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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
None, # background_image
*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
None, # background_image
*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
None, # background_image
*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
None, # background_image
*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"])
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