triangles of gradient mesh
This commit is contained in:
Jun Zhou
GitHub
142
apps/test_triangle.py
Normal file
142
apps/test_triangle.py
Normal file
@@ -0,0 +1,142 @@
|
||||
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([[20.0, 30.0], # base
|
||||
[50.0, 60.0], # control point
|
||||
[ 90.0, 198.0], # control point
|
||||
[ 60.0, 218.0], # base
|
||||
[ 90.0, 180.0], # control point
|
||||
[200.0, 85.0], # control point
|
||||
[230.0, 90.0], # 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/test_curve/target.png', gamma=2.2)
|
||||
target = img.clone()
|
||||
|
||||
# Load the obj file, get the vertices/control points
|
||||
obj = "imgs/Triangle.obj"
|
||||
vertices_tmp, faces_tmp = pydiffvg.obj_to_scene(obj)
|
||||
print(float(vertices_tmp[1][1]))
|
||||
print(faces_tmp)
|
||||
|
||||
vertices = []
|
||||
faces = []
|
||||
for v in vertices_tmp:
|
||||
vertices.append([float(v[1]), float(v[2])])
|
||||
for f in faces_tmp:
|
||||
vs_count = len(f)
|
||||
tmp = []
|
||||
for v in f[1:]:
|
||||
tmp.append(int(v))
|
||||
faces.append(tmp)
|
||||
print(vertices)
|
||||
print(faces)
|
||||
|
||||
# Ternary subdivision
|
||||
# Move the path to produce initial guess
|
||||
# normalize points for easier learning rate
|
||||
points_n = torch.tensor([[vertices[0][0]/256.0, vertices[0][1]/256.0], # base
|
||||
[70.0/256.0, 140.0/256.0], # control point
|
||||
[50.0/256.0, 100.0/256.0], # control point
|
||||
[vertices[1][0]/256.0, vertices[1][1]/256.0], # base
|
||||
[80.0/256.0, 40.0/256.0], # control point
|
||||
[120.0/256.0, 40.0/256.0], # control point
|
||||
[vertices[2][0]/256.0, vertices[2][1]/256.0], # base
|
||||
[150.0/256.0, 100.0/256.0], # control point
|
||||
[130.0/256.0, 140.0/256.0]], # 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/test_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/test_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/test_curve/final.png')
|
||||
|
||||
# Convert the intermediate renderings to a video.
|
||||
from subprocess import call
|
||||
call(["ffmpeg", "-framerate", "24", "-i",
|
||||
"results/test_curve/iter_%d.png", "-vb", "20M",
|
||||
"results/test_curve/out.mp4"])
|
||||
|
||||
144
apps/test_triangles.py
Normal file
144
apps/test_triangles.py
Normal file
@@ -0,0 +1,144 @@
|
||||
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([[20.0, 30.0], # base
|
||||
[50.0, 60.0], # control point
|
||||
[ 90.0, 198.0], # control point
|
||||
[ 60.0, 218.0], # base
|
||||
[ 90.0, 180.0], # control point
|
||||
[200.0, 85.0], # control point
|
||||
[230.0, 90.0], # 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/test_triangles/target.png', gamma=2.2)
|
||||
target = img.clone()
|
||||
|
||||
# Load the obj file, get the vertices/control points
|
||||
obj = "imgs/Triangles.obj"
|
||||
vertices_tmp, faces_tmp = pydiffvg.obj_to_scene(obj)
|
||||
print(float(vertices_tmp[1][1]))
|
||||
print(faces_tmp)
|
||||
|
||||
vertices = []
|
||||
faces = []
|
||||
for v in vertices_tmp:
|
||||
vertices.append([float(v[1]), float(v[2])])
|
||||
for f in faces_tmp:
|
||||
vs_count = len(f)
|
||||
tmp = []
|
||||
for v in f[1:]:
|
||||
tmp.append(int(v))
|
||||
faces.append(tmp)
|
||||
print(vertices)
|
||||
print(faces)
|
||||
|
||||
quit()
|
||||
|
||||
# Ternary subdivision
|
||||
# Move the path to produce initial guess
|
||||
# normalize points for easier learning rate
|
||||
points_n = torch.tensor([[vertices[0][0]/256.0, vertices[0][1]/256.0], # base
|
||||
[70.0/256.0, 140.0/256.0], # control point
|
||||
[50.0/256.0, 100.0/256.0], # control point
|
||||
[vertices[1][0]/256.0, vertices[1][1]/256.0], # base
|
||||
[80.0/256.0, 40.0/256.0], # control point
|
||||
[120.0/256.0, 40.0/256.0], # control point
|
||||
[vertices[2][0]/256.0, vertices[2][1]/256.0], # base
|
||||
[150.0/256.0, 100.0/256.0], # control point
|
||||
[130.0/256.0, 140.0/256.0]], # 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/test_triangles/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/test_triangles/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/test_triangles/final.png')
|
||||
|
||||
# Convert the intermediate renderings to a video.
|
||||
from subprocess import call
|
||||
call(["ffmpeg", "-framerate", "24", "-i",
|
||||
"results/test_triangles/iter_%d.png", "-vb", "20M",
|
||||
"results/test_triangles/out.mp4"])
|
||||
|
||||
Reference in New Issue
Block a user