxi) # leave some margin on edges + + # fill in dx, dy coordinates + sample[1:n+1, :2] = deltas + + # on paper indicator: 0 means touching paper in the 3d format, flip it + sample[1:n+1, 2] = 1 - sample_data[:, 2] + + # off-paper indicator, complement of previous flag + sample[1:n+1, 3] = 1 - sample[1:n+1, 2] + + # fill with end of sequence tokens for the remainder + sample[n+1:, 4] = 1 + + # Start of sequence token + sample[0] = [0, 0, 1, 0, 0] + + return sample + + def purify(self, strokes): + """removes to small or too long sequences + removes large gaps""" + data = [] + for seq in strokes: + if seq.shape[0] <= self.max_seq_length: + # and seq.shape[0] > 10: + + # Limit large spatial gaps + seq = np.minimum(seq, self.spatial_limit) + seq = np.maximum(seq, -self.spatial_limit) + seq = np.array(seq, dtype=np.float32) + data.append(seq) + return data + + def calculate_normalizing_scale_factor(self, strokes): + """Calculate the normalizing factor explained in appendix of + sketch-rnn.""" + data = [] + for i, stroke_i in enumerate(strokes): + for j, pt in enumerate(strokes[i]): + data.append(pt[0]) + data.append(pt[1]) + data = np.array(data) + return np.std(data) + + def normalize(self, strokes): + """Normalize entire dataset (delta_x, delta_y) by the scaling + factor.""" + data = [] + scale_factor = self.calculate_normalizing_scale_factor(strokes) + for seq in strokes: + seq[:, 0:2] /= scale_factor + data.append(seq) + return data + + +class FixedLengthQuickDrawDataset(QuickDrawDataset): + """A variant of the QuickDraw dataset where the strokes are represented as + a fixed-length sequence of triplets (dx, dy, opacity), where opacity = 0, 1. + """ + def __init__(self, *args, canvas_size=64, **kwargs): + super(FixedLengthQuickDrawDataset, self).__init__(*args, **kwargs) + self.canvas_size = canvas_size + + def __getitem__(self, idx): + sample = super(FixedLengthQuickDrawDataset, self).__getitem__(idx) + + # We construct a stroke opacity variable from the pen down state, dx, dy remain unchanged + strokes = sample[:, :3] + + im = np.zeros((1, 1)) + + # render image + # start = time.time() + im = rendering.opacityStroke2diffvg( + th.from_numpy(strokes).unsqueeze(0), canvas_size=self.canvas_size, + relative=True, debug=False) + im = im.squeeze(0).numpy() + # elapsed = (time.time() - start)*1000 + # print("item %d pipeline gt rendering took %.2fms" % (idx, elapsed)) + + return strokes, im + + +class MNISTDataset(th.utils.data.Dataset): + def __init__(self, imsize, train=True): + super(MNISTDataset, self).__init__() + self.mnist = dset.MNIST(root=os.path.join(DATA, "mnist"), + train=train, + download=True, + transform=transforms.Compose([ + transforms.Resize((imsize, imsize)), + transforms.ToTensor(), + ])) + + def __len__(self): + return len(self.mnist) + + def __getitem__(self, idx): + im, label = self.mnist[idx] + + # make sure data uses [0, 1] range + im -= im.min() + im /= im.max() + 1e-8 + + # Bring it to [-1, 1] + im -= 0.5 + im /= 0.5 + return im diff --git a/apps/generative_models/eval_gan.py b/apps/generative_models/eval_gan.py new file mode 100644 index 0000000..f415a51 --- /dev/null +++ b/apps/generative_models/eval_gan.py @@ -0,0 +1,182 @@ +"""Evaluate a pretrained GAN model. +Usage: + +`python eval_gan.py `, e.g. +`../results/quickdraw_gan_vector_bezier_fc_wgan`. + +""" +import os +import argparse +import torch as th +import numpy as np +import ttools +import imageio +from subprocess import call + +import pydiffvg + +import models + + +LOG = ttools.get_logger(__name__) + + +def postprocess(im, invert=False): + im = th.clamp((im + 1.0) / 2.0, 0, 1) + if invert: + im = (1.0 - im) + im = ttools.tensor2image(im) + return im + + +def imsave(im, path): + os.makedirs(os.path.dirname(path), exist_ok=True) + imageio.imwrite(path, im) + + +def save_scene(scn, path): + os.makedirs(os.path.dirname(path), exist_ok=True) + pydiffvg.save_svg(path, *scn, use_gamma=False) + + +def run(args): + th.manual_seed(0) + np.random.seed(0) + + meta = ttools.Checkpointer.load_meta(args.model, "vect_g_") + + if meta is None: + LOG.warning("Could not load metadata at %s, aborting.", args.model) + return + + LOG.info("Loaded model %s with metadata:\n %s", args.model, meta) + + if args.output_dir is None: + outdir = os.path.join(args.model, "eval") + else: + outdir = args.output_dir + os.makedirs(outdir, exist_ok=True) + + model_params = meta["model_params"] + if args.imsize is not None: + LOG.info("Overriding output image size to: %dx%d", args.imsize, + args.imsize) + old_size = model_params["imsize"] + scale = args.imsize * 1.0 / old_size + model_params["imsize"] = args.imsize + model_params["stroke_width"] = [w*scale for w in + model_params["stroke_width"]] + LOG.info("Overriding width to: %s", model_params["stroke_width"]) + + # task = meta["task"] + generator = meta["generator"] + if generator == "fc": + model = models.VectorGenerator(**model_params) + elif generator == "bezier_fc": + model = models.BezierVectorGenerator(**model_params) + elif generator in ["rnn"]: + model = models.RNNVectorGenerator(**model_params) + elif generator in ["chain_rnn"]: + model = models.ChainRNNVectorGenerator(**model_params) + else: + raise NotImplementedError() + model.eval() + + device = "cpu" + if th.cuda.is_available(): + device = "cuda" + + model.to(device) + + checkpointer = ttools.Checkpointer( + args.model, model, meta=meta, prefix="vect_g_") + checkpointer.load_latest() + + LOG.info("Computing latent space interpolation") + for i in range(args.nsamples): + z0 = model.sample_z(1) + z1 = model.sample_z(1) + + # interpolation + alpha = th.linspace(0, 1, args.nsteps).view(args.nsteps, 1).to(device) + alpha_video = th.linspace(0, 1, args.nframes).view(args.nframes, 1) + alpha_video = alpha_video.to(device) + + length = [args.nsteps, args.nframes] + for idx, a in enumerate([alpha, alpha_video]): + _z0 = z0.repeat(length[idx], 1).to(device) + _z1 = z1.repeat(length[idx], 1).to(device) + batch = _z0*(1-a) + _z1*a + out = model(batch) + if idx == 0: # image viz + n, c, h, w = out.shape + out = out.permute(1, 2, 0, 3) + out = out.contiguous().view(1, c, h, w*n) + out = postprocess(out, invert=args.invert) + imsave(out, os.path.join(outdir, + "latent_interp", "%03d.png" % i)) + + scenes = model.get_vector(batch) + for scn_idx, scn in enumerate(scenes): + save_scene(scn, os.path.join(outdir, "latent_interp_svg", + "%03d" % i, "%03d.svg" % + scn_idx)) + else: # video viz + anim_root = os.path.join(outdir, + "latent_interp_video", "%03d" % i) + LOG.info("Rendering animation %d", i) + for frame_idx, frame in enumerate(out): + LOG.info("frame %d", frame_idx) + frame = frame.unsqueeze(0) + frame = postprocess(frame, invert=args.invert) + imsave(frame, os.path.join(anim_root, + "frame%04d.png" % frame_idx)) + call(["ffmpeg", "-framerate", "30", "-i", + os.path.join(anim_root, "frame%04d.png"), "-vb", "20M", + os.path.join(outdir, + "latent_interp_video", "%03d.mp4" % i)]) + LOG.info(" saved %d", i) + + LOG.info("Sampling latent space") + + for i in range(args.nsamples): + n = 8 + bs = n*n + z = model.sample_z(bs).to(device) + out = model(z) + _, c, h, w = out.shape + out = out.view(n, n, c, h, w).permute(2, 0, 3, 1, 4) + out = out.contiguous().view(1, c, h*n, w*n) + out = postprocess(out) + imsave(out, os.path.join(outdir, "samples_%03d.png" % i)) + LOG.info(" saved %d", i) + + LOG.info("output images saved to %s", outdir) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument("model") + parser.add_argument("--output_dir", help="output directory for " + " the samples. Defaults to the model's path") + parser.add_argument("--nsamples", default=16, type=int, + help="number of output to compute") + parser.add_argument("--imsize", type=int, + help="if provided, override the raster output " + "resolution") + parser.add_argument("--nsteps", default=9, type=int, help="number of " + "interpolation steps for the interpolation") + parser.add_argument("--nframes", default=120, type=int, help="number of " + "frames for the interpolation video") + parser.add_argument("--invert", default=False, action="store_true", + help="if True, render black on white rather than the" + " opposite") + + args = parser.parse_args() + + pydiffvg.set_use_gpu(False) + + ttools.set_logger(False) + + run(args) diff --git a/apps/generative_models/losses.py b/apps/generative_models/losses.py new file mode 100644 index 0000000..8bebb7c --- /dev/null +++ b/apps/generative_models/losses.py @@ -0,0 +1,99 @@ +"""Losses for the generative models and baselines.""" +import torch as th +import numpy as np + +import ttools.modules.image_operators as imops + + +class KLDivergence(th.nn.Module): + """ + Args: + min_value(float): the loss is clipped so that value below this + number don't affect the optimization. + """ + def __init__(self, min_value=0.2): + super(KLDivergence, self).__init__() + self.min_value = min_value + + def forward(self, mu, log_sigma): + loss = -0.5 * (1.0 + log_sigma - mu.pow(2) - log_sigma.exp()) + loss = loss.mean() + loss = th.max(loss, self.min_value*th.ones_like(loss)) + return loss + + +class MultiscaleMSELoss(th.nn.Module): + def __init__(self, channels=3): + super(MultiscaleMSELoss, self).__init__() + self.blur = imops.GaussianBlur(1, channels=channels) + + def forward(self, im, target): + bs, c, h, w = im.shape + num_levels = max(int(np.ceil(np.log2(h))) - 2, 1) + + losses = [] + for lvl in range(num_levels): + loss = th.nn.functional.mse_loss(im, target) + losses.append(loss) + im = th.nn.functional.interpolate(self.blur(im), + scale_factor=0.5, + mode="nearest") + target = th.nn.functional.interpolate(self.blur(target), + scale_factor=0.5, + mode="nearest") + + losses = th.stack(losses) + return losses.sum() + + +def gaussian_pdfs(dx, dy, params): + """Returns the pdf at (dx, dy) for each Gaussian in the mixture. + """ + dx = dx.unsqueeze(-1) # replicate dx, dy to evaluate all pdfs at once + dy = dy.unsqueeze(-1) + + mu_x = params[..., 0] + mu_y = params[..., 1] + sigma_x = params[..., 2].exp() + sigma_y = params[..., 3].exp() + rho_xy = th.tanh(params[..., 4]) + + x = ((dx-mu_x) / sigma_x).pow(2) + y = ((dy-mu_y) / sigma_y).pow(2) + + xy = (dx-mu_x)*(dy-mu_y) / (sigma_x * sigma_y) + arg = x + y - 2.0*rho_xy*xy + pdf = th.exp(-arg / (2*(1.0 - rho_xy.pow(2)))) + norm = 2.0 * np.pi * sigma_x * sigma_y * (1.0 - rho_xy.pow(2)).sqrt() + + return pdf / norm + + +class GaussianMixtureReconstructionLoss(th.nn.Module): + """ + Args: + """ + def __init__(self, eps=1e-5): + super(GaussianMixtureReconstructionLoss, self).__init__() + self.eps = eps + + def forward(self, pen_logits, mixture_logits, gaussian_params, targets): + dx = targets[..., 0] + dy = targets[..., 1] + pen_state = targets[..., 2:].argmax(-1) # target index + + # Likelihood loss on the stroke position + # No need to predict accurate pen position for end-of-sequence tokens + valid_stroke = (targets[..., -1] != 1.0).float() + mixture_weights = th.nn.functional.softmax(mixture_logits, -1) + pdfs = gaussian_pdfs(dx, dy, gaussian_params) + position_loss = - th.log(self.eps + (pdfs * mixture_weights).sum(-1)) + + # by actual non-empty count + position_loss = (position_loss*valid_stroke).sum() / valid_stroke.sum() + + # Classification loss for the stroke mode + pen_loss = th.nn.functional.cross_entropy(pen_logits.view(-1, 3), + pen_state.view(-1)) + + return position_loss + pen_loss diff --git a/apps/generative_models/mnist_vae.py b/apps/generative_models/mnist_vae.py new file mode 100644 index 0000000..884c147 --- /dev/null +++ b/apps/generative_models/mnist_vae.py @@ -0,0 +1,1026 @@ +#!/bin/env python +"""Train a VAE MNIST generator. + +Usage: + +* Train a model: + +`python mnist_vae.py train` + +* Generate samples from a trained model: + +`python mnist_vae.py sample` + +* Generate latent space interpolations from a trained model: + +`python mnist_vae.py interpolate` +""" +import argparse +import os + +import numpy as np +import torch as th +from torch.utils.data import DataLoader +import torchvision.datasets as dset +import torchvision.transforms as transforms + +import ttools +import ttools.interfaces +from ttools.modules import networks + +from modules import Flatten + +import pydiffvg + +LOG = ttools.get_logger(__name__) + + +BASE_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), os.pardir) +VAE_OUTPUT = os.path.join(BASE_DIR, "results", "mnist_vae") +AE_OUTPUT = os.path.join(BASE_DIR, "results", "mnist_ae") + + +def _onehot(label): + bs = label.shape[0] + label_onehot = label.new(bs, 10) + label_onehot = label_onehot.zero_() + label_onehot.scatter_(1, label.unsqueeze(1), 1) + return label_onehot.float() + + +def render(canvas_width, canvas_height, shapes, shape_groups, samples=2): + _render = pydiffvg.RenderFunction.apply + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + img = _render(canvas_width, # width + canvas_height, # height + samples, # num_samples_x + samples, # num_samples_y + 0, # seed + None, # background + *scene_args) + return img + + +class MNISTCallback(ttools.callbacks.ImageDisplayCallback): + """Simple callback that visualize generated images during training.""" + def visualized_image(self, batch, step_data, is_val=False): + im = step_data["rendering"].detach().cpu() + im = 0.5 + 0.5*im + ref = batch[0].cpu() + + vizdata = [im, ref] + + # tensor to visualize, concatenate images + viz = th.clamp(th.cat(vizdata, 2), 0, 1) + return viz + + def caption(self, batch, step_data, is_val=False): + return "fake, real" + + +class VAEInterface(ttools.ModelInterface): + def __init__(self, model, lr=1e-4, cuda=True, max_grad_norm=10, + variational=True, w_kld=1.0): + super(VAEInterface, self).__init__() + + self.max_grad_norm = max_grad_norm + + self.model = model + + self.w_kld = w_kld + + self.variational = variational + + self.device = "cpu" + if cuda: + self.device = "cuda" + + self.model.to(self.device) + + self.opt = th.optim.Adam( + self.model.parameters(), lr=lr, betas=(0.5, 0.5), eps=1e-12) + + def training_step(self, batch): + im, label = batch[0], batch[1] + im = im.to(self.device) + label = label.to(self.device) + rendering, auxdata = self.model(im, label) + + im = batch[0] + im = im.to(self.device) + + logvar = auxdata["logvar"] + mu = auxdata["mu"] + + data_loss = th.nn.functional.mse_loss(rendering, im) + + ret = {} + if self.variational: # VAE mode + kld = -0.5 * th.sum(1 + logvar - mu.pow(2) - logvar.exp(), 1) + kld = kld.mean() + loss = data_loss + kld*self.w_kld + ret["kld"] = kld.item() + else: # Regular autoencoder + loss = data_loss + + # optimize + self.opt.zero_grad() + loss.backward() + + # Clip large gradients if needed + if self.max_grad_norm is not None: + nrm = th.nn.utils.clip_grad_norm_( + self.model.parameters(), self.max_grad_norm) + if nrm > self.max_grad_norm: + LOG.warning("Clipping generator gradients. norm = %.3f > %.3f", + nrm, self.max_grad_norm) + + self.opt.step() + + ret["loss"] = loss.item() + ret["data_loss"] = data_loss.item() + ret["auxdata"] = auxdata + ret["rendering"] = rendering + + return ret + + # def init_validation(self): + # return {"count": 0, "loss": 0} + # + # def update_validation(self, batch, fwd, running_data): + # with th.no_grad(): + # ref = batch[1].to(self.device) + # loss = th.nn.functional.mse_loss(fwd, ref) + # n = ref.shape[0] + # + # return { + # "loss": running_data["loss"] + loss.item()*n, + # "count": running_data["count"] + n + # } + # + # def finalize_validation(self, running_data): + # return { + # "loss": running_data["loss"] / running_data["count"] + # } + + +class MNISTGenerator(th.nn.Module): + def __init__(self, imsize=28): + super(MNISTGenerator, self).__init__() + if imsize != 28: + raise NotImplementedError() + + mul = 2 + self.convnet = th.nn.Sequential( + # 4x4 + th.nn.ConvTranspose2d(16 + 1, mul*32, 4, padding=1, stride=2), + th.nn.LeakyReLU(inplace=True), + th.nn.Conv2d(mul*32, mul*32, 3, padding=1), + th.nn.LeakyReLU(inplace=True), + + # 8x8 + th.nn.ConvTranspose2d(mul*32, mul*64, 4, padding=1, stride=2), + th.nn.LeakyReLU(inplace=True), + th.nn.Conv2d(mul*64, mul*64, 3, padding=1), + th.nn.LeakyReLU(inplace=True), + + # 16x16 + th.nn.ConvTranspose2d(mul*64, mul*128, 4, padding=1, stride=2), + th.nn.LeakyReLU(inplace=True), + th.nn.Conv2d(mul*128, mul*128, 3, padding=1), + th.nn.LeakyReLU(inplace=True), + + # 32x32 + th.nn.Conv2d(mul*128, mul*128, 3, padding=1), + th.nn.LeakyReLU(inplace=True), + + th.nn.Conv2d(mul*128, mul*128, 3, padding=1), + th.nn.LeakyReLU(inplace=True), + + th.nn.Conv2d(mul*128, 1, 1), + # th.nn.Tanh(), + ) + + def forward(self, im, label): + bs = im.shape[0] + + # sample a hidden vector + z = th.randn(bs, 16, 4, 4).to(im.device) + + # make the model conditional + in_ = th.cat([z, label.float().view(bs, 1, 1, 1).repeat(1, 1, 4, 4)], 1) + + out = self.convnet(in_) + return out, None + + +class VectorMNISTVAE(th.nn.Module): + def __init__(self, imsize=28, paths=4, segments=5, samples=2, zdim=128, + conditional=False, variational=True, raster=False, fc=False): + super(VectorMNISTVAE, self).__init__() + + # if imsize != 28: + # raise NotImplementedError() + + self.samples = samples + self.imsize = imsize + self.paths = paths + self.segments = segments + self.zdim = zdim + self.conditional = conditional + self.variational = variational + + ncond = 0 + if self.conditional: # one hot encoded input for conditional model + ncond = 10 + + self.fc = fc + mult = 1 + nc = 1024 + + if not self.fc: # conv model + self.encoder = th.nn.Sequential( + # 32x32 + th.nn.Conv2d(1 + ncond, mult*64, 4, padding=0, stride=2), + th.nn.LeakyReLU(0.2, inplace=True), + + # 16x16 + th.nn.Conv2d(mult*64, mult*128, 4, padding=0, stride=2), + th.nn.LeakyReLU(0.2, inplace=True), + + # 8x8 + th.nn.Conv2d(mult*128, mult*256, 4, padding=0, stride=2), + th.nn.LeakyReLU(0.2, inplace=True), + Flatten(), + ) + else: + self.encoder = th.nn.Sequential( + # 32x32 + Flatten(), + th.nn.Linear(28*28 + ncond, mult*256), + th.nn.LeakyReLU(0.2, inplace=True), + + # 8x8 + th.nn.Linear(mult*256, mult*256, 4), + th.nn.LeakyReLU(0.2, inplace=True), + ) + + self.mu_predictor = th.nn.Linear(256*1*1, zdim) + if self.variational: + self.logvar_predictor = th.nn.Linear(256*1*1, zdim) + + self.decoder = th.nn.Sequential( + th.nn.Linear(zdim + ncond, nc), + th.nn.SELU(inplace=True), + + th.nn.Linear(nc, nc), + th.nn.SELU(inplace=True), + ) + + + self.raster = raster + if self.raster: + self.raster_decoder = th.nn.Sequential( + th.nn.Linear(nc, imsize*imsize), + ) + else: + # 4 points bezier with n_segments -> 3*n_segments + 1 points + self.point_predictor = th.nn.Sequential( + th.nn.Linear(nc, 2*self.paths*(self.segments*3+1)), + th.nn.Tanh() # bound spatial extent + ) + + self.width_predictor = th.nn.Sequential( + th.nn.Linear(nc, self.paths), + th.nn.Tanh() + ) + + self.alpha_predictor = th.nn.Sequential( + th.nn.Linear(nc, self.paths), + th.nn.Tanh() + ) + + self._reset_weights() + + def _reset_weights(self): + for n, p in self.encoder.named_parameters(): + if 'bias' in n: + p.data.zero_() + elif 'weight' in n: + th.nn.init.kaiming_normal_(p.data, nonlinearity="leaky_relu") + + th.nn.init.kaiming_normal_(self.mu_predictor.weight.data, nonlinearity="linear") + if self.variational: + th.nn.init.kaiming_normal_(self.logvar_predictor.weight.data, nonlinearity="linear") + + for n, p in self.decoder.named_parameters(): + if 'bias' in n: + p.data.zero_() + elif 'weight' in n: + th.nn.init.kaiming_normal_(p, nonlinearity="linear") + + if not self.raster: + for n, p in self.point_predictor.named_parameters(): + pass + # if 'bias' in n: + # p.data.zero_() + # if 'weight' in n: + # th.nn.init.orthogonal_(p) + + for n, p in self.width_predictor.named_parameters(): + if 'bias' in n: + p.data.zero_() + elif 'weight' in n: + th.nn.init.orthogonal_(p) + + for n, p in self.alpha_predictor.named_parameters(): + if 'bias' in n: + p.data.zero_() + elif 'weight' in n: + th.nn.init.orthogonal_(p) + + def encode(self, im, label): + bs, _, h, w = im.shape + if self.conditional: + label_onehot = _onehot(label) + if not self.fc: + label_onehot = label_onehot.view(bs, 10, 1, 1).repeat(1, 1, h, w) + out = self.encoder(th.cat([im, label_onehot], 1)) + else: + out = self.encoder(th.cat([im.view(bs, -1), label_onehot], 1)) + else: + out = self.encoder(im) + mu = self.mu_predictor(out) + if self.variational: + logvar = self.logvar_predictor(out) + return mu, logvar + else: + return mu + + def reparameterize(self, mu, logvar): + std = th.exp(0.5*logvar) + eps = th.randn_like(logvar) + return mu + std*eps + + def _decode_features(self, z, label): + if label is not None: + assert self.conditional, "decoding with an input label requires a conditional AE" + label_onehot = _onehot(label) + z = th.cat([z, label_onehot], 1) + + decoded = self.decoder(z) + return decoded + + def decode(self, z, label=None): + bs = z.shape[0] + + feats = self._decode_features(z, label) + + if self.raster: + out = self.raster_decoder(feats).view(bs, 1, self.imsize, self.imsize) + return out, {} + + all_points = self.point_predictor(feats) + all_points = all_points.view(bs, self.paths, -1, 2) + + all_points = all_points*(self.imsize//2-2) + self.imsize//2 + + if False: + all_widths = th.ones(bs, self.paths) * 0.5 + else: + all_widths = self.width_predictor(feats) * 1.5 + .25 + + if False: + all_alphas = th.ones(bs, self.paths) + else: + all_alphas = self.alpha_predictor(feats) + + # Process the batch sequentially + outputs = [] + scenes = [] + for k in range(bs): + # Get point parameters from network + shapes = [] + shape_groups = [] + for p in range(self.paths): + points = all_points[k, p].contiguous().cpu() + width = all_widths[k, p].cpu() + alpha = all_alphas[k, p].cpu() + + color = th.cat([th.ones(3), alpha.view(1,)]) + num_ctrl_pts = th.zeros(self.segments, dtype=th.int32) + 2 + + path = pydiffvg.Path( + num_control_points=num_ctrl_pts, points=points, + stroke_width=width, is_closed=False) + + shapes.append(path) + path_group = pydiffvg.ShapeGroup( + shape_ids=th.tensor([len(shapes) - 1]), + fill_color=None, + stroke_color=color) + shape_groups.append(path_group) + + scenes.append( + [shapes, shape_groups, (self.imsize, self.imsize)]) + + # Rasterize + out = render(self.imsize, self.imsize, shapes, shape_groups, samples=self.samples) + + # Torch format, discard alpha, make gray + out = out.permute(2, 0, 1).view(4, self.imsize, self.imsize)[:3].mean(0, keepdim=True) + + outputs.append(out) + + output = th.stack(outputs).to(z.device) + + auxdata = { + "points": all_points, + "scenes": scenes, + } + + # map to [-1, 1] + output = output*2.0 - 1.0 + + return output, auxdata + + def forward(self, im, label): + bs = im.shape[0] + + if self.variational: + mu, logvar = self.encode(im, label) + z = self.reparameterize(mu, logvar) + else: + mu = self.encode(im, label) + z = mu + logvar = None + + if self.conditional: + output, aux = self.decode(z, label=label) + else: + output, aux = self.decode(z) + + aux["logvar"] = logvar + aux["mu"] = mu + + return output, aux + + +class VectorMNISTGenerator(th.nn.Module): + def __init__(self, imsize=28, paths=4, segments=5, samples=2, conditional=False, + zdim=20, fc=False): + super(VectorMNISTGenerator, self).__init__() + if imsize != 28: + raise NotImplementedError() + + self.samples = samples + self.imsize = imsize + self.paths = paths + self.segments = segments + self.conditional = conditional + self.zdim = zdim + self.fc = fc + + ncond = 0 + if self.conditional: # one hot encoded input for conditional model + ncond = 10 + + nc = 1024 + self.trunk = th.nn.Sequential( + th.nn.Linear(zdim + ncond, nc), # noise + one-hot + th.nn.SELU(inplace=True), + + # th.nn.Linear(nc, nc), + # th.nn.SELU(inplace=True), + + th.nn.Linear(nc, nc), + th.nn.SELU(inplace=True), + + # th.nn.Linear(nc, nc), + # th.nn.SELU(inplace=True), + ) + + # 4 points bezier so n_segments -> 3*n_segments + 1 points + self.point_predictor = th.nn.Sequential( + th.nn.Linear(nc, 2*self.paths*(self.segments*3+1)), + # th.nn.Linear(nc, 2*self.paths*(self.segments*1+1)), + th.nn.Tanh() # bound spatial extent + ) + + self.width_predictor = th.nn.Sequential( + th.nn.Linear(nc, self.paths), + th.nn.Tanh() + ) + + self.alpha_predictor = th.nn.Sequential( + th.nn.Linear(nc, self.paths), + th.nn.Tanh() + ) + + # self.postprocessor = th.nn.Sequential( + # th.nn.Conv2d(1, 32, 3, padding=1), + # th.nn.LeakyReLU(inplace=True), + # th.nn.Conv2d(32, 1, 1), + # ) + self._reset_weights() + + def _reset_weights(self): + for n, p in self.trunk.named_parameters(): + if 'bias' in n: + p.data.zero_() + elif 'weight' in n: + th.nn.init.kaiming_normal_(p) + p.data.mul_(0.7) + # th.nn.init.kaiming_normal_(p, nonlinearity="leaky_relu") + + for n, p in self.point_predictor.named_parameters(): + # if 'bias' in n: + # p.data.zero_() + if 'weight' in n: + th.nn.init.orthogonal_(p) + # th.nn.init.kaiming_normal_(p, nonlinearity="tanh") + + for n, p in self.width_predictor.named_parameters(): + if 'bias' in n: + p.data.zero_() + elif 'weight' in n: + # th.nn.init.orthogonal_(p) + th.nn.init.kaiming_normal_(p, nonlinearity="tanh") + + for n, p in self.alpha_predictor.named_parameters(): + if 'bias' in n: + p.data.zero_() + elif 'weight' in n: + th.nn.init.kaiming_normal_(p, nonlinearity="tanh") + # th.nn.init.orthogonal_(p) + + def sample_z(self, bs): + return th.randn(bs, self.zdim) + + def gen_sample(self, z, label=None): + bs = z.shape[0] + if self.conditional: + if label is None: + raise ValueError("GAN is conditional, please provide a label") + + # One-hot encoding of the image label + label_onehot = _onehot(label) + + # get some embedding + in_ = th.cat([z, label_onehot.float()], 1) + else: + in_ = z + + feats = self.trunk(in_) + + all_points = self.point_predictor(feats) + all_points = all_points.view(bs, self.paths, -1, 2) + + if False: + all_alphas = th.ones(bs, self.paths) + else: + all_alphas = self.alpha_predictor(feats) + + # stroke size between 0.5 and 3.5 px + if False: + all_widths = th.ones(bs, self.paths) * 1 + else: + all_widths = self.width_predictor(feats) + all_widths = 1.5*all_widths + 0.5 + + all_points = all_points*(self.imsize//2) + self.imsize//2 + + # Process the batch sequentially + outputs = [] + for k in range(bs): + # Get point parameters from network + shapes = [] + shape_groups = [] + for p in range(self.paths): + points = all_points[k, p].contiguous().cpu() + # num_ctrl_pts = th.zeros(self.segments, dtype=th.int32)+0 + num_ctrl_pts = th.zeros(self.segments, dtype=th.int32)+2 + width = all_widths[k, p].cpu() + alpha = all_alphas[k, p].cpu() + color = th.cat([th.ones(3), alpha.view(1,)]) + path = pydiffvg.Path( + num_control_points=num_ctrl_pts, points=points, + stroke_width=width, is_closed=False) + shapes.append(path) + path_group = pydiffvg.ShapeGroup( + shape_ids=th.tensor([len(shapes) - 1]), + fill_color=None, + stroke_color=color) + shape_groups.append(path_group) + + # Rasterize + out = render(self.imsize, self.imsize, shapes, shape_groups, samples=self.samples) + + # Torch format, discard alpha, make gray + out = out.permute(2, 0, 1).view(4, self.imsize, self.imsize)[:3].mean(0, keepdim=True) + + outputs.append(out) + + output = th.stack(outputs).to(z.device) + aux_data = { + "points": all_points, + "raw_vector": output, + } + + # output = self.postprocessor(output) + + # map to [-1, 1] + output = output*2.0 - 1.0 + + return output, aux_data + + def forward(self, im, label): + bs = label.shape[0] + + # sample a hidden vector (same dim as the raster version) + z = self.sample_z(bs).to(im.device) + if args.conditional: + return self.gen_sample(z, label=label) + else: + return self.gen_sample(z) + + + +class Discriminator(th.nn.Module): + def __init__(self, conditional=False, fc=False): + super(Discriminator, self).__init__() + + self.conditional = conditional + + ncond = 0 + if self.conditional: # one hot encoded input for conditional model + ncond = 10 + + sn = th.nn.utils.spectral_norm + # sn = lambda x: x + + self.fc = fc + + mult = 2 + if self.fc: + self.net = th.nn.Sequential( + Flatten(), + th.nn.Linear(28*28 + ncond, mult*256), + th.nn.LeakyReLU(0.2, inplace=True), + + # th.nn.Linear(mult*256, mult*256, 4), + # th.nn.LeakyReLU(0.2, inplace=True), + # th.nn.Dropout(0.5), + + th.nn.Linear(mult*256, mult*256, 4), + th.nn.LeakyReLU(0.2, inplace=True), + + th.nn.Linear(mult*256*1*1, 1), + ) + else: + self.net = th.nn.Sequential( + th.nn.Conv2d(1 + ncond, mult*64, 4, padding=0, stride=2), + + th.nn.LeakyReLU(0.2, inplace=True), + # 16x16 + + sn(th.nn.Conv2d(mult*64, mult*128, 4, padding=0, stride=2)), + th.nn.LeakyReLU(0.2, inplace=True), + # 8x8 + + sn(th.nn.Conv2d(mult*128, mult*256, 4, padding=0, stride=2)), + th.nn.LeakyReLU(0.2, inplace=True), + # 4x4 + + Flatten(), + + th.nn.Linear(mult*256*1*1, 1), + ) + + self._reset_weights() + + def _reset_weights(self): + for n, p in self.net.named_parameters(): + if 'bias' in n: + p.data.zero_() + if 'weight' in n: + th.nn.init.kaiming_normal_(p, nonlinearity="leaky_relu") + + def forward(self, x): + out = self.net(x) + return out + +class Dataset(th.utils.data.Dataset): + def __init__(self, data_dir, imsize): + super(Dataset, self).__init__() + self.mnist = dset.MNIST(root=data_dir, download=True, + transform=transforms.Compose([ + transforms.ToTensor(), + ])) + + def __len__(self): + return len(self.mnist) + + def __getitem__(self, idx): + im, label = self.mnist[idx] + + # make sure data uses [0, 1] range + im -= im.min() + im /= im.max() + 1e-8 + im -= 0.5 + im /= 0.5 + return im, label + + +def train(args): + th.manual_seed(0) + np.random.seed(0) + + pydiffvg.set_use_gpu(args.cuda) + + # Initialize datasets + imsize = 28 + dataset = Dataset(args.data_dir, imsize) + dataloader = DataLoader(dataset, batch_size=args.bs, + num_workers=4, shuffle=True) + + if args.generator in ["vae", "ae"]: + LOG.info("Vector config:\n samples %d\n" + " paths: %d\n segments: %d\n" + " zdim: %d\n" + " conditional: %d\n" + " fc: %d\n", + args.samples, args.paths, args.segments, + args.zdim, args.conditional, args.fc) + + model_params = dict(samples=args.samples, paths=args.paths, + segments=args.segments, conditional=args.conditional, + zdim=args.zdim, fc=args.fc) + + if args.generator == "vae": + model = VectorMNISTVAE(variational=True, **model_params) + chkpt = VAE_OUTPUT + name = "mnist_vae" + elif args.generator == "ae": + model = VectorMNISTVAE(variational=False, **model_params) + chkpt = AE_OUTPUT + name = "mnist_ae" + else: + raise ValueError("unknown generator") + + if args.conditional: + name += "_conditional" + chkpt += "_conditional" + + if args.fc: + name += "_fc" + chkpt += "_fc" + + # Resume from checkpoint, if any + checkpointer = ttools.Checkpointer( + chkpt, model, meta=model_params, prefix="g_") + extras, meta = checkpointer.load_latest() + + if meta is not None and meta != model_params: + LOG.info("Checkpoint's metaparams differ from CLI, aborting: %s and %s", + meta, model_params) + + # Hook interface + if args.generator in ["vae", "ae"]: + variational = args.generator == "vae" + if variational: + LOG.info("Using a VAE") + else: + LOG.info("Using an AE") + interface = VAEInterface(model, lr=args.lr, cuda=args.cuda, + variational=variational, w_kld=args.kld_weight) + + trainer = ttools.Trainer(interface) + + # Add callbacks + keys = ["loss_g", "loss_d"] + if args.generator == "vae": + keys = ["kld", "data_loss", "loss"] + elif args.generator == "ae": + keys = ["data_loss", "loss"] + port = 8097 + trainer.add_callback(ttools.callbacks.ProgressBarCallback( + keys=keys, val_keys=keys)) + trainer.add_callback(ttools.callbacks.VisdomLoggingCallback( + keys=keys, val_keys=keys, env=name, port=port)) + trainer.add_callback(MNISTCallback( + env=name, win="samples", port=port, frequency=args.freq)) + trainer.add_callback(ttools.callbacks.CheckpointingCallback( + checkpointer, max_files=2, interval=600, max_epochs=50)) + + # Start training + trainer.train(dataloader, num_epochs=args.num_epochs) + + +def generate_samples(args): + chkpt = VAE_OUTPUT + if args.conditional: + chkpt += "_conditional" + if args.fc: + chkpt += "_fc" + + meta = ttools.Checkpointer.load_meta(chkpt, prefix="g_") + if meta is None: + LOG.info("No metadata in checkpoint (or no checkpoint), aborting.") + return + + model = VectorMNISTVAE(**meta) + checkpointer = ttools.Checkpointer(chkpt, model, prefix="g_") + checkpointer.load_latest() + model.eval() + + # Sample some latent vectors + n = 8 + bs = n*n + z = th.randn(bs, model.zdim) + + imsize = 28 + dataset = Dataset(args.data_dir, imsize) + dataloader = DataLoader(dataset, batch_size=bs, + num_workers=1, shuffle=True) + + for batch in dataloader: + ref, label = batch + break + + autoencode = True + if autoencode: + LOG.info("Sampling with auto-encoder code") + if not args.conditional: + label = None + mu, logvar = model.encode(ref, label) + z = model.reparameterize(mu, logvar) + else: + label = None + if args.conditional: + label = th.clamp(th.rand(bs)*10, 0, 9).long() + if args.digit is not None: + label[:] = args.digit + + with th.no_grad(): + images, aux = model.decode(z, label=label) + scenes = aux["scenes"] + images += 1.0 + images /= 2.0 + + h = w = model.imsize + + images = images.view(n, n, h, w).permute(0, 2, 1, 3) + images = images.contiguous().view(n*h, n*w) + images = th.clamp(images, 0, 1).cpu().numpy() + path = os.path.join(chkpt, "samples.png") + pydiffvg.imwrite(images, path, gamma=2.2) + + if autoencode: + ref += 1.0 + ref /= 2.0 + ref = ref.view(n, n, h, w).permute(0, 2, 1, 3) + ref = ref.contiguous().view(n*h, n*w) + ref = th.clamp(ref, 0, 1).cpu().numpy() + path = os.path.join(chkpt, "ref.png") + pydiffvg.imwrite(ref, path, gamma=2.2) + + # merge scenes + all_shapes = [] + all_shape_groups = [] + cur_id = 0 + for idx, s in enumerate(scenes): + shapes, shape_groups, _ = s + # width, height = sizes + + # Shift digit on canvas + center_x = idx % n + center_y = idx // n + for shape in shapes: + shape.points[:, 0] += center_x * model.imsize + shape.points[:, 1] += center_y * model.imsize + all_shapes.append(shape) + for grp in shape_groups: + grp.shape_ids[:] = cur_id + cur_id += 1 + all_shape_groups.append(grp) + + LOG.info("Generated %d shapes", len(all_shapes)) + + fname = os.path.join(chkpt, "digits.svg") + pydiffvg.save_svg(fname, n*model.imsize, n*model.imsize, all_shapes, + all_shape_groups, use_gamma=False) + + LOG.info("Results saved to %s", chkpt) + + +def interpolate(args): + chkpt = VAE_OUTPUT + if args.conditional: + chkpt += "_conditional" + if args.fc: + chkpt += "_fc" + + meta = ttools.Checkpointer.load_meta(chkpt, prefix="g_") + if meta is None: + LOG.info("No metadata in checkpoint (or no checkpoint), aborting.") + return + + model = VectorMNISTVAE(imsize=128, **meta) + checkpointer = ttools.Checkpointer(chkpt, model, prefix="g_") + checkpointer.load_latest() + model.eval() + + # Sample some latent vectors + bs = 10 + z = th.randn(bs, model.zdim) + + label = None + label = th.arange(0, 10) + + animation = [] + nframes = 60 + with th.no_grad(): + for idx, _z in enumerate(z): + if idx == 0: # skip first + continue + _z0 = z[idx-1].unsqueeze(0).repeat(nframes, 1) + _z = _z.unsqueeze(0).repeat(nframes, 1) + if args.conditional: + _label = label[idx].unsqueeze(0).repeat(nframes) + else: + _label = None + + # interp weights + alpha = th.linspace(0, 1, nframes).view(nframes, 1) + batch = alpha*_z + (1.0 - alpha)*_z0 + images, aux = model.decode(batch, label=_label) + images += 1.0 + images /= 2.0 + animation.append(images) + + anim_dir = os.path.join(chkpt, "interpolation") + os.makedirs(anim_dir, exist_ok=True) + animation = th.cat(animation, 0) + for idx, frame in enumerate(animation): + frame = frame.squeeze() + frame = th.clamp(frame, 0, 1).cpu().numpy() + path = os.path.join(anim_dir, "frame%03d.png" % idx) + pydiffvg.imwrite(frame, path, gamma=2.2) + + LOG.info("Results saved to %s", anim_dir) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + subs = parser.add_subparsers() + + parser.add_argument("--cpu", dest="cuda", action="store_false", + default=th.cuda.is_available(), + help="if true, use CPU instead of GPU.") + parser.add_argument("--conditional", action="store_true", default=False) + parser.add_argument("--fc", action="store_true", default=False) + parser.add_argument("--data_dir", default="mnist", + help="path to download and store the data.") + + # -- Train ---------------------------------------------------------------- + parser_train = subs.add_parser("train") + parser_train.add_argument("--generator", choices=["vae", "ae"], + default="vae", + help="choice of regular or variational " + "autoencoder") + parser_train.add_argument("--freq", type=int, default=100, + help="number of steps between visualizations") + parser_train.add_argument("--lr", type=float, default=1e-4, + help="learning rate") + parser_train.add_argument("--kld_weight", type=float, default=1.0, + help="scalar weight for the KL divergence term.") + parser_train.add_argument("--bs", type=int, default=8, help="batch size") + parser_train.add_argument("--num_epochs", type=int, + help="max number of epochs") + # Vector configs + parser_train.add_argument("--paths", type=int, default=1, + help="number of unique vector paths to generate.") + parser_train.add_argument("--segments", type=int, default=3, + help="number of segments per vector path") + parser_train.add_argument("--samples", type=int, default=2, + help="number of samples in the MC rasterizer") + parser_train.add_argument("--zdim", type=int, default=20, + help="dimension of the latent space") + parser_train.set_defaults(func=train) + + # -- Eval ----------------------------------------------------------------- + parser_sample = subs.add_parser("sample") + parser_sample.add_argument("--digit", type=int, choices=list(range(10)), + help="digits to synthesize, " + "random if not specified") + parser_sample.set_defaults(func=generate_samples) + + parser_interpolate = subs.add_parser("interpolate") + parser_interpolate.set_defaults(func=interpolate) + + args = parser.parse_args() + + ttools.set_logger(True) + args.func(args) diff --git a/apps/generative_models/models.py b/apps/generative_models/models.py new file mode 100644 index 0000000..06d4b1c --- /dev/null +++ b/apps/generative_models/models.py @@ -0,0 +1,484 @@ +"""Collection of generative models.""" + +import torch as th +import ttools + +import rendering +import modules + +LOG = ttools.get_logger(__name__) + + +class BaseModel(th.nn.Module): + def sample_z(self, bs, device="cpu"): + return th.randn(bs, self.zdim).to(device) + + +class BaseVectorModel(BaseModel): + def get_vector(self, z): + _, scenes = self._forward(z) + return scenes + + def _forward(self, x): + raise NotImplementedError() + + def forward(self, z): + # Only return the raster + return self._forward(z)[0] + + +class BezierVectorGenerator(BaseVectorModel): + NUM_SEGMENTS = 2 + def __init__(self, num_strokes=4, + zdim=128, width=32, imsize=32, + color_output=False, + stroke_width=None): + super(BezierVectorGenerator, self).__init__() + + if stroke_width is None: + self.stroke_width = (0.5, 3.0) + LOG.warning("Setting default stroke with %s", self.stroke_width) + else: + self.stroke_width = stroke_width + + self.imsize = imsize + self.num_strokes = num_strokes + self.zdim = zdim + + self.trunk = th.nn.Sequential( + th.nn.Linear(zdim, width), + th.nn.SELU(inplace=True), + + th.nn.Linear(width, 2*width), + th.nn.SELU(inplace=True), + + th.nn.Linear(2*width, 4*width), + th.nn.SELU(inplace=True), + + th.nn.Linear(4*width, 8*width), + th.nn.SELU(inplace=True), + ) + + # 4 points bezier with n_segments -> 3*n_segments + 1 points + self.point_predictor = th.nn.Sequential( + th.nn.Linear(8*width, + 2*self.num_strokes*( + BezierVectorGenerator.NUM_SEGMENTS*3 + 1)), + th.nn.Tanh() # bound spatial extent + ) + + self.width_predictor = th.nn.Sequential( + th.nn.Linear(8*width, self.num_strokes), + th.nn.Sigmoid() + ) + + self.alpha_predictor = th.nn.Sequential( + th.nn.Linear(8*width, self.num_strokes), + th.nn.Sigmoid() + ) + + self.color_predictor = None + if color_output: + self.color_predictor = th.nn.Sequential( + th.nn.Linear(8*width, 3*self.num_strokes), + th.nn.Sigmoid() + ) + + def _forward(self, z): + bs = z.shape[0] + + feats = self.trunk(z) + all_points = self.point_predictor(feats) + all_alphas = self.alpha_predictor(feats) + + if self.color_predictor: + all_colors = self.color_predictor(feats) + all_colors = all_colors.view(bs, self.num_strokes, 3) + else: + all_colors = None + + all_widths = self.width_predictor(feats) + min_width = self.stroke_width[0] + max_width = self.stroke_width[1] + all_widths = (max_width - min_width) * all_widths + min_width + + all_points = all_points.view( + bs, self.num_strokes, BezierVectorGenerator.NUM_SEGMENTS*3+1, 2) + + output, scenes = rendering.bezier_render(all_points, all_widths, all_alphas, + colors=all_colors, + canvas_size=self.imsize) + + # map to [-1, 1] + output = output*2.0 - 1.0 + + return output, scenes + + +class VectorGenerator(BaseVectorModel): + def __init__(self, num_strokes=4, + zdim=128, width=32, imsize=32, + color_output=False, + stroke_width=None): + super(VectorGenerator, self).__init__() + + if stroke_width is None: + self.stroke_width = (0.5, 3.0) + LOG.warning("Setting default stroke with %s", self.stroke_width) + else: + self.stroke_width = stroke_width + + self.imsize = imsize + self.num_strokes = num_strokes + self.zdim = zdim + + self.trunk = th.nn.Sequential( + th.nn.Linear(zdim, width), + th.nn.SELU(inplace=True), + + th.nn.Linear(width, 2*width), + th.nn.SELU(inplace=True), + + th.nn.Linear(2*width, 4*width), + th.nn.SELU(inplace=True), + + th.nn.Linear(4*width, 8*width), + th.nn.SELU(inplace=True), + ) + + # straight lines so n_segments -> n_segments - 1 points + self.point_predictor = th.nn.Sequential( + th.nn.Linear(8*width, 2*(self.num_strokes*2)), + th.nn.Tanh() # bound spatial extent + ) + + self.width_predictor = th.nn.Sequential( + th.nn.Linear(8*width, self.num_strokes), + th.nn.Sigmoid() + ) + + self.alpha_predictor = th.nn.Sequential( + th.nn.Linear(8*width, self.num_strokes), + th.nn.Sigmoid() + ) + + self.color_predictor = None + if color_output: + self.color_predictor = th.nn.Sequential( + th.nn.Linear(8*width, 3*self.num_strokes), + th.nn.Sigmoid() + ) + + def _forward(self, z): + bs = z.shape[0] + + feats = self.trunk(z) + + all_points = self.point_predictor(feats) + + all_alphas = self.alpha_predictor(feats) + + if self.color_predictor: + all_colors = self.color_predictor(feats) + all_colors = all_colors.view(bs, self.num_strokes, 3) + else: + all_colors = None + + all_widths = self.width_predictor(feats) + min_width = self.stroke_width[0] + max_width = self.stroke_width[1] + all_widths = (max_width - min_width) * all_widths + min_width + + all_points = all_points.view(bs, self.num_strokes, 2, 2) + output, scenes = rendering.line_render(all_points, all_widths, all_alphas, + colors=all_colors, + canvas_size=self.imsize) + + # map to [-1, 1] + output = output*2.0 - 1.0 + + return output, scenes + + +class RNNVectorGenerator(BaseVectorModel): + def __init__(self, num_strokes=64, + zdim=128, width=32, imsize=32, + hidden_size=512, dropout=0.9, + color_output=False, + num_layers=3, stroke_width=None): + super(RNNVectorGenerator, self).__init__() + + + if stroke_width is None: + self.stroke_width = (0.5, 3.0) + LOG.warning("Setting default stroke with %s", self.stroke_width) + else: + self.stroke_width = stroke_width + + self.num_layers = num_layers + self.imsize = imsize + self.num_strokes = num_strokes + self.hidden_size = hidden_size + self.zdim = zdim + + self.hidden_cell_predictor = th.nn.Linear( + zdim, 2*hidden_size*num_layers) + + self.lstm = th.nn.LSTM( + zdim, hidden_size, + num_layers=self.num_layers, dropout=dropout, + batch_first=True) + + # straight lines so n_segments -> n_segments - 1 points + self.point_predictor = th.nn.Sequential( + th.nn.Linear(hidden_size, 2*2), # 2 points, (x,y) + th.nn.Tanh() # bound spatial extent + ) + + self.width_predictor = th.nn.Sequential( + th.nn.Linear(hidden_size, 1), + th.nn.Sigmoid() + ) + + self.alpha_predictor = th.nn.Sequential( + th.nn.Linear(hidden_size, 1), + th.nn.Sigmoid() + ) + + def _forward(self, z, hidden_and_cell=None): + steps = self.num_strokes + + # z is passed at each step, duplicate it + bs = z.shape[0] + expanded_z = z.unsqueeze(1).repeat(1, steps, 1) + + # First step in the RNN + if hidden_and_cell is None: + # Initialize from latent vector + hidden_and_cell = self.hidden_cell_predictor(th.tanh(z)) + hidden = hidden_and_cell[:, :self.hidden_size*self.num_layers] + hidden = hidden.view(-1, self.num_layers, self.hidden_size) + hidden = hidden.permute(1, 0, 2).contiguous() + cell = hidden_and_cell[:, self.hidden_size*self.num_layers:] + cell = cell.view(-1, self.num_layers, self.hidden_size) + cell = cell.permute(1, 0, 2).contiguous() + hidden_and_cell = (hidden, cell) + + feats, hidden_and_cell = self.lstm(expanded_z, hidden_and_cell) + hidden, cell = hidden_and_cell + + feats = feats.reshape(bs*steps, self.hidden_size) + + all_points = self.point_predictor(feats).view(bs, steps, 2, 2) + all_alphas = self.alpha_predictor(feats).view(bs, steps) + all_widths = self.width_predictor(feats).view(bs, steps) + + min_width = self.stroke_width[0] + max_width = self.stroke_width[1] + all_widths = (max_width - min_width) * all_widths + min_width + + output, scenes = rendering.line_render(all_points, all_widths, all_alphas, + canvas_size=self.imsize) + + # map to [-1, 1] + output = output*2.0 - 1.0 + + return output, scenes + + +class ChainRNNVectorGenerator(BaseVectorModel): + """Strokes form a single long chain.""" + def __init__(self, num_strokes=64, + zdim=128, width=32, imsize=32, + hidden_size=512, dropout=0.9, + color_output=False, + num_layers=3, stroke_width=None): + super(ChainRNNVectorGenerator, self).__init__() + + if stroke_width is None: + self.stroke_width = (0.5, 3.0) + LOG.warning("Setting default stroke with %s", self.stroke_width) + else: + self.stroke_width = stroke_width + + self.num_layers = num_layers + self.imsize = imsize + self.num_strokes = num_strokes + self.hidden_size = hidden_size + self.zdim = zdim + + self.hidden_cell_predictor = th.nn.Linear( + zdim, 2*hidden_size*num_layers) + + self.lstm = th.nn.LSTM( + zdim, hidden_size, + num_layers=self.num_layers, dropout=dropout, + batch_first=True) + + # straight lines so n_segments -> n_segments - 1 points + self.point_predictor = th.nn.Sequential( + th.nn.Linear(hidden_size, 2), # 1 point, (x,y) + th.nn.Tanh() # bound spatial extent + ) + + self.width_predictor = th.nn.Sequential( + th.nn.Linear(hidden_size, 1), + th.nn.Sigmoid() + ) + + self.alpha_predictor = th.nn.Sequential( + th.nn.Linear(hidden_size, 1), + th.nn.Sigmoid() + ) + + def _forward(self, z, hidden_and_cell=None): + steps = self.num_strokes + + # z is passed at each step, duplicate it + bs = z.shape[0] + expanded_z = z.unsqueeze(1).repeat(1, steps, 1) + + # First step in the RNN + if hidden_and_cell is None: + # Initialize from latent vector + hidden_and_cell = self.hidden_cell_predictor(th.tanh(z)) + hidden = hidden_and_cell[:, :self.hidden_size*self.num_layers] + hidden = hidden.view(-1, self.num_layers, self.hidden_size) + hidden = hidden.permute(1, 0, 2).contiguous() + cell = hidden_and_cell[:, self.hidden_size*self.num_layers:] + cell = cell.view(-1, self.num_layers, self.hidden_size) + cell = cell.permute(1, 0, 2).contiguous() + hidden_and_cell = (hidden, cell) + + feats, hidden_and_cell = self.lstm(expanded_z, hidden_and_cell) + hidden, cell = hidden_and_cell + + feats = feats.reshape(bs*steps, self.hidden_size) + + # Construct the chain + end_points = self.point_predictor(feats).view(bs, steps, 1, 2) + start_points = th.cat([ + # first point is canvas center + th.zeros(bs, 1, 1, 2, device=feats.device), + end_points[:, 1:, :, :]], 1) + all_points = th.cat([start_points, end_points], 2) + + all_alphas = self.alpha_predictor(feats).view(bs, steps) + all_widths = self.width_predictor(feats).view(bs, steps) + + min_width = self.stroke_width[0] + max_width = self.stroke_width[1] + all_widths = (max_width - min_width) * all_widths + min_width + + output, scenes = rendering.line_render(all_points, all_widths, all_alphas, + canvas_size=self.imsize) + + # map to [-1, 1] + output = output*2.0 - 1.0 + + return output, scenes + + +class Generator(BaseModel): + def __init__(self, width=64, imsize=32, zdim=128, + stroke_width=None, + color_output=False, + num_strokes=4): + super(Generator, self).__init__() + assert imsize == 32 + + self.imsize = imsize + self.zdim = zdim + + num_in_chans = self.zdim // (2*2) + num_out_chans = 3 if color_output else 1 + + self.net = th.nn.Sequential( + th.nn.ConvTranspose2d(num_in_chans, width*8, 4, padding=1, + stride=2), + th.nn.LeakyReLU(0.2, inplace=True), + th.nn.Conv2d(width*8, width*8, 3, padding=1), + th.nn.BatchNorm2d(width*8), + th.nn.LeakyReLU(0.2, inplace=True), + # 4x4 + + th.nn.ConvTranspose2d(8*width, 4*width, 4, padding=1, stride=2), + th.nn.LeakyReLU(0.2, inplace=True), + th.nn.Conv2d(4*width, 4*width, 3, padding=1), + th.nn.BatchNorm2d(width*4), + th.nn.LeakyReLU(0.2, inplace=True), + # 8x8 + + th.nn.ConvTranspose2d(4*width, 2*width, 4, padding=1, stride=2), + th.nn.LeakyReLU(0.2, inplace=True), + th.nn.Conv2d(2*width, 2*width, 3, padding=1), + th.nn.BatchNorm2d(width*2), + th.nn.LeakyReLU(0.2, inplace=True), + # 16x16 + + th.nn.ConvTranspose2d(2*width, width, 4, padding=1, stride=2), + th.nn.LeakyReLU(0.2, inplace=True), + th.nn.Conv2d(width, width, 3, padding=1), + th.nn.BatchNorm2d(width), + th.nn.LeakyReLU(0.2, inplace=True), + # 32x32 + + th.nn.Conv2d(width, width, 3, padding=1), + th.nn.BatchNorm2d(width), + th.nn.LeakyReLU(0.2, inplace=True), + th.nn.Conv2d(width, width, 3, padding=1), + th.nn.LeakyReLU(0.2, inplace=True), + th.nn.Conv2d(width, num_out_chans, 1), + + th.nn.Tanh(), + ) + + def forward(self, z): + bs = z.shape[0] + num_in_chans = self.zdim // (2*2) + raster = self.net(z.view(bs, num_in_chans, 2, 2)) + return raster + + +class Discriminator(th.nn.Module): + def __init__(self, conditional=False, width=64, color_output=False): + super(Discriminator, self).__init__() + + self.conditional = conditional + + sn = th.nn.utils.spectral_norm + + num_chan_in = 3 if color_output else 1 + + self.net = th.nn.Sequential( + th.nn.Conv2d(num_chan_in, width, 3, padding=1), + th.nn.LeakyReLU(0.2, inplace=True), + th.nn.Conv2d(width, 2*width, 4, padding=1, stride=2), + th.nn.LeakyReLU(0.2, inplace=True), + # 16x16 + + sn(th.nn.Conv2d(2*width, 2*width, 3, padding=1)), + th.nn.LeakyReLU(0.2, inplace=True), + sn(th.nn.Conv2d(2*width, 4*width, 4, padding=1, stride=2)), + th.nn.LeakyReLU(0.2, inplace=True), + # 8x8 + + sn(th.nn.Conv2d(4*width, 4*width, 3, padding=1)), + th.nn.LeakyReLU(0.2, inplace=True), + sn(th.nn.Conv2d(4*width, width*4, 4, padding=1, stride=2)), + th.nn.LeakyReLU(0.2, inplace=True), + # 4x4 + + sn(th.nn.Conv2d(4*width, 4*width, 3, padding=1)), + th.nn.LeakyReLU(0.2, inplace=True), + sn(th.nn.Conv2d(4*width, width*4, 4, padding=1, stride=2)), + th.nn.LeakyReLU(0.2, inplace=True), + # 2x2 + + modules.Flatten(), + th.nn.Linear(width*4*2*2, 1), + ) + + def forward(self, x): + out = self.net(x) + return out diff --git a/apps/generative_models/modules.py b/apps/generative_models/modules.py new file mode 100644 index 0000000..e8589ae --- /dev/null +++ b/apps/generative_models/modules.py @@ -0,0 +1,11 @@ +"""Helper modules to build our networks.""" +import torch as th + + +class Flatten(th.nn.Module): + def __init__(self): + super(Flatten, self).__init__() + + def forward(self, x): + bs = x.shape[0] + return x.view(bs, -1) diff --git a/apps/generative_models/rendering.py b/apps/generative_models/rendering.py new file mode 100644 index 0000000..4ef475e --- /dev/null +++ b/apps/generative_models/rendering.py @@ -0,0 +1,307 @@ +import os +import torch as th +import torch.multiprocessing as mp +import threading as mt +import numpy as np +import random + +import ttools + +import pydiffvg +import time + + +def render(canvas_width, canvas_height, shapes, shape_groups, samples=2, + seed=None): + if seed is None: + seed = random.randint(0, 1000000) + _render = pydiffvg.RenderFunction.apply + scene_args = pydiffvg.RenderFunction.serialize_scene( + canvas_width, canvas_height, shapes, shape_groups) + img = _render(canvas_width, canvas_height, samples, samples, + seed, # seed + None, # background image + *scene_args) + return img + + +def opacityStroke2diffvg(strokes, canvas_size=128, debug=False, relative=True, + force_cpu=True): + + dev = strokes.device + if force_cpu: + strokes = strokes.to("cpu") + + + # pydiffvg.set_use_gpu(False) + # if strokes.is_cuda: + # pydiffvg.set_use_gpu(True) + + """Rasterize strokes given in (dx, dy, opacity) sequence format.""" + bs, nsegs, dims = strokes.shape + out = [] + + start = time.time() + for batch_idx, stroke in enumerate(strokes): + + if relative: # Absolute coordinates + all_points = stroke[..., :2].cumsum(0) + else: + all_points = stroke[..., :2] + + all_opacities = stroke[..., 2] + + # Transform from [-1, 1] to canvas coordinates + # Make sure points are in canvas + all_points = 0.5*(all_points + 1.0) * canvas_size + # all_points = th.clamp(0.5*(all_points + 1.0), 0, 1) * canvas_size + + # Avoid overlapping points + eps = 1e-4 + all_points = all_points + eps*th.randn_like(all_points) + + shapes = [] + shape_groups = [] + + for start_idx in range(0, nsegs-1): + points = all_points[start_idx:start_idx+2].contiguous().float() + opacity = all_opacities[start_idx] + + num_ctrl_pts = th.zeros(points.shape[0] - 1, dtype=th.int32) + width = th.ones(1) + + path = pydiffvg.Path( + num_control_points=num_ctrl_pts, points=points, + stroke_width=width, is_closed=False) + + shapes.append(path) + + color = th.cat([th.ones(3, device=opacity.device), + opacity.unsqueeze(0)], 0) + path_group = pydiffvg.ShapeGroup( + shape_ids=th.tensor([len(shapes) - 1]), + fill_color=None, + stroke_color=color) + shape_groups.append(path_group) + + # Rasterize only if there are shapes + if shapes: + inner_start = time.time() + out.append(render(canvas_size, canvas_size, shapes, shape_groups, + samples=4)) + if debug: + inner_elapsed = time.time() - inner_start + print("diffvg call took %.2fms" % inner_elapsed) + else: + out.append(th.zeros(canvas_size, canvas_size, 4, + device=strokes.device)) + + if debug: + elapsed = (time.time() - start)*1000 + print("rendering took %.2fms" % elapsed) + images = th.stack(out, 0).permute(0, 3, 1, 2).contiguous() + + # Return data on the same device as input + return images.to(dev) + + +def stroke2diffvg(strokes, canvas_size=128): + """Rasterize strokes given some sequential data.""" + bs, nsegs, dims = strokes.shape + out = [] + for stroke_idx, stroke in enumerate(strokes): + end_of_stroke = stroke[:, 4] == 1 + last = end_of_stroke.cpu().numpy().argmax() + stroke = stroke[:last+1, :] + # stroke = stroke[~end_of_stroke] + # TODO: stop at the first end of stroke + # import ipdb; ipdb.set_trace() + split_idx = stroke[:, 3].nonzero().squeeze(1) + + # Absolute coordinates + all_points = stroke[..., :2].cumsum(0) + + # Transform to canvas coordinates + all_points[..., 0] += 0.5 + all_points[..., 0] *= canvas_size + all_points[..., 1] += 0.5 + all_points[..., 1] *= canvas_size + + # Make sure points are in canvas + all_points[..., :2] = th.clamp(all_points[..., :2], 0, canvas_size) + + shape_groups = [] + shapes = [] + start_idx = 0 + + for count, end_idx in enumerate(split_idx): + points = all_points[start_idx:end_idx+1].contiguous().float() + + if points.shape[0] <= 2: # we need at least 2 points for a line + continue + + num_ctrl_pts = th.zeros(points.shape[0] - 1, dtype=th.int32) + width = th.ones(1) + path = pydiffvg.Path( + num_control_points=num_ctrl_pts, points=points, + stroke_width=width, is_closed=False) + + start_idx = end_idx+1 + shapes.append(path) + + color = th.ones(4, 1) + path_group = pydiffvg.ShapeGroup( + shape_ids=th.tensor([len(shapes) - 1]), + fill_color=None, + stroke_color=color) + shape_groups.append(path_group) + + # Rasterize + if shapes: + # draw only if there are shapes + out.append(render(canvas_size, canvas_size, shapes, shape_groups, samples=2)) + else: + out.append(th.zeros(canvas_size, canvas_size, 4, + device=strokes.device)) + + return th.stack(out, 0).permute(0, 3, 1, 2)[:, :3].contiguous() + + +def line_render(all_points, all_widths, all_alphas, force_cpu=True, + canvas_size=32, colors=None): + dev = all_points.device + if force_cpu: + all_points = all_points.to("cpu") + all_widths = all_widths.to("cpu") + all_alphas = all_alphas.to("cpu") + + if colors is not None: + colors = colors.to("cpu") + + all_points = 0.5*(all_points + 1.0) * canvas_size + + eps = 1e-4 + all_points = all_points + eps*th.randn_like(all_points) + + bs, num_segments, _, _ = all_points.shape + n_out = 3 if colors is not None else 1 + output = th.zeros(bs, n_out, canvas_size, canvas_size, + device=all_points.device) + + scenes = [] + for k in range(bs): + shapes = [] + shape_groups = [] + for p in range(num_segments): + points = all_points[k, p].contiguous().cpu() + num_ctrl_pts = th.zeros(1, dtype=th.int32) + width = all_widths[k, p].cpu() + alpha = all_alphas[k, p].cpu() + if colors is not None: + color = colors[k, p] + else: + color = th.ones(3, device=alpha.device) + + color = th.cat([color, alpha.view(1,)]) + + path = pydiffvg.Path( + num_control_points=num_ctrl_pts, points=points, + stroke_width=width, is_closed=False) + shapes.append(path) + path_group = pydiffvg.ShapeGroup( + shape_ids=th.tensor([len(shapes) - 1]), + fill_color=None, + stroke_color=color) + shape_groups.append(path_group) + + # Rasterize + scenes.append((canvas_size, canvas_size, shapes, shape_groups)) + raster = render(canvas_size, canvas_size, shapes, shape_groups, + samples=2) + raster = raster.permute(2, 0, 1).view(4, canvas_size, canvas_size) + + alpha = raster[3:4] + if colors is not None: # color output + image = raster[:3] + alpha = alpha.repeat(3, 1, 1) + else: + image = raster[:1] + + # alpha compositing + image = image*alpha + output[k] = image + + output = output.to(dev) + + return output, scenes + + +def bezier_render(all_points, all_widths, all_alphas, force_cpu=True, + canvas_size=32, colors=None): + dev = all_points.device + if force_cpu: + all_points = all_points.to("cpu") + all_widths = all_widths.to("cpu") + all_alphas = all_alphas.to("cpu") + + if colors is not None: + colors = colors.to("cpu") + + all_points = 0.5*(all_points + 1.0) * canvas_size + + eps = 1e-4 + all_points = all_points + eps*th.randn_like(all_points) + + bs, num_strokes, num_pts, _ = all_points.shape + num_segments = (num_pts - 1) // 3 + n_out = 3 if colors is not None else 1 + output = th.zeros(bs, n_out, canvas_size, canvas_size, + device=all_points.device) + + scenes = [] + for k in range(bs): + shapes = [] + shape_groups = [] + for p in range(num_strokes): + points = all_points[k, p].contiguous().cpu() + # bezier + num_ctrl_pts = th.zeros(num_segments, dtype=th.int32) + 2 + width = all_widths[k, p].cpu() + alpha = all_alphas[k, p].cpu() + if colors is not None: + color = colors[k, p] + else: + color = th.ones(3, device=alpha.device) + + color = th.cat([color, alpha.view(1,)]) + + path = pydiffvg.Path( + num_control_points=num_ctrl_pts, points=points, + stroke_width=width, is_closed=False) + shapes.append(path) + path_group = pydiffvg.ShapeGroup( + shape_ids=th.tensor([len(shapes) - 1]), + fill_color=None, + stroke_color=color) + shape_groups.append(path_group) + + # Rasterize + scenes.append((canvas_size, canvas_size, shapes, shape_groups)) + raster = render(canvas_size, canvas_size, shapes, shape_groups, + samples=2) + raster = raster.permute(2, 0, 1).view(4, canvas_size, canvas_size) + + alpha = raster[3:4] + if colors is not None: # color output + image = raster[:3] + alpha = alpha.repeat(3, 1, 1) + else: + image = raster[:1] + + # alpha compositing + image = image*alpha + output[k] = image + + output = output.to(dev) + + return output, scenes diff --git a/apps/generative_models/sketch_rnn.py b/apps/generative_models/sketch_rnn.py new file mode 100755 index 0000000..2b88767 --- /dev/null +++ b/apps/generative_models/sketch_rnn.py @@ -0,0 +1,461 @@ +#!/bin/env python +"""Train a Sketch-RNN.""" +import argparse +from enum import Enum +import os +import wget + +import numpy as np +import torch as th +from torch.utils.data import DataLoader +import torchvision.datasets as dset +import torchvision.transforms as transforms + +import ttools +import ttools.interfaces +from ttools.modules import networks + +import pydiffvg + +import rendering +import losses +import data + +LOG = ttools.get_logger(__name__) + + +BASE_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), os.pardir) +OUTPUT = os.path.join(BASE_DIR, "results", "sketch_rnn_diffvg") +OUTPUT_BASELINE = os.path.join(BASE_DIR, "results", "sketch_rnn") + + +class SketchRNN(th.nn.Module): + class Encoder(th.nn.Module): + def __init__(self, hidden_size=512, dropout=0.9, zdim=128, + num_layers=1): + super(SketchRNN.Encoder, self).__init__() + self.hidden_size = hidden_size + self.num_layers = num_layers + self.zdim = zdim + + self.lstm = th.nn.LSTM(5, hidden_size, num_layers=self.num_layers, + dropout=dropout, bidirectional=True, + batch_first=True) + + # bidirectional model -> *2 + self.mu_predictor = th.nn.Linear(2*hidden_size, zdim) + self.sigma_predictor = th.nn.Linear(2*hidden_size, zdim) + + def forward(self, sequences, hidden_and_cell=None): + bs = sequences.shape[0] + if hidden_and_cell is None: + hidden = th.zeros(self.num_layers*2, bs, self.hidden_size).to( + sequences.device) + cell = th.zeros(self.num_layers*2, bs, self.hidden_size).to( + sequences.device) + hidden_and_cell = (hidden, cell) + + out, hidden_and_cell = self.lstm(sequences, hidden_and_cell) + hidden = hidden_and_cell[0] + + # Concat the forward/backward states + fc_input = th.cat([hidden[0], hidden[1]], 1) + + # VAE params + mu = self.mu_predictor(fc_input) + log_sigma = self.sigma_predictor(fc_input) + + # Sample a latent vector + sigma = th.exp(log_sigma/2.0) + z0 = th.randn(self.zdim, device=mu.device) + z = mu + sigma*z0 + + # KL divergence needs mu/sigma + return z, mu, log_sigma + + class Decoder(th.nn.Module): + """ + The decoder outputs a sequence where each time step models (dx, dy) as + a mixture of `num_gaussians` 2D Gaussians and the state triplet is a + categorical distribution. + + The model outputs at each time step: + - 5 parameters for each Gaussian: mu_x, mu_y, sigma_x, sigma_y, + rho_xy + - 1 logit for each Gaussian (the mixture weight) + - 3 logits for the state triplet probabilities + """ + def __init__(self, hidden_size=512, dropout=0.9, zdim=128, + num_layers=1, num_gaussians=20): + super(SketchRNN.Decoder, self).__init__() + self.hidden_size = hidden_size + self.num_layers = num_layers + self.zdim = zdim + self.num_gaussians = num_gaussians + + # Maps the latent vector to an initial cell/hidden vector + self.hidden_cell_predictor = th.nn.Linear(zdim, 2*hidden_size) + + self.lstm = th.nn.LSTM( + 5 + zdim, hidden_size, + num_layers=self.num_layers, dropout=dropout, + batch_first=True) + + self.parameters_predictor = th.nn.Linear( + hidden_size, num_gaussians + 5*num_gaussians + 3) + + def forward(self, inputs, z, hidden_and_cell=None): + # Every step in the sequence takes the latent vector as input so we + # replicate it here + expanded_z = z.unsqueeze(1).repeat(1, inputs.shape[1], 1) + inputs = th.cat([inputs, expanded_z], 2) + + bs, steps = inputs.shape[:2] + if hidden_and_cell is None: + # Initialize from latent vector + hidden_and_cell = self.hidden_cell_predictor(th.tanh(z)) + hidden = hidden_and_cell[:, :self.hidden_size] + hidden = hidden.unsqueeze(0).contiguous() + cell = hidden_and_cell[:, self.hidden_size:] + cell = cell.unsqueeze(0).contiguous() + hidden_and_cell = (hidden, cell) + + outputs, hidden_and_cell = self.lstm(inputs, hidden_and_cell) + hidden, cell = hidden_and_cell + + # if self.training: + # At train time we want parameters for each time step + outputs = outputs.reshape(bs*steps, self.hidden_size) + params = self.parameters_predictor(outputs).view(bs, steps, -1) + + pen_logits = params[..., -3:] + gaussian_params = params[..., :-3] + mixture_logits = gaussian_params[..., :self.num_gaussians] + gaussian_params = gaussian_params[..., self.num_gaussians:].view( + bs, steps, self.num_gaussians, -1) + + return pen_logits, mixture_logits, gaussian_params, hidden_and_cell + + def __init__(self, zdim=128, num_gaussians=20, encoder_dim=256, + decoder_dim=512): + super(SketchRNN, self).__init__() + self.encoder = SketchRNN.Encoder(zdim=zdim, hidden_size=encoder_dim) + self.decoder = SketchRNN.Decoder(zdim=zdim, hidden_size=decoder_dim, + num_gaussians=num_gaussians) + + def forward(self, sequences): + # Encode the sequences as latent vectors + # We skip the first time step since it is the same for all sequences: + # (0, 0, 1, 0, 0) + z, mu, log_sigma = self.encoder(sequences[:, 1:]) + + # Decode the latent vector into a model sequence + # Do not process the last time step (it is an end-of-sequence token) + pen_logits, mixture_logits, gaussian_params, hidden_and_cell = \ + self.decoder(sequences[:, :-1], z) + + return { + "pen_logits": pen_logits, + "mixture_logits": mixture_logits, + "gaussian_params": gaussian_params, + "z": z, + "mu": mu, + "log_sigma": log_sigma, + "hidden_and_cell": hidden_and_cell, + } + + def sample(self, sequences, temperature=1.0): + # Compute a latent vector conditionned based on a real sequence + z, _, _ = self.encoder(sequences[:, 1:]) + + start_of_seq = sequences[:, :1] + + max_steps = sequences.shape[1] - 1 # last step is an end-of-seq token + + output_sequences = th.zeros_like(sequences) + output_sequences[:, 0] = start_of_seq.squeeze(1) + + current_input = start_of_seq + hidden_and_cell = None + for step in range(max_steps): + pen_logits, mixture_logits, gaussian_params, hidden_and_cell = \ + self.decoder(current_input, z, hidden_and_cell=hidden_and_cell) + + # Pen and displacement state for the next step + next_state = th.zeros_like(current_input) + + # Adjust temperature to control randomness + mixture_logits = mixture_logits*temperature + pen_logits = pen_logits*temperature + + # Select one of 3 pen states + pen_distrib = \ + th.distributions.categorical.Categorical(logits=pen_logits) + pen_state = pen_distrib.sample() + + # One-hot encoding of the state + next_state[:, :, 2:].scatter_(2, pen_state.unsqueeze(-1), + th.ones_like(next_state[:, :, 2:])) + + # Select one of the Gaussians from the mixture + mixture_distrib = \ + th.distributions.categorical.Categorical(logits=mixture_logits) + mixture_idx = mixture_distrib.sample() + + # select the Gaussian parameter + mixture_idx = mixture_idx.unsqueeze(-1).unsqueeze(-1) + mixture_idx = mixture_idx.repeat(1, 1, 1, 5) + params = th.gather(gaussian_params, 2, mixture_idx).squeeze(2) + + # Sample a Gaussian from the corresponding Gaussian + mu = params[..., :2] + sigma_x = params[..., 2].exp() + sigma_y = params[..., 3].exp() + rho_xy = th.tanh(params[..., 4]) + cov = th.zeros(params.shape[0], params.shape[1], 2, 2, + device=params.device) + cov[..., 0, 0] = sigma_x.pow(2)*temperature + cov[..., 1, 1] = sigma_x.pow(2)*temperature + cov[..., 1, 0] = sigma_x*sigma_y*rho_xy*temperature + point_distrib = \ + th.distributions.multivariate_normal.MultivariateNormal( + mu, scale_tril=cov) + point = point_distrib.sample() + next_state[:, :, :2] = point + + # Commit step to output + output_sequences[:, step + 1] = next_state.squeeze(1) + + # Prepare next recurrent step + current_input = next_state + + return output_sequences + + +class SketchRNNCallback(ttools.callbacks.ImageDisplayCallback): + """Simple callback that visualize images.""" + def visualized_image(self, batch, step_data, is_val=False): + if not is_val: + # No need to render training data + return None + + with th.no_grad(): + # only display the first n drawings + n = 8 + batch = batch[:n] + + out_im = rendering.stroke2diffvg(step_data["sample"][:n]) + im = rendering.stroke2diffvg(batch) + im = th.cat([im, out_im], 2) + + return im + + def caption(self, batch, step_data, is_val=False): + if is_val: + return "top: truth, bottom: sample" + else: + return "top: truth, bottom: sample" + + +class Interface(ttools.ModelInterface): + def __init__(self, model, lr=1e-3, lr_decay=0.9999, + kl_weight=0.5, kl_min_weight=0.01, kl_decay=0.99995, + device="cpu", grad_clip=1.0, sampling_temperature=0.4): + super(Interface, self).__init__() + self.grad_clip = grad_clip + self.sampling_temperature = sampling_temperature + + self.model = model + self.device = device + self.model.to(self.device) + self.enc_opt = th.optim.Adam(self.model.encoder.parameters(), lr=lr) + self.dec_opt = th.optim.Adam(self.model.decoder.parameters(), lr=lr) + + self.kl_weight = kl_weight + self.kl_min_weight = kl_min_weight + self.kl_decay = kl_decay + self.kl_loss = losses.KLDivergence() + + self.schedulers = [ + th.optim.lr_scheduler.ExponentialLR(self.enc_opt, lr_decay), + th.optim.lr_scheduler.ExponentialLR(self.dec_opt, lr_decay), + ] + + self.reconstruction_loss = losses.GaussianMixtureReconstructionLoss() + + def optimizers(self): + return [self.enc_opt, self.dec_opt] + + def training_step(self, batch): + batch = batch.to(self.device) + out = self.model(batch) + + kl_loss = self.kl_loss( + out["mu"], out["log_sigma"]) + + # The target to predict is the next sequence step + targets = batch[:, 1:].to(self.device) + + # Scale the KL divergence weight + try: + state = self.enc_opt.state_dict()["param_groups"][0]["params"][0] + optim_step = self.enc_opt.state_dict()["state"][state]["step"] + except KeyError: + optim_step = 0 # no step taken yet + kl_scaling = 1.0 - (1.0 - + self.kl_min_weight)*(self.kl_decay**optim_step) + kl_weight = self.kl_weight * kl_scaling + + reconstruction_loss = self.reconstruction_loss( + out["pen_logits"], out["mixture_logits"], + out["gaussian_params"], targets) + loss = kl_loss*self.kl_weight + reconstruction_loss + + self.enc_opt.zero_grad() + self.dec_opt.zero_grad() + loss.backward() + + # clip gradients + enc_nrm = th.nn.utils.clip_grad_norm_( + self.model.encoder.parameters(), self.grad_clip) + dec_nrm = th.nn.utils.clip_grad_norm_( + self.model.decoder.parameters(), self.grad_clip) + + if enc_nrm > self.grad_clip: + LOG.debug("Clipped encoder gradient (%.5f) to %.2f", + enc_nrm, self.grad_clip) + + if dec_nrm > self.grad_clip: + LOG.debug("Clipped decoder gradient (%.5f) to %.2f", + dec_nrm, self.grad_clip) + + self.enc_opt.step() + self.dec_opt.step() + + return { + "loss": loss.item(), + "kl_loss": kl_loss.item(), + "kl_weight": kl_weight, + "recons_loss": reconstruction_loss.item(), + "lr": self.enc_opt.param_groups[0]["lr"], + } + + def init_validation(self): + return dict(sample=None) + + def validation_step(self, batch, running_data): + # Switch to eval mode for dropout, batchnorm, etc + self.model.eval() + with th.no_grad(): + sample = self.model.sample( + batch.to(self.device), temperature=self.sampling_temperature) + running_data["sample"] = sample + self.model.train() + return running_data + + +def train(args): + th.manual_seed(0) + np.random.seed(0) + + dataset = data.QuickDrawDataset(args.dataset) + dataloader = DataLoader( + dataset, batch_size=args.bs, num_workers=4, shuffle=True, + pin_memory=False) + + val_dataset = [s for idx, s in enumerate(dataset) if idx < 8] + val_dataloader = DataLoader( + val_dataset, batch_size=8, num_workers=4, shuffle=False, + pin_memory=False) + + model_params = { + "zdim": args.zdim, + "num_gaussians": args.num_gaussians, + "encoder_dim": args.encoder_dim, + "decoder_dim": args.decoder_dim, + } + model = SketchRNN(**model_params) + model.train() + + device = "cpu" + if th.cuda.is_available(): + device = "cuda" + LOG.info("Using CUDA") + + interface = Interface(model, lr=args.lr, lr_decay=args.lr_decay, + kl_decay=args.kl_decay, kl_weight=args.kl_weight, + sampling_temperature=args.sampling_temperature, + device=device) + + chkpt = OUTPUT_BASELINE + env_name = "sketch_rnn" + + # Resume from checkpoint, if any + checkpointer = ttools.Checkpointer( + chkpt, model, meta=model_params, + optimizers=interface.optimizers(), + schedulers=interface.schedulers) + extras, meta = checkpointer.load_latest() + epoch = extras["epoch"] if extras and "epoch" in extras.keys() else 0 + + if meta is not None and meta != model_params: + LOG.info("Checkpoint's metaparams differ " + "from CLI, aborting: %s and %s", meta, model_params) + + trainer = ttools.Trainer(interface) + + # Add callbacks + losses = ["loss", "kl_loss", "recons_loss"] + training_debug = ["lr", "kl_weight"] + trainer.add_callback(ttools.callbacks.ProgressBarCallback( + keys=losses, val_keys=None)) + trainer.add_callback(ttools.callbacks.VisdomLoggingCallback( + keys=losses, val_keys=None, env=env_name, port=args.port)) + trainer.add_callback(ttools.callbacks.VisdomLoggingCallback( + keys=training_debug, smoothing=0, val_keys=None, env=env_name, + port=args.port)) + trainer.add_callback(ttools.callbacks.CheckpointingCallback( + checkpointer, max_files=2, interval=600, max_epochs=10)) + trainer.add_callback( + ttools.callbacks.LRSchedulerCallback(interface.schedulers)) + + trainer.add_callback(SketchRNNCallback( + env=env_name, win="samples", port=args.port, frequency=args.freq)) + + # Start training + trainer.train(dataloader, starting_epoch=epoch, + val_dataloader=val_dataloader, + num_epochs=args.num_epochs) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--dataset", default="cat.npz") + + # Training params + parser.add_argument("--bs", type=int, default=100) + parser.add_argument("--num_epochs", type=int, default=10000) + parser.add_argument("--lr", type=float, default=1e-4) + parser.add_argument("--lr_decay", type=float, default=0.9999) + parser.add_argument("--kl_weight", type=float, default=0.5) + parser.add_argument("--kl_decay", type=float, default=0.99995) + + # Model configuration + parser.add_argument("--zdim", type=int, default=128) + parser.add_argument("--num_gaussians", type=int, default=20) + parser.add_argument("--encoder_dim", type=int, default=256) + parser.add_argument("--decoder_dim", type=int, default=512) + + parser.add_argument("--sampling_temperature", type=float, default=0.4, + help="controls sampling randomness. " + "0.0: deterministic, 1.0: unchanged") + + # Viz params + parser.add_argument("--freq", type=int, default=100) + parser.add_argument("--port", type=int, default=5000) + + args = parser.parse_args() + + pydiffvg.set_use_gpu(th.cuda.is_available()) + + train(args) diff --git a/apps/generative_models/sketch_vae.py b/apps/generative_models/sketch_vae.py new file mode 100755 index 0000000..797c3e5 --- /dev/null +++ b/apps/generative_models/sketch_vae.py @@ -0,0 +1,524 @@ +#!/bin/env python +"""Train a Sketch-VAE.""" +import argparse +from enum import Enum +import os +import wget +import time + +import numpy as np +import torch as th +from torch.utils.data import DataLoader +import torchvision.datasets as dset +import torchvision.transforms as transforms + +import ttools +import ttools.interfaces +from ttools.modules import networks + +import rendering +import losses +import modules +import data + +import pydiffvg + +LOG = ttools.get_logger(__name__) + + +BASE_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), os.pardir) +OUTPUT = os.path.join(BASE_DIR, "results") + + +class SketchVAE(th.nn.Module): + class ImageEncoder(th.nn.Module): + def __init__(self, image_size=64, width=64, zdim=128): + super(SketchVAE.ImageEncoder, self).__init__() + self.zdim = zdim + + self.net = th.nn.Sequential( + th.nn.Conv2d(4, width, 5, padding=2), + th.nn.InstanceNorm2d(width), + th.nn.ReLU(inplace=True), + # 64x64 + + th.nn.Conv2d(width, width, 5, padding=2), + th.nn.InstanceNorm2d(width), + th.nn.ReLU( inplace=True), + # 64x64 + + th.nn.Conv2d(width, 2*width, 5, stride=1, padding=2), + th.nn.InstanceNorm2d(2*width), + th.nn.ReLU( inplace=True), + # 32x32 + + th.nn.Conv2d(2*width, 2*width, 5, stride=2, padding=2), + th.nn.InstanceNorm2d(2*width), + th.nn.ReLU( inplace=True), + # 16x16 + + th.nn.Conv2d(2*width, 2*width, 5, stride=2, padding=2), + th.nn.InstanceNorm2d(2*width), + th.nn.ReLU( inplace=True), + # 16x16 + + th.nn.Conv2d(2*width, 2*width, 5, stride=2, padding=2), + th.nn.InstanceNorm2d(2*width), + th.nn.ReLU( inplace=True), + # 8x8 + + th.nn.Conv2d(2*width, 2*width, 5, stride=2, padding=2), + th.nn.InstanceNorm2d(2*width), + th.nn.ReLU( inplace=True), + # 4x4 + + modules.Flatten(), + th.nn.Linear(4*4*2*width, 2*zdim) + ) + + def forward(self, images): + features = self.net(images) + + # VAE params + mu = features[:, :self.zdim] + log_sigma = features[:, self.zdim:] + + # Sample a latent vector + sigma = th.exp(log_sigma/2.0) + z0 = th.randn(self.zdim, device=mu.device) + z = mu + sigma*z0 + + # KL divergence needs mu/sigma + return z, mu, log_sigma + + class ImageDecoder(th.nn.Module): + """""" + def __init__(self, zdim=128, image_size=64, width=64): + super(SketchVAE.ImageDecoder, self).__init__() + self.zdim = zdim + self.width = width + + self.embedding = th.nn.Linear(zdim, 4*4*2*width) + + self.net = th.nn.Sequential( + th.nn.ConvTranspose2d(2*width, 2*width, 4, padding=1, stride=2), + th.nn.InstanceNorm2d(2*width), + th.nn.ReLU( inplace=True), + # 8x8 + + th.nn.ConvTranspose2d(2*width, 2*width, 4, padding=1, stride=2), + th.nn.InstanceNorm2d(2*width), + th.nn.ReLU( inplace=True), + # 16x16 + + th.nn.ConvTranspose2d(2*width, 2*width, 4, padding=1, stride=2), + th.nn.InstanceNorm2d(2*width), + th.nn.ReLU( inplace=True), + # 16x16 + + th.nn.Conv2d(2*width, 2*width, 5, padding=2, stride=1), + th.nn.InstanceNorm2d(2*width), + th.nn.ReLU( inplace=True), + # 16x16 + + th.nn.ConvTranspose2d(2*width, 2*width, 4, padding=1, stride=2), + th.nn.InstanceNorm2d(2*width), + th.nn.ReLU( inplace=True), + # 32x32 + + th.nn.Conv2d(2*width, width, 5, padding=2, stride=1), + th.nn.InstanceNorm2d(width), + th.nn.ReLU( inplace=True), + # 32x32 + + th.nn.ConvTranspose2d(width, width, 5, padding=2, stride=1), + th.nn.InstanceNorm2d(width), + th.nn.ReLU( inplace=True), + # 64x64 + + th.nn.Conv2d(width, width, 5, padding=2, stride=1), + th.nn.InstanceNorm2d(width), + th.nn.ReLU( inplace=True), + # 64x64 + + th.nn.Conv2d(width, 4, 5, padding=2, stride=1), + ) + + def forward(self, z): + bs = z.shape[0] + im = self.embedding(z).view(bs, 2*self.width, 4, 4) + out = self.net(im) + return out + + class SketchDecoder(th.nn.Module): + """ + The decoder outputs a sequence where each time step models (dx, dy, + opacity). + """ + def __init__(self, sequence_length, hidden_size=512, dropout=0.9, + zdim=128, num_layers=3): + super(SketchVAE.SketchDecoder, self).__init__() + self.sequence_length = sequence_length + self.hidden_size = hidden_size + self.num_layers = num_layers + self.zdim = zdim + + # Maps the latent vector to an initial cell/hidden vector + self.hidden_cell_predictor = th.nn.Linear(zdim, 2*hidden_size*num_layers) + + self.lstm = th.nn.LSTM( + zdim, hidden_size, + num_layers=self.num_layers, dropout=dropout, + batch_first=True) + + self.dxdy_predictor = th.nn.Sequential( + th.nn.Linear(hidden_size, 2), + th.nn.Tanh(), + ) + self.opacity_predictor = th.nn.Sequential( + th.nn.Linear(hidden_size, 1), + th.nn.Sigmoid(), + ) + + def forward(self, z, hidden_and_cell=None): + # Every step in the sequence takes the latent vector as input so we + # replicate it here + bs = z.shape[0] + steps = self.sequence_length - 1 # no need to predict the start of sequence + expanded_z = z.unsqueeze(1).repeat(1, steps, 1) + + if hidden_and_cell is None: + # Initialize from latent vector + hidden_and_cell = self.hidden_cell_predictor( + th.tanh(z)) + hidden = hidden_and_cell[:, :self.hidden_size*self.num_layers] + hidden = hidden.view(-1, self.num_layers, self.hidden_size) + hidden = hidden.permute(1, 0, 2).contiguous() + # hidden = hidden.unsqueeze(1).contiguous() + cell = hidden_and_cell[:, self.hidden_size*self.num_layers:] + cell = cell.view(-1, self.num_layers, self.hidden_size) + cell = cell.permute(1, 0, 2).contiguous() + # cell = cell.unsqueeze(1).contiguous() + hidden_and_cell = (hidden, cell) + + outputs, hidden_and_cell = self.lstm(expanded_z, hidden_and_cell) + hidden, cell = hidden_and_cell + + dxdy = self.dxdy_predictor( + outputs.reshape(bs*steps, self.hidden_size)).view(bs, steps, -1) + + opacity = self.opacity_predictor( + outputs.reshape(bs*steps, self.hidden_size)).view(bs, steps, -1) + + strokes = th.cat([dxdy, opacity], -1) + + return strokes + + def __init__(self, sequence_length, zdim=128, image_size=64): + super(SketchVAE, self).