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"""
"""
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()
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