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# python finite_difference_comp.py imgs/tiger.svg
# python finite_difference_comp.py --use_prefiltering True imgs/tiger.svg
# python finite_difference_comp.py imgs/boston.svg
# python finite_difference_comp.py --use_prefiltering True imgs/boston.svg
# python finite_difference_comp.py imgs/contour.svg
# python finite_difference_comp.py --use_prefiltering True imgs/contour.svg
# python finite_difference_comp.py --size_scale 0.5 --clamping_factor 0.05 imgs/hawaii.svg
# python finite_difference_comp.py --size_scale 0.5 --clamping_factor 0.05 --use_prefiltering True imgs/hawaii.svg
# python finite_difference_comp.py imgs/mcseem2.svg
# python finite_difference_comp.py --use_prefiltering True imgs/mcseem2.svg
# python finite_difference_comp.py imgs/reschart.svg
# python finite_difference_comp.py --use_prefiltering True imgs/reschart.svg
import pydiffvg
import diffvg
from matplotlib import cm
import matplotlib.pyplot as plt
import argparse
import torch
pydiffvg.set_print_timing(True)
#pydiffvg.set_use_gpu(False)
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)
print(w, h)
curve_counts = 0
for s in shapes:
if isinstance(s, pydiffvg.Circle):
curve_counts += 1
elif isinstance(s, pydiffvg.Ellipse):
curve_counts += 1
elif isinstance(s, pydiffvg.Path):
curve_counts += len(s.num_control_points)
elif isinstance(s, pydiffvg.Polygon):
curve_counts += len(s.points) - 1
if s.is_closed:
curve_counts += 1
elif isinstance(s, pydiffvg.Rect):
curve_counts += 1
print('curve_counts:', curve_counts)
pfilter = pydiffvg.PixelFilter(type = diffvg.FilterType.box,
radius = torch.tensor(0.5))
use_prefiltering = args.use_prefiltering
print('use_prefiltering:', use_prefiltering)
scene_args = pydiffvg.RenderFunction.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups,
filter = pfilter,
use_prefiltering = use_prefiltering)
num_samples_x = args.num_spp
num_samples_y = args.num_spp
if (use_prefiltering):
num_samples_x = 1
num_samples_y = 1
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, # background_image
*scene_args)
pydiffvg.imwrite(img.cpu(), 'results/finite_difference_comp/img.png', gamma=1.0)
epsilon = 0.1
def perturb_scene(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, # background_image
*scene_args)
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, # background_image
*scene_args)
x_diff = (img0 - img1) / (2 * epsilon)
x_diff = x_diff.sum(axis = 2)
x_diff_max = x_diff.max() * args.clamping_factor
x_diff_min = x_diff.min() * args.clamping_factor
print(x_diff.max())
print(x_diff.min())
x_diff = cm.viridis(normalize(x_diff, x_diff_min, x_diff_max).cpu().numpy())
pydiffvg.imwrite(x_diff, 'results/finite_difference_comp/finite_x_diff.png', gamma=1.0)
perturb_scene(0, epsilon)
perturb_scene(1, 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, # background_image
*scene_args)
perturb_scene(1, -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, # background_image
*scene_args)
y_diff = (img0 - img1) / (2 * epsilon)
y_diff = y_diff.sum(axis = 2)
y_diff_max = y_diff.max() * args.clamping_factor
y_diff_min = y_diff.min() * args.clamping_factor
y_diff = cm.viridis(normalize(y_diff, y_diff_min, y_diff_max).cpu().numpy())
pydiffvg.imwrite(y_diff, 'results/finite_difference_comp/finite_y_diff.png', gamma=1.0)
perturb_scene(1, epsilon)
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, device = pydiffvg.get_device()),
w, # width
h, # height
num_samples_x, # num_samples_x
num_samples_y, # num_samples_y
0, # seed
None, # background_image
*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())
y_diff = cm.viridis(normalize(img_grad[:, :, 1], y_diff_min, y_diff_max).cpu().numpy())
pydiffvg.imwrite(x_diff, 'results/finite_difference_comp/ours_x_diff.png', gamma=1.0)
pydiffvg.imwrite(y_diff, 'results/finite_difference_comp/ours_y_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)
parser.add_argument("--clamping_factor", type=float, default=0.1)
parser.add_argument("--num_spp", type=int, default=4)
parser.add_argument("--use_prefiltering", type=bool, default=False)
args = parser.parse_args()
main(args)
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