__init__() + self.im_encoder = SketchVAE.ImageEncoder( + zdim=zdim, image_size=image_size) + self.im_decoder = SketchVAE.ImageDecoder( + zdim=zdim, image_size=image_size) + self.sketch_decoder = SketchVAE.SketchDecoder( + sequence_length, zdim=zdim) + + def forward(self, images): + # Encode the images as latent vectors + z, mu, log_sigma = self.im_encoder(images) + decoded_im = self.im_decoder(z) + decoded_sketch = self.sketch_decoder(z) + + return { + "decoded_im": decoded_im, + "decoded_sketch": decoded_sketch, + "z": z, + "mu": mu, + "log_sigma": log_sigma, + } + + +class SketchVAECallback(ttools.callbacks.ImageDisplayCallback): + """Simple callback that visualize images.""" + def visualized_image(self, batch, step_data, is_val=False): + if is_val: + return None + + # only display the first n drawings + n = 8 + gt = step_data["gt_image"][:n].detach() + vae_im = step_data["vae_image"][:n].detach() + sketch_im = step_data["sketch_image"][:n].detach() + + rendering = th.cat([gt, vae_im, sketch_im], 2) + rendering = th.clamp(rendering, 0, 1) + alpha = rendering[:, 3:4] + rendering = rendering[:, :3] * alpha + + return rendering + + def caption(self, batch, step_data, is_val=False): + if is_val: + return "" + else: + return "top: truth, middle: vae sample, output: rnn-output" + + + + +class Interface(ttools.ModelInterface): + def __init__(self, model, lr=1e-4, lr_decay=0.9999, + kl_weight=0.5, kl_min_weight=0.01, kl_decay=0.99995, + raster_resolution=64, absolute_coords=False, + device="cpu", grad_clip=1.0): + super(Interface, self).__init__() + + self.grad_clip = grad_clip + self.raster_resolution = raster_resolution + self.absolute_coords = absolute_coords + + self.model = model + self.device = device + self.model.to(self.device) + self.im_enc_opt = th.optim.Adam( + self.model.im_encoder.parameters(), lr=lr) + self.im_dec_opt = th.optim.Adam( + self.model.im_decoder.parameters(), lr=lr) + self.sketch_dec_opt = th.optim.Adam( + self.model.sketch_decoder.parameters(), lr=lr) + + self.kl_weight = kl_weight + self.kl_min_weight = kl_min_weight + self.kl_decay = kl_decay + self.kl_loss = losses.KLDivergence() + + self.schedulers = [ + th.optim.lr_scheduler.ExponentialLR(self.im_enc_opt, lr_decay), + th.optim.lr_scheduler.ExponentialLR(self.im_dec_opt, lr_decay), + th.optim.lr_scheduler.ExponentialLR(self.sketch_dec_opt, lr_decay), + ] + + # include loss on alpha + self.im_loss = losses.MultiscaleMSELoss(channels=4).to(self.device) + + def optimizers(self): + return [self.im_enc_opt, self.im_dec_opt, self.sketch_dec_opt] + + def kl_scaling(self): + # Scale the KL divergence weight + try: + state = self.im_enc_opt.state_dict()["param_groups"][0]["params"][0] + optim_step = self.im_enc_opt.state_dict()["state"][state]["step"] + except KeyError: + optim_step = 0 # no step taken yet + kl_scaling = 1.0 - (1.0 - + self.kl_min_weight)*(self.kl_decay**optim_step) + return kl_scaling + + def training_step(self, batch): + gt_strokes, gt_im = batch + gt_strokes = gt_strokes.to(self.device) + gt_im = gt_im.to(self.device) + + out = self.model(gt_im) + + kl_loss = self.kl_loss( + out["mu"], out["log_sigma"]) + kl_weight = self.kl_weight * self.kl_scaling() + + # add start of sequence + sos = gt_strokes[:, :1] + sketch = th.cat([sos, out["decoded_sketch"]], 1) + + vae_im = out["decoded_im"] + + # start = time.time() + sketch_im = rendering.opacityStroke2diffvg( + sketch, canvas_size=self.raster_resolution, debug=False, + force_cpu=True, relative=not self.absolute_coords) + # elapsed = (time.time() - start)*1000 + # print("out rendering took %.2fms" % elapsed) + + vae_im_loss = self.im_loss(vae_im, gt_im) + sketch_im_loss = self.im_loss(sketch_im, gt_im) + + # vae_im_loss = th.nn.functional.mse_loss(vae_im, gt_im) + # sketch_im_loss = th.nn.functional.mse_loss(sketch_im, gt_im) + + loss = vae_im_loss + kl_loss*kl_weight + sketch_im_loss + + self.im_enc_opt.zero_grad() + self.im_dec_opt.zero_grad() + self.sketch_dec_opt.zero_grad() + loss.backward() + + # clip gradients + enc_nrm = th.nn.utils.clip_grad_norm_( + self.model.im_encoder.parameters(), self.grad_clip) + dec_nrm = th.nn.utils.clip_grad_norm_( + self.model.im_decoder.parameters(), self.grad_clip) + sketch_dec_nrm = th.nn.utils.clip_grad_norm_( + self.model.sketch_decoder.parameters(), self.grad_clip) + + if enc_nrm > self.grad_clip: + LOG.debug("Clipped encoder gradient (%.5f) to %.2f", + enc_nrm, self.grad_clip) + + if dec_nrm > self.grad_clip: + LOG.debug("Clipped decoder gradient (%.5f) to %.2f", + dec_nrm, self.grad_clip) + + if sketch_dec_nrm > self.grad_clip: + LOG.debug("Clipped sketch decoder gradient (%.5f) to %.2f", + sketch_dec_nrm, self.grad_clip) + + self.im_enc_opt.step() + self.im_dec_opt.step() + self.sketch_dec_opt.step() + + return { + "vae_image": vae_im, + "sketch_image": sketch_im, + "gt_image": gt_im, + "loss": loss.item(), + "vae_im_loss": vae_im_loss.item(), + "sketch_im_loss": sketch_im_loss.item(), + "kl_loss": kl_loss.item(), + "kl_weight": kl_weight, + "lr": self.im_enc_opt.param_groups[0]["lr"], + } + + def init_validation(self): + return dict(sample=None) + + def validation_step(self, batch, running_data): + # Switch to eval mode for dropout, batchnorm, etc + # self.model.eval() + # with th.no_grad(): + # # sample = self.model.sample( + # # batch.to(self.device), temperature=self.sampling_temperature) + # # running_data["sample"] = sample + # self.model.train() + return running_data + + +def train(args): + th.manual_seed(0) + np.random.seed(0) + + dataset = data.FixedLengthQuickDrawDataset( + args.dataset, max_seq_length=args.sequence_length, + canvas_size=args.raster_resolution) + dataloader = DataLoader( + dataset, batch_size=args.bs, num_workers=args.workers, shuffle=True) + + # val_dataset = [s for idx, s in enumerate(dataset) if idx < 8] + # val_dataloader = DataLoader( + # val_dataset, batch_size=8, num_workers=4, shuffle=False) + + val_dataloader = None + + model_params = { + "zdim": args.zdim, + "sequence_length": args.sequence_length, + "image_size": args.raster_resolution, + # "encoder_dim": args.encoder_dim, + # "decoder_dim": args.decoder_dim, + } + model = SketchVAE(**model_params) + model.train() + + LOG.info("Model parameters:\n%s", model_params) + + device = "cpu" + if th.cuda.is_available(): + device = "cuda" + LOG.info("Using CUDA") + + interface = Interface(model, raster_resolution=args.raster_resolution, + lr=args.lr, lr_decay=args.lr_decay, + kl_decay=args.kl_decay, kl_weight=args.kl_weight, + absolute_coords=args.absolute_coordinates, + device=device) + + env_name = "sketch_vae" + if args.custom_name is not None: + env_name += "_" + args.custom_name + + if args.absolute_coordinates: + env_name += "_abs_coords" + + chkpt = os.path.join(OUTPUT, env_name) + + # Resume from checkpoint, if any + checkpointer = ttools.Checkpointer( + chkpt, model, meta=model_params, + optimizers=interface.optimizers(), + schedulers=interface.schedulers) + extras, meta = checkpointer.load_latest() + epoch = extras["epoch"] if extras and "epoch" in extras.keys() else 0 + + if meta is not None and meta != model_params: + LOG.info("Checkpoint's metaparams differ " + "from CLI, aborting: %s and %s", meta, model_params) + + trainer = ttools.Trainer(interface) + + # Add callbacks + losses = ["loss", "kl_loss", "vae_im_loss", "sketch_im_loss"] + training_debug = ["lr", "kl_weight"] + trainer.add_callback(ttools.callbacks.ProgressBarCallback( + keys=losses, val_keys=None)) + trainer.add_callback(ttools.callbacks.VisdomLoggingCallback( + keys=losses, val_keys=None, env=env_name, port=args.port)) + trainer.add_callback(ttools.callbacks.VisdomLoggingCallback( + keys=training_debug, smoothing=0, val_keys=None, env=env_name, + port=args.port)) + trainer.add_callback(ttools.callbacks.CheckpointingCallback( + checkpointer, max_files=2, interval=600, max_epochs=10)) + trainer.add_callback( + ttools.callbacks.LRSchedulerCallback(interface.schedulers)) + + trainer.add_callback(SketchVAECallback( + env=env_name, win="samples", port=args.port, frequency=args.freq)) + + # Start training + trainer.train(dataloader, starting_epoch=epoch, + val_dataloader=val_dataloader, + num_epochs=args.num_epochs) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--dataset", default="cat.npz") + + parser.add_argument("--absolute_coordinates", action="store_true", + default=False) + + parser.add_argument("--custom_name") + + # Training params + parser.add_argument("--bs", type=int, default=1) + parser.add_argument("--workers", type=int, default=0) + parser.add_argument("--num_epochs", type=int, default=10000) + parser.add_argument("--lr", type=float, default=1e-4) + parser.add_argument("--lr_decay", type=float, default=0.9999) + parser.add_argument("--kl_weight", type=float, default=0.5) + parser.add_argument("--kl_decay", type=float, default=0.99995) + + # Model configuration + parser.add_argument("--zdim", type=int, default=128) + parser.add_argument("--sequence_length", type=int, default=50) + parser.add_argument("--raster_resolution", type=int, default=64) + # parser.add_argument("--encoder_dim", type=int, default=256) + # parser.add_argument("--decoder_dim", type=int, default=512) + + # Viz params + parser.add_argument("--freq", type=int, default=10) + parser.add_argument("--port", type=int, default=5000) + + args = parser.parse_args() + + pydiffvg.set_use_gpu(False) + + train(args) diff --git a/apps/generative_models/train_gan.py b/apps/generative_models/train_gan.py new file mode 100755 index 0000000..9b5eaa1 --- /dev/null +++ b/apps/generative_models/train_gan.py @@ -0,0 +1,489 @@ +#!/bin/env python +"""Train a GAN. + +Usage: + +* Train a MNIST model: + +`python train_gan.py` + +* Train a Quickdraw model: + +`python train_gan.py --task quickdraw` + +""" +import argparse +import os + +import numpy as np +import torch as th +from torch.utils.data import DataLoader + +import ttools +import ttools.interfaces + +import losses +import data +import models + +import pydiffvg + +LOG = ttools.get_logger(__name__) + + +BASE_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), os.pardir) +OUTPUT = os.path.join(BASE_DIR, "results") + + +class Callback(ttools.callbacks.ImageDisplayCallback): + """Simple callback that visualize images.""" + def visualized_image(self, batch, step_data, is_val=False): + if is_val: + return + + gen = step_data["gen_image"][:16].detach() + ref = step_data["gt_image"][:16].detach() + + # tensor to visualize, concatenate images + vizdata = th.cat([ref, gen], 2) + + vector = step_data["vector_image"] + if vector is not None: + vector = vector[:16].detach() + vizdata = th.cat([vizdata, vector], 2) + + vizdata = (vizdata + 1.0 ) * 0.5 + viz = th.clamp(vizdata, 0, 1) + return viz + + def caption(self, batch, step_data, is_val=False): + if step_data["vector_image"] is not None: + s = "top: real, middle: raster, bottom: vector" + else: + s = "top: real, bottom: fake" + return s + + +class Interface(ttools.ModelInterface): + def __init__(self, generator, vect_generator, + discriminator, vect_discriminator, + lr=1e-4, lr_decay=0.9999, + gradient_penalty=10, + wgan_gp=False, + raster_resolution=32, device="cpu", grad_clip=1.0): + super(Interface, self).__init__() + + self.wgan_gp = wgan_gp + self.w_gradient_penalty = gradient_penalty + + self.n_critic = 1 + if self.wgan_gp: + self.n_critic = 5 + + self.grad_clip = grad_clip + self.raster_resolution = raster_resolution + + self.gen = generator + self.vect_gen = vect_generator + self.discrim = discriminator + self.vect_discrim = vect_discriminator + + self.device = device + self.gen.to(self.device) + self.discrim.to(self.device) + + beta1 = 0.5 + beta2 = 0.9 + + self.gen_opt = th.optim.Adam( + self.gen.parameters(), lr=lr, betas=(beta1, beta2)) + self.discrim_opt = th.optim.Adam( + self.discrim.parameters(), lr=lr, betas=(beta1, beta2)) + + self.schedulers = [ + th.optim.lr_scheduler.ExponentialLR(self.gen_opt, lr_decay), + th.optim.lr_scheduler.ExponentialLR(self.discrim_opt, lr_decay), + ] + + self.optimizers = [self.gen_opt, self.discrim_opt] + + if self.vect_gen is not None: + assert self.vect_discrim is not None + + self.vect_gen.to(self.device) + self.vect_discrim.to(self.device) + + self.vect_gen_opt = th.optim.Adam( + self.vect_gen.parameters(), lr=lr, betas=(beta1, beta2)) + self.vect_discrim_opt = th.optim.Adam( + self.vect_discrim.parameters(), lr=lr, betas=(beta1, beta2)) + + self.schedulers += [ + th.optim.lr_scheduler.ExponentialLR(self.vect_gen_opt, + lr_decay), + th.optim.lr_scheduler.ExponentialLR(self.vect_discrim_opt, + lr_decay), + ] + + self.optimizers += [self.vect_gen_opt, self.vect_discrim_opt] + + # include loss on alpha + self.im_loss = losses.MultiscaleMSELoss(channels=4).to(self.device) + + self.iter = 0 + + self.cross_entropy = th.nn.BCEWithLogitsLoss() + self.mse = th.nn.MSELoss() + + def _gradient_penalty(self, discrim, fake, real): + bs = real.size(0) + epsilon = th.rand(bs, 1, 1, 1, device=real.device) + epsilon = epsilon.expand_as(real) + + interpolation = epsilon * real.data + (1 - epsilon) * fake.data + interpolation = th.autograd.Variable(interpolation, requires_grad=True) + + interpolation_logits = discrim(interpolation) + grad_outputs = th.ones(interpolation_logits.size(), device=real.device) + + gradients = th.autograd.grad(outputs=interpolation_logits, + inputs=interpolation, + grad_outputs=grad_outputs, + create_graph=True, retain_graph=True)[0] + + gradients = gradients.view(bs, -1) + gradients_norm = th.sqrt(th.sum(gradients ** 2, dim=1) + 1e-12) + + # [Tanh-Tung 2019] https://openreview.net/pdf?id=ByxPYjC5KQ + return self.w_gradient_penalty * ((gradients_norm - 0) ** 2).mean() + + # return self.w_gradient_penalty * ((gradients_norm - 1) ** 2).mean() + + def _discriminator_step(self, discrim, opt, fake, real): + """Try to classify fake as 0 and real as 1.""" + + opt.zero_grad() + + # no backprop to gen + fake = fake.detach() + + fake_pred = discrim(fake) + real_pred = discrim(real) + + if self.wgan_gp: + gradient_penalty = self._gradient_penalty(discrim, fake, real) + loss_d = fake_pred.mean() - real_pred.mean() + gradient_penalty + gradient_penalty = gradient_penalty.item() + else: + fake_loss = self.cross_entropy(fake_pred, th.zeros_like(fake_pred)) + real_loss = self.cross_entropy(real_pred, th.ones_like(real_pred)) + # fake_loss = self.mse(fake_pred, th.zeros_like(fake_pred)) + # real_loss = self.mse(real_pred, th.ones_like(real_pred)) + loss_d = 0.5*(fake_loss + real_loss) + gradient_penalty = None + + loss_d.backward() + nrm = th.nn.utils.clip_grad_norm_( + discrim.parameters(), self.grad_clip) + if nrm > self.grad_clip: + LOG.debug("Clipped discriminator gradient (%.5f) to %.2f", + nrm, self.grad_clip) + + opt.step() + + return loss_d.item(), gradient_penalty + + def _generator_step(self, gen, discrim, opt, fake): + """Try to classify fake as 1.""" + + opt.zero_grad() + + fake_pred = discrim(fake) + + if self.wgan_gp: + loss_g = -fake_pred.mean() + else: + loss_g = self.cross_entropy(fake_pred, th.ones_like(fake_pred)) + # loss_g = self.mse(fake_pred, th.ones_like(fake_pred)) + + loss_g.backward() + + # clip gradients + nrm = th.nn.utils.clip_grad_norm_( + gen.parameters(), self.grad_clip) + if nrm > self.grad_clip: + LOG.debug("Clipped generator gradient (%.5f) to %.2f", + nrm, self.grad_clip) + + opt.step() + + return loss_g.item() + + def training_step(self, batch): + im = batch + im = im.to(self.device) + + z = self.gen.sample_z(im.shape[0], device=self.device) + + generated = self.gen(z) + + vect_generated = None + if self.vect_gen is not None: + vect_generated = self.vect_gen(z) + + loss_g = None + loss_d = None + loss_g_vect = None + loss_d_vect = None + + gp = None + gp_vect = None + + if self.iter < self.n_critic: # Discriminator update + self.iter += 1 + + loss_d, gp = self._discriminator_step( + self.discrim, self.discrim_opt, generated, im) + + if vect_generated is not None: + loss_d_vect, gp_vect = self._discriminator_step( + self.vect_discrim, self.vect_discrim_opt, vect_generated, im) + + else: # Generator update + self.iter = 0 + + loss_g = self._generator_step( + self.gen, self.discrim, self.gen_opt, generated) + + if vect_generated is not None: + loss_g_vect = self._generator_step( + self.vect_gen, self.vect_discrim, self.vect_gen_opt, vect_generated) + + return { + "loss_g": loss_g, + "loss_d": loss_d, + "loss_g_vect": loss_g_vect, + "loss_d_vect": loss_d_vect, + "gp": gp, + "gp_vect": gp_vect, + "gt_image": im, + "gen_image": generated, + "vector_image": vect_generated, + "lr": self.gen_opt.param_groups[0]["lr"], + } + + def init_validation(self): + return dict(sample=None) + + def validation_step(self, batch, running_data): + # Switch to eval mode for dropout, batchnorm, etc + self.model.eval() + return running_data + + +def train(args): + th.manual_seed(0) + np.random.seed(0) + + color_output = False + if args.task == "mnist": + dataset = data.MNISTDataset(args.raster_resolution, train=True) + elif args.task == "quickdraw": + dataset = data.QuickDrawImageDataset( + args.raster_resolution, train=True) + else: + raise NotImplementedError() + + dataloader = DataLoader( + dataset, batch_size=args.bs, num_workers=args.workers, shuffle=True) + + val_dataloader = None + + model_params = { + "zdim": args.zdim, + "num_strokes": args.num_strokes, + "imsize": args.raster_resolution, + "stroke_width": args.stroke_width, + "color_output": color_output, + } + gen = models.Generator(**model_params) + gen.train() + + discrim = models.Discriminator(color_output=color_output) + discrim.train() + + if args.raster_only: + vect_gen = None + vect_discrim = None + else: + if args.generator == "fc": + vect_gen = models.VectorGenerator(**model_params) + elif args.generator == "bezier_fc": + vect_gen = models.BezierVectorGenerator(**model_params) + elif args.generator in ["rnn"]: + vect_gen = models.RNNVectorGenerator(**model_params) + elif args.generator in ["chain_rnn"]: + vect_gen = models.ChainRNNVectorGenerator(**model_params) + else: + raise NotImplementedError() + vect_gen.train() + + vect_discrim = models.Discriminator(color_output=color_output) + vect_discrim.train() + + LOG.info("Model parameters:\n%s", model_params) + + device = "cpu" + if th.cuda.is_available(): + device = "cuda" + LOG.info("Using CUDA") + + interface = Interface(gen, vect_gen, discrim, vect_discrim, + raster_resolution=args.raster_resolution, lr=args.lr, + wgan_gp=args.wgan_gp, + lr_decay=args.lr_decay, device=device) + + env_name = args.task + "_gan" + + if args.raster_only: + env_name += "_raster" + else: + env_name += "_vector" + + env_name += "_" + args.generator + + if args.wgan_gp: + env_name += "_wgan" + + chkpt = os.path.join(OUTPUT, env_name) + + meta = { + "model_params": model_params, + "task": args.task, + "generator": args.generator, + } + checkpointer = ttools.Checkpointer( + chkpt, gen, meta=meta, + optimizers=interface.optimizers, + schedulers=interface.schedulers, + prefix="g_") + checkpointer_d = ttools.Checkpointer( + chkpt, discrim, + prefix="d_") + + # Resume from checkpoint, if any + extras, _ = checkpointer.load_latest() + checkpointer_d.load_latest() + + if not args.raster_only: + checkpointer_vect = ttools.Checkpointer( + chkpt, vect_gen, meta=meta, + optimizers=interface.optimizers, + schedulers=interface.schedulers, + prefix="vect_g_") + checkpointer_d_vect = ttools.Checkpointer( + chkpt, vect_discrim, + prefix="vect_d_") + extras, _ = checkpointer_vect.load_latest() + checkpointer_d_vect.load_latest() + + epoch = extras["epoch"] if extras and "epoch" in extras.keys() else 0 + + # if meta is not None and meta["model_parameters"] != model_params: + # LOG.info("Checkpoint's metaparams differ " + # "from CLI, aborting: %s and %s", meta, model_params) + + trainer = ttools.Trainer(interface) + + # Add callbacks + losses = ["loss_g", "loss_d", "loss_g_vect", "loss_d_vect", "gp", + "gp_vect"] + training_debug = ["lr"] + + trainer.add_callback(Callback( + env=env_name, win="samples", port=args.port, frequency=args.freq)) + trainer.add_callback(ttools.callbacks.ProgressBarCallback( + keys=losses, val_keys=None)) + trainer.add_callback(ttools.callbacks.MultiPlotCallback( + keys=losses, val_keys=None, env=env_name, port=args.port, + server=args.server, base_url=args.base_url, + win="losses", frequency=args.freq)) + trainer.add_callback(ttools.callbacks.VisdomLoggingCallback( + keys=training_debug, smoothing=0, val_keys=None, env=env_name, + server=args.server, base_url=args.base_url, + port=args.port)) + trainer.add_callback(ttools.callbacks.CheckpointingCallback( + checkpointer, max_files=2, interval=600, max_epochs=10)) + trainer.add_callback(ttools.callbacks.CheckpointingCallback( + checkpointer_d, max_files=2, interval=600, max_epochs=10)) + + if not args.raster_only: + trainer.add_callback(ttools.callbacks.CheckpointingCallback( + checkpointer_vect, max_files=2, interval=600, max_epochs=10)) + trainer.add_callback(ttools.callbacks.CheckpointingCallback( + checkpointer_d_vect, max_files=2, interval=600, max_epochs=10)) + + trainer.add_callback( + ttools.callbacks.LRSchedulerCallback(interface.schedulers)) + + # Start training + trainer.train(dataloader, starting_epoch=epoch, + val_dataloader=val_dataloader, + num_epochs=args.num_epochs) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--task", + default="mnist", + choices=["mnist", "quickdraw"]) + parser.add_argument("--generator", + default="bezier_fc", + choices=["bezier_fc", "fc", "rnn", "chain_rnn"], + help="model to use as generator") + + parser.add_argument("--raster_only", action="store_true", default=False, + help="if true only train the raster baseline") + + parser.add_argument("--standard_gan", dest="wgan_gp", action="store_false", + default=True, + help="if true, use regular GAN instead of WGAN") + + # Training params + parser.add_argument("--bs", type=int, default=4, help="batch size") + parser.add_argument("--workers", type=int, default=4, + help="number of dataloader threads") + parser.add_argument("--num_epochs", type=int, default=200, + help="number of epochs to train for") + parser.add_argument("--lr", type=float, default=1e-4, + help="learning rate") + parser.add_argument("--lr_decay", type=float, default=0.9999, + help="exponential learning rate decay rate") + + # Model configuration + parser.add_argument("--zdim", type=int, default=32, + help="latent space dimension") + parser.add_argument("--stroke_width", type=float, nargs=2, + default=(0.5, 1.5), + help="min and max stroke width") + parser.add_argument("--num_strokes", type=int, default=16, + help="number of strokes to generate") + parser.add_argument("--raster_resolution", type=int, default=32, + help="raster canvas resolution on each side") + + # Viz params + parser.add_argument("--freq", type=int, default=10, + help="visualization frequency") + parser.add_argument("--port", type=int, default=8097, + help="visdom port") + parser.add_argument("--server", default=None, + help="visdom server if not local.") + parser.add_argument("--base_url", default="", help="visdom entrypoint URL") + + args = parser.parse_args() + + pydiffvg.set_use_gpu(False) + + ttools.set_logger(False) + + train(args) diff --git a/apps/geometry.py b/apps/geometry.py new file mode 100644 index 0000000..59e00db --- /dev/null +++ b/apps/geometry.py @@ -0,0 +1,226 @@ +import math +import torch + +class GeometryLoss: + def __init__(self, pathObj, xyalign=True, parallel=True, smooth_node=True): + self.pathObj=pathObj + self.pathId=pathObj.id + self.get_segments(pathObj) + if xyalign: + self.make_hor_ver_constraints(pathObj) + + self.xyalign=xyalign + self.parallel=parallel + self.smooth_node=smooth_node + + if parallel: + self.make_parallel_constraints(pathObj) + + if smooth_node: + self.make_smoothness_constraints(pathObj) + + def make_smoothness_constraints(self,pathObj): + self.smooth_nodes=[] + for idx, node in enumerate(self.iterate_nodes()): + sm, t0, t1=self.node_smoothness(node,pathObj) + if abs(sm)<1e-2: + self.smooth_nodes.append((node,((t0.norm()/self.segment_approx_length(node[0],pathObj)).item(),(t1.norm()/self.segment_approx_length(node[1],pathObj)).item()))) + #print("Node {} is smooth (smoothness {})".format(idx,sm)) + else: + #print("Node {} is not smooth (smoothness {})".format(idx, sm)) + pass + + def node_smoothness(self,node,pathObj): + t0=self.tangent_out(node[0],pathObj) + t1=self.tangent_in(node[1],pathObj) + t1rot=torch.stack((-t1[1],t1[0])) + smoothness=t0.dot(t1rot)/(t0.norm()*t1.norm()) + + return smoothness, t0, t1 + + def segment_approx_length(self,segment,pathObj): + if segment[0]==0: + #line + idxs=self.segList[segment[0]][segment[1]] + #should have a pair of indices now + length=(pathObj.points[idxs[1],:]-pathObj.points[idxs[0],:]).norm() + return length + elif segment[0]==1: + #quadric + idxs = self.segList[segment[0]][segment[1]] + # should have a pair of indices now + length = (pathObj.points[idxs[1],:] - pathObj.points[idxs[0],:]).norm()+(pathObj.points[idxs[2],:] - pathObj.points[idxs[1],:]).norm() + return length + elif segment[0]==2: + #cubic + idxs = self.segList[segment[0]][segment[1]] + # should have a pair of indices now + length = (pathObj.points[idxs[1],:] - pathObj.points[idxs[0],:]).norm()+(pathObj.points[idxs[2],:] - pathObj.points[idxs[1],:]).norm()+(pathObj.points[idxs[3],:] - pathObj.points[idxs[2],:]).norm() + return length + + def tangent_in(self, segment,pathObj): + if segment[0]==0: + #line + idxs=self.segList[segment[0]][segment[1]] + #should have a pair of indices now + tangent=(pathObj.points[idxs[1],:]-pathObj.points[idxs[0],:])/2 + return tangent + elif segment[0]==1: + #quadric + idxs = self.segList[segment[0]][segment[1]] + # should have a pair of indices now + tangent = (pathObj.points[idxs[1],:] - pathObj.points[idxs[0],:]) + return tangent + elif segment[0]==2: + #cubic + idxs = self.segList[segment[0]][segment[1]] + # should have a pair of indices now + tangent = (pathObj.points[idxs[1],:] - pathObj.points[idxs[0],:]) + return tangent + + assert(False) + + def tangent_out(self, segment, pathObj): + if segment[0] == 0: + # line + idxs = self.segList[segment[0]][segment[1]] + # should have a pair of indices now + tangent = (pathObj.points[idxs[0],:] - pathObj.points[idxs[1],:]) / 2 + return tangent + elif segment[0] == 1: + # quadric + idxs = self.segList[segment[0]][segment[1]] + # should have a pair of indices now + tangent = (pathObj.points[idxs[1],:] - pathObj.points[idxs[2],:]) + return tangent + elif segment[0] == 2: + # cubic + idxs = self.segList[segment[0]][segment[1]] + # should have a pair of indices now + tangent = (pathObj.points[idxs[2],:] - pathObj.points[idxs[3],:]) + return tangent + + assert (False) + + def get_segments(self, pathObj): + self.segments=[] + self.lines = [] + self.quadrics=[] + self.cubics=[] + self.segList =(self.lines,self.quadrics,self.cubics) + idx=0 + total_points=pathObj.points.shape[0] + for ncp in pathObj.num_control_points.numpy(): + if ncp==0: + self.segments.append((0,len(self.lines))) + self.lines.append((idx, (idx + 1) % total_points)) + idx+=1 + elif ncp==1: + self.segments.append((1, len(self.quadrics))) + self.quadrics.append((idx, (idx + 1), (idx+2) % total_points)) + idx+=ncp+1 + elif ncp==2: + self.segments.append((2, len(self.cubics))) + self.cubics.append((idx, (idx + 1), (idx+2), (idx + 3) % total_points)) + idx += ncp + 1 + + def iterate_nodes(self): + for prev, next in zip([self.segments[-1]]+self.segments[:-1],self.segments): + yield (prev, next) + + def make_hor_ver_constraints(self, pathObj): + self.horizontals=[] + self.verticals=[] + for idx, line in enumerate(self.lines): + startPt=pathObj.points[line[0],:] + endPt=pathObj.points[line[1],:] + + dif=endPt-startPt + + if abs(dif[0])<1e-6: + #is horizontal + self.horizontals.append(idx) + + if abs(dif[1])<1e-6: + #is vertical + self.verticals.append(idx) + + def make_parallel_constraints(self,pathObj): + slopes=[] + for lidx, line in enumerate(self.lines): + startPt = pathObj.points[line[0], :] + endPt = pathObj.points[line[1], :] + + dif = endPt - startPt + + slope=math.atan2(dif[1],dif[0]) + if slope<0: + slope+=math.pi + + minidx=-1 + for idx, s in enumerate(slopes): + if abs(s[0]-slope)<1e-3: + minidx=idx + break + + if minidx>=0: + slopes[minidx][1].append(lidx) + else: + slopes.append((slope,[lidx])) + + self.parallel_groups=[sgroup[1] for sgroup in slopes if len(sgroup[1])>1 and (not self.xyalign or (sgroup[0]>1e-3 and abs(sgroup[0]-(math.pi/2))>1e-3))] + + def make_line_diff(self,pathObj,lidx): + line = self.lines[lidx] + startPt = pathObj.points[line[0], :] + endPt = pathObj.points[line[1], :] + + dif = endPt - startPt + return dif + + def calc_hor_ver_loss(self,loss,pathObj): + for lidx in self.horizontals: + dif = self.make_line_diff(pathObj,lidx) + loss+=dif[0].pow(2) + + for lidx in self.verticals: + dif = self.make_line_diff(pathObj,lidx) + loss += dif[1].pow(2) + + def calc_parallel_loss(self,loss,pathObj): + for group in self.parallel_groups: + diffs=[self.make_line_diff(pathObj,lidx) for lidx in group] + difmat=torch.stack(diffs,1) + lengths=difmat.pow(2).sum(dim=0).sqrt() + difmat=difmat/lengths + difmat=torch.cat((difmat,torch.zeros(1,difmat.shape[1]))) + rotmat=difmat[:,list(range(1,difmat.shape[1]))+[0]] + cross=difmat.cross(rotmat) + ploss=cross.pow(2).sum()*lengths.sum()*10 + loss+=ploss + + def calc_smoothness_loss(self,loss,pathObj): + for node, tlengths in self.smooth_nodes: + sl,t0,t1=self.node_smoothness(node,pathObj) + #add smoothness loss + loss+=sl.pow(2)*t0.norm().sqrt()*t1.norm().sqrt() + tl=((t0.norm()/self.segment_approx_length(node[0],pathObj))-tlengths[0]).pow(2)+((t1.norm()/self.segment_approx_length(node[1],pathObj))-tlengths[1]).pow(2) + loss+=tl*10 + + def compute(self, pathObj): + if pathObj.id != self.pathId: + raise ValueError("Path ID {} does not match construction-time ID {}".format(pathObj.id,self.pathId)) + + loss=torch.tensor(0.) + if self.xyalign: + self.calc_hor_ver_loss(loss,pathObj) + + if self.parallel: + self.calc_parallel_loss(loss, pathObj) + + if self.smooth_node: + self.calc_smoothness_loss(loss,pathObj) + + #print(loss.item()) + + return loss diff --git a/apps/image_compare.py b/apps/image_compare.py new file mode 100644 index 0000000..fc55aa2 --- /dev/null +++ b/apps/image_compare.py @@ -0,0 +1,45 @@ +import argparse +import skimage.io +import numpy as np +from matplotlib import cm +import math +from skimage.metrics import structural_similarity as ssim + +def normalize(x, min_, max_): + return (x - min_) / (max_ - min_) + +def main(args): + img1 = skimage.img_as_float(skimage.io.imread(args.img1)).astype(np.float32) + img2 = skimage.img_as_float(skimage.io.imread(args.img2)).astype(np.float32) + ref = skimage.img_as_float(skimage.io.imread(args.ref)).astype(np.float32) + img1 = img1[:, :, :3] + img2 = img2[:, :, :3] + ref = ref[:, :, :3] + + diff1 = np.sum(np.abs(img1 - ref), axis = 2) + diff2 = np.sum(np.abs(img2 - ref), axis = 2) + min_ = min(np.min(diff1), np.min(diff2)) + max_ = max(np.max(diff1), np.max(diff2)) * 0.5 + diff1 = cm.viridis(normalize(diff1, min_, max_)) + diff2 = cm.viridis(normalize(diff2, min_, max_)) + + # MSE + print('MSE img1:', np.mean(np.power(img1 - ref, 2.0))) + print('MSE img2:', np.mean(np.power(img2 - ref, 2.0))) + # PSNR + print('PSNR img1:', 20 * math.log10(1.0 / math.sqrt(np.mean(np.power(img1 - ref, 2.0))))) + print('PSNR img2:', 20 * math.log10(1.0 / math.sqrt(np.mean(np.power(img2 - ref, 2.0))))) + # SSIM + print('SSIM img1:', ssim(img1, ref, multichannel=True)) + print('SSIM img2:', ssim(img2, ref, multichannel=True)) + + skimage.io.imsave('diff1.png', (diff1 * 255).astype(np.uint8)) + skimage.io.imsave('diff2.png', (diff2 * 255).astype(np.uint8)) + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("img1", help="img1") + parser.add_argument("img2", help="img2") + parser.add_argument("ref", help="ref") + args = parser.parse_args() + main(args) diff --git a/apps/imgs/baboon.png b/apps/imgs/baboon.png new file mode 100644 index 0000000000000000000000000000000000000000..2b1499a5eb878c8b50149ce6ceb4853c7d5abe47 GIT binary patch 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z6tUDI$sIg~0o$3B1V&vz6}Sf7(hY$>wl)r)=*+t>o(frNYIOWCYLZX{En#3B1Gv}` ZrsGcMjA!C1mbW8J`unD7Fq0*J|Jlf0Y7zhd literal 0 HcmV?d00001 diff --git a/apps/imgs/tiger.svg b/apps/imgs/tiger.svg new file mode 100644 index 0000000..366de41 --- /dev/null +++ b/apps/imgs/tiger.svg @@ -0,0 +1,317 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/apps/optimize_pixel_filter.py b/apps/optimize_pixel_filter.py new file mode 100644 index 0000000..f5380fc --- /dev/null +++ b/apps/optimize_pixel_filter.py @@ -0,0 +1,115 @@ +import diffvg +import pydiffvg +import torch +import skimage +import numpy as np + +# Use GPU if available +pydiffvg.set_use_gpu(torch.cuda.is_available()) + +canvas_width = 256 +canvas_height = 256 +circle = pydiffvg.Circle(radius = torch.tensor(40.0), + center = torch.tensor([128.0, 128.0])) +shapes = [circle] +circle_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]), + fill_color = torch.tensor([0.3, 0.6, 0.3, 1.0])) +shape_groups = [circle_group] +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width=canvas_width, + canvas_height=canvas_height, + shapes=shapes, + shape_groups=shape_groups, + filter=pydiffvg.PixelFilter(type = diffvg.FilterType.hann, + radius = torch.tensor(8.0))) + +render = pydiffvg.RenderFunction.apply +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + None, + *scene_args) +# The output image is in linear RGB space. Do Gamma correction before saving the image. +pydiffvg.imwrite(img.cpu(), 'results/optimize_pixel_filter/target.png', gamma=2.2) +target = img.clone() + +# Change the pixel filter radius +radius = torch.tensor(1.0, requires_grad = True) +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width=canvas_width, + canvas_height=canvas_height, + shapes=shapes, + shape_groups=shape_groups, + filter=pydiffvg.PixelFilter(type = diffvg.FilterType.hann, + radius = radius)) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 1, # seed + None, + *scene_args) +pydiffvg.imwrite(img.cpu(), 'results/optimize_pixel_filter/init.png', gamma=2.2) + +# Optimize for radius & center +optimizer = torch.optim.Adam([radius], lr=1.0) +# Run 100 Adam iterations. +for t in range(100): + print('iteration:', t) + optimizer.zero_grad() + # Forward pass: render the image. + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width=canvas_width, + canvas_height=canvas_height, + shapes=shapes, + shape_groups=shape_groups, + filter=pydiffvg.PixelFilter(type = diffvg.FilterType.hann, + radius = radius)) + img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + t+1, # seed + None, + *scene_args) + # Save the intermediate render. + pydiffvg.imwrite(img.cpu(), 'results/optimize_pixel_filter/iter_{}.png'.format(t), gamma=2.2) + # Compute the loss function. Here it is L2. + loss = (img - target).pow(2).sum() + print('loss:', loss.item()) + + # Backpropagate the gradients. + loss.backward() + # Print the gradients + print('radius.grad:', radius.grad) + + # Take a gradient descent step. + optimizer.step() + # Print the current params. + print('radius:', radius) + +# Render the final result. +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width=canvas_width, + canvas_height=canvas_height, + shapes=shapes, + shape_groups=shape_groups, + filter=pydiffvg.PixelFilter(type = diffvg.FilterType.hann, + radius = radius)) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 102, # seed + None, + *scene_args) +# Save the images and differences. +pydiffvg.imwrite(img.cpu(), 'results/optimize_pixel_filter/final.png') + +# Convert the intermediate renderings to a video. +from subprocess import call +call(["ffmpeg", "-framerate", "24", "-i", + "results/optimize_pixel_filter/iter_%d.png", "-vb", "20M", + "results/optimize_pixel_filter/out.mp4"]) diff --git a/apps/painterly_rendering.py b/apps/painterly_rendering.py new file mode 100644 index 0000000..2b61f36 --- /dev/null +++ b/apps/painterly_rendering.py @@ -0,0 +1,223 @@ +""" +Scream: python painterly_rendering.py imgs/scream.jpg --num_paths 2048 --max_width 4.0 +Fallingwater: python painterly_rendering.py imgs/fallingwater.jpg --num_paths 2048 --max_width 4.0 +Fallingwater: python painterly_rendering.py imgs/fallingwater.jpg --num_paths 2048 --max_width 4.0 --use_lpips_loss +Baboon: python painterly_rendering.py imgs/baboon.png --num_paths 1024 --max_width 4.0 --num_iter 250 +Baboon Lpips: python painterly_rendering.py imgs/baboon.png --num_paths 1024 --max_width 4.0 --num_iter 500 --use_lpips_loss +Kitty: python painterly_rendering.py imgs/kitty.jpg --num_paths 1024 --use_blob +""" +import pydiffvg +import torch +import skimage +import skimage.io +import random +import ttools.modules +import argparse +import math + +pydiffvg.set_print_timing(True) + +gamma = 1.0 + +def main(args): + # Use GPU if available + pydiffvg.set_use_gpu(torch.cuda.is_available()) + + perception_loss = ttools.modules.LPIPS().to(pydiffvg.get_device()) + + #target = torch.from_numpy(skimage.io.imread('imgs/lena.png')).to(torch.float32) / 255.0 + target = torch.from_numpy(skimage.io.imread(args.target)).to(torch.float32) / 255.0 + target = target.pow(gamma) + target = target.to(pydiffvg.get_device()) + target = target.unsqueeze(0) + target = target.permute(0, 3, 1, 2) # NHWC -> NCHW + #target = torch.nn.functional.interpolate(target, size = [256, 256], mode = 'area') + canvas_width, canvas_height = target.shape[3], target.shape[2] + num_paths = args.num_paths + max_width = args.max_width + + random.seed(1234) + torch.manual_seed(1234) + + shapes = [] + shape_groups = [] + if args.use_blob: + for i in range(num_paths): + num_segments = random.randint(3, 5) + num_control_points = torch.zeros(num_segments, dtype = torch.int32) + 2 + points = [] + p0 = (random.random(), random.random()) + points.append(p0) + for j in range(num_segments): + radius = 0.05 + p1 = (p0[0] + radius * (random.random() - 0.5), p0[1] + radius * (random.random() - 0.5)) + p2 = (p1[0] + radius * (random.random() - 0.5), p1[1] + radius * (random.random() - 0.5)) + p3 = (p2[0] + radius * (random.random() - 0.5), p2[1] + radius * (random.random() - 0.5)) + points.append(p1) + points.append(p2) + if j < num_segments - 1: + points.append(p3) + p0 = p3 + points = torch.tensor(points) + points[:, 0] *= canvas_width + points[:, 1] *= canvas_height + path = pydiffvg.Path(num_control_points = num_control_points, + points = points, + stroke_width = torch.tensor(1.0), + is_closed = True) + shapes.append(path) + path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([len(shapes) - 1]), + fill_color = torch.tensor([random.random(), + random.random(), + random.random(), + random.random()])) + shape_groups.append(path_group) + else: + for i in range(num_paths): + num_segments = random.randint(1, 3) + num_control_points = torch.zeros(num_segments, dtype = torch.int32) + 2 + points = [] + p0 = (random.random(), random.random()) + points.append(p0) + for j in range(num_segments): + radius = 0.05 + p1 = (p0[0] + radius * (random.random() - 0.5), p0[1] + radius * (random.random() - 0.5)) + p2 = (p1[0] + radius * (random.random() - 0.5), p1[1] + radius * (random.random() - 0.5)) + p3 = (p2[0] + radius * (random.random() - 0.5), p2[1] + radius * (random.random() - 0.5)) + points.append(p1) + points.append(p2) + points.append(p3) + p0 = p3 + points = torch.tensor(points) + points[:, 0] *= canvas_width + points[:, 1] *= canvas_height + #points = torch.rand(3 * num_segments + 1, 2) * min(canvas_width, canvas_height) + path = pydiffvg.Path(num_control_points = num_control_points, + points = points, + stroke_width = torch.tensor(1.0), + is_closed = False) + shapes.append(path) + path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([len(shapes) - 1]), + fill_color = None, + stroke_color = torch.tensor([random.random(), + random.random(), + random.random(), + random.random()])) + shape_groups.append(path_group) + + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + + render = pydiffvg.RenderFunction.apply + img = render(canvas_width, # width + canvas_height, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + None, + *scene_args) + pydiffvg.imwrite(img.cpu(), 'results/painterly_rendering/init.png', gamma=gamma) + + points_vars = [] + stroke_width_vars = [] + color_vars = [] + for path in shapes: + path.points.requires_grad = True + points_vars.append(path.points) + if not args.use_blob: + for path in shapes: + path.stroke_width.requires_grad = True + stroke_width_vars.append(path.stroke_width) + if args.use_blob: + for group in shape_groups: + group.fill_color.requires_grad = True + color_vars.append(group.fill_color) + else: + for group in shape_groups: + group.stroke_color.requires_grad = True + color_vars.append(group.stroke_color) + + # Optimize + points_optim = torch.optim.Adam(points_vars, lr=1.0) + if len(stroke_width_vars) > 0: + width_optim = torch.optim.Adam(stroke_width_vars, lr=0.1) + color_optim = torch.optim.Adam(color_vars, lr=0.01) + # Adam iterations. + for t in range(args.num_iter): + print('iteration:', t) + points_optim.zero_grad() + if len(stroke_width_vars) > 0: + width_optim.zero_grad() + color_optim.zero_grad() + # Forward pass: render the image. + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + img = render(canvas_width, # width + canvas_height, # height + 2, # num_samples_x + 2, # num_samples_y + t, # seed + None, + *scene_args) + # Compose img with white background + img = img[:, :, 3:4] * img[:, :, :3] + torch.ones(img.shape[0], img.shape[1], 3, device = pydiffvg.get_device()) * (1 - img[:, :, 3:4]) + # Save the intermediate render. + pydiffvg.imwrite(img.cpu(), 'results/painterly_rendering/iter_{}.png'.format(t), gamma=gamma) + img = img[:, :, :3] + # Convert img from HWC to NCHW + img = img.unsqueeze(0) + img = img.permute(0, 3, 1, 2) # NHWC -> NCHW + if args.use_lpips_loss: + loss = perception_loss(img, target) + (img.mean() - target.mean()).pow(2) + else: + loss = (img - target).pow(2).mean() + print('render loss:', loss.item()) + + # Backpropagate the gradients. + loss.backward() + + # Take a gradient descent step. + points_optim.step() + if len(stroke_width_vars) > 0: + width_optim.step() + color_optim.step() + if len(stroke_width_vars) > 0: + for path in shapes: + path.stroke_width.data.clamp_(1.0, max_width) + if args.use_blob: + for group in shape_groups: + group.fill_color.data.clamp_(0.0, 1.0) + else: + for group in shape_groups: + group.stroke_color.data.clamp_(0.0, 1.0) + + if t % 10 == 0 or t == args.num_iter - 1: + pydiffvg.save_svg('results/painterly_rendering/iter_{}.svg'.format(t), + canvas_width, canvas_height, shapes, shape_groups) + + # Render the final result. + img = render(target.shape[1], # width + target.shape[0], # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + None, + *scene_args) + # Save the intermediate render. + pydiffvg.imwrite(img.cpu(), 'results/painterly_rendering/final.png'.format(t), gamma=gamma) + # Convert the intermediate renderings to a video. + from subprocess import call + call(["ffmpeg", "-framerate", "24", "-i", + "results/painterly_rendering/iter_%d.png", "-vb", "20M", + "results/painterly_rendering/out.mp4"]) + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("target", help="target image path") + parser.add_argument("--num_paths", type=int, default=512) + parser.add_argument("--max_width", type=float, default=2.0) + parser.add_argument("--use_lpips_loss", dest='use_lpips_loss', action='store_true') + parser.add_argument("--num_iter", type=int, default=500) + parser.add_argument("--use_blob", dest='use_blob', action='store_true') + args = parser.parse_args() + main(args) diff --git a/apps/quadratic_distance_approx.py b/apps/quadratic_distance_approx.py new file mode 100644 index 0000000..10e2a34 --- /dev/null +++ b/apps/quadratic_distance_approx.py @@ -0,0 +1,76 @@ +import pydiffvg +import torch +import skimage +import numpy as np +import matplotlib.pyplot as plt + +# Use GPU if available +pydiffvg.set_use_gpu(torch.cuda.is_available()) + +canvas_width, canvas_height = 256, 256 +num_control_points = torch.tensor([1]) +points = torch.tensor([[ 50.0, 30.0], # base + [125.0, 400.0], # control point + [170.0, 30.0]]) # base +path = pydiffvg.Path(num_control_points = num_control_points, + points = points, + stroke_width = torch.tensor([30.0]), + is_closed = False, + use_distance_approx = False) +shapes = [path] +path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]), + fill_color = None, + stroke_color = torch.tensor([0.5, 0.5, 0.5, 0.5])) +shape_groups = [path_group] +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + output_type = pydiffvg.OutputType.sdf) +render = pydiffvg.RenderFunction.apply +img = render(256, # width + 256, # height + 1, # num_samples_x + 1, # num_samples_y + 0, # seed + None, # background_image + *scene_args) +img /= 256.0 +cm = plt.get_cmap('viridis') +img = cm(img.squeeze()) +pydiffvg.imwrite(img, 'results/quadratic_distance_approx/ref_sdf.png') + +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + None, # background_image + *scene_args) +pydiffvg.imwrite(img, 'results/quadratic_distance_approx/ref_color.png') + +shapes[0].use_distance_approx = True +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + output_type = pydiffvg.OutputType.sdf) +img = render(256, # width + 256, # height + 1, # num_samples_x + 1, # num_samples_y + 0, # seed + None, # background_image + *scene_args) +img /= 256.0 +img = cm(img.squeeze()) +pydiffvg.imwrite(img, 'results/quadratic_distance_approx/approx_sdf.png') + +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + None, # background_image + *scene_args) +pydiffvg.imwrite(img, 'results/quadratic_distance_approx/approx_color.png') \ No newline at end of file diff --git a/apps/refine_svg.py b/apps/refine_svg.py new file mode 100644 index 0000000..7e324fb --- /dev/null +++ b/apps/refine_svg.py @@ -0,0 +1,115 @@ +import pydiffvg +import argparse +import ttools.modules +import torch +import skimage.io + +gamma = 1.0 + +def main(args): + perception_loss = ttools.modules.LPIPS().to(pydiffvg.get_device()) + + target = torch.from_numpy(skimage.io.imread(args.target)).to(torch.float32) / 255.0 + target = target.pow(gamma) + target = target.to(pydiffvg.get_device()) + target = target.unsqueeze(0) + target = target.permute(0, 3, 1, 2) # NHWC -> NCHW + + canvas_width, canvas_height, shapes, shape_groups = \ + pydiffvg.svg_to_scene(args.svg) + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + + render = pydiffvg.RenderFunction.apply + img = render(canvas_width, # width + canvas_height, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + None, # bg + *scene_args) + # The output image is in linear RGB space. Do Gamma correction before saving the image. + pydiffvg.imwrite(img.cpu(), 'results/refine_svg/init.png', gamma=gamma) + + points_vars = [] + for path in shapes: + path.points.requires_grad = True + points_vars.append(path.points) + color_vars = {} + for group in shape_groups: + group.fill_color.requires_grad = True + color_vars[group.fill_color.data_ptr()] = group.fill_color + color_vars = list(color_vars.values()) + + # Optimize + points_optim = torch.optim.Adam(points_vars, lr=1.0) + color_optim = torch.optim.Adam(color_vars, lr=0.01) + + # Adam iterations. + for t in range(args.num_iter): + print('iteration:', t) + points_optim.zero_grad() + color_optim.zero_grad() + # Forward pass: render the image. + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + img = render(canvas_width, # width + canvas_height, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + None, # bg + *scene_args) + # Compose img with white background + img = img[:, :, 3:4] * img[:, :, :3] + torch.ones(img.shape[0], img.shape[1], 3, device = pydiffvg.get_device()) * (1 - img[:, :, 3:4]) + # Save the intermediate render. + pydiffvg.imwrite(img.cpu(), 'results/refine_svg/iter_{}.png'.format(t), gamma=gamma) + img = img[:, :, :3] + # Convert img from HWC to NCHW + img = img.unsqueeze(0) + img = img.permute(0, 3, 1, 2) # NHWC -> NCHW + if args.use_lpips_loss: + loss = perception_loss(img, target) + else: + loss = (img - target).pow(2).mean() + print('render loss:', loss.item()) + + # Backpropagate the gradients. + loss.backward() + + # Take a gradient descent step. + points_optim.step() + color_optim.step() + for group in shape_groups: + group.fill_color.data.clamp_(0.0, 1.0) + + if t % 10 == 0 or t == args.num_iter - 1: + pydiffvg.save_svg('results/refine_svg/iter_{}.svg'.format(t), + canvas_width, canvas_height, shapes, shape_groups) + + # Render the final result. + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + img = render(canvas_width, # width + canvas_height, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + None, # bg + *scene_args) + # Save the intermediate render. + pydiffvg.imwrite(img.cpu(), 'results/refine_svg/final.png'.format(t), gamma=gamma) + # Convert the intermediate renderings to a video. + from subprocess import call + call(["ffmpeg", "-framerate", "24", "-i", + "results/refine_svg/iter_%d.png", "-vb", "20M", + "results/refine_svg/out.mp4"]) + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("svg", help="source SVG path") + parser.add_argument("target", help="target image path") + parser.add_argument("--use_lpips_loss", dest='use_lpips_loss', action='store_true') + parser.add_argument("--num_iter", type=int, default=250) + args = parser.parse_args() + main(args) diff --git a/apps/render_svg.py b/apps/render_svg.py new file mode 100644 index 0000000..0aa9273 --- /dev/null +++ b/apps/render_svg.py @@ -0,0 +1,41 @@ +""" +Simple utility to render an .svg to a .png +""" +import os +import argparse +import pydiffvg +import torch as th + + +def render(canvas_width, canvas_height, shapes, shape_groups): + _render = pydiffvg.RenderFunction.apply + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + img = _render(canvas_width, # width + canvas_height, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + None, + *scene_args) + return img + + +def main(args): + pydiffvg.set_device(th.device('cuda:1')) + + # Load SVG + svg = os.path.join(args.svg) + canvas_width, canvas_height, shapes, shape_groups = \ + pydiffvg.svg_to_scene(svg) + + # Save initial state + ref = render(canvas_width, canvas_height, shapes, shape_groups) + pydiffvg.imwrite(ref.cpu(), args.out, gamma=2.2) + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("svg", help="source SVG path") + parser.add_argument("out", help="output image path") + args = parser.parse_args() + main(args) diff --git a/apps/seam_carving.py b/apps/seam_carving.py new file mode 100644 index 0000000..aa0176e --- /dev/null +++ b/apps/seam_carving.py @@ -0,0 +1,284 @@ +"""Retargets an .svg using image-domain seam carving to shrink it.""" +import os +import pydiffvg +import argparse +import torch as th +import scipy.ndimage.filters as filters +import numba +import numpy as np +import skimage.io + + +def energy(im): + """Compute image energy. + + Args: + im(np.ndarray) with shape [h, w, 3]: input image. + + Returns: + (np.ndarray) with shape [h, w]: energy map. + """ + f_dx = np.array([ + [-1, 0, 1 ], + [-2, 0, 2 ], + [-1, 0, 1 ], + ]) + f_dy = f_dx.T + dx = filters.convolve(im.mean(2), f_dx) + dy = filters.convolve(im.mean(2), f_dy) + + return np.abs(dx) + np.abs(dy) + + +@numba.jit(nopython=True) +def min_seam(e): + """Finds the seam with minimal cost in an energy map. + + Args: + e(np.ndarray) with shape [h, w]: energy map. + + Returns: + min_e(np.ndarray) with shape [h, w]: for all (y,x) min_e[y, x] + is the cost of the minimal seam from 0 to y (top to bottom). + The minimal seam can be found by looking at the last row of min_e. + This is computed by dynamic programming. + argmin_e(np.ndarray) with shape [h, w]: for all (y,x) argmin_e[y, x] + contains the x coordinate corresponding to this seam in the + previous row (y-1). We use this for backtracking. + """ + # initialize to local energy + min_e = e.copy() + argmin_e = np.zeros_like(e, dtype=np.int64) + + h, w = e.shape + + # propagate vertically + for y in range(1, h): + for x in range(w): + if x == 0: + idx = np.argmin(e[y-1, x:x+2]) + argmin_e[y, x] = idx + x + mini = e[y-1, x + idx] + elif x == w-1: + idx = np.argmin(e[y-1, x-1:x+1]) + argmin_e[y, x] = idx + x - 1 + mini = e[y-1, x + idx - 1] + else: + idx = np.argmin(e[y-1, x-1:x+2]) + argmin_e[y, x] = idx + x - 1 + mini = e[y-1, x + idx - 1] + + min_e[y, x] = min_e[y, x] + mini + + return min_e, argmin_e + + +def carve_seam(im): + """Carves a vertical seam in an image, reducing it's horizontal size by 1. + + Args: + im(np.ndarray) with shape [h, w, 3]: input image. + + Returns: + (np.ndarray) with shape [h, w-1, 1]: the image with one seam removed. + """ + + e = energy(im) + min_e, argmin_e = min_seam(e) + h, w = im.shape[:2] + + # boolean flags for the pixels to preserve + to_keep = np.ones((h, w), dtype=np.bool) + + # get lowest energy (from last row) + x = np.argmin(min_e[-1]) + print("carving seam", x, "with energy", min_e[-1, x]) + + # backtract to identify the seam + for y in range(h-1, -1, -1): + # remove seam pixel + to_keep[y, x] = False + x = argmin_e[y, x] + + # replicate mask over color channels + to_keep = np.stack(3*[to_keep], axis=2) + new_im = im[to_keep].reshape((h, w-1, 3)) + return new_im + + +def render(canvas_width, canvas_height, shapes, shape_groups, samples=2): + _render = pydiffvg.RenderFunction.apply + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + + img = _render(canvas_width, # width + canvas_height, # height + samples, # num_samples_x + samples, # num_samples_y + 0, # seed + None, + *scene_args) + return img + + +def vector_rescale(shapes, scale_x=1.00, scale_y=1.00): + new_shapes = [] + for path in shapes: + path.points[..., 0] *= scale_x + path.points[..., 1] *= scale_y + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--svg", default=os.path.join("imgs", "hokusai.svg")) + parser.add_argument("--optim_steps", default=10, type=int) + parser.add_argument("--lr", default=1e-1, type=int) + args = parser.parse_args() + + name = os.path.splitext(os.path.basename(args.svg))[0] + root = os.path.join("results", "seam_carving", name) + svg_root = os.path.join(root, "svg") + os.makedirs(root, exist_ok=True) + os.makedirs(os.path.join(root, "svg"), exist_ok=True) + + pydiffvg.set_use_gpu(False) + # pydiffvg.set_device(th.device('cuda')) + + # Load SVG + print("loading svg %s" % args.svg) + canvas_width, canvas_height, shapes, shape_groups = \ + pydiffvg.svg_to_scene(args.svg) + print("done loading") + + max_size = 512 + scale_factor = max_size / max(canvas_width, canvas_height) + print("rescaling from %dx%d with scale %f" % (canvas_width, canvas_height, scale_factor)) + canvas_width = int(canvas_width*scale_factor) + canvas_height = int(canvas_height*scale_factor) + print("new shape %dx%d" % (canvas_width, canvas_height)) + vector_rescale(shapes, scale_x=scale_factor, scale_y=scale_factor) + + # Shrink image by 33 % + # num_seams_to_remove = 2 + num_seams_to_remove = canvas_width // 3 + new_canvas_width = canvas_width - num_seams_to_remove + scaling = new_canvas_width * 1.0 / canvas_width + + # Naive scaling baseline + print("rendering naive rescaling...") + vector_rescale(shapes, scale_x=scaling) + resized = render(new_canvas_width, canvas_height, shapes, shape_groups) + pydiffvg.imwrite(resized.cpu(), os.path.join(root, 'uniform_scaling.png'), gamma=2.2) + pydiffvg.save_svg(os.path.join(svg_root, 'uniform_scaling.svg') , canvas_width, + canvas_height, shapes, shape_groups, use_gamma=False) + vector_rescale(shapes, scale_x=1.0/scaling) # bring back original coordinates + print("saved naiving scaling") + + # Save initial state + print("rendering initial state...") + im = render(canvas_width, canvas_height, shapes, shape_groups) + pydiffvg.imwrite(im.cpu(), os.path.join(root, 'init.png'), gamma=2.2) + pydiffvg.save_svg(os.path.join(svg_root, 'init.svg'), canvas_width, + canvas_height, shapes, shape_groups, use_gamma=False) + print("saved initial state") + + # Optimize + # color_optim = th.optim.Adam(color_vars, lr=0.01) + + retargeted = im[..., :3].cpu().numpy() + previous_width = canvas_width + print("carving seams") + for seam_idx in range(num_seams_to_remove): + print('\nseam', seam_idx+1, 'of', num_seams_to_remove) + + # Remove a seam + retargeted = carve_seam(retargeted) + + current_width = canvas_width - seam_idx - 1 + scale_factor = current_width * 1.0 / previous_width + previous_width = current_width + + padded = np.zeros((canvas_height, canvas_width, 4)) + padded[:, :-seam_idx-1, :3] = retargeted + padded[:, :-seam_idx-1, -1] = 1.0 # alpha + padded = th.from_numpy(padded).to(im.device) + + # Remap points to the smaller canvas and + # collect variables to optimize + points_vars = [] + # width_vars = [] + mini, maxi = canvas_width, 0 + for path in shapes: + path.points.requires_grad = False + x = path.points[..., 0] + y = path.points[..., 1] + # rescale + + x = x * scale_factor + + # clip to canvas + path.points[..., 0] = th.clamp(x, 0, current_width) + path.points[..., 1] = th.clamp(y, 0, canvas_height) + + path.points.requires_grad = True + points_vars.append(path.points) + path.stroke_width.requires_grad = True + # width_vars.append(path.stroke_width) + + mini = min(mini, path.points.min().item()) + maxi = max(maxi, path.points.max().item()) + print("points", mini, maxi, "scale", scale_factor) + + # recreate an optimizer so we don't carry over the previous update + # (momentum)? + geom_optim = th.optim.Adam(points_vars, lr=args.lr) + + for step in range(args.optim_steps): + geom_optim.zero_grad() + + img = render(canvas_width, canvas_height, shapes, shape_groups, + samples=2) + + pydiffvg.imwrite( + img.cpu(), + os.path.join(root, "seam_%03d_iter_%02d.png" % (seam_idx, step)), gamma=2.2) + + # NO alpha + loss = (img - padded)[..., :3].pow(2).mean() + # loss = (img - padded).pow(2).mean() + print('render loss:', loss.item()) + + # Backpropagate the gradients. + loss.backward() + + # Take a gradient descent step. + geom_optim.step() + pydiffvg.save_svg(os.path.join(svg_root, "seam%03d.svg" % seam_idx), + canvas_width-seam_idx, canvas_height, shapes, + shape_groups, use_gamma=False) + + for path in shapes: + mini = min(mini, path.points.min().item()) + maxi = max(maxi, path.points.max().item()) + print("points", mini, maxi) + + img = render(canvas_width, canvas_height, shapes, shape_groups) + img = img[:, :-num_seams_to_remove] + + pydiffvg.imwrite(img.cpu(), os.path.join(root, 'final.png'), + gamma=2.2) + pydiffvg.imwrite(retargeted, os.path.join(root, 'ref.png'), + gamma=2.2) + + pydiffvg.save_svg(os.path.join(svg_root, 'final.svg'), + canvas_width-seam_idx, canvas_height, shapes, + shape_groups, use_gamma=False) + + # Convert the intermediate renderings to a video. + from subprocess import call + call(["ffmpeg", "-framerate", "24", "-i", os.path.join(root, "seam_%03d_iter_00.png"), "-vb", "20M", + os.path.join(root, "out.mp4")]) + + +if __name__ == "__main__": + main() diff --git a/apps/shared_edge_compare.py b/apps/shared_edge_compare.py new file mode 100644 index 0000000..e7a5aef --- /dev/null +++ b/apps/shared_edge_compare.py @@ -0,0 +1,127 @@ +import pydiffvg +import diffvg +from matplotlib import cm +import matplotlib.pyplot as plt +import argparse +import torch + +def normalize(x, min_, max_): + range = max(abs(min_), abs(max_)) + return (x + range) / (2 * range) + +def main(args): + canvas_width, canvas_height, shapes, shape_groups = \ + pydiffvg.svg_to_scene(args.svg_file) + + w = int(canvas_width * args.size_scale) + h = int(canvas_height * args.size_scale) + + pfilter = pydiffvg.PixelFilter(type = diffvg.FilterType.box, + radius = torch.tensor(0.5)) + + use_prefiltering = False + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + filter = pfilter, + use_prefiltering = use_prefiltering) + + num_samples_x = 16 + num_samples_y = 16 + render = pydiffvg.RenderFunction.apply + img = render(w, # width + h, # height + num_samples_x, # num_samples_x + num_samples_y, # num_samples_y + 0, # seed + None, + *scene_args) + pydiffvg.imwrite(img.cpu(), 'results/finite_difference_comp/img.png', gamma=1.0) + + epsilon = 0.1 + def perturb_scene(axis, epsilon): + shapes[2].points[:, axis] += epsilon + # for s in shapes: + # if isinstance(s, pydiffvg.Circle): + # s.center[axis] += epsilon + # elif isinstance(s, pydiffvg.Ellipse): + # s.center[axis] += epsilon + # elif isinstance(s, pydiffvg.Path): + # s.points[:, axis] += epsilon + # elif isinstance(s, pydiffvg.Polygon): + # s.points[:, axis] += epsilon + # elif isinstance(s, pydiffvg.Rect): + # s.p_min[axis] += epsilon + # s.p_max[axis] += epsilon + # for s in shape_groups: + # if isinstance(s.fill_color, pydiffvg.LinearGradient): + # s.fill_color.begin[axis] += epsilon + # s.fill_color.end[axis] += epsilon + + perturb_scene(0, epsilon) + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + filter = pfilter, + use_prefiltering = use_prefiltering) + render = pydiffvg.RenderFunction.apply + img0 = render(w, # width + h, # height + num_samples_x, # num_samples_x + num_samples_y, # num_samples_y + 0, # seed + None, + *scene_args) + + forward_diff = (img0 - img) / (epsilon) + forward_diff = forward_diff.sum(axis = 2) + x_diff_max = 1.5 + x_diff_min = -1.5 + print(forward_diff.max()) + print(forward_diff.min()) + forward_diff = cm.viridis(normalize(forward_diff, x_diff_min, x_diff_max).cpu().numpy()) + pydiffvg.imwrite(forward_diff, 'results/finite_difference_comp/shared_edge_forward_diff.png', gamma=1.0) + + perturb_scene(0, -2 * epsilon) + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + filter = pfilter, + use_prefiltering = use_prefiltering) + img1 = render(w, # width + h, # height + num_samples_x, # num_samples_x + num_samples_y, # num_samples_y + 0, # seed + None, + *scene_args) + backward_diff = (img - img1) / (epsilon) + backward_diff = backward_diff.sum(axis = 2) + print(backward_diff.max()) + print(backward_diff.min()) + backward_diff = cm.viridis(normalize(backward_diff, x_diff_min, x_diff_max).cpu().numpy()) + pydiffvg.imwrite(backward_diff, 'results/finite_difference_comp/shared_edge_backward_diff.png', gamma=1.0) + perturb_scene(0, epsilon) + + num_samples_x = 4 + num_samples_y = 4 + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + filter = pfilter, + use_prefiltering = use_prefiltering) + render_grad = pydiffvg.RenderFunction.render_grad + img_grad = render_grad(torch.ones(h, w, 4), + w, # width + h, # height + num_samples_x, # num_samples_x + num_samples_y, # num_samples_y + 0, # seed + *scene_args) + print(img_grad[:, :, 0].max()) + print(img_grad[:, :, 0].min()) + x_diff = cm.viridis(normalize(img_grad[:, :, 0], x_diff_min, x_diff_max).cpu().numpy()) + pydiffvg.imwrite(x_diff, 'results/finite_difference_comp/ours_x_diff.png', gamma=1.0) + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("svg_file", help="source SVG path") + parser.add_argument("--size_scale", type=float, default=1.0) + args = parser.parse_args() + main(args) diff --git a/apps/simple_transform_svg.py b/apps/simple_transform_svg.py new file mode 100644 index 0000000..3faec31 --- /dev/null +++ b/apps/simple_transform_svg.py @@ -0,0 +1,237 @@ +import pydiffvg +import torch +import torchvision +from PIL import Image +import numpy as np + +# Use GPU if available +pydiffvg.set_use_gpu(torch.cuda.is_available()) + +def inv_exp(a,x,xpow=1): + return pow(a,pow(1.-x,xpow)) + +import math +import numbers +import torch +from torch import nn +from torch.nn import functional as F + +import visdom + +class GaussianSmoothing(nn.Module): + """ + Apply gaussian smoothing on a + 1d, 2d or 3d tensor. Filtering is performed seperately for each channel + in the input using a depthwise convolution. + Arguments: + channels (int, sequence): Number of channels of the input tensors. Output will + have this number of channels as well. + kernel_size (int, sequence): Size of the gaussian kernel. + sigma (float, sequence): Standard deviation of the gaussian kernel. + dim (int, optional): The number of dimensions of the data. + Default value is 2 (spatial). + """ + def __init__(self, channels, kernel_size, sigma, dim=2): + super(GaussianSmoothing, self).__init__() + if isinstance(kernel_size, numbers.Number): + kernel_size = [kernel_size] * dim + if isinstance(sigma, numbers.Number): + sigma = [sigma] * dim + + # The gaussian kernel is the product of the + # gaussian function of each dimension. + kernel = 1 + meshgrids = torch.meshgrid( + [ + torch.arange(size, dtype=torch.float32) + for size in kernel_size + ] + ) + for size, std, mgrid in zip(kernel_size, sigma, meshgrids): + mean = (size - 1) / 2 + kernel *= 1 / (std * math.sqrt(2 * math.pi)) * \ + torch.exp(-((mgrid - mean) / std) ** 2 / 2) + + # Make sure sum of values in gaussian kernel equals 1. + kernel = kernel / torch.sum(kernel) + + # Reshape to depthwise convolutional weight + kernel = kernel.view(1, 1, *kernel.size()) + kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1)) + + self.register_buffer('weight', kernel) + self.groups = channels + + if dim == 1: + self.conv = F.conv1d + elif dim == 2: + self.conv = F.conv2d + elif dim == 3: + self.conv = F.conv3d + else: + raise RuntimeError( + 'Only 1, 2 and 3 dimensions are supported. Received {}.'.format(dim) + ) + + def forward(self, input): + """ + Apply gaussian filter to input. + Arguments: + input (torch.Tensor): Input to apply gaussian filter on. + Returns: + filtered (torch.Tensor): Filtered output. + """ + return self.conv(input, weight=self.weight, groups=self.groups) + +vis=visdom.Visdom(port=8080) + +smoothing = GaussianSmoothing(4, 5, 1) + +settings=pydiffvg.SvgOptimizationSettings() +settings.global_override(["optimize_color"],False) +settings.global_override(["optimize_alpha"],False) +settings.global_override(["gradients","optimize_color"],False) +settings.global_override(["gradients","optimize_alpha"],False) +settings.global_override(["gradients","optimize_stops"],False) +settings.global_override(["gradients","optimize_location"],False) +settings.global_override(["optimizer"],"Adam") +settings.global_override(["paths","optimize_points"],False) +settings.global_override(["transforms","transform_lr"],1e-2) +settings.undefault("linearGradient3152") +settings.retrieve("linearGradient3152")[0]["transforms"]["optimize_transforms"]=False + +#optim=pydiffvg.OptimizableSvg("note_small.svg",settings,verbose=True) +optim=pydiffvg.OptimizableSvg("heart_green.svg",settings,verbose=True) + +#img=torchvision.transforms.ToTensor()(Image.open("note_transformed.png")).permute(1,2,0) +img=torchvision.transforms.ToTensor()(Image.open("heart_green_90.png")).permute(1,2,0) + +name="heart_green_90" + +pydiffvg.imwrite(img.cpu(), 'results/simple_transform_svg/target.png') +target = img.clone().detach().requires_grad_(False) + +img=optim.render() +pydiffvg.imwrite(img.cpu(), 'results/simple_transform_svg/init.png') + +def smooth(input, kernel): + input=torch.nn.functional.pad(input.permute(2,0,1).unsqueeze(0), (2, 2, 2, 2), mode='reflect') + output=kernel(input) + return output + +def printimg(optim): + img=optim.render() + comp = img.clone().detach() + bg = torch.tensor([[[1., 1., 1.]]]) + comprgb = comp[:, :, 0:3] + compalpha = comp[:, :, 3].unsqueeze(2) + comp = comprgb * compalpha \ + + bg * (1 - compalpha) + return comp + +def comp_loss_and_grad(img, tgt, it, sz): + dif=img-tgt + + loss=dif.pow(2).mean() + + dif=dif.detach() + + cdif=dif.clone().abs() + cdif[:,:,3]=1. + + resdif=torch.nn.functional.interpolate(cdif.permute(2,0,1).unsqueeze(0),sz,mode='bilinear').squeeze().permute(1,2,0).abs() + pydiffvg.imwrite(resdif[:,:,0:4], 'results/simple_transform_svg/dif_{:04}.png'.format(it)) + + dif=dif.numpy() + padded=np.pad(dif,[(1,1),(1,1),(0,0)],mode='edge') + #print(padded[:-2,:,:].shape) + grad_x=(padded[:-2,:,:]-padded[2:,:,:])[:,1:-1,:] + grad_y=(padded[:,:-2,:]-padded[:,2:,:])[1:-1,:,:] + + resshape=dif.shape + resshape=(resshape[0],resshape[1],2) + res=np.zeros(resshape) + + for x in range(resshape[0]): + for y in range(resshape[1]): + A=np.concatenate((grad_x[x,y,:][:,np.newaxis],grad_y[x,y,:][:,np.newaxis]),axis=1) + b=-dif[x,y,:] + v=np.linalg.lstsq(np.dot(A.T,A),np.dot(A.T,b)) + res[x,y,:]=v[0] + + return loss, res + +import colorsys +def print_gradimg(gradimg,it,shape=None): + out=torch.zeros((gradimg.shape[0],gradimg.shape[1],3),requires_grad=False,dtype=torch.float32) + for x in range(gradimg.shape[0]): + for y in range(gradimg.shape[1]): + h=math.atan2(gradimg[x,y,1],gradimg[x,y,0]) + s=math.tanh(np.linalg.norm(gradimg[x,y,:])) + v=1. + vec=(gradimg[x,y,:].clip(min=-1,max=1)/2)+.5 + #out[x,y,:]=torch.tensor(colorsys.hsv_to_rgb(h,s,v),dtype=torch.float32) + out[x,y,:]=torch.tensor([vec[0],vec[1],0]) + + if shape is not None: + out=torch.nn.functional.interpolate(out.permute(2,0,1).unsqueeze(0),shape,mode='bilinear').squeeze().permute(1,2,0) + pydiffvg.imwrite(out.cpu(), 'results/simple_transform_svg/grad_{:04}.png'.format(it)) + +# Run 150 Adam iterations. +for t in range(1000): + print('iteration:', t) + optim.zero_grad() + with open('results/simple_transform_svg/viter_{:04}.svg'.format(t),"w") as f: + f.write(optim.write_xml()) + scale=inv_exp(1/16,math.pow(t/1000,1),0.5) + #print(scale) + #img = optim.render(seed=t+1,scale=scale) + img = optim.render(seed=t + 1, scale=None) + vis.line(torch.tensor([img.shape[0]]), X=torch.tensor([t]), win=name + " size", update="append", + opts={"title": name + " size"}) + #print(img.shape) + #img = optim.render(seed=t + 1) + + ptgt=target.permute(2,0,1).unsqueeze(0) + sz=img.shape[0:2] + restgt=torch.nn.functional.interpolate(ptgt,size=sz,mode='bilinear').squeeze().permute(1,2,0) + + # Compute the loss function. Here it is L2. + #loss = (smooth(img,smoothing) - smooth(restgt,smoothing)).pow(2).mean() + #loss = (img - restgt).pow(2).mean() + #loss=(img-target).pow(2).mean() + loss,gradimg=comp_loss_and_grad(img, restgt,t,target.shape[0:2]) + print_gradimg(gradimg,t,target.shape[0:2]) + print('loss:', loss.item()) + vis.line(loss.unsqueeze(0), X=torch.tensor([t]), win=name+" loss", update="append", + opts={"title": name + " loss"}) + + # Backpropagate the gradients. + loss.backward() + + # Take a gradient descent step. + optim.step() + + # Save the intermediate render. + comp=printimg(optim) + pydiffvg.imwrite(comp.cpu(), 'results/simple_transform_svg/iter_{:04}.png'.format(t)) + + +# Render the final result. + +img = optim.render() +# Save the images and differences. +pydiffvg.imwrite(img.cpu(), 'results/simple_transform_svg/final.png') +with open('results/simple_transform_svg/final.svg', "w") as f: + f.write(optim.write_xml()) + +# Convert the intermediate renderings to a video. +from subprocess import call +call(["ffmpeg", "-framerate", "24", "-i", + "results/simple_transform_svg/iter_%04d.png", "-vb", "20M", + "results/simple_transform_svg/out.mp4"]) + +call(["ffmpeg", "-framerate", "24", "-i", + "results/simple_transform_svg/grad_%04d.png", "-vb", "20M", + "results/simple_transform_svg/out_grad.mp4"]) + diff --git a/apps/single_circle.py b/apps/single_circle.py new file mode 100644 index 0000000..0f4070d --- /dev/null +++ b/apps/single_circle.py @@ -0,0 +1,106 @@ +import pydiffvg +import torch +import skimage +import numpy as np + +# Use GPU if available +pydiffvg.set_use_gpu(torch.cuda.is_available()) + +canvas_width = 256 +canvas_height = 256 +circle = pydiffvg.Circle(radius = torch.tensor(40.0), + center = torch.tensor([128.0, 128.0])) +shapes = [circle] +circle_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]), + fill_color = torch.tensor([0.3, 0.6, 0.3, 1.0])) +shape_groups = [circle_group] +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + +render = pydiffvg.RenderFunction.apply +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + None, + *scene_args) +# The output image is in linear RGB space. Do Gamma correction before saving the image. +pydiffvg.imwrite(img.cpu(), 'results/single_circle/target.png', gamma=2.2) +target = img.clone() + +# Move the circle to produce initial guess +# normalize radius & center for easier learning rate +radius_n = torch.tensor(20.0 / 256.0, requires_grad=True) +center_n = torch.tensor([108.0 / 256.0, 138.0 / 256.0], requires_grad=True) +color = torch.tensor([0.3, 0.2, 0.8, 1.0], requires_grad=True) +circle.radius = radius_n * 256 +circle.center = center_n * 256 +circle_group.fill_color = color +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 1, # seed + None, + *scene_args) +pydiffvg.imwrite(img.cpu(), 'results/single_circle/init.png', gamma=2.2) + +# Optimize for radius & center +optimizer = torch.optim.Adam([radius_n, center_n, color], lr=1e-2) +# Run 100 Adam iterations. +for t in range(100): + print('iteration:', t) + optimizer.zero_grad() + # Forward pass: render the image. + circle.radius = radius_n * 256 + circle.center = center_n * 256 + circle_group.fill_color = color + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + t+1, # seed + None, + *scene_args) + # Save the intermediate render. + pydiffvg.imwrite(img.cpu(), 'results/single_circle/iter_{}.png'.format(t), gamma=2.2) + # Compute the loss function. Here it is L2. + loss = (img - target).pow(2).sum() + print('loss:', loss.item()) + + # Backpropagate the gradients. + loss.backward() + # Print the gradients + print('radius.grad:', radius_n.grad) + print('center.grad:', center_n.grad) + print('color.grad:', color.grad) + + # Take a gradient descent step. + optimizer.step() + # Print the current params. + print('radius:', circle.radius) + print('center:', circle.center) + print('color:', circle_group.fill_color) + +# Render the final result. +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 102, # seed + *scene_args) +# Save the images and differences. +pydiffvg.imwrite(img.cpu(), 'results/single_circle/final.png') + +# Convert the intermediate renderings to a video. +from subprocess import call +call(["ffmpeg", "-framerate", "24", "-i", + "results/single_circle/iter_%d.png", "-vb", "20M", + "results/single_circle/out.mp4"]) diff --git a/apps/single_circle_outline.py b/apps/single_circle_outline.py new file mode 100644 index 0000000..78952df --- /dev/null +++ b/apps/single_circle_outline.py @@ -0,0 +1,118 @@ +import pydiffvg +import torch +import skimage +import numpy as np + +# Use GPU if available +pydiffvg.set_use_gpu(torch.cuda.is_available()) + +canvas_width, canvas_height = 256, 256 +circle = pydiffvg.Circle(radius = torch.tensor(40.0), + center = torch.tensor([128.0, 128.0]), + stroke_width = torch.tensor(5.0)) +shapes = [circle] +circle_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]), + fill_color = torch.tensor([0.3, 0.6, 0.3, 1.0]), + stroke_color = torch.tensor([0.6, 0.3, 0.6, 0.8])) +shape_groups = [circle_group] +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + +render = pydiffvg.RenderFunction.apply +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + None, + *scene_args) +# The output image is in linear RGB space. Do Gamma correction before saving the image. +pydiffvg.imwrite(img.cpu(), 'results/single_circle_outline/target.png', gamma=2.2) +target = img.clone() + +# Move the circle to produce initial guess +# normalize radius & center for easier learning rate +radius_n = torch.tensor(20.0 / 256.0, requires_grad=True) +center_n = torch.tensor([108.0 / 256.0, 138.0 / 256.0], requires_grad=True) +fill_color = torch.tensor([0.3, 0.2, 0.8, 1.0], requires_grad=True) +stroke_color = torch.tensor([0.4, 0.7, 0.5, 0.5], requires_grad=True) +stroke_width_n = torch.tensor(10.0 / 100.0, requires_grad=True) +circle.radius = radius_n * 256 +circle.center = center_n * 256 +circle.stroke_width = stroke_width_n * 100 +circle_group.fill_color = fill_color +circle_group.stroke_color = stroke_color +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 1, # seed + None, + *scene_args) +pydiffvg.imwrite(img.cpu(), 'results/single_circle_outline/init.png', gamma=2.2) + +# Optimize for radius & center +optimizer = torch.optim.Adam([radius_n, center_n, fill_color, stroke_color, stroke_width_n], lr=1e-2) +# Run 200 Adam iterations. +for t in range(200): + print('iteration:', t) + optimizer.zero_grad() + # Forward pass: render the image. + circle.radius = radius_n * 256 + circle.center = center_n * 256 + circle.stroke_width = stroke_width_n * 100 + circle_group.fill_color = fill_color + circle_group.stroke_color = stroke_color + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + t+1, # seed + None, + *scene_args) + # Save the intermediate render. + pydiffvg.imwrite(img.cpu(), 'results/single_circle_outline/iter_{}.png'.format(t), gamma=2.2) + # Compute the loss function. Here it is L2. + loss = (img - target).pow(2).sum() + print('loss:', loss.item()) + + # Backpropagate the gradients. + loss.backward() + # Print the gradients + print('radius.grad:', radius_n.grad) + print('center.grad:', center_n.grad) + print('fill_color.grad:', fill_color.grad) + print('stroke_color.grad:', stroke_color.grad) + print('stroke_width.grad:', stroke_width_n.grad) + + # Take a gradient descent step. + optimizer.step() + # Print the current params. + print('radius:', circle.radius) + print('center:', circle.center) + print('stroke_width:', circle.stroke_width) + print('fill_color:', circle_group.fill_color) + print('stroke_color:', circle_group.stroke_color) + +# Render the final result. +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 202, # seed + None, + *scene_args) +# Save the images and differences. +pydiffvg.imwrite(img.cpu(), 'results/single_circle_outline/final.png') + +# Convert the intermediate renderings to a video. +from subprocess import call +call(["ffmpeg", "-framerate", "24", "-i", + "results/single_circle_outline/iter_%d.png", "-vb", "20M", + "results/single_circle_outline/out.mp4"]) diff --git a/apps/single_circle_sdf.py b/apps/single_circle_sdf.py new file mode 100644 index 0000000..60c10cd --- /dev/null +++ b/apps/single_circle_sdf.py @@ -0,0 +1,114 @@ +import pydiffvg +import torch +import skimage +import numpy as np + +# Use GPU if available +pydiffvg.set_use_gpu(torch.cuda.is_available()) + +canvas_width = 256 +canvas_height = 256 +circle = pydiffvg.Circle(radius = torch.tensor(40.0), + center = torch.tensor([128.0, 128.0])) +shapes = [circle] +circle_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]), + fill_color = torch.tensor([0.3, 0.6, 0.3, 1.0])) +shape_groups = [circle_group] +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + output_type = pydiffvg.OutputType.sdf) + +render = pydiffvg.RenderFunction.apply +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + None, + *scene_args) +img = img / 256 # Normalize SDF to [0, 1] +pydiffvg.imwrite(img.cpu(), 'results/single_circle_sdf/target.png') +target = img.clone() + +# Move the circle to produce initial guess +# normalize radius & center for easier learning rate +radius_n = torch.tensor(20.0 / 256.0, requires_grad=True) +center_n = torch.tensor([108.0 / 256.0, 138.0 / 256.0], requires_grad=True) +color = torch.tensor([0.3, 0.2, 0.8, 1.0], requires_grad=True) +circle.radius = radius_n * 256 +circle.center = center_n * 256 +circle_group.fill_color = color +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + output_type = pydiffvg.OutputType.sdf) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 1, # seed + None, + *scene_args) +img = img / 256 # Normalize SDF to [0, 1] +pydiffvg.imwrite(img.cpu(), 'results/single_circle_sdf/init.png') + +# Optimize for radius & center +optimizer = torch.optim.Adam([radius_n, center_n, color], lr=1e-2) +# Run 100 Adam iterations. +for t in range(100): + print('iteration:', t) + optimizer.zero_grad() + # Forward pass: render the image. + circle.radius = radius_n * 256 + circle.center = center_n * 256 + circle_group.fill_color = color + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + output_type = pydiffvg.OutputType.sdf) + img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + t+1, # seed + None, + *scene_args) + img = img / 256 # Normalize SDF to [0, 1] + # Save the intermediate render. + pydiffvg.imwrite(img.cpu(), 'results/single_circle_sdf/iter_{}.png'.format(t), gamma=2.2) + # Compute the loss function. Here it is L2. + loss = (img - target).pow(2).sum() + print('loss:', loss.item()) + + # Backpropagate the gradients. + loss.backward() + # Print the gradients + print('radius.grad:', radius_n.grad) + print('center.grad:', center_n.grad) + print('color.grad:', color.grad) + + # Take a gradient descent step. + optimizer.step() + # Print the current params. + print('radius:', circle.radius) + print('center:', circle.center) + print('color:', circle_group.fill_color) + +# Render the final result. +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + output_type = pydiffvg.OutputType.sdf) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 102, # seed + None, + *scene_args) +img = img / 256 # Normalize SDF to [0, 1] +# Save the images and differences. +pydiffvg.imwrite(img.cpu(), 'results/single_circle_sdf/final.png') + +# Convert the intermediate renderings to a video. +from subprocess import call +call(["ffmpeg", "-framerate", "24", "-i", + "results/single_circle_sdf/iter_%d.png", "-vb", "20M", + "results/single_circle_sdf/out.mp4"]) \ No newline at end of file diff --git a/apps/single_circle_tf.py b/apps/single_circle_tf.py new file mode 100644 index 0000000..d77285d --- /dev/null +++ b/apps/single_circle_tf.py @@ -0,0 +1,94 @@ +import pydiffvg_tensorflow as pydiffvg +import tensorflow as tf +import skimage +import numpy as np + +canvas_width = 256 +canvas_height = 256 +circle = pydiffvg.Circle(radius = tf.constant(40.0), + center = tf.constant([128.0, 128.0])) +shapes = [circle] +circle_group = pydiffvg.ShapeGroup(shape_ids = tf.constant([0], dtype = tf.int32), + fill_color = tf.constant([0.3, 0.6, 0.3, 1.0])) +shape_groups = [circle_group] +scene_args = pydiffvg.