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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
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"])
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