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import pydiffvg
import diffvg
from matplotlib import cm
import matplotlib.pyplot as plt
import argparse
import torch
def normalize(x, min_, max_):
range = max(abs(min_), abs(max_))
return (x + range) / (2 * range)
def main(args):
canvas_width, canvas_height, shapes, shape_groups = \
pydiffvg.svg_to_scene(args.svg_file)
w = int(canvas_width * args.size_scale)
h = int(canvas_height * args.size_scale)
pfilter = pydiffvg.PixelFilter(type = diffvg.FilterType.box,
radius = torch.tensor(0.5))
use_prefiltering = False
scene_args = pydiffvg.RenderFunction.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups,
filter = pfilter,
use_prefiltering = use_prefiltering)
num_samples_x = 16
num_samples_y = 16
render = pydiffvg.RenderFunction.apply
img = render(w, # width
h, # height
num_samples_x, # num_samples_x
num_samples_y, # num_samples_y
0, # seed
None,
*scene_args)
pydiffvg.imwrite(img.cpu(), 'results/finite_difference_comp/img.png', gamma=1.0)
epsilon = 0.1
def perturb_scene(axis, epsilon):
shapes[2].points[:, axis] += epsilon
# for s in shapes:
# if isinstance(s, pydiffvg.Circle):
# s.center[axis] += epsilon
# elif isinstance(s, pydiffvg.Ellipse):
# s.center[axis] += epsilon
# elif isinstance(s, pydiffvg.Path):
# s.points[:, axis] += epsilon
# elif isinstance(s, pydiffvg.Polygon):
# s.points[:, axis] += epsilon
# elif isinstance(s, pydiffvg.Rect):
# s.p_min[axis] += epsilon
# s.p_max[axis] += epsilon
# for s in shape_groups:
# if isinstance(s.fill_color, pydiffvg.LinearGradient):
# s.fill_color.begin[axis] += epsilon
# s.fill_color.end[axis] += epsilon
perturb_scene(0, epsilon)
scene_args = pydiffvg.RenderFunction.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups,
filter = pfilter,
use_prefiltering = use_prefiltering)
render = pydiffvg.RenderFunction.apply
img0 = render(w, # width
h, # height
num_samples_x, # num_samples_x
num_samples_y, # num_samples_y
0, # seed
None,
*scene_args)
forward_diff = (img0 - img) / (epsilon)
forward_diff = forward_diff.sum(axis = 2)
x_diff_max = 1.5
x_diff_min = -1.5
print(forward_diff.max())
print(forward_diff.min())
forward_diff = cm.viridis(normalize(forward_diff, x_diff_min, x_diff_max).cpu().numpy())
pydiffvg.imwrite(forward_diff, 'results/finite_difference_comp/shared_edge_forward_diff.png', gamma=1.0)
perturb_scene(0, -2 * epsilon)
scene_args = pydiffvg.RenderFunction.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups,
filter = pfilter,
use_prefiltering = use_prefiltering)
img1 = render(w, # width
h, # height
num_samples_x, # num_samples_x
num_samples_y, # num_samples_y
0, # seed
None,
*scene_args)
backward_diff = (img - img1) / (epsilon)
backward_diff = backward_diff.sum(axis = 2)
print(backward_diff.max())
print(backward_diff.min())
backward_diff = cm.viridis(normalize(backward_diff, x_diff_min, x_diff_max).cpu().numpy())
pydiffvg.imwrite(backward_diff, 'results/finite_difference_comp/shared_edge_backward_diff.png', gamma=1.0)
perturb_scene(0, epsilon)
num_samples_x = 4
num_samples_y = 4
scene_args = pydiffvg.RenderFunction.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups,
filter = pfilter,
use_prefiltering = use_prefiltering)
render_grad = pydiffvg.RenderFunction.render_grad
img_grad = render_grad(torch.ones(h, w, 4),
w, # width
h, # height
num_samples_x, # num_samples_x
num_samples_y, # num_samples_y
0, # seed
*scene_args)
print(img_grad[:, :, 0].max())
print(img_grad[:, :, 0].min())
x_diff = cm.viridis(normalize(img_grad[:, :, 0], x_diff_min, x_diff_max).cpu().numpy())
pydiffvg.imwrite(x_diff, 'results/finite_difference_comp/ours_x_diff.png', gamma=1.0)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("svg_file", help="source SVG path")
parser.add_argument("--size_scale", type=float, default=1.0)
args = parser.parse_args()
main(args)
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