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + +render = pydiffvg.render +img = render(tf.constant(256), # width + tf.constant(256), # height + tf.constant(2), # num_samples_x + tf.constant(2), # num_samples_y + tf.constant(0), # seed + *scene_args) +# The output image is in linear RGB space. Do Gamma correction before saving the image. +pydiffvg.imwrite(img, 'results/single_circle_tf/target.png', gamma=2.2) +target = tf.identity(img) + +# Move the circle to produce initial guess +# normalize radius & center for easier learning rate +radius_n = tf.Variable(20.0 / 256.0) +center_n = tf.Variable([108.0 / 256.0, 138.0 / 256.0]) +color = tf.Variable([0.3, 0.2, 0.8, 1.0]) +circle.radius = radius_n * 256 +circle.center = center_n * 256 +circle_group.fill_color = color +scene_args = pydiffvg.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(tf.constant(256), # width + tf.constant(256), # height + tf.constant(2), # num_samples_x + tf.constant(2), # num_samples_y + tf.constant(1), # seed + *scene_args) +pydiffvg.imwrite(img, 'results/single_circle_tf/init.png', gamma=2.2) + +optimizer = tf.compat.v1.train.AdamOptimizer(1e-2) + +for t in range(100): + print('iteration:', t) + + with tf.GradientTape() as tape: + # Forward pass: render the image. + circle.radius = radius_n * 256 + circle.center = center_n * 256 + circle_group.fill_color = color + # Important to use a different seed every iteration, otherwise the result + # would be biased. + scene_args = pydiffvg.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + img = render(tf.constant(256), # width + tf.constant(256), # height + tf.constant(2), # num_samples_x + tf.constant(2), # num_samples_y + tf.constant(t+1), # seed, + *scene_args) + loss_value = tf.reduce_sum(tf.square(img - target)) + + print(f"loss_value: {loss_value}") + pydiffvg.imwrite(img, 'results/single_circle_tf/iter_{}.png'.format(t)) + + grads = tape.gradient(loss_value, [radius_n, center_n, color]) + print(grads) + optimizer.apply_gradients(zip(grads, [radius_n, center_n, color])) + +# Render the final result. +circle.radius = radius_n * 256 +circle.center = center_n * 256 +circle_group.fill_color = color +scene_args = pydiffvg.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(tf.constant(256), # width + tf.constant(256), # height + tf.constant(2), # num_samples_x + tf.constant(2), # num_samples_y + tf.constant(101), # seed + *scene_args) +# Save the images and differences. +pydiffvg.imwrite(img.cpu(), 'results/single_circle_tf/final.png') + +# Convert the intermediate renderings to a video. +from subprocess import call +call(["ffmpeg", "-framerate", "24", "-i", + "results/single_circle_tf/iter_%d.png", "-vb", "20M", + "results/single_circle_tf/out.mp4"]) diff --git a/apps/single_curve.py b/apps/single_curve.py new file mode 100644 index 0000000..cd64f47 --- /dev/null +++ b/apps/single_curve.py @@ -0,0 +1,121 @@ +import pydiffvg +import torch +import skimage +import numpy as np + +# Use GPU if available +pydiffvg.set_use_gpu(torch.cuda.is_available()) + +canvas_width, canvas_height = 256, 256 +num_control_points = torch.tensor([2, 2, 2]) +points = torch.tensor([[120.0, 30.0], # base + [150.0, 60.0], # control point + [ 90.0, 198.0], # control point + [ 60.0, 218.0], # base + [ 90.0, 180.0], # control point + [200.0, 65.0], # control point + [210.0, 98.0], # base + [220.0, 70.0], # control point + [130.0, 55.0]]) # control point +path = pydiffvg.Path(num_control_points = num_control_points, + points = points, + is_closed = True) +shapes = [path] +path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]), + fill_color = torch.tensor([0.3, 0.6, 0.3, 1.0])) +shape_groups = [path_group] +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + +render = pydiffvg.RenderFunction.apply +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + None, + *scene_args) +# The output image is in linear RGB space. Do Gamma correction before saving the image. +pydiffvg.imwrite(img.cpu(), 'results/single_curve/target.png', gamma=2.2) +target = img.clone() + +# Move the path to produce initial guess +# normalize points for easier learning rate +points_n = torch.tensor([[100.0/256.0, 40.0/256.0], # base + [155.0/256.0, 65.0/256.0], # control point + [100.0/256.0, 180.0/256.0], # control point + [ 65.0/256.0, 238.0/256.0], # base + [100.0/256.0, 200.0/256.0], # control point + [170.0/256.0, 55.0/256.0], # control point + [220.0/256.0, 100.0/256.0], # base + [210.0/256.0, 80.0/256.0], # control point + [140.0/256.0, 60.0/256.0]], # control point + requires_grad = True) +color = torch.tensor([0.3, 0.2, 0.5, 1.0], requires_grad=True) +path.points = points_n * 256 +path_group.fill_color = color +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 1, # seed + None, + *scene_args) +pydiffvg.imwrite(img.cpu(), 'results/single_curve/init.png', gamma=2.2) + +# Optimize +optimizer = torch.optim.Adam([points_n, color], lr=1e-2) +# Run 100 Adam iterations. +for t in range(100): + print('iteration:', t) + optimizer.zero_grad() + # Forward pass: render the image. + path.points = points_n * 256 + path_group.fill_color = color + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + t+1, # seed + None, + *scene_args) + # Save the intermediate render. + pydiffvg.imwrite(img.cpu(), 'results/single_curve/iter_{}.png'.format(t), gamma=2.2) + # Compute the loss function. Here it is L2. + loss = (img - target).pow(2).sum() + print('loss:', loss.item()) + + # Backpropagate the gradients. + loss.backward() + # Print the gradients + print('points_n.grad:', points_n.grad) + print('color.grad:', color.grad) + + # Take a gradient descent step. + optimizer.step() + # Print the current params. + print('points:', path.points) + print('color:', path_group.fill_color) + +# Render the final result. +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 102, # seed + None, + *scene_args) +# Save the images and differences. +pydiffvg.imwrite(img.cpu(), 'results/single_curve/final.png') + +# Convert the intermediate renderings to a video. +from subprocess import call +call(["ffmpeg", "-framerate", "24", "-i", + "results/single_curve/iter_%d.png", "-vb", "20M", + "results/single_curve/out.mp4"]) diff --git a/apps/single_curve_outline.py b/apps/single_curve_outline.py new file mode 100644 index 0000000..476da66 --- /dev/null +++ b/apps/single_curve_outline.py @@ -0,0 +1,132 @@ +import pydiffvg +import torch +import skimage + +# Use GPU if available +pydiffvg.set_use_gpu(torch.cuda.is_available()) + +canvas_width, canvas_height = 256, 256 +num_control_points = torch.tensor([2, 2, 2]) +points = torch.tensor([[120.0, 30.0], # base + [150.0, 60.0], # control point + [ 90.0, 198.0], # control point + [ 60.0, 218.0], # base + [ 90.0, 180.0], # control point + [200.0, 65.0], # control point + [210.0, 98.0], # base + [220.0, 70.0], # control point + [130.0, 55.0]]) # control point +path = pydiffvg.Path(num_control_points = num_control_points, + points = points, + is_closed = True, + stroke_width = torch.tensor(5.0)) +shapes = [path] +path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]), + fill_color = torch.tensor([0.3, 0.6, 0.3, 1.0]), + stroke_color = torch.tensor([0.6, 0.3, 0.6, 0.8])) +shape_groups = [path_group] +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + +render = pydiffvg.RenderFunction.apply +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + *scene_args) +# The output image is in linear RGB space. Do Gamma correction before saving the image. +pydiffvg.imwrite(img.cpu(), 'results/single_curve_outline/target.png', gamma=2.2) +target = img.clone() + +# Move the path to produce initial guess +# normalize points for easier learning rate +points_n = torch.tensor([[100.0/256.0, 40.0/256.0], # base + [155.0/256.0, 65.0/256.0], # control point + [100.0/256.0, 180.0/256.0], # control point + [ 65.0/256.0, 238.0/256.0], # base + [100.0/256.0, 200.0/256.0], # control point + [170.0/256.0, 55.0/256.0], # control point + [220.0/256.0, 100.0/256.0], # base + [210.0/256.0, 80.0/256.0], # control point + [140.0/256.0, 60.0/256.0]], # control point + requires_grad = True) +fill_color = torch.tensor([0.3, 0.2, 0.8, 1.0], requires_grad=True) +stroke_color = torch.tensor([0.4, 0.7, 0.5, 0.5], requires_grad=True) +stroke_width_n = torch.tensor(10.0 / 100.0, requires_grad=True) +path.points = points_n * 256 +path.stroke_width = stroke_width_n * 100 +path_group.fill_color = fill_color +path_group.stroke_color = stroke_color +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 1, # seed + *scene_args) +pydiffvg.imwrite(img.cpu(), 'results/single_curve_outline/init.png', gamma=2.2) + +# Optimize +optimizer = torch.optim.Adam([points_n, fill_color, stroke_color, stroke_width_n], lr=1e-2) +# Run 200 Adam iterations. +for t in range(200): + print('iteration:', t) + optimizer.zero_grad() + # Forward pass: render the image. + path.points = points_n * 256 + path.stroke_width = stroke_width_n * 100 + path_group.fill_color = fill_color + path_group.stroke_color = stroke_color + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + t+1, # seed + *scene_args) + # Save the intermediate render. + pydiffvg.imwrite(img.cpu(), 'results/single_curve_outline/iter_{}.png'.format(t), gamma=2.2) + # Compute the loss function. Here it is L2. + loss = (img - target).pow(2).sum() + print('loss:', loss.item()) + + # Backpropagate the gradients. + loss.backward() + # Print the gradients + print('points_n.grad:', points_n.grad) + print('fill_color.grad:', fill_color.grad) + print('stroke_color.grad:', stroke_color.grad) + print('stroke_width.grad:', stroke_width_n.grad) + + # Take a gradient descent step. + optimizer.step() + # Print the current params. + print('points:', path.points) + print('fill_color:', path_group.fill_color) + print('stroke_color:', path_group.stroke_color) + print('stroke_width:', path.stroke_width) + +# Render the final result. +path.points = points_n * 256 +path.stroke_width = stroke_width_n * 100 +path_group.fill_color = fill_color +path_group.stroke_color = stroke_color +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 202, # seed + *scene_args) +# Save the images and differences. +pydiffvg.imwrite(img.cpu(), 'results/single_curve_outline/final.png') + +# Convert the intermediate renderings to a video. +from subprocess import call +call(["ffmpeg", "-framerate", "24", "-i", + "results/single_curve_outline/iter_%d.png", "-vb", "20M", + "results/single_curve_outline/out.mp4"]) diff --git a/apps/single_curve_sdf.py b/apps/single_curve_sdf.py new file mode 100644 index 0000000..182cdad --- /dev/null +++ b/apps/single_curve_sdf.py @@ -0,0 +1,125 @@ +import pydiffvg +import torch +import skimage +import numpy as np + +# Use GPU if available +pydiffvg.set_use_gpu(torch.cuda.is_available()) + +canvas_width, canvas_height = 256, 256 +num_control_points = torch.tensor([2, 2, 2]) +points = torch.tensor([[120.0, 30.0], # base + [150.0, 60.0], # control point + [ 90.0, 198.0], # control point + [ 60.0, 218.0], # base + [ 90.0, 180.0], # control point + [200.0, 65.0], # control point + [210.0, 98.0], # base + [220.0, 70.0], # control point + [130.0, 55.0]]) # control point +path = pydiffvg.Path(num_control_points = num_control_points, + points = points, + is_closed = True) +shapes = [path] +path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]), + fill_color = torch.tensor([0.3, 0.6, 0.3, 1.0])) +shape_groups = [path_group] +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + output_type = pydiffvg.OutputType.sdf) + +render = pydiffvg.RenderFunction.apply +img = render(256, # width + 256, # height + 1, # num_samples_x + 1, # num_samples_y + 0, # seed + *scene_args) +img /= 256.0 +# The output image is in linear RGB space. Do Gamma correction before saving the image. +pydiffvg.imwrite(img.cpu(), 'results/single_curve_sdf/target.png', gamma=1.0) +target = img.clone() + +# Move the path to produce initial guess +# normalize points for easier learning rate +points_n = torch.tensor([[100.0/256.0, 40.0/256.0], # base + [155.0/256.0, 65.0/256.0], # control point + [100.0/256.0, 180.0/256.0], # control point + [ 65.0/256.0, 238.0/256.0], # base + [100.0/256.0, 200.0/256.0], # control point + [170.0/256.0, 55.0/256.0], # control point + [220.0/256.0, 100.0/256.0], # base + [210.0/256.0, 80.0/256.0], # control point + [140.0/256.0, 60.0/256.0]], # control point + requires_grad = True) +color = torch.tensor([0.3, 0.2, 0.5, 1.0], requires_grad=True) +path.points = points_n * 256 +path_group.fill_color = color +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + output_type = pydiffvg.OutputType.sdf) +img = render(256, # width + 256, # height + 1, # num_samples_x + 1, # num_samples_y + 1, # seed + *scene_args) +img /= 256.0 +pydiffvg.imwrite(img.cpu(), 'results/single_curve_sdf/init.png', gamma=1.0) + +# Optimize +optimizer = torch.optim.Adam([points_n, color], lr=1e-2) +# Run 100 Adam iterations. +for t in range(100): + print('iteration:', t) + optimizer.zero_grad() + # Forward pass: render the image. + path.points = points_n * 256 + path_group.fill_color = color + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + output_type = pydiffvg.OutputType.sdf) + img = render(256, # width + 256, # height + 1, # num_samples_x + 1, # num_samples_y + t+1, # seed + *scene_args) + img /= 256.0 + # Save the intermediate render. + pydiffvg.imwrite(img.cpu(), 'results/single_curve_sdf/iter_{}.png'.format(t), gamma=1.0) + # Compute the loss function. Here it is L2. + loss = (img - target).pow(2).sum() + print('loss:', loss.item()) + + # Backpropagate the gradients. + loss.backward() + # Print the gradients + print('points_n.grad:', points_n.grad) + print('color.grad:', color.grad) + + # Take a gradient descent step. + optimizer.step() + # Print the current params. + print('points:', path.points) + print('color:', path_group.fill_color) + +# Render the final result. +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + output_type = pydiffvg.OutputType.sdf) +img = render(256, # width + 256, # height + 1, # num_samples_x + 1, # num_samples_y + 102, # seed + *scene_args) +img /= 256.0 +# Save the images and differences. +pydiffvg.imwrite(img.cpu(), 'results/single_curve_sdf/final.png', gamma=1.0) + +# Convert the intermediate renderings to a video. +from subprocess import call +call(["ffmpeg", "-framerate", "24", "-i", + "results/single_curve_sdf/iter_%d.png", "-vb", "20M", + "results/single_curve_sdf/out.mp4"]) diff --git a/apps/single_curve_sdf_trans.py b/apps/single_curve_sdf_trans.py new file mode 100644 index 0000000..f7f5141 --- /dev/null +++ b/apps/single_curve_sdf_trans.py @@ -0,0 +1,172 @@ +import pydiffvg +import torch +import skimage +import numpy as np + +# Use GPU if available +pydiffvg.set_use_gpu(torch.cuda.is_available()) + +canvas_width, canvas_height = 256, 256 +num_control_points = torch.tensor([2]) +# points = torch.tensor([[120.0, 30.0], # base +# [150.0, 60.0], # control point +# [ 90.0, 198.0], # control point +# [ 60.0, 218.0], # base +# [ 90.0, 180.0], # control point +# [200.0, 65.0], # control point +# [210.0, 98.0], # base +# [220.0, 70.0], # control point +# [130.0, 55.0]]) # control point +points = torch.tensor([[ 20.0, 128.0], # base + [ 50.0, 128.0], # control point + [170.0, 128.0], # control point + [200.0, 128.0]]) # base +path = pydiffvg.Path(num_control_points = num_control_points, + points = points, + is_closed = False, + stroke_width = torch.tensor(10.0)) +shapes = [path] +path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]), + fill_color = None, + stroke_color = torch.tensor([0.3, 0.6, 0.3, 1.0])) +shape_groups = [path_group] +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + output_type = pydiffvg.OutputType.sdf) + +render = pydiffvg.RenderFunction.apply +img = render(256, # width + 256, # height + 1, # num_samples_x + 1, # num_samples_y + 0, # seed + *scene_args) + +path.points[:, 1] += 1e-3 +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + output_type = pydiffvg.OutputType.sdf) +img2 = render(256, # width + 256, # height + 1, # num_samples_x + 1, # num_samples_y + 0, # seed + *scene_args) + +# diff = img2 - img +# diff = diff[:, :, 0] / 1e-3 +# import matplotlib.pyplot as plt +# plt.imshow(diff) +# plt.show() + +# # The output image is in linear RGB space. Do Gamma correction before saving the image. +# pydiffvg.imwrite(img.cpu(), 'results/single_curve_sdf/target.png', gamma=1.0) +# target = img.clone() + +render_grad = pydiffvg.RenderFunction.render_grad +img = render_grad(torch.ones(256, 256, 1), # grad_img + 256, # width + 256, # height + 1, # num_samples_x + 1, # num_samples_y + 0, # seed + *scene_args) +img = img[:, :, 0] +import matplotlib.pyplot as plt +plt.imshow(img) +plt.show() + +# # Move the path to produce initial guess +# # normalize points for easier learning rate +# # points_n = torch.tensor([[100.0/256.0, 40.0/256.0], # base +# # [155.0/256.0, 65.0/256.0], # control point +# # [100.0/256.0, 180.0/256.0], # control point +# # [ 65.0/256.0, 238.0/256.0], # base +# # [100.0/256.0, 200.0/256.0], # control point +# # [170.0/256.0, 55.0/256.0], # control point +# # [220.0/256.0, 100.0/256.0], # base +# # [210.0/256.0, 80.0/256.0], # control point +# # [140.0/256.0, 60.0/256.0]], # control point +# # requires_grad = True) +# points_n = torch.tensor([[118.4274/256.0, 32.0159/256.0], +# [174.9657/256.0, 28.1877/256.0], +# [ 87.6629/256.0, 175.1049/256.0], +# [ 57.8093/256.0, 232.8987/256.0], +# [ 80.1829/256.0, 165.4280/256.0], +# [197.3640/256.0, 83.4058/256.0], +# [209.3676/256.0, 97.9176/256.0], +# [219.1048/256.0, 72.0000/256.0], +# [143.1226/256.0, 57.0636/256.0]], +# requires_grad = True) +# color = torch.tensor([0.3, 0.2, 0.5, 1.0], requires_grad=True) +# path.points = points_n * 256 +# path_group.fill_color = color +# scene_args = pydiffvg.RenderFunction.serialize_scene(\ +# canvas_width, canvas_height, shapes, shape_groups, +# output_type = pydiffvg.OutputType.sdf) +# img = render(256, # width +# 256, # height +# 1, # num_samples_x +# 1, # num_samples_y +# 1, # seed +# *scene_args) +# img /= 256.0 +# pydiffvg.imwrite(img.cpu(), 'results/single_curve_sdf/init.png', gamma=1.0) + +# # Optimize +# optimizer = torch.optim.Adam([points_n, color], lr=1e-3) +# # Run 100 Adam iterations. +# for t in range(2): +# print('iteration:', t) +# optimizer.zero_grad() +# # Forward pass: render the image. +# path.points = points_n * 256 +# path_group.fill_color = color +# scene_args = pydiffvg.RenderFunction.serialize_scene(\ +# canvas_width, canvas_height, shapes, shape_groups, +# output_type = pydiffvg.OutputType.sdf) +# img = render(256, # width +# 256, # height +# 1, # num_samples_x +# 1, # num_samples_y +# t+1, # seed +# *scene_args) +# img /= 256.0 +# # Save the intermediate render. +# pydiffvg.imwrite(img.cpu(), 'results/single_curve_sdf/iter_{}.png'.format(t), gamma=1.0) +# # Compute the loss function. Here it is L2. +# loss = (img - target).pow(2).sum() +# print('loss:', loss.item()) + +# # Backpropagate the gradients. +# loss.backward() +# # Print the gradients +# print('points_n.grad:', points_n.grad) +# print('color.grad:', color.grad) + +# # Take a gradient descent step. +# optimizer.step() +# # Print the current params. +# print('points:', path.points) +# print('color:', path_group.fill_color) +# exit() + +# # Render the final result. +# scene_args = pydiffvg.RenderFunction.serialize_scene(\ +# canvas_width, canvas_height, shapes, shape_groups, +# output_type = pydiffvg.OutputType.sdf) +# img = render(256, # width +# 256, # height +# 1, # num_samples_x +# 1, # num_samples_y +# 102, # seed +# *scene_args) +# img /= 256.0 +# # Save the images and differences. +# pydiffvg.imwrite(img.cpu(), 'results/single_curve_sdf/final.png', gamma=1.0) + +# # Convert the intermediate renderings to a video. +# from subprocess import call +# call(["ffmpeg", "-framerate", "24", "-i", +# "results/single_curve_sdf/iter_%d.png", "-vb", "20M", +# "results/single_curve_sdf/out.mp4"]) diff --git a/apps/single_ellipse.py b/apps/single_ellipse.py new file mode 100644 index 0000000..9d116e6 --- /dev/null +++ b/apps/single_ellipse.py @@ -0,0 +1,105 @@ +import pydiffvg +import torch +import skimage +import numpy as np + +# Use GPU if available +pydiffvg.set_use_gpu(torch.cuda.is_available()) + +canvas_width, canvas_height = 256, 256 +ellipse = pydiffvg.Ellipse(radius = torch.tensor([60.0, 30.0]), + center = torch.tensor([128.0, 128.0])) +shapes = [ellipse] +ellipse_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]), + fill_color = torch.tensor([0.3, 0.6, 0.3, 1.0])) +shape_groups = [ellipse_group] +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + +render = pydiffvg.RenderFunction.apply +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + *scene_args) +# The output image is in linear RGB space. Do Gamma correction before saving the image. +pydiffvg.imwrite(img.cpu(), 'results/single_ellipse/target.png', gamma=2.2) +target = img.clone() + +# Move the ellipse to produce initial guess +# normalize radius & center for easier learning rate +radius_n = torch.tensor([20.0 / 256.0, 40.0 / 256.0], requires_grad=True) +center_n = torch.tensor([108.0 / 256.0, 138.0 / 256.0], requires_grad=True) +color = torch.tensor([0.3, 0.2, 0.8, 1.0], requires_grad=True) +ellipse.radius = radius_n * 256 +ellipse.center = center_n * 256 +ellipse_group.fill_color = color +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 1, # seed + *scene_args) +pydiffvg.imwrite(img.cpu(), 'results/single_ellipse/init.png', gamma=2.2) + +# Optimize for radius & center +optimizer = torch.optim.Adam([radius_n, center_n, color], lr=1e-2) +# Run 50 Adam iterations. +for t in range(50): + print('iteration:', t) + optimizer.zero_grad() + # Forward pass: render the image. + ellipse.radius = radius_n * 256 + ellipse.center = center_n * 256 + ellipse_group.fill_color = color + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + t+1, # seed + *scene_args) + # Save the intermediate render. + pydiffvg.imwrite(img.cpu(), 'results/single_ellipse/iter_{}.png'.format(t), gamma=2.2) + # Compute the loss function. Here it is L2. + loss = (img - target).pow(2).sum() + print('loss:', loss.item()) + + # Backpropagate the gradients. + loss.backward() + # Print the gradients + print('radius.grad:', radius_n.grad) + print('center.grad:', center_n.grad) + print('color.grad:', color.grad) + + # Take a gradient descent step. + optimizer.step() + # Print the current params. + print('radius:', ellipse.radius) + print('center:', ellipse.center) + print('color:', ellipse_group.fill_color) + +# Render the final result. +ellipse.radius = radius_n * 256 +ellipse.center = center_n * 256 +ellipse_group.fill_color = color +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 52, # seed + *scene_args) +# Save the images and differences. +pydiffvg.imwrite(img.cpu(), 'results/single_ellipse/final.png') + +# Convert the intermediate renderings to a video. +from subprocess import call +call(["ffmpeg", "-framerate", "24", "-i", + "results/single_ellipse/iter_%d.png", "-vb", "20M", + "results/single_ellipse/out.mp4"]) diff --git a/apps/single_ellipse_transform.py b/apps/single_ellipse_transform.py new file mode 100644 index 0000000..0c11cac --- /dev/null +++ b/apps/single_ellipse_transform.py @@ -0,0 +1,108 @@ +import pydiffvg +import torch +import skimage +import numpy as np + +# Use GPU if available +pydiffvg.set_use_gpu(torch.cuda.is_available()) + +canvas_width, canvas_height = 256, 256 +ellipse = pydiffvg.Ellipse(radius = torch.tensor([60.0, 30.0]), + center = torch.tensor([128.0, 128.0])) +shapes = [ellipse] +ellipse_group = pydiffvg.ShapeGroup(\ + shape_ids = torch.tensor([0]), + fill_color = torch.tensor([0.3, 0.6, 0.3, 1.0]), + shape_to_canvas = torch.eye(3, 3)) +shape_groups = [ellipse_group] +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + +render = pydiffvg.RenderFunction.apply +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + *scene_args) +# The output image is in linear RGB space. Do Gamma correction before saving the image. +pydiffvg.imwrite(img.cpu(), 'results/single_ellipse_transform/target.png', gamma=2.2) +target = img.clone() + +# Affine transform the ellipse to produce initial guess +color = torch.tensor([0.3, 0.2, 0.8, 1.0], requires_grad=True) +affine = torch.zeros(2, 3) +affine[0, 0] = 1.3 +affine[0, 1] = 0.2 +affine[0, 2] = 0.1 +affine[1, 0] = 0.2 +affine[1, 1] = 0.6 +affine[1, 2] = 0.3 +affine.requires_grad = True +shape_to_canvas = torch.cat((affine, torch.tensor([[0.0, 0.0, 1.0]])), axis=0) +ellipse_group.fill_color = color +ellipse_group.shape_to_canvas = shape_to_canvas +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 1, # seed + *scene_args) +pydiffvg.imwrite(img.cpu(), 'results/single_ellipse_transform/init.png', gamma=2.2) + +# Optimize for radius & center +optimizer = torch.optim.Adam([color, affine], lr=1e-2) +# Run 150 Adam iterations. +for t in range(150): + print('iteration:', t) + optimizer.zero_grad() + # Forward pass: render the image. + ellipse_group.fill_color = color + ellipse_group.shape_to_canvas = torch.cat((affine, torch.tensor([[0.0, 0.0, 1.0]])), axis=0) + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + t+1, # seed + *scene_args) + # Save the intermediate render. + pydiffvg.imwrite(img.cpu(), 'results/single_ellipse_transform/iter_{}.png'.format(t), gamma=2.2) + # Compute the loss function. Here it is L2. + loss = (img - target).pow(2).sum() + print('loss:', loss.item()) + + # Backpropagate the gradients. + loss.backward() + # Print the gradients + print('color.grad:', color.grad) + print('affine.grad:', affine.grad) + + # Take a gradient descent step. + optimizer.step() + # Print the current params. + print('color:', ellipse_group.fill_color) + print('affine:', affine) + +# Render the final result. +ellipse_group.fill_color = color +ellipse_group.shape_to_canvas = torch.cat((affine, torch.tensor([[0.0, 0.0, 1.0]])), axis=0) +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 52, # seed + *scene_args) +# Save the images and differences. +pydiffvg.imwrite(img.cpu(), 'results/single_ellipse_transform/final.png') + +# Convert the intermediate renderings to a video. +from subprocess import call +call(["ffmpeg", "-framerate", "24", "-i", + "results/single_ellipse_transform/iter_%d.png", "-vb", "20M", + "results/single_ellipse_transform/out.mp4"]) diff --git a/apps/single_gradient.py b/apps/single_gradient.py new file mode 100644 index 0000000..289ff8a --- /dev/null +++ b/apps/single_gradient.py @@ -0,0 +1,127 @@ +import pydiffvg +import torch +import skimage +import numpy as np + +# Use GPU if available +pydiffvg.set_use_gpu(torch.cuda.is_available()) + +canvas_width, canvas_height = 256, 256 +color = pydiffvg.LinearGradient(\ + begin = torch.tensor([50.0, 50.0]), + end = torch.tensor([200.0, 200.0]), + offsets = torch.tensor([0.0, 1.0]), + stop_colors = torch.tensor([[0.2, 0.5, 0.7, 1.0], + [0.7, 0.2, 0.5, 1.0]])) +circle = pydiffvg.Circle(radius = torch.tensor(40.0), + center = torch.tensor([128.0, 128.0])) +shapes = [circle] +circle_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]), fill_color = color) +shape_groups = [circle_group] +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + +render = pydiffvg.RenderFunction.apply +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + *scene_args) +# The output image is in linear RGB space. Do Gamma correction before saving the image. +pydiffvg.imwrite(img.cpu(), 'results/single_gradient/target.png', gamma=2.2) +target = img.clone() + +# Move the circle to produce initial guess +# normalize radius & center for easier learning rate +radius_n = torch.tensor(20.0 / 256.0, requires_grad=True) +center_n = torch.tensor([108.0 / 256.0, 138.0 / 256.0], requires_grad=True) +begin_n = torch.tensor([100.0 / 256.0, 100.0 / 256.0], requires_grad=True) +end_n = torch.tensor([150.0 / 256.0, 150.0 / 256.0], requires_grad=True) +stop_colors = torch.tensor([[0.1, 0.9, 0.2, 1.0], + [0.5, 0.3, 0.6, 1.0]], requires_grad=True) +color.begin = begin_n * 256 +color.end = end_n * 256 +color.stop_colors = stop_colors +circle.radius = radius_n * 256 +circle.center = center_n * 256 +circle_group.fill_color = color +shapes = [circle] +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 1, # seed + *scene_args) +pydiffvg.imwrite(img.cpu(), 'results/single_gradient/init.png', gamma=2.2) + +# Optimize for radius & center +optimizer = torch.optim.Adam([radius_n, center_n, begin_n, end_n, stop_colors], lr=1e-2) +# Run 50 Adam iterations. +for t in range(100): + print('iteration:', t) + optimizer.zero_grad() + # Forward pass: render the image. + color.begin = begin_n * 256 + color.end = end_n * 256 + color.stop_colors = stop_colors + circle.radius = radius_n * 256 + circle.center = center_n * 256 + circle_group.fill_color = color + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + t+1, # seed + *scene_args) + # Save the intermediate render. + pydiffvg.imwrite(img.cpu(), 'results/single_gradient/iter_{}.png'.format(t), gamma=2.2) + # Compute the loss function. Here it is L2. + loss = (img - target).pow(2).sum() + print('loss:', loss.item()) + + # Backpropagate the gradients. + loss.backward() + # Print the gradients + print('radius.grad:', radius_n.grad) + print('center.grad:', center_n.grad) + print('begin.grad:', begin_n.grad) + print('end.grad:', end_n.grad) + print('stop_colors.grad:', stop_colors.grad) + + # Take a gradient descent step. + optimizer.step() + # Print the current params. + print('radius:', circle.radius) + print('center:', circle.center) + print('begin:', begin_n) + print('end:', end_n) + print('stop_colors:', stop_colors) + +# Render the final result. +color.begin = begin_n * 256 +color.end = end_n * 256 +color.stop_colors = stop_colors +circle.radius = radius_n * 256 +circle.center = center_n * 256 +circle_group.fill_color = color +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 52, # seed + *scene_args) +# Save the images and differences. +pydiffvg.imwrite(img.cpu(), 'results/single_gradient/final.png') + +# Convert the intermediate renderings to a video. +from subprocess import call +call(["ffmpeg", "-framerate", "24", "-i", + "results/single_gradient/iter_%d.png", "-vb", "20M", + "results/single_gradient/out.mp4"]) diff --git a/apps/single_open_curve.py b/apps/single_open_curve.py new file mode 100644 index 0000000..3f6b5d8 --- /dev/null +++ b/apps/single_open_curve.py @@ -0,0 +1,117 @@ +import pydiffvg +import torch +import skimage + +# Use GPU if available +pydiffvg.set_use_gpu(torch.cuda.is_available()) + +canvas_width, canvas_height = 256, 256 +num_control_points = torch.tensor([2]) +points = torch.tensor([[120.0, 30.0], # base + [150.0, 60.0], # control point + [ 90.0, 198.0], # control point + [ 60.0, 218.0]]) # base +path = pydiffvg.Path(num_control_points = num_control_points, + points = points, + thickness = None, + is_closed = False, + stroke_width = torch.tensor(5.0)) +shapes = [path] +path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]), + fill_color = None, + stroke_color = torch.tensor([0.6, 0.3, 0.6, 0.8])) +shape_groups = [path_group] +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + +render = pydiffvg.RenderFunction.apply +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + *scene_args) +# The output image is in linear RGB space. Do Gamma correction before saving the image. +pydiffvg.imwrite(img.cpu(), 'results/single_open_curve/target.png', gamma=2.2) +target = img.clone() + +# Move the path to produce initial guess +# normalize points for easier learning rate +points_n = torch.tensor([[100.0/256.0, 40.0/256.0], # base + [155.0/256.0, 65.0/256.0], # control point + [100.0/256.0, 180.0/256.0], # control point + [ 65.0/256.0, 238.0/256.0]], # base + requires_grad = True) +stroke_color = torch.tensor([0.4, 0.7, 0.5, 0.5], requires_grad=True) +stroke_width_n = torch.tensor(10.0 / 100.0, requires_grad=True) +path.points = points_n * 256 +path.stroke_width = stroke_width_n * 100 +path_group.stroke_color = stroke_color +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 1, # seed + *scene_args) +pydiffvg.imwrite(img.cpu(), 'results/single_open_curve/init.png', gamma=2.2) + +# Optimize +optimizer = torch.optim.Adam([points_n, stroke_color, stroke_width_n], lr=1e-2) +# Run 200 Adam iterations. +for t in range(200): + print('iteration:', t) + optimizer.zero_grad() + # Forward pass: render the image. + path.points = points_n * 256 + path.stroke_width = stroke_width_n * 100 + path_group.stroke_color = stroke_color + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + t+1, # seed + *scene_args) + # Save the intermediate render. + pydiffvg.imwrite(img.cpu(), 'results/single_open_curve/iter_{}.png'.format(t), gamma=2.2) + # Compute the loss function. Here it is L2. + loss = (img - target).pow(2).sum() + print('loss:', loss.item()) + + # Backpropagate the gradients. + loss.backward() + # Print the gradients + print('points_n.grad:', points_n.grad) + print('stroke_color.grad:', stroke_color.grad) + print('stroke_width.grad:', stroke_width_n.grad) + + # Take a gradient descent step. + optimizer.step() + # Print the current params. + print('points:', path.points) + print('stroke_color:', path_group.stroke_color) + print('stroke_width:', path.stroke_width) + +# Render the final result. +path.points = points_n * 256 +path.stroke_width = stroke_width_n * 100 +path_group.stroke_color = stroke_color +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 202, # seed + *scene_args) +# Save the images and differences. +pydiffvg.imwrite(img.cpu(), 'results/single_open_curve/final.png') + +# Convert the intermediate renderings to a video. +from subprocess import call +call(["ffmpeg", "-framerate", "24", "-i", + "results/single_open_curve/iter_%d.png", "-vb", "20M", + "results/single_open_curve/out.mp4"]) diff --git a/apps/single_open_curve_thickness.py b/apps/single_open_curve_thickness.py new file mode 100644 index 0000000..877d835 --- /dev/null +++ b/apps/single_open_curve_thickness.py @@ -0,0 +1,120 @@ +import pydiffvg +import torch +import skimage + +pydiffvg.set_print_timing(True) + +# Use GPU if available +pydiffvg.set_use_gpu(torch.cuda.is_available()) + +canvas_width, canvas_height = 256, 256 +num_control_points = torch.tensor([2]) +points = torch.tensor([[120.0, 30.0], # base + [150.0, 60.0], # control point + [ 90.0, 198.0], # control point + [ 60.0, 218.0]]) # base +thickness = torch.tensor([10.0, 5.0, 4.0, 20.0]) +path = pydiffvg.Path(num_control_points = num_control_points, + points = points, + is_closed = False, + stroke_width = thickness) +shapes = [path] +path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]), + fill_color = None, + stroke_color = torch.tensor([0.6, 0.3, 0.6, 0.8])) +shape_groups = [path_group] +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + +render = pydiffvg.RenderFunction.apply +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + *scene_args) +# The output image is in linear RGB space. Do Gamma correction before saving the image. +pydiffvg.imwrite(img.cpu(), 'results/single_open_curve_thickness/target.png', gamma=2.2) +target = img.clone() + +# Move the path to produce initial guess +# normalize points for easier learning rate +points_n = torch.tensor([[100.0/256.0, 40.0/256.0], # base + [155.0/256.0, 65.0/256.0], # control point + [100.0/256.0, 180.0/256.0], # control point + [ 65.0/256.0, 238.0/256.0]], # base + requires_grad = True) +thickness_n = torch.tensor([10.0 / 100.0, 10.0 / 100.0, 10.0 / 100.0, 10.0 / 100.0], + requires_grad = True) +stroke_color = torch.tensor([0.4, 0.7, 0.5, 0.5], requires_grad=True) +path.points = points_n * 256 +path.stroke_width = thickness_n * 100 +path_group.stroke_color = stroke_color +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 1, # seed + *scene_args) +pydiffvg.imwrite(img.cpu(), 'results/single_open_curve_thickness/init.png', gamma=2.2) + +# Optimize +optimizer = torch.optim.Adam([points_n, thickness_n, stroke_color], lr=1e-2) +# Run 200 Adam iterations. +for t in range(200): + print('iteration:', t) + optimizer.zero_grad() + # Forward pass: render the image. + path.points = points_n * 256 + path.stroke_width = thickness_n * 100 + path_group.stroke_color = stroke_color + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + t+1, # seed + *scene_args) + # Save the intermediate render. + pydiffvg.imwrite(img.cpu(), 'results/single_open_curve_thickness/iter_{}.png'.format(t), gamma=2.2) + # Compute the loss function. Here it is L2. + loss = (img - target).pow(2).sum() + print('loss:', loss.item()) + + # Backpropagate the gradients. + loss.backward() + # Print the gradients + print('points_n.grad:', points_n.grad) + print('thickness_n.grad:', thickness_n.grad) + print('stroke_color.grad:', stroke_color.grad) + + # Take a gradient descent step. + optimizer.step() + # Print the current params. + print('points:', path.points) + print('thickness:', path.stroke_width) + print('stroke_color:', path_group.stroke_color) + +# Render the final result. +path.points = points_n * 256 +path.stroke_width = thickness_n * 100 +path_group.stroke_color = stroke_color +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 202, # seed + *scene_args) +# Save the images and differences. +pydiffvg.imwrite(img.cpu(), 'results/single_open_curve_thickness/final.png') + +# Convert the intermediate renderings to a video. +from subprocess import call +call(["ffmpeg", "-framerate", "24", "-i", + "results/single_open_curve_thickness/iter_%d.png", "-vb", "20M", + "results/single_open_curve_thickness/out.mp4"]) diff --git a/apps/single_path.py b/apps/single_path.py new file mode 100644 index 0000000..5261cfb --- /dev/null +++ b/apps/single_path.py @@ -0,0 +1,99 @@ +import pydiffvg +import torch +import skimage + +# Use GPU if available +pydiffvg.set_use_gpu(torch.cuda.is_available()) + +canvas_width, canvas_height = 510, 510 +# https://www.flaticon.com/free-icon/black-plane_61212#term=airplane&page=1&position=8 +shapes = pydiffvg.from_svg_path('M510,255c0-20.4-17.85-38.25-38.25-38.25H331.5L204,12.75h-51l63.75,204H76.5l-38.25-51H0L25.5,255L0,344.25h38.25l38.25-51h140.25l-63.75,204h51l127.5-204h140.25C492.15,293.25,510,275.4,510,255z') +path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]), + fill_color = torch.tensor([0.3, 0.6, 0.3, 1.0])) +shape_groups = [path_group] +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + +render = pydiffvg.RenderFunction.apply +img = render(510, # width + 510, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + *scene_args) +# The output image is in linear RGB space. Do Gamma correction before saving the image. +pydiffvg.imwrite(img.cpu(), 'results/single_path/target.png', gamma=2.2) +target = img.clone() + +# Move the path to produce initial guess +# normalize points for easier learning rate +noise = torch.FloatTensor(shapes[0].points.shape).uniform_(0.0, 1.0) +points_n = (shapes[0].points.clone() + (noise * 60 - 30)) / 510.0 +points_n.requires_grad = True +color = torch.tensor([0.3, 0.2, 0.5, 1.0], requires_grad=True) +shapes[0].points = points_n * 510 +path_group.fill_color = color +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(510, # width + 510, # height + 2, # num_samples_x + 2, # num_samples_y + 1, # seed + *scene_args) +pydiffvg.imwrite(img.cpu(), 'results/single_path/init.png', gamma=2.2) + +# Optimize +optimizer = torch.optim.Adam([points_n, color], lr=1e-2) +# Run 100 Adam iterations. +for t in range(100): + print('iteration:', t) + optimizer.zero_grad() + # Forward pass: render the image. + shapes[0].points = points_n * 510 + path_group.fill_color = color + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + img = render(510, # width + 510, # height + 2, # num_samples_x + 2, # num_samples_y + t+1, # seed + *scene_args) + # Save the intermediate render. + pydiffvg.imwrite(img.cpu(), 'results/single_path/iter_{:02}.png'.format(t), gamma=2.2) + # Compute the loss function. Here it is L2. + loss = (img - target).pow(2).sum() + print('loss:', loss.item()) + + # Backpropagate the gradients. + loss.backward() + # Print the gradients + print('points_n.grad:', points_n.grad) + print('color.grad:', color.grad) + + # Take a gradient descent step. + optimizer.step() + # Print the current params. + print('points:', shapes[0].points) + print('color:', path_group.fill_color) + +# Render the final result. +shapes[0].points = points_n * 510 +path_group.fill_color = color +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(510, # width + 510, # height + 2, # num_samples_x + 2, # num_samples_y + 102, # seed + *scene_args) +# Save the images and differences. +pydiffvg.imwrite(img.cpu(), 'results/single_path/final.png') + +# Convert the intermediate renderings to a video. +from subprocess import call +call(["ffmpeg", "-framerate", "20", "-i", + "results/single_path/iter_%02d.png", "-vb", "20M", + "results/single_path/out.mp4"]) diff --git a/apps/single_path_sdf.py b/apps/single_path_sdf.py new file mode 100644 index 0000000..b225f5a --- /dev/null +++ b/apps/single_path_sdf.py @@ -0,0 +1,105 @@ +import pydiffvg +import torch +import skimage + +# Use GPU if available +pydiffvg.set_use_gpu(torch.cuda.is_available()) + +canvas_width, canvas_height = 510, 510 +# https://www.flaticon.com/free-icon/black-plane_61212#term=airplane&page=1&position=8 +shapes = pydiffvg.from_svg_path('M510,255c0-20.4-17.85-38.25-38.25-38.25H331.5L204,12.75h-51l63.75,204H76.5l-38.25-51H0L25.5,255L0,344.25h38.25l38.25-51h140.25l-63.75,204h51l127.5-204h140.25C492.15,293.25,510,275.4,510,255z') +path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]), + fill_color = torch.tensor([0.3, 0.6, 0.3, 1.0])) +shape_groups = [path_group] +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + output_type = pydiffvg.OutputType.sdf) + +render = pydiffvg.RenderFunction.apply +img = render(510, # width + 510, # height + 1, # num_samples_x + 1, # num_samples_y + 0, # seed + *scene_args) +img = img / 510 # Normalize SDF to [0, 1] +pydiffvg.imwrite(img.cpu(), 'results/single_path_sdf/target.png', gamma=1.0) +target = img.clone() + +# Move the path to produce initial guess +# normalize points for easier learning rate +noise = torch.FloatTensor(shapes[0].points.shape).uniform_(0.0, 1.0) +points_n = (shapes[0].points.clone() + (noise * 60 - 30)) / 510.0 +points_n.requires_grad = True +color = torch.tensor([0.3, 0.2, 0.5, 1.0], requires_grad=True) +shapes[0].points = points_n * 510 +path_group.fill_color = color +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + output_type = pydiffvg.OutputType.sdf) +img = render(510, # width + 510, # height + 1, # num_samples_x + 1, # num_samples_y + 1, # seed + *scene_args) +img = img / 510 # Normalize SDF to [0, 1] +pydiffvg.imwrite(img.cpu(), 'results/single_path_sdf/init.png', gamma=1.0) + +# Optimize +optimizer = torch.optim.Adam([points_n, color], lr=1e-2) +# Run 100 Adam iterations. +for t in range(100): + print('iteration:', t) + optimizer.zero_grad() + # Forward pass: render the image. + shapes[0].points = points_n * 510 + path_group.fill_color = color + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + output_type = pydiffvg.OutputType.sdf) + img = render(510, # width + 510, # height + 1, # num_samples_x + 1, # num_samples_y + t+1, # seed + *scene_args) + img = img / 510 # Normalize SDF to [0, 1] + # Save the intermediate render. + pydiffvg.imwrite(img.cpu(), 'results/single_path_sdf/iter_{}.png'.format(t), gamma=1.0) + # Compute the loss function. Here it is L2. + loss = (img - target).pow(2).sum() + print('loss:', loss.item()) + + # Backpropagate the gradients. + loss.backward() + # Print the gradients + print('points_n.grad:', points_n.grad) + print('color.grad:', color.grad) + + # Take a gradient descent step. + optimizer.step() + # Print the current params. + print('points:', shapes[0].points) + print('color:', path_group.fill_color) + +# Render the final result. +shapes[0].points = points_n * 510 +path_group.fill_color = color +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + output_type = pydiffvg.OutputType.sdf) +img = render(510, # width + 510, # height + 1, # num_samples_x + 1, # num_samples_y + 102, # seed + *scene_args) +# Save the images and differences. +pydiffvg.imwrite(img.cpu(), 'results/single_path_sdf/final.png') + +# Convert the intermediate renderings to a video. +from subprocess import call +call(["ffmpeg", "-framerate", "24", "-i", + "results/single_path_sdf/iter_%d.png", "-vb", "20M", + "results/single_path_sdf/out.mp4"]) diff --git a/apps/single_polygon.py b/apps/single_polygon.py new file mode 100644 index 0000000..88a7c15 --- /dev/null +++ b/apps/single_polygon.py @@ -0,0 +1,108 @@ +import pydiffvg +import torch +import skimage +import numpy as np + +# Use GPU if available +pydiffvg.set_use_gpu(torch.cuda.is_available()) + +canvas_width, canvas_height = 256, 256 +# https://www.w3schools.com/graphics/svg_polygon.asp +points = torch.tensor([[120.0, 30.0], + [ 60.0, 218.0], + [210.0, 98.0], + [ 30.0, 98.0], + [180.0, 218.0]]) +polygon = pydiffvg.Polygon(points = points, is_closed = True) +shapes = [polygon] +polygon_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]), + fill_color = torch.tensor([0.3, 0.6, 0.3, 1.0])) +shape_groups = [polygon_group] +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + +render = pydiffvg.RenderFunction.apply +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + *scene_args) +# The output image is in linear RGB space. Do Gamma correction before saving the image. +pydiffvg.imwrite(img.cpu(), 'results/single_polygon/target.png', gamma=2.2) +target = img.clone() + +# Move the polygon to produce initial guess +# normalize points for easier learning rate +points_n = torch.tensor([[140.0 / 256.0, 20.0 / 256.0], + [ 65.0 / 256.0, 228.0 / 256.0], + [215.0 / 256.0, 100.0 / 256.0], + [ 35.0 / 256.0, 90.0 / 256.0], + [160.0 / 256.0, 208.0 / 256.0]], requires_grad=True) +color = torch.tensor([0.3, 0.2, 0.5, 1.0], requires_grad=True) +polygon.points = points_n * 256 +polygon_group.color = color +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 1, # seed + *scene_args) +pydiffvg.imwrite(img.cpu(), 'results/single_polygon/init.png', gamma=2.2) + +# Optimize for radius & center +optimizer = torch.optim.Adam([points_n, color], lr=1e-2) +# Run 100 Adam iterations. +for t in range(100): + print('iteration:', t) + optimizer.zero_grad() + # Forward pass: render the image. + polygon.points = points_n * 256 + polygon_group.color = color + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + t+1, # seed + *scene_args) + # Save the intermediate render. + pydiffvg.imwrite(img.cpu(), 'results/single_polygon/iter_{}.png'.format(t), gamma=2.2) + # Compute the loss function. Here it is L2. + loss = (img - target).pow(2).sum() + print('loss:', loss.item()) + + # Backpropagate the gradients. + loss.backward() + # Print the gradients + print('points_n.grad:', points_n.grad) + print('color.grad:', color.grad) + + # Take a gradient descent step. + optimizer.step() + # Print the current params. + print('points:', polygon.points) + print('color:', polygon_group.fill_color) + +# Render the final result. +polygon.points = points_n * 256 +polygon_group.color = color +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 102, # seed + *scene_args) +# Save the images and differences. +pydiffvg.imwrite(img.cpu(), 'results/single_polygon/final.png') + +# Convert the intermediate renderings to a video. +from subprocess import call +call(["ffmpeg", "-framerate", "24", "-i", + "results/single_polygon/iter_%d.png", "-vb", "20M", + "results/single_polygon/out.mp4"]) diff --git a/apps/single_rect.py b/apps/single_rect.py new file mode 100644 index 0000000..4fc51e3 --- /dev/null +++ b/apps/single_rect.py @@ -0,0 +1,102 @@ +import pydiffvg +import torch +import skimage +import numpy as np + +# Use GPU if available +pydiffvg.set_use_gpu(torch.cuda.is_available()) + +canvas_width, canvas_height = 256 ,256 +rect = pydiffvg.Rect(p_min = torch.tensor([40.0, 40.0]), + p_max = torch.tensor([160.0, 160.0])) +shapes = [rect] +rect_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]), + fill_color = torch.tensor([0.3, 0.6, 0.3, 1.0])) +shape_groups = [rect_group] +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + +render = pydiffvg.RenderFunction.apply +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + *scene_args) +# The output image is in linear RGB space. Do Gamma correction before saving the image. +pydiffvg.imwrite(img.cpu(), 'results/single_rect/target.png', gamma=2.2) +target = img.clone() + +# Move the rect to produce initial guess +# normalize p_min & p_max for easier learning rate +p_min_n = torch.tensor([80.0 / 256.0, 20.0 / 256.0], requires_grad=True) +p_max_n = torch.tensor([100.0 / 256.0, 60.0 / 256.0], requires_grad=True) +color = torch.tensor([0.3, 0.2, 0.5, 1.0], requires_grad=True) +rect.p_min = p_min_n * 256 +rect.p_max = p_max_n * 256 +rect_group.fill_color = color +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 1, # seed + *scene_args) +pydiffvg.imwrite(img.cpu(), 'results/single_rect/init.png', gamma=2.2) + +# Optimize for radius & center +optimizer = torch.optim.Adam([p_min_n, p_max_n, color], lr=1e-2) +# Run 100 Adam iterations. +for t in range(100): + print('iteration:', t) + optimizer.zero_grad() + # Forward pass: render the image. + rect.p_min = p_min_n * 256 + rect.p_max = p_max_n * 256 + rect_group.fill_color = color + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + t+1, # seed + *scene_args) + # Save the intermediate render. + pydiffvg.imwrite(img.cpu(), 'results/single_rect/iter_{}.png'.format(t), gamma=2.2) + # Compute the loss function. Here it is L2. + loss = (img - target).pow(2).sum() + print('loss:', loss.item()) + + # Backpropagate the gradients. + loss.backward() + # Print the gradients + print('p_min.grad:', p_min_n.grad) + print('p_max.grad:', p_max_n.grad) + print('color.grad:', color.grad) + + # Take a gradient descent step. + optimizer.step() + # Print the current params. + print('p_min:', rect.p_min) + print('p_max:', rect.p_max) + print('color:', rect_group.fill_color) + +# Render the final result. +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 102, # seed + *scene_args) +# Save the images and differences. +pydiffvg.imwrite(img.cpu(), 'results/single_rect/final.png') + +# Convert the intermediate renderings to a video. +from subprocess import call +call(["ffmpeg", "-framerate", "24", "-i", + "results/single_rect/iter_%d.png", "-vb", "20M", + "results/single_rect/out.mp4"]) diff --git a/apps/single_stroke.py b/apps/single_stroke.py new file mode 100644 index 0000000..256f79e --- /dev/null +++ b/apps/single_stroke.py @@ -0,0 +1,117 @@ +import pydiffvg +import torch +import skimage +import numpy as np + +# Use GPU if available +pydiffvg.set_use_gpu(torch.cuda.is_available()) + +canvas_width, canvas_height = 256, 256 +num_control_points = torch.tensor([2]) +points = torch.tensor([[120.0, 30.0], # base + [150.0, 60.0], # control point + [ 90.0, 198.0], # control point + [ 60.0, 218.0]]) # base +path = pydiffvg.Path(num_control_points = num_control_points, + points = points, + is_closed = False, + stroke_width = torch.tensor(5.0)) +shapes = [path] +path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]), + fill_color = torch.tensor([0.0, 0.0, 0.0, 0.0]), + stroke_color = torch.tensor([0.6, 0.3, 0.6, 0.8])) +shape_groups = [path_group] +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + +render = pydiffvg.RenderFunction.apply +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + *scene_args) +# The output image is in linear RGB space. Do Gamma correction before saving the image. +pydiffvg.imwrite(img.cpu(), 'results/single_stroke/target.png', gamma=2.2) +target = img.clone() + +# Move the path to produce initial guess +# normalize points for easier learning rate +points_n = torch.tensor([[100.0/256.0, 40.0/256.0], # base + [155.0/256.0, 65.0/256.0], # control point + [100.0/256.0, 180.0/256.0], # control point + [ 65.0/256.0, 238.0/256.0]], # base + requires_grad = True) +stroke_color = torch.tensor([0.4, 0.7, 0.5, 0.5], requires_grad=True) +stroke_width_n = torch.tensor(10.0 / 100.0, requires_grad=True) +path.points = points_n * 256 +path.stroke_width = stroke_width_n * 100 +path_group.stroke_color = stroke_color +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 1, # seed + *scene_args) +pydiffvg.imwrite(img.cpu(), 'results/single_stroke/init.png', gamma=2.2) + +# Optimize +optimizer = torch.optim.Adam([points_n, stroke_color, stroke_width_n], lr=1e-2) +# Run 200 Adam iterations. +for t in range(200): + print('iteration:', t) + optimizer.zero_grad() + # Forward pass: render the image. + path.points = points_n * 256 + path.stroke_width = stroke_width_n * 100 + path_group.stroke_color = stroke_color + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + t+1, # seed + *scene_args) + # Save the intermediate render. + pydiffvg.imwrite(img.cpu(), 'results/single_stroke/iter_{}.png'.format(t), gamma=2.2) + # Compute the loss function. Here it is L2. + loss = (img - target).pow(2).sum() + print('loss:', loss.item()) + + # Backpropagate the gradients. + loss.backward() + # Print the gradients + print('points_n.grad:', points_n.grad) + print('stroke_color.grad:', stroke_color.grad) + print('stroke_width.grad:', stroke_width_n.grad) + + # Take a gradient descent step. + optimizer.step() + # Print the current params. + print('points:', path.points) + print('stroke_color:', path_group.stroke_color) + print('stroke_width:', path.stroke_width) + +# Render the final result. +path.points = points_n * 256 +path.stroke_width = stroke_width_n * 100 +path_group.stroke_color = stroke_color +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 202, # seed + *scene_args) +# Save the images and differences. +pydiffvg.imwrite(img.cpu(), 'results/single_stroke/final.png') + +# Convert the intermediate renderings to a video. +from subprocess import call +call(["ffmpeg", "-framerate", "24", "-i", + "results/single_stroke/iter_%d.png", "-vb", "20M", + "results/single_stroke/out.mp4"]) diff --git a/apps/sketch_gan.py b/apps/sketch_gan.py new file mode 100644 index 0000000..6f3da59 --- /dev/null +++ b/apps/sketch_gan.py @@ -0,0 +1,213 @@ +"""A simple training interface using ttools.""" +import argparse +import os +import logging +import random + +import numpy as np +import torch +from torchvision.datasets import MNIST +import torchvision.transforms as xforms +from torch.utils.data import DataLoader + +import ttools +import ttools.interfaces + +import pydiffvg + +LOG = ttools.get_logger(__name__) + +pydiffvg.render_pytorch.print_timing = False + +torch.manual_seed(123) +np.random.seed(123) +torch.backends.cudnn.deterministic = True + +latent_dim = 100 +img_size = 32 +num_paths = 8 +num_segments = 8 + +def weights_init_normal(m): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + torch.nn.init.normal_(m.weight.data, 0.0, 0.02) + elif classname.find("BatchNorm2d") != -1: + torch.nn.init.normal_(m.weight.data, 1.0, 0.02) + torch.nn.init.constant_(m.bias.data, 0.0) + +class VisdomImageCallback(ttools.callbacks.ImageDisplayCallback): + def visualized_image(self, batch, fwd_result): + return torch.cat([batch[0], fwd_result.cpu()], dim = 2) + +# From https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/dcgan/dcgan.py +class Generator(torch.nn.Module): + def __init__(self): + super(Generator, self).__init__() + + self.fc = torch.nn.Sequential( + torch.nn.Linear(latent_dim, 128), + torch.nn.LeakyReLU(0.2, inplace=True), + torch.nn.Linear(128, 256), + torch.nn.LeakyReLU(0.2, inplace=True), + torch.nn.Linear(256, 512), + torch.nn.LeakyReLU(0.2, inplace=True), + torch.nn.Linear(512, 1024), + torch.nn.LeakyReLU(0.2, inplace=True), + torch.nn.Linear(1024, 2 * num_paths * (num_segments + 1) + num_paths + num_paths), + torch.nn.Sigmoid() + ) + + def forward(self, z): + out = self.fc(z) + # construct paths + imgs = [] + for b in range(out.shape[0]): + index = 0 + shapes = [] + shape_groups = [] + for i in range(num_paths): + points = img_size * out[b, index: index + 2 * (num_segments + 1)].view(-1, 2).cpu() + index += 2 * (num_segments + 1) + stroke_width = img_size * out[b, index].view(1).cpu() + index += 1 + + num_control_points = torch.zeros(num_segments, dtype = torch.int32) + 2 + path = pydiffvg.Path(num_control_points = num_control_points, + points = points, + stroke_width = stroke_width, + is_closed = False) + shapes.append(path) + + stroke_color = out[b, index].view(1).cpu() + index += 1 + stroke_color = torch.cat([stroke_color, torch.tensor([0.0, 0.0, 1.0])]) + path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([len(shapes) - 1]), + fill_color = None, + stroke_color = stroke_color) + shape_groups.append(path_group) + scene_args = pydiffvg.RenderFunction.serialize_scene(img_size, img_size, shapes, shape_groups) + render = pydiffvg.RenderFunction.apply + img = render(img_size, # width + img_size, # height + 2, # num_samples_x + 2, # num_samples_y + random.randint(0, 1048576), # seed + None, + *scene_args) + img = img[:, :, :1] + # HWC -> NCHW + img = img.unsqueeze(0) + img = img.permute(0, 3, 1, 2) # NHWC -> NCHW + imgs.append(img) + img = torch.cat(imgs, dim = 0) + return img + +class Discriminator(torch.nn.Module): + def __init__(self): + super(Discriminator, self).__init__() + + def discriminator_block(in_filters, out_filters, bn=True): + block = [torch.nn.Conv2d(in_filters, out_filters, 3, 2, 1), + torch.nn.LeakyReLU(0.2, inplace=True), + torch.nn.Dropout2d(0.25)] + if bn: + block.append(torch.nn.BatchNorm2d(out_filters, 0.8)) + return block + + self.model = torch.nn.Sequential( + *discriminator_block(1, 16, bn=False), + *discriminator_block(16, 32), + *discriminator_block(32, 64), + *discriminator_block(64, 128), + ) + + # The height and width of downsampled image + ds_size = img_size // 2 ** 4 + self.adv_layer = torch.nn.Sequential( + torch.nn.Linear(128 * ds_size ** 2, 1), + torch.nn.Sigmoid()) + + def forward(self, img): + out = self.model(img) + out = out.view(out.shape[0], -1) + validity = self.adv_layer(out) + + return validity + +class MNISTInterface(ttools.interfaces.SGANInterface): + """An adapter to run or train a model.""" + + def __init__(self, gen, discrim, lr=2e-4): + super(MNISTInterface, self).__init__(gen, discrim, lr, opt = 'adam') + + def forward(self, batch): + return self.gen(torch.zeros([batch[0].shape[0], latent_dim], device = self.device).normal_()) + + def _discriminator_input(self, batch, fwd_data, fake=False): + if fake: + return fwd_data + else: + return batch[0].to(self.device) + +def train(args): + """Train a MNIST classifier.""" + + # Setup train and val data + _xform = xforms.Compose([xforms.Resize([32, 32]), xforms.ToTensor()]) + data = MNIST("data/mnist", train=True, download=True, transform=_xform) + + # Initialize asynchronous dataloaders + loader = DataLoader(data, batch_size=args.bs, num_workers=2) + + # Instantiate the models + gen = Generator() + discrim = Discriminator() + + gen.apply(weights_init_normal) + discrim.apply(weights_init_normal) + + # Checkpointer to save/recall model parameters + checkpointer_gen = ttools.Checkpointer(os.path.join(args.out, "checkpoints"), model=gen, prefix="gen_") + checkpointer_discrim = ttools.Checkpointer(os.path.join(args.out, "checkpoints"), model=discrim, prefix="discrim_") + + # resume from a previous checkpoint, if any + checkpointer_gen.load_latest() + checkpointer_discrim.load_latest() + + # Setup a training interface for the model + interface = MNISTInterface(gen, discrim, lr=args.lr) + + # Create a training looper with the interface we defined + trainer = ttools.Trainer(interface) + + # Adds several callbacks, that will be called by the trainer -------------- + # A periodic checkpointing operation + trainer.add_callback(ttools.callbacks.CheckpointingCallback(checkpointer_gen)) + trainer.add_callback(ttools.callbacks.CheckpointingCallback(checkpointer_discrim)) + # A simple progress bar + trainer.add_callback(ttools.callbacks.ProgressBarCallback( + keys=["loss_g", "loss_d", "loss"])) + # A volatile logging using visdom + trainer.add_callback(ttools.callbacks.VisdomLoggingCallback( + keys=["loss_g", "loss_d", "loss"], + port=8080, env="mnist_demo")) + # Image + trainer.add_callback(VisdomImageCallback(port=8080, env="mnist_demo")) + # ------------------------------------------------------------------------- + + # Start the training + LOG.info("Training started, press Ctrl-C to interrupt.") + trainer.train(loader, num_epochs=args.epochs) + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + # TODO: subparsers + parser.add_argument("data", help="directory where we download and store the MNIST dataset.") + parser.add_argument("out", help="directory where we write the checkpoints and visualizations.") + parser.add_argument("--lr", type=float, default=1e-4, help="learning rate for the optimizer.") + parser.add_argument("--epochs", type=int, default=500, help="number of epochs to train for.") + parser.add_argument("--bs", type=int, default=64, help="number of elements per batch.") + args = parser.parse_args() + ttools.set_logger(True) # activate debug prints + train(args) diff --git a/apps/style_transfer.py b/apps/style_transfer.py new file mode 100644 index 0000000..43ba38c --- /dev/null +++ b/apps/style_transfer.py @@ -0,0 +1,291 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +import torchvision.transforms as transforms +import torchvision.models as models +from PIL import Image +import copy +import pydiffvg +import argparse + +def main(args): + pydiffvg.set_use_gpu(torch.cuda.is_available()) + + canvas_width, canvas_height, shapes, shape_groups = pydiffvg.svg_to_scene(args.content_file) + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + render = pydiffvg.RenderFunction.apply + img = render(canvas_width, # width + canvas_height, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + None, + *scene_args) + # Transform to gamma space + pydiffvg.imwrite(img.cpu(), 'results/style_transfer/init.png', gamma=1.0) + # HWC -> NCHW + img = img.unsqueeze(0) + img = img.permute(0, 3, 1, 2) # NHWC -> NCHW + + loader = transforms.Compose([ + transforms.ToTensor()]) # transform it into a torch tensor + + def image_loader(image_name): + image = Image.open(image_name) + # fake batch dimension required to fit network's input dimensions + image = loader(image).unsqueeze(0) + return image.to(pydiffvg.get_device(), torch.float) + + style_img = image_loader(args.style_img) + # alpha blend content with a gray background + content_img = img[:, :3, :, :] * img[:, 3, :, :] + \ + 0.5 * torch.ones([1, 3, img.shape[2], img.shape[3]]) * \ + (1 - img[:, 3, :, :]) + + assert style_img.size() == content_img.size(), \ + "we need to import style and content images of the same size" + + unloader = transforms.ToPILImage() # reconvert into PIL image + + class ContentLoss(nn.Module): + def __init__(self, target,): + super(ContentLoss, self).__init__() + # we 'detach' the target content from the tree used + # to dynamically compute the gradient: this is a stated value, + # not a variable. Otherwise the forward method of the criterion + # will throw an error. + self.target = target.detach() + + def forward(self, input): + self.loss = F.mse_loss(input, self.target) + return input + + def gram_matrix(input): + a, b, c, d = input.size() # a=batch size(=1) + # b=number of feature maps + # (c,d)=dimensions of a f. map (N=c*d) + + features = input.view(a * b, c * d) # resise F_XL into \hat F_XL + + G = torch.mm(features, features.t()) # compute the gram product + + # we 'normalize' the values of the gram matrix + # by dividing by the number of element in each feature maps. + return G.div(a * b * c * d) + + class StyleLoss(nn.Module): + + def __init__(self, target_feature): + super(StyleLoss, self).__init__() + self.target = gram_matrix(target_feature).detach() + + def forward(self, input): + G = gram_matrix(input) + self.loss = F.mse_loss(G, self.target) + return input + + device = pydiffvg.get_device() + cnn = models.vgg19(pretrained=True).features.to(device).eval() + + cnn_normalization_mean = torch.tensor([0.485, 0.456, 0.406]).to(device) + cnn_normalization_std = torch.tensor([0.229, 0.224, 0.225]).to(device) + + # create a module to normalize input image so we can easily put it in a + # nn.Sequential + class Normalization(nn.Module): + def __init__(self, mean, std): + super(Normalization, self).__init__() + # .view the mean and std to make them [C x 1 x 1] so that they can + # directly work with image Tensor of shape [B x C x H x W]. + # B is batch size. C is number of channels. H is height and W is width. + self.mean = mean.clone().view(-1, 1, 1) + self.std = std.clone().view(-1, 1, 1) + + def forward(self, img): + # normalize img + return (img - self.mean) / self.std + + # desired depth layers to compute style/content losses : + content_layers_default = ['conv_4'] + style_layers_default = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5'] + + def get_style_model_and_losses(cnn, normalization_mean, normalization_std, + style_img, content_img, + content_layers=content_layers_default, + style_layers=style_layers_default): + cnn = copy.deepcopy(cnn) + + # normalization module + normalization = Normalization(normalization_mean, normalization_std).to(device) + + # just in order to have an iterable access to or list of content/syle + # losses + content_losses = [] + style_losses = [] + + # assuming that cnn is a nn.Sequential, so we make a new nn.Sequential + # to put in modules that are supposed to be activated sequentially + model = nn.Sequential(normalization) + + i = 0 # increment every time we see a conv + for layer in cnn.children(): + if isinstance(layer, nn.Conv2d): + i += 1 + name = 'conv_{}'.format(i) + elif isinstance(layer, nn.ReLU): + name = 'relu_{}'.format(i) + # The in-place version doesn't play very nicely with the ContentLoss + # and StyleLoss we insert below. So we replace with out-of-place + # ones here. + layer = nn.ReLU(inplace=False) + elif isinstance(layer, nn.MaxPool2d): + name = 'pool_{}'.format(i) + elif isinstance(layer, nn.BatchNorm2d): + name = 'bn_{}'.format(i) + else: + raise RuntimeError('Unrecognized layer: {}'.format(layer.__class__.__name__)) + + model.add_module(name, layer) + + if name in content_layers: + # add content loss: + target = model(content_img).detach() + content_loss = ContentLoss(target) + model.add_module("content_loss_{}".format(i), content_loss) + content_losses.append(content_loss) + + if name in style_layers: + # add style loss: + target_feature = model(style_img).detach() + style_loss = StyleLoss(target_feature) + model.add_module("style_loss_{}".format(i), style_loss) + style_losses.append(style_loss) + + # now we trim off the layers after the last content and style losses + for i in range(len(model) - 1, -1, -1): + if isinstance(model[i], ContentLoss) or isinstance(model[i], StyleLoss): + break + + model = model[:(i + 1)] + + return model, style_losses, content_losses + + def run_style_transfer(cnn, normalization_mean, normalization_std, + content_img, style_img, + canvas_width, canvas_height, + shapes, shape_groups, + num_steps=500, style_weight=5000, content_weight=1): + """Run the style transfer.""" + print('Building the style transfer model..') + model, style_losses, content_losses = get_style_model_and_losses(cnn, + normalization_mean, normalization_std, style_img, content_img) + point_params = [] + color_params = [] + stroke_width_params = [] + for shape in shapes: + if isinstance(shape, pydiffvg.Path): + point_params.append(shape.points.requires_grad_()) + stroke_width_params.append(shape.stroke_width.requires_grad_()) + for shape_group in shape_groups: + if isinstance(shape_group.fill_color, torch.Tensor): + color_params.append(shape_group.fill_color.requires_grad_()) + elif isinstance(shape_group.fill_color, pydiffvg.LinearGradient): + point_params.append(shape_group.fill_color.begin.requires_grad_()) + point_params.append(shape_group.fill_color.end.requires_grad_()) + color_params.append(shape_group.fill_color.stop_colors.requires_grad_()) + if isinstance(shape_group.stroke_color, torch.Tensor): + color_params.append(shape_group.stroke_color.requires_grad_()) + elif isinstance(shape_group.stroke_color, pydiffvg.LinearGradient): + point_params.append(shape_group.stroke_color.begin.requires_grad_()) + point_params.append(shape_group.stroke_color.end.requires_grad_()) + color_params.append(shape_group.stroke_color.stop_colors.requires_grad_()) + + point_optimizer = optim.Adam(point_params, lr=1.0) + color_optimizer = optim.Adam(color_params, lr=0.01) + stroke_width_optimizers = optim.Adam(stroke_width_params, lr=0.1) + print('Optimizing..') + run = [0] + while run[0] <= num_steps: + point_optimizer.zero_grad() + color_optimizer.zero_grad() + stroke_width_optimizers.zero_grad() + + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + render = pydiffvg.RenderFunction.apply + img = render(canvas_width, # width + canvas_height, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + None, + *scene_args) + # alpha blend img with a gray background + img = img[:, :, :3] * img[:, :, 3:4] + \ + 0.5 * torch.ones([img.shape[0], img.shape[1], 3]) * \ + (1 - img[:, :, 3:4]) + + pydiffvg.imwrite(img.cpu(), + 'results/style_transfer/step_{}.png'.format(run[0]), + gamma=1.0) + + # HWC to NCHW + img = img.permute([2, 0, 1]).unsqueeze(0) + model(img) + style_score = 0 + content_score = 0 + + for sl in style_losses: + style_score += sl.loss + for cl in content_losses: + content_score += cl.loss + + style_score *= style_weight + content_score *= content_weight + + loss = style_score + content_score + loss.backward() + + run[0] += 1 + if run[0] % 1 == 0: + print("run {}:".format(run)) + print('Style Loss : {:4f} Content Loss: {:4f}'.format( + style_score.item(), content_score.item())) + print() + + point_optimizer.step() + color_optimizer.step() + stroke_width_optimizers.step() + + for color in color_params: + color.data.clamp_(0, 1) + for w in stroke_width_params: + w.data.clamp_(0.5, 4.0) + + return shapes, shape_groups + + shapes, shape_groups = run_style_transfer(\ + cnn, cnn_normalization_mean, cnn_normalization_std, + content_img, style_img, + canvas_width, canvas_height, shapes, shape_groups) + + scene_args = pydiffvg.RenderFunction.serialize_scene(shapes, shape_groups) + render = pydiffvg.RenderFunction.apply + img = render(canvas_width, # width + canvas_height, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + None, + *scene_args) + # Transform to gamma space + pydiffvg.imwrite(img.cpu(), 'results/style_transfer/output.png', gamma=1.0) + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("content_file", help="source SVG path") + parser.add_argument("style_img", help="target image path") + args = parser.parse_args() + main(args) diff --git a/apps/svg_brush.py b/apps/svg_brush.py new file mode 100644 index 0000000..de54e48 --- /dev/null +++ b/apps/svg_brush.py @@ -0,0 +1,167 @@ +import sys +sys.path.append("../svg") +from geometry import GeometryLoss +import numpy as np +import pygame as pg +import torch +import pydiffvg +import tkinter as tk +from tkinter import filedialog + +def box_kernel(val): + return np.heaviside(-val+1,0) + +def cone_kernel(val): + return np.maximum(0,1-val) + +def nptosurf(arr): + if arr.shape[2]==1: + #greyscale + shape=arr.shape + shape=(shape[0],shape[1],3) + arr=np.broadcast_to(arr,shape) + return pg.surfarray.make_surface(arr*255) + +def brush_tensor(screen_size,coords,radius,kernel): + coordarr=np.stack(np.meshgrid(np.linspace(0,screen_size[0]-1,screen_size[0]),np.linspace(0,screen_size[1]-1,screen_size[1]),indexing='ij'),axis=2) + ctrarr = np.reshape(np.array(coords), [1, 1, 2]) + distarr=np.sqrt(np.sum(np.power(coordarr-ctrarr,2),axis=2)) + valarr=kernel(distarr/radius) + return torch.tensor(valarr,requires_grad=False,dtype=torch.float32) + +def checkerboard(shape, square_size=2): + xv,yv=np.meshgrid(np.floor(np.linspace(0,shape[1]-1,shape[1])/square_size),np.floor(np.linspace(0,shape[0]-1,shape[0])/square_size)) + bin=np.expand_dims(((xv+yv)%2),axis=2) + res=bin*np.array([[[1., 1., 1.,]]])+(1-bin)*np.array([[[.75, .75, .75,]]]) + return torch.tensor(res,requires_grad=False,dtype=torch.float32) + +def render(optim, viewport): + scene_args = pydiffvg.RenderFunction.serialize_scene(*optim.build_scene()) + render = pydiffvg.RenderFunction.apply + img = render(viewport[0], # width + viewport[1], # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + None, + *scene_args) + return img + +def optimize(optim, viewport, brush_kernel, increase=True, strength=0.1): + optim.zero_grad() + + geomLoss=torch.tensor(0.) + + for shape, gloss in zip(optim.scene[2],geometryLosses): + geomLoss+=gloss.compute(shape) + + img=render(optim,viewport) + + imalpha=img[:,:,3] + + multiplied=imalpha*brush_kernel + + loss=((1-multiplied).mean() if increase else multiplied.mean())*strength + + loss+=geomLoss + + loss.backward() + + optim.step() + + return render(optim,viewport) + +def get_infile(): + pydiffvg.set_use_gpu(False) + root = tk.Tk() + #root.withdraw() + + file_path = filedialog.askopenfilename(initialdir = ".",title = "Select graphic to optimize",filetypes = (("SVG files","*.svg"),("all files","*.