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import pydiffvg
import argparse
import ttools.modules
import torch
import skimage.io
gamma = 1.0
def main(args):
perception_loss = ttools.modules.LPIPS().to(pydiffvg.get_device())
target = torch.from_numpy(skimage.io.imread(args.target)).to(torch.float32) / 255.0
target = target.pow(gamma)
target = target.to(pydiffvg.get_device())
target = target.unsqueeze(0)
target = target.permute(0, 3, 1, 2) # NHWC -> NCHW
canvas_width, canvas_height, shapes, shape_groups = \
pydiffvg.svg_to_scene(args.svg)
scene_args = pydiffvg.RenderFunction.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
render = pydiffvg.RenderFunction.apply
img = render(canvas_width, # width
canvas_height, # height
2, # num_samples_x
2, # num_samples_y
0, # seed
None, # bg
*scene_args)
# The output image is in linear RGB space. Do Gamma correction before saving the image.
pydiffvg.imwrite(img.cpu(), 'results/refine_svg/init.png', gamma=gamma)
points_vars = []
for path in shapes:
path.points.requires_grad = True
points_vars.append(path.points)
color_vars = {}
for group in shape_groups:
group.fill_color.requires_grad = True
color_vars[group.fill_color.data_ptr()] = group.fill_color
color_vars = list(color_vars.values())
# Optimize
points_optim = torch.optim.Adam(points_vars, lr=1.0)
color_optim = torch.optim.Adam(color_vars, lr=0.01)
# Adam iterations.
for t in range(args.num_iter):
print('iteration:', t)
points_optim.zero_grad()
color_optim.zero_grad()
# Forward pass: render the image.
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, # bg
*scene_args)
# Compose img with white background
img = img[:, :, 3:4] * img[:, :, :3] + torch.ones(img.shape[0], img.shape[1], 3, device = pydiffvg.get_device()) * (1 - img[:, :, 3:4])
# Save the intermediate render.
pydiffvg.imwrite(img.cpu(), 'results/refine_svg/iter_{}.png'.format(t), gamma=gamma)
img = img[:, :, :3]
# Convert img from HWC to NCHW
img = img.unsqueeze(0)
img = img.permute(0, 3, 1, 2) # NHWC -> NCHW
if args.use_lpips_loss:
loss = perception_loss(img, target)
else:
loss = (img - target).pow(2).mean()
print('render loss:', loss.item())
# Backpropagate the gradients.
loss.backward()
# Take a gradient descent step.
points_optim.step()
color_optim.step()
for group in shape_groups:
group.fill_color.data.clamp_(0.0, 1.0)
if t % 10 == 0 or t == args.num_iter - 1:
pydiffvg.save_svg('results/refine_svg/iter_{}.svg'.format(t),
canvas_width, canvas_height, shapes, shape_groups)
# Render the final result.
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, # bg
*scene_args)
# Save the intermediate render.
pydiffvg.imwrite(img.cpu(), 'results/refine_svg/final.png'.format(t), gamma=gamma)
# Convert the intermediate renderings to a video.
from subprocess import call
call(["ffmpeg", "-framerate", "24", "-i",
"results/refine_svg/iter_%d.png", "-vb", "20M",
"results/refine_svg/out.mp4"])
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("svg", help="source SVG path")
parser.add_argument("target", help="target image path")
parser.add_argument("--use_lpips_loss", dest='use_lpips_loss', action='store_true')
parser.add_argument("--num_iter", type=int, default=250)
args = parser.parse_args()
main(args)
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