initial commit

2020-09-03 22:30:30 -04:00
commit 413a3e5cee
148 changed files with 138536 additions and 0 deletions
--- a/apps/single_polygon.py
+++ 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"])