*"))) + + root.destroy() + + return file_path + +def compositebg(img): + bg=checkerboard(img.shape,2) + color=img[:,:,0:3] + alpha=img[:,:,3] + composite=alpha.unsqueeze(2)*color+(1-alpha).unsqueeze(2)*bg + + return composite + +def main(): + infile=get_infile() + + settings=pydiffvg.SvgOptimizationSettings() + settings.global_override(["optimize_color"],False) + settings.global_override(["transforms","optimize_transforms"], False) + settings.global_override(["optimizer"], "SGD") + settings.global_override(["paths","shape_lr"], 1e-1) + + optim=pydiffvg.OptimizableSvg(infile,settings) + + global geometryLosses + geometryLosses = [] + + for shape in optim.build_scene()[2]: + geometryLosses.append(GeometryLoss(shape)) + + scaling=1 + brush_radius=100 + graphic_size=optim.canvas + screen_size=(graphic_size[1]*scaling, graphic_size[0]*scaling) + + pg.init() + + screen=pg.display.set_mode(screen_size) + screen.fill((255,255,255)) + + img=render(optim,graphic_size) + print(img.max()) + + npsurf = pg.transform.scale(nptosurf(compositebg(img).detach().permute(1,0,2).numpy()), screen_size) + + screen.blit(npsurf,(0,0)) + + pg.display.update() + clock=pg.time.Clock() + + z=0 + btn=0 + + while True: + clock.tick(60) + for event in pg.event.get(): + if event.type==pg.QUIT: + pg.quit() + sys.exit() + + y, x = pg.mouse.get_pos() + if event.type == pg.MOUSEBUTTONDOWN: + if event.button in [1,3]: + z=1 + btn=event.button + elif event.button == 4: + brush_radius*=1.1 + elif event.button == 5: + brush_radius/=1.1 + brush_radius=max(brush_radius,5) + elif event.type == pg.MOUSEBUTTONUP: + if event.button in [1,3]: + z=0 + + if z==1: + brush=brush_tensor((graphic_size[0],graphic_size[1]), (x/scaling, y/scaling), brush_radius, box_kernel) + img=optimize(optim,graphic_size,brush,btn==1) + npsurf = pg.transform.scale(nptosurf(compositebg(img).detach().permute(1,0,2).numpy()), screen_size) + + + screen.blit(npsurf,(0,0)) + pg.draw.circle(screen, (255,255,255), (y,x), int(brush_radius*scaling), 1) + pg.display.update() + + +if __name__ == '__main__': + main() + diff --git a/apps/svg_parse_test.py b/apps/svg_parse_test.py new file mode 100644 index 0000000..f6fb5ea --- /dev/null +++ b/apps/svg_parse_test.py @@ -0,0 +1,63 @@ +import pydiffvg +import sys +import numpy as np +import torch +sys.path.append("../pydiffvg") + +from optimize_svg import OptimizableSvg + +pydiffvg.set_use_gpu(False) + +""" +for x in range(100000): + inmat=np.eye(3) + inmat[0:2,:]=(np.random.rand(2,3)-0.5)*2 + decomp=OptimizableSvg.TransformTools.decompose(inmat) + outmat=OptimizableSvg.TransformTools.recompose(torch.tensor(decomp[0],dtype=torch.float32),torch.tensor(decomp[1],dtype=torch.float32),torch.tensor(decomp[2],dtype=torch.float32),torch.tensor(decomp[3],dtype=torch.float32)).numpy() + dif=np.linalg.norm(inmat-outmat) + if dif > 1e-3: + print(dif) + print(inmat) + print(outmat) + print(decomp)""" + +#infile='../../data/test_data/linear_grad_alpha_aspaths.svg' +#infile='../../data/note_small.svg' +infile='linux.svg' + +canvas_width, canvas_height, shapes, shape_groups = \ + pydiffvg.svg_to_scene(infile) +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) +render = pydiffvg.RenderFunction.apply +img = render(canvas_width, # width + canvas_height, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + *scene_args) +# The output image is in linear RGB space. Do Gamma correction before saving the image. +pydiffvg.imwrite(img.cpu(), 'test_old.png', gamma=1.0) + +#optim=OptimizableSvg('linux.svg',verbose=True) +optim=OptimizableSvg(infile,verbose=True) + +scene=optim.build_scene() +scene_args = pydiffvg.RenderFunction.serialize_scene(*scene) +render = pydiffvg.RenderFunction.apply +img = render(scene[0], # width + scene[1], # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + *scene_args) + + + +with open("resaved.svg","w") as f: + f.write(optim.write_xml()) + +# The output image is in linear RGB space. Do Gamma correction before saving the image. +pydiffvg.imwrite(img.cpu(), 'test_new.png', gamma=1.0) + +print("Done!") \ No newline at end of file diff --git a/apps/test_eval_positions.py b/apps/test_eval_positions.py new file mode 100644 index 0000000..c79e3a8 --- /dev/null +++ b/apps/test_eval_positions.py @@ -0,0 +1,109 @@ +import pydiffvg +import torch +import skimage +import numpy as np + +# Use GPU if available +pydiffvg.set_use_gpu(torch.cuda.is_available()) + +canvas_width = 256 +canvas_height = 256 +circle = pydiffvg.Circle(radius = torch.tensor(40.0), + center = torch.tensor([128.0, 128.0])) +shapes = [circle] +circle_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]), + fill_color = torch.tensor([0.3, 0.6, 0.3, 1.0])) +shape_groups = [circle_group] +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + output_type = pydiffvg.OutputType.sdf) + +render = pydiffvg.RenderFunction.apply +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 0, # seed + *scene_args) +img = img / 256 # Normalize SDF to [0, 1] +pydiffvg.imwrite(img.cpu(), 'results/test_eval_positions/target.png') +target = img.clone() + +# Move the circle to produce initial guess +# normalize radius & center for easier learning rate +radius_n = torch.tensor(20.0 / 256.0, requires_grad=True) +center_n = torch.tensor([108.0 / 256.0, 138.0 / 256.0], requires_grad=True) +color = torch.tensor([0.3, 0.2, 0.8, 1.0], requires_grad=True) +circle.radius = radius_n * 256 +circle.center = center_n * 256 +circle_group.fill_color = color +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + output_type = pydiffvg.OutputType.sdf) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 1, # seed + *scene_args) +img = img / 256 # Normalize SDF to [0, 1] +pydiffvg.imwrite(img.cpu(), 'results/test_eval_positions/init.png') + +# Optimize for radius & center +optimizer = torch.optim.Adam([radius_n, center_n, color], lr=1e-2) +# Run 200 Adam iterations. +for t in range(200): + print('iteration:', t) + optimizer.zero_grad() + # Forward pass: render the image. + circle.radius = radius_n * 256 + circle.center = center_n * 256 + circle_group.fill_color = color + # Evaluate 1000 positions + eval_positions = torch.rand(1000, 2) * 256 + # for grid_sample() + grid_eval_positions = (eval_positions / 256.0) * 2.0 - 1.0 + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + output_type = pydiffvg.OutputType.sdf, + eval_positions = eval_positions) + samples = render(256, # width + 256, # height + 0, # num_samples_x + 0, # num_samples_y + t+1, # seed + *scene_args) + samples = samples / 256 # Normalize SDF to [0, 1] + target_sampled = torch.nn.functional.grid_sample(\ + target.view(1, 1, target.shape[0], target.shape[1]), + grid_eval_positions.view(1, -1, 1, 2), mode='nearest') + loss = (samples - target_sampled).pow(2).mean() + print('loss:', loss.item()) + + # Backpropagate the gradients. + loss.backward() + # Print the gradients + print('radius.grad:', radius_n.grad) + print('center.grad:', center_n.grad) + print('color.grad:', color.grad) + + # Take a gradient descent step. + optimizer.step() + # Print the current params. + print('radius:', circle.radius) + print('center:', circle.center) + print('color:', circle_group.fill_color) + +# Render the final result. +scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups, + output_type = pydiffvg.OutputType.sdf) +img = render(256, # width + 256, # height + 2, # num_samples_x + 2, # num_samples_y + 102, # seed + *scene_args) +img = img / 256 # Normalize SDF to [0, 1] +# Save the images and differences. +pydiffvg.imwrite(img.cpu(), 'results/test_eval_positions/final.png') diff --git a/apps/textureSyn/1.jpg b/apps/textureSyn/1.jpg new file mode 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zPSrNT5CuaGr1C_=9T{8(3^@c2c%q6iTOh*xO#I{cV{;~(`(OAb_7?vDN|)>bo&NxN z{{Su!zX7ra7@T7m>C=k+Ch+CD-*{T<&D`5{4Mct8&f5iL&#(H%9Q6bPkN^c0o-gBv zq0vL(kljLNgTydft6~`K;&op}RCfnGbGYX{``2ILYsa$qdE#wCA1*uSQ_4x!H4n8A zl?3CFli#m=(M5Mi;E%$7Nq*F`H~cIe6?5|HnmqcY&;yBXM3VmV*qxvdFbK#Xf{_0K{jne$To!tlzUm n0: + nonchosenPatchId = nonchosenPatchId % self.total_patches_count + #exlcude chosen from nonchosen + nonchosenPatchId = np.delete(nonchosenPatchId, np.where(nonchosenPatchId == chosenPatchId)) + #highlight non chosen candidates + c = [0.25, 0.9 ,0.45] + self.highlightPatches(result, nonchosenPatchId, color=c, highlight_width = 4, alpha = 0.5) + + #hightlight chosen + c = [1.0, 0.25, 0.15] + self.highlightPatches(result, [chosenPatchId], color=c, highlight_width = 4, alpha = 1) + + return result + + def highlightPatches(self, writeResult, patchesIDs, color, highlight_width = 2, solid = False, alpha = 0.1): + + searchWindow = self.patchSize + 2*self.overlapSize + + #number of possible steps + row_steps = floor((np.shape(writeResult)[0] - searchWindow) / self.windowStep) + 1 + col_steps = floor((np.shape(writeResult)[1] - searchWindow) / self.windowStep) + 1 + + for i in range(len(patchesIDs)): + + chosenPatchId = patchesIDs[i] + + #patch Id to step + patch_row = floor(chosenPatchId / col_steps) + patch_col = chosenPatchId - patch_row * col_steps + + #highlight chosen patch (below are boundaries of the example patch) + row_start = self.windowStep* patch_row + row_end = self.windowStep * patch_row + searchWindow + col_start = self.windowStep * patch_col + col_end = self.windowStep * patch_col + searchWindow + + if not solid: + w = highlight_width + overlap = np.copy(writeResult[row_start:row_start+w, col_start:col_end]) + writeResult[row_start:row_start+w, col_start:col_end] = overlap * (1-alpha) + (np.zeros(np.shape(overlap))+color) * alpha #top + overlap = np.copy(writeResult[row_end-w:row_end, col_start:col_end]) + writeResult[row_end-w:row_end, col_start:col_end] = overlap * (1-alpha) + (np.zeros(np.shape(overlap))+color) * alpha #bot + overlap = np.copy( writeResult[row_start:row_end, col_start:col_start+w]) + writeResult[row_start:row_end, col_start:col_start+w] = overlap * (1-alpha) + (np.zeros(np.shape(overlap))+color) * alpha #left + overlap = np.copy(writeResult[row_start:row_end, col_end-w:col_end]) + writeResult[row_start:row_end, col_end-w:col_end] = overlap * (1-alpha) + (np.zeros(np.shape(overlap))+color) * alpha #end + else: + a = alpha + writeResult[row_start:row_end, col_start:col_end] = writeResult[row_start:row_end, col_start:col_end] * (1-a) + (np.zeros(np.shape(writeResult[row_start:row_end, col_start:col_end]))+color) * a + + + def resize(self, imgArray, targetSize): + img = Image.fromarray(np.uint8(imgArray*255)) + img = img.resize((targetSize[0], targetSize[1]), resample=0, box=None) + return np.array(img)/255 + + def findMostSimilarPatches(self, overlapArea_Top, overlapArea_Left, coord, in_k=5): + + #check which KD tree we need to use + if (overlapArea_Top is not None) and (overlapArea_Left is not None): + combined = self.getCombinedOverlap(overlapArea_Top.reshape(-1), overlapArea_Left.reshape(-1)) + dist, ind = self.kdtree_combined.query([combined], k=in_k) + elif overlapArea_Top is not None: + dist, ind = self.kdtree_topOverlap.query([overlapArea_Top.reshape(-1)], k=in_k) + elif overlapArea_Left is not None: + dist, ind = self.kdtree_leftOverlap.query([overlapArea_Left.reshape(-1)], k=in_k) + else: + raise Exception("ERROR: no valid overlap area is passed to -findMostSimilarPatch-") + dist = dist[0] + ind = ind[0] + + return dist, ind + + #disallow visually similar blocks to be placed next to each other + def checkForMirrors(self, dist, ind, coord, thres = 3): + remove_i = [] + #do I have a top or left neighbour + if coord[0]-1>-1: + top_neigh = int(self.idMap[coord[0]-1, coord[1]]) + for i in range(len(ind)): + if (abs(ind[i]%self.total_patches_count - top_neigh%self.total_patches_count) < thres): + remove_i.append(i) + if coord[1]-1>-1: + left_neigh = int(self.idMap[coord[0], coord[1]-1]) + for i in range(len(ind)): + if (abs(ind[i]%self.total_patches_count - left_neigh%self.total_patches_count) < thres): + remove_i.append(i) + + dist = np.delete(dist, remove_i) + ind = np.delete(ind, remove_i) + + return dist, ind + + + def distances2probability(self, distances, PARM_truncation, PARM_attenuation): + + probabilities = 1 - distances / np.max(distances) + probabilities *= (probabilities > PARM_truncation) + probabilities = pow(probabilities, PARM_attenuation) #attenuate the values + #check if we didn't truncate everything! + if np.sum(probabilities) == 0: + #then just revert it + probabilities = 1 - distances / np.max(distances) + probabilities *= (probabilities > PARM_truncation*np.max(probabilities)) # truncate the values (we want top truncate%) + probabilities = pow(probabilities, PARM_attenuation) + probabilities /= np.sum(probabilities) #normalize so they add up to one + + return probabilities + + def getOverlapAreaTop(self, coord): + #do I have a top neighbour + if coord[0]-1>-1: + canvasPatch = self.patchCoord2canvasPatch(coord) + return canvasPatch[0:self.overlapSize, :, :] + else: + return None + + def getOverlapAreaLeft(self, coord): + #do I have a left neighbour + if coord[1]-1>-1: + canvasPatch = self.patchCoord2canvasPatch(coord) + return canvasPatch[:, 0:self.overlapSize, :] + else: + return None + + def initKDtrees(self): + #prepate overlap patches + topOverlap = self.examplePatches[:, 0:self.overlapSize, :, :] + leftOverlap = self.examplePatches[:, :, 0:self.overlapSize, :] + shape_top = np.shape(topOverlap) + shape_left = np.shape(leftOverlap) + + flatten_top = topOverlap.reshape(shape_top[0], -1) + flatten_left = leftOverlap.reshape(shape_left[0], -1) + flatten_combined = self.getCombinedOverlap(flatten_top, flatten_left) + + tree_top = KDTree(flatten_top) + tree_left = KDTree(flatten_left) + tree_combined = KDTree(flatten_combined) + + return tree_top, tree_left, tree_combined + + #the corner of 2 overlaps is counted double + def getCombinedOverlap(self, top, left): + shape = np.shape(top) + if len(shape) > 1: + combined = np.zeros((shape[0], shape[1]*2)) + combined[0:shape[0], 0:shape[1]] = top + combined[0:shape[0], shape[1]:shape[1]*2] = left + else: + combined = np.zeros((shape[0]*2)) + combined[0:shape[0]] = top + combined[shape[0]:shape[0]*2] = left + return combined + + def initFirstPatch(self): + #grab a random block + patchId = randint(0, np.shape(self.examplePatches)[0]) + #mark out fill map + self.filledMap[0, 0] = 1 + self.idMap[0, 0] = patchId % self.total_patches_count + #update canvas + self.updateCanvas(patchId, 0, 0, False, False) + #visualize + # self.visualize([0,0], [patchId], []) + + + def prepareExamplePatches(self): + + searchKernelSize = self.patchSize + 2 * self.overlapSize + + result = view_as_windows(self.exampleMap, [searchKernelSize, searchKernelSize, 3] , self.windowStep) + shape = np.shape(result) + result = result.reshape(shape[0]*shape[1], searchKernelSize, searchKernelSize, 3) + + self.total_patches_count = shape[0]*shape[1] + + if self.mirror_hor: + #flip along horizonal axis + hor_result = np.zeros(np.shape(result)) + + for i in range(self.total_patches_count): + hor_result[i] = result[i][::-1, :, :] + + result = np.concatenate((result, hor_result)) + if self.mirror_vert: + vert_result = np.zeros((shape[0]*shape[1], searchKernelSize, searchKernelSize, 3)) + + for i in range(self.total_patches_count): + vert_result[i] = result[i][:, ::-1, :] + + result = np.concatenate((result, vert_result)) + + return result + + def initCanvas(self): + + #check whether the outputSize adheres to patch+overlap size + num_patches_X = ceil((self.outputSize[0]-self.overlapSize)/(self.patchSize+self.overlapSize)) + num_patches_Y = ceil((self.outputSize[1]-self.overlapSize)/(self.patchSize+self.overlapSize)) + #calc needed output image size + required_size_X = num_patches_X*self.patchSize + (num_patches_X+1)*self.overlapSize + required_size_Y = num_patches_Y*self.patchSize + (num_patches_X+1)*self.overlapSize + + #create empty canvas + canvas = np.zeros((required_size_X, required_size_Y, 3)) + filledMap = np.zeros((num_patches_X, num_patches_Y)) #map showing which patches have been resolved + idMap = np.zeros((num_patches_X, num_patches_Y)) - 1 #stores patches id + + print("modified output size: ", np.shape(canvas)) + print("number of patches: ", np.shape(filledMap)[0]) + + return canvas, filledMap, idMap + + def idCoordTo2DCoord(self, idCoord, imgSize): + row = int(floor(idCoord / imgSize[0])) + col = int(idCoord - row * imgSize[1]) + return [row, col] + + def updateCanvas(self, inputPatchId, coord_X, coord_Y, blendTop = False, blendLeft = False): + #translate Patch coordinate into Canvas coordinate + x_range = self.patchCoord2canvasCoord(coord_X) + y_range = self.patchCoord2canvasCoord(coord_Y) + examplePatch = self.examplePatches[inputPatchId] + if blendLeft: + canvasOverlap = self.canvas[x_range[0]:x_range[1], y_range[0]:y_range[0]+self.overlapSize] + examplePatchOverlap = np.copy(examplePatch[0][:, 0:self.overlapSize]) + examplePatch[0][:, 0:self.overlapSize] = self.linearBlendOverlaps(canvasOverlap, examplePatchOverlap, 'left') + if blendTop: + canvasOverlap = self.canvas[x_range[0]:x_range[0]+self.overlapSize, y_range[0]:y_range[1]] + examplePatchOverlap = np.copy(examplePatch[0][0:self.overlapSize, :]) + examplePatch[0][0:self.overlapSize, :] = self.linearBlendOverlaps(canvasOverlap, examplePatchOverlap, 'top') + self.canvas[x_range[0]:x_range[1], y_range[0]:y_range[1]] = examplePatch + + def linearBlendOverlaps(self, canvasOverlap, examplePatchOverlap, mode): + if mode == 'left': + mask = np.repeat(np.arange(self.overlapSize)[np.newaxis, :], np.shape(canvasOverlap)[0], axis=0) / self.overlapSize + elif mode == 'top': + mask = np.repeat(np.arange(self.overlapSize)[:, np.newaxis], np.shape(canvasOverlap)[1], axis=1) / self.overlapSize + mask = np.repeat(mask[:, :, np.newaxis], 3, axis=2) #cast to 3d array + return canvasOverlap * (1 - mask) + examplePatchOverlap * mask + + #def minimumBoundaryError(self, canvasOverlap, examplePatchOverlap, mode) + + def patchCoord2canvasCoord(self, coord): + return [(self.patchSize+self.overlapSize)*coord, (self.patchSize+self.overlapSize)*(coord+1) + self.overlapSize] + + def patchCoord2canvasPatch(self, coord): + x_range = self.patchCoord2canvasCoord(coord[0]) + y_range = self.patchCoord2canvasCoord(coord[1]) + return np.copy(self.canvas[x_range[0]:x_range[1], y_range[0]:y_range[1]]) + + def loadExampleMap(self, exampleMapPath): + exampleMap = io.imread(exampleMapPath) #returns an MxNx3 array + exampleMap = exampleMap / 255.0 #normalize + #make sure it is 3channel RGB + if (np.shape(exampleMap)[-1] > 3): + exampleMap = exampleMap[:,:,:3] #remove Alpha Channel + elif (len(np.shape(exampleMap)) == 2): + exampleMap = 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/apps/texture_synthesis.py b/apps/texture_synthesis.py new file mode 100644 index 0000000..3a7ccce --- /dev/null +++ b/apps/texture_synthesis.py @@ -0,0 +1,180 @@ +import os, sys +import pydiffvg +import argparse +import torch +# import torch as th +import scipy.ndimage.filters as filters +# import numba +import numpy as np +from skimage import io +sys.path.append('./textureSyn') +from patchBasedTextureSynthesis import * +from make_gif import make_gif +import random +import ttools.modules + +from svgpathtools import svg2paths2, Path, is_path_segment +""" +python texture_synthesis.py textureSyn/traced_1.png --svg-path textureSyn/traced_1.svg --case 1 +""" + +def texture_syn(img_path): + ## get the width and height first + # input_img = io.imread(img_path) # returns an MxNx3 array + # output_size = [input_img.shape[1], input_img.shape[0]] + # output_path = "textureSyn/1/" + output_path = "results/texture_synthesis/%d"%(args.case) + patch_size = 40 # size of the patch (without the overlap) + overlap_size = 10 # the width of the overlap region + output_size = [300, 300] + pbts = patchBasedTextureSynthesis(img_path, output_path, output_size, patch_size, overlap_size, in_windowStep=5, + in_mirror_hor=True, in_mirror_vert=True, in_shapshots=False) + target_img = pbts.resolveAll() + return np.array(target_img) + + +def render(canvas_width, canvas_height, shapes, shape_groups, samples=2): + _render = pydiffvg.RenderFunction.apply + scene_args = pydiffvg.RenderFunction.serialize_scene(\ + canvas_width, canvas_height, shapes, shape_groups) + img = _render(canvas_width, # width + canvas_height, # height + samples, # num_samples_x + samples, # num_samples_y + 0, # seed + None, + *scene_args) + return img + +def big_bounding_box(paths_n_stuff): + """Finds a BB containing a collection of paths, Bezier path segments, and + points (given as complex numbers).""" + bbs = [] + for thing in paths_n_stuff: + if is_path_segment(thing) or isinstance(thing, Path): + bbs.append(thing.bbox()) + elif isinstance(thing, complex): + bbs.append((thing.real, thing.real, thing.imag, thing.imag)) + else: + try: + complexthing = complex(thing) + bbs.append((complexthing.real, complexthing.real, + complexthing.imag, complexthing.imag)) + except ValueError: + raise TypeError( + "paths_n_stuff can only contains Path, CubicBezier, " + "QuadraticBezier, Line, and complex objects.") + xmins, xmaxs, ymins, ymaxs = list(zip(*bbs)) + xmin = min(xmins) + xmax = max(xmaxs) + ymin = min(ymins) + ymax = max(ymaxs) + return xmin, xmax, ymin, ymax + + +def main(args): + ## set device -> use cpu now since I haven't solved the nvcc issue + pydiffvg.set_use_gpu(False) + # pydiffvg.set_device(torch.device('cuda:1')) + ## use L2 for now + # perception_loss = ttools.modules.LPIPS().to(pydiffvg.get_device()) + + ## generate a texture synthesized + target_img = texture_syn(args.target) + tar_h, tar_w = target_img.shape[1], target_img.shape[0] + canvas_width, canvas_height, shapes, shape_groups = \ + pydiffvg.svg_to_scene(args.svg_path) + + + ## svgpathtools for checking the bounding box + # paths, _, _ = svg2paths2(args.svg_path) + # print(len(paths)) + # xmin, xmax, ymin, ymax = big_bounding_box(paths) + # print(xmin, xmax, ymin, ymax) + # input("check") + + + print('tar h : %d tar w : %d'%(tar_h, tar_w)) + print('canvas h : %d canvas w : %d' % (canvas_height, canvas_width)) + scale_ratio = tar_h / canvas_height + print("scale ratio : ", scale_ratio) + # input("check") + for path in shapes: + path.points[..., 0] = path.points[..., 0] * scale_ratio + path.points[..., 1] = path.points[..., 1] * scale_ratio + + init_img = render(tar_w, tar_h, shapes, shape_groups) + pydiffvg.imwrite(init_img.cpu(), 'results/texture_synthesis/%d/init.png'%(args.case), gamma=2.2) + # input("check") + random.seed(1234) + torch.manual_seed(1234) + + points_vars = [] + for path in shapes: + path.points.requires_grad = True + points_vars.append(path.points) + color_vars = [] + for group in shape_groups: + group.fill_color.requires_grad = True + color_vars.append(group.fill_color) + # Optimize + points_optim = torch.optim.Adam(points_vars, lr=1.0) + color_optim = torch.optim.Adam(color_vars, lr=0.01) + + target = torch.from_numpy(target_img).to(torch.float32) / 255.0 + target = target.pow(2.2) + target = target.to(pydiffvg.get_device()) + target = target.unsqueeze(0) + target = target.permute(0, 3, 1, 2) # NHWC -> NCHW + canvas_width, canvas_height = target.shape[3], target.shape[2] + # print('canvas h : %d canvas w : %d' % (canvas_height, canvas_width)) + # input("check") + + for t in range(args.max_iter): + print('iteration:', t) + points_optim.zero_grad() + color_optim.zero_grad() + cur_img = render(canvas_width, canvas_height, shapes, shape_groups) + pydiffvg.imwrite(cur_img.cpu(), 'results/texture_synthesis/%d/iter_%d.png'%(args.case, t), gamma=2.2) + cur_img = cur_img[:, :, :3] + cur_img = cur_img.unsqueeze(0) + cur_img = cur_img.permute(0, 3, 1, 2) # NHWC -> NCHW + + ## perceptual loss + # loss = perception_loss(cur_img, target) + ## l2 loss + loss = (cur_img - target).pow(2).mean() + print('render loss:', loss.item()) + loss.backward() + + points_optim.step() + color_optim.step() + + for group in shape_groups: + group.fill_color.data.clamp_(0.0, 1.0) + ## write svg + if t % 10 == 0 or t == args.max_iter - 1: + pydiffvg.save_svg('results/texture_synthesis/%d/iter_%d.svg'%(args.case, t), + canvas_width, canvas_height, shapes, shape_groups) + + ## render final result + final_img = render(tar_h, tar_w, shapes, shape_groups) + pydiffvg.imwrite(final_img.cpu(), 'results/texture_synthesis/%d/final.png'%(args.case), gamma=2.2) + + + from subprocess import call + call(["ffmpeg", "-framerate", "24", "-i", + "results/texture_synthesis/%d/iter_%d.png"%(args.case), "-vb", "20M", + "results/texture_synthesis/%d/out.mp4"%(args.case)]) + ## make gif + make_gif("results/texture_synthesis/%d"%(args.case), "results/texture_synthesis/%d/out.gif"%(args.case), frame_every_X_steps=1, repeat_ending=3, total_iter=args.max_iter) + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + ## target image path + parser.add_argument("target", help="target image path") + parser.add_argument("--svg-path", type=str, help="the corresponding svg file path") + parser.add_argument("--max-iter", type=int, default=500, help="the max optimization iterations") + parser.add_argument("--case", type=int, default=1, help="just the case id for a separate result folder") + args = parser.parse_args() + main(args) \ No newline at end of file diff --git a/atomic.cpp b/atomic.cpp new file mode 100644 index 0000000..9c642b9 --- /dev/null +++ b/atomic.cpp @@ -0,0 +1,27 @@ +//A hacky solution to get around the Ellipse include + +#ifdef WIN32 +#include +#include + +float win_atomic_add(float &target, float source) { + union { int i; float f; } old_val; + union { int i; float f; } new_val; + do { + old_val.f = target; + new_val.f = old_val.f + (float)source; + } while (InterlockedCompareExchange((LONG*)&target, (LONG)new_val.i, (LONG)old_val.i) != old_val.i); + return old_val.f; +} + +double win_atomic_add(double &target, double source) { + union { int64_t i; double f; } old_val; + union { int64_t i; double f; } new_val; + do { + old_val.f = target; + new_val.f = old_val.f + (double)source; + } while (InterlockedCompareExchange64((LONG64*)&target, (LONG64)new_val.i, (LONG64)old_val.i) != old_val.i); + return old_val.f; +} + +#endif \ No newline at end of file diff --git a/atomic.h b/atomic.h new file mode 100644 index 0000000..c721722 --- /dev/null +++ b/atomic.h @@ -0,0 +1,139 @@ +#pragma once + +#include "diffvg.h" +#include "vector.h" +#include "matrix.h" + +// https://stackoverflow.com/questions/39274472/error-function-atomicadddouble-double-has-already-been-defined +#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 600 +#else +static inline DEVICE double atomicAdd(double *address, double val) { + unsigned long long int* address_as_ull = (unsigned long long int*)address; + unsigned long long int old = *address_as_ull, assumed; + if (val == 0.0) + return __longlong_as_double(old); + do { + assumed = old; + old = atomicCAS(address_as_ull, assumed, __double_as_longlong(val +__longlong_as_double(assumed))); + } while (assumed != old); + return __longlong_as_double(old); +} +#endif + +#ifndef WIN32 + template + DEVICE + inline T0 atomic_add_(T0 &target, T1 source) { + #ifdef __CUDA_ARCH__ + return atomicAdd(&target, (T0)source); + #else + T0 old_val; + T0 new_val; + do { + old_val = target; + new_val = old_val + source; + } while (!__atomic_compare_exchange(&target, &old_val, &new_val, true, + std::memory_order::memory_order_seq_cst, + std::memory_order::memory_order_seq_cst)); + return old_val; + #endif + } + + DEVICE + inline + float atomic_add(float &target, float source) { + return atomic_add_(target, source); + } + DEVICE + inline + double atomic_add(double &target, double source) { + return atomic_add_(target, source); + } +#else + float win_atomic_add(float &target, float source); + double win_atomic_add(double &target, double source); + DEVICE + static float atomic_add(float &target, float source) { + #ifdef __CUDA_ARCH__ + return atomicAdd(&target, source); + #else + return win_atomic_add(target, source); + #endif + } + DEVICE + static double atomic_add(double &target, double source) { + #ifdef __CUDA_ARCH__ + return atomicAdd(&target, (double)source); + #else + return win_atomic_add(target, source); + #endif + } +#endif + +template +DEVICE +inline T0 atomic_add(T0 *target, T1 source) { + return atomic_add(*target, (T0)source); +} + +template +DEVICE +inline TVector2 atomic_add(TVector2 &target, const TVector2 &source) { + atomic_add(target[0], source[0]); + atomic_add(target[1], source[1]); + return target; +} + +template +DEVICE +inline void atomic_add(T0 *target, const TVector2 &source) { + atomic_add(target[0], (T0)source[0]); + atomic_add(target[1], (T0)source[1]); +} + +template +DEVICE +inline TVector3 atomic_add(TVector3 &target, const TVector3 &source) { + atomic_add(target[0], source[0]); + atomic_add(target[1], source[1]); + atomic_add(target[2], source[2]); + return target; +} + +template +DEVICE +inline void atomic_add(T0 *target, const TVector3 &source) { + atomic_add(target[0], (T0)source[0]); + atomic_add(target[1], (T0)source[1]); + atomic_add(target[2], (T0)source[2]); +} + +template +DEVICE +inline TVector4 atomic_add(TVector4 &target, const TVector4 &source) { + atomic_add(target[0], source[0]); + atomic_add(target[1], source[1]); + atomic_add(target[2], source[2]); + atomic_add(target[3], source[3]); + return target; +} + +template +DEVICE +inline void atomic_add(T0 *target, const TVector4 &source) { + atomic_add(target[0], (T0)source[0]); + atomic_add(target[1], (T0)source[1]); + atomic_add(target[2], (T0)source[2]); + atomic_add(target[3], (T0)source[3]); +} + +template +DEVICE +inline void atomic_add(T0 *target, const TMatrix3x3 &source) { + for (int i = 0; i < 3; i++) { + for (int j = 0; j < 3; j++) { + atomic_add(target[3 * i + j], (T0)source(i, j)); + } + } +} + diff --git a/cdf.h b/cdf.h new file mode 100644 index 0000000..48a64f8 --- /dev/null +++ b/cdf.h @@ -0,0 +1,29 @@ +#pragma once + +#include "diffvg.h" + +DEVICE int sample(const float *cdf, int num_entries, float u, float *updated_u = nullptr) { + // Binary search the cdf + auto lb = 0; + auto len = num_entries - 1 - lb; + while (len > 0) { + auto half_len = len / 2; + auto mid = lb + half_len; + assert(mid >= 0 && mid < num_entries); + if (u < cdf[mid]) { + len = half_len; + } else { + lb = mid + 1; + len = len - half_len - 1; + } + } + lb = clamp(lb, 0, num_entries - 1); + if (updated_u != nullptr) { + if (lb > 0) { + *updated_u = (u - cdf[lb - 1]) / (cdf[lb] - cdf[lb - 1]); + } else { + *updated_u = u / cdf[lb]; + } + } + return lb; +} diff --git a/cmake/FindTensorFlow.cmake b/cmake/FindTensorFlow.cmake new file mode 100644 index 0000000..b251b10 --- /dev/null +++ b/cmake/FindTensorFlow.cmake @@ -0,0 +1,34 @@ +# https://github.com/PatWie/tensorflow-cmake/blob/master/cmake/modules/FindTensorFlow.cmake + +execute_process( + COMMAND python -c "exec(\"try:\\n import tensorflow as tf; print(tf.__version__); print(tf.__cxx11_abi_flag__);print(tf.sysconfig.get_include()); print(tf.sysconfig.get_lib())\\nexcept ImportError:\\n exit(1)\")" + OUTPUT_VARIABLE TF_INFORMATION_STRING + OUTPUT_STRIP_TRAILING_WHITESPACE + RESULT_VARIABLE retcode) + +if("${retcode}" STREQUAL "0") + string(REPLACE "\n" ";" TF_INFORMATION_LIST ${TF_INFORMATION_STRING}) + list(GET TF_INFORMATION_LIST 0 TF_DETECTED_VERSION) + list(GET TF_INFORMATION_LIST 1 TF_DETECTED_ABI) + list(GET TF_INFORMATION_LIST 2 TF_DETECTED_INCLUDE_DIR) + list(GET TF_INFORMATION_LIST 3 TF_DETECTED_LIBRARY_DIR) + if(WIN32) + find_library(TF_DETECTED_LIBRARY NAMES _pywrap_tensorflow_internal PATHS + ${TF_DETECTED_LIBRARY_DIR}/python) + else() + # For some reason my tensorflow doesn't have a .so file + list(APPEND CMAKE_FIND_LIBRARY_SUFFIXES .so.1) + list(APPEND CMAKE_FIND_LIBRARY_SUFFIXES .so.2) + find_library(TF_DETECTED_LIBRARY NAMES tensorflow_framework PATHS + ${TF_DETECTED_LIBRARY_DIR}) + endif() + set(TensorFlow_VERSION ${TF_DETECTED_VERSION}) + set(TensorFlow_ABI ${TF_DETECTED_ABI}) + set(TensorFlow_INCLUDE_DIR ${TF_DETECTED_INCLUDE_DIR}) + set(TensorFlow_LIBRARY ${TF_DETECTED_LIBRARY}) + if(TensorFlow_LIBRARY AND TensorFlow_INCLUDE_DIR) + set(TensorFlow_FOUND TRUE) + else() + set(TensorFlow_FOUND FALSE) + endif() +endif() diff --git a/cmake/FindThrust.cmake b/cmake/FindThrust.cmake new file mode 100644 index 0000000..61eef29 --- /dev/null +++ b/cmake/FindThrust.cmake @@ -0,0 +1,40 @@ +##============================================================================= +## +## Copyright (c) Kitware, Inc. +## All rights reserved. +## See LICENSE.txt for details. +## +## This software is distributed WITHOUT ANY WARRANTY; without even +## the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR +## PURPOSE. See the above copyright notice for more information. +## +## Copyright 2012 Sandia Corporation. +## Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation, +## the U.S. Government retains certain rights in this software. +## +##============================================================================= + +# +# FindThrust +# +# This module finds the Thrust header files and extrats their version. It +# sets the following variables. +# +# THRUST_INCLUDE_DIR - Include directory for thrust header files. (All header +# files will actually be in the thrust subdirectory.) +# THRUST_VERSION - Version of thrust in the form "major.minor.patch". +# + +find_path(THRUST_INCLUDE_DIR + HINTS /usr/include/cuda + /usr/local/include + /usr/local/cuda/include + ${CUDA_INCLUDE_DIRS} + ./thrust + ../thrust + NAMES thrust/version.h +) + +if (THRUST_INCLUDE_DIR) + set(THRUST_FOUND TRUE) +endif () \ No newline at end of file diff --git a/color.cpp b/color.cpp new file mode 100644 index 0000000..2a2e8ab --- /dev/null +++ b/color.cpp @@ -0,0 +1,25 @@ +#include "color.h" + +void LinearGradient::copy_to(ptr stop_offsets, + ptr stop_colors) const { + float *o = stop_offsets.get(); + float *c = stop_colors.get(); + for (int i = 0; i < num_stops; i++) { + o[i] = this->stop_offsets[i]; + } + for (int i = 0; i < 4 * num_stops; i++) { + c[i] = this->stop_colors[i]; + } +} + +void RadialGradient::copy_to(ptr stop_offsets, + ptr stop_colors) const { + float *o = stop_offsets.get(); + float *c = stop_colors.get(); + for (int i = 0; i < num_stops; i++) { + o[i] = this->stop_offsets[i]; + } + for (int i = 0; i < 4 * num_stops; i++) { + c[i] = this->stop_colors[i]; + } +} diff --git a/color.h b/color.h new file mode 100644 index 0000000..c787105 --- /dev/null +++ b/color.h @@ -0,0 +1,63 @@ +#pragma once + +#include "diffvg.h" +#include "vector.h" +#include "ptr.h" + +enum class ColorType { + Constant, + LinearGradient, + RadialGradient +}; + +struct Constant { + Vector4f color; + + ptr get_ptr() { + return ptr(this); + } +}; + +struct LinearGradient { + LinearGradient(const Vector2f &begin, + const Vector2f &end, + int num_stops, + ptr stop_offsets, + ptr stop_colors) + : begin(begin), end(end), num_stops(num_stops), + stop_offsets(stop_offsets.get()), stop_colors(stop_colors.get()) {} + + ptr get_ptr() { + return ptr(this); + } + + void copy_to(ptr stop_offset, + ptr stop_colors) const; + + Vector2f begin, end; + int num_stops; + float *stop_offsets; + float *stop_colors; // rgba +}; + +struct RadialGradient { + RadialGradient(const Vector2f ¢er, + const Vector2f &radius, + int num_stops, + ptr stop_offsets, + ptr stop_colors) + : center(center), radius(radius), num_stops(num_stops), + stop_offsets(stop_offsets.get()), stop_colors(stop_colors.get()) {} + + ptr get_ptr() { + return ptr(this); + } + + void copy_to(ptr stop_offset, + ptr stop_colors) const; + + Vector2f center, radius; + int num_stops; + float *stop_offsets; + float *stop_colors; // rgba +}; diff --git a/compute_distance.h b/compute_distance.h new file mode 100644 index 0000000..c125641 --- /dev/null +++ b/compute_distance.h @@ -0,0 +1,949 @@ +#pragma once + +#include "diffvg.h" +#include "edge_query.h" +#include "scene.h" +#include "shape.h" +#include "solve.h" +#include "vector.h" + +#include + +struct ClosestPointPathInfo { + int base_point_id; + int point_id; + float t_root; +}; + +DEVICE +inline +bool closest_point(const Circle &circle, const Vector2f &pt, + Vector2f *result) { + *result = circle.center + circle.radius * normalize(pt - circle.center); + return false; +} + +DEVICE +inline +bool closest_point(const Path &path, const BVHNode *bvh_nodes, const Vector2f &pt, float max_radius, + ClosestPointPathInfo *path_info, + Vector2f *result) { + auto min_dist = max_radius; + auto ret_pt = Vector2f{0, 0}; + auto found = false; + auto num_segments = path.num_base_points; + constexpr auto max_bvh_size = 128; + int bvh_stack[max_bvh_size]; + auto stack_size = 0; + bvh_stack[stack_size++] = 2 * num_segments - 2; + while (stack_size > 0) { + const BVHNode &node = bvh_nodes[bvh_stack[--stack_size]]; + if (node.child1 < 0) { + // leaf + auto base_point_id = node.child0; + auto point_id = - node.child1 - 1; + assert(base_point_id < num_segments); + assert(point_id < path.num_points); + auto dist = 0.f; + auto closest_pt = Vector2f{0, 0}; + auto t_root = 0.f; + if (path.num_control_points[base_point_id] == 0) { + // Straight line + auto i0 = point_id; + auto i1 = (point_id + 1) % path.num_points; + auto p0 = Vector2f{path.points[2 * i0], path.points[2 * i0 + 1]}; + auto p1 = Vector2f{path.points[2 * i1], path.points[2 * i1 + 1]}; + // project pt to line + auto t = dot(pt - p0, p1 - p0) / dot(p1 - p0, p1 - p0); + if (t < 0) { + dist = distance(p0, pt); + closest_pt = p0; + t_root = 0; + } else if (t > 1) { + dist = distance(p1, pt); + closest_pt = p1; + t_root = 1; + } else { + dist = distance(p0 + t * (p1 - p0), pt); + closest_pt = p0 + t * (p1 - p0); + t_root = t; + } + } else if (path.num_control_points[base_point_id] == 1) { + // Quadratic Bezier curve + auto i0 = point_id; + auto i1 = point_id + 1; + auto i2 = (point_id + 2) % path.num_points; + auto p0 = Vector2f{path.points[2 * i0], path.points[2 * i0 + 1]}; + auto p1 = Vector2f{path.points[2 * i1], path.points[2 * i1 + 1]}; + auto p2 = Vector2f{path.points[2 * i2], path.points[2 * i2 + 1]}; + if (path.use_distance_approx) { + closest_pt = quadratic_closest_pt_approx(p0, p1, p2, pt, &t_root); + dist = distance(closest_pt, pt); + } else { + auto eval = [&](float t) -> Vector2f { + auto tt = 1 - t; + return (tt*tt)*p0 + (2*tt*t)*p1 + (t*t)*p2; + }; + auto pt0 = eval(0); + auto pt1 = eval(1); + auto dist0 = distance(pt0, pt); + auto dist1 = distance(pt1, pt); + { + dist = dist0; + closest_pt = pt0; + t_root = 0; + } + if (dist1 < dist) { + dist = dist1; + closest_pt = pt1; + t_root = 1; + } + // The curve is (1-t)^2p0 + 2(1-t)tp1 + t^2p2 + // = (p0-2p1+p2)t^2+(-2p0+2p1)t+p0 = q + // Want to solve (q - pt) dot q' = 0 + // q' = (p0-2p1+p2)t + (-p0+p1) + // Expanding (p0-2p1+p2)^2 t^3 + + // 3(p0-2p1+p2)(-p0+p1) t^2 + + // (2(-p0+p1)^2+(p0-2p1+p2)(p0-pt))t + + // (-p0+p1)(p0-pt) = 0 + auto A = sum((p0-2*p1+p2)*(p0-2*p1+p2)); + auto B = sum(3*(p0-2*p1+p2)*(-p0+p1)); + auto C = sum(2*(-p0+p1)*(-p0+p1)+(p0-2*p1+p2)*(p0-pt)); + auto D = sum((-p0+p1)*(p0-pt)); + float t[3]; + int num_sol = solve_cubic(A, B, C, D, t); + for (int j = 0; j < num_sol; j++) { + if (t[j] >= 0 && t[j] <= 1) { + auto p = eval(t[j]); + auto distp = distance(p, pt); + if (distp < dist) { + dist = distp; + closest_pt = p; + t_root = t[j]; + } + } + } + } + } else if (path.num_control_points[base_point_id] == 2) { + // Cubic Bezier curve + auto i0 = point_id; + auto i1 = point_id + 1; + auto i2 = point_id + 2; + auto i3 = (point_id + 3) % path.num_points; + auto p0 = Vector2f{path.points[2 * i0], path.points[2 * i0 + 1]}; + auto p1 = Vector2f{path.points[2 * i1], path.points[2 * i1 + 1]}; + auto p2 = Vector2f{path.points[2 * i2], path.points[2 * i2 + 1]}; + auto p3 = Vector2f{path.points[2 * i3], path.points[2 * i3 + 1]}; + auto eval = [&](float t) -> Vector2f { + auto tt = 1 - t; + return (tt*tt*tt)*p0 + (3*tt*tt*t)*p1 + (3*tt*t*t)*p2 + (t*t*t)*p3; + }; + auto pt0 = eval(0); + auto pt1 = eval(1); + auto dist0 = distance(pt0, pt); + auto dist1 = distance(pt1, pt); + { + dist = dist0; + closest_pt = pt0; + t_root = 0; + } + if (dist1 < dist) { + dist = dist1; + closest_pt = pt1; + t_root = 1; + } + // The curve is (1 - t)^3 p0 + 3 * (1 - t)^2 t p1 + 3 * (1 - t) t^2 p2 + t^3 p3 + // = (-p0+3p1-3p2+p3) t^3 + (3p0-6p1+3p2) t^2 + (-3p0+3p1) t + p0 + // Want to solve (q - pt) dot q' = 0 + // q' = 3*(-p0+3p1-3p2+p3)t^2 + 2*(3p0-6p1+3p2)t + (-3p0+3p1) + // Expanding + // 3*(-p0+3p1-3p2+p3)^2 t^5 + // 5*(-p0+3p1-3p2+p3)(3p0-6p1+3p2) t^4 + // 4*(-p0+3p1-3p2+p3)(-3p0+3p1) + 2*(3p0-6p1+3p2)^2 t^3 + // 3*(3p0-6p1+3p2)(-3p0+3p1) + 3*(-p0+3p1-3p2+p3)(p0-pt) t^2 + // (-3p0+3p1)^2+2(p0-pt)(3p0-6p1+3p2) t + // (p0-pt)(-3p0+3p1) + double A = 3*sum((-p0+3*p1-3*p2+p3)*(-p0+3*p1-3*p2+p3)); + double B = 5*sum((-p0+3*p1-3*p2+p3)*(3*p0-6*p1+3*p2)); + double C = 4*sum((-p0+3*p1-3*p2+p3)*(-3*p0+3*p1)) + 2*sum((3*p0-6*p1+3*p2)*(3*p0-6*p1+3*p2)); + double D = 3*(sum((3*p0-6*p1+3*p2)*(-3*p0+3*p1)) + sum((-p0+3*p1-3*p2+p3)*(p0-pt))); + double E = sum((-3*p0+3*p1)*(-3*p0+3*p1)) + 2*sum((p0-pt)*(3*p0-6*p1+3*p2)); + double F = sum((p0-pt)*(-3*p0+3*p1)); + // normalize the polynomial + B /= A; + C /= A; + D /= A; + E /= A; + F /= A; + // Isolator Polynomials: + // https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.133.2233&rep=rep1&type=pdf + // x/5 + B/25 + // /----------------------------------------------------- + // 5x^4 + 4B x^3 + 3C x^2 + 2D x + E / x^5 + B x^4 + C x^3 + D x^2 + E x + F + // x^5 + 4B/5 x^4 + 3C/5 x^3 + 2D/5 x^2 + E/5 x + // ---------------------------------------------------- + // B/5 x^4 + 2C/5 x^3 + 3D/5 x^2 + 4E/5 x + F + // B/5 x^4 + 4B^2/25 x^3 + 3BC/25 x^2 + 2BD/25 x + BE/25 + // ---------------------------------------------------- + // (2C/5 - 4B^2/25)x^3 + (3D/5-3BC/25)x^2 + (4E/5-2BD/25) + (F-BE/25) + auto p1A = ((2 / 5.f) * C - (4 / 25.f) * B * B); + auto p1B = ((3 / 5.f) * D - (3 / 25.f) * B * C); + auto p1C = ((4 / 5.f) * E - (2 / 25.f) * B * D); + auto p1D = F - B * E / 25.f; + // auto q1A = 1 / 5.f; + // auto q1B = B / 25.f; + // x/5 + B/25 = 0 + // x = -B/5 + auto q_root = -B/5.f; + double p_roots[3]; + int num_sol = solve_cubic(p1A, p1B, p1C, p1D, p_roots); + float intervals[4]; + if (q_root >= 0 && q_root <= 1) { + intervals[0] = q_root; + } + for (int j = 0; j < num_sol; j++) { + intervals[j + 1] = p_roots[j]; + } + auto num_intervals = 1 + num_sol; + // sort intervals + for (int j = 1; j < num_intervals; j++) { + for (int k = j; k > 0 && intervals[k - 1] > intervals[k]; k--) { + auto tmp = intervals[k]; + intervals[k] = intervals[k - 1]; + intervals[k - 1] = tmp; + } + } + auto eval_polynomial = [&] (double t) { + return t*t*t*t*t+ + B*t*t*t*t+ + C*t*t*t+ + D*t*t+ + E*t+ + F; + }; + auto eval_polynomial_deriv = [&] (double t) { + return 5*t*t*t*t+ + 4*B*t*t*t+ + 3*C*t*t+ + 2*D*t+ + E; + }; + auto lower_bound = 0.f; + for (int j = 0; j < num_intervals + 1; j++) { + if (j < num_intervals && intervals[j] < 0.f) { + continue; + } + auto upper_bound = j < num_intervals ? + min(intervals[j], 1.f) : 1.f; + auto lb = lower_bound; + auto ub = upper_bound; + auto lb_eval = eval_polynomial(lb); + auto ub_eval = eval_polynomial(ub); + if (lb_eval * ub_eval > 0) { + // Doesn't have root + continue; + } + if (lb_eval > ub_eval) { + swap_(lb, ub); + } + auto t = 0.5f * (lb + ub); + auto num_iter = 20; + for (int it = 0; it < num_iter; it++) { + if (!(t >= lb && t <= ub)) { + t = 0.5f * (lb + ub); + } + auto value = eval_polynomial(t); + if (fabs(value) < 1e-5f || it == num_iter - 1) { + break; + } + // The derivative may not be entirely accurate, + // but the bisection is going to handle this + if (value > 0.f) { + ub = t; + } else { + lb = t; + } + auto derivative = eval_polynomial_deriv(t); + t -= value / derivative; + } + auto p = eval(t); + auto distp = distance(p, pt); + if (distp < dist) { + dist = distp; + closest_pt = p; + t_root = t; + } + if (upper_bound >= 1.f) { + break; + } + lower_bound = upper_bound; + } + } else { + assert(false); + } + if (dist < min_dist) { + min_dist = dist; + ret_pt = closest_pt; + path_info->base_point_id = base_point_id; + path_info->point_id = point_id; + path_info->t_root = t_root; + found = true; + } + } else { + assert(node.child0 >= 0 && node.child1 >= 0); + const AABB &b0 = bvh_nodes[node.child0].box; + if (within_distance(b0, pt, min_dist)) { + bvh_stack[stack_size++] = node.child0; + } + const AABB &b1 = bvh_nodes[node.child1].box; + if (within_distance(b1, pt, min_dist)) { + bvh_stack[stack_size++] = node.child1; + } + assert(stack_size <= max_bvh_size); + } + } + if (found) { + assert(path_info->base_point_id < num_segments); + } + *result = ret_pt; + return found; +} + +DEVICE +inline +bool closest_point(const Rect &rect, const Vector2f &pt, + Vector2f *result) { + auto min_dist = 0.f; + auto closest_pt = Vector2f{0, 0}; + auto update = [&](const Vector2f &p0, const Vector2f &p1, bool first) { + // project pt to line + auto t = dot(pt - p0, p1 - p0) / dot(p1 - p0, p1 - p0); + if (t < 0) { + auto d = distance(p0, pt); + if (first || d < min_dist) { + min_dist = d; + closest_pt = p0; + } + } else if (t > 1) { + auto d = distance(p1, pt); + if (first || d < min_dist) { + min_dist = d; + closest_pt = p1; + } + } else { + auto p = p0 + t * (p1 - p0); + auto d = distance(p, pt); + if (first || d < min_dist) { + min_dist = d; + closest_pt = p0; + } + } + }; + auto left_top = rect.p_min; + auto right_top = Vector2f{rect.p_max.x, rect.p_min.y}; + auto left_bottom = Vector2f{rect.p_min.x, rect.p_max.y}; + auto right_bottom = rect.p_max; + update(left_top, left_bottom, true); + update(left_top, right_top, false); + update(right_top, right_bottom, false); + update(left_bottom, right_bottom, false); + *result = closest_pt; + return true; +} + +DEVICE +inline +bool closest_point(const Shape &shape, const BVHNode *bvh_nodes, const Vector2f &pt, float max_radius, + ClosestPointPathInfo *path_info, + Vector2f *result) { + switch (shape.type) { + case ShapeType::Circle: + return closest_point(*(const Circle *)shape.ptr, pt, result); + case ShapeType::Ellipse: + // https://www.geometrictools.com/Documentation/DistancePointEllipseEllipsoid.pdf + assert(false); + return false; + case ShapeType::Path: + return closest_point(*(const Path *)shape.ptr, bvh_nodes, pt, max_radius, path_info, result); + case ShapeType::Rect: + return closest_point(*(const Rect *)shape.ptr, pt, result); + } + assert(false); + return false; +} + +DEVICE +inline +bool compute_distance(const SceneData &scene, + int shape_group_id, + const Vector2f &pt, + float max_radius, + int *min_shape_id, + Vector2f *closest_pt_, + ClosestPointPathInfo *path_info, + float *result) { + const ShapeGroup &shape_group = scene.shape_groups[shape_group_id]; + // pt is in canvas space, transform it to shape's local space + auto local_pt = xform_pt(shape_group.canvas_to_shape, pt); + + constexpr auto max_bvh_stack_size = 64; + int bvh_stack[max_bvh_stack_size]; + auto stack_size = 0; + bvh_stack[stack_size++] = 2 * shape_group.num_shapes - 2; + const auto &bvh_nodes = scene.shape_groups_bvh_nodes[shape_group_id]; + + auto min_dist = max_radius; + auto found = false; + + while (stack_size > 0) { + const BVHNode &node = bvh_nodes[bvh_stack[--stack_size]]; + if (node.child1 < 0) { + // leaf + auto shape_id = node.child0; + const auto &shape = scene.shapes[shape_id]; + ClosestPointPathInfo local_path_info{-1, -1}; + auto local_closest_pt = Vector2f{0, 0}; + if (closest_point(shape, scene.path_bvhs[shape_id], local_pt, max_radius, &local_path_info, &local_closest_pt)) { + auto closest_pt = xform_pt(shape_group.shape_to_canvas, local_closest_pt); + auto dist = distance(closest_pt, pt); + if (!found || dist < min_dist) { + found = true; + min_dist = dist; + if (min_shape_id != nullptr) { + *min_shape_id = shape_id; + } + if (closest_pt_ != nullptr) { + *closest_pt_ = closest_pt; + } + if (path_info != nullptr) { + *path_info = local_path_info; + } + } + } + } else { + assert(node.child0 >= 0 && node.child1 >= 0); + const AABB &b0 = bvh_nodes[node.child0].box; + if (inside(b0, local_pt, max_radius)) { + bvh_stack[stack_size++] = node.child0; + } + const AABB &b1 = bvh_nodes[node.child1].box; + if (inside(b1, local_pt, max_radius)) { + bvh_stack[stack_size++] = node.child1; + } + assert(stack_size <= max_bvh_stack_size); + } + } + + *result = min_dist; + return found; +} + + +DEVICE +inline +void d_closest_point(const Circle &circle, + const Vector2f &pt, + const Vector2f &d_closest_pt, + Circle &d_circle, + Vector2f &d_pt) { + // return circle.center + circle.radius * normalize(pt - circle.center); + auto d_center = d_closest_pt * + (1 + d_normalize(pt - circle.center, circle.radius * d_closest_pt)); + atomic_add(&d_circle.center.x, d_center); + atomic_add(&d_circle.radius, dot(d_closest_pt, normalize(pt - circle.center))); +} + +DEVICE +inline +void d_closest_point(const Path &path, + const Vector2f &pt, + const Vector2f &d_closest_pt, + const ClosestPointPathInfo &path_info, + Path &d_path, + Vector2f &d_pt) { + auto base_point_id = path_info.base_point_id; + auto point_id = path_info.point_id; + auto min_t_root = path_info.t_root; + + if (path.num_control_points[base_point_id] == 0) { + // Straight line + auto i0 = point_id; + auto i1 = (point_id + 1) % path.num_points; + auto p0 = Vector2f{path.points[2 * i0], path.points[2 * i0 + 1]}; + auto p1 = Vector2f{path.points[2 * i1], path.points[2 * i1 + 1]}; + // project pt to line + auto t = dot(pt - p0, p1 - p0) / dot(p1 - p0, p1 - p0); + auto d_p0 = Vector2f{0, 0}; + auto d_p1 = Vector2f{0, 0}; + if (t < 0) { + d_p0 += d_closest_pt; + } else if (t > 1) { + d_p1 += d_closest_pt; + } else { + auto d_p = d_closest_pt; + // p = p0 + t * (p1 - p0) + d_p0 += d_p * (1 - t); + d_p1 += d_p * t; + } + atomic_add(d_path.points + 2 * i0, d_p0); + atomic_add(d_path.points + 2 * i1, d_p1); + } else if (path.num_control_points[base_point_id] == 1) { + // Quadratic Bezier curve + auto i0 = point_id; + auto i1 = point_id + 1; + auto i2 = (point_id + 2) % path.num_points; + auto p0 = Vector2f{path.points[2 * i0], path.points[2 * i0 + 1]}; + auto p1 = Vector2f{path.points[2 * i1], path.points[2 * i1 + 1]}; + auto p2 = Vector2f{path.points[2 * i2], path.points[2 * i2 + 1]}; + // auto eval = [&](float t) -> Vector2f { + // auto tt = 1 - t; + // return (tt*tt)*p0 + (2*tt*t)*p1 + (t*t)*p2; + // }; + // auto dist0 = distance(eval(0), pt); + // auto dist1 = distance(eval(1), pt); + auto d_p0 = Vector2f{0, 0}; + auto d_p1 = Vector2f{0, 0}; + auto d_p2 = Vector2f{0, 0}; + auto t = min_t_root; + if (t == 0) { + d_p0 += d_closest_pt; + } else if (t == 1) { + d_p2 += d_closest_pt; + } else { + // The curve is (1-t)^2p0 + 2(1-t)tp1 + t^2p2 + // = (p0-2p1+p2)t^2+(-2p0+2p1)t+p0 = q + // Want to solve (q - pt) dot q' = 0 + // q' = (p0-2p1+p2)t + (-p0+p1) + // Expanding (p0-2p1+p2)^2 t^3 + + // 3(p0-2p1+p2)(-p0+p1) t^2 + + // (2(-p0+p1)^2+(p0-2p1+p2)(p0-pt))t + + // (-p0+p1)(p0-pt) = 0 + auto A = sum((p0-2*p1+p2)*(p0-2*p1+p2)); + auto B = sum(3*(p0-2*p1+p2)*(-p0+p1)); + auto C = sum(2*(-p0+p1)*(-p0+p1)+(p0-2*p1+p2)*(p0-pt)); + // auto D = sum((-p0+p1)*(p0-pt)); + auto d_p = d_closest_pt; + // p = eval(t) + auto tt = 1 - t; + // (tt*tt)*p0 + (2*tt*t)*p1 + (t*t)*p2 + auto d_tt = 2 * tt * dot(d_p, p0) + 2 * t * dot(d_p, p1); + auto d_t = -d_tt + 2 * tt * dot(d_p, p1) + 2 * t * dot(d_p, p2); + auto d_p0 = d_p * tt * tt; + auto d_p1 = 2 * d_p * tt * t; + auto d_p2 = d_p * t * t; + // implicit function theorem: dt/dA = -1/(p'(t)) * dp/dA + auto poly_deriv_t = 3 * A * t * t + 2 * B * t + C; + if (fabs(poly_deriv_t) > 1e-6f) { + auto d_A = - (d_t / poly_deriv_t) * t * t * t; + auto d_B = - (d_t / poly_deriv_t) * t * t; + auto d_C = - (d_t / poly_deriv_t) * t; + auto d_D = - (d_t / poly_deriv_t); + // A = sum((p0-2*p1+p2)*(p0-2*p1+p2)) + // B = sum(3*(p0-2*p1+p2)*(-p0+p1)) + // C = sum(2*(-p0+p1)*(-p0+p1)+(p0-2*p1+p2)*(p0-pt)) + // D = sum((-p0+p1)*(p0-pt)) + d_p0 += 2*d_A*(p0-2*p1+p2)+ + 3*d_B*((-p0+p1)-(p0-2*p1+p2))+ + 2*d_C*(-2*(-p0+p1))+ + d_C*((p0-pt)+(p0-2*p1+p2))+ + 2*d_D*(-(p0-pt)+(-p0+p1)); + d_p1 += (-2)*2*d_A*(p0-2*p1+p2)+ + 3*d_B*(-2*(-p0+p1)+(p0-2*p1+p2))+ + 2*d_C*(2*(-p0+p1))+ + d_C*((-2)*(p0-pt))+ + d_D*(p0-pt); + d_p2 += 2*d_A*(p0-2*p1+p2)+ + 3*d_B*(-p0+p1)+ + d_C*(p0-pt); + d_pt += d_C*(-(p0-2*p1+p2))+ + d_D*(-(-p0+p1)); + } + } + atomic_add(d_path.points + 2 * i0, d_p0); + atomic_add(d_path.points + 2 * i1, d_p1); + atomic_add(d_path.points + 2 * i2, d_p2); + } else if (path.num_control_points[base_point_id] == 2) { + // Cubic Bezier curve + auto i0 = point_id; + auto i1 = point_id + 1; + auto i2 = point_id + 2; + auto i3 = (point_id + 3) % path.num_points; + auto p0 = Vector2f{path.points[2 * i0], path.points[2 * i0 + 1]}; + auto p1 = Vector2f{path.points[2 * i1], path.points[2 * i1 + 1]}; + auto p2 = Vector2f{path.points[2 * i2], path.points[2 * i2 + 1]}; + auto p3 = Vector2f{path.points[2 * i3], path.points[2 * i3 + 1]}; + // auto eval = [&](float t) -> Vector2f { + // auto tt = 1 - t; + // return (tt*tt*tt)*p0 + (3*tt*tt*t)*p1 + (3*tt*t*t)*p2 + (t*t*t)*p3; + // }; + auto d_p0 = Vector2f{0, 0}; + auto d_p1 = Vector2f{0, 0}; + auto d_p2 = Vector2f{0, 0}; + auto d_p3 = Vector2f{0, 0}; + auto t = min_t_root; + if (t == 0) { + // closest_pt = p0 + d_p0 += d_closest_pt; + } else if (t == 1) { + // closest_pt = p1 + d_p3 += d_closest_pt; + } else { + // The curve is (1 - t)^3 p0 + 3 * (1 - t)^2 t p1 + 3 * (1 - t) t^2 p2 + t^3 p3 + // = (-p0+3p1-3p2+p3) t^3 + (3p0-6p1+3p2) t^2 + (-3p0+3p1) t + p0 + // Want to solve (q - pt) dot q' = 0 + // q' = 3*(-p0+3p1-3p2+p3)t^2 + 2*(3p0-6p1+3p2)t + (-3p0+3p1) + // Expanding + // 3*(-p0+3p1-3p2+p3)^2 t^5 + // 5*(-p0+3p1-3p2+p3)(3p0-6p1+3p2) t^4 + // 4*(-p0+3p1-3p2+p3)(-3p0+3p1) + 2*(3p0-6p1+3p2)^2 t^3 + // 3*(3p0-6p1+3p2)(-3p0+3p1) + 3*(-p0+3p1-3p2+p3)(p0-pt) t^2 + // (-3p0+3p1)^2+2(p0-pt)(3p0-6p1+3p2) t + // (p0-pt)(-3p0+3p1) + double A = 3*sum((-p0+3*p1-3*p2+p3)*(-p0+3*p1-3*p2+p3)); + double B = 5*sum((-p0+3*p1-3*p2+p3)*(3*p0-6*p1+3*p2)); + double C = 4*sum((-p0+3*p1-3*p2+p3)*(-3*p0+3*p1)) + 2*sum((3*p0-6*p1+3*p2)*(3*p0-6*p1+3*p2)); + double D = 3*(sum((3*p0-6*p1+3*p2)*(-3*p0+3*p1)) + sum((-p0+3*p1-3*p2+p3)*(p0-pt))); + double E = sum((-3*p0+3*p1)*(-3*p0+3*p1)) + 2*sum((p0-pt)*(3*p0-6*p1+3*p2)); + double F = sum((p0-pt)*(-3*p0+3*p1)); + B /= A; + C /= A; + D /= A; + E /= A; + F /= A; + // auto eval_polynomial = [&] (double t) { + // return t*t*t*t*t+ + // B*t*t*t*t+ + // C*t*t*t+ + // D*t*t+ + // E*t+ + // F; + // }; + auto eval_polynomial_deriv = [&] (double t) { + return 5*t*t*t*t+ + 4*B*t*t*t+ + 3*C*t*t+ + 2*D*t+ + E; + }; + + // auto p = eval(t); + auto d_p = d_closest_pt; + // (tt*tt*tt)*p0 + (3*tt*tt*t)*p1 + (3*tt*t*t)*p2 + (t*t*t)*p3 + auto tt = 1 - t; + auto d_tt = 3 * tt * tt * dot(d_p, p0) + + 6 * tt * t * dot(d_p, p1) + + 3 * t * t * dot(d_p, p2); + auto d_t = -d_tt + + 3 * tt * tt * dot(d_p, p1) + + 6 * tt * t * dot(d_p, p2) + + 3 * t * t * dot(d_p, p3); + d_p0 += d_p * (tt * tt * tt); + d_p1 += d_p * (3 * tt * tt * t); + d_p2 += d_p * (3 * tt * t * t); + d_p3 += d_p * (t * t * t); + // implicit function theorem: dt/dA = -1/(p'(t)) * dp/dA + auto poly_deriv_t = eval_polynomial_deriv(t); + if (fabs(poly_deriv_t) > 1e-10f) { + auto d_B = -(d_t / poly_deriv_t) * t * t * t * t; + auto d_C = -(d_t / poly_deriv_t) * t * t * t; + auto d_D = -(d_t / poly_deriv_t) * t * t; + auto d_E = -(d_t / poly_deriv_t) * t; + auto d_F = -(d_t / poly_deriv_t); + // B = B' / A + // C = C' / A + // D = D' / A + // E = E' / A + // F = F' / A + auto d_A = -d_B * B / A + -d_C * C / A + -d_D * D / A + -d_E * E / A + -d_F * F / A; + d_B /= A; + d_C /= A; + d_D /= A; + d_E /= A; + d_F /= A; + { + double A = 3*sum((-p0+3*p1-3*p2+p3)*(-p0+3*p1-3*p2+p3)) + 1e-3; + double B = 5*sum((-p0+3*p1-3*p2+p3)*(3*p0-6*p1+3*p2)); + double C = 4*sum((-p0+3*p1-3*p2+p3)*(-3*p0+3*p1)) + 2*sum((3*p0-6*p1+3*p2)*(3*p0-6*p1+3*p2)); + double D = 3*(sum((3*p0-6*p1+3*p2)*(-3*p0+3*p1)) + sum((-p0+3*p1-3*p2+p3)*(p0-pt))); + double E = sum((-3*p0+3*p1)*(-3*p0+3*p1)) + 2*sum((p0-pt)*(3*p0-6*p1+3*p2)); + double F = sum((p0-pt)*(-3*p0+3*p1)); + B /= A; + C /= A; + D /= A; + E /= A; + F /= A; + auto eval_polynomial = [&] (double t) { + return t*t*t*t*t+ + B*t*t*t*t+ + C*t*t*t+ + D*t*t+ + E*t+ + F; + }; + auto eval_polynomial_deriv = [&] (double t) { + return 5*t*t*t*t+ + 4*B*t*t*t+ + 3*C*t*t+ + 2*D*t+ + E; + }; + auto lb = t - 1e-2f; + auto ub = t + 1e-2f; + auto lb_eval = eval_polynomial(lb); + auto ub_eval = eval_polynomial(ub); + if (lb_eval > ub_eval) { + swap_(lb, ub); + } + auto t_ = 0.5f * (lb + ub); + auto num_iter = 20; + for (int it = 0; it < num_iter; it++) { + if (!(t_ >= lb && t_ <= ub)) { + t_ = 0.5f * (lb + ub); + } + auto value = eval_polynomial(t_); + if (fabs(value) < 1e-5f || it == num_iter - 1) { + break; + } + // The derivative may not be entirely accurate, + // but the bisection is going to handle this + if (value > 0.f) { + ub = t_; + } else { + lb = t_; + } + auto derivative = eval_polynomial_deriv(t); + t_ -= value / derivative; + } + } + // A = 3*sum((-p0+3*p1-3*p2+p3)*(-p0+3*p1-3*p2+p3)) + d_p0 += d_A * 3 * (-1) * 2 * (-p0+3*p1-3*p2+p3); + d_p1 += d_A * 3 * 3 * 2 * (-p0+3*p1-3*p2+p3); + d_p2 += d_A * 3 * (-3) * 2 * (-p0+3*p1-3*p2+p3); + d_p3 += d_A * 3 * 1 * 2 * (-p0+3*p1-3*p2+p3); + // B = 5*sum((-p0+3*p1-3*p2+p3)*(3*p0-6*p1+3*p2)) + d_p0 += d_B * 5 * ((-1) * (3*p0-6*p1+3*p2) + 3 * (-p0+3*p1-3*p2+p3)); + d_p1 += d_B * 5 * (3 * (3*p0-6*p1+3*p2) + (-6) * (-p0+3*p1-3*p2+p3)); + d_p2 += d_B * 5 * ((-3) * (3*p0-6*p1+3*p2) + 3 * (-p0+3*p1-3*p2+p3)); + d_p3 += d_B * 5 * (3*p0-6*p1+3*p2); + // C = 4*sum((-p0+3*p1-3*p2+p3)*(-3*p0+3*p1)) + 2*sum((3*p0-6*p1+3*p2)*(3*p0-6*p1+3*p2)) + d_p0 += d_C * 4 * ((-1) * (-3*p0+3*p1) + (-3) * (-p0+3*p1-3*p2+p3)) + + d_C * 2 * (3 * 2 * (3*p0-6*p1+3*p2)); + d_p1 += d_C * 4 * (3 * (-3*p0+3*p1) + 3 * (-p0+3*p1-3*p2+p3)) + + d_C * 2 * ((-6) * 2 * (3*p0-6*p1+3*p2)); + d_p2 += d_C * 4 * ((-3) * (-3*p0+3*p1)) + + d_C * 2 * (3 * 2 * (3*p0-6*p1+3*p2)); + d_p3 += d_C * 4 * (-3*p0+3*p1); + // D = 3*(sum((3*p0-6*p1+3*p2)*(-3*p0+3*p1)) + sum((-p0+3*p1-3*p2+p3)*(p0-pt))) + d_p0 += d_D * 3 * (3 * (-3*p0+3*p1) + (-3) * (3*p0-6*p1+3*p2)) + + d_D * 3 * ((-1) * (p0-pt) + 1 * (-p0+3*p1-3*p2+p3)); + d_p1 += d_D * 3 * ((-6) * (-3*p0+3*p1) + (3) * (3*p0-6*p1+3*p2)) + + d_D * 3 * (3 * (p0-pt)); + d_p2 += d_D * 3 * (3 * (-3*p0+3*p1)) + + d_D * 3 * ((-3) * (p0-pt)); + d_pt += d_D * 3 * ((-1) * (-p0+3*p1-3*p2+p3)); + // E = sum((-3*p0+3*p1)*(-3*p0+3*p1)) + 2*sum((p0-pt)*(3*p0-6*p1+3*p2)) + d_p0 += d_E * ((-3) * 2 * (-3*p0+3*p1)) + + d_E * 2 * (1 * (3*p0-6*p1+3*p2) + 3 * (p0-pt)); + d_p1 += d_E * ( 3 * 2 * (-3*p0+3*p1)) + + d_E * 2 * ((-6) * (p0-pt)); + d_p2 += d_E * 2 * ( 3 * (p0-pt)); + d_pt += d_E * 2 * ((-1) * (3*p0-6*p1+3*p2)); + // F = sum((p0-pt)*(-3*p0+3*p1)) + d_p0 += d_F * (1 * (-3*p0+3*p1)) + + d_F * ((-3) * (p0-pt)); + d_p1 += d_F * (3 * (p0-pt)); + d_pt += d_F * ((-1) * (-3*p0+3*p1)); + } + } + atomic_add(d_path.points + 2 * i0, d_p0); + atomic_add(d_path.points + 2 * i1, d_p1); + atomic_add(d_path.points + 2 * i2, d_p2); + atomic_add(d_path.points + 2 * i3, d_p3); + } else { + assert(false); + } +} + +DEVICE +inline +void d_closest_point(const Rect &rect, + const Vector2f &pt, + const Vector2f &d_closest_pt, + Rect &d_rect, + Vector2f &d_pt) { + auto dist = [&](const Vector2f &p0, const Vector2f &p1) -> float { + // project pt to line + auto t = dot(pt - p0, p1 - p0) / dot(p1 - p0, p1 - p0); + if (t < 0) { + return distance(p0, pt); + } else if (t > 1) { + return distance(p1, pt); + } else { + return distance(p0 + t * (p1 - p0), pt); + } + // return 0; + }; + auto left_top = rect.p_min; + auto right_top = Vector2f{rect.p_max.x, rect.p_min.y}; + auto left_bottom = Vector2f{rect.p_min.x, rect.p_max.y}; + auto right_bottom = rect.p_max; + auto left_dist = dist(left_top, left_bottom); + auto top_dist = dist(left_top, right_top); + auto right_dist = dist(right_top, right_bottom); + auto bottom_dist = dist(left_bottom, right_bottom); + int min_id = 0; + auto min_dist = left_dist; + if (top_dist < min_dist) { min_dist = top_dist; min_id = 1; } + if (right_dist < min_dist) { min_dist = right_dist; min_id = 2; } + if (bottom_dist < min_dist) { min_dist = bottom_dist; min_id = 3; } + + auto d_update = [&](const Vector2f &p0, const Vector2f &p1, + const Vector2f &d_closest_pt, + Vector2f &d_p0, Vector2f &d_p1) { + // project pt to line + auto t = dot(pt - p0, p1 - p0) / dot(p1 - p0, p1 - p0); + if (t < 0) { + d_p0 += d_closest_pt; + } else if (t > 1) { + d_p1 += d_closest_pt; + } else { + // p = p0 + t * (p1 - p0) + auto d_p = d_closest_pt; + d_p0 += d_p * (1 - t); + d_p1 += d_p * t; + auto d_t = sum(d_p * (p1 - p0)); + // t = dot(pt - p0, p1 - p0) / dot(p1 - p0, p1 - p0) + auto d_numerator = d_t / dot(p1 - p0, p1 - p0); + auto d_denominator = d_t * (-t) / dot(p1 - p0, p1 - p0); + // numerator = dot(pt - p0, p1 - p0) + d_pt += (p1 - p0) * d_numerator; + d_p1 += (pt - p0) * d_numerator; + d_p0 += ((p0 - p1) + (p0 - pt)) * d_numerator; + // denominator = dot(p1 - p0, p1 - p0) + d_p1 += 2 * (p1 - p0) * d_denominator; + d_p0 += 2 * (p0 - p1) * d_denominator; + } + }; + auto d_left_top = Vector2f{0, 0}; + auto d_right_top = Vector2f{0, 0}; + auto d_left_bottom = Vector2f{0, 0}; + auto d_right_bottom = Vector2f{0, 0}; + if (min_id == 0) { + d_update(left_top, left_bottom, d_closest_pt, d_left_top, d_left_bottom); + } else if (min_id == 1) { + d_update(left_top, right_top, d_closest_pt, d_left_top, d_right_top); + } else if (min_id == 2) { + d_update(right_top, right_bottom, d_closest_pt, d_right_top, d_right_bottom); + } else { + assert(min_id == 3); + d_update(left_bottom, right_bottom, d_closest_pt, d_left_bottom, d_right_bottom); + } + auto d_p_min = Vector2f{0, 0}; + auto d_p_max = Vector2f{0, 0}; + // left_top = rect.p_min + // right_top = Vector2f{rect.p_max.x, rect.p_min.y} + // left_bottom = Vector2f{rect.p_min.x, rect.p_max.y} + // right_bottom = rect.p_max + d_p_min += d_left_top; + d_p_max.x += d_right_top.x; + d_p_min.y += d_right_top.y; + d_p_min.x += d_left_bottom.x; + d_p_max.y += d_left_bottom.y; + d_p_max += d_right_bottom; + atomic_add(d_rect.p_min, d_p_min); + atomic_add(d_rect.p_max, d_p_max); +} + +DEVICE +inline +void d_closest_point(const Shape &shape, + const Vector2f &pt, + const Vector2f &d_closest_pt, + const ClosestPointPathInfo &path_info, + Shape &d_shape, + Vector2f &d_pt) { + switch (shape.type) { + case ShapeType::Circle: + d_closest_point(*(const Circle *)shape.ptr, + pt, + d_closest_pt, + *(Circle *)d_shape.ptr, + d_pt); + break; + case ShapeType::Ellipse: + // https://www.geometrictools.com/Documentation/DistancePointEllipseEllipsoid.pdf + assert(false); + break; + case ShapeType::Path: + d_closest_point(*(const Path *)shape.ptr, + pt, + d_closest_pt, + path_info, + *(Path *)d_shape.ptr, + d_pt); + break; + case ShapeType::Rect: + d_closest_point(*(const Rect *)shape.ptr, + pt, + d_closest_pt, + *(Rect *)d_shape.ptr, + d_pt); + break; + } +} + +DEVICE +inline +void d_compute_distance(const Matrix3x3f &canvas_to_shape, + const Matrix3x3f &shape_to_canvas, + const Shape &shape, + const Vector2f &pt, + const Vector2f &closest_pt, + const ClosestPointPathInfo &path_info, + float d_dist, + Matrix3x3f &d_shape_to_canvas, + Shape &d_shape, + float *d_translation) { + if (distance_squared(pt, closest_pt) < 1e-10f) { + // The derivative at distance=0 is undefined + return; + } + assert(isfinite(d_dist)); + // pt is in canvas space, transform it to shape's local space + auto local_pt = xform_pt(canvas_to_shape, pt); + auto local_closest_pt = xform_pt(canvas_to_shape, closest_pt); + // auto local_closest_pt = closest_point(shape, local_pt); + // auto closest_pt = xform_pt(shape_group.shape_to_canvas, local_closest_pt); + // auto dist = distance(closest_pt, pt); + auto d_pt = Vector2f{0, 0}; + auto d_closest_pt = Vector2f{0, 0}; + d_distance(closest_pt, pt, d_dist, d_closest_pt, d_pt); + assert(isfinite(d_pt)); + assert(isfinite(d_closest_pt)); + // auto closest_pt = xform_pt(shape_group.shape_to_canvas, local_closest_pt); + auto d_local_closest_pt = Vector2f{0, 0}; + auto d_shape_to_canvas_ = Matrix3x3f(); + d_xform_pt(shape_to_canvas, local_closest_pt, d_closest_pt, + d_shape_to_canvas_, d_local_closest_pt); + assert(isfinite(d_local_closest_pt)); + auto d_local_pt = Vector2f{0, 0}; + d_closest_point(shape, local_pt, d_local_closest_pt, path_info, d_shape, d_local_pt); + assert(isfinite(d_local_pt)); + auto d_canvas_to_shape = Matrix3x3f(); + d_xform_pt(canvas_to_shape, + pt, + d_local_pt, + d_canvas_to_shape, + d_pt); + // http://jack.valmadre.net/notes/2016/09/04/back-prop-differentials/#back-propagation-using-differentials + auto tc2s = transpose(canvas_to_shape); + d_shape_to_canvas_ += -tc2s * d_canvas_to_shape * tc2s; + atomic_add(&d_shape_to_canvas(0, 0), d_shape_to_canvas_); + if (d_translation != nullptr) { + atomic_add(d_translation, -d_pt); + } +} diff --git a/cuda_utils.h b/cuda_utils.h new file mode 100644 index 0000000..1e4609b --- /dev/null +++ b/cuda_utils.h @@ -0,0 +1,53 @@ +#pragma once + +#ifdef __CUDACC__ + #include + #include +#endif +#include +#include +#include + +#ifdef __CUDACC__ +#define checkCuda(x) do { if((x)!=cudaSuccess) { \ + printf("CUDA Runtime Error: %s at %s:%d\n",\ + cudaGetErrorString(x),__FILE__,__LINE__);\ + exit(1);}} while(0) +#endif + +template +DEVICE +inline T infinity() { +#ifdef __CUDA_ARCH__ + const unsigned long long ieee754inf = 0x7ff0000000000000; + return __longlong_as_double(ieee754inf); +#else + return std::numeric_limits::infinity(); +#endif +} + +template <> +DEVICE +inline double infinity() { +#ifdef __CUDA_ARCH__ + return __longlong_as_double(0x7ff0000000000000ULL); +#else + return std::numeric_limits::infinity(); +#endif +} + +template <> +DEVICE +inline float infinity() { +#ifdef __CUDA_ARCH__ + return __int_as_float(0x7f800000); +#else + return std::numeric_limits::infinity(); +#endif +} + +inline void cuda_synchronize() { +#ifdef __CUDACC__ + checkCuda(cudaDeviceSynchronize()); +#endif +} diff --git a/diffvg.cpp b/diffvg.cpp new file mode 100644 index 0000000..7346d24 --- /dev/null +++ b/diffvg.cpp @@ -0,0 +1,1792 @@ +#include "diffvg.h" +#include "aabb.h" +#include "shape.h" +#include "sample_boundary.h" +#include "atomic.h" +#include "cdf.h" +#include "compute_distance.h" +#include "cuda_utils.h" +#include "edge_query.h" +#include "filter.h" +#include "matrix.h" +#include "parallel.h" +#include "pcg.h" +#include "ptr.h" +#include "scene.h" +#include "vector.h" +#include "winding_number.h" +#include "within_distance.h" +#include +#include +#include +#include +#include + +namespace py = pybind11; + +struct Command { + int shape_group_id; + int shape_id; + int point_id; // Only used by path +}; + +DEVICE +bool is_inside(const SceneData &scene_data, + int shape_group_id, + const Vector2f &pt, + EdgeQuery *edge_query) { + const ShapeGroup &shape_group = scene_data.shape_groups[shape_group_id]; + // pt is in canvas space, transform it to shape's local space + auto local_pt = xform_pt(shape_group.canvas_to_shape, pt); + const auto &bvh_nodes = scene_data.shape_groups_bvh_nodes[shape_group_id]; + const AABB &bbox = bvh_nodes[2 * shape_group.num_shapes - 2].box; + if (!inside(bbox, local_pt)) { + return false; + } + auto winding_number = 0; + // Traverse the shape group BVH + constexpr auto max_bvh_stack_size = 64; + int bvh_stack[max_bvh_stack_size]; + auto stack_size = 0; + bvh_stack[stack_size++] = 2 * shape_group.num_shapes - 2; + while (stack_size > 0) { + const BVHNode &node = bvh_nodes[bvh_stack[--stack_size]]; + if (node.child1 < 0) { + // leaf + auto shape_id = node.child0; + auto w = compute_winding_number( + scene_data.shapes[shape_id], scene_data.path_bvhs[shape_id], local_pt); + winding_number += w; + if (edge_query != nullptr) { + if (edge_query->shape_group_id == shape_group_id && + edge_query->shape_id == shape_id) { + if ((shape_group.use_even_odd_rule && abs(w) % 2 == 1) || + (!shape_group.use_even_odd_rule && w != 0)) { + edge_query->hit = true; + } + } + } + } else { + assert(node.child0 >= 0 && node.child1 >= 0); + const AABB &b0 = bvh_nodes[node.child0].box; + if (inside(b0, local_pt)) { + bvh_stack[stack_size++] = node.child0; + } + const AABB &b1 = bvh_nodes[node.child1].box; + if (inside(b1, local_pt)) { + bvh_stack[stack_size++] = node.child1; + } + assert(stack_size <= max_bvh_stack_size); + } + } + if (shape_group.use_even_odd_rule) { + return abs(winding_number) % 2 == 1; + } else { + return winding_number != 0; + } +} + +DEVICE void accumulate_boundary_gradient(const Shape &shape, + float contrib, + float t, + const Vector2f &normal, + const BoundaryData &boundary_data, + Shape &d_shape, + const Matrix3x3f &shape_to_canvas, + const Vector2f &local_boundary_pt, + Matrix3x3f &d_shape_to_canvas) { + assert(isfinite(contrib)); + assert(isfinite(normal)); + // According to Reynold transport theorem, + // the Jacobian of the boundary integral is dot(velocity, normal), + // where the velocity depends on the variable being differentiated with. + if (boundary_data.is_stroke) { + auto has_path_thickness = false; + if (shape.type == ShapeType::Path) { + const Path &path = *(const Path *)shape.ptr; + has_path_thickness = path.thickness != nullptr; + } + // differentiate stroke width: velocity is the same as normal + if (has_path_thickness) { + Path *d_p = (Path*)d_shape.ptr; + auto base_point_id = boundary_data.path.base_point_id; + auto point_id = boundary_data.path.point_id; + auto t = boundary_data.path.t; + const Path &path = *(const Path *)shape.ptr; + if (path.num_control_points[base_point_id] == 0) { + // Straight line + auto i0 = point_id; + auto i1 = (point_id + 1) % path.num_points; + // r = r0 + t * (r1 - r0) + atomic_add(&d_p->thickness[i0], (1 - t) * contrib); + atomic_add(&d_p->thickness[i1], ( t) * contrib); + } else if (path.num_control_points[base_point_id] == 1) { + // Quadratic Bezier curve + auto i0 = point_id; + auto i1 = point_id + 1; + auto i2 = (point_id + 2) % path.num_points; + // r = (1-t)^2r0 + 2(1-t)t r1 + t^2 r2 + atomic_add(&d_p->thickness[i0], square(1 - t) * contrib); + atomic_add(&d_p->thickness[i1], (2*(1-t)*t) * contrib); + atomic_add(&d_p->thickness[i2], (t*t) * contrib); + } else if (path.num_control_points[base_point_id] == 2) { + auto i0 = point_id; + auto i1 = point_id + 1; + auto i2 = point_id + 2; + auto i3 = (point_id + 3) % path.num_points; + // r = (1-t)^3r0 + 3*(1-t)^2tr1 + 3*(1-t)t^2r2 + t^3r3 + atomic_add(&d_p->thickness[i0], cubic(1 - t) * contrib); + atomic_add(&d_p->thickness[i1], 3 * square(1 - t) * t * contrib); + atomic_add(&d_p->thickness[i2], 3 * (1 - t) * t * t * contrib); + atomic_add(&d_p->thickness[i3], t * t * t * contrib); + } else { + assert(false); + } + } else { + atomic_add(&d_shape.stroke_width, contrib); + } + } + switch (shape.type) { + case ShapeType::Circle: { + Circle *d_p = (Circle*)d_shape.ptr; + // velocity for the center is (1, 0) for x and (0, 1) for y + atomic_add(&d_p->center[0], normal * contrib); + // velocity for the radius is the same as the normal + atomic_add(&d_p->radius, contrib); + break; + } case ShapeType::Ellipse: { + Ellipse *d_p = (Ellipse*)d_shape.ptr; + // velocity for the center is (1, 0) for x and (0, 1) for y + atomic_add(&d_p->center[0], normal * contrib); + // velocity for the radius: + // x = center.x + r.x * cos(2pi * t) + // y = center.y + r.y * sin(2pi * t) + // for r.x: (cos(2pi * t), 0) + // for r.y: (0, sin(2pi * t)) + atomic_add(&d_p->radius.x, cos(2 * float(M_PI) * t) * normal.x * contrib); + atomic_add(&d_p->radius.y, sin(2 * float(M_PI) * t) * normal.y * contrib); + break; + } case ShapeType::Path: { + Path *d_p = (Path*)d_shape.ptr; + auto base_point_id = boundary_data.path.base_point_id; + auto point_id = boundary_data.path.point_id; + auto t = boundary_data.path.t; + const Path &path = *(const Path *)shape.ptr; + if (path.num_control_points[base_point_id] == 0) { + // Straight line + auto i0 = point_id; + auto i1 = (point_id + 1) % path.num_points; + // pt = p0 + t * (p1 - p0) + // velocity for p0.x: (1 - t, 0) + // p0.y: ( 0, 1 - t) + // p1.x: ( t, 0) + // p1.y: ( 0, t) + atomic_add(&d_p->points[2 * i0 + 0], (1 - t) * normal.x * contrib); + atomic_add(&d_p->points[2 * i0 + 1], (1 - t) * normal.y * contrib); + atomic_add(&d_p->points[2 * i1 + 0], ( t) * normal.x * contrib); + atomic_add(&d_p->points[2 * i1 + 1], ( t) * normal.y * contrib); + } else if (path.num_control_points[base_point_id] == 1) { + // Quadratic Bezier curve + auto i0 = point_id; + auto i1 = point_id + 1; + auto i2 = (point_id + 2) % path.num_points; + // pt = (1-t)^2p0 + 2(1-t)t p1 + t^2 p2 + // velocity for p0.x: ((1-t)^2, 0) + // p0.y: ( 0, (1-t)^2) + // p1.x: (2(1-t)t, 0) + // p1.y: ( 0, 2(1-t)t) + // p1.x: ( t^2, 0) + // p1.y: ( 0, t^2) + atomic_add(&d_p->points[2 * i0 + 0], square(1 - t) * normal.x * contrib); + atomic_add(&d_p->points[2 * i0 + 1], square(1 - t) * normal.y * contrib); + atomic_add(&d_p->points[2 * i1 + 0], (2*(1-t)*t) * normal.x * contrib); + atomic_add(&d_p->points[2 * i1 + 1], (2*(1-t)*t) * normal.y * contrib); + atomic_add(&d_p->points[2 * i2 + 0], (t*t) * normal.x * contrib); + atomic_add(&d_p->points[2 * i2 + 1], (t*t) * normal.y * contrib); + } else if (path.num_control_points[base_point_id] == 2) { + auto i0 = point_id; + auto i1 = point_id + 1; + auto i2 = point_id + 2; + auto i3 = (point_id + 3) % path.num_points; + // pt = (1-t)^3p0 + 3*(1-t)^2tp1 + 3*(1-t)t^2p2 + t^3p3 + // velocity for p0.x: ( (1-t)^3, 0) + // p0.y: ( 0, (1-t)^3) + // p1.x: (3*(1-t)^2t, 0) + // p1.y: ( 0, 3*(1-t)^2t) + // p2.x: (3*(1-t)t^2, 0) + // p2.y: ( 0, 3*(1-t)t^2) + // p2.x: ( t^3, 0) + // p2.y: ( 0, t^3) + atomic_add(&d_p->points[2 * i0 + 0], cubic(1 - t) * normal.x * contrib); + atomic_add(&d_p->points[2 * i0 + 1], cubic(1 - t) * normal.y * contrib); + atomic_add(&d_p->points[2 * i1 + 0], 3 * square(1 - t) * t * normal.x * contrib); + atomic_add(&d_p->points[2 * i1 + 1], 3 * square(1 - t) * t * normal.y * contrib); + atomic_add(&d_p->points[2 * i2 + 0], 3 * (1 - t) * t * t * normal.x * contrib); + atomic_add(&d_p->points[2 * i2 + 1], 3 * (1 - t) * t * t * normal.y * contrib); + atomic_add(&d_p->points[2 * i3 + 0], t * t * t * normal.x * contrib); + atomic_add(&d_p->points[2 * i3 + 1], t * t * t * normal.y * contrib); + } else { + assert(false); + } + break; + } case ShapeType::Rect: { + Rect *d_p = (Rect*)d_shape.ptr; + // The velocity depends on the position of the boundary + if (normal == Vector2f{-1, 0}) { + // left + // velocity for p_min is (1, 0) for x and (0, 0) for y + atomic_add(&d_p->p_min.x, -contrib); + } else if (normal == Vector2f{1, 0}) { + // right + // velocity for p_max is (1, 0) for x and (0, 0) for y + atomic_add(&d_p->p_max.x, contrib); + } else if (normal == Vector2f{0, -1}) { + // top + // velocity for p_min is (0, 0) for x and (0, 1) for y + atomic_add(&d_p->p_min.y, -contrib); + } else if (normal == Vector2f{0, 1}) { + // bottom + // velocity for p_max is (0, 0) for x and (0, 1) for y + atomic_add(&d_p->p_max.y, contrib); + } else { + // incorrect normal assignment? + assert(false); + } + break; + } default: { + assert(false); + break; + } + } + // for shape_to_canvas we have the following relationship: + // boundary_pt = xform_pt(shape_to_canvas, local_pt) + // the velocity is the derivative of boundary_pt with respect to shape_to_canvas + // we can use reverse-mode AD to compute the dot product of the velocity and the Jacobian + // by passing the normal in d_xform_pt + auto d_shape_to_canvas_ = Matrix3x3f(); + auto d_local_boundary_pt = Vector2f{0, 0}; + d_xform_pt(shape_to_canvas, + local_boundary_pt, + normal * contrib, + d_shape_to_canvas_, + d_local_boundary_pt); + atomic_add(&d_shape_to_canvas(0, 0), d_shape_to_canvas_); +} + +DEVICE +Vector4f sample_color(const ColorType &color_type, + void *color, + const Vector2f &pt) { + switch (color_type) { + case ColorType::Constant: { + auto c = (const Constant*)color; + assert(isfinite(c->color)); + return c->color; + } case ColorType::LinearGradient: { + auto c = (const LinearGradient*)color; + // Project pt to (c->begin, c->end) + auto beg = c->begin; + auto end = c->end; + auto t = dot(pt - beg, end - beg) / max(dot(end - beg, end - beg), 1e-3f); + // Find the correponding stop: + if (t < c->stop_offsets[0]) { + return Vector4f{c->stop_colors[0], + c->stop_colors[1], + c->stop_colors[2], + c->stop_colors[3]}; + } + for (int i = 0; i < c->num_stops - 1; i++) { + auto offset_curr = c->stop_offsets[i]; + auto offset_next = c->stop_offsets[i + 1]; + assert(offset_next > offset_curr); + if (t >= offset_curr && t < offset_next) { + auto color_curr = Vector4f{ + c->stop_colors[4 * i + 0], + c->stop_colors[4 * i + 1], + c->stop_colors[4 * i + 2], + c->stop_colors[4 * i + 3]}; + auto color_next = Vector4f{ + c->stop_colors[4 * (i + 1) + 0], + c->stop_colors[4 * (i + 1) + 1], + c->stop_colors[4 * (i + 1) + 2], + c->stop_colors[4 * (i + 1) + 3]}; + auto tt = (t - offset_curr) / (offset_next - offset_curr); + assert(isfinite(tt)); + assert(isfinite(color_curr)); + assert(isfinite(color_next)); + return color_curr * (1 - tt) + color_next * tt; + } + } + return Vector4f{c->stop_colors[4 * (c->num_stops - 1) + 0], + c->stop_colors[4 * (c->num_stops - 1) + 1], + c->stop_colors[4 * (c->num_stops - 1) + 2], + c->stop_colors[4 * (c->num_stops - 1) + 3]}; + } case ColorType::RadialGradient: { + auto c = (const RadialGradient*)color; + // Distance from pt to center + auto offset = pt - c->center; + auto normalized_offset = offset / c->radius; + auto t = length(normalized_offset); + // Find the correponding stop: + if (t < c->stop_offsets[0]) { + return Vector4f{c->stop_colors[0], + c->stop_colors[1], + c->stop_colors[2], + c->stop_colors[3]}; + } + for (int i = 0; i < c->num_stops - 1; i++) { + auto offset_curr = c->stop_offsets[i]; + auto offset_next = c->stop_offsets[i + 1]; + assert(offset_next > offset_curr); + if (t >= offset_curr && t < offset_next) { + auto color_curr = Vector4f{ + c->stop_colors[4 * i + 0], + c->stop_colors[4 * i + 1], + c->stop_colors[4 * i + 2], + c->stop_colors[4 * i + 3]}; + auto color_next = Vector4f{ + c->stop_colors[4 * (i + 1) + 0], + c->stop_colors[4 * (i + 1) + 1], + c->stop_colors[4 * (i + 1) + 2], + c->stop_colors[4 * (i + 1) + 3]}; + auto tt = (t - offset_curr) / (offset_next - offset_curr); + assert(isfinite(tt)); + assert(isfinite(color_curr)); + assert(isfinite(color_next)); + return color_curr * (1 - tt) + color_next * tt; + } + } + return Vector4f{c->stop_colors[4 * (c->num_stops - 1) + 0], + c->stop_colors[4 * (c->num_stops - 1) + 1], + c->stop_colors[4 * (c->num_stops - 1) + 2], + c->stop_colors[4 * (c->num_stops - 1) + 3]}; + } default: { + assert(false); + } + } + return Vector4f{}; +} + +DEVICE +void d_sample_color(const ColorType &color_type, + void *color_ptr, + const Vector2f &pt, + const Vector4f &d_color, + void *d_color_ptr, + float *d_translation) { + switch (color_type) { + case ColorType::Constant: { + auto d_c = (Constant*)d_color_ptr; + atomic_add(&d_c->color[0], d_color); + return; + } case ColorType::LinearGradient: { + auto c = (const LinearGradient*)color_ptr; + auto d_c = (LinearGradient*)d_color_ptr; + // Project pt to (c->begin, c->end) + auto beg = c->begin; + auto end = c->end; + auto t = dot(pt - beg, end - beg) / max(dot(end - beg, end - beg), 1e-3f); + // Find the correponding stop: + if (t < c->stop_offsets[0]) { + atomic_add(&d_c->stop_colors[0], d_color); + return; + } + for (int i = 0; i < c->num_stops - 1; i++) { + auto offset_curr = c->stop_offsets[i]; + auto offset_next = c->stop_offsets[i + 1]; + assert(offset_next > offset_curr); + if (t >= offset_curr && t < offset_next) { + auto color_curr = Vector4f{ + c->stop_colors[4 * i + 0], + c->stop_colors[4 * i + 1], + c->stop_colors[4 * i + 2], + c->stop_colors[4 * i + 3]}; + auto color_next = Vector4f{ + c->stop_colors[4 * (i + 1) + 0], + c->stop_colors[4 * (i + 1) + 1], + c->stop_colors[4 * (i + 1) + 2], + c->stop_colors[4 * (i + 1) + 3]}; + auto tt = (t - offset_curr) / (offset_next - offset_curr); + // return color_curr * (1 - tt) + color_next * tt; + auto d_color_curr = d_color * (1 - tt); + auto d_color_next = d_color * tt; + auto d_tt = sum(d_color * (color_next - color_curr)); + auto d_offset_next = -d_tt * tt / (offset_next - offset_curr); + auto d_offset_curr = d_tt * ((tt - 1.f) / (offset_next - offset_curr)); + auto d_t = d_tt / (offset_next - offset_curr); + assert(isfinite(d_tt)); + atomic_add(&d_c->stop_colors[4 * i], d_color_curr); + atomic_add(&d_c->stop_colors[4 * (i + 1)], d_color_next); + atomic_add(&d_c->stop_offsets[i], d_offset_curr); + atomic_add(&d_c->stop_offsets[i + 1], d_offset_next); + // auto t = dot(pt - beg, end - beg) / max(dot(end - beg, end - beg), 1e-6f); + // l = max(dot(end - beg, end - beg), 1e-3f) + // t = dot(pt - beg, end - beg) / l; + auto l = max(dot(end - beg, end - beg), 1e-3f); + auto d_beg = d_t * (-(pt - beg)-(end - beg)) / l; + auto d_end = d_t * (pt - beg) / l; + auto d_l = -d_t * t / l; + if (dot(end - beg, end - beg) > 1e-3f) { + d_beg += 2 * d_l * (beg - end); + d_end += 2 * d_l * (end - beg); + } + atomic_add(&d_c->begin[0], d_beg); + atomic_add(&d_c->end[0], d_end); + if (d_translation != nullptr) { + atomic_add(d_translation, (d_beg + d_end)); + } + return; + } + } + atomic_add(&d_c->stop_colors[4 * (c->num_stops - 1)], d_color); + return; + } case ColorType::RadialGradient: { + auto c = (const RadialGradient*)color_ptr; + auto d_c = (RadialGradient*)d_color_ptr; + // Distance from pt to center + auto offset = pt - c->center; + auto normalized_offset = offset / c->radius; + auto t = length(normalized_offset); + // Find the correponding stop: + if (t < c->stop_offsets[0]) { + atomic_add(&d_c->stop_colors[0], d_color); + return; + } + for (int i = 0; i < c->num_stops - 1; i++) { + auto offset_curr = c->stop_offsets[i]; + auto offset_next = c->stop_offsets[i + 1]; + assert(offset_next > offset_curr); + if (t >= offset_curr && t < offset_next) { + auto color_curr = Vector4f{ + c->stop_colors[4 * i + 0], + c->stop_colors[4 * i + 1], + c->stop_colors[4 * i + 2], + c->stop_colors[4 * i + 3]}; + auto color_next = Vector4f{ + c->stop_colors[4 * (i + 1) + 0], + c->stop_colors[4 * (i + 1) + 1], + c->stop_colors[4 * (i + 1) + 2], + c->stop_colors[4 * (i + 1) + 3]}; + auto tt = (t - offset_curr) / (offset_next - offset_curr); + assert(isfinite(tt)); + // return color_curr * (1 - tt) + color_next * tt; + auto d_color_curr = d_color * (1 - tt); + auto d_color_next = d_color * tt; + auto d_tt = sum(d_color * (color_next - color_curr)); + auto d_offset_next = -d_tt * tt / (offset_next - offset_curr); + auto d_offset_curr = d_tt * ((tt - 1.f) / (offset_next - offset_curr)); + auto d_t = d_tt / (offset_next - offset_curr); + assert(isfinite(d_t)); + atomic_add(&d_c->stop_colors[4 * i], d_color_curr); + atomic_add(&d_c->stop_colors[4 * (i + 1)], d_color_next); + atomic_add(&d_c->stop_offsets[i], d_offset_curr); + atomic_add(&d_c->stop_offsets[i + 1], d_offset_next); + // offset = pt - c->center + // normalized_offset = offset / c->radius + // t = length(normalized_offset) + auto d_normalized_offset = d_length(normalized_offset, d_t); + auto d_offset = d_normalized_offset / c->radius; + auto d_radius = -d_normalized_offset * offset / (c->radius * c->radius); + auto d_center = -d_offset; + atomic_add(&d_c->center[0], d_center); + atomic_add(&d_c->radius[0], d_radius); + if (d_translation != nullptr) { + atomic_add(d_translation, d_center); + } + } + } + atomic_add(&d_c->stop_colors[4 * (c->num_stops - 1)], d_color); + return; + } default: { + assert(false); + } + } +} + +struct Fragment { + Vector3f color; + float alpha; + int group_id; + bool is_stroke; +}; + +struct PrefilterFragment { + Vector3f color; + float alpha; + int group_id; + bool is_stroke; + int shape_id; + float distance; + Vector2f closest_pt; + ClosestPointPathInfo path_info; + bool within_distance; +}; + +DEVICE +Vector4f sample_color(const SceneData &scene, + const Vector4f *background_color, + const Vector2f &screen_pt, + const Vector4f *d_color = nullptr, + EdgeQuery *edge_query = nullptr, + Vector4f *d_background_color = nullptr, + float *d_translation = nullptr) { + if (edge_query != nullptr) { + edge_query->hit = false; + } + + // screen_pt is in screen space ([0, 1), [0, 1)), + // need to transform to canvas space + auto pt = screen_pt; + pt.x *= scene.canvas_width; + pt.y *= scene.canvas_height; + constexpr auto max_hit_shapes = 256; + constexpr auto max_bvh_stack_size = 64; + Fragment fragments[max_hit_shapes]; + int bvh_stack[max_bvh_stack_size]; + auto stack_size = 0; + auto num_fragments = 0; + bvh_stack[stack_size++] = 2 * scene.num_shape_groups - 2; + while (stack_size > 0) { + const BVHNode &node = scene.bvh_nodes[bvh_stack[--stack_size]]; + if (node.child1 < 0) { + // leaf + auto group_id = node.child0; + const ShapeGroup &shape_group = scene.shape_groups[group_id]; + if (shape_group.stroke_color != nullptr) { + if (within_distance(scene, group_id, pt, edge_query)) { + auto color_alpha = sample_color(shape_group.stroke_color_type, + shape_group.stroke_color, + pt); + Fragment f; + f.color = Vector3f{color_alpha[0], color_alpha[1], color_alpha[2]}; + f.alpha = color_alpha[3]; + f.group_id = group_id; + f.is_stroke = true; + assert(num_fragments < max_hit_shapes); + fragments[num_fragments++] = f; + } + } + if (shape_group.fill_color != nullptr) { + if (is_inside(scene, group_id, pt, edge_query)) { + auto color_alpha = sample_color(shape_group.fill_color_type, + shape_group.fill_color, + pt); + Fragment f; + f.color = Vector3f{color_alpha[0], color_alpha[1], color_alpha[2]}; + f.alpha = color_alpha[3]; + f.group_id = group_id; + f.is_stroke = false; + assert(num_fragments < max_hit_shapes); + fragments[num_fragments++] = f; + } + } + } else { + assert(node.child0 >= 0 && node.child1 >= 0); + const AABB &b0 = scene.bvh_nodes[node.child0].box; + if (inside(b0, pt, scene.bvh_nodes[node.child0].max_radius)) { + bvh_stack[stack_size++] = node.child0; + } + const AABB &b1 = scene.bvh_nodes[node.child1].box; + if (inside(b1, pt, scene.bvh_nodes[node.child1].max_radius)) { + bvh_stack[stack_size++] = node.child1; + } + assert(stack_size <= max_bvh_stack_size); + } + } + if (num_fragments <= 0) { + if (background_color != nullptr) { + if (d_background_color != nullptr) { + *d_background_color = *d_color; + } + return *background_color; + } + return Vector4f{0, 0, 0, 0}; + } + // Sort the fragments from back to front (i.e. increasing order of group id) + // https://github.com/frigaut/yorick-imutil/blob/master/insort.c#L37 + for (int i = 1; i < num_fragments; i++) { + auto j = i; + auto temp = fragments[j]; + while (j > 0 && fragments[j - 1].group_id > temp.group_id) { + fragments[j] = fragments[j - 1]; + j--; + } + fragments[j] = temp; + } + // Blend the color + Vector3f accum_color[max_hit_shapes]; + float accum_alpha[max_hit_shapes]; + // auto hit_opaque = false; + auto first_alpha = 0.f; + auto first_color = Vector3f{0, 0, 0}; + if (background_color != nullptr) { + first_alpha = background_color->w; + first_color = Vector3f{background_color->x, + background_color->y, + background_color->z}; + } + for (int i = 0; i < num_fragments; i++) { + const Fragment &fragment = fragments[i]; + auto new_color = fragment.color; + auto new_alpha = fragment.alpha; + auto prev_alpha = i > 0 ? accum_alpha[i - 1] : first_alpha; + auto prev_color = i > 0 ? accum_color[i - 1] : first_color; + if (edge_query != nullptr) { + // Do we hit the target shape? + if (new_alpha >= 1.f && edge_query->hit) { + // A fully opaque shape in front of the target occludes it + edge_query->hit = false; + } + if (edge_query->shape_group_id == fragment.group_id) { + edge_query->hit = true; + } + } + // prev_color is alpha premultiplied, don't need to multiply with + // prev_alpha + accum_color[i] = prev_color * (1 - new_alpha) + new_alpha * new_color; + accum_alpha[i] = prev_alpha * (1 - new_alpha) + new_alpha; + } + auto final_color = accum_color[num_fragments - 1]; + auto final_alpha = accum_alpha[num_fragments - 1]; + if (final_alpha > 1e-6f) { + final_color /= final_alpha; + } + assert(isfinite(final_color)); + assert(isfinite(final_alpha)); + if (d_color != nullptr) { + // Backward pass + auto d_final_color = Vector3f{(*d_color)[0], (*d_color)[1], (*d_color)[2]}; + auto d_final_alpha = (*d_color)[3]; + auto d_curr_color = d_final_color; + auto d_curr_alpha = d_final_alpha; + if (final_alpha > 1e-6f) { + // final_color = curr_color / final_alpha + d_curr_color = d_final_color / final_alpha; + d_curr_alpha -= sum(d_final_color * final_color) / final_alpha; + } + assert(isfinite(*d_color)); + assert(isfinite(d_curr_color)); + assert(isfinite(d_curr_alpha)); + for (int i = num_fragments - 1; i >= 0; i--) { + // color[n] = prev_color * (1 - new_alpha) + new_alpha * new_color; + // alpha[n] = prev_alpha * (1 - new_alpha) + new_alpha; + auto prev_alpha = i > 0 ? accum_alpha[i - 1] : first_alpha; + auto prev_color = i > 0 ? accum_color[i - 1] : first_color; + auto d_prev_alpha = d_curr_alpha * (1.f - fragments[i].alpha); + auto d_alpha_i = d_curr_alpha * (1.f - prev_alpha); + d_alpha_i += sum(d_curr_color * (fragments[i].color - prev_color)); + auto d_prev_color = d_curr_color * (1 - fragments[i].alpha); + auto d_color_i = d_curr_color * fragments[i].alpha; + auto group_id = fragments[i].group_id; + if (fragments[i].is_stroke) { + d_sample_color(scene.shape_groups[group_id].stroke_color_type, + scene.shape_groups[group_id].stroke_color, + pt, + Vector4f{d_color_i[0], d_color_i[1], d_color_i[2], d_alpha_i}, + scene.d_shape_groups[group_id].stroke_color, + d_translation); + } else { + d_sample_color(scene.shape_groups[group_id].fill_color_type, + scene.shape_groups[group_id].fill_color, + pt, + Vector4f{d_color_i[0], d_color_i[1], d_color_i[2], d_alpha_i}, + scene.d_shape_groups[group_id].fill_color, + d_translation); + } + d_curr_color = d_prev_color; + d_curr_alpha = d_prev_alpha; + } + if (d_background_color != nullptr) { + d_background_color->x += d_curr_color.x; + d_background_color->y += d_curr_color.y; + d_background_color->z += d_curr_color.z; + d_background_color->w += d_curr_alpha; + } + } + return Vector4f{final_color[0], final_color[1], final_color[2], final_alpha}; +} + +DEVICE +float sample_distance(const SceneData &scene, + const Vector2f &screen_pt, + float weight, + const float *d_dist = nullptr, + float *d_translation = nullptr) { + // screen_pt is in screen space ([0, 1), [0, 1)), + // need to transform to canvas space + auto pt = screen_pt; + pt.x *= scene.canvas_width; + pt.y *= scene.canvas_height; + // for each shape + auto min_group_id = -1; + auto min_distance = 0.f; + auto min_shape_id = -1; + auto closest_pt = Vector2f{0, 0}; + auto min_path_info = ClosestPointPathInfo{-1, -1, 0}; + for (int group_id = scene.num_shape_groups - 1; group_id >= 0; group_id--) { + auto s = -1; + auto p = Vector2f{0, 0}; + ClosestPointPathInfo local_path_info; + auto d = infinity(); + if (compute_distance(scene, group_id, pt, infinity(), &s, &p, &local_path_info, &d)) { + if (min_group_id == -1 || d < min_distance) { + min_distance = d; + min_group_id = group_id; + min_shape_id = s; + closest_pt = p; + min_path_info = local_path_info; + } + } + } + if (min_group_id == -1) { + return min_distance; + } + min_distance *= weight; + auto inside = false; + const ShapeGroup &shape_group = scene.shape_groups[min_group_id]; + if (shape_group.fill_color != nullptr) { + inside = is_inside(scene, + min_group_id, + pt, + nullptr); + if (inside) { + min_distance = -min_distance; + } + } + assert((min_group_id >= 0 && min_shape_id >= 0) || scene.num_shape_groups == 0); + if (d_dist != nullptr) { + auto d_abs_dist = inside ? -(*d_dist) : (*d_dist); + const ShapeGroup &shape_group = scene.shape_groups[min_group_id]; + const Shape &shape = scene.shapes[min_shape_id]; + ShapeGroup &d_shape_group = scene.d_shape_groups[min_group_id]; + Shape &d_shape = scene.d_shapes[min_shape_id]; + d_compute_distance(shape_group.canvas_to_shape, + shape_group.shape_to_canvas, + shape, + pt, + closest_pt, + min_path_info, + d_abs_dist, + d_shape_group.shape_to_canvas, + d_shape, + d_translation); + } + return min_distance; +} + +// Gather d_color from d_image inside the filter kernel, normalize by +// weight_image. +DEVICE +Vector4f gather_d_color(const Filter &filter, + const float *d_color_image, + const float *weight_image, + int width, + int height, + const Vector2f &pt) { + auto x = int(pt.x); + auto y = int(pt.y); + auto radius = filter.radius; + assert(radius > 0); + auto ri = (int)ceil(radius); + auto d_color = Vector4f{0, 0, 0, 0}; + for (int dy = -ri; dy <= ri; dy++) { + for (int dx = -ri; dx <= ri; dx++) { + auto xx = x + dx; + auto yy = y + dy; + if (xx >= 0 && xx < width && yy >= 0 && yy < height) { + auto xc = xx + 0.5f; + auto yc = yy + 0.5f; + auto filter_weight = + compute_filter_weight(filter, xc - pt.x, yc - pt.y); + // pixel = \sum weight * color / \sum weight + auto weight_sum = weight_image[yy * width + xx]; + if (weight_sum > 0) { + d_color += (filter_weight / weight_sum) * Vector4f{ + d_color_image[4 * (yy * width + xx) + 0], + d_color_image[4 * (yy * width + xx) + 1], + d_color_image[4 * (yy * width + xx) + 2], + d_color_image[4 * (yy * width + xx) + 3], + }; + } + } + } + } + return d_color; +} + +DEVICE +float smoothstep(float d) { + auto t = clamp((d + 1.f) / 2.f, 0.f, 1.f); + return t * t * (3 - 2 * t); +} + +DEVICE +float d_smoothstep(float d, float d_ret) { + if (d < -1.f || d > 1.f) { + return 0.f; + } + auto t = (d + 1.f) / 2.f; + // ret = t * t * (3 - 2 * t) + // = 3 * t * t - 2 * t * t * t + auto d_t = d_ret * (6 * t - 6 * t * t); + return d_t / 2.f; +} + +DEVICE +Vector4f sample_color_prefiltered(const SceneData &scene, + const Vector4f *background_color, + const Vector2f &screen_pt, + const Vector4f *d_color = nullptr, + Vector4f *d_background_color = nullptr, + float *d_translation = nullptr) { + // screen_pt is in screen space ([0, 1), [0, 1)), + // need to transform to canvas space + auto pt = screen_pt; + pt.x *= scene.canvas_width; + pt.y *= scene.canvas_height; + constexpr auto max_hit_shapes = 64; + constexpr auto max_bvh_stack_size = 64; + PrefilterFragment fragments[max_hit_shapes]; + int bvh_stack[max_bvh_stack_size]; + auto stack_size = 0; + auto num_fragments = 0; + bvh_stack[stack_size++] = 2 * scene.num_shape_groups - 2; + while (stack_size > 0) { + const BVHNode &node = scene.bvh_nodes[bvh_stack[--stack_size]]; + if (node.child1 < 0) { + // leaf + auto group_id = node.child0; + const ShapeGroup &shape_group = scene.shape_groups[group_id]; + if (shape_group.stroke_color != nullptr) { + auto min_shape_id = -1; + auto closest_pt = Vector2f{0, 0}; + auto local_path_info = ClosestPointPathInfo{-1, -1, 0}; + auto d = infinity(); + compute_distance(scene, group_id, pt, infinity(), + &min_shape_id, &closest_pt, &local_path_info, &d); + assert(min_shape_id != -1); + const auto &shape = scene.shapes[min_shape_id]; + auto w = smoothstep(fabs(d) + shape.stroke_width) - + smoothstep(fabs(d) - shape.stroke_width); + if (w > 0) { + auto color_alpha = sample_color(shape_group.stroke_color_type, + shape_group.stroke_color, + pt); + color_alpha[3] *= w; + + PrefilterFragment f; + f.color = Vector3f{color_alpha[0], color_alpha[1], color_alpha[2]}; + f.alpha = color_alpha[3]; + f.group_id = group_id; + f.shape_id = min_shape_id; + f.distance = d; + f.closest_pt = closest_pt; + f.is_stroke = true; + f.path_info = local_path_info; + f.within_distance = true; + assert(num_fragments < max_hit_shapes); + fragments[num_fragments++] = f; + } + } + if (shape_group.fill_color != nullptr) { + auto min_shape_id = -1; + auto closest_pt = Vector2f{0, 0}; + auto local_path_info = ClosestPointPathInfo{-1, -1, 0}; + auto d = infinity(); + auto found = compute_distance(scene, + group_id, + pt, + 1.f, + &min_shape_id, + &closest_pt, + &local_path_info, + &d); + auto inside = is_inside(scene, group_id, pt, nullptr); + if (found || inside) { + if (!inside) { + d = -d; + } + auto w = smoothstep(d); + if (w > 0) { + auto color_alpha = sample_color(shape_group.fill_color_type, + shape_group.fill_color, + pt); + color_alpha[3] *= w; + + PrefilterFragment f; + f.color = Vector3f{color_alpha[0], color_alpha[1], color_alpha[2]}; + f.alpha = color_alpha[3]; + f.group_id = group_id; + f.shape_id = min_shape_id; + f.distance = d; + f.closest_pt = closest_pt; + f.is_stroke = false; + f.path_info = local_path_info; + f.within_distance = found; + assert(num_fragments < max_hit_shapes); + fragments[num_fragments++] = f; + } + } + } + } else { + assert(node.child0 >= 0 && node.child1 >= 0); + const AABB &b0 = scene.bvh_nodes[node.child0].box; + if (inside(b0, pt, scene.bvh_nodes[node.child0].max_radius)) { + bvh_stack[stack_size++] = node.child0; + } + const AABB &b1 = scene.bvh_nodes[node.child1].box; + if (inside(b1, pt, scene.bvh_nodes[node.child1].max_radius)) { + bvh_stack[stack_size++] = node.child1; + } + assert(stack_size <= max_bvh_stack_size); + } + } + if (num_fragments <= 0) { + if (background_color != nullptr) { + if (d_background_color != nullptr) { + *d_background_color = *d_color; + } + return *background_color; + } + return Vector4f{0, 0, 0, 0}; + } + // Sort the fragments from back to front (i.e. increasing order of group id) + // https://github.com/frigaut/yorick-imutil/blob/master/insort.c#L37 + for (int i = 1; i < num_fragments; i++) { + auto j = i; + auto temp = fragments[j]; + while (j > 0 && fragments[j - 1].group_id > temp.group_id) { + fragments[j] = fragments[j - 1]; + j--; + } + fragments[j] = temp; + } + // Blend the color + Vector3f accum_color[max_hit_shapes]; + float accum_alpha[max_hit_shapes]; + auto first_alpha = 0.f; + auto first_color = Vector3f{0, 0, 0}; + if (background_color != nullptr) { + first_alpha = background_color->w; + first_color = Vector3f{background_color->x, + background_color->y, + background_color->z}; + } + for (int i = 0; i < num_fragments; i++) { + const PrefilterFragment &fragment = fragments[i]; + auto new_color = fragment.color; + auto new_alpha = fragment.alpha; + auto prev_alpha = i > 0 ? accum_alpha[i - 1] : first_alpha; + auto prev_color = i > 0 ? accum_color[i - 1] : first_color; + // prev_color is alpha premultiplied, don't need to multiply with + // prev_alpha + accum_color[i] = prev_color * (1 - new_alpha) + new_alpha * new_color; + accum_alpha[i] = prev_alpha * (1 - new_alpha) + new_alpha; + } + auto final_color = accum_color[num_fragments - 1]; + auto final_alpha = accum_alpha[num_fragments - 1]; + if (final_alpha > 1e-6f) { + final_color /= final_alpha; + } + assert(isfinite(final_color)); + assert(isfinite(final_alpha)); + if (d_color != nullptr) { + // Backward pass + auto d_final_color = Vector3f{(*d_color)[0], (*d_color)[1], (*d_color)[2]}; + auto d_final_alpha = (*d_color)[3]; + auto d_curr_color = d_final_color; + auto d_curr_alpha = d_final_alpha; + if (final_alpha > 1e-6f) { + // final_color = curr_color / final_alpha + d_curr_color = d_final_color / final_alpha; + d_curr_alpha -= sum(d_final_color * final_color) / final_alpha; + } + assert(isfinite(*d_color)); + assert(isfinite(d_curr_color)); + assert(isfinite(d_curr_alpha)); + for (int i = num_fragments - 1; i >= 0; i--) { + // color[n] = prev_color * (1 - new_alpha) + new_alpha * new_color; + // alpha[n] = prev_alpha * (1 - new_alpha) + new_alpha; + auto prev_alpha = i > 0 ? accum_alpha[i - 1] : first_alpha; + auto prev_color = i > 0 ? accum_color[i - 1] : first_color; + auto d_prev_alpha = d_curr_alpha * (1.f - fragments[i].alpha); + auto d_alpha_i = d_curr_alpha * (1.f - prev_alpha); + d_alpha_i += sum(d_curr_color * (fragments[i].color - prev_color)); + auto d_prev_color = d_curr_color * (1 - fragments[i].alpha); + auto d_color_i = d_curr_color * fragments[i].alpha; + auto group_id = fragments[i].group_id; + if (fragments[i].is_stroke) { + const auto &shape = scene.shapes[fragments[i].shape_id]; + auto d = fragments[i].distance; + auto abs_d_plus_width = fabs(d) + shape.stroke_width; + auto abs_d_minus_width = fabs(d) - shape.stroke_width; + auto w = smoothstep(abs_d_plus_width) - + smoothstep(abs_d_minus_width); + if (w != 0) { + auto d_w = w > 0 ? (fragments[i].alpha / w) * d_alpha_i : 0.f; + d_alpha_i *= w; + + // Backprop to color + d_sample_color(scene.shape_groups[group_id].stroke_color_type, + scene.shape_groups[group_id].stroke_color, + pt, + Vector4f{d_color_i[0], d_color_i[1], d_color_i[2], d_alpha_i}, + scene.d_shape_groups[group_id].stroke_color, + d_translation); + + auto d_abs_d_plus_width = d_smoothstep(abs_d_plus_width, d_w); + auto d_abs_d_minus_width = -d_smoothstep(abs_d_minus_width, d_w); + + auto d_d = d_abs_d_plus_width + d_abs_d_minus_width; + if (d < 0) { + d_d = -d_d; + } + auto d_stroke_width = d_abs_d_plus_width - d_abs_d_minus_width; + + const auto &shape_group = scene.shape_groups[group_id]; + ShapeGroup &d_shape_group = scene.d_shape_groups[group_id]; + Shape &d_shape = scene.d_shapes[fragments[i].shape_id]; + if (fabs(d_d) > 1e-10f) { + d_compute_distance(shape_group.canvas_to_shape, + shape_group.shape_to_canvas, + shape, + pt, + fragments[i].closest_pt, + fragments[i].path_info, + d_d, + d_shape_group.shape_to_canvas, + d_shape, + d_translation); + } + atomic_add(&d_shape.stroke_width, d_stroke_width); + } + } else { + const auto &shape = scene.shapes[fragments[i].shape_id]; + auto d = fragments[i].distance; + auto w = smoothstep(d); + if (w != 0) { + // color_alpha[3] = color_alpha[3] * w; + auto d_w = w > 0 ? (fragments[i].alpha / w) * d_alpha_i : 0.f; + d_alpha_i *= w; + + d_sample_color(scene.shape_groups[group_id].fill_color_type, + scene.shape_groups[group_id].fill_color, + pt, + Vector4f{d_color_i[0], d_color_i[1], d_color_i[2], d_alpha_i}, + scene.d_shape_groups[group_id].fill_color, + d_translation); + + // w = smoothstep(d) + auto d_d = d_smoothstep(d, d_w); + if (d < 0) { + d_d = -d_d; + } + + const auto &shape_group = scene.shape_groups[group_id]; + ShapeGroup &d_shape_group = scene.d_shape_groups[group_id]; + Shape &d_shape = scene.d_shapes[fragments[i].shape_id]; + if (fabs(d_d) > 1e-10f && fragments[i].within_distance) { + d_compute_distance(shape_group.canvas_to_shape, + shape_group.shape_to_canvas, + shape, + pt, + fragments[i].closest_pt, + fragments[i].path_info, + d_d, + d_shape_group.shape_to_canvas, + d_shape, + d_translation); + } + } + } + d_curr_color = d_prev_color; + d_curr_alpha = d_prev_alpha; + } + if (d_background_color != nullptr) { + d_background_color->x += d_curr_color.x; + d_background_color->y += d_curr_color.y; + d_background_color->z += d_curr_color.z; + d_background_color->w += d_curr_alpha; + } + } + return Vector4f{final_color[0], final_color[1], final_color[2], final_alpha}; +} + +struct weight_kernel { + DEVICE void operator()(int idx) { + auto rng_state = init_pcg32(idx, seed); + // height * width * num_samples_y * num_samples_x + auto sx = idx % num_samples_x; + auto sy = (idx / num_samples_x) % num_samples_y; + auto x = (idx / (num_samples_x * num_samples_y)) % width; + auto y = (idx / (num_samples_x * num_samples_y * width)); + assert(y < height); + auto rx = next_pcg32_float(&rng_state); + auto ry = next_pcg32_float(&rng_state); + if (use_prefiltering) { + rx = ry = 0.5f; + } + auto pt = Vector2f{x + ((float)sx + rx) / num_samples_x, + y + ((float)sy + ry) / num_samples_y}; + auto radius = scene.filter->radius; + assert(radius >= 0); + auto ri = (int)ceil(radius); + for (int dy = -ri; dy <= ri; dy++) { + for (int dx = -ri; dx <= ri; dx++) { + auto xx = x + dx; + auto yy = y + dy; + if (xx >= 0 && xx < width && yy >= 0 && yy < height) { + auto xc = xx + 0.5f; + auto yc = yy + 0.5f; + auto filter_weight = compute_filter_weight(*scene.filter, + xc - pt.x, + yc - pt.y); + atomic_add(weight_image[yy * width + xx], filter_weight); + } + } + } + } + + SceneData scene; + float *weight_image; + int width; + int height; + int num_samples_x; + int num_samples_y; + uint64_t seed; + bool use_prefiltering; +}; + +// We use a "mega kernel" for rendering +struct render_kernel { + DEVICE void operator()(int idx) { + // height * width * num_samples_y * num_samples_x + auto pt = Vector2f{0, 0}; + auto x = 0; + auto y = 0; + if (eval_positions == nullptr) { + auto rng_state = init_pcg32(idx, seed); + auto sx = idx % num_samples_x; + auto sy = (idx / num_samples_x) % num_samples_y; + x = (idx / (num_samples_x * num_samples_y)) % width; + y = (idx / (num_samples_x * num_samples_y * width)); + assert(x < width && y < height); + auto rx = next_pcg32_float(&rng_state); + auto ry = next_pcg32_float(&rng_state); + if (use_prefiltering) { + rx = ry = 0.5f; + } + pt = Vector2f{x + ((float)sx + rx) / num_samples_x, + y + ((float)sy + ry) / num_samples_y}; + } else { + pt = Vector2f{eval_positions[2 * idx], + eval_positions[2 * idx + 1]}; + x = int(pt.x); + y = int(pt.y); + } + + // normalize pt to [0, 1] + auto npt = pt; + npt.x /= width; + npt.y /= height; + auto num_samples = num_samples_x * num_samples_y; + if (render_image != nullptr || d_render_image != nullptr) { + Vector4f d_color = Vector4f{0, 0, 0, 0}; + if (d_render_image != nullptr) { + // Gather d_color from d_render_image inside the filter kernel + // normalize using weight_image + d_color = gather_d_color(*scene.filter, + d_render_image, + weight_image, + width, + height, + pt); + } + auto color = Vector4f{0, 0, 0, 0}; + if (use_prefiltering) { + color = sample_color_prefiltered(scene, + background_image != nullptr ? (const Vector4f*)&background_image[4 * ((y * width) + x)] : nullptr, + npt, + d_render_image != nullptr ? &d_color : nullptr, + d_background_image != nullptr ? (Vector4f*)&d_background_image[4 * ((y * width) + x)] : nullptr, + d_translation != nullptr ? &d_translation[2 * (y * width + x)] : nullptr); + } else { + color = sample_color(scene, + background_image != nullptr ? (const Vector4f*)&background_image[4 * ((y * width) + x)] : nullptr, + npt, + d_render_image != nullptr ? &d_color : nullptr, + nullptr, + d_background_image != nullptr ? (Vector4f*)&d_background_image[4 * ((y * width) + x)] : nullptr, + d_translation != nullptr ? &d_translation[2 * (y * width + x)] : nullptr); + } + assert(isfinite(color)); + // Splat color onto render_image + auto radius = scene.filter->radius; + assert(radius >= 0); + auto ri = (int)ceil(radius); + for (int dy = -ri; dy <= ri; dy++) { + for (int dx = -ri; dx <= ri; dx++) { + auto xx = x + dx; + auto yy = y + dy; + if (xx >= 0 && xx < width && yy >= 0 && yy < height && + weight_image[yy * width + xx] > 0) { + auto weight_sum = weight_image[yy * width + xx]; + auto xc = xx + 0.5f; + auto yc = yy + 0.5f; + auto filter_weight = compute_filter_weight(*scene.filter, + xc - pt.x, + yc - pt.y); + auto weighted_color = filter_weight * color / weight_sum; + if (render_image != nullptr) { + atomic_add(render_image[4 * (yy * width + xx) + 0], + weighted_color[0]); + atomic_add(render_image[4 * (yy * width + xx) + 1], + weighted_color[1]); + atomic_add(render_image[4 * (yy * width + xx) + 2], + weighted_color[2]); + atomic_add(render_image[4 * (yy * width + xx) + 3], + weighted_color[3]); + } + if (d_render_image != nullptr) { + // Backprop to filter_weight + // pixel = \sum weight * color / \sum weight + auto d_pixel = Vector4f{ + d_render_image[4 * (yy * width + xx) + 0], + d_render_image[4 * (yy * width + xx) + 1], + d_render_image[4 * (yy * width + xx) + 2], + d_render_image[4 * (yy * width + xx) + 3], + }; + auto d_weight = + (dot(d_pixel, color) * weight_sum - + filter_weight * dot(d_pixel, color) * (weight_sum - filter_weight)) / + square(weight_sum); + d_compute_filter_weight(*scene.filter, + xc - pt.x, + yc - pt.y, + d_weight, + scene.d_filter); + } + } + } + } + } + if (sdf_image != nullptr || d_sdf_image != nullptr) { + float d_dist = 0.f; + if (d_sdf_image != nullptr) { + if (eval_positions == nullptr) { + d_dist = d_sdf_image[y * width + x]; + } else { + d_dist = d_sdf_image[idx]; + } + } + auto weight = eval_positions == nullptr ? 1.f / num_samples : 1.f; + auto dist = sample_distance(scene, npt, weight, + d_sdf_image != nullptr ? &d_dist : nullptr, + d_translation != nullptr ? &d_translation[2 * (y * width + x)] : nullptr); + if (sdf_image != nullptr) { + if (eval_positions == nullptr) { + atomic_add(sdf_image[y * width + x], dist); + } else { + atomic_add(sdf_image[idx], dist); + } + } + } + } + + SceneData scene; + float *background_image; + float *render_image; + float *weight_image; + float *sdf_image; + float *d_background_image; + float *d_render_image; + float *d_sdf_image; + float *d_translation; + int width; + int height; + int num_samples_x; + int num_samples_y; + uint64_t seed; + bool use_prefiltering; + float *eval_positions; +}; + +struct BoundarySample { + Vector2f pt; + Vector2f local_pt; + Vector2f normal; + int shape_group_id; + int shape_id; + float t; + BoundaryData data; + float pdf; +}; + +struct sample_boundary_kernel { + DEVICE void operator()(int idx) { + boundary_samples[idx].pt = Vector2f{0, 0}; + boundary_samples[idx].shape_id = -1; + boundary_ids[idx] = idx; + morton_codes[idx] = 0; + + auto rng_state = init_pcg32(idx, seed); + auto u = next_pcg32_float(&rng_state); + // Sample a shape + auto sample_id = sample(scene.sample_shapes_cdf, + scene.num_total_shapes, + u); + assert(sample_id >= 0 && sample_id < scene.num_total_shapes); + auto shape_id = scene.sample_shape_id[sample_id]; + assert(shape_id >= 0 && shape_id < scene.num_shapes); + auto shape_group_id = scene.sample_group_id[sample_id]; + assert(shape_group_id >= 0 && shape_group_id < scene.num_shape_groups); + auto shape_pmf = scene.sample_shapes_pmf[shape_id]; + if (shape_pmf <= 0) { + return; + } + // Sample a point on the boundary of the shape + auto boundary_pdf = 0.f; + auto normal = Vector2f{0, 0}; + auto t = next_pcg32_float(&rng_state); + BoundaryData boundary_data; + const ShapeGroup &shape_group = scene.shape_groups[shape_group_id]; + auto local_boundary_pt = sample_boundary( + scene, shape_group_id, shape_id, + t, normal, boundary_pdf, boundary_data); + if (boundary_pdf <= 0) { + return; + } + + // local_boundary_pt & normal are in shape's local space, + // transform them to canvas space + auto boundary_pt = xform_pt(shape_group.shape_to_canvas, local_boundary_pt); + normal = xform_normal(shape_group.canvas_to_shape, normal); + // Normalize boundary_pt to [0, 1) + boundary_pt.x /= scene.canvas_width; + boundary_pt.y /= scene.canvas_height; + + boundary_samples[idx].pt = boundary_pt; + boundary_samples[idx].local_pt = local_boundary_pt; + boundary_samples[idx].normal = normal; + boundary_samples[idx].shape_group_id = shape_group_id; + boundary_samples[idx].shape_id = shape_id; + boundary_samples[idx].t = t; + boundary_samples[idx].data = boundary_data; + boundary_samples[idx].pdf = shape_pmf * boundary_pdf; + TVector2 p_i{boundary_pt.x * 1023, boundary_pt.y * 1023}; + morton_codes[idx] = (expand_bits(p_i.x) << 1u) | + (expand_bits(p_i.y) << 0u); + } + + SceneData scene; + uint64_t seed; + BoundarySample *boundary_samples; + int *boundary_ids; + uint32_t *morton_codes; +}; + +struct render_edge_kernel { + DEVICE void operator()(int idx) { + auto bid = boundary_ids[idx]; + if (boundary_samples[bid].shape_id == -1) { + return; + } + auto boundary_pt = boundary_samples[bid].pt; + auto local_boundary_pt = boundary_samples[bid].local_pt; + auto normal = boundary_samples[bid].normal; + auto shape_group_id = boundary_samples[bid].shape_group_id; + auto shape_id = boundary_samples[bid].shape_id; + auto t = boundary_samples[bid].t; + auto boundary_data = boundary_samples[bid].data; + auto pdf = boundary_samples[bid].pdf; + + const ShapeGroup &shape_group = scene.shape_groups[shape_group_id]; + + auto bx = int(boundary_pt.x * width); + auto by = int(boundary_pt.y * height); + if (bx < 0 || bx >= width || by < 0 || by >= height) { + return; + } + + // Sample the two sides of the boundary + auto inside_query = EdgeQuery{shape_group_id, shape_id, false}; + auto outside_query = EdgeQuery{shape_group_id, shape_id, false}; + auto color_inside = sample_color(scene, + background_image != nullptr ? (const Vector4f *)&background_image[4 * ((by * width) + bx)] : nullptr, + boundary_pt - 1e-4f * normal, + nullptr, &inside_query); + auto color_outside = sample_color(scene, + background_image != nullptr ? (const Vector4f *)&background_image[4 * ((by * width) + bx)] : nullptr, + boundary_pt + 1e-4f * normal, + nullptr, &outside_query); + if (!inside_query.hit && !outside_query.hit) { + // occluded + return; + } + if (!inside_query.hit) { + normal = -normal; + swap_(inside_query, outside_query); + swap_(color_inside, color_outside); + } + // Boundary point in screen space + auto sboundary_pt = boundary_pt; + sboundary_pt.x *= width; + sboundary_pt.y *= height; + auto d_color = gather_d_color(*scene.filter, + d_render_image, + weight_image, + width, + height, + sboundary_pt); + // Normalization factor + d_color /= float(scene.canvas_width * scene.canvas_height); + + assert(isfinite(d_color)); + assert(isfinite(pdf) && pdf > 0); + auto contrib = dot(color_inside - color_outside, d_color) / pdf; + ShapeGroup &d_shape_group = scene.d_shape_groups[shape_group_id]; + accumulate_boundary_gradient(scene.shapes[shape_id], + contrib, t, normal, boundary_data, scene.d_shapes[shape_id], + shape_group.shape_to_canvas, local_boundary_pt, d_shape_group.shape_to_canvas); + // Don't need to backprop to filter weights: + // \int f'(x) g(x) dx doesn't contain discontinuities + // if f is continuous, even if g is discontinuous + if (d_translation != nullptr) { + // According to Reynold transport theorem, + // the Jacobian of the boundary integral is dot(velocity, normal) + // The velocity of the object translating x is (1, 0) + // The velocity of the object translating y is (0, 1) + atomic_add(&d_translation[2 * (by * width + bx) + 0], normal.x * contrib); + atomic_add(&d_translation[2 * (by * width + bx) + 1], normal.y * contrib); + } + } + + SceneData scene; + const float *background_image; + const BoundarySample *boundary_samples; + const int *boundary_ids; + float *weight_image; + float *d_render_image; + float *d_translation; + int width; + int height; + int num_samples_x; + int num_samples_y; +}; + +void render(std::shared_ptr scene, + ptr background_image, + ptr render_image, + ptr render_sdf, + int width, + int height, + int num_samples_x, + int num_samples_y, + uint64_t seed, + ptr d_background_image, + ptr d_render_image, + ptr d_render_sdf, + ptr d_translation, + bool use_prefiltering, + ptr eval_positions, + int num_eval_positions) { +#ifdef __NVCC__ + int old_device_id = -1; + if (scene->use_gpu) { + checkCuda(cudaGetDevice(&old_device_id)); + if (scene->gpu_index != -1) { + checkCuda(cudaSetDevice(scene->gpu_index)); + } + } +#endif + parallel_init(); + + float *weight_image = nullptr; + // Allocate and zero the weight image + if (scene->use_gpu) { +#ifdef __CUDACC__ + if (eval_positions.get() == nullptr) { + checkCuda(cudaMallocManaged(&weight_image, width * height * sizeof(float))); + cudaMemset(weight_image, 0, width * height * sizeof(float)); + } +#else + assert(false); +#endif + } else { + if (eval_positions.get() == nullptr) { + weight_image = (float*)malloc(width * height * sizeof(float)); + memset(weight_image, 0, width * height * sizeof(float)); + } + } + + if (render_image.get() != nullptr || d_render_image.get() != nullptr || + render_sdf.get() != nullptr || d_render_sdf.get() != nullptr) { + if (weight_image != nullptr) { + parallel_for(weight_kernel{ + get_scene_data(*scene.get()), + weight_image, + width, + height, + num_samples_x, + num_samples_y, + seed + }, width * height * num_samples_x * num_samples_y, scene->use_gpu); + } + + auto num_samples = eval_positions.get() == nullptr ? + width * height * num_samples_x * num_samples_y : num_eval_positions; + parallel_for(render_kernel{ + get_scene_data(*scene.get()), + background_image.get(), + render_image.get(), + weight_image, + render_sdf.get(), + d_background_image.get(), + d_render_image.get(), + d_render_sdf.get(), + d_translation.get(), + width, + height, + num_samples_x, + num_samples_y, + seed, + use_prefiltering, + eval_positions.get() + }, num_samples, scene->use_gpu); + } + + // Boundary sampling + if (!use_prefiltering && d_render_image.get() != nullptr) { + auto num_samples = width * height * num_samples_x * num_samples_y; + BoundarySample *boundary_samples = nullptr; + int *boundary_ids = nullptr; // for sorting + uint32_t *morton_codes = nullptr; // for sorting + // Allocate boundary samples + if (scene->use_gpu) { +#ifdef __CUDACC__ + checkCuda(cudaMallocManaged(&boundary_samples, + num_samples * sizeof(BoundarySample))); + checkCuda(cudaMallocManaged(&boundary_ids, + num_samples * sizeof(int))); + checkCuda(cudaMallocManaged(&morton_codes, + num_samples * sizeof(uint32_t))); +#else + assert(false); + #endif + } else { + boundary_samples = (BoundarySample*)malloc( + num_samples * sizeof(BoundarySample)); + boundary_ids = (int*)malloc( + num_samples * sizeof(int)); + morton_codes = (uint32_t*)malloc( + num_samples * sizeof(uint32_t)); + } + + // Edge sampling + // We sort the boundary samples for better thread coherency + parallel_for(sample_boundary_kernel{ + get_scene_data(*scene.get()), + seed, + boundary_samples, + boundary_ids, + morton_codes + }, num_samples, scene->use_gpu); + if (scene->use_gpu) { + thrust::sort_by_key(thrust::device, morton_codes, morton_codes + num_samples, boundary_ids); + } else { + // Don't need to sort for CPU, we are not using SIMD hardware anyway. + // thrust::sort_by_key(thrust::host, morton_codes, morton_codes + num_samples, boundary_ids); + } + parallel_for(render_edge_kernel{ + get_scene_data(*scene.get()), + background_image.get(), + boundary_samples, + boundary_ids, + weight_image, + d_render_image.get(), + d_translation.get(), + width, + height, + num_samples_x, + num_samples_y + }, num_samples, scene->use_gpu); + if (scene->use_gpu) { +#ifdef __CUDACC__ + checkCuda(cudaFree(boundary_samples)); + checkCuda(cudaFree(boundary_ids)); + checkCuda(cudaFree(morton_codes)); +#else + assert(false); +#endif + } else { + free(boundary_samples); + free(boundary_ids); + free(morton_codes); + } + } + + // Clean up weight image + if (scene->use_gpu) { +#ifdef __CUDACC__ + checkCuda(cudaFree(weight_image)); +#else + assert(false); +#endif + } else { + free(weight_image); + } + + if (scene->use_gpu) { + cuda_synchronize(); + } + + parallel_cleanup(); +#ifdef __NVCC__ + if (old_device_id != -1) { + checkCuda(cudaSetDevice(old_device_id)); + } +#endif +} + +PYBIND11_MODULE(diffvg, m) { + m.doc() = "Differential Vector Graphics"; + + py::class_>(m, "void_ptr") + .def(py::init()) + .def("as_size_t", &ptr::as_size_t); + py::class_>(m, "float_ptr") + .def(py::init()); + py::class_>(m, "int_ptr") + .def(py::init()); + + py::class_(m, "Vector2f") + .def(py::init()) + .def_readwrite("x", &Vector2f::x) + .def_readwrite("y", &Vector2f::y); + + py::class_(m, "Vector3f") + .def(py::init()) + .def_readwrite("x", &Vector3f::x) + .def_readwrite("y", &Vector3f::y) + .def_readwrite("z", &Vector3f::z); + + py::class_(m, "Vector4f") + .def(py::init()) + .def_readwrite("x", &Vector4f::x) + .def_readwrite("y", &Vector4f::y) + .def_readwrite("z", &Vector4f::z) + .def_readwrite("w", &Vector4f::w); + + py::enum_(m, "ShapeType") + .value("circle", ShapeType::Circle) + .value("ellipse", ShapeType::Ellipse) + .value("path", ShapeType::Path) + .value("rect", ShapeType::Rect); + + py::class_(m, "Circle") + .def(py::init()) + .def("get_ptr", &Circle::get_ptr) + .def_readonly("radius", &Circle::radius) + .def_readonly("center", &Circle::center); + + py::class_(m, "Ellipse") + .def(py::init()) + .def("get_ptr", &Ellipse::get_ptr) + .def_readonly("radius", &Ellipse::radius) + .def_readonly("center", &Ellipse::center); + + py::class_(m, "Path") + .def(py::init, ptr, ptr, int, int, bool, bool>()) + .def("get_ptr", &Path::get_ptr) + .def("has_thickness", &Path::has_thickness) + .def("copy_to", &Path::copy_to) + .def_readonly("num_points", &Path::num_points); + + py::class_(m, "Rect") + .def(py::init()) + .def("get_ptr", &Rect::get_ptr) + .def_readonly("p_min", &Rect::p_min) + .def_readonly("p_max", &Rect::p_max); + + py::enum_(m, "ColorType") + .value("constant", ColorType::Constant) + .value("linear_gradient", ColorType::LinearGradient) + .value("radial_gradient", ColorType::RadialGradient); + + py::class_(m, "Constant") + .def(py::init()) + .def("get_ptr", &Constant::get_ptr) + .def_readonly("color", &Constant::color); + + py::class_(m, "LinearGradient") + .def(py::init, ptr>()) + .def("get_ptr", &LinearGradient::get_ptr) + .def("copy_to", &LinearGradient::copy_to) + .def_readonly("begin", &LinearGradient::begin) + .def_readonly("end", &LinearGradient::end) + .def_readonly("num_stops", &LinearGradient::num_stops); + + py::class_(m, "RadialGradient") + .def(py::init, ptr>()) + .def("get_ptr", &RadialGradient::get_ptr) + .def("copy_to", &RadialGradient::copy_to) + .def_readonly("center", &RadialGradient::center) + .def_readonly("radius", &RadialGradient::radius) + .def_readonly("num_stops", &RadialGradient::num_stops); + + py::class_(m, "Shape") + .def(py::init, float>()) + .def("as_circle", &Shape::as_circle) + .def("as_ellipse", &Shape::as_ellipse) + .def("as_path", &Shape::as_path) + .def("as_rect", &Shape::as_rect) + .def_readonly("type", &Shape::type) + .def_readonly("stroke_width", &Shape::stroke_width); + + py::class_(m, "ShapeGroup") + .def(py::init, + int, + ColorType, + ptr, + ColorType, + ptr, + bool, + ptr>()) + .def("fill_color_as_constant", &ShapeGroup::fill_color_as_constant) + .def("fill_color_as_linear_gradient", &ShapeGroup::fill_color_as_linear_gradient) + .def("fill_color_as_radial_gradient", &ShapeGroup::fill_color_as_radial_gradient) + .def("stroke_color_as_constant", &ShapeGroup::stroke_color_as_constant) + .def("stroke_color_as_linear_gradient", &ShapeGroup::stroke_color_as_linear_gradient) + .def("stroke_color_as_radial_gradient", &ShapeGroup::fill_color_as_radial_gradient) + .def("has_fill_color", &ShapeGroup::has_fill_color) + .def("has_stroke_color", &ShapeGroup::has_stroke_color) + .def("copy_to", &ShapeGroup::copy_to) + .def_readonly("fill_color_type", &ShapeGroup::fill_color_type) + .def_readonly("stroke_color_type", &ShapeGroup::stroke_color_type); + + py::enum_(m, "FilterType") + .value("box", FilterType::Box) + .value("tent", FilterType::Tent) + .value("parabolic", FilterType::RadialParabolic) + .value("hann", FilterType::Hann); + + py::class_(m, "Filter") + .def(py::init()); + + py::class_>(m, "Scene") + .def(py::init &, + const std::vector &, + const Filter &, + bool, + int>()) + .def("get_d_shape", &Scene::get_d_shape) + .def("get_d_shape_group", &Scene::get_d_shape_group) + .def("get_d_filter_radius", &Scene::get_d_filter_radius) + .def_readonly("num_shapes", &Scene::num_shapes) + .def_readonly("num_shape_groups", &Scene::num_shape_groups); + + m.def("render", &render, ""); +} diff --git a/diffvg.h b/diffvg.h new file mode 100644 index 0000000..400e4dc --- /dev/null +++ b/diffvg.h @@ -0,0 +1,156 @@ +#pragma once + +#ifdef __NVCC__ + #define DEVICE __device__ __host__ +#else + #define DEVICE +#endif + +#ifndef __NVCC__ + #include + namespace { + inline float fmodf(float a, float b) { + return std::fmod(a, b); + } + inline double fmod(double a, double b) { + return std::fmod(a, b); + } + } + using std::isfinite; +#endif + +#ifndef M_PI +#define M_PI 3.14159265358979323846 +#endif + +#include +#include + +// We use Real for most of the internal computation. +// However, for PyTorch interfaces, Optix Prime and Embree queries +// we use float +using Real = float; + +template +DEVICE +inline T square(const T &x) { + return x * x; +} + +template +DEVICE +inline T cubic(const T &x) { + return x * x * x; +} + +template +DEVICE +inline T clamp(const T &v, const T &lo, const T &hi) { + if (v < lo) return lo; + else if (v > hi) return hi; + else return v; +} + +DEVICE +inline int modulo(int a, int b) { + auto r = a % b; + return (r < 0) ? r+b : r; +} + +DEVICE +inline float modulo(float a, float b) { + float r = ::fmodf(a, b); + return (r < 0.0f) ? r+b : r; +} + +DEVICE +inline double modulo(double a, double b) { + double r = ::fmod(a, b); + return (r < 0.0) ? r+b : r; +} + +template +DEVICE +inline T max(const T &a, const T &b) { + return a > b ? a : b; +} + +template +DEVICE +inline T min(const T &a, const T &b) { + return a < b ? a : b; +} + +/// Return ceil(x/y) for integers x and y +inline int idiv_ceil(int x, int y) { + return (x + y-1) / y; +} + +template +DEVICE +inline void swap_(T &a, T &b) { + T tmp = a; + a = b; + b = tmp; +} + +inline double log2(double x) { + return log(x) / log(Real(2)); +} + +template +DEVICE +inline T safe_acos(const T &x) { + if (x >= 1) return T(0); + else if(x <= -1) return T(M_PI); + return acos(x); +} + +// For Morton code computation. This can be made faster. +DEVICE +inline uint32_t expand_bits(uint32_t x) { + // Insert one zero after every bit given a 10-bit integer + constexpr uint64_t mask = 0x1u; + // We start from LSB (bit 31) + auto result = (x & (mask << 0u)); + result |= ((x & (mask << 1u)) << 1u); + result |= ((x & (mask << 2u)) << 2u); + result |= ((x & (mask << 3u)) << 3u); + result |= ((x & (mask << 4u)) << 4u); + result |= ((x & (mask << 5u)) << 5u); + result |= ((x & (mask << 6u)) << 6u); + result |= ((x & (mask << 7u)) << 7u); + result |= ((x & (mask << 8u)) << 8u); + result |= ((x & (mask << 9u)) << 9u); + return result; +} + +// DEVICE +// inline int clz(uint64_t x) { +// #ifdef __CUDA_ARCH__ +// return __clzll(x); +// #else +// // TODO: use _BitScanReverse in windows +// return x == 0 ? 64 : __builtin_clzll(x); +// #endif +// } + +// DEVICE +// inline int ffs(uint8_t x) { +// #ifdef __CUDA_ARCH__ +// return __ffs(x); +// #else +// // TODO: use _BitScanReverse in windows +// return __builtin_ffs(x); +// #endif +// } + +// DEVICE +// inline int popc(uint8_t x) { +// #ifdef __CUDA_ARCH__ +// return __popc(x); +// #else +// // TODO: use _popcnt in windows +// return __builtin_popcount(x); +// #endif +// } diff --git a/edge_query.h b/edge_query.h new file mode 100644 index 0000000..57f233a --- /dev/null +++ b/edge_query.h @@ -0,0 +1,7 @@ +#pragma once + +struct EdgeQuery { + int shape_group_id; + int shape_id; + bool hit; // Do we hit the specified shape_group_id & shape_id? +}; diff --git a/filter.h b/filter.h new file mode 100644 index 0000000..2dd0b62 --- /dev/null +++ b/filter.h @@ -0,0 +1,106 @@ +#pragma once + +#include "diffvg.h" +#include "atomic.h" + +enum class FilterType { + Box, + Tent, + RadialParabolic, // 4/3(1 - (d/r)) + Hann // https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows +}; + +struct Filter { + FilterType type; + float radius; +}; + +struct DFilter { + float radius; +}; + +DEVICE +inline +float compute_filter_weight(const Filter &filter, + float dx, + float dy) { + if (fabs(dx) > filter.radius || fabs(dy) > filter.radius) { + return 0; + } + if (filter.type == FilterType::Box) { + return 1.f / square(2 * filter.radius); + } else if (filter.type == FilterType::Tent) { + return (filter.radius - fabs(dx)) * (filter.radius - fabs(dy)) / + square(square(filter.radius)); + } else if (filter.type == FilterType::RadialParabolic) { + return (4.f / 3.f) * (1 - square(dx / filter.radius)) * + (4.f / 3.f) * (1 - square(dy / filter.radius)); + } else { + assert(filter.type == FilterType::Hann); + // normalize dx, dy to [0, 1] + auto ndx = (dx / (2*filter.radius)) + 0.5f; + auto ndy = (dy / (2*filter.radius)) + 0.5f; + // the normalization factor is R^2 + return 0.5f * (1.f - cos(float(2 * M_PI) * ndx)) * + 0.5f * (1.f - cos(float(2 * M_PI) * ndy)) / + square(filter.radius); + } +} + +DEVICE +inline +void d_compute_filter_weight(const Filter &filter, + float dx, + float dy, + float d_return, + DFilter *d_filter) { + if (filter.type == FilterType::Box) { + // return 1.f / square(2 * filter.radius); + atomic_add(d_filter->radius, + d_return * (-2) * 2 * filter.radius / cubic(2 * filter.radius)); + } else if (filter.type == FilterType::Tent) { + // return (filer.radius - fabs(dx)) * (filer.radius - fabs(dy)) / + // square(square(filter.radius)); + auto fx = filter.radius - fabs(dx); + auto fy = filter.radius - fabs(dy); + auto norm = 1 / square(filter.radius); + auto d_fx = d_return * fy * norm; + auto d_fy = d_return * fx * norm; + auto d_norm = d_return * fx * fy; + atomic_add(d_filter->radius, + d_fx + d_fy + (-4) * d_norm / pow(filter.radius, 5)); + } else if (filter.type == FilterType::RadialParabolic) { + // return (4.f / 3.f) * (1 - square(dx / filter.radius)) * + // (4.f / 3.f) * (1 - square(dy / filter.radius)); + // auto d_square_x = d_return * (-4.f / 3.f); + // auto d_square_y = d_return * (-4.f / 3.f); + auto r3 = filter.radius * filter.radius * filter.radius; + auto d_radius = -(2 * square(dx) + 2 * square(dy)) / r3; + atomic_add(d_filter->radius, d_radius); + } else { + assert(filter.type == FilterType::Hann); + // // normalize dx, dy to [0, 1] + // auto ndx = (dx / (2*filter.radius)) + 0.5f; + // auto ndy = (dy / (2*filter.radius)) + 0.5f; + // // the normalization factor is R^2 + // return 0.5f * (1.f - cos(float(2 * M_PI) * ndx)) * + // 0.5f * (1.f - cos(float(2 * M_PI) * ndy)) / + // square(filter.radius); + + // normalize dx, dy to [0, 1] + auto ndx = (dx / (2*filter.radius)) + 0.5f; + auto ndy = (dy / (2*filter.radius)) + 0.5f; + auto fx = 0.5f * (1.f - cos(float(2*M_PI) * ndx)); + auto fy = 0.5f * (1.f - cos(float(2*M_PI) * ndy)); + auto norm = 1 / square(filter.radius); + auto d_fx = d_return * fy * norm; + auto d_fy = d_return * fx * norm; + auto d_norm = d_return * fx * fy; + auto d_ndx = d_fx * 0.5f * sin(float(2*M_PI) * ndx) * float(2*M_PI); + auto d_ndy = d_fy * 0.5f * sin(float(2*M_PI) * ndy) * float(2*M_PI); + atomic_add(d_filter->radius, + d_ndx * (-2*dx / square(2*filter.radius)) + + d_ndy * (-2*dy / square(2*filter.radius)) + + (-2) * d_norm / cubic(filter.radius)); + } +} diff --git a/matrix.h b/matrix.h new file mode 100644 index 0000000..b53f484 --- /dev/null +++ b/matrix.h @@ -0,0 +1,544 @@ +#pragma once + +#include "diffvg.h" +#include "vector.h" +#include + +template +struct TMatrix3x3 { + DEVICE + TMatrix3x3() { + for (int i = 0; i < 3; i++) { + for (int j = 0; j < 3; j++) { + data[i][j] = T(0); + } + } + } + + template + DEVICE + TMatrix3x3(T2 *arr) { + data[0][0] = arr[0]; + data[0][1] = arr[1]; + data[0][2] = arr[2]; + data[1][0] = arr[3]; + data[1][1] = arr[4]; + data[1][2] = arr[5]; + data[2][0] = arr[6]; + data[2][1] = arr[7]; + data[2][2] = arr[8]; + } + DEVICE + TMatrix3x3(T v00, T v01, T v02, + T v10, T v11, T v12, + T v20, T v21, T v22) { + data[0][0] = v00; + data[0][1] = v01; + data[0][2] = v02; + data[1][0] = v10; + data[1][1] = v11; + data[1][2] = v12; + data[2][0] = v20; + data[2][1] = v21; + data[2][2] = v22; + } + + DEVICE + const T& operator()(int i, int j) const { + return data[i][j]; + } + DEVICE + T& operator()(int i, int j) { + return data[i][j]; + } + DEVICE + static TMatrix3x3 identity() { + TMatrix3x3 m(1, 0, 0, + 0, 1, 0, + 0, 0, 1); + return m; + } + + T data[3][3]; +}; + +using Matrix3x3 = TMatrix3x3; +using Matrix3x3f = TMatrix3x3; + +template +struct TMatrix4x4 { + DEVICE TMatrix4x4() { + for (int i = 0; i < 4; i++) { + for (int j = 0; j < 4; j++) { + data[i][j] = T(0); + } + } + } + + template + DEVICE TMatrix4x4(const T2 *arr) { + for (int i = 0; i < 4; i++) { + for (int j = 0; j < 4; j++) { + data[i][j] = (T)arr[i * 4 + j]; + } + } + } + + template + DEVICE TMatrix4x4(const TMatrix4x4 &m) { + for (int i = 0; i < 4; i++) { + for (int j = 0; j < 4; j++) { + data[i][j] = T(m.data[i][j]); + } + } + } + + template + DEVICE TMatrix4x4(T2 v00, T2 v01, T2 v02, T2 v03, + T2 v10, T2 v11, T2 v12, T2 v13, + T2 v20, T2 v21, T2 v22, T2 v23, + T2 v30, T2 v31, T2 v32, T2 v33) { + data[0][0] = (T)v00; + data[0][1] = (T)v01; + data[0][2] = (T)v02; + data[0][3] = (T)v03; + data[1][0] = (T)v10; + data[1][1] = (T)v11; + data[1][2] = (T)v12; + data[1][3] = (T)v13; + data[2][0] = (T)v20; + data[2][1] = (T)v21; + data[2][2] = (T)v22; + data[2][3] = (T)v23; + data[3][0] = (T)v30; + data[3][1] = (T)v31; + data[3][2] = (T)v32; + data[3][3] = (T)v33; + } + + DEVICE + const T& operator()(int i, int j) const { + return data[i][j]; + } + + DEVICE + T& operator()(int i, int j) { + return data[i][j]; + } + + DEVICE + static TMatrix4x4 identity() { + TMatrix4x4 m(1, 0, 0, 0, + 0, 1, 0, 0, + 0, 0, 1, 0, + 0, 0, 0, 1); + return m; + } + + T data[4][4]; +}; + +using Matrix4x4 = TMatrix4x4; +using Matrix4x4f = TMatrix4x4; + +template +DEVICE +inline auto operator+(const TMatrix3x3 &m0, const TMatrix3x3 &m1) -> TMatrix3x3 { + TMatrix3x3 m; + for (int i = 0; i < 3; i++) { + for (int j = 0; j < 3; j++) { + m(i, j) = m0(i, j) + m1(i, j); + } + } + return m; +} + +template +DEVICE +inline auto operator-(const TMatrix3x3 &m0, const TMatrix3x3 &m1) -> TMatrix3x3 { + TMatrix3x3 m; + for (int i = 0; i < 3; i++) { + for (int j = 0; j < 3; j++) { + m(i, j) = m0(i, j) - m1(i, j); + } + } + return m; +} + +template +DEVICE +inline auto operator*(const TMatrix3x3 &m0, const TMatrix3x3 &m1) -> TMatrix3x3 { + TMatrix3x3 ret; + for (int i = 0; i < 3; i++) { + for (int j = 0; j < 3; j++) { + ret(i, j) = T(0); + for (int k = 0; k < 3; k++) { + ret(i, j) += m0(i, k) * m1(k, j); + } + } + } + return ret; +} + +template +DEVICE +inline auto operator*(const TVector3 &v, const TMatrix3x3 &m) -> TVector3 { + TVector3 ret; + for (int i = 0; i < 3; i++) { + ret[i] = T(0); + for (int j = 0; j < 3; j++) { + ret[i] += v[j] * m(j, i); + } + } + return ret; +} + +template +DEVICE +inline auto operator*(const TMatrix3x3 &m, const TVector3 &v) -> TVector3 { + TVector3 ret; + for (int i = 0; i < 3; i++) { + ret[i] = 0.f; + for (int j = 0; j < 3; j++) { + ret[i] += m(i, j) * v[j]; + } + } + return ret; +} + +template +DEVICE +inline auto inverse(const TMatrix3x3 &m) -> TMatrix3x3 { + // computes the inverse of a matrix m + auto det = m(0, 0) * (m(1, 1) * m(2, 2) - m(2, 1) * m(1, 2)) - + m(0, 1) * (m(1, 0) * m(2, 2) - m(1, 2) * m(2, 0)) + + m(0, 2) * (m(1, 0) * m(2, 1) - m(1, 1) * m(2, 0)); + + auto invdet = 1 / det; + + auto m_inv = TMatrix3x3{}; + m_inv(0, 0) = (m(1, 1) * m(2, 2) - m(2, 1) * m(1, 2)) * invdet; + m_inv(0, 1) = (m(0, 2) * m(2, 1) - m(0, 1) * m(2, 2)) * invdet; + m_inv(0, 2) = (m(0, 1) * m(1, 2) - m(0, 2) * m(1, 1)) * invdet; + m_inv(1, 0) = (m(1, 2) * m(2, 0) - m(1, 0) * m(2, 2)) * invdet; + m_inv(1, 1) = (m(0, 0) * m(2, 2) - m(0, 2) * m(2, 0)) * invdet; + m_inv(1, 2) = (m(1, 0) * m(0, 2) - m(0, 0) * m(1, 2)) * invdet; + m_inv(2, 0) = (m(1, 0) * m(2, 1) - m(2, 0) * m(1, 1)) * invdet; + m_inv(2, 1) = (m(2, 0) * m(0, 1) - m(0, 0) * m(2, 1)) * invdet; + m_inv(2, 2) = (m(0, 0) * m(1, 1) - m(1, 0) * m(0, 1)) * invdet; + return m_inv; +} + +template +DEVICE +inline auto operator+(const TMatrix4x4 &m0, const TMatrix4x4 &m1) -> TMatrix4x4 { + TMatrix4x4 m; + for (int i = 0; i < 4; i++) { + for (int j = 0; j < 4; j++) { + m(i, j) = m0(i, j) + m1(i, j); + } + } + return m; +} + +template +DEVICE +TMatrix3x3 transpose(const TMatrix3x3 &m) { + return TMatrix3x3(m(0, 0), m(1, 0), m(2, 0), + m(0, 1), m(1, 1), m(2, 1), + m(0, 2), m(1, 2), m(2, 2)); +} + +template +DEVICE +TMatrix4x4 transpose(const TMatrix4x4 &m) { + return TMatrix4x4(m(0, 0), m(1, 0), m(2, 0), m(3, 0), + m(0, 1), m(1, 1), m(2, 1), m(3, 1), + m(0, 2), m(1, 2), m(2, 2), m(3, 2), + m(0, 3), m(1, 3), m(2, 3), m(3, 3)); +} + +template +DEVICE +inline TMatrix3x3 operator-(const TMatrix3x3 &m0) { + TMatrix3x3 m; + for (int i = 0; i < 3; i++) { + for (int j = 0; j < 3; j++) { + m(i, j) = -m0(i, j); + } + } + return m; +} + +template +DEVICE +inline TMatrix4x4 operator-(const TMatrix4x4 &m0) { + TMatrix4x4 m; + for (int i = 0; i < 4; i++) { + for (int j = 0; j < 4; j++) { + m(i, j) = -m0(i, j); + } + } + return m; +} + +template +DEVICE +inline TMatrix4x4 operator-(const TMatrix4x4 &m0, const TMatrix4x4 &m1) { + TMatrix4x4 m; + for (int i = 0; i < 4; i++) { + for (int j = 0; j < 4; j++) { + m(i, j) = m0(i, j) - m1(i, j); + } + } + return m; +} + +template +DEVICE +inline TMatrix3x3& operator+=(TMatrix3x3 &m0, const TMatrix3x3 &m1) { + for (int i = 0; i < 3; i++) { + for (int j = 0; j < 3; j++) { + m0(i, j) += m1(i, j); + } + } + return m0; +} + +template +DEVICE +inline TMatrix4x4& operator+=(TMatrix4x4 &m0, const TMatrix4x4 &m1) { + for (int i = 0; i < 4; i++) { + for (int j = 0; j < 4; j++) { + m0(i, j) += m1(i, j); + } + } + return m0; +} + +template +DEVICE +inline TMatrix4x4& operator-=(TMatrix4x4 &m0, const TMatrix4x4 &m1) { + for (int i = 0; i < 4; i++) { + for (int j = 0; j < 4; j++) { + m0(i, j) -= m1(i, j); + } + } + return m0; +} + +template +DEVICE +inline TMatrix4x4 operator*(const TMatrix4x4 &m0, const TMatrix4x4 &m1) { + TMatrix4x4 m; + for (int i = 0; i < 4; i++) { + for (int j = 0; j < 4; j++) { + for (int k = 0; k < 4; k++) { + m(i, j) += m0(i, k) * m1(k, j); + } + } + } + return m; +} + +template +DEVICE +TMatrix4x4 inverse(const TMatrix4x4 &m) { + // https://stackoverflow.com/questions/1148309/inverting-a-4x4-matrix + TMatrix4x4 inv; + + inv(0, 0) = m(1, 1) * m(2, 2) * m(3, 3) - + m(1, 1) * m(2, 3) * m(3, 2) - + m(2, 1) * m(1, 2) * m(3, 3) + + m(2, 1) * m(1, 3) * m(3, 2) + + m(3, 1) * m(1, 2) * m(2, 3) - + m(3, 1) * m(1, 3) * m(2, 2); + + inv(1, 0) = -m(1, 0) * m(2, 2) * m(3, 3) + + m(1, 0) * m(2, 3) * m(3, 2) + + m(2, 0) * m(1, 2) * m(3, 3) - + m(2, 0) * m(1, 3) * m(3, 2) - + m(3, 0) * m(1, 2) * m(2, 3) + + m(3, 0) * m(1, 3) * m(2, 2); + + inv(2, 0) = m(1, 0) * m(2, 1) * m(3, 3) - + m(1, 0) * m(2, 3) * m(3, 1) - + m(2, 0) * m(1, 1) * m(3, 3) + + m(2, 0) * m(1, 3) * m(3, 1) + + m(3, 0) * m(1, 1) * m(2, 3) - + m(3, 0) * m(1, 3) * m(2, 1); + + inv(3, 0) = -m(1, 0) * m(2, 1) * m(3, 2) + + m(1, 0) * m(2, 2) * m(3, 1) + + m(2, 0) * m(1, 1) * m(3, 2) - + m(2, 0) * m(1, 2) * m(3, 1) - + m(3, 0) * m(1, 1) * m(2, 2) + + m(3, 0) * m(1, 2) * m(2, 1); + + inv(0, 1) = -m(0, 1) * m(2, 2) * m(3, 3) + + m(0, 1) * m(2, 3) * m(3, 2) + + m(2, 1) * m(0, 2) * m(3, 3) - + m(2, 1) * m(0, 3) * m(3, 2) - + m(3, 1) * m(0, 2) * m(2, 3) + + m(3, 1) * m(0, 3) * m(2, 2); + + inv(1, 1) = m(0, 0) * m(2, 2) * m(3, 3) - + m(0, 0) * m(2, 3) * m(3, 2) - + m(2, 0) * m(0, 2) * m(3, 3) + + m(2, 0) * m(0, 3) * m(3, 2) + + m(3, 0) * m(0, 2) * m(2, 3) - + m(3, 0) * m(0, 3) * m(2, 2); + + inv(2, 1) = -m(0, 0) * m(2, 1) * m(3, 3) + + m(0, 0) * m(2, 3) * m(3, 1) + + m(2, 0) * m(0, 1) * m(3, 3) - + m(2, 0) * m(0, 3) * m(3, 1) - + m(3, 0) * m(0, 1) * m(2, 3) + + m(3, 0) * m(0, 3) * m(2, 1); + + inv(3, 1) = m(0, 0) * m(2, 1) * m(3, 2) - + m(0, 0) * m(2, 2) * m(3, 1) - + m(2, 0) * m(0, 1) * m(3, 2) + + m(2, 0) * m(0, 2) * m(3, 1) + + m(3, 0) * m(0, 1) * m(2, 2) - + m(3, 0) * m(0, 2) * m(2, 1); + + inv(0, 2) = m(0, 1) * m(1, 2) * m(3, 3) - + m(0, 1) * m(1, 3) * m(3, 2) - + m(1, 1) * m(0, 2) * m(3, 3) + + m(1, 1) * m(0, 3) * m(3, 2) + + m(3, 1) * m(0, 2) * m(1, 3) - + m(3, 1) * m(0, 3) * m(1, 2); + + inv(1, 2) = -m(0, 0) * m(1, 2) * m(3, 3) + + m(0, 0) * m(1, 3) * m(3, 2) + + m(1, 0) * m(0, 2) * m(3, 3) - + m(1, 0) * m(0, 3) * m(3, 2) - + m(3, 0) * m(0, 2) * m(1, 3) + + m(3, 0) * m(0, 3) * m(1, 2); + + inv(2, 2) = m(0, 0) * m(1, 1) * m(3, 3) - + m(0, 0) * m(1, 3) * m(3, 1) - + m(1, 0) * m(0, 1) * m(3, 3) + + m(1, 0) * m(0, 3) * m(3, 1) + + m(3, 0) * m(0, 1) * m(1, 3) - + m(3, 0) * m(0, 3) * m(1, 1); + + inv(3, 2) = -m(0, 0) * m(1, 1) * m(3, 2) + + m(0, 0) * m(1, 2) * m(3, 1) + + m(1, 0) * m(0, 1) * m(3, 2) - + m(1, 0) * m(0, 2) * m(3, 1) - + m(3, 0) * m(0, 1) * m(1, 2) + + m(3, 0) * m(0, 2) * m(1, 1); + + inv(0, 3) = -m(0, 1) * m(1, 2) * m(2, 3) + + m(0, 1) * m(1, 3) * m(2, 2) + + m(1, 1) * m(0, 2) * m(2, 3) - + m(1, 1) * m(0, 3) * m(2, 2) - + m(2, 1) * m(0, 2) * m(1, 3) + + m(2, 1) * m(0, 3) * m(1, 2); + + inv(1, 3) = m(0, 0) * m(1, 2) * m(2, 3) - + m(0, 0) * m(1, 3) * m(2, 2) - + m(1, 0) * m(0, 2) * m(2, 3) + + m(1, 0) * m(0, 3) * m(2, 2) + + m(2, 0) * m(0, 2) * m(1, 3) - + m(2, 0) * m(0, 3) * m(1, 2); + + inv(2, 3) = -m(0, 0) * m(1, 1) * m(2, 3) + + m(0, 0) * m(1, 3) * m(2, 1) + + m(1, 0) * m(0, 1) * m(2, 3) - + m(1, 0) * m(0, 3) * m(2, 1) - + m(2, 0) * m(0, 1) * m(1, 3) + + m(2, 0) * m(0, 3) * m(1, 1); + + inv(3, 3) = m(0, 0) * m(1, 1) * m(2, 2) - + m(0, 0) * m(1, 2) * m(2, 1) - + m(1, 0) * m(0, 1) * m(2, 2) + + m(1, 0) * m(0, 2) * m(2, 1) + + m(2, 0) * m(0, 1) * m(1, 2) - + m(2, 0) * m(0, 2) * m(1, 1); + + auto det = m(0, 0) * inv(0, 0) + + m(0, 1) * inv(1, 0) + + m(0, 2) * inv(2, 0) + + m(0, 3) * inv(3, 0); + + if (det == 0) { + return TMatrix4x4{}; + } + + auto inv_det = 1.0 / det; + + for (int i = 0; i < 4; i++) { + for (int j = 0; j < 4; j++) { + inv(i, j) *= inv_det; + } + } + + return inv; +} + +template +inline std::ostream& operator<<(std::ostream &os, const TMatrix3x3 &m) { + for (int i = 0; i < 3; i++) { + for (int j = 0; j < 3; j++) { + os << m(i, j) << " "; + } + os << std::endl; + } + return os; +} + +template +inline std::ostream& operator<<(std::ostream &os, const TMatrix4x4 &m) { + for (int i = 0; i < 4; i++) { + for (int j = 0; j < 4; j++) { + os << m(i, j) << " "; + } + os << std::endl; + } + return os; +} + +template +DEVICE +TVector2 xform_pt(const TMatrix3x3 &m, const TVector2 &pt) { + TVector3 t{m(0, 0) * pt[0] + m(0, 1) * pt[1] + m(0, 2), + m(1, 0) * pt[0] + m(1, 1) * pt[1] + m(1, 2), + m(2, 0) * pt[0] + m(2, 1) * pt[1] + m(2, 2)}; + return TVector2{t[0] / t[2], t[1] / t[2]}; +} + +template +DEVICE +void d_xform_pt(const TMatrix3x3 &m, const TVector2 &pt, + const TVector2 &d_out, + TMatrix3x3 &d_m, + TVector2 &d_pt) { + TVector3 t{m(0, 0) * pt[0] + m(0, 1) * pt[1] + m(0, 2), + m(1, 0) * pt[0] + m(1, 1) * pt[1] + m(1, 2), + m(2, 0) * pt[0] + m(2, 1) * pt[1] + m(2, 2)}; + auto out = TVector2{t[0] / t[2], t[1] / t[2]}; + TVector3 d_t{d_out[0] / t[2], + d_out[1] / t[2], + -(d_out[0] * out[0] + d_out[1] * out[1]) / t[2]}; + d_m(0, 0) += d_t[0] * pt[0]; + d_m(0, 1) += d_t[0] * pt[1]; + d_m(0, 2) += d_t[0]; + d_m(1, 0) += d_t[1] * pt[0]; + d_m(1, 1) += d_t[1] * pt[1]; + d_m(1, 2) += d_t[1]; + d_m(2, 0) += d_t[2] * pt[0]; + d_m(2, 1) += d_t[2] * pt[1]; + d_m(2, 2) += d_t[2]; + d_pt[0] += d_t[0] * m(0, 0) + d_t[1] * m(1, 0) + d_t[2] * m(2, 0); + d_pt[1] += d_t[0] * m(0, 1) + d_t[1] * m(1, 1) + d_t[2] * m(2, 1); +} + +template +DEVICE +TVector2 xform_normal(const TMatrix3x3 &m_inv, const TVector2 &n) { + return normalize(TVector2{m_inv(0, 0) * n[0] + m_inv(1, 0) * n[1], + m_inv(0, 1) * n[0] + m_inv(1, 1) * n[1]}); +} diff --git a/parallel.cpp b/parallel.cpp new file mode 100644 index 0000000..365fc5b --- /dev/null +++ b/parallel.cpp @@ -0,0 +1,273 @@ +#include "parallel.h" +#include +#include +#include +#include +#include + +// From https://github.com/mmp/pbrt-v3/blob/master/src/core/parallel.cpp + +static std::vector threads; +static bool shutdownThreads = false; +struct ParallelForLoop; +static ParallelForLoop *workList = nullptr; +static std::mutex workListMutex; + +struct ParallelForLoop { + ParallelForLoop(std::function func1D, int64_t maxIndex, int chunkSize) + : func1D(std::move(func1D)), maxIndex(maxIndex), chunkSize(chunkSize) { + } + ParallelForLoop(const std::function &f, const Vector2i count) + : func2D(f), maxIndex(count[0] * count[1]), chunkSize(1) { + nX = count[0]; + } + + std::function func1D; + std::function func2D; + const int64_t maxIndex; + const int chunkSize; + int64_t nextIndex = 0; + int activeWorkers = 0; + ParallelForLoop *next = nullptr; + int nX = -1; + + bool Finished() const { + return nextIndex >= maxIndex && activeWorkers == 0; + } +}; + +void Barrier::Wait() { + std::unique_lock lock(mutex); + assert(count > 0); + if (--count == 0) { + // This is the last thread to reach the barrier; wake up all of the + // other ones before exiting. + cv.notify_all(); + } else { + // Otherwise there are still threads that haven't reached it. Give + // up the lock and wait to be notified. + cv.wait(lock, [this] { return count == 0; }); + } +} + +static std::condition_variable workListCondition; + +static void worker_thread_func(const int tIndex, std::shared_ptr barrier) { + ThreadIndex = tIndex; + + // The main thread sets up a barrier so that it can be sure that all + // workers have called ProfilerWorkerThreadInit() before it continues + // (and actually starts the profiling system). + barrier->Wait(); + + // Release our reference to the Barrier so that it's freed once all of + // the threads have cleared it. + barrier.reset(); + + std::unique_lock lock(workListMutex); + while (!shutdownThreads) { + if (!workList) { + // Sleep until there are more tasks to run + workListCondition.wait(lock); + } else { + // Get work from _workList_ and run loop iterations + ParallelForLoop &loop = *workList; + + // Run a chunk of loop iterations for _loop_ + + // Find the set of loop iterations to run next + int64_t indexStart = loop.nextIndex; + int64_t indexEnd = std::min(indexStart + loop.chunkSize, loop.maxIndex); + + // Update _loop_ to reflect iterations this thread will run + loop.nextIndex = indexEnd; + if (loop.nextIndex == loop.maxIndex) + workList = loop.next; + loop.activeWorkers++; + + // Run loop indices in _[indexStart, indexEnd)_ + lock.unlock(); + for (int64_t index = indexStart; index < indexEnd; ++index) { + if (loop.func1D) { + loop.func1D(index); + } + // Handle other types of loops + else { + assert(loop.func2D != nullptr); + loop.func2D(Vector2i{int(index % loop.nX), + int(index / loop.nX)}); + } + } + lock.lock(); + + // Update _loop_ to reflect completion of iterations + loop.activeWorkers--; + if (loop.Finished()) { + workListCondition.notify_all(); + } + } + } +} + +void parallel_for_host(const std::function &func, + int64_t count, + int chunkSize) { + // Run iterations immediately if not using threads or if _count_ is small + if (threads.empty() || count < chunkSize) { + for (int64_t i = 0; i < count; ++i) { + func(i); + } + return; + } + + // Create and enqueue _ParallelForLoop_ for this loop + ParallelForLoop loop(func, count, chunkSize); + workListMutex.lock(); + loop.next = workList; + workList = &loop; + workListMutex.unlock(); + + // Notify worker threads of work to be done + std::unique_lock lock(workListMutex); + workListCondition.notify_all(); + + // Help out with parallel loop iterations in the current thread + while (!loop.Finished()) { + // Run a chunk of loop iterations for _loop_ + + // Find the set of loop iterations to run next + int64_t indexStart = loop.nextIndex; + int64_t indexEnd = std::min(indexStart + loop.chunkSize, loop.maxIndex); + + // Update _loop_ to reflect iterations this thread will run + loop.nextIndex = indexEnd; + if (loop.nextIndex == loop.maxIndex) { + workList = loop.next; + } + loop.activeWorkers++; + + // Run loop indices in _[indexStart, indexEnd)_ + lock.unlock(); + for (int64_t index = indexStart; index < indexEnd; ++index) { + if (loop.func1D) { + loop.func1D(index); + } + // Handle other types of loops + else { + assert(loop.func2D != nullptr); + loop.func2D(Vector2i{int(index % loop.nX), + int(index / loop.nX)}); + } + } + lock.lock(); + + // Update _loop_ to reflect completion of iterations + loop.activeWorkers--; + } +} + +thread_local int ThreadIndex; + +void parallel_for_host( + std::function func, const Vector2i count) { + // Launch worker threads if needed + if (threads.empty() || count.x * count.y <= 1) { + for (int y = 0; y < count.y; ++y) { + for (int x = 0; x < count.x; ++x) { + func(Vector2i{x, y}); + } + } + return; + } + + ParallelForLoop loop(std::move(func), count); + { + std::lock_guard lock(workListMutex); + loop.next = workList; + workList = &loop; + } + + std::unique_lock lock(workListMutex); + workListCondition.notify_all(); + + // Help out with parallel loop iterations in the current thread + while (!loop.Finished()) { + // Run a chunk of loop iterations for _loop_ + + // Find the set of loop iterations to run next + int64_t indexStart = loop.nextIndex; + int64_t indexEnd = std::min(indexStart + loop.chunkSize, loop.maxIndex); + + // Update _loop_ to reflect iterations this thread will run + loop.nextIndex = indexEnd; + if (loop.nextIndex == loop.maxIndex) { + workList = loop.next; + } + loop.activeWorkers++; + + // Run loop indices in _[indexStart, indexEnd)_ + lock.unlock(); + for (int64_t index = indexStart; index < indexEnd; ++index) { + if (loop.func1D) { + loop.func1D(index); + } + // Handle other types of loops + else { + assert(loop.func2D != nullptr); + loop.func2D(Vector2i{int(index % loop.nX), + int(index / loop.nX)}); + } + } + lock.lock(); + + // Update _loop_ to reflect completion of iterations + loop.activeWorkers--; + } +} + +int num_system_cores() { + // return 1; + int ret = std::thread::hardware_concurrency(); + if (ret == 0) { + return 16; + } + return ret; +} + +void parallel_init() { + assert(threads.size() == 0); + int nThreads = num_system_cores(); + ThreadIndex = 0; + + // Create a barrier so that we can be sure all worker threads get past + // their call to ProfilerWorkerThreadInit() before we return from this + // function. In turn, we can be sure that the profiling system isn't + // started until after all worker threads have done that. + std::shared_ptr barrier = std::make_shared(nThreads); + + // Launch one fewer worker thread than the total number we want doing + // work, since the main thread helps out, too. + for (int i = 0; i < nThreads - 1; ++i) { + threads.push_back(std::thread(worker_thread_func, i + 1, barrier)); + } + + barrier->Wait(); +} + +void parallel_cleanup() { + if (threads.empty()) { + return; + } + + { + std::lock_guard lock(workListMutex); + shutdownThreads = true; + workListCondition.notify_all(); + } + + for (std::thread &thread : threads) { + thread.join(); + } + threads.erase(threads.begin(), threads.end()); + shutdownThreads = false; +} diff --git a/parallel.h b/parallel.h new file mode 100644 index 0000000..b7f9c71 --- /dev/null +++ b/parallel.h @@ -0,0 +1,91 @@ +#pragma once + +#include "vector.h" + +#include +#include +#include +#include +#include +#include +#include +// From https://github.com/mmp/pbrt-v3/blob/master/src/core/parallel.h + +class Barrier { + public: + Barrier(int count) : count(count) { assert(count > 0); } + ~Barrier() { assert(count == 0); } + void Wait(); + + private: + std::mutex mutex; + std::condition_variable cv; + int count; +}; + +void parallel_for_host(const std::function &func, + int64_t count, + int chunkSize = 1); +extern thread_local int ThreadIndex; +void parallel_for_host( + std::function func, const Vector2i count); +int num_system_cores(); + +void parallel_init(); +void parallel_cleanup(); + +#ifdef __CUDACC__ +template +__global__ void parallel_for_device_kernel(T functor, int count) { + auto idx = threadIdx.x + blockIdx.x * blockDim.x; + if (idx >= count) { + return; + } + functor(idx); +} +template +inline void parallel_for_device(T functor, + int count, + int work_per_thread = 256) { + if (count <= 0) { + return; + } + auto block_size = work_per_thread; + auto block_count = idiv_ceil(count, block_size); + parallel_for_device_kernel<<>>(functor, count); +} +#endif + +template +inline void parallel_for(T functor, + int count, + bool use_gpu, + int work_per_thread = -1) { + if (work_per_thread == -1) { + work_per_thread = use_gpu ? 64 : 256; + } + if (count <= 0) { + return; + } + if (use_gpu) { +#ifdef __CUDACC__ + auto block_size = work_per_thread; + auto block_count = idiv_ceil(count, block_size); + parallel_for_device_kernel<<>>(functor, count); +#else + throw std::runtime_error("diffvg not compiled with GPU"); + assert(false); +#endif + } else { + auto num_threads = idiv_ceil(count, work_per_thread); + parallel_for_host([&](int thread_index) { + auto id_offset = work_per_thread * thread_index; + auto work_end = std::min(id_offset + work_per_thread, count); + for (int work_id = id_offset; work_id < work_end; work_id++) { + auto idx = work_id; + assert(idx < count); + functor(idx); + } + }, num_threads); + } +} diff --git a/pcg.h b/pcg.h new file mode 100644 index 0000000..55859a1 --- /dev/null +++ b/pcg.h @@ -0,0 +1,40 @@ +#pragma once + +#include "diffvg.h" + +// http://www.pcg-random.org/download.html +struct pcg32_state { + uint64_t state; + uint64_t inc; +}; + +DEVICE inline uint32_t next_pcg32(pcg32_state *rng) { + uint64_t oldstate = rng->state; + // Advance internal state + rng->state = oldstate * 6364136223846793005ULL + (rng->inc|1); + // Calculate output function (XSH RR), uses old state for max ILP + uint32_t xorshifted = ((oldstate >> 18u) ^ oldstate) >> 27u; + uint32_t rot = oldstate >> 59u; + return (xorshifted >> rot) | (xorshifted << ((-rot) & 31)); +} + +// https://github.com/wjakob/pcg32/blob/master/pcg32.h +DEVICE inline float next_pcg32_float(pcg32_state *rng) { + union { + uint32_t u; + float f; + } x; + x.u = (next_pcg32(rng) >> 9) | 0x3f800000u; + return x.f - 1.0f; +} + +// Initialize each pixel with a PCG rng with a different stream +DEVICE inline pcg32_state init_pcg32(int idx, uint64_t seed) { + pcg32_state state; + state.state = 0U; + state.inc = (((uint64_t)idx + 1) << 1u) | 1u; + next_pcg32(&state); + state.state += (0x853c49e6748fea9bULL + seed); + next_pcg32(&state); + return state; +} diff --git a/ptr.h b/ptr.h new file mode 100644 index 0000000..f3f8e43 --- /dev/null +++ b/ptr.h @@ -0,0 +1,23 @@ +#pragma once + +#include + +/** + * Python doesn't have a pointer type, therefore we create a pointer wrapper + * see https://stackoverflow.com/questions/48982143/returning-and-passing-around-raw-pod-pointers-arrays-with-python-c-and-pyb?rq=1 + */ +template +class ptr { +public: + ptr() : p(nullptr) {} + ptr(T* p) : p(p) {} + ptr(std::size_t p) : p((T*)p) {} + ptr(const ptr& other) : ptr(other.p) {} + T* operator->() const { return p; } + T* get() const { return p; } + void destroy() { delete p; } + bool is_null() const { return p == nullptr; } + size_t as_size_t() const {return (size_t)p;} +private: + T* p; +}; diff --git a/pydiffvg/__init__.py b/pydiffvg/__init__.py new file mode 100644 index 0000000..24f3dd1 --- /dev/null +++ b/pydiffvg/__init__.py @@ -0,0 +1,9 @@ +from .device import * +from .shape import * +from .pixel_filter import * +from .render_pytorch import * +from .image import * +from .parse_svg import * +from .color import * +from .optimize_svg import * +from .save_svg import * \ No newline at end of file diff --git a/pydiffvg/color.py b/pydiffvg/color.py new file mode 100644 index 0000000..68c360f --- /dev/null +++ b/pydiffvg/color.py @@ -0,0 +1,24 @@ +import pydiffvg +import torch + +class LinearGradient: + def __init__(self, + begin = torch.tensor([0.0, 0.0]), + end = torch.tensor([0.0, 0.0]), + offsets = torch.tensor([0.0]), + stop_colors = torch.tensor([0.0, 0.0, 0.0, 0.0])): + self.begin = begin + self.end = end + self.offsets = offsets + self.stop_colors = stop_colors + +class RadialGradient: + def __init__(self, + center = torch.tensor([0.0, 0.0]), + radius = torch.tensor([0.0, 0.0]), + offsets = torch.tensor([0.0]), + stop_colors = torch.tensor([0.0, 0.0, 0.0, 0.0])): + self.center = center + self.radius = radius + self.offsets = offsets + self.stop_colors = stop_colors diff --git a/pydiffvg/device.py b/pydiffvg/device.py new file mode 100644 index 0000000..420883d --- /dev/null +++ b/pydiffvg/device.py @@ -0,0 +1,25 @@ +import torch + +use_gpu = torch.cuda.is_available() +device = torch.device('cuda') if use_gpu else torch.device('cpu') + +def set_use_gpu(v): + global use_gpu + global device + use_gpu = v + if not use_gpu: + device = torch.device('cpu') + +def get_use_gpu(): + global use_gpu + return use_gpu + +def set_device(d): + global device + global use_gpu + device = d + use_gpu = device.type == 'cuda' + +def get_device(): + global device + return device diff --git a/pydiffvg/image.py b/pydiffvg/image.py new file mode 100644 index 0000000..f83fea2 --- /dev/null +++ b/pydiffvg/image.py @@ -0,0 +1,22 @@ +import numpy as np +import skimage +import skimage.io +import os + +def imwrite(img, filename, gamma = 2.2, normalize = False): + directory = os.path.dirname(filename) + if directory != '' and not os.path.exists(directory): + os.makedirs(directory) + + if not isinstance(img, np.ndarray): + img = img.data.numpy() + if normalize: + img_rng = np.max(img) - np.min(img) + if img_rng > 0: + img = (img - np.min(img)) / img_rng + img = np.clip(img, 0.0, 1.0) + if img.ndim==2: + #repeat along the third dimension + img=np.expand_dims(img,2) + img[:, :, :3] = np.power(img[:, :, :3], 1.0/gamma) + skimage.io.imsave(filename, (img * 255).astype(np.uint8)) \ No newline at end of file diff --git a/pydiffvg/optimize_svg.py b/pydiffvg/optimize_svg.py new file mode 100644 index 0000000..a3a58ab --- /dev/null +++ b/pydiffvg/optimize_svg.py @@ -0,0 +1,1606 @@ +import json +import copy +import xml.etree.ElementTree as etree +from xml.dom import minidom +import warnings +import torch +import numpy as np +import re +import sys +import pydiffvg +import math +from collections import namedtuple +import cssutils + +class SvgOptimizationSettings: + + default_params = { + "optimize_color": True, + "color_lr": 2e-3, + "optimize_alpha": False, + "alpha_lr": 2e-3, + "optimizer": "Adam", + "transforms": { + "optimize_transforms":True, + "transform_mode":"rigid", + "translation_mult":1e-3, + "transform_lr":2e-3 + }, + "circles": { + "optimize_center": True, + "optimize_radius": True, + "shape_lr": 2e-1 + }, + "paths": { + "optimize_points": True, + "shape_lr": 2e-1 + }, + "gradients": { + "optimize_stops": True, + "stop_lr": 2e-3, + "optimize_color": True, + "color_lr": 2e-3, + "optimize_alpha": False, + "alpha_lr": 2e-3, + "optimize_location": True, + "location_lr": 2e-1 + } + } + + optims = { + "Adam": torch.optim.Adam, + "SGD": torch.optim.SGD, + "ASGD": torch.optim.ASGD, + } + + #region methods + def __init__(self, f=None): + self.store = {} + if f is None: + self.store["default"] = copy.deepcopy(SvgOptimizationSettings.default_params) + else: + self.store = json.load(f) + + # create default alias for root + def default_name(self, dname): + self.dname = dname + if dname not in self.store: + self.store[dname] = self.store["default"] + + def retrieve(self, node_id): + if node_id not in self.store: + return (self.store["default"], False) + else: + return (self.store[node_id], True) + + def reset_to_defaults(self, node_id): + if node_id in self.store: + del self.store[node_id] + + return self.store["default"] + + def undefault(self, node_id): + if node_id not in self.store: + self.store[node_id] = copy.deepcopy(self.store["default"]) + + return self.store[node_id] + + def override_optimizer(self, optimizer): + if optimizer is not None: + for v in self.store.values(): + v["optimizer"] = optimizer + + def global_override(self, path, value): + for store in self.store.values(): + d = store + for key in path[:-1]: + d = d[key] + + d[path[-1]] = value + + def save(self, file): + self.store["default"] = self.store[self.dname] + json.dump(self.store, file, indent="\t") + #endregion + +class OptimizableSvg: + + class TransformTools: + @staticmethod + def parse_matrix(vals): + assert(len(vals)==6) + return np.array([[vals[0],vals[2],vals[4]],[vals[1], vals[3], vals[5]],[0,0,1]]) + + @staticmethod + def parse_translate(vals): + assert(len(vals)>=1 and len(vals)<=2) + mat=np.eye(3) + mat[0,2]=vals[0] + if len(vals)>1: + mat[1,2]=vals[1] + return mat + + @staticmethod + def parse_rotate(vals): + assert (len(vals) == 1 or len(vals) == 3) + mat = np.eye(3) + rads=math.radians(vals[0]) + sint=math.sin(rads) + cost=math.cos(rads) + mat[0:2, 0:2] = np.array([[cost,-sint],[sint,cost]]) + if len(vals) > 1: + tr1=parse_translate(vals[1:3]) + tr2=parse_translate([-vals[1],-vals[2]]) + mat=tr1 @ mat @ tr2 + return mat + + @staticmethod + def parse_scale(vals): + assert (len(vals) >= 1 and len(vals) <= 2) + d=np.array([vals[0], vals[1] if len(vals)>1 else vals[0],1]) + return np.diag(d) + + @staticmethod + def parse_skewx(vals): + assert(len(vals)==1) + m=np.eye(3) + m[0,1]=vals[0] + return m + + @staticmethod + def parse_skewy(vals): + assert (len(vals) == 1) + m = np.eye(3) + m[1, 0] = vals[0] + return m + + @staticmethod + def transformPoints(pointsTensor, transform): + assert(transform is not None) + one=torch.ones((pointsTensor.shape[0],1),device=pointsTensor.device) + homo_points = torch.cat([pointsTensor, one], dim=1) + mult = transform.mm(homo_points.permute(1,0)).permute(1,0) + tfpoints=mult[:, 0:2].contiguous() + #print(torch.norm(mult[:,2]-one)) + assert(pointsTensor.shape == tfpoints.shape) + return tfpoints + + @staticmethod + def promote_numpy(M): + ret = np.eye(3) + ret[0:2, 0:2] = M + return ret + + @staticmethod + def recompose_numpy(Theta,ScaleXY,ShearX,TXY): + cost=math.cos(Theta) + sint=math.sin(Theta) + Rot=np.array([[cost, -sint],[sint, cost]]) + Scale=np.diag(ScaleXY) + Shear=np.eye(2) + Shear[0,1]=ShearX + + Translate=np.eye(3) + Translate[0:2,2]=TXY + + M=OptimizableSvg.TransformTools.promote_numpy(Rot @ Scale @ Shear) @ Translate + return M + + @staticmethod + def promote(m): + M=torch.eye(3).to(m.device) + M[0:2,0:2]=m + return M + + @staticmethod + def make_rot(Theta): + sint=Theta.sin().squeeze() + cost=Theta.cos().squeeze() + #m=torch.tensor([[cost, -sint],[sint, cost]]) + Rot=torch.stack((torch.stack((cost,-sint)),torch.stack((sint,cost)))) + return Rot + + @staticmethod + def make_scale(ScaleXY): + if ScaleXY.squeeze().dim()==0: + ScaleXY=ScaleXY.squeeze() + #uniform scale + return torch.diag(torch.stack([ScaleXY,ScaleXY])).to(ScaleXY.device) + else: + return torch.diag(ScaleXY).to(ScaleXY.device) + + @staticmethod + def make_shear(ShearX): + m=torch.eye(2).to(ShearX.device) + m[0,1]=ShearX + return m + + @staticmethod + def make_translate(TXY): + m=torch.eye(3).to(TXY.device) + m[0:2,2]=TXY + return m + + @staticmethod + def recompose(Theta,ScaleXY,ShearX,TXY): + Rot=OptimizableSvg.TransformTools.make_rot(Theta) + Scale=OptimizableSvg.TransformTools.make_scale(ScaleXY) + Shear=OptimizableSvg.TransformTools.make_shear(ShearX) + Translate=OptimizableSvg.TransformTools.make_translate(TXY) + + return OptimizableSvg.TransformTools.promote(Rot.mm(Scale).mm(Shear)).mm(Translate) + + TransformDecomposition=namedtuple("TransformDecomposition","theta scale shear translate") + TransformProperties=namedtuple("TransformProperties", "has_rotation has_scale has_mirror scale_uniform has_shear has_translation") + + @staticmethod + def make_named(decomp): + if not isinstance(decomp,OptimizableSvg.TransformTools.TransformDecomposition): + decomp=OptimizableSvg.TransformTools.TransformDecomposition(theta=decomp[0],scale=decomp[1],shear=decomp[2],translate=decomp[3]) + return decomp + + @staticmethod + def analyze_transform(decomp): + decomp=OptimizableSvg.TransformTools.make_named(decomp) + epsilon=1e-3 + has_rotation=abs(decomp.theta)>epsilon + has_scale=abs((abs(decomp.scale)-1)).max()>epsilon + scale_len=decomp.scale.squeeze().ndim>0 if isinstance(decomp.scale,np.ndarray) else decomp.scale.squeeze().dim() > 0 + has_mirror=scale_len and decomp.scale[0]*decomp.scale[1] < 0 + scale_uniform=not scale_len or abs(abs(decomp.scale[0])-abs(decomp.scale[1]))epsilon + has_translate=max(abs(decomp.translate[0]),abs(decomp.translate[1]))>epsilon + + return OptimizableSvg.TransformTools.TransformProperties(has_rotation=has_rotation,has_scale=has_scale,has_mirror=has_mirror,scale_uniform=scale_uniform,has_shear=has_shear,has_translation=has_translate) + + @staticmethod + def check_and_decomp(M): + decomp=OptimizableSvg.TransformTools.decompose(M) if M is not None else OptimizableSvg.TransformTools.TransformDecomposition(theta=0,scale=(1,1),shear=0,translate=(0,0)) + props=OptimizableSvg.TransformTools.analyze_transform(decomp) + return (decomp, props) + + @staticmethod + def tf_to_string(M): + tfstring = "matrix({} {} {} {} {} {})".format(M[0, 0], M[1, 0], M[0, 1], M[1, 1], M[0, 2], M[1, 2]) + return tfstring + + @staticmethod + def decomp_to_string(decomp): + decomp = OptimizableSvg.TransformTools.make_named(decomp) + ret="" + props=OptimizableSvg.TransformTools.analyze_transform(decomp) + if props.has_rotation: + ret+="rotate({}) ".format(math.degrees(decomp.theta.item())) + if props.has_scale: + if decomp.scale.dim()==0: + ret += "scale({}) ".format(decomp.scale.item()) + else: + ret+="scale({} {}) ".format(decomp.scale[0], decomp.scale[1]) + if props.has_shear: + ret+="skewX({}) ".format(decomp.shear.item()) + if props.has_translation: + ret+="translate({} {}) ".format(decomp.translate[0],decomp.translate[1]) + + return ret + + @staticmethod + def decompose(M): + m = M[0:2, 0:2] + t0=M[0:2, 2] + #get translation so that we can post-multiply with it + TXY=np.linalg.solve(m,t0) + + T=np.eye(3) + T[0:2,2]=TXY + + q, r = np.linalg.qr(m) + + ref = np.array([[1, 0], [0, np.sign(np.linalg.det(q))]]) + + Rot = np.dot(q, ref) + + ref2 = np.array([[1, 0], [0, np.sign(np.linalg.det(r))]]) + + r2 = np.dot(ref2, r) + + Ref = np.dot(ref, ref2) + + sc = np.diag(r2) + Scale = np.diagflat(sc) + + Shear = np.eye(2) + Shear[0, 1] = r2[0, 1] / sc[0] + #the actual shear coefficient + ShearX=r2[0, 1] / sc[0] + + if np.sum(sc) < 0: + # both scales are negative, flip this and add a 180 rotation + Rot = np.dot(Rot, -np.eye(2)) + Scale = -Scale + + Theta = math.atan2(Rot[1, 0], Rot[0, 0]) + ScaleXY = np.array([Scale[0,0],Scale[1,1]*Ref[1,1]]) + + return OptimizableSvg.TransformTools.TransformDecomposition(theta=Theta, scale=ScaleXY, shear=ShearX, translate=TXY) + + #region suboptimizers + + #optimizes color, but really any tensor that needs to stay between 0 and 1 per-entry + class ColorOptimizer: + def __init__(self,tensor,optim_type,lr): + self.tensor=tensor + self.optim=optim_type([tensor],lr=lr) + + def zero_grad(self): + self.optim.zero_grad() + + def step(self): + self.optim.step() + self.tensor.data.clamp_(min=1e-4,max=1.) + + #optimizes gradient stop positions + class StopOptimizer: + def __init__(self,stops,optim_type,lr): + self.stops=stops + self.optim=optim_type([stops],lr=lr) + + def zero_grad(self): + self.optim.zero_grad() + + def step(self): + self.optim.step() + self.stops.data.clamp_(min=0., max=1.) + self.stops.data, _ = self.stops.sort() + self.stops.data[0] = 0. + self.stops.data[-1]=1. + + #optimizes gradient: stop, positions, colors+opacities, locations + class GradientOptimizer: + def __init__(self, begin, end, offsets, stops, optim_params): + self.begin=begin.clone().detach() if begin is not None else None + self.end=end.clone().detach() if end is not None else None + self.offsets=offsets.clone().detach() if offsets is not None else None + self.stop_colors=stops[:,0:3].clone().detach() if stops is not None else None + self.stop_alphas=stops[:,3].clone().detach() if stops is not None else None + self.optimizers=[] + + if optim_params["gradients"]["optimize_stops"] and self.offsets is not None: + self.offsets.requires_grad_(True) + self.optimizers.append(OptimizableSvg.StopOptimizer(self.offsets,SvgOptimizationSettings.optims[optim_params["optimizer"]],optim_params["gradients"]["stop_lr"])) + if optim_params["gradients"]["optimize_color"] and self.stop_colors is not None: + self.stop_colors.requires_grad_(True) + self.optimizers.append(OptimizableSvg.ColorOptimizer(self.stop_colors,SvgOptimizationSettings.optims[optim_params["optimizer"]],optim_params["gradients"]["color_lr"])) + if optim_params["gradients"]["optimize_alpha"] and self.stop_alphas is not None: + self.stop_alphas.requires_grad_(True) + self.optimizers.append(OptimizableSvg.ColorOptimizer(self.stop_alphas,SvgOptimizationSettings.optims[optim_params["optimizer"]],optim_params["gradients"]["alpha_lr"])) + if optim_params["gradients"]["optimize_location"] and self.begin is not None and self.end is not None: + self.begin.requires_grad_(True) + self.end.requires_grad_(True) + self.optimizers.append(SvgOptimizationSettings.optims[optim_params["optimizer"]]([self.begin,self.end],lr=optim_params["gradients"]["location_lr"])) + + + def get_vals(self): + return self.begin, self.end, self.offsets, torch.cat((self.stop_colors,self.stop_alphas.unsqueeze(1)),1) if self.stop_colors is not None and self.stop_alphas is not None else None + + def zero_grad(self): + for optim in self.optimizers: + optim.zero_grad() + + def step(self): + for optim in self.optimizers: + optim.step() + + class TransformOptimizer: + def __init__(self,transform,optim_params): + self.transform=transform + self.optimizes=optim_params["transforms"]["optimize_transforms"] and transform is not None + self.params=copy.deepcopy(optim_params) + self.transform_mode=optim_params["transforms"]["transform_mode"] + + if self.optimizes: + optimvars=[] + self.residual=None + lr=optim_params["transforms"]["transform_lr"] + tmult=optim_params["transforms"]["translation_mult"] + decomp,props=OptimizableSvg.TransformTools.check_and_decomp(transform.cpu().numpy()) + if self.transform_mode=="move": + #only translation and rotation should be set + if props.has_scale or props.has_shear or props.has_mirror: + print("Warning: set to optimize move only, but input transform has residual scale or shear") + self.residual=self.transform.clone().detach().requires_grad_(False) + self.Theta=torch.tensor(0,dtype=torch.float32,requires_grad=True,device=transform.device) + self.translation=torch.tensor([0, 0],dtype=torch.float32,requires_grad=True,device=transform.device) + else: + self.residual=None + self.Theta=torch.tensor(decomp.theta,dtype=torch.float32,requires_grad=True,device=transform.device) + self.translation=torch.tensor(decomp.translate,dtype=torch.float32,requires_grad=True,device=transform.device) + optimvars+=[{'params':x,'lr':lr} for x in [self.Theta]]+[{'params':self.translation,'lr':lr*tmult}] + elif self.transform_mode=="rigid": + #only translation, rotation, and uniform scale should be set + if props.has_shear or props.has_mirror or not props.scale_uniform: + print("Warning: set to optimize rigid transform only, but input transform has residual shear, mirror or non-uniform scale") + self.residual = self.transform.clone().detach().requires_grad_(False) + self.Theta = torch.tensor(0, dtype=torch.float32, requires_grad=True,device=transform.device) + self.translation = torch.tensor([0, 0], dtype=torch.float32, requires_grad=True,device=transform.device) + self.scale=torch.tensor(1, dtype=torch.float32, requires_grad=True,device=transform.device) + else: + self.residual = None + self.Theta = torch.tensor(decomp.theta, dtype=torch.float32, requires_grad=True,device=transform.device) + self.translation = torch.tensor(decomp.translate, dtype=torch.float32, requires_grad=True,device=transform.device) + self.scale = torch.tensor(decomp.scale[0], dtype=torch.float32, requires_grad=True,device=transform.device) + optimvars += [{'params':x,'lr':lr} for x in [self.Theta, self.scale]]+[{'params':self.translation,'lr':lr*tmult}] + elif self.transform_mode=="similarity": + if props.has_shear or not props.scale_uniform: + print("Warning: set to optimize rigid transform only, but input transform has residual shear or non-uniform scale") + self.residual = self.transform.clone().detach().requires_grad_(False) + self.Theta = torch.tensor(0, dtype=torch.float32, requires_grad=True,device=transform.device) + self.translation = torch.tensor([0, 0], dtype=torch.float32, requires_grad=True,device=transform.device) + self.scale=torch.tensor(1, dtype=torch.float32, requires_grad=True,device=transform.device) + self.scale_sign=torch.tensor(1,dtype=torch.float32,requires_grad=False,device=transform.device) + else: + self.residual = None + self.Theta = torch.tensor(decomp.theta, dtype=torch.float32, requires_grad=True,device=transform.device) + self.translation = torch.tensor(decomp.translate, dtype=torch.float32, requires_grad=True,device=transform.device) + self.scale = torch.tensor(decomp.scale[0], dtype=torch.float32, requires_grad=True,device=transform.device) + self.scale_sign = torch.tensor(np.sign(decomp.scale[0]*decomp.scale[1]), dtype=torch.float32, requires_grad=False,device=transform.device) + optimvars += [{'params':x,'lr':lr} for x in [self.Theta, self.scale]]+[{'params':self.translation,'lr':lr*tmult}] + elif self.transform_mode=="affine": + self.Theta = torch.tensor(decomp.theta, dtype=torch.float32, requires_grad=True,device=transform.device) + self.translation = torch.tensor(decomp.translate, dtype=torch.float32, requires_grad=True,device=transform.device) + self.scale = torch.tensor(decomp.scale, dtype=torch.float32, requires_grad=True,device=transform.device) + self.shear = torch.tensor(decomp.shear, dtype=torch.float32, requires_grad=True,device=transform.device) + optimvars += [{'params':x,'lr':lr} for x in [self.Theta, self.scale, self.shear]]+[{'params':self.translation,'lr':lr*tmult}] + else: + raise ValueError("Unrecognized transform mode '{}'".format(self.transform_mode)) + self.optimizer=SvgOptimizationSettings.optims[optim_params["optimizer"]](optimvars) + + def get_transform(self): + if not self.optimizes: + return self.transform + else: + if self.transform_mode == "move": + composed=OptimizableSvg.TransformTools.recompose(self.Theta,torch.tensor([1.],device=self.Theta.device),torch.tensor(0.,device=self.Theta.device),self.translation) + return self.residual.mm(composed) if self.residual is not None else composed + elif self.transform_mode == "rigid": + composed = OptimizableSvg.TransformTools.recompose(self.Theta, self.scale, torch.tensor(0.,device=self.Theta.device), + self.translation) + return self.residual.mm(composed) if self.residual is not None else composed + elif self.transform_mode == "similarity": + composed=OptimizableSvg.TransformTools.recompose(self.Theta, torch.cat((self.scale,self.scale*self.scale_sign)),torch.tensor(0.,device=self.Theta.device),self.translation) + return self.residual.mm(composed) if self.residual is not None else composed + elif self.transform_mode == "affine": + composed = OptimizableSvg.TransformTools.recompose(self.Theta, self.scale, self.shear, self.translation) + return composed + else: + raise ValueError("Unrecognized transform mode '{}'".format(self.transform_mode)) + + def tfToString(self): + if self.transform is None: + return None + elif not self.optimizes: + return OptimizableSvg.TransformTools.tf_to_string(self.transform) + else: + if self.transform_mode == "move": + str=OptimizableSvg.TransformTools.decomp_to_string((self.Theta,torch.tensor([1.]),torch.tensor(0.),self.translation)) + return (OptimizableSvg.TransformTools.tf_to_string(self.residual) if self.residual is not None else "")+" "+str + elif self.transform_mode == "rigid": + str = OptimizableSvg.TransformTools.decomp_to_string((self.Theta, self.scale, torch.tensor(0.), + self.translation)) + return (OptimizableSvg.TransformTools.tf_to_string(self.residual) if self.residual is not None else "")+" "+str + elif self.transform_mode == "similarity": + str=OptimizableSvg.TransformTools.decomp_to_string((self.Theta, torch.cat((self.scale,self.scale*self.scale_sign)),torch.tensor(0.),self.translation)) + return (OptimizableSvg.TransformTools.tf_to_string(self.residual) if self.residual is not None else "")+" "+str + elif self.transform_mode == "affine": + str = OptimizableSvg.TransformTools.decomp_to_string((self.Theta, self.scale, self.shear, self.translation)) + return composed + + def zero_grad(self): + if self.optimizes: + self.optimizer.zero_grad() + + def step(self): + if self.optimizes: + self.optimizer.step() + + #endregion + + #region Nodes + class SvgNode: + def __init__(self,id,transform,appearance,settings): + self.id=id + self.children=[] + self.optimizers=[] + self.device = settings.device + self.transform=torch.tensor(transform,dtype=torch.float32,device=self.device) if transform is not None else None + self.transform_optim=OptimizableSvg.TransformOptimizer(self.transform,settings.retrieve(self.id)[0]) + self.optimizers.append(self.transform_optim) + self.proc_appearance(appearance,settings.retrieve(self.id)[0]) + + def tftostring(self): + return self.transform_optim.tfToString() + + def appearanceToString(self): + appstring="" + for key,value in self.appearance.items(): + if key in ["fill", "stroke"]: + #a paint-type value + if value[0] == "none": + appstring+="{}:none;".format(key) + elif value[0] == "solid": + appstring += "{}:{};".format(key,OptimizableSvg.rgb_to_string(value[1])) + elif value[0] == "url": + appstring += "{}:url(#{});".format(key,value[1].id) + #appstring += "{}:{};".format(key,"#ff00ff") + elif key in ["opacity", "fill-opacity", "stroke-opacity", "stroke-width", "fill-rule"]: + appstring+="{}:{};".format(key,value) + else: + raise ValueError("Don't know how to write appearance parameter '{}'".format(key)) + return appstring + + + def write_xml_common_attrib(self,node,tfname="transform"): + if self.transform is not None: + node.set(tfname,self.tftostring()) + if len(self.appearance)>0: + node.set('style',self.appearanceToString()) + if self.id is not None: + node.set('id',self.id) + + + def proc_appearance(self,appearance,optim_params): + self.appearance=appearance + for key, value in appearance.items(): + if key == "fill" or key == "stroke": + if optim_params["optimize_color"] and value[0]=="solid": + value[1].requires_grad_(True) + self.optimizers.append(OptimizableSvg.ColorOptimizer(value[1],SvgOptimizationSettings.optims[optim_params["optimizer"]],optim_params["color_lr"])) + elif key == "fill-opacity" or key == "stroke-opacity" or key == "opacity": + if optim_params["optimize_alpha"]: + value[1].requires_grad_(True) + self.optimizers.append(OptimizableSvg.ColorOptimizer(value[1], optim_params["optimizer"], + optim_params["alpha_lr"])) + elif key == "fill-rule" or key == "stroke-width": + pass + else: + raise RuntimeError("Unrecognized appearance key '{}'".format(key)) + + def prop_transform(self,intform): + return intform.matmul(self.transform_optim.get_transform()) if self.transform is not None else intform + + def prop_appearance(self,inappearance): + outappearance=copy.copy(inappearance) + for key,value in self.appearance.items(): + if key == "fill": + #gets replaced + outappearance[key]=value + elif key == "fill-opacity": + #gets multiplied + outappearance[key] = outappearance[key]*value + elif key == "fill-rule": + #gets replaced + outappearance[key] = value + elif key =="opacity": + # gets multiplied + outappearance[key] = outappearance[key]*value + elif key == "stroke": + # gets replaced + outappearance[key] = value + elif key == "stroke-opacity": + # gets multiplied + outappearance[key] = outappearance[key]*value + elif key =="stroke-width": + # gets replaced + outappearance[key] = value + else: + raise RuntimeError("Unrecognized appearance key '{}'".format(key)) + return outappearance + + def zero_grad(self): + for optim in self.optimizers: + optim.zero_grad() + for child in self.children: + child.zero_grad() + + def step(self): + for optim in self.optimizers: + optim.step() + for child in self.children: + child.step() + + def get_type(self): + return "Generic node" + + def is_shape(self): + return False + + def build_scene(self,shapes,shape_groups,transform,appearance): + raise NotImplementedError("Abstract SvgNode cannot recurse") + + class GroupNode(SvgNode): + def __init__(self, id, transform, appearance,settings): + super().__init__(id, transform, appearance,settings) + + def get_type(self): + return "Group node" + + def build_scene(self,shapes,shape_groups,transform,appearance): + outtf=self.prop_transform(transform) + outapp=self.prop_appearance(appearance) + for child in self.children: + child.build_scene(shapes,shape_groups,outtf,outapp) + + def write_xml(self, parent): + elm=etree.SubElement(parent,"g") + self.write_xml_common_attrib(elm) + + for child in self.children: + child.write_xml(elm) + + class RootNode(SvgNode): + def __init__(self, id, transform, appearance,settings): + super().__init__(id, transform, appearance,settings) + + def write_xml(self,document): + elm=etree.Element('svg') + self.write_xml_common_attrib(elm) + elm.set("version","2.0") + elm.set("width",str(document.canvas[0])) + elm.set("height", str(document.canvas[1])) + elm.set("xmlns","http://www.w3.org/2000/svg") + elm.set("xmlns:xlink","http://www.w3.org/1999/xlink") + #write definitions before we write any children + document.write_defs(elm) + + #write the children + for child in self.children: + child.write_xml(elm) + + return elm + + def get_type(self): + return "Root node" + + def build_scene(self,shapes,shape_groups,transform,appearance): + outtf = self.prop_transform(transform).to(self.device) + for child in self.children: + child.build_scene(shapes,shape_groups,outtf,appearance) + + @staticmethod + def get_default_appearance(device): + default_appearance = {"fill": ("solid", torch.tensor([0., 0., 0.],device=device)), + "fill-opacity": torch.tensor([1.],device=device), + "fill-rule": "nonzero", + "opacity": torch.tensor([1.],device=device), + "stroke": ("none", None), + "stroke-opacity": torch.tensor([1.],device=device), + "stroke-width": torch.tensor([0.],device=device)} + return default_appearance + + @staticmethod + def get_default_transform(): + return torch.eye(3) + + + + class ShapeNode(SvgNode): + def __init__(self, id, transform, appearance,settings): + super().__init__(id, transform, appearance,settings) + + def get_type(self): + return "Generic shape node" + + def is_shape(self): + return True + + def construct_paint(self,value,combined_opacity,transform): + if value[0] == "none": + return None + elif value[0] == "solid": + return torch.cat([value[1],combined_opacity]).to(self.device) + elif value[0] == "url": + #get the gradient object from this node + return value[1].getGrad(combined_opacity,transform) + else: + raise ValueError("Unknown paint value type '{}'".format(value[0])) + + def make_shape_group(self,appearance,transform,num_shapes,num_subobjects): + fill=self.construct_paint(appearance["fill"],appearance["opacity"]*appearance["fill-opacity"],transform) + stroke=self.construct_paint(appearance["stroke"],appearance["opacity"]*appearance["stroke-opacity"],transform) + sg = pydiffvg.ShapeGroup(shape_ids=torch.tensor(range(num_shapes, num_shapes + num_subobjects)), + fill_color=fill, + use_even_odd_rule=appearance["fill-rule"]=="evenodd", + stroke_color=stroke, + shape_to_canvas=transform, + id=self.id) + return sg + + class PathNode(ShapeNode): + def __init__(self, id, transform, appearance,settings, paths): + super().__init__(id, transform, appearance,settings) + self.proc_paths(paths,settings.retrieve(self.id)[0]) + + def proc_paths(self,paths,optim_params): + self.paths=paths + if optim_params["paths"]["optimize_points"]: + ptlist=[] + for path in paths: + ptlist.append(path.points.requires_grad_(True)) + self.optimizers.append(SvgOptimizationSettings.optims[optim_params["optimizer"]](ptlist,lr=optim_params["paths"]["shape_lr"])) + + def get_type(self): + return "Path node" + + def build_scene(self,shapes,shape_groups,transform,appearance): + applytf=self.prop_transform(transform) + applyapp = self.prop_appearance(appearance) + sg=self.make_shape_group(applyapp,applytf,len(shapes),len(self.paths)) + for path in self.paths: + disp_path=pydiffvg.Path(path.num_control_points,path.points,path.is_closed,applyapp["stroke-width"],path.id) + shapes.append(disp_path) + shape_groups.append(sg) + + def path_to_string(self,path): + path_string = "M {},{} ".format(path.points[0][0].item(), path.points[0][1].item()) + idx = 1 + numpoints = path.points.shape[0] + for type in path.num_control_points: + toproc = type + 1 + if type == 0: + # add line + path_string += "L " + elif type == 1: + # add quadric + path_string += "Q " + elif type == 2: + # add cubic + path_string += "C " + while toproc > 0: + path_string += "{},{} ".format(path.points[idx % numpoints][0].item(), + path.points[idx % numpoints][1].item()) + idx += 1 + toproc -= 1 + if path.is_closed: + path_string += "Z " + + return path_string + + def paths_string(self): + pstr="" + for path in self.paths: + pstr+=self.path_to_string(path) + return pstr + + def write_xml(self, parent): + elm = etree.SubElement(parent, "path") + self.write_xml_common_attrib(elm) + elm.set("d",self.paths_string()) + + for child in self.children: + child.write_xml(elm) + + class RectNode(ShapeNode): + def __init__(self, id, transform, appearance,settings, rect): + super().__init__(id, transform, appearance,settings) + self.rect=torch.tensor(rect,dtype=torch.float,device=settings.device) + optim_params=settings.retrieve(self.id)[0] + #borrowing path settings for this + if optim_params["paths"]["optimize_points"]: + self.optimizers.append(SvgOptimizationSettings.optims[optim_params["optimizer"]]([self.rect],lr=optim_params["paths"]["shape_lr"])) + + def get_type(self): + return "Rect node" + + def build_scene(self,shapes,shape_groups,transform,appearance): + applytf=self.prop_transform(transform) + applyapp = self.prop_appearance(appearance) + sg=self.make_shape_group(applyapp,applytf,len(shapes),1) + shapes.append(pydiffvg.Rect(self.rect[0:2],self.rect[0:2]+self.rect[2:4],applyapp["stroke-width"],self.id)) + shape_groups.append(sg) + + def write_xml(self, parent): + elm = etree.SubElement(parent, "rect") + self.write_xml_common_attrib(elm) + elm.set("x",str(self.rect[0])) + elm.set("y", str(self.rect[1])) + elm.set("width", str(self.rect[2])) + elm.set("height", str(self.rect[3])) + + for child in self.children: + child.write_xml(elm) + + class CircleNode(ShapeNode): + def __init__(self, id, transform, appearance,settings, rect): + super().__init__(id, transform, appearance,settings) + self.circle=torch.tensor(rect,dtype=torch.float,device=settings.device) + optim_params=settings.retrieve(self.id)[0] + #borrowing path settings for this + if optim_params["paths"]["optimize_points"]: + self.optimizers.append(SvgOptimizationSettings.optims[optim_params["optimizer"]]([self.circle],lr=optim_params["paths"]["shape_lr"])) + + def get_type(self): + return "Circle node" + + def build_scene(self,shapes,shape_groups,transform,appearance): + applytf=self.prop_transform(transform) + applyapp = self.prop_appearance(appearance) + sg=self.make_shape_group(applyapp,applytf,len(shapes),1) + shapes.append(pydiffvg.Circle(self.circle[2],self.circle[0:2],applyapp["stroke-width"],self.id)) + shape_groups.append(sg) + + def write_xml(self, parent): + elm = etree.SubElement(parent, "circle") + self.write_xml_common_attrib(elm) + elm.set("cx",str(self.circle[0])) + elm.set("cy", str(self.circle[1])) + elm.set("r", str(self.circle[2])) + + for child in self.children: + child.write_xml(elm) + + + class EllipseNode(ShapeNode): + def __init__(self, id, transform, appearance,settings, ellipse): + super().__init__(id, transform, appearance,settings) + self.ellipse=torch.tensor(ellipse,dtype=torch.float,device=settings.device) + optim_params=settings.retrieve(self.id)[0] + #borrowing path settings for this + if optim_params["paths"]["optimize_points"]: + self.optimizers.append(SvgOptimizationSettings.optims[optim_params["optimizer"]]([self.ellipse],lr=optim_params["paths"]["shape_lr"])) + + def get_type(self): + return "Ellipse node" + + def build_scene(self,shapes,shape_groups,transform,appearance): + applytf=self.prop_transform(transform) + applyapp = self.prop_appearance(appearance) + sg=self.make_shape_group(applyapp,applytf,len(shapes),1) + shapes.append(pydiffvg.Ellipse(self.ellipse[2:4],self.ellipse[0:2],applyapp["stroke-width"],self.id)) + shape_groups.append(sg) + + def write_xml(self, parent): + elm = etree.SubElement(parent, "ellipse") + self.write_xml_common_attrib(elm) + elm.set("cx", str(self.ellipse[0])) + elm.set("cy", str(self.ellipse[1])) + elm.set("rx", str(self.ellipse[2])) + elm.set("ry", str(self.ellipse[3])) + + for child in self.children: + child.write_xml(elm) + + class PolygonNode(ShapeNode): + def __init__(self, id, transform, appearance,settings, points): + super().__init__(id, transform, appearance,settings) + self.points=points + optim_params=settings.retrieve(self.id)[0] + #borrowing path settings for this + if optim_params["paths"]["optimize_points"]: + self.optimizers.append(SvgOptimizationSettings.optims[optim_params["optimizer"]]([self.points],lr=optim_params["paths"]["shape_lr"])) + + def get_type(self): + return "Polygon node" + + def build_scene(self,shapes,shape_groups,transform,appearance): + applytf=self.prop_transform(transform) + applyapp = self.prop_appearance(appearance) + sg=self.make_shape_group(applyapp,applytf,len(shapes),1) + shapes.append(pydiffvg.Polygon(self.points,True,applyapp["stroke-width"],self.id)) + shape_groups.append(sg) + + def point_string(self): + ret="" + for i in range(self.points.shape[0]): + pt=self.points[i,:] + #assert pt.shape == (1,2) + ret+= str(pt[0])+","+str(pt[1])+" " + return ret + + def write_xml(self, parent): + elm = etree.SubElement(parent, "polygon") + self.write_xml_common_attrib(elm) + elm.set("points",self.point_string()) + + for child in self.children: + child.write_xml(elm) + + class GradientNode(SvgNode): + def __init__(self, id, transform,settings,begin,end,offsets,stops,href): + super().__init__(id, transform, {},settings) + self.optim=OptimizableSvg.GradientOptimizer(begin, end, offsets, stops, settings.retrieve(id)[0]) + self.optimizers.append(self.optim) + self.href=href + + def is_ref(self): + return self.href is not None + + def get_type(self): + return "Gradient node" + + def get_stops(self): + _, _, offsets, stops=self.optim.get_vals() + return offsets, stops + + def get_points(self): + begin, end, _, _ =self.optim.get_vals() + return begin, end + + def write_xml(self, parent): + elm = etree.SubElement(parent, "linearGradient") + self.write_xml_common_attrib(elm,tfname="gradientTransform") + + begin, end, offsets, stops = self.optim.get_vals() + + if self.href is None: + #we have stops + for idx, offset in enumerate(offsets): + stop=etree.SubElement(elm,"stop") + stop.set("offset",str(offset.item())) + stop.set("stop-color",OptimizableSvg.rgb_to_string(stops[idx,0:3])) + stop.set("stop-opacity",str(stops[idx,3].item())) + else: + elm.set('xlink:href', "#{}".format(self.href.id)) + + if begin is not None and end is not None: + #no stops + elm.set('x1', str(begin[0].item())) + elm.set('y1', str(begin[1].item())) + elm.set('x2', str(end[0].item())) + elm.set('y2', str(end[1].item())) + + # magic value to make this work + elm.set("gradientUnits", "userSpaceOnUse") + + for child in self.children: + child.write_xml(elm) + + def getGrad(self,combined_opacity,transform): + if self.is_ref(): + offsets, stops=self.href.get_stops() + else: + offsets, stops=self.get_stops() + + stops=stops.clone() + stops[:,3]*=combined_opacity + + begin,end = self.get_points() + + applytf=self.prop_transform(transform) + begin=OptimizableSvg.TransformTools.transformPoints(begin.unsqueeze(0),applytf).squeeze() + end = OptimizableSvg.TransformTools.transformPoints(end.unsqueeze(0), applytf).squeeze() + + return pydiffvg.LinearGradient(begin, end, offsets, stops) + #endregion + + def __init__(self, filename, settings=SvgOptimizationSettings(),optimize_background=False, verbose=False, device=torch.device("cpu")): + self.settings=settings + self.verbose=verbose + self.device=device + self.settings.device=device + + tree = etree.parse(filename) + root = tree.getroot() + + #in case we need global optimization + self.optimizers=[] + self.background=torch.tensor([1.,1.,1.],dtype=torch.float32,requires_grad=optimize_background,device=self.device) + + if optimize_background: + p=settings.retrieve("default")[0] + self.optimizers.append(OptimizableSvg.ColorOptimizer(self.background,SvgOptimizationSettings.optims[p["optimizer"]],p["color_lr"])) + + self.defs={} + + self.depth=0 + + self.dirty=True + self.scene=None + + self.parseRoot(root) + + recognised_shapes=["path","circle","rect","ellipse","polygon"] + + #region core functionality + def build_scene(self): + if self.dirty: + shape_groups=[] + shapes=[] + self.root.build_scene(shapes,shape_groups,OptimizableSvg.RootNode.get_default_transform().to(self.device),OptimizableSvg.RootNode.get_default_appearance(self.device)) + self.scene=(self.canvas[0],self.canvas[1],shapes,shape_groups) + self.dirty=False + return self.scene + + def zero_grad(self): + self.root.zero_grad() + for optim in self.optimizers: + optim.zero_grad() + for item in self.defs.values(): + if issubclass(item.__class__,OptimizableSvg.SvgNode): + item.zero_grad() + + def render(self,scale=None,seed=0): + #render at native resolution + scene = self.build_scene() + scene_args = pydiffvg.RenderFunction.serialize_scene(*scene) + render = pydiffvg.RenderFunction.apply + out_size=(scene[0],scene[1]) if scale is None else (int(scene[0]*scale),int(scene[1]*scale)) + img = render(out_size[0], # width + out_size[1], # height + 2, # num_samples_x + 2, # num_samples_y + seed, # seed + *scene_args) + return img + + def step(self): + self.dirty=True + self.root.step() + for optim in self.optimizers: + optim.step() + for item in self.defs.values(): + if issubclass(item.__class__, OptimizableSvg.SvgNode): + item.step() + #endregion + + #region reporting + + def offset_str(self,s): + return ("\t"*self.depth)+s + + def reportSkippedAttribs(self, node, non_skipped=[]): + skipped=set([k for k in node.attrib.keys() if not OptimizableSvg.is_namespace(k)])-set(non_skipped) + if len(skipped)>0: + tag=OptimizableSvg.remove_namespace(node.tag) if "id" not in node.attrib else "{}#{}".format(OptimizableSvg.remove_namespace(node.tag),node.attrib["id"]) + print(self.offset_str("Warning: Skipping the following attributes of node '{}': {}".format(tag,", ".join(["'{}'".format(atr) for atr in skipped])))) + + def reportSkippedChildren(self,node,skipped): + skipped_names=["{}#{}".format(elm.tag,elm.attrib["id"]) if "id" in elm.attrib else elm.tag for elm in skipped] + if len(skipped)>0: + tag = OptimizableSvg.remove_namespace(node.tag) if "id" not in node.attrib else "{}#{}".format(OptimizableSvg.remove_namespace(node.tag), + node.attrib["id"]) + print(self.offset_str("Warning: Skipping the following children of node '{}': {}".format(tag,", ".join(["'{}'".format(name) for name in skipped_names])))) + + #endregion + + #region parsing + @staticmethod + def remove_namespace(s): + """ + {...} ... -> ... + """ + return re.sub('{.*}', '', s) + + @staticmethod + def is_namespace(s): + return re.match('{.*}', s) is not None + + @staticmethod + def parseTransform(node): + if "transform" not in node.attrib and "gradientTransform" not in node.attrib: + return None + + tf_string=node.attrib["transform"] if "transform" in node.attrib else node.attrib["gradientTransform"] + tforms=tf_string.split(")")[:-1] + mat=np.eye(3) + for tform in tforms: + type = tform.split("(")[0] + args = [float(val) for val in re.split("[, ]+",tform.split("(")[1])] + if type == "matrix": + mat=mat @ OptimizableSvg.TransformTools.parse_matrix(args) + elif type == "translate": + mat = mat @ OptimizableSvg.TransformTools.parse_translate(args) + elif type == "rotate": + mat = mat @ OptimizableSvg.TransformTools.parse_rotate(args) + elif type == "scale": + mat = mat @ OptimizableSvg.TransformTools.parse_scale(args) + elif type == "skewX": + mat = mat @ OptimizableSvg.TransformTools.parse_skewx(args) + elif type == "skewY": + mat = mat @ OptimizableSvg.TransformTools.parse_skewy(args) + else: + raise ValueError("Unknown transform type '{}'".format(type)) + return mat + + #dictionary that defines what constant do we need to multiply different units to get the value in pixels + #gleaned from the CSS definition + unit_dict = {"px":1, + "mm":4, + "cm":40, + "in":25.4*4, + "pt":25.4*4/72, + "pc":25.4*4/6 + } + + @staticmethod + def parseLength(s): + #length is a number followed possibly by a unit definition + #we assume that default unit is the pixel (px) equal to 0.25mm + #last two characters might be unit + val=None + for i in range(len(s)): + try: + val=float(s[:len(s)-i]) + unit=s[len(s)-i:] + break + except ValueError: + continue + if len(unit)>0 and unit not in OptimizableSvg.unit_dict: + raise ValueError("Unknown or unsupported unit '{}' encountered while parsing".format(unit)) + if unit != "": + val*=OptimizableSvg.unit_dict[unit] + return val + + @staticmethod + def parseOpacity(s): + is_percent=s.endswith("%") + s=s.rstrip("%") + val=float(s) + if is_percent: + val=val/100 + return np.clip(val,0.,1.) + + @staticmethod + def parse_color(s): + """ + Hex to tuple + """ + if s[0] != '#': + raise ValueError("Color argument `{}` not supported".format(s)) + s = s.lstrip('#') + if len(s)==6: + rgb = tuple(int(s[i:i + 2], 16) for i in (0, 2, 4)) + return torch.tensor([rgb[0] / 255.0, rgb[1] / 255.0, rgb[2] / 255.0]) + elif len(s)==3: + rgb = tuple((int(s[i:i + 1], 16)) for i in (0, 1, 2)) + return torch.tensor([rgb[0] / 15.0, rgb[1] / 15.0, rgb[2] / 15.0]) + else: + raise ValueError("Color argument `{}` not supported".format(s)) + # sRGB to RGB + # return torch.pow(torch.tensor([rgb[0] / 255.0, rgb[1] / 255.0, rgb[2] / 255.0]), 2.2) + + + @staticmethod + def rgb_to_string(val): + byte_rgb=(val.clone().detach()*255).type(torch.int) + byte_rgb.clamp_(min=0,max=255) + s="#{:02x}{:02x}{:02x}".format(*byte_rgb) + return s + + #parses a "paint" string for use in fill and stroke definitions + @staticmethod + def parsePaint(paintStr,defs,device): + paintStr=paintStr.strip() + if paintStr=="none": + return ("none", None) + elif paintStr[0]=="#": + return ("solid",OptimizableSvg.parse_color(paintStr).to(device)) + elif paintStr.startswith("url"): + url=paintStr.lstrip("url(").rstrip(")").strip("\'\"").lstrip("#") + if url not in defs: + raise ValueError("Paint-type attribute referencing an unknown object with ID '#{}'".format(url)) + return ("url",defs[url]) + else: + raise ValueError("Unrecognized paint string: '{}'".format(paintStr)) + + appearance_keys=["fill","fill-opacity","fill-rule","opacity","stroke","stroke-opacity","stroke-width"] + + @staticmethod + def parseAppearance(node, defs, device): + ret={} + parse_keys = OptimizableSvg.appearance_keys + local_dict={key:value for key,value in node.attrib.items() if key in parse_keys} + css_dict={} + style_dict={} + appearance_dict={} + if "class" in node.attrib: + cls=node.attrib["class"] + if "."+cls in defs: + css_string=defs["."+cls] + css_dict={item.split(":")[0]:item.split(":")[1] for item in css_string.split(";") if len(item)>0 and item.split(":")[0] in parse_keys} + if "style" in node.attrib: + style_string=node.attrib["style"] + style_dict={item.split(":")[0]:item.split(":")[1] for item in style_string.split(";") if len(item)>0 and item.split(":")[0] in parse_keys} + appearance_dict.update(css_dict) + appearance_dict.update(style_dict) + appearance_dict.update(local_dict) + for key,value in appearance_dict.items(): + if key=="fill": + ret[key]=OptimizableSvg.parsePaint(value,defs,device) + elif key == "fill-opacity": + ret[key]=torch.tensor(OptimizableSvg.parseOpacity(value),device=device) + elif key == "fill-rule": + ret[key]=value + elif key == "opacity": + ret[key]=torch.tensor(OptimizableSvg.parseOpacity(value),device=device) + elif key == "stroke": + ret[key]=OptimizableSvg.parsePaint(value,defs,device) + elif key == "stroke-opacity": + ret[key]=torch.tensor(OptimizableSvg.parseOpacity(value),device=device) + elif key == "stroke-width": + ret[key]=torch.tensor(OptimizableSvg.parseLength(value),device=device) + else: + raise ValueError("Error while parsing appearance attributes: key '{}' should not be here".format(key)) + + return ret + + def parseRoot(self,root): + if self.verbose: + print(self.offset_str("Parsing root")) + self.depth += 1 + + # get document canvas dimensions + self.parseViewport(root) + canvmax=np.max(self.canvas) + self.settings.global_override(["transforms","translation_mult"],canvmax) + id=root.attrib["id"] if "id" in root.attrib else None + + transform=OptimizableSvg.parseTransform(root) + appearance=OptimizableSvg.parseAppearance(root,self.defs,self.device) + + version=root.attrib["version"] if "version" in root.attrib else "" + if version != "2.0": + print(self.offset_str("Warning: Version {} is not 2.0, strange things may happen".format(version))) + + self.root=OptimizableSvg.RootNode(id,transform,appearance,self.settings) + + if self.verbose: + self.reportSkippedAttribs(root, ["width", "height", "id", "transform","version", "style"]+OptimizableSvg.appearance_keys) + + #go through the root children and parse them appropriately + skipped=[] + for child in root: + if OptimizableSvg.remove_namespace(child.tag) in OptimizableSvg.recognised_shapes: + self.parseShape(child,self.root) + elif OptimizableSvg.remove_namespace(child.tag) == "defs": + self.parseDefs(child) + elif OptimizableSvg.remove_namespace(child.tag) == "style": + self.parseStyle(child) + elif OptimizableSvg.remove_namespace(child.tag) == "g": + self.parseGroup(child,self.root) + else: + skipped.append(child) + + if self.verbose: + self.reportSkippedChildren(root,skipped) + + self.depth-=1 + + def parseShape(self,shape,parent): + tag=OptimizableSvg.remove_namespace(shape.tag) + if self.verbose: + print(self.offset_str("Parsing {}#{}".format(tag,shape.attrib["id"] if "id" in shape.attrib else ""))) + + self.depth+=1 + if tag == "path": + self.parsePath(shape,parent) + elif tag == "circle": + self.parseCircle(shape,parent) + elif tag == "rect": + self.parseRect(shape,parent) + elif tag == "ellipse": + self.parseEllipse(shape,parent) + elif tag == "polygon": + self.parsePolygon(shape,parent) + else: + raise ValueError("Encountered unknown shape type '{}'".format(tag)) + self.depth -= 1 + + def parsePath(self,shape,parent): + path_string=shape.attrib['d'] + name = '' + if 'id' in shape.attrib: + name = shape.attrib['id'] + paths = pydiffvg.from_svg_path(path_string) + for idx, path in enumerate(paths): + path.stroke_width = torch.tensor([0.],device=self.device) + path.num_control_points=path.num_control_points.to(self.device) + path.points=path.points.to(self.device) + path.source_id = name + path.id = "{}-{}".format(name,idx) if len(paths)>1 else name + transform = OptimizableSvg.parseTransform(shape) + appearance = OptimizableSvg.parseAppearance(shape,self.defs,self.device) + node=OptimizableSvg.PathNode(name,transform,appearance,self.settings,paths) + parent.children.append(node) + + if self.verbose: + self.reportSkippedAttribs(shape, ["id","d","transform","style"]+OptimizableSvg.appearance_keys) + self.reportSkippedChildren(shape,list(shape)) + + def parseEllipse(self, shape, parent): + cx = float(shape.attrib["cx"]) if "cx" in shape.attrib else 0. + cy = float(shape.attrib["cy"]) if "cy" in shape.attrib else 0. + rx = float(shape.attrib["rx"]) + ry = float(shape.attrib["ry"]) + name = '' + if 'id' in shape.attrib: + name = shape.attrib['id'] + transform = OptimizableSvg.parseTransform(shape) + appearance = OptimizableSvg.parseAppearance(shape, self.defs, self.device) + node = OptimizableSvg.EllipseNode(name, transform, appearance, self.settings, (cx, cy, rx, ry)) + parent.children.append(node) + + if self.verbose: + self.reportSkippedAttribs(shape, ["id", "x", "y", "r", "transform", + "style"] + OptimizableSvg.appearance_keys) + self.reportSkippedChildren(shape, list(shape)) + + def parsePolygon(self, shape, parent): + points_string = shape.attrib['points'] + name = '' + points=[] + for point_string in points_string.split(" "): + if len(point_string) == 0: + continue + coord_strings=point_string.split(",") + assert len(coord_strings)==2 + points.append([float(coord_strings[0]),float(coord_strings[1])]) + points=torch.tensor(points,dtype=torch.float,device=self.device) + if 'id' in shape.attrib: + name = shape.attrib['id'] + transform = OptimizableSvg.parseTransform(shape) + appearance = OptimizableSvg.parseAppearance(shape, self.defs, self.device) + node = OptimizableSvg.PolygonNode(name, transform, appearance, self.settings, points) + parent.children.append(node) + + if self.verbose: + self.reportSkippedAttribs(shape, ["id", "points", "transform", "style"] + OptimizableSvg.appearance_keys) + self.reportSkippedChildren(shape, list(shape)) + + def parseCircle(self,shape,parent): + cx = float(shape.attrib["cx"]) if "cx" in shape.attrib else 0. + cy = float(shape.attrib["cy"]) if "cy" in shape.attrib else 0. + r = float(shape.attrib["r"]) + name = '' + if 'id' in shape.attrib: + name = shape.attrib['id'] + transform = OptimizableSvg.parseTransform(shape) + appearance = OptimizableSvg.parseAppearance(shape, self.defs, self.device) + node = OptimizableSvg.CircleNode(name, transform, appearance, self.settings, (cx, cy, r)) + parent.children.append(node) + + if self.verbose: + self.reportSkippedAttribs(shape, ["id", "x", "y", "r", "transform", + "style"] + OptimizableSvg.appearance_keys) + self.reportSkippedChildren(shape, list(shape)) + + def parseRect(self,shape,parent): + x = float(shape.attrib["x"]) if "x" in shape.attrib else 0. + y = float(shape.attrib["y"]) if "y" in shape.attrib else 0. + width = float(shape.attrib["width"]) + height = float(shape.attrib["height"]) + name = '' + if 'id' in shape.attrib: + name = shape.attrib['id'] + transform = OptimizableSvg.parseTransform(shape) + appearance = OptimizableSvg.parseAppearance(shape, self.defs, self.device) + node = OptimizableSvg.RectNode(name, transform, appearance, self.settings, (x,y,width,height)) + parent.children.append(node) + + if self.verbose: + self.reportSkippedAttribs(shape, ["id", "x", "y", "width", "height", "transform", "style"] + OptimizableSvg.appearance_keys) + self.reportSkippedChildren(shape, list(shape)) + + def parseGroup(self,group,parent): + tag = OptimizableSvg.remove_namespace(group.tag) + id = group.attrib["id"] if "id" in group.attrib else "" + if self.verbose: + print(self.offset_str("Parsing {}#{}".format(tag, id))) + + self.depth+=1 + + transform=self.parseTransform(group) + + #todo process more attributes + appearance=OptimizableSvg.parseAppearance(group,self.defs,self.device) + node=OptimizableSvg.GroupNode(id,transform,appearance,self.settings) + parent.children.append(node) + + if self.verbose: + self.reportSkippedAttribs(group,["id","transform","style"]+OptimizableSvg.appearance_keys) + + skipped_children=[] + for child in group: + if OptimizableSvg.remove_namespace(child.tag) in OptimizableSvg.recognised_shapes: + self.parseShape(child,node) + elif OptimizableSvg.remove_namespace(child.tag) == "defs": + self.parseDefs(child) + elif OptimizableSvg.remove_namespace(child.tag) == "style": + self.parseStyle(child) + elif OptimizableSvg.remove_namespace(child.tag) == "g": + self.parseGroup(child,node) + else: + skipped_children.append(child) + + if self.verbose: + self.reportSkippedChildren(group,skipped_children) + + self.depth-=1 + + def parseStyle(self,style_node): + tag = OptimizableSvg.remove_namespace(style_node.tag) + id = style_node.attrib["id"] if "id" in style_node.attrib else "" + if self.verbose: + print(self.offset_str("Parsing {}#{}".format(tag, id))) + + if style_node.attrib["type"] != "text/css": + raise ValueError("Only text/css style recognized, got {}".format(style_node.attrib["type"])) + + self.depth += 1 + + # creating only a dummy node + node = OptimizableSvg.SvgNode(id, None, {}, self.settings) + + if self.verbose: + self.reportSkippedAttribs(def_node, ["id"]) + + if len(style_node)>0: + raise ValueError("Style node should not have children (has {})".format(len(style_node))) + + # collect CSS classes + sheet = cssutils.parseString(style_node.text) + for rule in sheet: + if hasattr(rule, 'selectorText') and hasattr(rule, 'style'): + name = rule.selectorText + if len(name) >= 2 and name[0] == '.': + self.defs[name] = rule.style.getCssText().replace("\n","") + else: + raise ValueError("Unrecognized CSS selector {}".format(name)) + else: + raise ValueError("No style or selector text in CSS rule") + + if self.verbose: + self.reportSkippedChildren(def_node, skipped_children) + + self.depth -= 1 + + def parseDefs(self,def_node): + #only linear gradients are currently supported + tag = OptimizableSvg.remove_namespace(def_node.tag) + id = def_node.attrib["id"] if "id" in def_node.attrib else "" + if self.verbose: + print(self.offset_str("Parsing {}#{}".format(tag, id))) + + self.depth += 1 + + + # creating only a dummy node + node = OptimizableSvg.SvgNode(id, None, {},self.settings) + + if self.verbose: + self.reportSkippedAttribs(def_node, ["id"]) + + skipped_children = [] + for child in def_node: + if OptimizableSvg.remove_namespace(child.tag) == "linearGradient": + self.parseGradient(child,node) + elif OptimizableSvg.remove_namespace(child.tag) in OptimizableSvg.recognised_shapes: + raise NotImplementedError("Definition/instantiation of shapes not supported") + elif OptimizableSvg.remove_namespace(child.tag) == "defs": + raise NotImplementedError("Definition within definition not supported") + elif OptimizableSvg.remove_namespace(child.tag) == "g": + raise NotImplementedError("Groups within definition not supported") + else: + skipped_children.append(child) + + if len(node.children)>0: + #take this node out and enter it into defs + self.defs[node.children[0].id]=node.children[0] + node.children.pop() + + + if self.verbose: + self.reportSkippedChildren(def_node, skipped_children) + + self.depth -= 1 + + def parseGradientStop(self,stop): + param_dict={key:value for key,value in stop.attrib.items() if key in ["id","offset","stop-color","stop-opacity"]} + style_dict={} + if "style" in stop.attrib: + style_dict={item.split(":")[0]:item.split(":")[1] for item in stop.attrib["style"].split(";") if len(item)>0} + param_dict.update(style_dict) + + offset=OptimizableSvg.parseOpacity(param_dict["offset"]) + color=OptimizableSvg.parse_color(param_dict["stop-color"]) + opacity=OptimizableSvg.parseOpacity(param_dict["stop-opacity"]) if "stop-opacity" in param_dict else 1. + + return offset, color, opacity + + def parseGradient(self, gradient_node, parent): + tag = OptimizableSvg.remove_namespace(gradient_node.tag) + id = gradient_node.attrib["id"] if "id" in gradient_node.attrib else "" + if self.verbose: + print(self.offset_str("Parsing {}#{}".format(tag, id))) + + self.depth += 1 + if "stop" not in [OptimizableSvg.remove_namespace(child.tag) for child in gradient_node]\ + and "href" not in [OptimizableSvg.remove_namespace(key) for key in gradient_node.attrib.keys()]: + raise ValueError("Gradient {} has neither stops nor a href link to them".format(id)) + + transform=self.parseTransform(gradient_node) + begin=None + end = None + offsets=[] + stops=[] + href=None + + if "x1" in gradient_node.attrib or "y1" in gradient_node.attrib: + begin=np.array([0.,0.]) + if "x1" in gradient_node.attrib: + begin[0] = float(gradient_node.attrib["x1"]) + if "y1" in gradient_node.attrib: + begin[1] = float(gradient_node.attrib["y1"]) + begin = torch.tensor(begin.transpose(),dtype=torch.float32) + + if "x2" in gradient_node.attrib or "y2" in gradient_node.attrib: + end=np.array([0.,0.]) + if "x2" in gradient_node.attrib: + end[0] = float(gradient_node.attrib["x2"]) + if "y2" in gradient_node.attrib: + end[1] = float(gradient_node.attrib["y2"]) + end=torch.tensor(end.transpose(),dtype=torch.float32) + + stop_nodes=[node for node in list(gradient_node) if OptimizableSvg.remove_namespace(node.tag)=="stop"] + if len(stop_nodes)>0: + stop_nodes=sorted(stop_nodes,key=lambda n: float(n.attrib["offset"])) + + for stop in stop_nodes: + offset, color, opacity = self.parseGradientStop(stop) + offsets.append(offset) + stops.append(np.concatenate((color,np.array([opacity])))) + + hkey=next((value for key,value in gradient_node.attrib.items() if OptimizableSvg.remove_namespace(key)=="href"),None) + if hkey is not None: + href=self.defs[hkey.lstrip("#")] + + parent.children.append(OptimizableSvg.GradientNode(id,transform,self.settings,begin.to(self.device) if begin is not None else begin,end.to(self.device) if end is not None else end,torch.tensor(offsets,dtype=torch.float32,device=self.device) if len(offsets)>0 else None,torch.tensor(np.array(stops),dtype=torch.float32,device=self.device) if len(stops)>0 else None,href)) + + self.depth -= 1 + + def parseViewport(self, root): + if "width" in root.attrib and "height" in root.attrib: + self.canvas = np.array([int(math.ceil(float(root.attrib["width"]))), int(math.ceil(float(root.attrib["height"])))]) + elif "viewBox" in root.attrib: + s=root.attrib["viewBox"].split(" ") + w=s[2] + h=s[3] + self.canvas = np.array( + [int(math.ceil(float(w))), int(math.ceil(float(h)))]) + else: + raise ValueError("Size information is missing from document definition") + #endregion + + #region writing + def write_xml(self): + tree=self.root.write_xml(self) + + return minidom.parseString(etree.tostring(tree, 'utf-8')).toprettyxml(indent=" ") + + def write_defs(self,root): + if len(self.defs)==0: + return + + defnode = etree.SubElement(root, 'defs') + stylenode = etree.SubElement(root,'style') + stylenode.set('type','text/css') + stylenode.text="" + + defcpy=copy.copy(self.defs) + while len(defcpy)>0: + torem=[] + for key,value in defcpy.items(): + if issubclass(value.__class__,OptimizableSvg.SvgNode): + if value.href is None or value.href not in defcpy: + value.write_xml(defnode) + torem.append(key) + else: + continue + else: + #this is a string, and hence a CSS attribute + stylenode.text+=key+" {"+value+"}\n" + torem.append(key) + + for key in torem: + del defcpy[key] + #endregion + + diff --git a/pydiffvg/parse_svg.py b/pydiffvg/parse_svg.py new file mode 100644 index 0000000..17d91b3 --- /dev/null +++ b/pydiffvg/parse_svg.py @@ -0,0 +1,578 @@ +import torch +import xml.etree.ElementTree as etree +import numpy as np +import diffvg +import os +import pydiffvg +import svgpathtools +import svgpathtools.parser +import re +import warnings +import cssutils +import logging +cssutils.log.setLevel(logging.ERROR) + +def remove_namespaces(s): + """ + {...} ... -> ... + """ + return re.sub('{.*}', '', s) + +def parse_style(s, defs): + style_dict = {} + for e in s.split(';'): + key_value = e.split(':') + if len(key_value) == 2: + key = key_value[0].strip() + value = key_value[1].strip() + if key == 'fill' or key == 'stroke': + # Special case: convert colors into tensor in definitions so + # that different shapes can share the same color + value = parse_color(value, defs) + style_dict[key] = value + return style_dict + +def parse_hex(s): + """ + Hex to tuple + """ + s = s.lstrip('#') + if len(s) == 3: + s = s[0] + s[0] + s[1] + s[1] + s[2] + s[2] + rgb = tuple(int(s[i:i+2], 16) for i in (0, 2, 4)) + # sRGB to RGB + # return torch.pow(torch.tensor([rgb[0] / 255.0, rgb[1] / 255.0, rgb[2] / 255.0]), 2.2) + return torch.pow(torch.tensor([rgb[0] / 255.0, rgb[1] / 255.0, rgb[2] / 255.0]), 1.0) + +def parse_int(s): + """ + trim alphabets + """ + return int(float(''.join(i for i in s if (not i.isalpha())))) + +def parse_color(s, defs): + if s is None: + return None + if isinstance(s, torch.Tensor): + return s + s = s.lstrip(' ') + color = torch.tensor([0.0, 0.0, 0.0, 1.0]) + if s[0] == '#': + color[:3] = parse_hex(s) + elif s[:3] == 'url': + # url(#id) + color = defs[s[4:-1].lstrip('#')] + elif s == 'none': + color = None + elif s[:4] == 'rgb(': + rgb = s[4:-1].split(',') + color = torch.tensor([int(rgb[0]) / 255.0, int(rgb[1]) / 255.0, int(rgb[2]) / 255.0, 1.0]) + elif s == 'none': + return None + else: + warnings.warn('Unknown color command ' + s) + return color + +# https://github.com/mathandy/svgpathtools/blob/7ebc56a831357379ff22216bec07e2c12e8c5bc6/svgpathtools/parser.py +def _parse_transform_substr(transform_substr): + type_str, value_str = transform_substr.split('(') + value_str = value_str.replace(',', ' ') + values = list(map(float, filter(None, value_str.split(' ')))) + + transform = np.identity(3) + if 'matrix' in type_str: + transform[0:2, 0:3] = np.array([values[0:6:2], values[1:6:2]]) + elif 'translate' in transform_substr: + transform[0, 2] = values[0] + if len(values) > 1: + transform[1, 2] = values[1] + elif 'scale' in transform_substr: + x_scale = values[0] + y_scale = values[1] if (len(values) > 1) else x_scale + transform[0, 0] = x_scale + transform[1, 1] = y_scale + elif 'rotate' in transform_substr: + angle = values[0] * np.pi / 180.0 + if len(values) == 3: + offset = values[1:3] + else: + offset = (0, 0) + tf_offset = np.identity(3) + tf_offset[0:2, 2:3] = np.array([[offset[0]], [offset[1]]]) + tf_rotate = np.identity(3) + tf_rotate[0:2, 0:2] = np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]]) + tf_offset_neg = np.identity(3) + tf_offset_neg[0:2, 2:3] = np.array([[-offset[0]], [-offset[1]]]) + + transform = tf_offset.dot(tf_rotate).dot(tf_offset_neg) + elif 'skewX' in transform_substr: + transform[0, 1] = np.tan(values[0] * np.pi / 180.0) + elif 'skewY' in transform_substr: + transform[1, 0] = np.tan(values[0] * np.pi / 180.0) + else: + # Return an identity matrix if the type of transform is unknown, and warn the user + warnings.warn('Unknown SVG transform type: {0}'.format(type_str)) + return transform + +def parse_transform(transform_str): + """ + Converts a valid SVG transformation string into a 3x3 matrix. + If the string is empty or null, this returns a 3x3 identity matrix + """ + if not transform_str: + return np.identity(3) + elif not isinstance(transform_str, str): + raise TypeError('Must provide a string to parse') + + total_transform = np.identity(3) + transform_substrs = transform_str.split(')')[:-1] # Skip the last element, because it should be empty + for substr in transform_substrs: + total_transform = total_transform.dot(_parse_transform_substr(substr)) + + return torch.from_numpy(total_transform).type(torch.float32) + +def parse_linear_gradient(node, transform, defs): + begin = torch.tensor([0.0, 0.0]) + end = torch.tensor([0.0, 0.0]) + offsets = [] + stop_colors = [] + # Inherit from parent + for key in node.attrib: + if remove_namespaces(key) == 'href': + value = node.attrib[key] + parent = defs[value.lstrip('#')] + begin = parent.begin + end = parent.end + offsets = parent.offsets + stop_colors = parent.stop_colors + + for attrib in node.attrib: + attrib = remove_namespaces(attrib) + if attrib == 'x1': + begin[0] = float(node.attrib['x1']) + elif attrib == 'y1': + begin[1] = float(node.attrib['y1']) + elif attrib == 'x2': + end[0] = float(node.attrib['x2']) + elif attrib == 'y2': + end[1] = float(node.attrib['y2']) + elif attrib == 'gradientTransform': + transform = transform @ parse_transform(node.attrib['gradientTransform']) + + begin = transform @ torch.cat((begin, torch.ones([1]))) + begin = begin / begin[2] + begin = begin[:2] + end = transform @ torch.cat((end, torch.ones([1]))) + end = end / end[2] + end = end[:2] + + for child in node: + tag = remove_namespaces(child.tag) + if tag == 'stop': + offset = float(child.attrib['offset']) + color = [0.0, 0.0, 0.0, 1.0] + if 'stop-color' in child.attrib: + c = parse_color(child.attrib['stop-color'], defs) + color[:3] = [c[0], c[1], c[2]] + if 'stop-opacity' in child.attrib: + color[3] = float(child.attrib['stop-opacity']) + if 'style' in child.attrib: + style = parse_style(child.attrib['style'], defs) + if 'stop-color' in style: + c = parse_color(style['stop-color'], defs) + color[:3] = [c[0], c[1], c[2]] + if 'stop-opacity' in style: + color[3] = float(style['stop-opacity']) + offsets.append(offset) + stop_colors.append(color) + if isinstance(offsets, list): + offsets = torch.tensor(offsets) + if isinstance(stop_colors, list): + stop_colors = torch.tensor(stop_colors) + + return pydiffvg.LinearGradient(begin, end, offsets, stop_colors) + + +def parse_radial_gradient(node, transform, defs): + begin = torch.tensor([0.0, 0.0]) + end = torch.tensor([0.0, 0.0]) + center = torch.tensor([0.0, 0.0]) + radius = torch.tensor([0.0, 0.0]) + offsets = [] + stop_colors = [] + # Inherit from parent + for key in node.attrib: + if remove_namespaces(key) == 'href': + value = node.attrib[key] + parent = defs[value.lstrip('#')] + begin = parent.begin + end = parent.end + offsets = parent.offsets + stop_colors = parent.stop_colors + + for attrib in node.attrib: + attrib = remove_namespaces(attrib) + if attrib == 'cx': + center[0] = float(node.attrib['cx']) + elif attrib == 'cy': + center[1] = float(node.attrib['cy']) + elif attrib == 'fx': + radius[0] = float(node.attrib['fx']) + elif attrib == 'fy': + radius[1] = float(node.attrib['fy']) + elif attrib == 'fr': + radius[0] = float(node.attrib['fr']) + radius[1] = float(node.attrib['fr']) + elif attrib == 'gradientTransform': + transform = transform @ parse_transform(node.attrib['gradientTransform']) + + # TODO: this is incorrect + center = transform @ torch.cat((center, torch.ones([1]))) + center = center / center[2] + center = center[:2] + + for child in node: + tag = remove_namespaces(child.tag) + if tag == 'stop': + offset = float(child.attrib['offset']) + color = [0.0, 0.0, 0.0, 1.0] + if 'stop-color' in child.attrib: + c = parse_color(child.attrib['stop-color'], defs) + color[:3] = [c[0], c[1], c[2]] + if 'stop-opacity' in child.attrib: + color[3] = float(child.attrib['stop-opacity']) + if 'style' in child.attrib: + style = parse_style(child.attrib['style'], defs) + if 'stop-color' in style: + c = parse_color(style['stop-color'], defs) + color[:3] = [c[0], c[1], c[2]] + if 'stop-opacity' in style: + color[3] = float(style['stop-opacity']) + offsets.append(offset) + stop_colors.append(color) + if isinstance(offsets, list): + offsets = torch.tensor(offsets) + if isinstance(stop_colors, list): + stop_colors = torch.tensor(stop_colors) + + return pydiffvg.RadialGradient(begin, end, offsets, stop_colors) + +def parse_stylesheet(node, transform, defs): + # collect CSS classes + sheet = cssutils.parseString(node.text) + for rule in sheet: + if hasattr(rule, 'selectorText') and hasattr(rule, 'style'): + name = rule.selectorText + if len(name) >= 2 and name[0] == '.': + defs[name[1:]] = parse_style(rule.style.getCssText(), defs) + return defs + +def parse_defs(node, transform, defs): + for child in node: + tag = remove_namespaces(child.tag) + if tag == 'linearGradient': + if 'id' in child.attrib: + defs[child.attrib['id']] = parse_linear_gradient(child, transform, defs) + elif tag == 'radialGradient': + if 'id' in child.attrib: + defs[child.attrib['id']] = parse_radial_gradient(child, transform, defs) + elif tag == 'style': + defs = parse_stylesheet(child, transform, defs) + return defs + +def parse_common_attrib(node, transform, fill_color, defs): + attribs = {} + if 'class' in node.attrib: + attribs.update(defs[node.attrib['class']]) + attribs.update(node.attrib) + + name = '' + if 'id' in node.attrib: + name = node.attrib['id'] + + stroke_color = None + stroke_width = torch.tensor(0.5) + use_even_odd_rule = False + + new_transform = transform + if 'transform' in attribs: + new_transform = transform @ parse_transform(attribs['transform']) + if 'fill' in attribs: + fill_color = parse_color(attribs['fill'], defs) + fill_opacity = 1.0 + if 'fill-opacity' in attribs: + fill_opacity *= float(attribs['fill-opacity']) + if 'opacity' in attribs: + fill_opacity *= float(attribs['opacity']) + # Ignore opacity if the color is a gradient + if isinstance(fill_color, torch.Tensor): + fill_color[3] = fill_opacity + + if 'fill-rule' in attribs: + if attribs['fill-rule'] == "evenodd": + use_even_odd_rule = True + elif attribs['fill-rule'] == "nonzero": + use_even_odd_rule = False + else: + warnings.warn('Unknown fill-rule: {}'.format(attribs['fill-rule'])) + + if 'stroke' in attribs: + stroke_color = parse_color(attribs['stroke'], defs) + + if 'stroke-width' in attribs: + stroke_width = attribs['stroke-width'] + if stroke_width[-2:] == 'px': + stroke_width = stroke_width[:-2] + stroke_width = torch.tensor(float(stroke_width) / 2.0) + + if 'style' in attribs: + style = parse_style(attribs['style'], defs) + if 'fill' in style: + fill_color = parse_color(style['fill'], defs) + fill_opacity = 1.0 + if 'fill-opacity' in style: + fill_opacity *= float(style['fill-opacity']) + if 'opacity' in style: + fill_opacity *= float(style['opacity']) + if 'fill-rule' in style: + if style['fill-rule'] == "evenodd": + use_even_odd_rule = True + elif style['fill-rule'] == "nonzero": + use_even_odd_rule = False + else: + warnings.warn('Unknown fill-rule: {}'.format(style['fill-rule'])) + # Ignore opacity if the color is a gradient + if isinstance(fill_color, torch.Tensor): + fill_color[3] = fill_opacity + if 'stroke' in style: + if style['stroke'] != 'none': + stroke_color = parse_color(style['stroke'], defs) + # Ignore opacity if the color is a gradient + if isinstance(stroke_color, torch.Tensor): + if 'stroke-opacity' in style: + stroke_color[3] = float(style['stroke-opacity']) + if 'opacity' in style: + stroke_color[3] *= float(style['opacity']) + if 'stroke-width' in style: + stroke_width = style['stroke-width'] + if stroke_width[-2:] == 'px': + stroke_width = stroke_width[:-2] + stroke_width = torch.tensor(float(stroke_width) / 2.0) + + if isinstance(fill_color, pydiffvg.LinearGradient): + fill_color.begin = new_transform @ torch.cat((fill_color.begin, torch.ones([1]))) + fill_color.begin = fill_color.begin / fill_color.begin[2] + fill_color.begin = fill_color.begin[:2] + fill_color.end = new_transform @ torch.cat((fill_color.end, torch.ones([1]))) + fill_color.end = fill_color.end / fill_color.end[2] + fill_color.end = fill_color.end[:2] + if isinstance(stroke_color, pydiffvg.LinearGradient): + stroke_color.begin = new_transform @ torch.cat((stroke_color.begin, torch.ones([1]))) + stroke_color.begin = stroke_color.begin / stroke_color.begin[2] + stroke_color.begin = stroke_color.begin[:2] + stroke_color.end = new_transform @ torch.cat((stroke_color.end, torch.ones([1]))) + stroke_color.end = stroke_color.end / stroke_color.end[2] + stroke_color.end = stroke_color.end[:2] + if 'filter' in style: + print('*** WARNING ***: Ignoring filter for path with id "{}"'.format(name)) + + return new_transform, fill_color, stroke_color, stroke_width, use_even_odd_rule + +def is_shape(tag): + return tag == 'path' or tag == 'polygon' or tag == 'line' or tag == 'circle' or tag == 'rect' + +def parse_shape(node, transform, fill_color, shapes, shape_groups, defs): + tag = remove_namespaces(node.tag) + new_transform, new_fill_color, stroke_color, stroke_width, use_even_odd_rule = \ + parse_common_attrib(node, transform, fill_color, defs) + if tag == 'path': + d = node.attrib['d'] + name = '' + if 'id' in node.attrib: + name = node.attrib['id'] + force_closing = new_fill_color is not None + paths = pydiffvg.from_svg_path(d, new_transform, force_closing) + for idx, path in enumerate(paths): + assert(path.points.shape[1] == 2) + path.stroke_width = stroke_width + path.source_id = name + path.id = "{}-{}".format(name,idx) if len(paths)>1 else name + prev_shapes_size = len(shapes) + shapes = shapes + paths + shape_ids = torch.tensor(list(range(prev_shapes_size, len(shapes)))) + shape_groups.append(pydiffvg.ShapeGroup(\ + shape_ids = shape_ids, + fill_color = new_fill_color, + stroke_color = stroke_color, + use_even_odd_rule = use_even_odd_rule, + id = name)) + elif tag == 'polygon': + name = '' + if 'id' in node.attrib: + name = node.attrib['id'] + force_closing = new_fill_color is not None + pts = node.attrib['points'].strip() + pts = pts.split(' ') + # import ipdb; ipdb.set_trace() + pts = [[float(y) for y in re.split(',| ', x)] for x in pts if x] + pts = torch.tensor(pts, dtype=torch.float32).view(-1, 2) + polygon = pydiffvg.Polygon(pts, force_closing) + polygon.stroke_width = stroke_width + shape_ids = torch.tensor([len(shapes)]) + shapes.append(polygon) + shape_groups.append(pydiffvg.ShapeGroup(\ + shape_ids = shape_ids, + fill_color = new_fill_color, + stroke_color = stroke_color, + use_even_odd_rule = use_even_odd_rule, + shape_to_canvas = new_transform, + id = name)) + elif tag == 'line': + x1 = float(node.attrib['x1']) + y1 = float(node.attrib['y1']) + x2 = float(node.attrib['x2']) + y2 = float(node.attrib['y2']) + p1 = torch.tensor([x1, y1]) + p2 = torch.tensor([x2, y2]) + points = torch.stack((p1, p2)) + line = pydiffvg.Polygon(points, False) + line.stroke_width = stroke_width + shape_ids = torch.tensor([len(shapes)]) + shapes.append(line) + shape_groups.append(pydiffvg.ShapeGroup(\ + shape_ids = shape_ids, + fill_color = new_fill_color, + stroke_color = stroke_color, + use_even_odd_rule = use_even_odd_rule, + shape_to_canvas = new_transform)) + elif tag == 'circle': + radius = float(node.attrib['r']) + cx = float(node.attrib['cx']) + cy = float(node.attrib['cy']) + name = '' + if 'id' in node.attrib: + name = node.attrib['id'] + center = torch.tensor([cx, cy]) + circle = pydiffvg.Circle(radius = torch.tensor(radius), + center = center) + circle.stroke_width = stroke_width + shape_ids = torch.tensor([len(shapes)]) + shapes.append(circle) + shape_groups.append(pydiffvg.ShapeGroup(\ + shape_ids = shape_ids, + fill_color = new_fill_color, + stroke_color = stroke_color, + use_even_odd_rule = use_even_odd_rule, + shape_to_canvas = new_transform)) + elif tag == 'ellipse': + rx = float(node.attrib['rx']) + ry = float(node.attrib['ry']) + cx = float(node.attrib['cx']) + cy = float(node.attrib['cy']) + name = '' + if 'id' in node.attrib: + name = node.attrib['id'] + center = torch.tensor([cx, cy]) + circle = pydiffvg.Circle(radius = torch.tensor(radius), + center = center) + circle.stroke_width = stroke_width + shape_ids = torch.tensor([len(shapes)]) + shapes.append(circle) + shape_groups.append(pydiffvg.ShapeGroup(\ + shape_ids = shape_ids, + fill_color = new_fill_color, + stroke_color = stroke_color, + use_even_odd_rule = use_even_odd_rule, + shape_to_canvas = new_transform)) + elif tag == 'rect': + x = 0.0 + y = 0.0 + if x in node.attrib: + x = float(node.attrib['x']) + if y in node.attrib: + y = float(node.attrib['y']) + w = float(node.attrib['width']) + h = float(node.attrib['height']) + p_min = torch.tensor([x, y]) + p_max = torch.tensor([x + w, x + h]) + rect = pydiffvg.Rect(p_min = p_min, p_max = p_max) + rect.stroke_width = stroke_width + shape_ids = torch.tensor([len(shapes)]) + shapes.append(rect) + shape_groups.append(pydiffvg.ShapeGroup(\ + shape_ids = shape_ids, + fill_color = new_fill_color, + stroke_color = stroke_color, + use_even_odd_rule = use_even_odd_rule, + shape_to_canvas = new_transform)) + return shapes, shape_groups + +def parse_group(node, transform, fill_color, shapes, shape_groups, defs): + if 'transform' in node.attrib: + transform = transform @ parse_transform(node.attrib['transform']) + if 'fill' in node.attrib: + fill_color = parse_color(node.attrib['fill'], defs) + for child in node: + tag = remove_namespaces(child.tag) + if is_shape(tag): + shapes, shape_groups = parse_shape(\ + child, transform, fill_color, shapes, shape_groups, defs) + elif tag == 'g': + shapes, shape_groups = parse_group(\ + child, transform, fill_color, shapes, shape_groups, defs) + return shapes, shape_groups + +def parse_scene(node): + canvas_width = -1 + canvas_height = -1 + defs = {} + shapes = [] + shape_groups = [] + fill_color = torch.tensor([0.0, 0.0, 0.0, 1.0]) + transform = torch.eye(3) + if 'viewBox' in node.attrib: + view_box_array = node.attrib['viewBox'].split() + canvas_width = parse_int(view_box_array[2]) + canvas_height = parse_int(view_box_array[3]) + else: + if 'width' in node.attrib: + canvas_width = parse_int(node.attrib['width']) + else: + print('Warning: Can\'t find canvas width.') + if 'height' in node.attrib: + canvas_height = parse_int(node.attrib['height']) + else: + print('Warning: Can\'t find canvas height.') + for child in node: + tag = remove_namespaces(child.tag) + if tag == 'defs': + defs = parse_defs(child, transform, defs) + elif tag == 'style': + defs = parse_stylesheet(child, transform, defs) + elif tag == 'linearGradient': + if 'id' in child.attrib: + defs[child.attrib['id']] = parse_linear_gradient(child, transform, defs) + elif tag == 'radialGradient': + if 'id' in child.attrib: + defs[child.attrib['id']] = parse_radial_gradient(child, transform, defs) + elif is_shape(tag): + shapes, shape_groups = parse_shape(\ + child, transform, fill_color, shapes, shape_groups, defs) + elif tag == 'g': + shapes, shape_groups = parse_group(\ + child, transform, fill_color, shapes, shape_groups, defs) + return canvas_width, canvas_height, shapes, shape_groups + +def svg_to_scene(filename): + """ + Load from a SVG file and convert to PyTorch tensors. + """ + + tree = etree.parse(filename) + root = tree.getroot() + cwd = os.getcwd() + if (os.path.dirname(filename) != ''): + os.chdir(os.path.dirname(filename)) + ret = parse_scene(root) + os.chdir(cwd) + return ret diff --git a/pydiffvg/pixel_filter.py b/pydiffvg/pixel_filter.py new file mode 100644 index 0000000..9b0ff22 --- /dev/null +++ b/pydiffvg/pixel_filter.py @@ -0,0 +1,9 @@ +import torch +import pydiffvg + +class PixelFilter: + def __init__(self, + type, + radius = torch.tensor(0.5)): + self.type = type + self.radius = radius diff --git a/pydiffvg/render_pytorch.py b/pydiffvg/render_pytorch.py new file mode 100644 index 0000000..b776ce6 --- /dev/null +++ b/pydiffvg/render_pytorch.py @@ -0,0 +1,870 @@ +import torch +import diffvg +import pydiffvg +import time +from enum import IntEnum +import warnings + +print_timing = False + +def set_print_timing(val): + global print_timing + print_timing=val + +class OutputType(IntEnum): + color = 1 + sdf = 2 + +class RenderFunction(torch.autograd.Function): + """ + The PyTorch interface of diffvg. + """ + @staticmethod + def serialize_scene(canvas_width, + canvas_height, + shapes, + shape_groups, + filter = pydiffvg.PixelFilter(type = diffvg.FilterType.box, + radius = torch.tensor(0.5)), + output_type = OutputType.color, + use_prefiltering = False, + eval_positions = torch.tensor([])): + """ + Given a list of shapes, convert them to a linear list of argument, + so that we can use it in PyTorch. + """ + num_shapes = len(shapes) + num_shape_groups = len(shape_groups) + args = [] + args.append(canvas_width) + args.append(canvas_height) + args.append(num_shapes) + args.append(num_shape_groups) + args.append(output_type) + args.append(use_prefiltering) + args.append(eval_positions.to(pydiffvg.get_device())) + for shape in shapes: + use_thickness = False + if isinstance(shape, pydiffvg.Circle): + assert(shape.center.is_contiguous()) + args.append(diffvg.ShapeType.circle) + args.append(shape.radius.cpu()) + args.append(shape.center.cpu()) + elif isinstance(shape, pydiffvg.Ellipse): + assert(shape.radius.is_contiguous()) + assert(shape.center.is_contiguous()) + args.append(diffvg.ShapeType.ellipse) + args.append(shape.radius.cpu()) + args.append(shape.center.cpu()) + elif isinstance(shape, pydiffvg.Path): + assert(shape.num_control_points.is_contiguous()) + assert(shape.points.is_contiguous()) + assert(shape.points.shape[1] == 2) + assert(torch.isfinite(shape.points).all()) + args.append(diffvg.ShapeType.path) + args.append(shape.num_control_points.to(torch.int32).cpu()) + args.append(shape.points.cpu()) + if len(shape.stroke_width.shape) > 0 and shape.stroke_width.shape[0] > 1: + assert(torch.isfinite(shape.stroke_width).all()) + use_thickness = True + args.append(shape.stroke_width.cpu()) + else: + args.append(None) + args.append(shape.is_closed) + args.append(shape.use_distance_approx) + elif isinstance(shape, pydiffvg.Polygon): + assert(shape.points.is_contiguous()) + assert(shape.points.shape[1] == 2) + args.append(diffvg.ShapeType.path) + if shape.is_closed: + args.append(torch.zeros(shape.points.shape[0], dtype = torch.int32)) + else: + args.append(torch.zeros(shape.points.shape[0] - 1, dtype = torch.int32)) + args.append(shape.points.cpu()) + args.append(None) + args.append(shape.is_closed) + args.append(False) # use_distance_approx + elif isinstance(shape, pydiffvg.Rect): + assert(shape.p_min.is_contiguous()) + assert(shape.p_max.is_contiguous()) + args.append(diffvg.ShapeType.rect) + args.append(shape.p_min.cpu()) + args.append(shape.p_max.cpu()) + else: + assert(False) + if use_thickness: + args.append(torch.tensor(0.0)) + else: + args.append(shape.stroke_width.cpu()) + + for shape_group in shape_groups: + assert(shape_group.shape_ids.is_contiguous()) + args.append(shape_group.shape_ids.to(torch.int32).cpu()) + # Fill color + if shape_group.fill_color is None: + args.append(None) + elif isinstance(shape_group.fill_color, torch.Tensor): + assert(shape_group.fill_color.is_contiguous()) + args.append(diffvg.ColorType.constant) + args.append(shape_group.fill_color.cpu()) + elif isinstance(shape_group.fill_color, pydiffvg.LinearGradient): + assert(shape_group.fill_color.begin.is_contiguous()) + assert(shape_group.fill_color.end.is_contiguous()) + assert(shape_group.fill_color.offsets.is_contiguous()) + assert(shape_group.fill_color.stop_colors.is_contiguous()) + args.append(diffvg.ColorType.linear_gradient) + args.append(shape_group.fill_color.begin.cpu()) + args.append(shape_group.fill_color.end.cpu()) + args.append(shape_group.fill_color.offsets.cpu()) + args.append(shape_group.fill_color.stop_colors.cpu()) + elif isinstance(shape_group.fill_color, pydiffvg.RadialGradient): + assert(shape_group.fill_color.center.is_contiguous()) + assert(shape_group.fill_color.radius.is_contiguous()) + assert(shape_group.fill_color.offsets.is_contiguous()) + assert(shape_group.fill_color.stop_colors.is_contiguous()) + args.append(diffvg.ColorType.radial_gradient) + args.append(shape_group.fill_color.center.cpu()) + args.append(shape_group.fill_color.radius.cpu()) + args.append(shape_group.fill_color.offsets.cpu()) + args.append(shape_group.fill_color.stop_colors.cpu()) + + if shape_group.fill_color is not None: + # go through the underlying shapes and check if they are all closed + for shape_id in shape_group.shape_ids: + if isinstance(shapes[shape_id], pydiffvg.Path): + if not shapes[shape_id].is_closed: + warnings.warn("Detected non-closed paths with fill color. This might causes unexpected results.", Warning) + + # Stroke color + if shape_group.stroke_color is None: + args.append(None) + elif isinstance(shape_group.stroke_color, torch.Tensor): + assert(shape_group.stroke_color.is_contiguous()) + args.append(diffvg.ColorType.constant) + args.append(shape_group.stroke_color.cpu()) + elif isinstance(shape_group.stroke_color, pydiffvg.LinearGradient): + assert(shape_group.stroke_color.begin.is_contiguous()) + assert(shape_group.stroke_color.end.is_contiguous()) + assert(shape_group.stroke_color.offsets.is_contiguous()) + assert(shape_group.stroke_color.stop_colors.is_contiguous()) + assert(torch.isfinite(shape_group.stroke_color.stop_colors).all()) + args.append(diffvg.ColorType.linear_gradient) + args.append(shape_group.stroke_color.begin.cpu()) + args.append(shape_group.stroke_color.end.cpu()) + args.append(shape_group.stroke_color.offsets.cpu()) + args.append(shape_group.stroke_color.stop_colors.cpu()) + elif isinstance(shape_group.stroke_color, pydiffvg.RadialGradient): + assert(shape_group.stroke_color.center.is_contiguous()) + assert(shape_group.stroke_color.radius.is_contiguous()) + assert(shape_group.stroke_color.offsets.is_contiguous()) + assert(shape_group.stroke_color.stop_colors.is_contiguous()) + assert(torch.isfinite(shape_group.stroke_color.stop_colors).all()) + args.append(diffvg.ColorType.radial_gradient) + args.append(shape_group.stroke_color.center.cpu()) + args.append(shape_group.stroke_color.radius.cpu()) + args.append(shape_group.stroke_color.offsets.cpu()) + args.append(shape_group.stroke_color.stop_colors.cpu()) + args.append(shape_group.use_even_odd_rule) + # Transformation + args.append(shape_group.shape_to_canvas.contiguous().cpu()) + args.append(filter.type) + args.append(filter.radius.cpu()) + return args + + @staticmethod + def forward(ctx, + width, + height, + num_samples_x, + num_samples_y, + seed, + background_image, + *args): + """ + Forward rendering pass. + """ + # Unpack arguments + current_index = 0 + canvas_width = args[current_index] + current_index += 1 + canvas_height = args[current_index] + current_index += 1 + num_shapes = args[current_index] + current_index += 1 + num_shape_groups = args[current_index] + current_index += 1 + output_type = args[current_index] + current_index += 1 + use_prefiltering = args[current_index] + current_index += 1 + eval_positions = args[current_index] + current_index += 1 + shapes = [] + shape_groups = [] + shape_contents = [] # Important to avoid GC deleting the shapes + color_contents = [] # Same as above + for shape_id in range(num_shapes): + shape_type = args[current_index] + current_index += 1 + if shape_type == diffvg.ShapeType.circle: + radius = args[current_index] + current_index += 1 + center = args[current_index] + current_index += 1 + shape = diffvg.Circle(radius, diffvg.Vector2f(center[0], center[1])) + elif shape_type == diffvg.ShapeType.ellipse: + radius = args[current_index] + current_index += 1 + center = args[current_index] + current_index += 1 + shape = diffvg.Ellipse(diffvg.Vector2f(radius[0], radius[1]), + diffvg.Vector2f(center[0], center[1])) + elif shape_type == diffvg.ShapeType.path: + num_control_points = args[current_index] + current_index += 1 + points = args[current_index] + current_index += 1 + thickness = args[current_index] + current_index += 1 + is_closed = args[current_index] + current_index += 1 + use_distance_approx = args[current_index] + current_index += 1 + shape = diffvg.Path(diffvg.int_ptr(num_control_points.data_ptr()), + diffvg.float_ptr(points.data_ptr()), + diffvg.float_ptr(thickness.data_ptr() if thickness is not None else 0), + num_control_points.shape[0], + points.shape[0], + is_closed, + use_distance_approx) + elif shape_type == diffvg.ShapeType.rect: + p_min = args[current_index] + current_index += 1 + p_max = args[current_index] + current_index += 1 + shape = diffvg.Rect(diffvg.Vector2f(p_min[0], p_min[1]), + diffvg.Vector2f(p_max[0], p_max[1])) + else: + assert(False) + stroke_width = args[current_index] + current_index += 1 + shapes.append(diffvg.Shape(\ + shape_type, shape.get_ptr(), stroke_width.item())) + shape_contents.append(shape) + + for shape_group_id in range(num_shape_groups): + shape_ids = args[current_index] + current_index += 1 + fill_color_type = args[current_index] + current_index += 1 + if fill_color_type == diffvg.ColorType.constant: + color = args[current_index] + current_index += 1 + fill_color = diffvg.Constant(\ + diffvg.Vector4f(color[0], color[1], color[2], color[3])) + elif fill_color_type == diffvg.ColorType.linear_gradient: + beg = args[current_index] + current_index += 1 + end = args[current_index] + current_index += 1 + offsets = args[current_index] + current_index += 1 + stop_colors = args[current_index] + current_index += 1 + assert(offsets.shape[0] == stop_colors.shape[0]) + fill_color = diffvg.LinearGradient(diffvg.Vector2f(beg[0], beg[1]), + diffvg.Vector2f(end[0], end[1]), + offsets.shape[0], + diffvg.float_ptr(offsets.data_ptr()), + diffvg.float_ptr(stop_colors.data_ptr())) + elif fill_color_type == diffvg.ColorType.radial_gradient: + center = args[current_index] + current_index += 1 + radius = args[current_index] + current_index += 1 + offsets = args[current_index] + current_index += 1 + stop_colors = args[current_index] + current_index += 1 + assert(offsets.shape[0] == stop_colors.shape[0]) + fill_color = diffvg.RadialGradient(diffvg.Vector2f(center[0], center[1]), + diffvg.Vector2f(radius[0], radius[1]), + offsets.shape[0], + diffvg.float_ptr(offsets.data_ptr()), + diffvg.float_ptr(stop_colors.data_ptr())) + elif fill_color_type is None: + fill_color = None + else: + assert(False) + stroke_color_type = args[current_index] + current_index += 1 + if stroke_color_type == diffvg.ColorType.constant: + color = args[current_index] + current_index += 1 + stroke_color = diffvg.Constant(\ + diffvg.Vector4f(color[0], color[1], color[2], color[3])) + elif stroke_color_type == diffvg.ColorType.linear_gradient: + beg = args[current_index] + current_index += 1 + end = args[current_index] + current_index += 1 + offsets = args[current_index] + current_index += 1 + stop_colors = args[current_index] + current_index += 1 + assert(offsets.shape[0] == stop_colors.shape[0]) + stroke_color = diffvg.LinearGradient(diffvg.Vector2f(beg[0], beg[1]), + diffvg.Vector2f(end[0], end[1]), + offsets.shape[0], + diffvg.float_ptr(offsets.data_ptr()), + diffvg.float_ptr(stop_colors.data_ptr())) + elif stroke_color_type == diffvg.ColorType.radial_gradient: + center = args[current_index] + current_index += 1 + radius = args[current_index] + current_index += 1 + offsets = args[current_index] + current_index += 1 + stop_colors = args[current_index] + current_index += 1 + assert(offsets.shape[0] == stop_colors.shape[0]) + stroke_color = diffvg.RadialGradient(diffvg.Vector2f(center[0], center[1]), + diffvg.Vector2f(radius[0], radius[1]), + offsets.shape[0], + diffvg.float_ptr(offsets.data_ptr()), + diffvg.float_ptr(stop_colors.data_ptr())) + elif stroke_color_type is None: + stroke_color = None + else: + assert(False) + use_even_odd_rule = args[current_index] + current_index += 1 + shape_to_canvas = args[current_index] + current_index += 1 + + if fill_color is not None: + color_contents.append(fill_color) + if stroke_color is not None: + color_contents.append(stroke_color) + shape_groups.append(diffvg.ShapeGroup(\ + diffvg.int_ptr(shape_ids.data_ptr()), + shape_ids.shape[0], + diffvg.ColorType.constant if fill_color_type is None else fill_color_type, + diffvg.void_ptr(0) if fill_color is None else fill_color.get_ptr(), + diffvg.ColorType.constant if stroke_color_type is None else stroke_color_type, + diffvg.void_ptr(0) if stroke_color is None else stroke_color.get_ptr(), + use_even_odd_rule, + diffvg.float_ptr(shape_to_canvas.data_ptr()))) + + filter_type = args[current_index] + current_index += 1 + filter_radius = args[current_index] + current_index += 1 + filt = diffvg.Filter(filter_type, filter_radius) + + start = time.time() + scene = diffvg.Scene(canvas_width, canvas_height, + shapes, shape_groups, filt, pydiffvg.get_use_gpu(), + pydiffvg.get_device().index if pydiffvg.get_device().index is not None else -1) + time_elapsed = time.time() - start + global print_timing + if print_timing: + print('Scene construction, time: %.5f s' % time_elapsed) + + if output_type == OutputType.color: + assert(eval_positions.shape[0] == 0) + rendered_image = torch.zeros(height, width, 4, device = pydiffvg.get_device()) + else: + assert(output_type == OutputType.sdf) + if eval_positions.shape[0] == 0: + rendered_image = torch.zeros(height, width, 1, device = pydiffvg.get_device()) + else: + rendered_image = torch.zeros(eval_positions.shape[0], 1, device = pydiffvg.get_device()) + + if background_image is not None: + background_image = background_image.to(pydiffvg.get_device()) + if background_image.shape[2] == 3: + background_image = torch.cat((\ + background_image, torch.ones(background_image.shape[0], background_image.shape[1], 1, + device = background_image.device)), dim = 2) + background_image = background_image.contiguous() + assert(background_image.shape[0] == rendered_image.shape[0]) + assert(background_image.shape[1] == rendered_image.shape[1]) + assert(background_image.shape[2] == 4) + + start = time.time() + diffvg.render(scene, + diffvg.float_ptr(background_image.data_ptr() if background_image is not None else 0), + diffvg.float_ptr(rendered_image.data_ptr() if output_type == OutputType.color else 0), + diffvg.float_ptr(rendered_image.data_ptr() if output_type == OutputType.sdf else 0), + width, + height, + num_samples_x, + num_samples_y, + seed, + diffvg.float_ptr(0), # d_background_image + diffvg.float_ptr(0), # d_render_image + diffvg.float_ptr(0), # d_render_sdf + diffvg.float_ptr(0), # d_translation + use_prefiltering, + diffvg.float_ptr(eval_positions.data_ptr()), + eval_positions.shape[0]) + assert(torch.isfinite(rendered_image).all()) + time_elapsed = time.time() - start + if print_timing: + print('Forward pass, time: %.5f s' % time_elapsed) + + ctx.scene = scene + ctx.background_image = background_image + ctx.shape_contents = shape_contents + ctx.color_contents = color_contents + ctx.filter = filt + ctx.width = width + ctx.height = height + ctx.num_samples_x = num_samples_x + ctx.num_samples_y = num_samples_y + ctx.seed = seed + ctx.output_type = output_type + ctx.use_prefiltering = use_prefiltering + ctx.eval_positions = eval_positions + return rendered_image + + @staticmethod + def render_grad(grad_img, + width, + height, + num_samples_x, + num_samples_y, + seed, + background_image, + *args): + if not grad_img.is_contiguous(): + grad_img = grad_img.contiguous() + assert(torch.isfinite(grad_img).all()) + + # Unpack arguments + current_index = 0 + canvas_width = args[current_index] + current_index += 1 + canvas_height = args[current_index] + current_index += 1 + num_shapes = args[current_index] + current_index += 1 + num_shape_groups = args[current_index] + current_index += 1 + output_type = args[current_index] + current_index += 1 + use_prefiltering = args[current_index] + current_index += 1 + eval_positions = args[current_index] + current_index += 1 + shapes = [] + shape_groups = [] + shape_contents = [] # Important to avoid GC deleting the shapes + color_contents = [] # Same as above + for shape_id in range(num_shapes): + shape_type = args[current_index] + current_index += 1 + if shape_type == diffvg.ShapeType.circle: + radius = args[current_index] + current_index += 1 + center = args[current_index] + current_index += 1 + shape = diffvg.Circle(radius, diffvg.Vector2f(center[0], center[1])) + elif shape_type == diffvg.ShapeType.ellipse: + radius = args[current_index] + current_index += 1 + center = args[current_index] + current_index += 1 + shape = diffvg.Ellipse(diffvg.Vector2f(radius[0], radius[1]), + diffvg.Vector2f(center[0], center[1])) + elif shape_type == diffvg.ShapeType.path: + num_control_points = args[current_index] + current_index += 1 + points = args[current_index] + current_index += 1 + thickness = args[current_index] + current_index += 1 + is_closed = args[current_index] + current_index += 1 + use_distance_approx = args[current_index] + current_index += 1 + shape = diffvg.Path(diffvg.int_ptr(num_control_points.data_ptr()), + diffvg.float_ptr(points.data_ptr()), + diffvg.float_ptr(thickness.data_ptr() if thickness is not None else 0), + num_control_points.shape[0], + points.shape[0], + is_closed, + use_distance_approx) + elif shape_type == diffvg.ShapeType.rect: + p_min = args[current_index] + current_index += 1 + p_max = args[current_index] + current_index += 1 + shape = diffvg.Rect(diffvg.Vector2f(p_min[0], p_min[1]), + diffvg.Vector2f(p_max[0], p_max[1])) + else: + assert(False) + stroke_width = args[current_index] + current_index += 1 + shapes.append(diffvg.Shape(\ + shape_type, shape.get_ptr(), stroke_width.item())) + shape_contents.append(shape) + + for shape_group_id in range(num_shape_groups): + shape_ids = args[current_index] + current_index += 1 + fill_color_type = args[current_index] + current_index += 1 + if fill_color_type == diffvg.ColorType.constant: + color = args[current_index] + current_index += 1 + fill_color = diffvg.Constant(\ + diffvg.Vector4f(color[0], color[1], color[2], color[3])) + elif fill_color_type == diffvg.ColorType.linear_gradient: + beg = args[current_index] + current_index += 1 + end = args[current_index] + current_index += 1 + offsets = args[current_index] + current_index += 1 + stop_colors = args[current_index] + current_index += 1 + assert(offsets.shape[0] == stop_colors.shape[0]) + fill_color = diffvg.LinearGradient(diffvg.Vector2f(beg[0], beg[1]), + diffvg.Vector2f(end[0], end[1]), + offsets.shape[0], + diffvg.float_ptr(offsets.data_ptr()), + diffvg.float_ptr(stop_colors.data_ptr())) + elif fill_color_type == diffvg.ColorType.radial_gradient: + center = args[current_index] + current_index += 1 + radius = args[current_index] + current_index += 1 + offsets = args[current_index] + current_index += 1 + stop_colors = args[current_index] + current_index += 1 + assert(offsets.shape[0] == stop_colors.shape[0]) + fill_color = diffvg.RadialGradient(diffvg.Vector2f(center[0], center[1]), + diffvg.Vector2f(radius[0], radius[1]), + offsets.shape[0], + diffvg.float_ptr(offsets.data_ptr()), + diffvg.float_ptr(stop_colors.data_ptr())) + elif fill_color_type is None: + fill_color = None + else: + assert(False) + stroke_color_type = args[current_index] + current_index += 1 + if stroke_color_type == diffvg.ColorType.constant: + color = args[current_index] + current_index += 1 + stroke_color = diffvg.Constant(\ + diffvg.Vector4f(color[0], color[1], color[2], color[3])) + elif stroke_color_type == diffvg.ColorType.linear_gradient: + beg = args[current_index] + current_index += 1 + end = args[current_index] + current_index += 1 + offsets = args[current_index] + current_index += 1 + stop_colors = args[current_index] + current_index += 1 + assert(offsets.shape[0] == stop_colors.shape[0]) + stroke_color = diffvg.LinearGradient(diffvg.Vector2f(beg[0], beg[1]), + diffvg.Vector2f(end[0], end[1]), + offsets.shape[0], + diffvg.float_ptr(offsets.data_ptr()), + diffvg.float_ptr(stop_colors.data_ptr())) + elif stroke_color_type == diffvg.ColorType.radial_gradient: + center = args[current_index] + current_index += 1 + radius = args[current_index] + current_index += 1 + offsets = args[current_index] + current_index += 1 + stop_colors = args[current_index] + current_index += 1 + assert(offsets.shape[0] == stop_colors.shape[0]) + stroke_color = diffvg.RadialGradient(diffvg.Vector2f(center[0], center[1]), + diffvg.Vector2f(radius[0], radius[1]), + offsets.shape[0], + diffvg.float_ptr(offsets.data_ptr()), + diffvg.float_ptr(stop_colors.data_ptr())) + elif stroke_color_type is None: + stroke_color = None + else: + assert(False) + use_even_odd_rule = args[current_index] + current_index += 1 + shape_to_canvas = args[current_index] + current_index += 1 + + if fill_color is not None: + color_contents.append(fill_color) + if stroke_color is not None: + color_contents.append(stroke_color) + shape_groups.append(diffvg.ShapeGroup(\ + diffvg.int_ptr(shape_ids.data_ptr()), + shape_ids.shape[0], + diffvg.ColorType.constant if fill_color_type is None else fill_color_type, + diffvg.void_ptr(0) if fill_color is None else fill_color.get_ptr(), + diffvg.ColorType.constant if stroke_color_type is None else stroke_color_type, + diffvg.void_ptr(0) if stroke_color is None else stroke_color.get_ptr(), + use_even_odd_rule, + diffvg.float_ptr(shape_to_canvas.data_ptr()))) + + filter_type = args[current_index] + current_index += 1 + filter_radius = args[current_index] + current_index += 1 + filt = diffvg.Filter(filter_type, filter_radius) + + scene = diffvg.Scene(canvas_width, canvas_height, + shapes, shape_groups, filt, pydiffvg.get_use_gpu(), + pydiffvg.get_device().index if pydiffvg.get_device().index is not None else -1) + + if output_type == OutputType.color: + assert(grad_img.shape[2] == 4) + else: + assert(grad_img.shape[2] == 1) + + if background_image is not None: + background_image = background_image.to(pydiffvg.get_device()) + if background_image.shape[2] == 3: + background_image = torch.cat((\ + background_image, torch.ones(background_image.shape[0], background_image.shape[1], 1, + device = background_image.device)), dim = 2) + background_image = background_image.contiguous() + assert(background_image.shape[0] == rendered_image.shape[0]) + assert(background_image.shape[1] == rendered_image.shape[1]) + assert(background_image.shape[2] == 4) + + translation_grad_image = \ + torch.zeros(height, width, 2, device = pydiffvg.get_device()) + start = time.time() + diffvg.render(scene, + diffvg.float_ptr(background_image.data_ptr() if background_image is not None else 0), + diffvg.float_ptr(0), # render_image + diffvg.float_ptr(0), # render_sdf + width, + height, + num_samples_x, + num_samples_y, + seed, + diffvg.float_ptr(0), # d_background_image + diffvg.float_ptr(grad_img.data_ptr() if output_type == OutputType.color else 0), + diffvg.float_ptr(grad_img.data_ptr() if output_type == OutputType.sdf else 0), + diffvg.float_ptr(translation_grad_image.data_ptr()), + use_prefiltering, + diffvg.float_ptr(eval_positions.data_ptr()), + eval_positions.shape[0]) + time_elapsed = time.time() - start + if print_timing: + print('Gradient pass, time: %.5f s' % time_elapsed) + assert(torch.isfinite(translation_grad_image).all()) + + return translation_grad_image + + @staticmethod + def backward(ctx, + grad_img): + if not grad_img.is_contiguous(): + grad_img = grad_img.contiguous() + assert(torch.isfinite(grad_img).all()) + + scene = ctx.scene + width = ctx.width + height = ctx.height + num_samples_x = ctx.num_samples_x + num_samples_y = ctx.num_samples_y + seed = ctx.seed + output_type = ctx.output_type + use_prefiltering = ctx.use_prefiltering + eval_positions = ctx.eval_positions + background_image = ctx.background_image + + if background_image is not None: + d_background_image = torch.zeros_like(background_image) + else: + d_background_image = None + + start = time.time() + diffvg.render(scene, + diffvg.float_ptr(background_image.data_ptr() if background_image is not None else 0), + diffvg.float_ptr(0), # render_image + diffvg.float_ptr(0), # render_sdf + width, + height, + num_samples_x, + num_samples_y, + seed, + diffvg.float_ptr(d_background_image.data_ptr() if background_image is not None else 0), + diffvg.float_ptr(grad_img.data_ptr() if output_type == OutputType.color else 0), + diffvg.float_ptr(grad_img.data_ptr() if output_type == OutputType.sdf else 0), + diffvg.float_ptr(0), # d_translation + use_prefiltering, + diffvg.float_ptr(eval_positions.data_ptr()), + eval_positions.shape[0]) + time_elapsed = time.time() - start + global print_timing + if print_timing: + print('Backward pass, time: %.5f s' % time_elapsed) + + d_args = [] + d_args.append(None) # width + d_args.append(None) # height + d_args.append(None) # num_samples_x + d_args.append(None) # num_samples_y + d_args.append(None) # seed + d_args.append(d_background_image) + d_args.append(None) # canvas_width + d_args.append(None) # canvas_height + d_args.append(None) # num_shapes + d_args.append(None) # num_shape_groups + d_args.append(None) # output_type + d_args.append(None) # use_prefiltering + d_args.append(None) # eval_positions + for shape_id in range(scene.num_shapes): + d_args.append(None) # type + d_shape = scene.get_d_shape(shape_id) + use_thickness = False + if d_shape.type == diffvg.ShapeType.circle: + d_circle = d_shape.as_circle() + radius = torch.tensor(d_circle.radius) + assert(torch.isfinite(radius).all()) + d_args.append(radius) + c = d_circle.center + c = torch.tensor((c.x, c.y)) + assert(torch.isfinite(c).all()) + d_args.append(c) + elif d_shape.type == diffvg.ShapeType.ellipse: + d_ellipse = d_shape.as_ellipse() + r = d_ellipse.radius + r = torch.tensor((d_ellipse.radius.x, d_ellipse.radius.y)) + assert(torch.isfinite(r).all()) + d_args.append(r) + c = d_ellipse.center + c = torch.tensor((c.x, c.y)) + assert(torch.isfinite(c).all()) + d_args.append(c) + elif d_shape.type == diffvg.ShapeType.path: + d_path = d_shape.as_path() + points = torch.zeros((d_path.num_points, 2)) + thickness = None + if d_path.has_thickness(): + use_thickness = True + thickness = torch.zeros(d_path.num_points) + d_path.copy_to(diffvg.float_ptr(points.data_ptr()), diffvg.float_ptr(thickness.data_ptr())) + else: + d_path.copy_to(diffvg.float_ptr(points.data_ptr()), diffvg.float_ptr(0)) + assert(torch.isfinite(points).all()) + if thickness is not None: + assert(torch.isfinite(thickness).all()) + d_args.append(None) # num_control_points + d_args.append(points) + d_args.append(thickness) + d_args.append(None) # is_closed + d_args.append(None) # use_distance_approx + elif d_shape.type == diffvg.ShapeType.rect: + d_rect = d_shape.as_rect() + p_min = torch.tensor((d_rect.p_min.x, d_rect.p_min.y)) + p_max = torch.tensor((d_rect.p_max.x, d_rect.p_max.y)) + assert(torch.isfinite(p_min).all()) + assert(torch.isfinite(p_max).all()) + d_args.append(p_min) + d_args.append(p_max) + else: + assert(False) + if use_thickness: + d_args.append(None) + else: + w = torch.tensor((d_shape.stroke_width)) + assert(torch.isfinite(w).all()) + d_args.append(w) + + for group_id in range(scene.num_shape_groups): + d_shape_group = scene.get_d_shape_group(group_id) + d_args.append(None) # shape_ids + d_args.append(None) # fill_color_type + if d_shape_group.has_fill_color(): + if d_shape_group.fill_color_type == diffvg.ColorType.constant: + d_constant = d_shape_group.fill_color_as_constant() + c = d_constant.color + d_args.append(torch.tensor((c.x, c.y, c.z, c.w))) + elif d_shape_group.fill_color_type == diffvg.ColorType.linear_gradient: + d_linear_gradient = d_shape_group.fill_color_as_linear_gradient() + beg = d_linear_gradient.begin + d_args.append(torch.tensor((beg.x, beg.y))) + end = d_linear_gradient.end + d_args.append(torch.tensor((end.x, end.y))) + offsets = torch.zeros((d_linear_gradient.num_stops)) + stop_colors = torch.zeros((d_linear_gradient.num_stops, 4)) + d_linear_gradient.copy_to(\ + diffvg.float_ptr(offsets.data_ptr()), + diffvg.float_ptr(stop_colors.data_ptr())) + assert(torch.isfinite(stop_colors).all()) + d_args.append(offsets) + d_args.append(stop_colors) + elif d_shape_group.fill_color_type == diffvg.ColorType.radial_gradient: + d_radial_gradient = d_shape_group.fill_color_as_radial_gradient() + center = d_radial_gradient.center + d_args.append(torch.tensor((center.x, center.y))) + radius = d_radial_gradient.radius + d_args.append(torch.tensor((radius.x, radius.y))) + offsets = torch.zeros((d_radial_gradient.num_stops)) + stop_colors = torch.zeros((d_radial_gradient.num_stops, 4)) + d_radial_gradient.copy_to(\ + diffvg.float_ptr(offsets.data_ptr()), + diffvg.float_ptr(stop_colors.data_ptr())) + assert(torch.isfinite(stop_colors).all()) + d_args.append(offsets) + d_args.append(stop_colors) + else: + assert(False) + d_args.append(None) # stroke_color_type + if d_shape_group.has_stroke_color(): + if d_shape_group.stroke_color_type == diffvg.ColorType.constant: + d_constant = d_shape_group.stroke_color_as_constant() + c = d_constant.color + d_args.append(torch.tensor((c.x, c.y, c.z, c.w))) + elif d_shape_group.stroke_color_type == diffvg.ColorType.linear_gradient: + d_linear_gradient = d_shape_group.stroke_color_as_linear_gradient() + beg = d_linear_gradient.begin + d_args.append(torch.tensor((beg.x, beg.y))) + end = d_linear_gradient.end + d_args.append(torch.tensor((end.x, end.y))) + offsets = torch.zeros((d_linear_gradient.num_stops)) + stop_colors = torch.zeros((d_linear_gradient.num_stops, 4)) + d_linear_gradient.copy_to(\ + diffvg.float_ptr(offsets.data_ptr()), + diffvg.float_ptr(stop_colors.data_ptr())) + assert(torch.isfinite(stop_colors).all()) + d_args.append(offsets) + d_args.append(stop_colors) + elif d_shape_group.fill_color_type == diffvg.ColorType.radial_gradient: + d_radial_gradient = d_shape_group.stroke_color_as_radial_gradient() + center = d_radial_gradient.center + d_args.append(torch.tensor((center.x, center.y))) + radius = d_radial_gradient.radius + d_args.append(torch.tensor((radius.x, radius.y))) + offsets = torch.zeros((d_radial_gradient.num_stops)) + stop_colors = torch.zeros((d_radial_gradient.num_stops, 4)) + d_radial_gradient.copy_to(\ + diffvg.float_ptr(offsets.data_ptr()), + diffvg.float_ptr(stop_colors.data_ptr())) + assert(torch.isfinite(stop_colors).all()) + d_args.append(offsets) + d_args.append(stop_colors) + else: + assert(False) + d_args.append(None) # use_even_odd_rule + d_shape_to_canvas = torch.zeros((3, 3)) + d_shape_group.copy_to(diffvg.float_ptr(d_shape_to_canvas.data_ptr())) + assert(torch.isfinite(d_shape_to_canvas).all()) + d_args.append(d_shape_to_canvas) + d_args.append(None) # filter_type + d_args.append(torch.tensor(scene.get_d_filter_radius())) + + return tuple(d_args) diff --git a/pydiffvg/save_svg.py b/pydiffvg/save_svg.py new file mode 100644 index 0000000..15dd370 --- /dev/null +++ b/pydiffvg/save_svg.py @@ -0,0 +1,150 @@ +import torch +import pydiffvg +import xml.etree.ElementTree as etree +from xml.dom import minidom + +def prettify(elem): + """Return a pretty-printed XML string for the Element. + """ + rough_string = etree.tostring(elem, 'utf-8') + reparsed = minidom.parseString(rough_string) + return reparsed.toprettyxml(indent=" ") + +def save_svg(filename, width, height, shapes, shape_groups, use_gamma = False): + root = etree.Element('svg') + root.set('version', '1.1') + root.set('xmlns', 'http://www.w3.org/2000/svg') + root.set('width', str(width)) + root.set('height', str(height)) + defs = etree.SubElement(root, 'defs') + g = etree.SubElement(root, 'g') + if use_gamma: + f = etree.SubElement(defs, 'filter') + f.set('id', 'gamma') + f.set('x', '0') + f.set('y', '0') + f.set('width', '100%') + f.set('height', '100%') + gamma = etree.SubElement(f, 'feComponentTransfer') + gamma.set('color-interpolation-filters', 'sRGB') + feFuncR = etree.SubElement(gamma, 'feFuncR') + feFuncR.set('type', 'gamma') + feFuncR.set('amplitude', str(1)) + feFuncR.set('exponent', str(1/2.2)) + feFuncG = etree.SubElement(gamma, 'feFuncG') + feFuncG.set('type', 'gamma') + feFuncG.set('amplitude', str(1)) + feFuncG.set('exponent', str(1/2.2)) + feFuncB = etree.SubElement(gamma, 'feFuncB') + feFuncB.set('type', 'gamma') + feFuncB.set('amplitude', str(1)) + feFuncB.set('exponent', str(1/2.2)) + feFuncA = etree.SubElement(gamma, 'feFuncA') + feFuncA.set('type', 'gamma') + feFuncA.set('amplitude', str(1)) + feFuncA.set('exponent', str(1/2.2)) + g.set('style', 'filter:url(#gamma)') + + # Store color + for i, shape_group in enumerate(shape_groups): + def add_color(shape_color, name): + if isinstance(shape_color, pydiffvg.LinearGradient): + lg = shape_color + color = etree.SubElement(defs, 'linearGradient') + color.set('id', name) + color.set('x1', str(lg.begin[0].item())) + color.set('y1', str(lg.begin[1].item())) + color.set('x2', str(lg.end[0].item())) + color.set('y2', str(lg.end[1].item())) + offsets = lg.offsets.data.cpu().numpy() + stop_colors = lg.stop_colors.data.cpu().numpy() + for j in range(offsets.shape[0]): + stop = etree.SubElement(color, 'stop') + stop.set('offset', offsets[j]) + c = lg.stop_colors[j, :] + stop.set('stop-color', 'rgb({}, {}, {})'.format(\ + int(255 * c[0]), int(255 * c[1]), int(255 * c[2]))) + stop.set('stop-opacity', '{}'.format(c[3])) + + if shape_group.fill_color is not None: + add_color(shape_group.fill_color, 'shape_{}_fill'.format(i)) + if shape_group.stroke_color is not None: + add_color(shape_group.stroke_color, 'shape_{}_stroke'.format(i)) + + for i, shape_group in enumerate(shape_groups): + shape = shapes[shape_group.shape_ids[0]] + if isinstance(shape, pydiffvg.Circle): + shape_node = etree.SubElement(g, 'circle') + shape_node.set('r', shape.radius.item()) + shape_node.set('cx', shape.center[0].item()) + shape_node.set('cy', shape.center[1].item()) + elif isinstance(shape, pydiffvg.Polygon): + shape_node = etree.SubElement(g, 'polygon') + points = shape.points.data.cpu().numpy() + path_str = '' + for j in range(0, shape.points.shape[0]): + path_str += '{} {}'.format(points[j, 0], points[j, 1]) + if j != shape.points.shape[0] - 1: + path_str += ' ' + shape_node.set('points', path_str) + elif isinstance(shape, pydiffvg.Path): + shape_node = etree.SubElement(g, 'path') + num_segments = shape.num_control_points.shape[0] + num_control_points = shape.num_control_points.data.cpu().numpy() + points = shape.points.data.cpu().numpy() + num_points = shape.points.shape[0] + path_str = 'M {} {}'.format(points[0, 0], points[0, 1]) + point_id = 1 + for j in range(0, num_segments): + if num_control_points[j] == 0: + p = point_id % num_points + path_str += ' L {} {}'.format(\ + points[p, 0], points[p, 1]) + point_id += 1 + elif num_control_points[j] == 1: + p1 = (point_id + 1) % num_points + path_str += ' Q {} {} {} {}'.format(\ + points[point_id, 0], points[point_id, 1], + points[p1, 0], points[p1, 1]) + point_id += 2 + elif num_control_points[j] == 2: + p2 = (point_id + 2) % num_points + path_str += ' C {} {} {} {} {} {}'.format(\ + points[point_id, 0], points[point_id, 1], + points[point_id + 1, 0], points[point_id + 1, 1], + points[p2, 0], points[p2, 1]) + point_id += 3 + shape_node.set('d', path_str) + elif isinstance(shape, pydiffvg.Rect): + shape_node = etree.SubElement(g, 'rect') + shape_node.set('x', shape.p_min[0].item()) + shape_node.set('y', shape.p_min[1].item()) + shape_node.set('width', shape.p_max[0].item() - shape.p_min[0].item()) + shape_node.set('height', shape.p_max[1].item() - shape.p_min[1].item()) + else: + assert(False) + + shape_node.set('stroke-width', str(2 * shape.stroke_width.data.cpu().item())) + if shape_group.fill_color is not None: + if isinstance(shape_group.fill_color, pydiffvg.LinearGradient): + shape_node.set('fill', 'url(#shape_{}_fill)'.format(i)) + else: + c = shape_group.fill_color.data.cpu().numpy() + shape_node.set('fill', 'rgb({}, {}, {})'.format(\ + int(255 * c[0]), int(255 * c[1]), int(255 * c[2]))) + shape_node.set('opacity', str(c[3])) + else: + shape_node.set('fill', 'none') + if shape_group.stroke_color is not None: + if isinstance(shape_group.stroke_color, pydiffvg.LinearGradient): + shape_node.set('stroke', 'url(#shape_{}_stroke)'.format(i)) + else: + c = shape_group.stroke_color.data.cpu().numpy() + shape_node.set('stroke', 'rgb({}, {}, {})'.format(\ + int(255 * c[0]), int(255 * c[1]), int(255 * c[2]))) + shape_node.set('stroke-opacity', str(c[3])) + shape_node.set('stroke-linecap', 'round') + shape_node.set('stroke-linejoin', 'round') + + with open(filename, "w") as f: + f.write(prettify(root)) diff --git a/pydiffvg/shape.py b/pydiffvg/shape.py new file mode 100644 index 0000000..a87e9e5 --- /dev/null +++ b/pydiffvg/shape.py @@ -0,0 +1,172 @@ +import torch +import svgpathtools +import math + +class Circle: + def __init__(self, radius, center, stroke_width = torch.tensor(1.0), id = ''): + self.radius = radius + self.center = center + self.stroke_width = stroke_width + self.id = id + +class Ellipse: + def __init__(self, radius, center, stroke_width = torch.tensor(1.0), id = ''): + self.radius = radius + self.center = center + self.stroke_width = stroke_width + self.id = id + +class Path: + def __init__(self, + num_control_points, + points, + is_closed, + stroke_width = torch.tensor(1.0), + id = '', + use_distance_approx = False): + self.num_control_points = num_control_points + self.points = points + self.is_closed = is_closed + self.stroke_width = stroke_width + self.id = id + self.use_distance_approx = use_distance_approx + +class Polygon: + def __init__(self, points, is_closed, stroke_width = torch.tensor(1.0), id = ''): + self.points = points + self.is_closed = is_closed + self.stroke_width = stroke_width + self.id = id + +class Rect: + def __init__(self, p_min, p_max, stroke_width = torch.tensor(1.0), id = ''): + self.p_min = p_min + self.p_max = p_max + self.stroke_width = stroke_width + self.id = id + +class ShapeGroup: + def __init__(self, + shape_ids, + fill_color, + use_even_odd_rule = True, + stroke_color = None, + shape_to_canvas = torch.eye(3), + id = ''): + self.shape_ids = shape_ids + self.fill_color = fill_color + self.use_even_odd_rule = use_even_odd_rule + self.stroke_color = stroke_color + self.shape_to_canvas = shape_to_canvas + self.id = id + +def from_svg_path(path_str, shape_to_canvas = torch.eye(3), force_close = False): + path = svgpathtools.parse_path(path_str) + if len(path) == 0: + return [] + ret_paths = [] + subpaths = path.continuous_subpaths() + for subpath in subpaths: + if subpath.isclosed(): + if len(subpath) > 1 and isinstance(subpath[-1], svgpathtools.Line) and subpath[-1].length() < 1e-5: + subpath.remove(subpath[-1]) + subpath[-1].end = subpath[0].start # Force closing the path + subpath.end = subpath[-1].end + assert(subpath.isclosed()) + else: + beg = subpath[0].start + end = subpath[-1].end + if abs(end - beg) < 1e-5: + subpath[-1].end = beg # Force closing the path + subpath.end = subpath[-1].end + assert(subpath.isclosed()) + elif force_close: + subpath.append(svgpathtools.Line(end, beg)) + subpath.end = subpath[-1].end + assert(subpath.isclosed()) + + num_control_points = [] + points = [] + + for i, e in enumerate(subpath): + if i == 0: + points.append((e.start.real, e.start.imag)) + else: + # Must begin from the end of previous segment + assert(e.start.real == points[-1][0]) + assert(e.start.imag == points[-1][1]) + if isinstance(e, svgpathtools.Line): + num_control_points.append(0) + elif isinstance(e, svgpathtools.QuadraticBezier): + num_control_points.append(1) + points.append((e.control.real, e.control.imag)) + elif isinstance(e, svgpathtools.CubicBezier): + num_control_points.append(2) + points.append((e.control1.real, e.control1.imag)) + points.append((e.control2.real, e.control2.imag)) + elif isinstance(e, svgpathtools.Arc): + # Convert to Cubic curves + # https://www.joecridge.me/content/pdf/bezier-arcs.pdf + start = e.theta * math.pi / 180.0 + stop = (e.theta + e.delta) * math.pi / 180.0 + + sign = 1.0 + if stop < start: + sign = -1.0 + + epsilon = 0.00001 + debug = abs(e.delta) >= 90.0 + while (sign * (stop - start) > epsilon): + arc_to_draw = stop - start + if arc_to_draw > 0.0: + arc_to_draw = min(arc_to_draw, 0.5 * math.pi) + else: + arc_to_draw = max(arc_to_draw, -0.5 * math.pi) + alpha = arc_to_draw / 2.0 + cos_alpha = math.cos(alpha) + sin_alpha = math.sin(alpha) + cot_alpha = 1.0 / math.tan(alpha) + phi = start + alpha + cos_phi = math.cos(phi) + sin_phi = math.sin(phi) + lambda_ = (4.0 - cos_alpha) / 3.0 + mu = sin_alpha + (cos_alpha - lambda_) * cot_alpha + last = sign * (stop - (start + arc_to_draw)) <= epsilon + num_control_points.append(2) + rx = e.radius.real + ry = e.radius.imag + cx = e.center.real + cy = e.center.imag + rot = e.phi * math.pi / 180.0 + cos_rot = math.cos(rot) + sin_rot = math.sin(rot) + x = lambda_ * cos_phi + mu * sin_phi + y = lambda_ * sin_phi - mu * cos_phi + xx = x * cos_rot - y * sin_rot + yy = x * sin_rot + y * cos_rot + points.append((cx + rx * xx, cy + ry * yy)) + x = lambda_ * cos_phi - mu * sin_phi + y = lambda_ * sin_phi + mu * cos_phi + xx = x * cos_rot - y * sin_rot + yy = x * sin_rot + y * cos_rot + points.append((cx + rx * xx, cy + ry * yy)) + if not last: + points.append((cx + rx * math.cos(rot + start + arc_to_draw), + cy + ry * math.sin(rot + start + arc_to_draw))) + start += arc_to_draw + first = False + if i != len(subpath) - 1: + points.append((e.end.real, e.end.imag)) + else: + if subpath.isclosed(): + # Must end at the beginning of first segment + assert(e.end.real == points[0][0]) + assert(e.end.imag == points[0][1]) + else: + points.append((e.end.real, e.end.imag)) + points = torch.tensor(points) + points = torch.cat((points, torch.ones([points.shape[0], 1])), dim = 1) @ torch.transpose(shape_to_canvas, 0, 1) + points = points / points[:, 2:3] + points = points[:, :2].contiguous() + ret_paths.append(Path(torch.tensor(num_control_points), points, subpath.isclosed())) + return ret_paths diff --git a/pydiffvg_tensorflow/__init__.py b/pydiffvg_tensorflow/__init__.py new file mode 100644 index 0000000..2686524 --- /dev/null +++ b/pydiffvg_tensorflow/__init__.py @@ -0,0 +1,24 @@ +import tensorflow as tf +try: + import diffvg +except ImportError: + print("Warning: diffvg is not installed when you import pydiffvg_tensorflow.") +from .device import * +from .shape import * +from .pixel_filter import * +from .render_tensorflow import * +from .image import * +from .color import * +import os.path + +print(os.path.dirname(diffvg.__file__)) + +if tf.__cxx11_abi_flag__ == 0: + __data_ptr_module = tf.load_op_library(os.path.join(os.path.dirname(diffvg.__file__), 'libdiffvg_tf_data_ptr_no_cxx11_abi.so')) +else: + assert(tf.__cxx11_abi_flag__ == 1) + __data_ptr_module = tf.load_op_library(os.path.join(os.path.dirname(diffvg.__file__), 'libdiffvg_tf_data_ptr_cxx11_abi.so')) + +def data_ptr(tensor): + addr_as_uint64 = __data_ptr_module.data_ptr(tensor) + return int(addr_as_uint64) diff --git a/pydiffvg_tensorflow/color.py b/pydiffvg_tensorflow/color.py new file mode 100644 index 0000000..e0db612 --- /dev/null +++ b/pydiffvg_tensorflow/color.py @@ -0,0 +1,23 @@ +import tensorflow as tf + +class LinearGradient: + def __init__(self, + begin = tf.constant([0.0, 0.0]), + end = tf.constant([0.0, 0.0]), + offsets = tf.constant([0.0]), + stop_colors = tf.constant([0.0, 0.0, 0.0, 0.0])): + self.begin = begin + self.end = end + self.offsets = offsets + self.stop_colors = stop_colors + +class RadialGradient: + def __init__(self, + center = tf.constant([0.0, 0.0]), + radius = tf.constant([0.0, 0.0]), + offsets = tf.constant([0.0]), + stop_colors = tf.constant([0.0, 0.0, 0.0, 0.0])): + self.center = center + self.radius = radius + self.offsets = offsets + self.stop_colors = stop_colors diff --git a/pydiffvg_tensorflow/custom_ops/CMakeLists.txt b/pydiffvg_tensorflow/custom_ops/CMakeLists.txt new file mode 100644 index 0000000..e15c953 --- /dev/null +++ b/pydiffvg_tensorflow/custom_ops/CMakeLists.txt @@ -0,0 +1,29 @@ +cmake_minimum_required(VERSION 3.12) + +set(CMAKE_CXX_STANDARD 11) +set(CMAKE_CXX_STANDARD_REQUIRED ON) + +project(diffvgTFCustomOp) + +set(CMAKE_POSITION_INDEPENDENT_CODE ON) + +include_directories(SYSTEM ${TensorFlow_INCLUDE_DIR}) + +# Compile two versions of the library +add_library(diffvg_tf_data_ptr_cxx11_abi SHARED data_ptr.cc) +set_target_properties(diffvg_tf_data_ptr_cxx11_abi PROPERTIES COMPILE_FLAGS -D_GLIBCXX_USE_CXX11_ABI=1) +set_target_properties(diffvg_tf_data_ptr_cxx11_abi PROPERTIES LINK_FLAGS -D_GLIBCXX_USE_CXX11_ABI=1) +if(APPLE) + # .so instead of .dylib + set_target_properties(diffvg_tf_data_ptr_cxx11_abi PROPERTIES SUFFIX .so) +endif() +target_link_libraries(diffvg_tf_data_ptr_cxx11_abi ${TensorFlow_LIBRARY}) + +add_library(diffvg_tf_data_ptr_no_cxx11_abi SHARED data_ptr.cc) +set_target_properties(diffvg_tf_data_ptr_no_cxx11_abi PROPERTIES COMPILE_FLAGS -D_GLIBCXX_USE_CXX11_ABI=0) +set_target_properties(diffvg_tf_data_ptr_no_cxx11_abi PROPERTIES LINK_FLAGS -D_GLIBCXX_USE_CXX11_ABI=0) +if(APPLE) + # .so instead of .dylib + set_target_properties(diffvg_tf_data_ptr_no_cxx11_abi PROPERTIES SUFFIX .so) +endif() +target_link_libraries(diffvg_tf_data_ptr_no_cxx11_abi ${TensorFlow_LIBRARY}) diff --git a/pydiffvg_tensorflow/custom_ops/data_ptr.cc b/pydiffvg_tensorflow/custom_ops/data_ptr.cc new file mode 100644 index 0000000..cb3caff --- /dev/null +++ b/pydiffvg_tensorflow/custom_ops/data_ptr.cc @@ -0,0 +1,88 @@ +// TODO: add back acknowledgement to the original author when release. + +#pragma warning(disable : 4003 4061 4100 4127 4242 4244 4267 4355 4365 4388 4464 4514 4574 4623 4625 4626 4647 4668 4710 4820 4946 5026 5027 5031 5039) + +// For windows +#define NOMINMAX + +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/shape_inference.h" +#include "tensorflow/core/framework/op_kernel.h" +#include +#include + +using namespace tensorflow; + +/* Tensorflow custom ops does not allow parameter types of list of + various data types. Therefore, we can't pass a list but we have + to pass each objects individually. + + Consult Tensorflow source code: /tensorflow/core/framework/tensor.h + for what is supported by Tensorflow +*/ + +REGISTER_OP("DataPtr") + .Attr("T: {float, int32} = DT_INT32") // To preserve backwards compatibility, you should specify a default value when adding an attr to an existing op: + .Input("input: T") // Tensor + .Output("output: uint64") // scalar + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + c->set_output(0, {}); // scalar + return Status::OK(); + }); + +template +class DataPtrOp : public OpKernel { + public: + explicit DataPtrOp(OpKernelConstruction* context) : OpKernel(context) {} + + void Compute(OpKernelContext* context) override { + // Grab the input tensor + const Tensor& input_tensor = context->input(0); + const T *tensor = input_tensor.flat().data(); + + // Create an output tensor + // NOTE: The output datatype must match the Ops definition!!!. + Tensor* output_tensor = NULL; + // Always allocate on CPU + AllocatorAttributes alloc_attr; + alloc_attr.set_on_host(true); + OP_REQUIRES_OK(context, + context->allocate_output(0, {}, // Initialize a one-element scalar + &output_tensor, + alloc_attr) + ); + auto output_flat = output_tensor->flat(); + + // Cast pointer to unsigned long int + uintptr_t addr = (uintptr_t)tensor; + + // Cast unsigned long int -> unsigned int64 + uint64 addr_converted = addr; + + output_flat(0) = addr_converted; + } +}; + +// Polymorphism: https://www.tensorflow.org/guide/extend/op#polymorphism +REGISTER_KERNEL_BUILDER( + Name("DataPtr") + .Device(DEVICE_CPU) + .TypeConstraint("T"), + DataPtrOp); +REGISTER_KERNEL_BUILDER( + Name("DataPtr") + .Device(DEVICE_CPU) + .TypeConstraint("T"), + DataPtrOp); +REGISTER_KERNEL_BUILDER( + Name("DataPtr") + .Device(DEVICE_GPU) + .TypeConstraint("T") + .HostMemory("output"), + DataPtrOp); +REGISTER_KERNEL_BUILDER( + Name("DataPtr") + .Device(DEVICE_GPU) + .TypeConstraint("T") + .HostMemory("output"), + DataPtrOp); diff --git a/pydiffvg_tensorflow/device.py b/pydiffvg_tensorflow/device.py new file mode 100644 index 0000000..271b6bd --- /dev/null +++ b/pydiffvg_tensorflow/device.py @@ -0,0 +1,59 @@ +import tensorflow as tf + +use_gpu = tf.test.is_gpu_available( + cuda_only=True, + min_cuda_compute_capability=None +) +cpu_device_id = 0 +gpu_device_id = 0 + +def get_device_name(): + """ + Get the current tensorflow device name we are using. + """ + global use_gpu + global cpu_device_id + global gpu_device_id + return '/device:gpu:' + str(gpu_device_id) if use_gpu else '/device:cpu:' + str(cpu_device_id) + +def set_use_gpu(v: bool): + """ + Set whether to use CUDA or not. + """ + global use_gpu + use_gpu = v + +def get_use_gpu(): + """ + Get whether we are using CUDA or not. + """ + global use_gpu + return use_gpu + +def set_cpu_device_id(did: int): + """ + Set the cpu device id we are using. + """ + global cpu_device_id + cpu_device_id = did + +def get_cpu_device_id(): + """ + Get the cpu device id we are using. + """ + global cpu_device_id + return cpu_device_id + +def set_gpu_device_id(did: int): + """ + Set the gpu device id we are using. + """ + global gpu_device_id + gpu_device_id = did + +def get_gpu_device_id(): + """ + Get the gpu device id we are using. + """ + global gpu_device_id + return gpu_device_id diff --git a/pydiffvg_tensorflow/image.py b/pydiffvg_tensorflow/image.py new file mode 100644 index 0000000..18eb1e6 --- /dev/null +++ b/pydiffvg_tensorflow/image.py @@ -0,0 +1,22 @@ +import numpy as np +import skimage +import skimage.io +import os + +def imwrite(img, filename, gamma = 2.2, normalize = False): + directory = os.path.dirname(filename) + if directory != '' and not os.path.exists(directory): + os.makedirs(directory) + + if not isinstance(img, np.ndarray): + img = img.numpy() + if normalize: + img_rng = np.max(img) - np.min(img) + if img_rng > 0: + img = (img - np.min(img)) / img_rng + img = np.clip(img, 0.0, 1.0) + if img.ndim==2: + #repeat along the third dimension + img=np.expand_dims(img,2) + img[:, :, :3] = np.power(img[:, :, :3], 1.0/gamma) + skimage.io.imsave(filename, (img * 255).astype(np.uint8)) \ No newline at end of file diff --git a/pydiffvg_tensorflow/pixel_filter.py b/pydiffvg_tensorflow/pixel_filter.py new file mode 100644 index 0000000..0eff017 --- /dev/null +++ b/pydiffvg_tensorflow/pixel_filter.py @@ -0,0 +1,8 @@ +import tensorflow as tf + +class PixelFilter: + def __init__(self, + type, + radius = tf.constant(0.5)): + self.type = type + self.radius = radius diff --git a/pydiffvg_tensorflow/render_tensorflow.py b/pydiffvg_tensorflow/render_tensorflow.py new file mode 100644 index 0000000..6a7875b --- /dev/null +++ b/pydiffvg_tensorflow/render_tensorflow.py @@ -0,0 +1,649 @@ +import tensorflow as tf +import diffvg +import pydiffvg_tensorflow as pydiffvg +import time +from enum import IntEnum +import warnings + +print_timing = False +__EMPTY_TENSOR = tf.constant([]) + +def is_empty_tensor(tensor): + return tf.equal(tf.size(tensor), 0) + +def set_print_timing(val): + global print_timing + print_timing=val + +class OutputType(IntEnum): + color = 1 + sdf = 2 + +class ShapeType: + __shapetypes = [ + diffvg.ShapeType.circle, + diffvg.ShapeType.ellipse, + diffvg.ShapeType.path, + diffvg.ShapeType.rect + ] + + @staticmethod + def asTensor(type): + for i in range(len(ShapeType.__shapetypes)): + if ShapeType.__shapetypes[i] == type: + return tf.constant(i) + + @staticmethod + def asShapeType(index: tf.Tensor): + if is_empty_tensor(index): + return None + try: + type = ShapeType.__shapetypes[index] + except IndexError: + print(f'{index} is out of range: [0, {len(ShapeType.__shapetypes)})') + import sys + sys.exit() + else: + return type + +class ColorType: + __colortypes = [ + diffvg.ColorType.constant, + diffvg.ColorType.linear_gradient, + diffvg.ColorType.radial_gradient + ] + + @staticmethod + def asTensor(type): + for i in range(len(ColorType.__colortypes)): + if ColorType.__colortypes[i] == type: + return tf.constant(i) + + @staticmethod + def asColorType(index: tf.Tensor): + if is_empty_tensor(index): + return None + try: + type = ColorType.__colortypes[index] + except IndexError: + print(f'{index} is out of range: [0, {len(ColorType.__colortypes)})') + import sys + sys.exit() + else: + return type + +class FilterType: + __filtertypes = [ + diffvg.FilterType.box, + diffvg.FilterType.tent, + diffvg.FilterType.hann + ] + + @staticmethod + def asTensor(type): + for i in range(len(FilterType.__filtertypes)): + if FilterType.__filtertypes[i] == type: + return tf.constant(i) + + @staticmethod + def asFilterType(index: tf.Tensor): + if is_empty_tensor(index): + return None + try: + type = FilterType.__filtertypes[index] + except IndexError: + print(f'{index} is out of range: [0, {len(FilterType.__filtertypes)})') + import sys + sys.exit() + else: + return type + +def serialize_scene(canvas_width, + canvas_height, + shapes, + shape_groups, + filter = pydiffvg.PixelFilter(type = diffvg.FilterType.box, + radius = tf.constant(0.5)), + output_type = OutputType.color, + use_prefiltering = False): + """ + Given a list of shapes, convert them to a linear list of argument, + so that we can use it in TF. + """ + with tf.device('/device:cpu:' + str(pydiffvg.get_cpu_device_id())): + num_shapes = len(shapes) + num_shape_groups = len(shape_groups) + args = [] + args.append(tf.constant(canvas_width)) + args.append(tf.constant(canvas_height)) + args.append(tf.constant(num_shapes)) + args.append(tf.constant(num_shape_groups)) + args.append(tf.constant(output_type)) + args.append(tf.constant(use_prefiltering)) + for shape in shapes: + if isinstance(shape, pydiffvg.Circle): + args.append(ShapeType.asTensor(diffvg.ShapeType.circle)) + args.append(tf.identity(shape.radius)) + args.append(tf.identity(shape.center)) + elif isinstance(shape, pydiffvg.Ellipse): + args.append(ShapeType.asTensor(diffvg.ShapeType.ellipse)) + args.append(tf.identity(shape.radius)) + args.append(tf.identity(shape.center)) + elif isinstance(shape, pydiffvg.Path): + assert(shape.points.shape[1] == 2) + args.append(ShapeType.asTensor(diffvg.ShapeType.path)) + args.append(tf.identity(shape.num_control_points, type=tf.int32)) + args.append(tf.identity(shape.points)) + args.append(tf.constant(shape.is_closed)) + elif isinstance(shape, pydiffvg.Polygon): + assert(shape.points.shape[1] == 2) + args.append(ShapeType.asTensor(diffvg.ShapeType.path)) + if shape.is_closed: + args.append(tf.zeros(shape.points.shape[0], dtype = tf.int32)) + else: + args.append(tf.zeros(shape.points.shape[0] - 1, dtype = tf.int32)) + args.append(tf.identity(shape.points)) + args.append(tf.constant(shape.is_closed)) + elif isinstance(shape, pydiffvg.Rect): + args.append(ShapeType.asTensor(diffvg.ShapeType.rect)) + args.append(tf.identity(shape.p_min)) + args.append(tf.identity(shape.p_max)) + else: + assert(False) + args.append(tf.identity(shape.stroke_width)) + + for shape_group in shape_groups: + args.append(tf.identity(shape_group.shape_ids)) + # Fill color + if shape_group.fill_color is None: + args.append(__EMPTY_TENSOR) + elif tf.is_tensor(shape_group.fill_color): + args.append(ColorType.asTensor(diffvg.ColorType.constant)) + args.append(tf.identity(shape_group.fill_color)) + elif isinstance(shape_group.fill_color, pydiffvg.LinearGradient): + args.append(ColorType.asTensor(diffvg.ColorType.linear_gradient)) + args.append(tf.identity(shape_group.fill_color.begin)) + args.append(tf.identity(shape_group.fill_color.end)) + args.append(tf.identity(shape_group.fill_color.offsets)) + args.append(tf.identity(shape_group.fill_color.stop_colors)) + elif isinstance(shape_group.fill_color, pydiffvg.RadialGradient): + args.append(ColorType.asTensor(diffvg.ColorType.radial_gradient)) + args.append(tf.identity(shape_group.fill_color.center)) + args.append(tf.identity(shape_group.fill_color.radius)) + args.append(tf.identity(shape_group.fill_color.offsets)) + args.append(tf.identity(shape_group.fill_color.stop_colors)) + + if shape_group.fill_color is not None: + # go through the underlying shapes and check if they are all closed + for shape_id in shape_group.shape_ids: + if isinstance(shapes[shape_id], pydiffvg.Path): + if not shapes[shape_id].is_closed: + warnings.warn("Detected non-closed paths with fill color. This might causes unexpected results.", Warning) + + # Stroke color + if shape_group.stroke_color is None: + args.append(__EMPTY_TENSOR) + elif tf.is_tensor(shape_group.stroke_color): + args.append(tf.constant(0)) + args.append(tf.identity(shape_group.stroke_color)) + elif isinstance(shape_group.stroke_color, pydiffvg.LinearGradient): + args.append(ColorType.asTensor(diffvg.ColorType.linear_gradient)) + args.append(tf.identity(shape_group.stroke_color.begin)) + args.append(tf.identity(shape_group.stroke_color.end)) + args.append(tf.identity(shape_group.stroke_color.offsets)) + args.append(tf.identity(shape_group.stroke_color.stop_colors)) + elif isinstance(shape_group.stroke_color, pydiffvg.RadialGradient): + args.append(ColorType.asTensor(diffvg.ColorType.radial_gradient)) + args.append(tf.identity(shape_group.stroke_color.center)) + args.append(tf.identity(shape_group.stroke_color.radius)) + args.append(tf.identity(shape_group.stroke_color.offsets)) + args.append(tf.identity(shape_group.stroke_color.stop_colors)) + args.append(tf.constant(shape_group.use_even_odd_rule)) + # Transformation + args.append(tf.identity(shape_group.shape_to_canvas)) + args.append(FilterType.asTensor(filter.type)) + args.append(tf.constant(filter.radius)) + return args + +class Context: pass + +def forward(width, + height, + num_samples_x, + num_samples_y, + seed, + *args): + """ + Forward rendering pass: given a serialized scene and output an image. + """ + # Unpack arguments + with tf.device('/device:cpu:' + str(pydiffvg.get_cpu_device_id())): + current_index = 0 + canvas_width = int(args[current_index]) + current_index += 1 + canvas_height = int(args[current_index]) + current_index += 1 + num_shapes = int(args[current_index]) + current_index += 1 + num_shape_groups = int(args[current_index]) + current_index += 1 + output_type = OutputType(int(args[current_index])) + current_index += 1 + use_prefiltering = bool(args[current_index]) + current_index += 1 + shapes = [] + shape_groups = [] + shape_contents = [] # Important to avoid GC deleting the shapes + color_contents = [] # Same as above + for shape_id in range(num_shapes): + shape_type = ShapeType.asShapeType(args[current_index]) + current_index += 1 + if shape_type == diffvg.ShapeType.circle: + radius = args[current_index] + current_index += 1 + center = args[current_index] + current_index += 1 + shape = diffvg.Circle(float(radius), + diffvg.Vector2f(float(center[0]), float(center[1]))) + elif shape_type == diffvg.ShapeType.ellipse: + radius = args[current_index] + current_index += 1 + center = args[current_index] + current_index += 1 + shape = diffvg.Ellipse(diffvg.Vector2f(float(radius[0]), float(radius[1])), + diffvg.Vector2f(float(center[0]), float(center[1]))) + elif shape_type == diffvg.ShapeType.path: + num_control_points = args[current_index] + current_index += 1 + points = args[current_index] + current_index += 1 + is_closed = args[current_index] + current_index += 1 + shape = diffvg.Path(diffvg.int_ptr(pydiffvg.data_ptr(num_control_points)), + diffvg.float_ptr(pydiffvg.data_ptr(points)), + num_control_points.shape[0], + points.shape[0], + is_closed) + elif shape_type == diffvg.ShapeType.rect: + p_min = args[current_index] + current_index += 1 + p_max = args[current_index] + current_index += 1 + shape = diffvg.Rect(diffvg.Vector2f(float(p_min[0]), float(p_min[1])), + diffvg.Vector2f(float(p_max[0]), float(p_max[1]))) + else: + assert(False) + stroke_width = args[current_index] + current_index += 1 + shapes.append(diffvg.Shape(\ + shape_type, shape.get_ptr(), float(stroke_width))) + shape_contents.append(shape) + + for shape_group_id in range(num_shape_groups): + shape_ids = args[current_index] + current_index += 1 + fill_color_type = ColorType.asColorType(args[current_index]) + current_index += 1 + if fill_color_type == diffvg.ColorType.constant: + color = args[current_index] + current_index += 1 + fill_color = diffvg.Constant(\ + diffvg.Vector4f(color[0], color[1], color[2], color[3])) + elif fill_color_type == diffvg.ColorType.linear_gradient: + beg = args[current_index] + current_index += 1 + end = args[current_index] + current_index += 1 + offsets = args[current_index] + current_index += 1 + stop_colors = args[current_index] + current_index += 1 + assert(offsets.shape[0] == stop_colors.shape[0]) + fill_color = diffvg.LinearGradient(diffvg.Vector2f(float(beg[0]), float(beg[1])), + diffvg.Vector2f(float(end[0]), float(end[1])), + offsets.shape[0], + diffvg.float_ptr(pydiffvg.data_ptr(offsets)), + diffvg.float_ptr(pydiffvg.data_ptr(stop_colors))) + elif fill_color_type == diffvg.ColorType.radial_gradient: + center = args[current_index] + current_index += 1 + radius = args[current_index] + current_index += 1 + offsets = args[current_index] + current_index += 1 + stop_colors = args[current_index] + current_index += 1 + assert(offsets.shape[0] == stop_colors.shape[0]) + fill_color = diffvg.RadialGradient(diffvg.Vector2f(float(center[0]), float(center[1])), + diffvg.Vector2f(float(radius[0]), float(radius[1])), + offsets.shape[0], + diffvg.float_ptr(pydiffvg.data_ptr(offsets)), + diffvg.float_ptr(pydiffvg.data_ptr(stop_colors))) + elif fill_color_type is None: + fill_color = None + else: + assert(False) + + stroke_color_type = ColorType.asColorType(args[current_index]) + current_index += 1 + if stroke_color_type == diffvg.ColorType.constant: + color = args[current_index] + current_index += 1 + stroke_color = diffvg.Constant(\ + diffvg.Vector4f(float(color[0]), + float(color[1]), + float(color[2]), + float(color[3]))) + elif stroke_color_type == diffvg.ColorType.linear_gradient: + beg = args[current_index] + current_index += 1 + end = args[current_index] + current_index += 1 + offsets = args[current_index] + current_index += 1 + stop_colors = args[current_index] + current_index += 1 + assert(offsets.shape[0] == stop_colors.shape[0]) + stroke_color = diffvg.LinearGradient(\ + diffvg.Vector2f(float(beg[0]), float(beg[1])), + diffvg.Vector2f(float(end[0]), float(end[1])), + offsets.shape[0], + diffvg.float_ptr(pydiffvg.data_ptr(offsets)), + diffvg.float_ptr(stop_colors.data_ptr())) + elif stroke_color_type == diffvg.ColorType.radial_gradient: + center = args[current_index] + current_index += 1 + radius = args[current_index] + current_index += 1 + offsets = args[current_index] + current_index += 1 + stop_colors = args[current_index] + current_index += 1 + assert(offsets.shape[0] == stop_colors.shape[0]) + stroke_color = diffvg.RadialGradient(\ + diffvg.Vector2f(float(center[0]), float(center[1])), + diffvg.Vector2f(float(radius[0]), float(radius[1])), + offsets.shape[0], + diffvg.float_ptr(pydiffvg.data_ptr(offsets)), + diffvg.float_ptr(pydiffvg.data_ptr(stop_colors))) + elif stroke_color_type is None: + stroke_color = None + else: + assert(False) + use_even_odd_rule = bool(args[current_index]) + current_index += 1 + shape_to_canvas = args[current_index] + current_index += 1 + + if fill_color is not None: + color_contents.append(fill_color) + if stroke_color is not None: + color_contents.append(stroke_color) + shape_groups.append(diffvg.ShapeGroup(\ + diffvg.int_ptr(pydiffvg.data_ptr(shape_ids)), + shape_ids.shape[0], + diffvg.ColorType.constant if fill_color_type is None else fill_color_type, + diffvg.void_ptr(0) if fill_color is None else fill_color.get_ptr(), + diffvg.ColorType.constant if stroke_color_type is None else stroke_color_type, + diffvg.void_ptr(0) if stroke_color is None else stroke_color.get_ptr(), + use_even_odd_rule, + diffvg.float_ptr(pydiffvg.data_ptr(shape_to_canvas)))) + + filter_type = FilterType.asFilterType(args[current_index]) + current_index += 1 + filter_radius = args[current_index] + current_index += 1 + filt = diffvg.Filter(filter_type, filter_radius) + + device_name = pydiffvg.get_device_name() + device_spec = tf.DeviceSpec.from_string(device_name) + use_gpu = device_spec.device_type == 'GPU' + gpu_index = device_spec.device_index if device_spec.device_index is not None else 0 + + start = time.time() + scene = diffvg.Scene(canvas_width, + canvas_height, + shapes, + shape_groups, + filt, + use_gpu, + gpu_index) + time_elapsed = time.time() - start + global print_timing + if print_timing: + print('Scene construction, time: %.5f s' % time_elapsed) + + with tf.device(device_name): + if output_type == OutputType.color: + rendered_image = tf.zeros((int(height), int(width), 4), dtype = tf.float32) + else: + assert(output_type == OutputType.sdf) + rendered_image = tf.zeros((int(height), int(width), 1), dtype = tf.float32) + + start = time.time() + diffvg.render(scene, + diffvg.float_ptr(pydiffvg.data_ptr(rendered_image) if output_type == OutputType.color else 0), + diffvg.float_ptr(pydiffvg.data_ptr(rendered_image) if output_type == OutputType.sdf else 0), + width, + height, + int(num_samples_x), + int(num_samples_y), + seed, + diffvg.float_ptr(0), # d_render_image + diffvg.float_ptr(0), # d_render_sdf + diffvg.float_ptr(0), # d_translation + use_prefiltering) + time_elapsed = time.time() - start + if print_timing: + print('Forward pass, time: %.5f s' % time_elapsed) + + ctx = Context() + ctx.scene = scene + ctx.shape_contents = shape_contents + ctx.color_contents = color_contents + ctx.filter = filt + ctx.width = width + ctx.height = height + ctx.num_samples_x = num_samples_x + ctx.num_samples_y = num_samples_y + ctx.seed = seed + ctx.output_type = output_type + ctx.use_prefiltering = use_prefiltering + return rendered_image, ctx + +@tf.custom_gradient +def render(*x): + """ + The main TensorFlow interface of C++ diffvg. + """ + assert(tf.executing_eagerly()) + if pydiffvg.get_use_gpu() and os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] != 'true': + print('******************** WARNING ********************') + print('Tensorflow by default allocates all GPU memory,') + print('causing huge amount of page faults when rendering.') + print('Please set the environment variable TF_FORCE_GPU_ALLOW_GROWTH to true,') + print('so that Tensorflow allocates memory on demand.') + print('*************************************************') + + width = x[0] + height = x[1] + num_samples_x = x[2] + num_samples_y = x[3] + seed = x[4] + args = x[5:] + img, ctx = forward(width, height, num_samples_x, num_samples_y, seed, *args) + + def backward(grad_img): + scene = ctx.scene + width = ctx.width + height = ctx.height + num_samples_x = ctx.num_samples_x + num_samples_y = ctx.num_samples_y + seed = ctx.seed + output_type = ctx.output_type + use_prefiltering = ctx.use_prefiltering + + start = time.time() + with tf.device(pydiffvg.get_device_name()): + diffvg.render(scene, + diffvg.float_ptr(0), # render_image + diffvg.float_ptr(0), # render_sdf + width, + height, + num_samples_x, + num_samples_y, + seed, + diffvg.float_ptr(pydiffvg.data_ptr(grad_img) if output_type == OutputType.color else 0), + diffvg.float_ptr(pydiffvg.data_ptr(grad_img) if output_type == OutputType.sdf else 0), + diffvg.float_ptr(0), # d_translation + use_prefiltering) + time_elapsed = time.time() - start + global print_timing + if print_timing: + print('Backward pass, time: %.5f s' % time_elapsed) + + with tf.device('/device:cpu:' + str(pydiffvg.get_cpu_device_id())): + d_args = [] + d_args.append(None) # width + d_args.append(None) # height + d_args.append(None) # num_samples_x + d_args.append(None) # num_samples_y + d_args.append(None) # seed + d_args.append(None) # canvas_width + d_args.append(None) # canvas_height + d_args.append(None) # num_shapes + d_args.append(None) # num_shape_groups + d_args.append(None) # output_type + d_args.append(None) # use_prefiltering + for shape_id in range(scene.num_shapes): + d_args.append(None) # type + d_shape = scene.get_d_shape(shape_id) + if d_shape.type == diffvg.ShapeType.circle: + d_circle = d_shape.as_circle() + radius = tf.constant(d_circle.radius) + d_args.append(radius) + c = d_circle.center + c = tf.constant((c.x, c.y)) + d_args.append(c) + elif d_shape.type == diffvg.ShapeType.ellipse: + d_ellipse = d_shape.as_ellipse() + r = d_ellipse.radius + r = tf.constant((d_ellipse.radius.x, d_ellipse.radius.y)) + d_args.append(r) + c = d_ellipse.center + c = tf.constant((c.x, c.y)) + d_args.append(c) + elif d_shape.type == diffvg.ShapeType.path: + d_path = d_shape.as_path() + points = tf.zeros((d_path.num_points, 2), dtype=tf.float32) + d_path.copy_to(diffvg.float_ptr(points.data_ptr())) + d_args.append(None) # num_control_points + d_args.append(points) + d_args.append(None) # is_closed + elif d_shape.type == diffvg.ShapeType.rect: + d_rect = d_shape.as_rect() + p_min = tf.constant((d_rect.p_min.x, d_rect.p_min.y)) + p_max = tf.constant((d_rect.p_max.x, d_rect.p_max.y)) + d_args.append(p_min) + d_args.append(p_max) + else: + assert(False) + w = tf.constant((d_shape.stroke_width)) + d_args.append(w) + + for group_id in range(scene.num_shape_groups): + d_shape_group = scene.get_d_shape_group(group_id) + d_args.append(None) # shape_ids + d_args.append(None) # fill_color_type + if d_shape_group.has_fill_color(): + if d_shape_group.fill_color_type == diffvg.ColorType.constant: + d_constant = d_shape_group.fill_color_as_constant() + c = d_constant.color + d_args.append(tf.constant((c.x, c.y, c.z, c.w))) + elif d_shape_group.fill_color_type == diffvg.ColorType.linear_gradient: + d_linear_gradient = d_shape_group.fill_color_as_linear_gradient() + beg = d_linear_gradient.begin + d_args.append(tf.constant((beg.x, beg.y))) + end = d_linear_gradient.end + d_args.append(tf.constant((end.x, end.y))) + offsets = tf.zeros((d_linear_gradient.num_stops), dtype=tf.float32) + stop_colors = tf.zeros((d_linear_gradient.num_stops, 4), dtype=tf.float32) + # HACK: tensorflow's eager mode uses a cache to store scalar + # constants to avoid memory copy. If we pass scalar tensors + # into the C++ code and modify them, we would corrupt the + # cache, causing incorrect result in future scalar constant + # creations. Thus we force tensorflow to copy by plusing a zero. + # (also see https://github.com/tensorflow/tensorflow/issues/11186 + # for more discussion regarding copying tensors) + if offsets.shape.num_elements() == 1: + offsets = offsets + 0 + d_linear_gradient.copy_to(\ + diffvg.float_ptr(pydiffvg.data_ptr(offsets)), + diffvg.float_ptr(pydiffvg.data_ptr(stop_colors))) + d_args.append(offsets) + d_args.append(stop_colors) + elif d_shape_group.fill_color_type == diffvg.ColorType.radial_gradient: + d_radial_gradient = d_shape_group.fill_color_as_radial_gradient() + center = d_radial_gradient.center + d_args.append(tf.constant((center.x, center.y))) + radius = d_radial_gradient.radius + d_args.append(tf.constant((radius.x, radius.y))) + offsets = tf.zeros((d_radial_gradient.num_stops)) + if offsets.shape.num_elements() == 1: + offsets = offsets + 0 + stop_colors = tf.zeros((d_radial_gradient.num_stops, 4)) + d_radial_gradient.copy_to(\ + diffvg.float_ptr(pydiffvg.data_ptr(offsets)), + diffvg.float_ptr(pydiffvg.data_ptr(stop_colors))) + d_args.append(offsets) + d_args.append(stop_colors) + else: + assert(False) + d_args.append(None) # stroke_color_type + if d_shape_group.has_stroke_color(): + if d_shape_group.stroke_color_type == diffvg.ColorType.constant: + d_constant = d_shape_group.stroke_color_as_constant() + c = d_constant.color + d_args.append(tf.constant((c.x, c.y, c.z, c.w))) + elif d_shape_group.stroke_color_type == diffvg.ColorType.linear_gradient: + d_linear_gradient = d_shape_group.stroke_color_as_linear_gradient() + beg = d_linear_gradient.begin + d_args.append(tf.constant((beg.x, beg.y))) + end = d_linear_gradient.end + d_args.append(tf.constant((end.x, end.y))) + offsets = tf.zeros((d_linear_gradient.num_stops)) + stop_colors = tf.zeros((d_linear_gradient.num_stops, 4)) + if offsets.shape.num_elements() == 1: + offsets = offsets + 0 + d_linear_gradient.copy_to(\ + diffvg.float_ptr(pydiffvg.data_ptr(offsets)), + diffvg.float_ptr(pydiffvg.data_ptr(stop_colors))) + d_args.append(offsets) + d_args.append(stop_colors) + elif d_shape_group.fill_color_type == diffvg.ColorType.radial_gradient: + d_radial_gradient = d_shape_group.stroke_color_as_radial_gradient() + center = d_radial_gradient.center + d_args.append(tf.constant((center.x, center.y))) + radius = d_radial_gradient.radius + d_args.append(tf.constant((radius.x, radius.y))) + offsets = tf.zeros((d_radial_gradient.num_stops)) + stop_colors = tf.zeros((d_radial_gradient.num_stops, 4)) + if offsets.shape.num_elements() == 1: + offsets = offsets + 0 + d_radial_gradient.copy_to(\ + diffvg.float_ptr(pydiffvg.data_ptr(offsets)), + diffvg.float_ptr(pydiffvg.data_ptr(stop_colors))) + d_args.append(offsets) + d_args.append(stop_colors) + else: + assert(False) + d_args.append(None) # use_even_odd_rule + d_shape_to_canvas = tf.zeros((3, 3), dtype = tf.float32) + d_shape_group.copy_to(diffvg.float_ptr(pydiffvg.data_ptr(d_shape_to_canvas))) + d_args.append(d_shape_to_canvas) + d_args.append(None) # filter_type + d_args.append(tf.constant(scene.get_d_filter_radius())) + + return d_args + + return img, backward \ No newline at end of file diff --git a/pydiffvg_tensorflow/shape.py b/pydiffvg_tensorflow/shape.py new file mode 100644 index 0000000..f71c2c7 --- /dev/null +++ b/pydiffvg_tensorflow/shape.py @@ -0,0 +1,53 @@ +import tensorflow as tf +import math + +class Circle: + def __init__(self, radius, center, stroke_width = tf.constant(1.0), id = ''): + self.radius = radius + self.center = center + self.stroke_width = stroke_width + self.id = id + +class Ellipse: + def __init__(self, radius, center, stroke_width = tf.constant(1.0), id = ''): + self.radius = radius + self.center = center + self.stroke_width = stroke_width + self.id = id + +class Path: + def __init__(self, num_control_points, points, is_closed, stroke_width = tf.constant(1.0), id = ''): + self.num_control_points = num_control_points + self.points = points + self.is_closed = is_closed + self.stroke_width = stroke_width + self.id = id + +class Polygon: + def __init__(self, points, is_closed, stroke_width = tf.constant(1.0), id = ''): + self.points = points + self.is_closed = is_closed + self.stroke_width = stroke_width + self.id = id + +class Rect: + def __init__(self, p_min, p_max, stroke_width = tf.constant(1.0), id = ''): + self.p_min = p_min + self.p_max = p_max + self.stroke_width = stroke_width + self.id = id + +class ShapeGroup: + def __init__(self, + shape_ids, + fill_color, + use_even_odd_rule = True, + stroke_color = None, + shape_to_canvas = tf.eye(3), + id = ''): + self.shape_ids = shape_ids + self.fill_color = fill_color + self.use_even_odd_rule = use_even_odd_rule + self.stroke_color = stroke_color + self.shape_to_canvas = shape_to_canvas + self.id = id diff --git a/sample_boundary.h b/sample_boundary.h new file mode 100644 index 0000000..28af129 --- /dev/null +++ b/sample_boundary.h @@ -0,0 +1,454 @@ +#pragma once + +#include "diffvg.h" +#include "shape.h" +#include "scene.h" +#include "vector.h" +#include "cdf.h" + +struct PathBoundaryData { + int base_point_id; + int point_id; + float t; +}; + +struct BoundaryData { + PathBoundaryData path; + bool is_stroke; +}; + +DEVICE +Vector2f sample_boundary(const Circle &circle, + float t, + Vector2f &normal, + float &pdf, + BoundaryData &, + float stroke_perturb_direction, + float stroke_radius) { + // Parametric form of a circle (t in [0, 1)): + // x = center.x + r * cos(2pi * t) + // y = center.y + r * sin(2pi * t) + auto offset = Vector2f{ + circle.radius * cos(2 * float(M_PI) * t), + circle.radius * sin(2 * float(M_PI) * t) + }; + normal = normalize(offset); + pdf /= (2 * float(M_PI) * circle.radius); + auto ret = circle.center + offset; + if (stroke_perturb_direction != 0.f) { + ret += stroke_perturb_direction * stroke_radius * normal; + if (stroke_perturb_direction < 0) { + // normal should point towards the perturb direction + normal = -normal; + } + } + return ret; +} + +DEVICE +Vector2f sample_boundary(const Ellipse &ellipse, + float t, + Vector2f &normal, + float &pdf, + BoundaryData &, + float stroke_perturb_direction, + float stroke_radius) { + // Parametric form of a ellipse (t in [0, 1)): + // x = center.x + r.x * cos(2pi * t) + // y = center.y + r.y * sin(2pi * t) + const auto &r = ellipse.radius; + auto offset = Vector2f{ + r.x * cos(2 * float(M_PI) * t), + r.y * sin(2 * float(M_PI) * t) + }; + auto dxdt = -r.x * sin(2 * float(M_PI) * t) * 2 * float(M_PI); + auto dydt = r.y * cos(2 * float(M_PI) * t) * 2 * float(M_PI); + // tangent is normalize(dxdt, dydt) + normal = normalize(Vector2f{dydt, -dxdt}); + pdf /= sqrt(square(dxdt) + square(dydt)); + auto ret = ellipse.center + offset; + if (stroke_perturb_direction != 0.f) { + ret += stroke_perturb_direction * stroke_radius * normal; + if (stroke_perturb_direction < 0) { + // normal should point towards the perturb direction + normal = -normal; + } + } + return ret; +} + +DEVICE +Vector2f sample_boundary(const Path &path, + const float *path_length_cdf, + const float *path_length_pmf, + const int *point_id_map, + float path_length, + float t, + Vector2f &normal, + float &pdf, + BoundaryData &data, + float stroke_perturb_direction, + float stroke_radius) { + if (stroke_perturb_direction != 0.f && !path.is_closed) { + // We need to samples the "caps" of the path + // length of a cap is pi * abs(stroke_perturb_direction) + // there are two caps + auto cap_length = 0.f; + if (path.thickness != nullptr) { + auto r0 = path.thickness[0]; + auto r1 = path.thickness[path.num_points - 1]; + cap_length = float(M_PI) * (r0 + r1); + } else { + cap_length = 2 * float(M_PI) * stroke_radius; + } + auto cap_prob = cap_length / (cap_length + path_length); + if (t < cap_prob) { + t = t / cap_prob; + pdf *= cap_prob; + auto r0 = stroke_radius; + auto r1 = stroke_radius; + if (path.thickness != nullptr) { + r0 = path.thickness[0]; + r1 = path.thickness[path.num_points - 1]; + } + // HACK: in theory we want to compute the tangent and + // sample the hemi-circle, but here we just sample the + // full circle since it's less typing + if (stroke_perturb_direction < 0) { + // Sample the cap at the beginning + auto p0 = Vector2f{path.points[0], path.points[1]}; + auto offset = Vector2f{ + r0 * cos(2 * float(M_PI) * t), + r0 * sin(2 * float(M_PI) * t) + }; + normal = normalize(offset); + pdf /= (2 * float(M_PI) * r0); + data.path.base_point_id = 0; + data.path.point_id = 0; + data.path.t = 0; + return p0 + offset; + } else { + // Sample the cap at the end + auto p0 = Vector2f{path.points[2 * (path.num_points - 1)], + path.points[2 * (path.num_points - 1) + 1]}; + auto offset = Vector2f{ + r1 * cos(2 * float(M_PI) * t), + r1 * sin(2 * float(M_PI) * t) + }; + normal = normalize(offset); + pdf /= (2 * float(M_PI) * r1); + data.path.base_point_id = path.num_base_points - 1; + data.path.point_id = path.num_points - 2 - + path.num_control_points[data.path.base_point_id]; + data.path.t = 1; + return p0 + offset; + } + } else { + t = (t - cap_prob) / (1 - cap_prob); + pdf *= (1 - cap_prob); + } + } + // Binary search on path_length_cdf + auto sample_id = sample(path_length_cdf, + path.num_base_points, + t, + &t); + assert(sample_id >= 0 && sample_id < path.num_base_points); + auto point_id = point_id_map[sample_id]; + if (path.num_control_points[sample_id] == 0) { + // Straight line + auto i0 = point_id; + auto i1 = (i0 + 1) % path.num_points; + assert(i0 < path.num_points); + auto p0 = Vector2f{path.points[2 * i0], path.points[2 * i0 + 1]}; + auto p1 = Vector2f{path.points[2 * i1], path.points[2 * i1 + 1]}; + data.path.base_point_id = sample_id; + data.path.point_id = point_id; + data.path.t = t; + if (t < -1e-3f || t > 1+1e-3f) { + // return invalid sample + pdf = 0; + return Vector2f{0, 0}; + } + auto tangent = (p1 - p0); + auto tan_len = length(tangent); + if (tan_len == 0) { + // return invalid sample + pdf = 0; + return Vector2f{0, 0}; + } + normal = Vector2f{-tangent.y, tangent.x} / tan_len; + // length of tangent is the Jacobian of the sampling transformation + pdf *= path_length_pmf[sample_id] / tan_len; + auto ret = p0 + t * (p1 - p0); + if (stroke_perturb_direction != 0.f) { + auto r0 = stroke_radius; + auto r1 = stroke_radius; + if (path.thickness != nullptr) { + r0 = path.thickness[i0]; + r1 = path.thickness[i1]; + } + auto r = r0 + t * (r1 - r0); + ret += stroke_perturb_direction * r * normal; + if (stroke_perturb_direction < 0) { + // normal should point towards the perturb direction + normal = -normal; + } + } + return ret; + } else if (path.num_control_points[sample_id] == 1) { + // Quadratic Bezier curve + auto i0 = point_id; + auto i1 = i0 + 1; + auto i2 = (i0 + 2) % path.num_points; + auto p0 = Vector2f{path.points[2 * i0], path.points[2 * i0 + 1]}; + auto p1 = Vector2f{path.points[2 * i1], path.points[2 * i1 + 1]}; + auto p2 = Vector2f{path.points[2 * i2], path.points[2 * i2 + 1]}; + auto eval = [&](float t) -> Vector2f { + auto tt = 1 - t; + return (tt*tt)*p0 + (2*tt*t)*p1 + (t*t)*p2; + }; + data.path.base_point_id = sample_id; + data.path.point_id = point_id; + data.path.t = t; + if (t < -1e-3f || t > 1+1e-3f) { + // return invalid sample + pdf = 0; + return Vector2f{0, 0}; + } + auto tangent = 2 * (1 - t) * (p1 - p0) + 2 * t * (p2 - p1); + auto tan_len = length(tangent); + if (tan_len == 0) { + // return invalid sample + pdf = 0; + return Vector2f{0, 0}; + } + normal = Vector2f{-tangent.y, tangent.x} / tan_len; + // length of tangent is the Jacobian of the sampling transformation + pdf *= path_length_pmf[sample_id] / tan_len; + auto ret = eval(t); + if (stroke_perturb_direction != 0.f) { + auto r0 = stroke_radius; + auto r1 = stroke_radius; + auto r2 = stroke_radius; + if (path.thickness != nullptr) { + r0 = path.thickness[i0]; + r1 = path.thickness[i1]; + r2 = path.thickness[i2]; + } + auto tt = 1 - t; + auto r = (tt*tt)*r0 + (2*tt*t)*r1 + (t*t)*r2; + ret += stroke_perturb_direction * r * normal; + if (stroke_perturb_direction < 0) { + // normal should point towards the perturb direction + normal = -normal; + } + } + return ret; + } else if (path.num_control_points[sample_id] == 2) { + // Cubic Bezier curve + auto i0 = point_id; + auto i1 = point_id + 1; + auto i2 = point_id + 2; + auto i3 = (point_id + 3) % path.num_points; + assert(i0 >= 0 && i2 < path.num_points); + auto p0 = Vector2f{path.points[2 * i0], path.points[2 * i0 + 1]}; + auto p1 = Vector2f{path.points[2 * i1], path.points[2 * i1 + 1]}; + auto p2 = Vector2f{path.points[2 * i2], path.points[2 * i2 + 1]}; + auto p3 = Vector2f{path.points[2 * i3], path.points[2 * i3 + 1]}; + auto eval = [&](float t) -> Vector2f { + auto tt = 1 - t; + return (tt*tt*tt)*p0 + (3*tt*tt*t)*p1 + (3*tt*t*t)*p2 + (t*t*t)*p3; + }; + data.path.base_point_id = sample_id; + data.path.point_id = point_id; + data.path.t = t; + if (t < -1e-3f || t > 1+1e-3f) { + // return invalid sample + pdf = 0; + return Vector2f{0, 0}; + } + auto tangent = 3 * square(1 - t) * (p1 - p0) + 6 * (1 - t) * t * (p2 - p1) + 3 * t * t * (p3 - p2); + auto tan_len = length(tangent); + if (tan_len == 0) { + // return invalid sample + pdf = 0; + return Vector2f{0, 0}; + } + normal = Vector2f{-tangent.y, tangent.x} / tan_len; + // length of tangent is the Jacobian of the sampling transformation + pdf *= path_length_pmf[sample_id] / tan_len; + auto ret = eval(t); + if (stroke_perturb_direction != 0.f) { + auto r0 = stroke_radius; + auto r1 = stroke_radius; + auto r2 = stroke_radius; + auto r3 = stroke_radius; + if (path.thickness != nullptr) { + r0 = path.thickness[i0]; + r1 = path.thickness[i1]; + r2 = path.thickness[i2]; + r3 = path.thickness[i3]; + } + auto tt = 1 - t; + auto r = (tt*tt*tt)*r0 + (3*tt*tt*t)*r1 + (3*tt*t*t)*r2 + (t*t*t)*r3; + ret += stroke_perturb_direction * r * normal; + if (stroke_perturb_direction < 0) { + // normal should point towards the perturb direction + normal = -normal; + } + } + return ret; + } else { + assert(false); + } + assert(false); + return Vector2f{0, 0}; +} + +DEVICE +Vector2f sample_boundary(const Rect &rect, + float t, Vector2f &normal, + float &pdf, + BoundaryData &, + float stroke_perturb_direction, + float stroke_radius) { + // Roll a dice to decide whether to sample width or height + auto w = rect.p_max.x - rect.p_min.x; + auto h = rect.p_max.y - rect.p_min.y; + pdf /= (2 * (w +h)); + if (t <= w / (w + h)) { + // Sample width + // reuse t for the next dice + t *= (w + h) / w; + // Roll a dice to decide whether to sample upper width or lower width + if (t < 0.5f) { + // Sample upper width + normal = Vector2f{0, -1}; + auto ret = rect.p_min + 2 * t * Vector2f{rect.p_max.x - rect.p_min.x, 0.f}; + if (stroke_perturb_direction != 0.f) { + ret += stroke_perturb_direction * stroke_radius * normal; + if (stroke_perturb_direction < 0) { + // normal should point towards the perturb direction + normal = -normal; + } + } + return ret; + } else { + // Sample lower width + normal = Vector2f{0, 1}; + auto ret = Vector2f{rect.p_min.x, rect.p_max.y} + + 2 * (t - 0.5f) * Vector2f{rect.p_max.x - rect.p_min.x, 0.f}; + if (stroke_perturb_direction != 0.f) { + ret += stroke_perturb_direction * stroke_radius * normal; + if (stroke_perturb_direction < 0) { + // normal should point towards the perturb direction + normal = -normal; + } + } + return ret; + } + } else { + // Sample height + // reuse t for the next dice + assert(h > 0); + t = (t - w / (w + h)) * (w + h) / h; + // Roll a dice to decide whether to sample left height or right height + if (t < 0.5f) { + // Sample left height + normal = Vector2f{-1, 0}; + auto ret = rect.p_min + 2 * t * Vector2f{0.f, rect.p_max.y - rect.p_min.y}; + if (stroke_perturb_direction != 0.f) { + ret += stroke_perturb_direction * stroke_radius * normal; + if (stroke_perturb_direction < 0) { + // normal should point towards the perturb direction + normal = -normal; + } + } + return ret; + } else { + // Sample right height + normal = Vector2f{1, 0}; + auto ret = Vector2f{rect.p_max.x, rect.p_min.y} + + 2 * (t - 0.5f) * Vector2f{0.f, rect.p_max.y - rect.p_min.y}; + if (stroke_perturb_direction != 0.f) { + ret += stroke_perturb_direction * stroke_radius * normal; + if (stroke_perturb_direction < 0) { + // normal should point towards the perturb direction + normal = -normal; + } + } + return ret; + } + } +} + +DEVICE +Vector2f sample_boundary(const SceneData &scene, + int shape_group_id, + int shape_id, + float t, + Vector2f &normal, + float &pdf, + BoundaryData &data) { + const ShapeGroup &shape_group = scene.shape_groups[shape_group_id]; + const Shape &shape = scene.shapes[shape_id]; + pdf = 1; + // Choose which one to sample: stroke discontinuities or fill discontinuities. + // TODO: we don't need to sample fill discontinuities when stroke alpha is 1 and both + // fill and stroke color exists + auto stroke_perturb = false; + if (shape_group.fill_color != nullptr && shape_group.stroke_color != nullptr) { + if (t < 0.5f) { + stroke_perturb = false; + t = 2 * t; + pdf = 0.5f; + } else { + stroke_perturb = true; + t = 2 * (t - 0.5f); + pdf = 0.5f; + } + } else if (shape_group.stroke_color != nullptr) { + stroke_perturb = true; + } + data.is_stroke = stroke_perturb; + auto stroke_perturb_direction = 0.f; + if (stroke_perturb) { + if (t < 0.5f) { + stroke_perturb_direction = -1.f; + t = 2 * t; + pdf *= 0.5f; + } else { + stroke_perturb_direction = 1.f; + t = 2 * (t - 0.5f); + pdf *= 0.5f; + } + } + switch (shape.type) { + case ShapeType::Circle: + return sample_boundary( + *(const Circle *)shape.ptr, t, normal, pdf, data, stroke_perturb_direction, shape.stroke_width); + case ShapeType::Ellipse: + return sample_boundary( + *(const Ellipse *)shape.ptr, t, normal, pdf, data, stroke_perturb_direction, shape.stroke_width); + case ShapeType::Path: + return sample_boundary( + *(const Path *)shape.ptr, + scene.path_length_cdf[shape_id], + scene.path_length_pmf[shape_id], + scene.path_point_id_map[shape_id], + scene.shapes_length[shape_id], + t, + normal, + pdf, + data, + stroke_perturb_direction, + shape.stroke_width); + case ShapeType::Rect: + return sample_boundary( + *(const Rect *)shape.ptr, t, normal, pdf, data, stroke_perturb_direction, shape.stroke_width); + } + assert(false); + return Vector2f{}; +} + diff --git a/scene.cpp b/scene.cpp new file mode 100644 index 0000000..e024488 --- /dev/null +++ b/scene.cpp @@ -0,0 +1,1035 @@ +#include "scene.h" +#include "aabb.h" +#include "cuda_utils.h" +#include "filter.h" +#include "shape.h" +#include +#include +#include +#include +#include + +size_t align(size_t s) { + auto a = alignof(std::max_align_t); + return ((s + a - 1) / a) * a; +} + +template +void allocate(bool use_gpu, T **p) { + if (use_gpu) { +#ifdef __NVCC__ + checkCuda(cudaMallocManaged(p, sizeof(T))); +#else + throw std::runtime_error("diffvg not compiled with GPU"); + assert(false); +#endif + } else { + *p = (T*)malloc(sizeof(T)); + } +} + +template +void allocate(bool use_gpu, size_t size, T **p) { + if (use_gpu) { +#ifdef __NVCC__ + checkCuda(cudaMallocManaged(p, size * sizeof(T))); +#else + throw std::runtime_error("diffvg not compiled with GPU"); + assert(false); +#endif + } else { + *p = (T*)malloc(size * sizeof(T)); + } +} + +void copy_and_init_shapes(Scene &scene, + const std::vector &shape_list) { + for (int shape_id = 0; shape_id < scene.num_shapes; shape_id++) { + switch (shape_list[shape_id]->type) { + case ShapeType::Circle: { + Circle *p = (Circle *)scene.shapes[shape_id].ptr; + const Circle *p_ = (const Circle*)(shape_list[shape_id]->ptr); + *p = *p_; + Circle *d_p = (Circle *)scene.d_shapes[shape_id].ptr; + d_p->radius = 0; + d_p->center = Vector2f{0, 0}; + break; + } case ShapeType::Ellipse: { + Ellipse *p = (Ellipse *)scene.shapes[shape_id].ptr; + const Ellipse *p_ = (const Ellipse*)(shape_list[shape_id]->ptr); + *p = *p_; + Ellipse *d_p = (Ellipse *)scene.d_shapes[shape_id].ptr; + d_p->radius = Vector2f{0, 0}; + d_p->center = Vector2f{0, 0}; + break; + } case ShapeType::Path: { + Path *p = (Path *)scene.shapes[shape_id].ptr; + const Path *p_ = (const Path*)(shape_list[shape_id]->ptr); + p->num_points = p_->num_points; + p->num_base_points = p_->num_base_points; + for (int i = 0; i < p_->num_base_points; i++) { + p->num_control_points[i] = p_->num_control_points[i]; + } + for (int i = 0; i < 2 * p_->num_points; i++) { + p->points[i] = p_->points[i]; + } + p->is_closed = p_->is_closed; + p->use_distance_approx = p_->use_distance_approx; + Path *d_p = (Path *)scene.d_shapes[shape_id].ptr; + d_p->num_points = p_->num_points; + d_p->num_base_points = p_->num_base_points; + for (int i = 0; i < 2 * p_->num_points; i++) { + d_p->points[i] = 0; + } + d_p->is_closed = p_->is_closed; + if (p_->thickness != nullptr) { + for (int i = 0; i < p_->num_points; i++) { + p->thickness[i] = p_->thickness[i]; + d_p->thickness[i] = 0; + } + } + d_p->use_distance_approx = p_->use_distance_approx; + break; + } case ShapeType::Rect: { + Rect *p = (Rect *)scene.shapes[shape_id].ptr; + const Rect *p_ = (const Rect*)(shape_list[shape_id]->ptr); + *p = *p_; + Rect *d_p = (Rect *)scene.d_shapes[shape_id].ptr; + d_p->p_min = Vector2f{0, 0}; + d_p->p_max = Vector2f{0, 0}; + break; + } default: { + assert(false); + break; + } + } + scene.shapes[shape_id].type = shape_list[shape_id]->type; + scene.shapes[shape_id].stroke_width = shape_list[shape_id]->stroke_width; + scene.d_shapes[shape_id].type = shape_list[shape_id]->type; + scene.d_shapes[shape_id].stroke_width = 0; + } +} + +std::vector +compute_shape_length(const std::vector &shape_list) { + int num_shapes = (int)shape_list.size(); + std::vector shape_length_list(num_shapes, 0.f); + for (int shape_id = 0; shape_id < num_shapes; shape_id++) { + auto shape_length = 0.f; + switch (shape_list[shape_id]->type) { + case ShapeType::Circle: { + const Circle *p_ = (const Circle*)(shape_list[shape_id]->ptr); + shape_length += float(2.f * M_PI) * p_->radius; + break; + } case ShapeType::Ellipse: { + const Ellipse *p_ = (const Ellipse*)(shape_list[shape_id]->ptr); + // https://en.wikipedia.org/wiki/Ellipse#Circumference + // Ramanujan's ellipse circumference approximation + auto a = p_->radius.x; + auto b = p_->radius.y; + shape_length += float(M_PI) * (3 * (a + b) - sqrt((3 * a + b) * (a + 3 * b))); + break; + } case ShapeType::Path: { + const Path *p_ = (const Path*)(shape_list[shape_id]->ptr); + auto length = 0.f; + auto point_id = 0; + for (int i = 0; i < p_->num_base_points; i++) { + if (p_->num_control_points[i] == 0) { + // Straight line + auto i0 = point_id; + assert(i0 < p_->num_points); + auto i1 = (i0 + 1) % p_->num_points; + point_id += 1; + auto p0 = Vector2f{p_->points[2 * i0], p_->points[2 * i0 + 1]}; + auto p1 = Vector2f{p_->points[2 * i1], p_->points[2 * i1 + 1]}; + length += distance(p1, p0); + } else if (p_->num_control_points[i] == 1) { + // Quadratic Bezier curve + auto i0 = point_id; + auto i1 = i0 + 1; + auto i2 = (i0 + 2) % p_->num_points; + point_id += 2; + auto p0 = Vector2f{p_->points[2 * i0], p_->points[2 * i0 + 1]}; + auto p1 = Vector2f{p_->points[2 * i1], p_->points[2 * i1 + 1]}; + auto p2 = Vector2f{p_->points[2 * i2], p_->points[2 * i2 + 1]}; + auto eval = [&](float t) -> Vector2f { + auto tt = 1 - t; + return (tt*tt)*p0 + (2*tt*t)*p1 + (t*t)*p2; + }; + // We use 3-point samples to approximate the length + auto v0 = p0; + auto v1 = eval(0.5f); + auto v2 = p2; + length += distance(v1, v0) + distance(v1, v2); + } else if (p_->num_control_points[i] == 2) { + // Cubic Bezier curve + auto i0 = point_id; + auto i1 = i0 + 1; + auto i2 = i0 + 2; + auto i3 = (i0 + 3) % p_->num_points; + point_id += 3; + auto p0 = Vector2f{p_->points[2 * i0], p_->points[2 * i0 + 1]}; + auto p1 = Vector2f{p_->points[2 * i1], p_->points[2 * i1 + 1]}; + auto p2 = Vector2f{p_->points[2 * i2], p_->points[2 * i2 + 1]}; + auto p3 = Vector2f{p_->points[2 * i3], p_->points[2 * i3 + 1]}; + auto eval = [&](float t) -> Vector2f { + auto tt = 1 - t; + return (tt*tt*tt)*p0 + (3*tt*tt*t)*p1 + (3*tt*t*t)*p2 + (t*t*t)*p3; + }; + // We use 4-point samples to approximate the length + auto v0 = p0; + auto v1 = eval(1.f/3.f); + auto v2 = eval(2.f/3.f); + auto v3 = p3; + length += distance(v1, v0) + distance(v1, v2) + distance(v2, v3); + } else { + assert(false); + } + } + assert(isfinite(length)); + shape_length += length; + break; + } case ShapeType::Rect: { + const Rect *p_ = (const Rect*)(shape_list[shape_id]->ptr); + shape_length += 2 * (p_->p_max.x - p_->p_min.x + p_->p_max.y - p_->p_min.y); + break; + } default: { + assert(false); + break; + } + } + assert(isfinite(shape_length)); + shape_length_list[shape_id] = shape_length; + } + return shape_length_list; +} + +void build_shape_cdfs(Scene &scene, + const std::vector &shape_group_list, + const std::vector &shape_length_list) { + int sample_id = 0; + for (int shape_group_id = 0; shape_group_id < (int)shape_group_list.size(); shape_group_id++) { + const ShapeGroup *shape_group = shape_group_list[shape_group_id]; + for (int i = 0; i < shape_group->num_shapes; i++) { + int shape_id = shape_group->shape_ids[i]; + float length = shape_length_list[shape_id]; + scene.sample_shape_id[sample_id] = shape_id; + if (sample_id == 0) { + scene.sample_shapes_cdf[sample_id] = length; + } else { + scene.sample_shapes_cdf[sample_id] = length + + scene.sample_shapes_cdf[sample_id - 1]; + } + assert(isfinite(length)); + scene.sample_shapes_pmf[sample_id] = length; + scene.sample_group_id[sample_id] = shape_group_id; + sample_id++; + } + } + assert(sample_id == scene.num_total_shapes); + auto normalization = scene.sample_shapes_cdf[scene.num_total_shapes - 1]; + if (normalization <= 0) { + char buf[256]; + sprintf(buf, "The total length of the shape boundaries in the scene is equal or less than 0. Length = %f", normalization); + throw std::runtime_error(buf); + } + if (!isfinite(normalization)) { + char buf[256]; + sprintf(buf, "The total length of the shape boundaries in the scene is not a number. Length = %f", normalization); + throw std::runtime_error(buf); + } + assert(normalization > 0); + for (int sample_id = 0; sample_id < scene.num_total_shapes; sample_id++) { + scene.sample_shapes_cdf[sample_id] /= normalization; + scene.sample_shapes_pmf[sample_id] /= normalization; + } +} + +void build_path_cdfs(Scene &scene, + const std::vector &shape_list, + const std::vector &shape_length_list) { + for (int shape_id = 0; shape_id < scene.num_shapes; shape_id++) { + if (shape_list[shape_id]->type == ShapeType::Path) { + const Path &path = shape_list[shape_id]->as_path(); + float *pmf = scene.path_length_pmf[shape_id]; + float *cdf = scene.path_length_cdf[shape_id]; + int *point_id_map = scene.path_point_id_map[shape_id]; + auto path_length = shape_length_list[shape_id]; + auto inv_length = 1.f / path_length; + auto point_id = 0; + for (int i = 0; i < path.num_base_points; i++) { + point_id_map[i] = point_id; + if (path.num_control_points[i] == 0) { + // Straight line + auto i0 = point_id; + auto i1 = (i0 + 1) % path.num_points; + point_id += 1; + auto p0 = Vector2f{path.points[2 * i0], path.points[2 * i0 + 1]}; + auto p1 = Vector2f{path.points[2 * i1], path.points[2 * i1 + 1]}; + auto d = distance(p0, p1) * inv_length; + pmf[i] = d; + if (i == 0) { + cdf[i] = d; + } else { + cdf[i] = d + cdf[i - 1]; + } + } else if (path.num_control_points[i] == 1) { + // Quadratic Bezier curve + auto i0 = point_id; + auto i1 = i0 + 1; + auto i2 = (i0 + 2) % path.num_points; + point_id += 2; + auto p0 = Vector2f{path.points[2 * i0], path.points[2 * i0 + 1]}; + auto p1 = Vector2f{path.points[2 * i1], path.points[2 * i1 + 1]}; + auto p2 = Vector2f{path.points[2 * i2], path.points[2 * i2 + 1]}; + auto eval = [&](float t) -> Vector2f { + auto tt = 1 - t; + return (tt*tt)*p0 + (2*tt*t)*p1 + (t*t)*p2; + }; + // We use 3-point samples to approximate the length + auto v0 = p0; + auto v1 = eval(0.5f); + auto v2 = p2; + auto d = (distance(v0, v1) + distance(v1, v2)) * inv_length; + pmf[i] = d; + if (i == 0) { + cdf[i] = d; + } else { + cdf[i] = d + cdf[i - 1]; + } + } else if (path.num_control_points[i] == 2) { + // Cubic Bezier curve + auto i0 = point_id; + auto i1 = point_id + 1; + auto i2 = point_id + 2; + auto i3 = (point_id + 3) % path.num_points; + point_id += 3; + auto p0 = Vector2f{path.points[2 * i0], path.points[2 * i0 + 1]}; + auto p1 = Vector2f{path.points[2 * i1], path.points[2 * i1 + 1]}; + auto p2 = Vector2f{path.points[2 * i2], path.points[2 * i2 + 1]}; + auto p3 = Vector2f{path.points[2 * i3], path.points[2 * i3 + 1]}; + auto eval = [&](float t) -> Vector2f { + auto tt = 1 - t; + return (tt*tt*tt)*p0 + (3*tt*tt*t)*p1 + (3*tt*t*t)*p2 + (t*t*t)*p3; + }; + // We use 4-point samples to approximate the length + auto v0 = p0; + auto v1 = eval(1.f/3.f); + auto v2 = eval(2.f/3.f); + auto v3 = p3; + auto d = (distance(v1, v0) + distance(v1, v2) + distance(v2, v3)) * inv_length; + pmf[i] = d; + if (i == 0) { + cdf[i] = d; + } else { + cdf[i] = d + cdf[i - 1]; + } + } else { + assert(false); + } + } + } + } +} + +void copy_and_init_shape_groups(Scene &scene, + const std::vector &shape_group_list) { + for (int group_id = 0; group_id < scene.num_shape_groups; group_id++) { + const ShapeGroup *shape_group = shape_group_list[group_id]; + auto copy_and_init_color = [&](const ColorType &color_type, void *color_ptr, void *target_ptr, void *d_target_ptr) { + switch (color_type) { + case ColorType::Constant: { + Constant *c = (Constant*)target_ptr; + Constant *d_c = (Constant*)d_target_ptr; + const Constant *c_ = (const Constant*)color_ptr; + *c = *c_; + d_c->color = Vector4{0, 0, 0, 0}; + break; + } case ColorType::LinearGradient: { + LinearGradient *c = (LinearGradient*)target_ptr; + LinearGradient *d_c = (LinearGradient*)d_target_ptr; + const LinearGradient *c_ = (const LinearGradient*)color_ptr; + c->begin = c_->begin; + c->end = c_->end; + c->num_stops = c_->num_stops; + for (int i = 0; i < c_->num_stops; i++) { + c->stop_offsets[i] = c_->stop_offsets[i]; + } + for (int i = 0; i < 4 * c_->num_stops; i++) { + c->stop_colors[i] = c_->stop_colors[i]; + } + d_c->begin = Vector2f{0, 0}; + d_c->end = Vector2f{0, 0}; + d_c->num_stops = c_->num_stops; + for (int i = 0; i < c_->num_stops; i++) { + d_c->stop_offsets[i] = 0; + } + for (int i = 0; i < 4 * c_->num_stops; i++) { + d_c->stop_colors[i] = 0; + } + break; + } case ColorType::RadialGradient: { + RadialGradient *c = (RadialGradient*)target_ptr; + RadialGradient *d_c = (RadialGradient*)d_target_ptr; + const RadialGradient *c_ = (const RadialGradient*)color_ptr; + c->center = c_->center; + c->radius = c_->radius; + c->num_stops = c_->num_stops; + for (int i = 0; i < c_->num_stops; i++) { + c->stop_offsets[i] = c_->stop_offsets[i]; + } + for (int i = 0; i < 4 * c_->num_stops; i++) { + c->stop_colors[i] = c_->stop_colors[i]; + } + d_c->center = Vector2f{0, 0}; + d_c->radius = Vector2f{0, 0}; + d_c->num_stops = c_->num_stops; + for (int i = 0; i < c_->num_stops; i++) { + d_c->stop_offsets[i] = 0; + } + for (int i = 0; i < 4 * c_->num_stops; i++) { + d_c->stop_colors[i] = 0; + } + break; + } default: { + assert(false); + } + } + }; + for (int i = 0; i < shape_group->num_shapes; i++) { + scene.shape_groups[group_id].shape_ids[i] = shape_group->shape_ids[i]; + } + scene.shape_groups[group_id].num_shapes = shape_group->num_shapes; + scene.shape_groups[group_id].use_even_odd_rule = shape_group->use_even_odd_rule; + scene.shape_groups[group_id].canvas_to_shape = shape_group->canvas_to_shape; + scene.shape_groups[group_id].shape_to_canvas = shape_group->shape_to_canvas; + scene.d_shape_groups[group_id].shape_ids = nullptr; + scene.d_shape_groups[group_id].num_shapes = shape_group->num_shapes; + scene.d_shape_groups[group_id].use_even_odd_rule = shape_group->use_even_odd_rule; + scene.d_shape_groups[group_id].canvas_to_shape = Matrix3x3f{}; + scene.d_shape_groups[group_id].shape_to_canvas = Matrix3x3f{}; + + scene.shape_groups[group_id].fill_color_type = shape_group->fill_color_type; + scene.d_shape_groups[group_id].fill_color_type = shape_group->fill_color_type; + if (shape_group->fill_color != nullptr) { + copy_and_init_color(shape_group->fill_color_type, + shape_group->fill_color, + scene.shape_groups[group_id].fill_color, + scene.d_shape_groups[group_id].fill_color); + } + scene.shape_groups[group_id].stroke_color_type = shape_group->stroke_color_type; + scene.d_shape_groups[group_id].stroke_color_type = shape_group->stroke_color_type; + if (shape_group->stroke_color != nullptr) { + copy_and_init_color(shape_group->stroke_color_type, + shape_group->stroke_color, + scene.shape_groups[group_id].stroke_color, + scene.d_shape_groups[group_id].stroke_color); + } + } +} + +DEVICE uint32_t morton2D(const Vector2f &p, int canvas_width, int canvas_height) { + auto scene_bounds = Vector2f{canvas_width, canvas_height}; + auto pp = p / scene_bounds; + TVector2 pp_i{pp.x * 1023, pp.y * 1023}; + return (expand_bits(pp_i.x) << 1u) | + (expand_bits(pp_i.y) << 0u); +} + +template +void build_bvh(const Scene &scene, BVHNode *nodes, int num_primitives) { + auto bvh_size = 2 * num_primitives - 1; + if (bvh_size > 1) { + if (sort) { + // Sort by Morton code + std::sort(nodes, nodes + num_primitives, + [&] (const BVHNode &n0, const BVHNode &n1) { + auto p0 = 0.5f * (n0.box.p_min + n0.box.p_max); + auto p1 = 0.5f * (n1.box.p_min + n1.box.p_max); + auto m0 = morton2D(p0, scene.canvas_width, scene.canvas_height); + auto m1 = morton2D(p1, scene.canvas_width, scene.canvas_height); + return m0 < m1; + }); + } + for (int i = num_primitives; i < bvh_size; i++) { + nodes[i] = BVHNode{-1, -1, AABB{}, 0.f}; + } + int prev_beg = 0; + int prev_end = num_primitives; + // For handling odd number of nodes at a level + int leftover = prev_end % 2 == 0 ? -1 : prev_end - 1; + while (prev_end - prev_beg >= 1 || leftover != -1) { + int length = (prev_end - prev_beg) / 2; + if ((prev_end - prev_beg) % 2 == 1 && leftover != -1 && + leftover != prev_end - 1) { + length += 1; + } + for (int i = 0; i < length; i++) { + BVHNode node; + node.child0 = prev_beg + 2 * i; + node.child1 = prev_beg + 2 * i + 1; + if (node.child1 >= prev_end) { + assert(leftover != -1); + node.child1 = leftover; + leftover = -1; + } + AABB child0_box = nodes[node.child0].box; + AABB child1_box = nodes[node.child1].box; + node.box = merge(child0_box, child1_box); + node.max_radius = std::max(nodes[node.child0].max_radius, + nodes[node.child1].max_radius); + nodes[prev_end + i] = node; + } + if (length == 1 && leftover == -1) { + break; + } + prev_beg = prev_end; + prev_end = prev_beg + length; + if (length % 2 == 1 && leftover == -1) { + leftover = prev_end - 1; + } + } + } + assert(nodes[2 * num_primitives - 2].child0 != -1); +} + +void compute_bounding_boxes(Scene &scene, + const std::vector &shape_list, + const std::vector &shape_group_list) { + for (int shape_id = 0; shape_id < scene.num_shapes; shape_id++) { + switch (shape_list[shape_id]->type) { + case ShapeType::Circle: { + const Circle *p = (const Circle*)(shape_list[shape_id]->ptr); + scene.shapes_bbox[shape_id] = AABB{p->center - p->radius, + p->center + p->radius}; + break; + } case ShapeType::Ellipse: { + const Ellipse *p = (const Ellipse*)(shape_list[shape_id]->ptr); + scene.shapes_bbox[shape_id] = AABB{p->center - p->radius, + p->center + p->radius}; + break; + } case ShapeType::Path: { + const Path *p = (const Path*)(shape_list[shape_id]->ptr); + AABB box; + if (p->num_points > 0) { + box = AABB{Vector2f{p->points[0], p->points[1]}, + Vector2f{p->points[0], p->points[1]}}; + } + for (int i = 1; i < p->num_points; i++) { + box = merge(box, Vector2f{p->points[2 * i], p->points[2 * i + 1]}); + } + scene.shapes_bbox[shape_id] = box; + std::vector boxes(p->num_base_points); + std::vector thickness(p->num_base_points); + std::vector first_point_id(p->num_base_points); + auto r = shape_list[shape_id]->stroke_width; + auto point_id = 0; + for (int i = 0; i < p->num_base_points; i++) { + first_point_id[i] = point_id; + if (p->num_control_points[i] == 0) { + // Straight line + auto i0 = point_id; + auto i1 = (i0 + 1) % p->num_points; + point_id += 1; + auto p0 = Vector2f{p->points[2 * i0], p->points[2 * i0 + 1]}; + auto p1 = Vector2f{p->points[2 * i1], p->points[2 * i1 + 1]}; + boxes[i] = AABB(); + boxes[i] = merge(boxes[i], p0); + boxes[i] = merge(boxes[i], p1); + auto r0 = r; + auto r1 = r; + // override radius if path has thickness + if (p->thickness != nullptr) { + r0 = p->thickness[i0]; + r1 = p->thickness[i1]; + } + thickness[i] = max(r0, r1); + } else if (p->num_control_points[i] == 1) { + // Quadratic Bezier curve + auto i0 = point_id; + auto i1 = i0 + 1; + auto i2 = (i0 + 2) % p->num_points; + point_id += 2; + auto p0 = Vector2f{p->points[2 * i0], p->points[2 * i0 + 1]}; + auto p1 = Vector2f{p->points[2 * i1], p->points[2 * i1 + 1]}; + auto p2 = Vector2f{p->points[2 * i2], p->points[2 * i2 + 1]}; + boxes[i] = AABB(); + boxes[i] = merge(boxes[i], p0); + boxes[i] = merge(boxes[i], p1); + boxes[i] = merge(boxes[i], p2); + auto r0 = r; + auto r1 = r; + auto r2 = r; + // override radius if path has thickness + if (p->thickness != nullptr) { + r0 = p->thickness[i0]; + r1 = p->thickness[i1]; + r2 = p->thickness[i2]; + } + thickness[i] = max(max(r0, r1), r2); + } else if (p->num_control_points[i] == 2) { + // Cubic Bezier curve + auto i0 = point_id; + auto i1 = i0 + 1; + auto i2 = i0 + 2; + auto i3 = (i0 + 3) % p->num_points; + point_id += 3; + auto p0 = Vector2f{p->points[2 * i0], p->points[2 * i0 + 1]}; + auto p1 = Vector2f{p->points[2 * i1], p->points[2 * i1 + 1]}; + auto p2 = Vector2f{p->points[2 * i2], p->points[2 * i2 + 1]}; + auto p3 = Vector2f{p->points[2 * i3], p->points[2 * i3 + 1]}; + boxes[i] = AABB(); + boxes[i] = merge(boxes[i], p0); + boxes[i] = merge(boxes[i], p1); + boxes[i] = merge(boxes[i], p2); + boxes[i] = merge(boxes[i], p3); + auto r0 = r; + auto r1 = r; + auto r2 = r; + auto r3 = r; + // override radius if path has thickness + if (p->thickness != nullptr) { + r0 = p->thickness[i0]; + r1 = p->thickness[i1]; + r2 = p->thickness[i2]; + r3 = p->thickness[i3]; + } + thickness[i] = max(max(max(r0, r1), r2), r3); + } else { + assert(false); + } + } + // Sort the boxes by y + std::vector idx(boxes.size()); + std::iota(idx.begin(), idx.end(), 0); + std::sort(idx.begin(), idx.end(), [&](int i0, int i1) { + const AABB &b0 = boxes[i0]; + const AABB &b1 = boxes[i1]; + auto b0y = 0.5f * (b0.p_min.y + b0.p_max.y); + auto b1y = 0.5f * (b1.p_min.y + b1.p_max.y); + return b0y < b1y; + }); + BVHNode *nodes = scene.path_bvhs[shape_id]; + for (int i = 0; i < (int)idx.size(); i++) { + nodes[i] = BVHNode{idx[i], + -(first_point_id[idx[i]]+1), + boxes[idx[i]], + thickness[idx[i]]}; + } + build_bvh(scene, nodes, boxes.size()); + break; + } case ShapeType::Rect: { + const Rect *p = (const Rect*)(shape_list[shape_id]->ptr); + scene.shapes_bbox[shape_id] = AABB{p->p_min, p->p_max}; + break; + } default: { + assert(false); + break; + } + } + } + + for (int shape_group_id = 0; shape_group_id < (int)shape_group_list.size(); shape_group_id++) { + const ShapeGroup *shape_group = shape_group_list[shape_group_id]; + // Build a BVH for each shape group + BVHNode *nodes = scene.shape_groups_bvh_nodes[shape_group_id]; + for (int i = 0; i < shape_group->num_shapes; i++) { + auto shape_id = shape_group->shape_ids[i]; + auto r = shape_group->stroke_color == nullptr ? 0 : shape_list[shape_id]->stroke_width; + nodes[i] = BVHNode{shape_id, + -1, + scene.shapes_bbox[shape_id], + r}; + } + build_bvh(scene, nodes, shape_group->num_shapes); + } + + BVHNode *nodes = scene.bvh_nodes; + for (int shape_group_id = 0; shape_group_id < (int)shape_group_list.size(); shape_group_id++) { + const ShapeGroup *shape_group = shape_group_list[shape_group_id]; + auto max_radius = shape_list[shape_group->shape_ids[0]]->stroke_width; + if (shape_list[shape_group->shape_ids[0]]->type == ShapeType::Path) { + const Path *p = (const Path*)(shape_list[shape_group->shape_ids[0]]->ptr); + if (p->thickness != nullptr) { + const BVHNode *nodes = scene.path_bvhs[shape_group->shape_ids[0]]; + max_radius = nodes[0].max_radius; + } + } + for (int i = 1; i < shape_group->num_shapes; i++) { + auto shape_id = shape_group->shape_ids[i]; + auto shape = shape_list[shape_id]; + auto r = shape->stroke_width; + if (shape->type == ShapeType::Path) { + const Path *p = (const Path*)(shape_list[shape_id]->ptr); + if (p->thickness != nullptr) { + const BVHNode *nodes = scene.path_bvhs[shape_id]; + r = nodes[0].max_radius; + } + } + max_radius = std::max(max_radius, r); + } + // Fetch group bbox from BVH + auto bbox = scene.shape_groups_bvh_nodes[shape_group_id][2 * shape_group->num_shapes - 2].box; + // Transform box from local to world space + nodes[shape_group_id].child0 = shape_group_id; + nodes[shape_group_id].child1 = -1; + nodes[shape_group_id].box = transform(shape_group->shape_to_canvas, bbox); + if (shape_group->stroke_color == nullptr) { + nodes[shape_group_id].max_radius = 0; + } else { + nodes[shape_group_id].max_radius = max_radius; + } + } + build_bvh(scene, nodes, shape_group_list.size()); +} + +template +size_t allocate_buffers(Scene &scene, + const std::vector &shape_list, + const std::vector &shape_group_list) { + auto num_shapes = shape_list.size(); + auto num_shape_groups = shape_group_list.size(); + + size_t buffer_size = 0; + if (alloc_mode) scene.shapes = (Shape*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(Shape) * num_shapes); + if (alloc_mode) scene.d_shapes = (Shape*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(Shape) * num_shapes); + if (alloc_mode) scene.shape_groups = (ShapeGroup*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(ShapeGroup) * num_shape_groups); + if (alloc_mode) scene.d_shape_groups = (ShapeGroup*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(ShapeGroup) * num_shape_groups); + if (alloc_mode) scene.sample_shapes_cdf = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * scene.num_total_shapes); + if (alloc_mode) scene.sample_shapes_pmf = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * scene.num_total_shapes); + if (alloc_mode) scene.sample_shape_id = (int*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(int) * scene.num_total_shapes); + if (alloc_mode) scene.sample_group_id = (int*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(int) * scene.num_total_shapes); + if (alloc_mode) scene.shapes_length = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * num_shapes); + if (alloc_mode) scene.path_length_cdf = (float**)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float*) * num_shapes); + if (alloc_mode) scene.path_length_pmf = (float**)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float*) * num_shapes); + if (alloc_mode) scene.path_point_id_map = (int**)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(int*) * num_shapes); + if (alloc_mode) scene.filter = (Filter*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(Filter)); + if (alloc_mode) scene.d_filter = (DFilter*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(DFilter)); + if (alloc_mode) scene.shapes_bbox = (AABB*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(AABB) * num_shapes); + if (alloc_mode) scene.path_bvhs = (BVHNode**)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(BVHNode*) * num_shapes); + if (alloc_mode) scene.shape_groups_bvh_nodes = (BVHNode**)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(BVHNode*) * num_shape_groups); + if (alloc_mode) scene.bvh_nodes = (BVHNode*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(BVHNode) * (2 * num_shape_groups - 1)); + + if (alloc_mode) { + for (int i = 0; i < num_shapes; i++) { + scene.path_length_cdf[i] = nullptr; + scene.path_length_pmf[i] = nullptr; + scene.path_point_id_map[i] = nullptr; + scene.path_bvhs[i] = nullptr; + } + } + + for (int shape_id = 0; shape_id < scene.num_shapes; shape_id++) { + switch (shape_list[shape_id]->type) { + case ShapeType::Circle: { + if (alloc_mode) scene.shapes[shape_id].ptr = (Circle*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(Circle)); // scene.shapes[shape_id].ptr + if (alloc_mode) scene.d_shapes[shape_id].ptr = (Circle*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(Circle)); // scene.d_shapes[shape_id].ptr + break; + } case ShapeType::Ellipse: { + if (alloc_mode) scene.shapes[shape_id].ptr = (Ellipse*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(Ellipse)); // scene.shapes[shape_id].ptr + if (alloc_mode) scene.d_shapes[shape_id].ptr = (Ellipse*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(Ellipse)); // scene.d_shapes[shape_id].ptr + break; + } case ShapeType::Path: { + if (alloc_mode) scene.shapes[shape_id].ptr = (Path*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(Path)); // scene.shapes[shape_id].ptr + if (alloc_mode) scene.d_shapes[shape_id].ptr = (Path*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(Path)); // scene.d_shapes[shape_id].ptr + + const Path *p_ = (const Path*)(shape_list[shape_id]->ptr); + Path *p = nullptr, *d_p = nullptr; + if (alloc_mode) p = (Path*)scene.shapes[shape_id].ptr; + if (alloc_mode) d_p = (Path*)scene.d_shapes[shape_id].ptr; + if (alloc_mode) p->num_control_points = (int*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(int) * p_->num_base_points); // p->num_control_points + if (alloc_mode) p->points = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * (2 * p_->num_points)); // p->points + if (alloc_mode) d_p->points = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * (2 * p_->num_points)); // d_p->points + if (p_->thickness != nullptr) { + if (alloc_mode) p->thickness = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * p_->num_points); // p->thickness + if (alloc_mode) d_p->thickness = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * p_->num_points); // d_p->thickness + } else { + if (alloc_mode) p->thickness = nullptr; + if (alloc_mode) d_p->thickness = nullptr; + } + if (alloc_mode) scene.path_length_pmf[shape_id] = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * p_->num_base_points); // scene.path_length_pmf + if (alloc_mode) scene.path_length_cdf[shape_id] = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * p_->num_base_points); // scene.path_length_cdf + if (alloc_mode) scene.path_point_id_map[shape_id] = (int*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(int) * p_->num_base_points); // scene.path_point_id_map + if (alloc_mode) scene.path_bvhs[shape_id] = (BVHNode*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(BVHNode) * (2 * p_->num_base_points - 1)); + break; + } case ShapeType::Rect: { + if (alloc_mode) scene.shapes[shape_id].ptr = (Ellipse*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(Rect)); // scene.shapes[shape_id].ptr + if (alloc_mode) scene.d_shapes[shape_id].ptr = (Ellipse*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(Rect)); // scene.d_shapes[shape_id].ptr + break; + } default: { + assert(false); + break; + } + } + } + + for (int group_id = 0; group_id < scene.num_shape_groups; group_id++) { + const ShapeGroup *shape_group = shape_group_list[group_id]; + if (shape_group->fill_color != nullptr) { + switch (shape_group->fill_color_type) { + case ColorType::Constant: { + if (alloc_mode) scene.shape_groups[group_id].fill_color = (Constant*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(Constant)); // color + if (alloc_mode) scene.d_shape_groups[group_id].fill_color = (Constant*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(Constant)); // d_color + break; + } case ColorType::LinearGradient: { + if (alloc_mode) scene.shape_groups[group_id].fill_color = (LinearGradient*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(LinearGradient)); // color + if (alloc_mode) scene.shape_groups[group_id].fill_color = (LinearGradient*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(LinearGradient)); // d_color + + const LinearGradient *c_ = (const LinearGradient *)shape_group->fill_color; + LinearGradient *c = nullptr, *d_c = nullptr; + if (alloc_mode) c = (LinearGradient *)scene.shape_groups[group_id].fill_color; + if (alloc_mode) d_c = (LinearGradient *)scene.d_shape_groups[group_id].fill_color; + if (alloc_mode) c->stop_offsets = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * c_->num_stops); // c->stop_offsets + if (alloc_mode) c->stop_colors = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * 4 * c_->num_stops); // c->stop_colors + if (alloc_mode) d_c->stop_offsets = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * c_->num_stops); // d_c->stop_offsets + if (alloc_mode) d_c->stop_colors = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * 4 * c_->num_stops); // d_c->stop_colors + break; + } case ColorType::RadialGradient: { + if (alloc_mode) scene.shape_groups[group_id].fill_color = (RadialGradient*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(RadialGradient)); // color + if (alloc_mode) scene.shape_groups[group_id].fill_color = (RadialGradient*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(RadialGradient)); // d_color + + const RadialGradient *c_ = (const RadialGradient *)shape_group->fill_color; + RadialGradient *c = nullptr, *d_c = nullptr; + if (alloc_mode) c = (RadialGradient *)scene.shape_groups[group_id].fill_color; + if (alloc_mode) d_c = (RadialGradient *)scene.d_shape_groups[group_id].fill_color; + if (alloc_mode) c->stop_offsets = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * c_->num_stops); // c->stop_offsets + if (alloc_mode) c->stop_colors = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * 4 * c_->num_stops); // c->stop_colors + if (alloc_mode) d_c->stop_offsets = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * c_->num_stops); // d_c->stop_offsets + if (alloc_mode) d_c->stop_colors = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * 4 * c_->num_stops); // d_c->stop_colors + break; + } default: { + assert(false); + } + } + } else { + if (alloc_mode) scene.shape_groups[group_id].fill_color = nullptr; + if (alloc_mode) scene.d_shape_groups[group_id].fill_color = nullptr; + } + if (shape_group->stroke_color != nullptr) { + switch (shape_group->stroke_color_type) { + case ColorType::Constant: { + if (alloc_mode) scene.shape_groups[group_id].stroke_color = (Constant*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(Constant)); // color + if (alloc_mode) scene.d_shape_groups[group_id].stroke_color = (Constant*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(Constant)); // d_color + break; + } case ColorType::LinearGradient: { + if (alloc_mode) scene.shape_groups[group_id].stroke_color = (LinearGradient*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(LinearGradient)); // color + if (alloc_mode) scene.shape_groups[group_id].stroke_color = (LinearGradient*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(LinearGradient)); // d_color + + const LinearGradient *c_ = (const LinearGradient *)shape_group->stroke_color; + LinearGradient *c = nullptr, *d_c = nullptr; + if (alloc_mode) c = (LinearGradient *)scene.shape_groups[group_id].stroke_color; + if (alloc_mode) d_c = (LinearGradient *)scene.d_shape_groups[group_id].stroke_color; + if (alloc_mode) c->stop_offsets = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * c_->num_stops); // c->stop_offsets + if (alloc_mode) c->stop_colors = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * 4 * c_->num_stops); // c->stop_colors + if (alloc_mode) d_c->stop_offsets = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * c_->num_stops); // d_c->stop_offsets + if (alloc_mode) d_c->stop_colors = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * 4 * c_->num_stops); // d_c->stop_colors + break; + } case ColorType::RadialGradient: { + if (alloc_mode) scene.shape_groups[group_id].stroke_color = (RadialGradient*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(RadialGradient)); // color + if (alloc_mode) scene.shape_groups[group_id].stroke_color = (RadialGradient*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(RadialGradient)); // d_color + + const RadialGradient *c_ = (const RadialGradient *)shape_group->stroke_color; + RadialGradient *c = nullptr, *d_c = nullptr; + if (alloc_mode) c = (RadialGradient *)scene.shape_groups[group_id].stroke_color; + if (alloc_mode) d_c = (RadialGradient *)scene.d_shape_groups[group_id].stroke_color; + if (alloc_mode) c->stop_offsets = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * c_->num_stops); // c->stop_offsets + if (alloc_mode) c->stop_colors = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * 4 * c_->num_stops); // c->stop_colors + if (alloc_mode) d_c->stop_offsets = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * c_->num_stops); // d_c->stop_offsets + if (alloc_mode) d_c->stop_colors = (float*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(float) * 4 * c_->num_stops); // d_c->stop_colors + break; + } default: { + assert(false); + } + } + } else { + if (alloc_mode) scene.shape_groups[group_id].stroke_color = nullptr; + if (alloc_mode) scene.d_shape_groups[group_id].stroke_color = nullptr; + } + if (alloc_mode) scene.shape_groups[group_id].shape_ids = (int*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(int) * shape_group->num_shapes); // shape_group->shape_ids + if (alloc_mode) scene.shape_groups_bvh_nodes[group_id] = (BVHNode*)&scene.buffer[buffer_size]; + buffer_size += align(sizeof(BVHNode) * (2 * shape_group->num_shapes - 1)); // scene.shape_groups_bvh_nodes[group_id] + } + return buffer_size; +} + +Scene::Scene(int canvas_width, + int canvas_height, + const std::vector &shape_list, + const std::vector &shape_group_list, + const Filter &filter, + bool use_gpu, + int gpu_index) + : canvas_width(canvas_width), + canvas_height(canvas_height), + num_shapes(shape_list.size()), + num_shape_groups(shape_group_list.size()), + use_gpu(use_gpu), + gpu_index(gpu_index) { + if (num_shapes == 0) { + return; + } + // Shape group may reuse some of the shapes, + // record the total number of shapes. + int num_total_shapes = 0; + for (const ShapeGroup *sg : shape_group_list) { + num_total_shapes += sg->num_shapes; + } + this->num_total_shapes = num_total_shapes; + + // Memory initialization +#ifdef __NVCC__ + int old_device_id = -1; +#endif + if (use_gpu) { +#ifdef __NVCC__ + checkCuda(cudaGetDevice(&old_device_id)); + if (gpu_index != -1) { + checkCuda(cudaSetDevice(gpu_index)); + } +#else + throw std::runtime_error("diffvg not compiled with GPU"); + assert(false); +#endif + } + + size_t buffer_size = allocate_buffers(*this, shape_list, shape_group_list); + // Allocate a huge buffer for everything + allocate(use_gpu, buffer_size, &buffer); + // memset(buffer, 111, buffer_size); + // Actually distribute the buffer + allocate_buffers(*this, shape_list, shape_group_list); + copy_and_init_shapes(*this, shape_list); + copy_and_init_shape_groups(*this, shape_group_list); + + std::vector shape_length_list = compute_shape_length(shape_list); + // Copy shape_length + if (use_gpu) { +#ifdef __NVCC__ + checkCuda(cudaMemcpy(this->shapes_length, &shape_length_list[0], num_shapes * sizeof(float), cudaMemcpyHostToDevice)); +#else + throw std::runtime_error("diffvg not compiled with GPU"); + assert(false); +#endif + } else { + memcpy(this->shapes_length, &shape_length_list[0], num_shapes * sizeof(float)); + } + build_shape_cdfs(*this, shape_group_list, shape_length_list); + build_path_cdfs(*this, shape_list, shape_length_list); + compute_bounding_boxes(*this, shape_list, shape_group_list); + + // Filter initialization + *(this->filter) = filter; + this->d_filter->radius = 0; + + if (use_gpu) { +#ifdef __NVCC__ + if (old_device_id != -1) { + checkCuda(cudaSetDevice(old_device_id)); + } +#else + throw std::runtime_error("diffvg not compiled with GPU"); + assert(false); +#endif + } +} + +Scene::~Scene() { + if (num_shapes == 0) { + return; + } + if (use_gpu) { +#ifdef __NVCC__ + int old_device_id = -1; + checkCuda(cudaGetDevice(&old_device_id)); + if (gpu_index != -1) { + checkCuda(cudaSetDevice(gpu_index)); + } + + checkCuda(cudaFree(buffer)); + + checkCuda(cudaSetDevice(old_device_id)); +#else + // Don't throw because C++ don't want a destructor to throw. + std::cerr << "diffvg not compiled with GPU"; + exit(1); +#endif + } else { + free(buffer); + } +} + +Shape Scene::get_d_shape(int shape_id) const { + return d_shapes[shape_id]; +} + +ShapeGroup Scene::get_d_shape_group(int group_id) const { + return d_shape_groups[group_id]; +} + +float Scene::get_d_filter_radius() const { + return d_filter->radius; +} diff --git a/scene.h b/scene.h new file mode 100644 index 0000000..e2f452d --- /dev/null +++ b/scene.h @@ -0,0 +1,120 @@ +#pragma once + +#include "diffvg.h" +#include "aabb.h" +#include + +struct Shape; +struct ShapeGroup; +struct Filter; +struct DFilter; + +struct BVHNode { + int child0, child1; // child1 is negative if it is a leaf + AABB box; + float max_radius; +}; + +struct Scene { + Scene(int canvas_width, + int canvas_height, + const std::vector &shape_list, + const std::vector &shape_group_list, + const Filter &filter, + bool use_gpu, + int gpu_index); + + ~Scene(); + + int canvas_width; + int canvas_height; + + uint8_t *buffer; + + Shape *shapes; + Shape *d_shapes; + ShapeGroup *shape_groups; + ShapeGroup *d_shape_groups; + Filter *filter; + DFilter *d_filter; + // For accelerating intersection + AABB *shapes_bbox; + BVHNode **path_bvhs; // Only for Path + BVHNode **shape_groups_bvh_nodes; // One BVH for each shape group + BVHNode *bvh_nodes; + + int num_shapes; + int num_shape_groups; + // shape_groups reuse shape, so the total number of shapes + // doesn't equal to num_shapes + int num_total_shapes; + bool use_gpu; + int gpu_index; + + // For edge sampling + float *shapes_length; + float *sample_shapes_cdf; + float *sample_shapes_pmf; + int *sample_shape_id; + int *sample_group_id; + float **path_length_cdf; + float **path_length_pmf; + int **path_point_id_map; + + ShapeGroup get_d_shape_group(int group_id) const; + Shape get_d_shape(int shape_id) const; + float get_d_filter_radius() const; +}; + +struct SceneData { + int canvas_width; + int canvas_height; + Shape *shapes; + Shape *d_shapes; + ShapeGroup *shape_groups; + ShapeGroup *d_shape_groups; + Filter *filter; + DFilter *d_filter; + AABB *shapes_bbox; + BVHNode **path_bvhs; // Only for Path + BVHNode **shape_groups_bvh_nodes; + BVHNode *bvh_nodes; + int num_shapes; + int num_shape_groups; + int num_total_shapes; + // For edge sampling + float *shapes_length; + float *sample_shapes_cdf; + float *sample_shapes_pmf; + int *sample_shape_id; + int *sample_group_id; + float **path_length_cdf; + float **path_length_pmf; + int **path_point_id_map; +}; + +inline SceneData get_scene_data(const Scene &scene) { + return SceneData{scene.canvas_width, + scene.canvas_height, + scene.shapes, + scene.d_shapes, + scene.shape_groups, + scene.d_shape_groups, + scene.filter, + scene.d_filter, + scene.shapes_bbox, + scene.path_bvhs, + scene.shape_groups_bvh_nodes, + scene.bvh_nodes, + scene.num_shapes, + scene.num_shape_groups, + scene.num_total_shapes, + scene.shapes_length, + scene.sample_shapes_cdf, + scene.sample_shapes_pmf, + scene.sample_shape_id, + scene.sample_group_id, + scene.path_length_cdf, + scene.path_length_pmf, + scene.path_point_id_map}; +} diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..e01f5a2 --- /dev/null +++ b/setup.py @@ -0,0 +1,96 @@ +# Adapted from https://github.com/pybind/cmake_example/blob/master/setup.py +import os +import re +import sys +import platform +import subprocess +import importlib +from sysconfig import get_paths + +import importlib +from setuptools import setup, Extension +from setuptools.command.build_ext import build_ext +from setuptools.command.install import install +from distutils.sysconfig import get_config_var +from distutils.version import LooseVersion + +class CMakeExtension(Extension): + def __init__(self, name, sourcedir, build_with_cuda): + Extension.__init__(self, name, sources=[]) + self.sourcedir = os.path.abspath(sourcedir) + self.build_with_cuda = build_with_cuda + +class Build(build_ext): + def run(self): + try: + out = subprocess.check_output(['cmake', '--version']) + except OSError: + raise RuntimeError("CMake must be installed to build the following extensions: " + + ", ".join(e.name for e in self.extensions)) + + super().run() + + def build_extension(self, ext): + if isinstance(ext, CMakeExtension): + extdir = os.path.abspath(os.path.dirname(self.get_ext_fullpath(ext.name))) + info = get_paths() + include_path = info['include'] + cmake_args = ['-DCMAKE_LIBRARY_OUTPUT_DIRECTORY=' + extdir, + '-DPYTHON_INCLUDE_PATH=' + include_path] + + cfg = 'Debug' if self.debug else 'Release' + build_args = ['--config', cfg] + + if platform.system() == "Windows": + cmake_args += ['-DCMAKE_LIBRARY_OUTPUT_DIRECTORY_{}={}'.format(cfg.upper(), extdir), + '-DCMAKE_RUNTIME_OUTPUT_DIRECTORY_{}={}'.format(cfg.upper(), extdir)] + if sys.maxsize > 2**32: + cmake_args += ['-A', 'x64'] + build_args += ['--', '/m'] + else: + cmake_args += ['-DCMAKE_BUILD_TYPE=' + cfg] + build_args += ['--', '-j8'] + + if ext.build_with_cuda: + cmake_args += ['-DDIFFVG_CUDA=1'] + + env = os.environ.copy() + env['CXXFLAGS'] = '{} -DVERSION_INFO=\\"{}\\"'.format(env.get('CXXFLAGS', ''), + self.distribution.get_version()) + if not os.path.exists(self.build_temp): + os.makedirs(self.build_temp) + subprocess.check_call(['cmake', ext.sourcedir] + cmake_args, cwd=self.build_temp, env=env) + subprocess.check_call(['cmake', '--build', '.'] + build_args, cwd=self.build_temp) + else: + super().build_extension(ext) + +torch_spec = importlib.util.find_spec("torch") +tf_spec = importlib.util.find_spec("tensorflow") +packages = [] +build_with_cuda = False +if torch_spec is not None: + packages.append('pydiffvg') + import torch + if torch.cuda.is_available(): + build_with_cuda = True +if tf_spec is not None and sys.platform != 'win32': + packages.append('pydiffvg_tensorflow') + if not build_with_cuda: + import tensorflow as tf + if tf.test.is_gpu_available(cuda_only=True, min_cuda_compute_capability=None): + build_with_cuda = True +if len(packages) == 0: + print('Error: PyTorch or Tensorflow must be installed. For Windows platform only PyTorch is supported.') + exit() +# Override build_with_cuda with environment variable +if 'DIFFVG_CUDA' in os.environ: + build_with_cuda = os.environ['DIFFVG_CUDA'] == '1' + +setup(name = 'diffvg', + version = '0.0.1', + install_requires = ["svgpathtools"], + description = 'Differentiable Vector Graphics', + ext_modules = [CMakeExtension('diffvg', '', build_with_cuda)], + cmdclass = dict(build_ext=Build, install=install), + packages = packages, + zip_safe = False) diff --git a/shape.cpp b/shape.cpp new file mode 100644 index 0000000..19a3096 --- /dev/null +++ b/shape.cpp @@ -0,0 +1,22 @@ +#include "shape.h" + +void Path::copy_to(ptr points, ptr thickness) const { + float *p = points.get(); + for (int i = 0; i < 2 * num_points; i++) { + p[i] = this->points[i]; + } + if (this->thickness != nullptr) { + float *t = thickness.get(); + for (int i = 0; i < num_points; i++) { + t[i] = this->thickness[i]; + } + } +} + +void ShapeGroup::copy_to(ptr shape_to_canvas) const { + for (int i = 0; i < 3; i++) { + for (int j = 0; j < 3; j++) { + shape_to_canvas.get()[i * 3 + j] = this->shape_to_canvas(i, j); + } + } +} diff --git a/shape.h b/shape.h new file mode 100644 index 0000000..b549f31 --- /dev/null +++ b/shape.h @@ -0,0 +1,169 @@ +#pragma once + +#include "diffvg.h" +#include "color.h" +#include "ptr.h" +#include "vector.h" +#include "matrix.h" + +enum class ShapeType { + Circle, + Ellipse, + Path, + Rect +}; + +struct Circle { + float radius; + Vector2f center; + + ptr get_ptr() { + return ptr(this); + } +}; + +struct Ellipse { + Vector2f radius; + Vector2f center; + + ptr get_ptr() { + return ptr(this); + } +}; + +struct Path { + Path(ptr num_control_points, + ptr points, + ptr thickness, + int num_base_points, + int num_points, + bool is_closed, + bool use_distance_approx) : + num_control_points(num_control_points.get()), + points(points.get()), + thickness(thickness.get()), + num_base_points(num_base_points), + num_points(num_points), + is_closed(is_closed), + use_distance_approx(use_distance_approx) {} + + int *num_control_points; + float *points; + float *thickness; + int num_base_points; + int num_points; + bool is_closed; + bool use_distance_approx; + + bool has_thickness() const { + return thickness != nullptr; + } + void copy_to(ptr points, ptr thickness) const; + + ptr get_ptr() { + return ptr(this); + } +}; + +struct Rect { + Vector2f p_min; + Vector2f p_max; + + ptr get_ptr() { + return ptr(this); + } +}; + +struct Shape { + Shape() {} + Shape(const ShapeType &type, + ptr shape_ptr, + float stroke_width) + : type(type), ptr(shape_ptr.get()), stroke_width(stroke_width) {} + + Circle as_circle() const { + return *(Circle*)ptr; + } + + Ellipse as_ellipse() const { + return *(Ellipse*)ptr; + } + + Path as_path() const { + return *(Path*)ptr; + } + + Rect as_rect() const { + return *(Rect*)ptr; + } + + ShapeType type; + void *ptr; + float stroke_width; +}; + +struct ShapeGroup { + ShapeGroup() {} + ShapeGroup(ptr shape_ids, + int num_shapes, + const ColorType &fill_color_type, + ptr fill_color, + const ColorType &stroke_color_type, + ptr stroke_color, + bool use_even_odd_rule, + ptr shape_to_canvas) + : shape_ids(shape_ids.get()), + num_shapes(num_shapes), + fill_color_type(fill_color_type), + fill_color(fill_color.get()), + stroke_color_type(stroke_color_type), + stroke_color(stroke_color.get()), + use_even_odd_rule(use_even_odd_rule), + shape_to_canvas(shape_to_canvas.get()) { + canvas_to_shape = inverse(this->shape_to_canvas); + } + + bool has_fill_color() const { + return fill_color != nullptr; + } + + Constant fill_color_as_constant() const { + return *(Constant*)fill_color; + } + + LinearGradient fill_color_as_linear_gradient() const { + return *(LinearGradient*)fill_color; + } + + RadialGradient fill_color_as_radial_gradient() const { + return *(RadialGradient*)fill_color; + } + + bool has_stroke_color() const { + return stroke_color != nullptr; + } + + Constant stroke_color_as_constant() const { + return *(Constant*)stroke_color; + } + + LinearGradient stroke_color_as_linear_gradient() const { + return *(LinearGradient*)stroke_color; + } + + RadialGradient stroke_color_as_radial_gradient() const { + return *(RadialGradient*)stroke_color; + } + + void copy_to(ptr shape_to_canvas) const; + + int *shape_ids; + int num_shapes; + ColorType fill_color_type; + void *fill_color; + ColorType stroke_color_type; + void *stroke_color; + bool use_even_odd_rule; + Matrix3x3f canvas_to_shape; + Matrix3x3f shape_to_canvas; +}; diff --git a/solve.h b/solve.h new file mode 100644 index 0000000..99f730d --- /dev/null +++ b/solve.h @@ -0,0 +1,59 @@ +#pragma once + +#include "diffvg.h" + +template +DEVICE +inline bool solve_quadratic(T a, T b, T c, T *t0, T *t1) { + // From https://github.com/mmp/pbrt-v3/blob/master/src/core/pbrt.h#L419 + T discrim = square(b) - 4 * a * c; + if (discrim < 0) { + return false; + } + T root_discrim = sqrt(discrim); + + T q; + if (b < 0) { + q = -0.5f * (b - root_discrim); + } else { + q = -0.5f * (b + root_discrim); + } + *t0 = q / a; + *t1 = c / q; + if (*t0 > *t1) { + swap_(*t0, *t1); + } + return true; +} + +template +DEVICE +inline int solve_cubic(T a, T b, T c, T d, T t[3]) { + if (fabs(a) < 1e-6f) { + if (solve_quadratic(b, c, d, &t[0], &t[1])) { + return 2; + } else { + return 0; + } + } + // normalize cubic equation + b /= a; + c /= a; + d /= a; + T Q = (b * b - 3 * c) / 9.f; + T R = (2 * b * b * b - 9 * b * c + 27 * d) / 54.f; + if (R * R < Q * Q * Q) { + // 3 real roots + T theta = acos(R / sqrt(Q * Q * Q)); + t[0] = -2.f * sqrt(Q) * cos(theta / 3.f) - b / 3.f; + t[1] = -2.f * sqrt(Q) * cos((theta + 2.f * T(M_PI)) / 3.f) - b / 3.f; + t[2] = -2.f * sqrt(Q) * cos((theta - 2.f * T(M_PI)) / 3.f) - b / 3.f; + return 3; + } else { + T A = R > 0 ? -pow(R + sqrt(R * R - Q * Q * Q), T(1./3.)): + pow(-R + sqrt(R * R - Q * Q * Q), T(1./3.)); + T B = fabs(A) > 1e-6f ? Q / A : T(0); + t[0] = (A + B) - b / T(3); + return 1; + } +} diff --git a/vector.h b/vector.h new file mode 100644 index 0000000..3575b26 --- /dev/null +++ b/vector.h @@ -0,0 +1,817 @@ +#pragma once + +#include "diffvg.h" +#include +#include + +template +struct TVector2 { + DEVICE TVector2() {} + + template + DEVICE + TVector2(T2 x, T2 y) : x(T(x)), y(T(y)) {} + + template + DEVICE + TVector2(const TVector2 &v) : x(T(v.x)), y(T(v.y)) {} + + DEVICE T& operator[](int i) { + return *(&x + i); + } + + DEVICE T operator[](int i) const { + return *(&x + i); + } + + T x, y; +}; + +template +struct TVector3 { + DEVICE TVector3() {} + + template + DEVICE + TVector3(T2 x, T2 y, T2 z) : x(T(x)), y(T(y)), z(T(z)) {} + + template + DEVICE + TVector3(const TVector3 &v) : x(T(v.x)), y(T(v.y)), z(T(v.z)) {} + + DEVICE T& operator[](int i) { + return *(&x + i); + } + + DEVICE T operator[](int i) const { + return *(&x + i); + } + + T x, y, z; +}; + +template +struct TVector4 { + DEVICE TVector4() {} + + template + DEVICE + TVector4(T2 x, T2 y, T2 z, T2 w) : x(T(x)), y(T(y)), z(T(z)), w(T(w)) {} + + template + DEVICE + TVector4(const TVector4 &v) : x(T(v.x)), y(T(v.y)), z(T(v.z)), w(T(v.w)) {} + + + DEVICE T& operator[](int i) { + return *(&x + i); + } + + DEVICE T operator[](int i) const { + return *(&x + i); + } + + T x, y, z, w; +}; + +using Vector2f = TVector2; +using Vector2d = TVector2; +using Vector2i = TVector2; +using Vector2 = TVector2; +using Vector3i = TVector3; +using Vector3f = TVector3; +using Vector3d = TVector3; +using Vector3 = TVector3; +using Vector4f = TVector4; +using Vector4d = TVector4; +using Vector4 = TVector4; + +template +DEVICE +inline auto operator+(const TVector2 &v0, + const TVector2 &v1) -> TVector2 { + return TVector2{ + v0[0] + v1[0], v0[1] + v1[1]}; +} + +template +DEVICE +inline auto operator+(const T0 &v0, + const TVector2 &v1) -> TVector2 { + return TVector2{v0 + v1[0], v0 + v1[1]}; +} + +template +DEVICE +inline auto operator+(const T0 &v0, + const TVector3 &v1) -> TVector3 { + return TVector3{ + v0 + v1[0], v0 + v1[1], v0 + v1[2]}; +} + +template +DEVICE +inline auto operator+(const TVector2 &v0, + const T1 &v1) -> TVector2 { + return TVector2{ + v0[0] + v1, v0[1] + v1}; +} + +template +DEVICE +inline auto operator+(const TVector3 &v0, + const T1 &v1) -> TVector3 { + return TVector3{ + v0[0] + v1, v0[1] + v1, v0[2] + v1}; +} + +template +DEVICE +inline auto operator+(const TVector3 &v0, + const TVector3 &v1) -> TVector3 { + return TVector3{ + v0[0] + v1[0], v0[1] + v1[1], v0[2] + v1[2]}; +} + +template +DEVICE +inline auto operator+(const TVector4 &v0, + const TVector4 &v1) -> TVector4 { + return TVector4{ + v0[0] + v1[0], v0[1] + v1[1], v0[2] + v1[2], v0[3] + v1[3]}; +} + +template +DEVICE +inline auto operator+=(TVector2 &v0, + const TVector2 &v1) -> TVector2& { + v0[0] += v1[0]; + v0[1] += v1[1]; + return v0; +} + +template +DEVICE +inline auto operator+=(TVector3 &v0, + const TVector3 &v1) -> TVector3& { + v0[0] += v1[0]; + v0[1] += v1[1]; + v0[2] += v1[2]; + return v0; +} + +template +DEVICE +inline auto operator+=(TVector3 &v0, + const T1 &v1) -> TVector3& { + v0[0] += v1; + v0[1] += v1; + v0[2] += v1; + return v0; +} + +template +DEVICE +inline auto operator+=(TVector4 &v0, + const TVector4 &v1) -> TVector4& { + v0[0] += v1[0]; + v0[1] += v1[1]; + v0[2] += v1[2]; + v0[3] += v1[3]; + return v0; +} + +template +DEVICE +inline auto operator+=(TVector4 &v0, + const T1 &v1) -> TVector4& { + v0[0] += v1; + v0[1] += v1; + v0[2] += v1; + v0[3] += v1; + return v0; +} + +template +DEVICE +inline auto operator-(const T0 &v0, + const TVector2 &v1) -> TVector2 { + return TVector2{v0 - v1[0], v0 - v1[1]}; +} + +template +DEVICE +inline auto operator-(const T0 &v0, + const TVector3 &v1) -> TVector2 { + return TVector3{v0 - v1[0], v0 - v1[1], v0 - v1[2]}; +} + +template +DEVICE +inline auto operator-(const TVector2 &v0, + const T1 &v1) -> TVector2 { + return TVector2{v0[0] - v1, v0[1] - v1}; +} + +template +DEVICE +inline auto operator-(const TVector3 &v0, + const T1 &v1) -> TVector3 { + return TVector3{v0[0] - v1, v0[1] - v1, v0[2] - v1}; +} + +template +DEVICE +inline auto operator-(const TVector2 &v0, + const TVector2 &v1) -> TVector2 { + return TVector2{ + v0[0] - v1[0], v0[1] - v1[1]}; +} + +template +DEVICE +inline auto operator-(const TVector2 &v) -> TVector2 { + return TVector2{-v[0], -v[1]}; +} + +template +DEVICE +inline auto operator-(const TVector3 &v) -> TVector3 { + return TVector3{-v[0], -v[1], -v[2]}; +} + +template +DEVICE +inline auto operator-(const TVector3 &v0, + const TVector3 &v1) -> TVector3 { + return TVector3{ + v0[0] - v1[0], v0[1] - v1[1], v0[2] - v1[2]}; +} + +template +DEVICE +inline auto operator-(const TVector4 &v0, + const TVector4 &v1) -> TVector4 { + return TVector4{ + v0[0] - v1[0], v0[1] - v1[1], v0[2] - v1[2], v0[3] - v1[3]}; +} + +template +DEVICE +inline auto operator-=(TVector2 &v0, + const TVector2 &v1) -> TVector2& { + v0[0] -= v1[0]; + v0[1] -= v1[1]; + return v0; +} + +template +DEVICE +inline auto operator-=(TVector3 &v0, + const TVector3 &v1) -> TVector3& { + v0[0] -= v1[0]; + v0[1] -= v1[1]; + v0[2] -= v1[2]; + return v0; +} + +template +DEVICE +inline auto operator*(const TVector2 &v0, + const TVector2 &v1) -> TVector2 { + return TVector2{ + v0[0] * v1[0], v0[1] * v1[1]}; +} + +template +DEVICE +inline auto operator*(const TVector2 &v0, + const T1 &s) -> TVector2 { + return TVector2{ + v0[0] * s, v0[1] * s}; +} + +template +DEVICE +inline auto operator*(const T0 &s, + const TVector2 &v0) -> TVector2 { + return TVector2{s * v0[0], s * v0[1]}; +} + +template +DEVICE +inline auto operator*=(TVector2 &v0, + const T1 &s) -> TVector2& { + v0[0] *= s; + v0[1] *= s; + return v0; +} + +template +DEVICE +inline auto operator*(const TVector3 &v0, + const T1 &s) -> TVector3 { + return TVector3{ + v0[0] * s, v0[1] * s, v0[2] * s}; +} + +template +DEVICE +inline auto operator*(const T0 &s, + const TVector3 &v0) -> TVector3 { + return TVector3{ + s * v0[0], s * v0[1], s * v0[2]}; +} + +template +DEVICE +inline auto operator*=(TVector3 &v0, + const T1 &s) -> TVector3& { + v0[0] *= s; + v0[1] *= s; + v0[2] *= s; + return v0; +} + +template +DEVICE +inline auto operator*=(TVector4 &v0, + const T1 &s) -> TVector4& { + v0[0] *= s; + v0[1] *= s; + v0[2] *= s; + v0[3] *= s; + return v0; +} + +template +DEVICE +inline auto operator*(const TVector3 &v0, + const TVector3 &v1) -> TVector3 { + return TVector3{ + v0[0] * v1[0], v0[1] * v1[1], v0[2] * v1[2]}; +} + +template +DEVICE +inline auto operator*(const TVector4 &v0, + const T1 &s) -> TVector4 { + return TVector4{ + v0[0] * s, v0[1] * s, v0[2] * s, v0[3] * s}; +} + +template +DEVICE +inline auto operator*(const T0 &s, + const TVector4 &v0) -> TVector4 { + return TVector4{ + s * v0[0], s * v0[1], s * v0[2], s * v0[3]}; +} + +template +DEVICE +inline auto operator*(const TVector4 &v0, + const TVector4 &v1) -> TVector4 { + return TVector4{ + v0[0] * v1[0], v0[1] * v1[1], v0[2] * v1[2], v0[3] * v1[3]}; +} + +template +DEVICE +inline auto operator/(const TVector2 &v0, + const T1 &s) -> TVector2 { + auto inv_s = 1.f / s; + return v0 * inv_s; +} + +template +DEVICE +inline auto operator/(const TVector3 &v0, + const T1 &s) -> TVector3 { + auto inv_s = 1.f / s; + return v0 * inv_s; +} + +template +DEVICE +inline auto operator/(const TVector4 &v0, + const T1 &s) -> TVector4 { + auto inv_s = 1.f / s; + return v0 * inv_s; +} + +template +DEVICE +inline auto operator/(const T0 &s, + const TVector3 &v1) -> TVector3 { + return TVector3{ + s / v1[0], s / v1[2], s / v1[2]}; +} + +template +DEVICE +inline auto operator/(const TVector3 &v0, + const TVector3 &v1) -> TVector3 { + return TVector3{ + v0[0] / v1[0], v0[1] / v1[2], v0[2] / v1[2]}; +} + +template +DEVICE +inline auto operator/(const TVector2 &v0, + const TVector2 &v1) -> TVector2 { + return TVector2{ + v0[0] / v1[0], v0[1] / v1[1]}; +} + +template +DEVICE +inline auto operator/=(TVector3 &v0, + const T1 &s) -> TVector3& { + auto inv_s = 1.f / s; + v0[0] *= inv_s; + v0[1] *= inv_s; + v0[2] *= inv_s; + return v0; +} + +template +DEVICE +inline auto operator/=(TVector4 &v0, + const T1 &s) -> TVector4& { + auto inv_s = 1.f / s; + v0[0] *= inv_s; + v0[1] *= inv_s; + v0[2] *= inv_s; + v0[3] *= inv_s; + return v0; +} + +template +DEVICE +inline bool operator==(const TVector2 &v0, + const TVector2 &v1) { + return v0.x == v1.x && v0.y == v1.y; +} + +template +DEVICE +inline bool operator==(const TVector3 &v0, + const TVector3 &v1) { + return v0.x == v1.x && v0.y == v1.y && v0.z == v1.z; +} + +template +DEVICE +inline bool operator!=(const TVector3 &v0, + const TVector3 &v1) { + return v0.x != v1.x || v0.y != v1.y || v0.z != v1.z; +} + +template +DEVICE +inline TVector2 get_normal(const TVector2 &v) { + return TVector2{v.y, -v.x}; +} + +template +DEVICE +inline T length_squared(const TVector2 &v0) { + return square(v0[0]) + square(v0[1]); +} + +template +DEVICE +inline TVector2 d_length_squared(const TVector2 &v0, const T &d_l_sq) { + //l_sq = square(v0[0]) + square(v0[1]) + return 2 * d_l_sq * v0; +} + +template +DEVICE +inline T length(const TVector2 &v0) { + return sqrt(length_squared(v0)); +} + +template +DEVICE +inline TVector2 d_length(const TVector2 &v0, const T &d_l) { + auto l_sq = length_squared(v0); + auto l = sqrt(l_sq); + auto d_l_sq = 0.5f * d_l / l; + return d_length_squared(v0, T(d_l_sq)); +} + +template +DEVICE +inline T length_squared(const TVector3 &v0) { + return square(v0[0]) + square(v0[1]) + square(v0[2]); +} + +template +DEVICE +inline TVector3 d_length_squared(const TVector3 &v0, const T &d_l_sq) { + //l_sq = square(v0[0]) + square(v0[1]) + square(v0[2]) + return 2 * d_l_sq * v0; +} + +template +DEVICE +inline T length(const TVector3 &v0) { + return sqrt(length_squared(v0)); +} + +template +DEVICE +inline TVector3 d_length(const TVector3 &v0, const T &d_l) { + auto l_sq = length_squared(v0); + auto l = sqrt(l_sq); + auto d_l_sq = 0.5f * d_l / l; + return d_length_squared(v0, d_l_sq); +} + +template +DEVICE +inline auto distance_squared(const TVector2 &v0, + const TVector2 &v1) -> decltype(length_squared(v1 - v0)) { + return length_squared(v1 - v0); +} + +template +DEVICE +inline auto distance_squared(const TVector3 &v0, + const TVector3 &v1) -> decltype(length_squared(v1 - v0)) { + return length_squared(v1 - v0); +} + +template +DEVICE +inline auto distance(const TVector2 &v0, + const TVector2 &v1) -> decltype(length(v1 - v0)) { + return length(v1 - v0); +} + +template +DEVICE +inline void d_distance(const TVector2 &v0, + const TVector2 &v1, + const T &d_output, + TVector2 &d_v0, + TVector2 &d_v1) { + auto d_v1_v0 = d_length(v1 - v0, d_output); + d_v0 -= d_v1_v0; + d_v1 += d_v1_v0; +} + +template +DEVICE +inline auto distance(const TVector3 &v0, + const TVector3 &v1) -> decltype(length(v1 - v0)) { + return length(v1 - v0); +} + +template +DEVICE +inline void d_distance(const TVector3 &v0, + const TVector3 &v1, + const T &d_output, + TVector3 &d_v0, + TVector3 &d_v1) { + auto d_v1_v0 = d_length(v1 - v0, d_output); + d_v0 -= d_v1_v0; + d_v1 += d_v1_v0; +} + +template +DEVICE +inline TVector2 normalize(const TVector2 &v0) { + return v0 / length(v0); +} + +template +DEVICE +inline TVector2 d_normalize(const TVector2 &v0, const TVector2 &d_n) { + auto l = length(v0); + auto n = v0 / l; + auto d_v0 = d_n / l; + auto d_l = -dot(d_n, n) / l; + // l = length(v0) + d_v0 += d_length(v0, d_l); + return d_v0; +} + +template +DEVICE +inline TVector3 normalize(const TVector3 &v0) { + return v0 / length(v0); +} + +template +DEVICE +inline TVector3 d_normalize(const TVector3 &v0, const TVector3 &d_n) { + auto l = length(v0); + auto n = v0 / l; + auto d_v0 = d_n / l; + auto d_l = -dot(d_n, n) / l; + // l = length(v0) + d_v0 += d_length(v0, d_l); + return d_v0; +} + +template +DEVICE +inline auto dot(const TVector2 &v0, const TVector2 &v1) -> decltype(v0[0] * v1[0]) { + return v0[0] * v1[0] + + v0[1] * v1[1]; +} + +template +DEVICE +inline auto dot(const TVector3 &v0, const TVector3 &v1) -> decltype(v0[0] * v1[0]) { + return v0[0] * v1[0] + + v0[1] * v1[1] + + v0[2] * v1[2]; +} + +template +DEVICE +inline auto dot(const TVector4 &v0, const TVector4 &v1) -> decltype(v0[0] * v1[0]) { + return v0[0] * v1[0] + + v0[1] * v1[1] + + v0[2] * v1[2] + + v0[3] * v1[3]; +} + +template +DEVICE +inline auto cross(const TVector3 &v0, const TVector3 &v1) -> TVector3 { + return TVector3{ + v0[1] * v1[2] - v0[2] * v1[1], + v0[2] * v1[0] - v0[0] * v1[2], + v0[0] * v1[1] - v0[1] * v1[0]}; +} + +template +DEVICE +inline void d_cross(const TVector3 &v0, const TVector3 &v1, const TVector3 &d_output, + TVector3 &d_v0, TVector3 &d_v1) { + d_v0 += cross(v1, d_output); + d_v1 += cross(d_output, v0); +} + +template +DEVICE +inline T luminance(const TVector3 &v) { + return 0.212671f * v[0] + + 0.715160f * v[1] + + 0.072169f * v[2]; +} + +template +DEVICE +inline T sum(const T &v) { + return v; +} + +template +DEVICE +inline T sum(const TVector2 &v) { + return v[0] + v[1]; +} + +template +DEVICE +inline T sum(const TVector3 &v) { + return v[0] + v[1] + v[2]; +} + +template +DEVICE +inline T sum(const TVector4 &v) { + return v[0] + v[1] + v[2] + v[3]; +} + +template +DEVICE +void coordinate_system(const TVector3 &n, TVector3 &x, TVector3 &y) { + if (n[2] < -1.f + 1e-6f) { + x = TVector3{T(0), T(-1), T(0)}; + y = TVector3{T(-1), T(0), T(0)}; + } else { + auto a = 1.f / (1.f + n[2]); + auto b = -n[0] * n[1] * a; + x = TVector3{1.f - square(n[0]) * a, b, -n[0]}; + y = TVector3{b, 1.f - square(n[1]) * a, -n[1]}; + } +} + +template +DEVICE +void d_coordinate_system(const TVector3 &n, const TVector3 &d_x, const TVector3 &d_y, + TVector3 &d_n) { + if (n[2] < -1.f + 1e-6f) { + //x = TVector3{T(0), T(-1), T(0)}; + //y = TVector3{T(-1), T(0), T(0)}; + // don't need to do anything + } else { + auto a = 1.f / (1.f + n[2]); + // auto b = -n[0] * n[1] * a; + // x = TVector3{1.f - square(n[0]) * a, b, -n[0]} + d_n[0] -= 2.f * n[0] * d_x[0] * a; + auto d_a = -square(n[0]) * d_x[0]; + auto d_b = d_x[1]; + d_n[0] -= d_x[2]; + // y = TVector3{b, 1.f - square(n[1]) * a, -n[1]} + d_b += d_y[0]; + d_n[1] -= 2.f * d_y[1] * n[1] * a; + d_a -= d_y[1] * square(n[1]); + d_n[1] -= d_y[2]; + // b = -n[0] * n[1] * a + d_n[0] -= d_b * n[1] * a; + d_n[1] -= d_b * n[0] * a; + d_a -= d_b * n[0] * n[1]; + // a = 1 / (1 + n[2]) + d_n[2] -= d_a * a / (1 + n[2]); + } +} + +DEVICE +inline bool isfinite(const Vector2 &v) { + return isfinite(v.x) && + isfinite(v.y); +} + +DEVICE +inline bool isfinite(const Vector3 &v) { + return isfinite(v.x) && + isfinite(v.y) && + isfinite(v.z); +} + +DEVICE +inline bool isfinite(const Vector4 &v) { + return isfinite(v.x) && + isfinite(v.y) && + isfinite(v.z) && + isfinite(v.w); +} + +DEVICE +inline bool is_zero(const Vector3 &v) { + return v.x == 0 && v.y == 0 && v.z == 0; +} + +template +inline std::ostream& operator<<(std::ostream &os, const TVector2 &v) { + return os << "(" << v[0] << ", " << v[1] << ")"; +} + +template +inline std::ostream& operator<<(std::ostream &os, const TVector3 &v) { + return os << "(" << v[0] << ", " << v[1] << ", " << v[2] << ")"; +} + +template +inline std::ostream& operator<<(std::ostream &os, const TVector4 &v) { + return os << "(" << v[0] << ", " << v[1] << ", " << v[2] << ", " << v[3] << ")"; +} + +DEVICE +inline +float det(const Vector2f &a, const Vector2f &b) { + return a.x*b.y-b.x*a.y; +} + +DEVICE +inline +Vector2f quadratic_closest_pt_approx(const Vector2f &b0, + const Vector2f &b1, + const Vector2f &b2, + float *t_ = nullptr) { + // From http://w3.impa.br/~diego/publications/NehHop08.pdf + float a=det(b0,b2), b=2*det(b1,b0), d=2*det(b2,b1); + float f=b*d-a*a; + Vector2f d21=b2-b1, d10=b1-b0, d20=b2-b0; + Vector2f gf=2*(b*d21+d*d10+a*d20); + gf=Vector2f(gf.y,-gf.x); + Vector2f pp=-f*gf/dot(gf,gf); + Vector2f d0p=b0-pp; + float ap=det(d0p,d20), bp=2*det(d10,d0p); + float t=clamp((ap+bp)/(2*a+b+d),0.f,1.f); + float tt = 1 - t; + if (t_ != nullptr) { + *t_ = t; + } + return (tt*tt)*b0 + (2*tt*t)*b1 + (t*t)*b2; +} + +DEVICE +inline +Vector2f quadratic_closest_pt_approx(const Vector2f &b0, + const Vector2f &b1, + const Vector2f &b2, + const Vector2f &pt, + float *t = nullptr) { + // Approximate closest point to a quadratic curve + return quadratic_closest_pt_approx(b0 - pt, b1 - pt, b2 - pt, t) + pt; +} diff --git a/winding_number.h b/winding_number.h new file mode 100644 index 0000000..8791a4c --- /dev/null +++ b/winding_number.h @@ -0,0 +1,202 @@ +#pragma once + +#include "diffvg.h" +#include "scene.h" +#include "shape.h" +#include "solve.h" +#include "vector.h" + +DEVICE +int compute_winding_number(const Circle &circle, const Vector2f &pt) { + const auto &c = circle.center; + auto r = circle.radius; + // inside the circle: return 1, outside the circle: return 0 + if (distance_squared(c, pt) < r * r) { + return 1; + } else { + return 0; + } +} + +DEVICE +int compute_winding_number(const Ellipse &ellipse, const Vector2f &pt) { + const auto &c = ellipse.center; + const auto &r = ellipse.radius; + // inside the ellipse: return 1, outside the ellipse: return 0 + if (square(c.x - pt.x) / square(r.x) + square(c.y - pt.y) / square(r.y) < 1) { + return 1; + } else { + return 0; + } +} + +DEVICE +bool intersect(const AABB &box, const Vector2f &pt) { + if (pt.y < box.p_min.y || pt.y > box.p_max.y) { + return false; + } + if (pt.x > box.p_max.x) { + return false; + } + return true; +} + +DEVICE +int compute_winding_number(const Path &path, const BVHNode *bvh_nodes, const Vector2f &pt) { + // Shoot a horizontal ray from pt to right, intersect with all curves of the path, + // count intersection + auto num_segments = path.num_base_points; + constexpr auto max_bvh_size = 128; + int bvh_stack[max_bvh_size]; + auto stack_size = 0; + auto winding_number = 0; + bvh_stack[stack_size++] = 2 * num_segments - 2; + while (stack_size > 0) { + const BVHNode &node = bvh_nodes[bvh_stack[--stack_size]]; + if (node.child1 < 0) { + // leaf + auto base_point_id = node.child0; + auto point_id = - node.child1 - 1; + assert(base_point_id < num_segments); + assert(point_id < path.num_points); + if (path.num_control_points[base_point_id] == 0) { + // Straight line + auto i0 = point_id; + auto i1 = (point_id + 1) % path.num_points; + auto p0 = Vector2f{path.points[2 * i0], path.points[2 * i0 + 1]}; + auto p1 = Vector2f{path.points[2 * i1], path.points[2 * i1 + 1]}; + // intersect p0 + t * (p1 - p0) with pt + t' * (1, 0) + // solve: + // pt.x + t' = v0.x + t * (v1.x - v0.x) + // pt.y = v0.y + t * (v1.y - v0.y) + if (p1.y != p0.y) { + auto t = (pt.y - p0.y) / (p1.y - p0.y); + if (t >= 0 && t <= 1) { + auto tp = p0.x - pt.x + t * (p1.x - p0.x); + if (tp >= 0) { + if (p1.y - p0.y > 0) { + winding_number += 1; + } else { + winding_number -= 1; + } + } + } + } + } else if (path.num_control_points[base_point_id] == 1) { + // Quadratic Bezier curve + auto i0 = point_id; + auto i1 = point_id + 1; + auto i2 = (point_id + 2) % path.num_points; + auto p0 = Vector2f{path.points[2 * i0], path.points[2 * i0 + 1]}; + auto p1 = Vector2f{path.points[2 * i1], path.points[2 * i1 + 1]}; + auto p2 = Vector2f{path.points[2 * i2], path.points[2 * i2 + 1]}; + // The curve is (1-t)^2p0 + 2(1-t)tp1 + t^2p2 + // = (p0-2p1+p2)t^2+(-2p0+2p1)t+p0 + // intersect with pt + t' * (1 0) + // solve + // pt.y = (p0-2p1+p2)t^2+(-2p0+2p1)t+p0 + float t[2]; + if (solve_quadratic(p0.y-2*p1.y+p2.y, + -2*p0.y+2*p1.y, + p0.y-pt.y, + &t[0], &t[1])) { + for (int j = 0; j < 2; j++) { + if (t[j] >= 0 && t[j] <= 1) { + auto tp = (p0.x-2*p1.x+p2.x)*t[j]*t[j] + + (-2*p0.x+2*p1.x)*t[j] + + p0.x-pt.x; + if (tp >= 0) { + if (2*(p0.y-2*p1.y+p2.y)*t[j]+(-2*p0.y+2*p1.y) > 0) { + winding_number += 1; + } else { + winding_number -= 1; + } + } + } + } + } + } else if (path.num_control_points[base_point_id] == 2) { + // Cubic Bezier curve + auto i0 = point_id; + auto i1 = point_id + 1; + auto i2 = point_id + 2; + auto i3 = (point_id + 3) % path.num_points; + auto p0 = Vector2f{path.points[2 * i0], path.points[2 * i0 + 1]}; + auto p1 = Vector2f{path.points[2 * i1], path.points[2 * i1 + 1]}; + auto p2 = Vector2f{path.points[2 * i2], path.points[2 * i2 + 1]}; + auto p3 = Vector2f{path.points[2 * i3], path.points[2 * i3 + 1]}; + // The curve is (1 - t)^3 p0 + 3 * (1 - t)^2 t p1 + 3 * (1 - t) t^2 p2 + t^3 p3 + // = (-p0+3p1-3p2+p3) t^3 + (3p0-6p1+3p2) t^2 + (-3p0+3p1) t + p0 + // intersect with pt + t' * (1 0) + // solve: + // pt.y = (-p0+3p1-3p2+p3) t^3 + (3p0-6p1+3p2) t^2 + (-3p0+3p1) t + p0 + double t[3]; + int num_sol = solve_cubic(double(-p0.y+3*p1.y-3*p2.y+p3.y), + double(3*p0.y-6*p1.y+3*p2.y), + double(-3*p0.y+3*p1.y), + double(p0.y-pt.y), + t); + for (int j = 0; j < num_sol; j++) { + if (t[j] >= 0 && t[j] <= 1) { + // t' = (-p0+3p1-3p2+p3) t^3 + (3p0-6p1+3p2) t^2 + (-3p0+3p1) t + p0 - pt.x + auto tp = (-p0.x+3*p1.x-3*p2.x+p3.x)*t[j]*t[j]*t[j]+ + (3*p0.x-6*p1.x+3*p2.x)*t[j]*t[j]+ + (-3*p0.x+3*p1.x)*t[j]+ + p0.x-pt.x; + if (tp > 0) { + if (3*(-p0.y+3*p1.y-3*p2.y+p3.y)*t[j]*t[j]+ + 2*(3*p0.y-6*p1.y+3*p2.y)*t[j]+ + (-3*p0.y+3*p1.y) > 0) { + winding_number += 1; + } else { + winding_number -= 1; + } + } + } + } + } else { + assert(false); + } + } else { + assert(node.child0 >= 0 && node.child1 >= 0); + const AABB &b0 = bvh_nodes[node.child0].box; + if (intersect(b0, pt)) { + bvh_stack[stack_size++] = node.child0; + } + const AABB &b1 = bvh_nodes[node.child1].box; + if (intersect(b1, pt)) { + bvh_stack[stack_size++] = node.child1; + } + assert(stack_size <= max_bvh_size); + } + } + return winding_number; +} + +DEVICE +int compute_winding_number(const Rect &rect, const Vector2f &pt) { + const auto &p_min = rect.p_min; + const auto &p_max = rect.p_max; + // inside the rectangle: return 1, outside the rectangle: return 0 + if (pt.x > p_min.x && pt.x < p_max.x && pt.y > p_min.y && pt.y < p_max.y) { + return 1; + } else { + return 0; + } +} + +DEVICE +int compute_winding_number(const Shape &shape, const BVHNode *bvh_nodes, const Vector2f &pt) { + switch (shape.type) { + case ShapeType::Circle: + return compute_winding_number(*(const Circle *)shape.ptr, pt); + case ShapeType::Ellipse: + return compute_winding_number(*(const Ellipse *)shape.ptr, pt); + case ShapeType::Path: + return compute_winding_number(*(const Path *)shape.ptr, bvh_nodes, pt); + case ShapeType::Rect: + return compute_winding_number(*(const Rect *)shape.ptr, pt); + } + assert(false); + return 0; +} diff --git a/within_distance.h b/within_distance.h new file mode 100644 index 0000000..e815377 --- /dev/null +++ b/within_distance.h @@ -0,0 +1,446 @@ +#pragma once + +#include "diffvg.h" +#include "edge_query.h" +#include "shape.h" +#include "vector.h" + +DEVICE +inline +bool within_distance(const Circle &circle, const Vector2f &pt, float r) { + auto dist_to_center = distance(circle.center, pt); + if (fabs(dist_to_center - circle.radius) < r) { + return true; + } + return false; +} + +DEVICE +inline +bool within_distance(const Path &path, const BVHNode *bvh_nodes, const Vector2f &pt, float r) { + auto num_segments = path.num_base_points; + constexpr auto max_bvh_size = 128; + int bvh_stack[max_bvh_size]; + auto stack_size = 0; + bvh_stack[stack_size++] = 2 * num_segments - 2; + while (stack_size > 0) { + const BVHNode &node = bvh_nodes[bvh_stack[--stack_size]]; + if (node.child1 < 0) { + // leaf + auto base_point_id = node.child0; + auto point_id = - node.child1 - 1; + assert(base_point_id < num_segments); + assert(point_id < path.num_points); + if (path.num_control_points[base_point_id] == 0) { + // Straight line + auto i0 = point_id; + auto i1 = (point_id + 1) % path.num_points; + auto p0 = Vector2f{path.points[2 * i0], path.points[2 * i0 + 1]}; + auto p1 = Vector2f{path.points[2 * i1], path.points[2 * i1 + 1]}; + // project pt to line + auto t = dot(pt - p0, p1 - p0) / dot(p1 - p0, p1 - p0); + auto r0 = r; + auto r1 = r; + // override radius if path has thickness + if (path.thickness != nullptr) { + r0 = path.thickness[i0]; + r1 = path.thickness[i1]; + } + if (t < 0) { + if (distance_squared(p0, pt) < r0 * r0) { + return true; + } + } else if (t > 1) { + if (distance_squared(p1, pt) < r1 * r1) { + return true; + } + } else { + auto r = r0 + t * (r1 - r0); + if (distance_squared(p0 + t * (p1 - p0), pt) < r * r) { + return true; + } + } + } else if (path.num_control_points[base_point_id] == 1) { + // Quadratic Bezier curve + auto i0 = point_id; + auto i1 = point_id + 1; + auto i2 = (point_id + 2) % path.num_points; + auto p0 = Vector2f{path.points[2 * i0], path.points[2 * i0 + 1]}; + auto p1 = Vector2f{path.points[2 * i1], path.points[2 * i1 + 1]}; + auto p2 = Vector2f{path.points[2 * i2], path.points[2 * i2 + 1]}; + if (path.use_distance_approx) { + auto cp = quadratic_closest_pt_approx(p0, p1, p2, pt); + return distance_squared(cp, pt) < r * r; + } + auto eval = [&](float t) -> Vector2f { + auto tt = 1 - t; + return (tt*tt)*p0 + (2*tt*t)*p1 + (t*t)*p2; + }; + auto r0 = r; + auto r1 = r; + auto r2 = r; + // override radius if path has thickness + if (path.thickness != nullptr) { + r0 = path.thickness[i0]; + r1 = path.thickness[i1]; + r2 = path.thickness[i2]; + } + if (distance_squared(eval(0), pt) < r0 * r0) { + return true; + } + if (distance_squared(eval(1), pt) < r2 * r2) { + return true; + } + + // The curve is (1-t)^2p0 + 2(1-t)tp1 + t^2p2 + // = (p0-2p1+p2)t^2+(-2p0+2p1)t+p0 = q + // Want to solve (q - pt) dot q' = 0 + // q' = (p0-2p1+p2)t + (-p0+p1) + // Expanding (p0-2p1+p2)^2 t^3 + + // 3(p0-2p1+p2)(-p0+p1) t^2 + + // (2(-p0+p1)^2+(p0-2p1+p2)(p0-pt))t + + // (-p0+p1)(p0-pt) = 0 + auto A = sum((p0-2*p1+p2)*(p0-2*p1+p2)); + auto B = sum(3*(p0-2*p1+p2)*(-p0+p1)); + auto C = sum(2*(-p0+p1)*(-p0+p1)+(p0-2*p1+p2)*(p0-pt)); + auto D = sum((-p0+p1)*(p0-pt)); + float t[3]; + int num_sol = solve_cubic(A, B, C, D, t); + for (int j = 0; j < num_sol; j++) { + if (t[j] >= 0 && t[j] <= 1) { + auto tt = 1 - t[j]; + auto r = (tt*tt)*r0 + (2*tt*t[j])*r1 + (t[j]*t[j])*r2; + auto p = eval(t[j]); + if (distance_squared(p, pt) < r*r) { + return true; + } + } + } + } else if (path.num_control_points[base_point_id] == 2) { + // Cubic Bezier curve + auto i0 = point_id; + auto i1 = point_id + 1; + auto i2 = point_id + 2; + auto i3 = (point_id + 3) % path.num_points; + auto p0 = Vector2f{path.points[2 * i0], path.points[2 * i0 + 1]}; + auto p1 = Vector2f{path.points[2 * i1], path.points[2 * i1 + 1]}; + auto p2 = Vector2f{path.points[2 * i2], path.points[2 * i2 + 1]}; + auto p3 = Vector2f{path.points[2 * i3], path.points[2 * i3 + 1]}; + auto eval = [&](float t) -> Vector2f { + auto tt = 1 - t; + return (tt*tt*tt)*p0 + (3*tt*tt*t)*p1 + (3*tt*t*t)*p2 + (t*t*t)*p3; + }; + auto r0 = r; + auto r1 = r; + auto r2 = r; + auto r3 = r; + // override radius if path has thickness + if (path.thickness != nullptr) { + r0 = path.thickness[i0]; + r1 = path.thickness[i1]; + r2 = path.thickness[i2]; + r3 = path.thickness[i3]; + } + if (distance_squared(eval(0), pt) < r0*r0) { + return true; + } + if (distance_squared(eval(1), pt) < r3*r3) { + return true; + } + // The curve is (1 - t)^3 p0 + 3 * (1 - t)^2 t p1 + 3 * (1 - t) t^2 p2 + t^3 p3 + // = (-p0+3p1-3p2+p3) t^3 + (3p0-6p1+3p2) t^2 + (-3p0+3p1) t + p0 + // Want to solve (q - pt) dot q' = 0 + // q' = 3*(-p0+3p1-3p2+p3)t^2 + 2*(3p0-6p1+3p2)t + (-3p0+3p1) + // Expanding + // 3*(-p0+3p1-3p2+p3)^2 t^5 + // 5*(-p0+3p1-3p2+p3)(3p0-6p1+3p2) t^4 + // 4*(-p0+3p1-3p2+p3)(-3p0+3p1) + 2*(3p0-6p1+3p2)^2 t^3 + // 3*(3p0-6p1+3p2)(-3p0+3p1) + 3*(-p0+3p1-3p2+p3)(p0-pt) t^2 + // (-3p0+3p1)^2+2(p0-pt)(3p0-6p1+3p2) t + // (p0-pt)(-3p0+3p1) + double A = 3*sum((-p0+3*p1-3*p2+p3)*(-p0+3*p1-3*p2+p3)); + double B = 5*sum((-p0+3*p1-3*p2+p3)*(3*p0-6*p1+3*p2)); + double C = 4*sum((-p0+3*p1-3*p2+p3)*(-3*p0+3*p1)) + 2*sum((3*p0-6*p1+3*p2)*(3*p0-6*p1+3*p2)); + double D = 3*(sum((3*p0-6*p1+3*p2)*(-3*p0+3*p1)) + sum((-p0+3*p1-3*p2+p3)*(p0-pt))); + double E = sum((-3*p0+3*p1)*(-3*p0+3*p1)) + 2*sum((p0-pt)*(3*p0-6*p1+3*p2)); + double F = sum((p0-pt)*(-3*p0+3*p1)); + // normalize the polynomial + B /= A; + C /= A; + D /= A; + E /= A; + F /= A; + // Isolator Polynomials: + // https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.133.2233&rep=rep1&type=pdf + // x/5 + B/25 + // /----------------------------------------------------- + // 5x^4 + 4B x^3 + 3C x^2 + 2D x + E / x^5 + B x^4 + C x^3 + D x^2 + E x + F + // x^5 + 4B/5 x^4 + 3C/5 x^3 + 2D/5 x^2 + E/5 x + // ---------------------------------------------------- + // B/5 x^4 + 2C/5 x^3 + 3D/5 x^2 + 4E/5 x + F + // B/5 x^4 + 4B^2/25 x^3 + 3BC/25 x^2 + 2BD/25 x + BE/25 + // ---------------------------------------------------- + // (2C/5 - 4B^2/25)x^3 + (3D/5-3BC/25)x^2 + (4E/5-2BD/25) + (F-BE/25) + auto p1A = ((2 / 5.f) * C - (4 / 25.f) * B * B); + auto p1B = ((3 / 5.f) * D - (3 / 25.f) * B * C); + auto p1C = ((4 / 5.f) * E - (2 / 25.f) * B * D); + auto p1D = F - B * E / 25.f; + // auto q1A = 1 / 5.f; + // auto q1B = B / 25.f; + // x/5 + B/25 = 0 + // x = -B/5 + auto q_root = -B/5.f; + double p_roots[3]; + int num_sol = solve_cubic(p1A, p1B, p1C, p1D, p_roots); + float intervals[4]; + if (q_root >= 0 && q_root <= 1) { + intervals[0] = q_root; + } + for (int j = 0; j < num_sol; j++) { + intervals[j + 1] = p_roots[j]; + } + auto num_intervals = 1 + num_sol; + // sort intervals + for (int j = 1; j < num_intervals; j++) { + for (int k = j; k > 0 && intervals[k - 1] > intervals[k]; k--) { + auto tmp = intervals[k]; + intervals[k] = intervals[k - 1]; + intervals[k - 1] = tmp; + } + } + auto eval_polynomial = [&] (double t) { + return t*t*t*t*t+ + B*t*t*t*t+ + C*t*t*t+ + D*t*t+ + E*t+ + F; + }; + auto eval_polynomial_deriv = [&] (double t) { + return 5*t*t*t*t+ + 4*B*t*t*t+ + 3*C*t*t+ + 2*D*t+ + E; + }; + auto lower_bound = 0.f; + for (int j = 0; j < num_intervals + 1; j++) { + if (j < num_intervals && intervals[j] < 0.f) { + continue; + } + auto upper_bound = j < num_intervals ? + min(intervals[j], 1.f) : 1.f; + auto lb = lower_bound; + auto ub = upper_bound; + auto lb_eval = eval_polynomial(lb); + auto ub_eval = eval_polynomial(ub); + if (lb_eval * ub_eval > 0) { + // Doesn't have root + continue; + } + if (lb_eval > ub_eval) { + swap_(lb, ub); + } + auto t = 0.5f * (lb + ub); + for (int it = 0; it < 20; it++) { + if (!(t >= lb && t <= ub)) { + t = 0.5f * (lb + ub); + } + auto value = eval_polynomial(t); + if (fabs(value) < 1e-5f || it == 19) { + break; + } + // The derivative may not be entirely accurate, + // but the bisection is going to handle this + if (value > 0.f) { + ub = t; + } else { + lb = t; + } + auto derivative = eval_polynomial_deriv(t); + t -= value / derivative; + } + auto tt = 1 - t; + auto r = (tt*tt*tt)*r0 + (3*tt*tt*t)*r1 + (3*tt*t*t)*r2 + (t*t*t)*r3; + if (distance_squared(eval(t), pt) < r * r) { + return true; + } + if (upper_bound >= 1.f) { + break; + } + lower_bound = upper_bound; + } + } else { + assert(false); + } + } else { + assert(node.child0 >= 0 && node.child1 >= 0); + const AABB &b0 = bvh_nodes[node.child0].box; + if (within_distance(b0, pt, bvh_nodes[node.child0].max_radius)) { + bvh_stack[stack_size++] = node.child0; + } + const AABB &b1 = bvh_nodes[node.child1].box; + if (within_distance(b1, pt, bvh_nodes[node.child1].max_radius)) { + bvh_stack[stack_size++] = node.child1; + } + assert(stack_size <= max_bvh_size); + } + } + return false; +} + +DEVICE +inline +int within_distance(const Rect &rect, const Vector2f &pt, float r) { + auto test = [&](const Vector2f &p0, const Vector2f &p1) { + // project pt to line + auto t = dot(pt - p0, p1 - p0) / dot(p1 - p0, p1 - p0); + if (t < 0) { + if (distance_squared(p0, pt) < r * r) { + return true; + } + } else if (t > 1) { + if (distance_squared(p1, pt) < r * r) { + return true; + } + } else { + if (distance_squared(p0 + t * (p1 - p0), pt) < r * r) { + return true; + } + } + return false; + }; + auto left_top = rect.p_min; + auto right_top = Vector2f{rect.p_max.x, rect.p_min.y}; + auto left_bottom = Vector2f{rect.p_min.x, rect.p_max.y}; + auto right_bottom = rect.p_max; + // left + if (test(left_top, left_bottom)) { + return true; + } + // top + if (test(left_top, right_top)) { + return true; + } + // right + if (test(right_top, right_bottom)) { + return true; + } + // bottom + if (test(left_bottom, right_bottom)) { + return true; + } + return false; +} + +DEVICE +inline +bool within_distance(const Shape &shape, const BVHNode *bvh_nodes, const Vector2f &pt, float r) { + switch (shape.type) { + case ShapeType::Circle: + return within_distance(*(const Circle *)shape.ptr, pt, r); + case ShapeType::Ellipse: + // https://www.geometrictools.com/Documentation/DistancePointEllipseEllipsoid.pdf + assert(false); + return false; + case ShapeType::Path: + return within_distance(*(const Path *)shape.ptr, bvh_nodes, pt, r); + case ShapeType::Rect: + return within_distance(*(const Rect *)shape.ptr, pt, r); + } + assert(false); + return false; +} + +DEVICE +inline +bool within_distance(const SceneData &scene, + int shape_group_id, + const Vector2f &pt) { + const ShapeGroup &shape_group = scene.shape_groups[shape_group_id]; + // pt is in canvas space, transform it to shape's local space + auto local_pt = xform_pt(shape_group.canvas_to_shape, pt); + + constexpr auto max_bvh_stack_size = 64; + int bvh_stack[max_bvh_stack_size]; + auto stack_size = 0; + bvh_stack[stack_size++] = 2 * shape_group.num_shapes - 2; + const auto &bvh_nodes = scene.shape_groups_bvh_nodes[shape_group_id]; + + while (stack_size > 0) { + const BVHNode &node = bvh_nodes[bvh_stack[--stack_size]]; + if (node.child1 < 0) { + // leaf + auto shape_id = node.child0; + const auto &shape = scene.shapes[shape_id]; + if (within_distance(shape, scene.path_bvhs[shape_id], + local_pt, shape.stroke_width)) { + return true; + } + } else { + assert(node.child0 >= 0 && node.child1 >= 0); + const AABB &b0 = bvh_nodes[node.child0].box; + if (inside(b0, local_pt, bvh_nodes[node.child0].max_radius)) { + bvh_stack[stack_size++] = node.child0; + } + const AABB &b1 = bvh_nodes[node.child1].box; + if (inside(b1, local_pt, bvh_nodes[node.child1].max_radius)) { + bvh_stack[stack_size++] = node.child1; + } + assert(stack_size <= max_bvh_stack_size); + } + } + + return false; +} + +DEVICE +inline +bool within_distance(const SceneData &scene, + int shape_group_id, + const Vector2f &pt, + EdgeQuery *edge_query) { + if (edge_query == nullptr || shape_group_id != edge_query->shape_group_id) { + // Specialized version + return within_distance(scene, shape_group_id, pt); + } + const ShapeGroup &shape_group = scene.shape_groups[shape_group_id]; + // pt is in canvas space, transform it to shape's local space + auto local_pt = xform_pt(shape_group.canvas_to_shape, pt); + + constexpr auto max_bvh_stack_size = 64; + int bvh_stack[max_bvh_stack_size]; + auto stack_size = 0; + bvh_stack[stack_size++] = 2 * shape_group.num_shapes - 2; + const auto &bvh_nodes = scene.shape_groups_bvh_nodes[shape_group_id]; + + auto ret = false; + while (stack_size > 0) { + const BVHNode &node = bvh_nodes[bvh_stack[--stack_size]]; + if (node.child1 < 0) { + // leaf + auto shape_id = node.child0; + const auto &shape = scene.shapes[shape_id]; + if (within_distance(shape, scene.path_bvhs[shape_id], + local_pt, shape.stroke_width)) { + ret = true; + if (shape_id == edge_query->shape_id) { + edge_query->hit = true; + } + } + } else { + assert(node.child0 >= 0 && node.child1 >= 0); + const AABB &b0 = bvh_nodes[node.child0].box; + if (inside(b0, local_pt, bvh_nodes[node.child0].max_radius)) { + bvh_stack[stack_size++] = node.child0; + } + const AABB &b1 = bvh_nodes[node.child1].box; + if (inside(b1, local_pt, bvh_nodes[node.child1].max_radius)) { + bvh_stack[stack_size++] = node.child1; + } + assert(stack_size <= max_bvh_stack_size); + } + } + + return ret; +}

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