def parse_linear_gradient(node, transform, defs):
begin = torch.tensor([0.0, 0.0])
end = torch.tensor([0.0, 0.0])
offsets = []
stop_colors = []
# Inherit from parent
for key in node.attrib:
if remove_namespaces(key) == 'href':
value = node.attrib[key]
parent = defs[value.lstrip('#')]
begin = parent.begin
end = parent.end
offsets = parent.offsets
stop_colors = parent.stop_colors
for attrib in node.attrib:
attrib = remove_namespaces(attrib)
if attrib == 'x1':
begin[0] = float(node.attrib['x1'])
elif attrib == 'y1':
begin[1] = float(node.attrib['y1'])
elif attrib == 'x2':
end[0] = float(node.attrib['x2'])
elif attrib == 'y2':
end[1] = float(node.attrib['y2'])
elif attrib == 'gradientTransform':
transform = transform @ parse_transform(node.attrib['gradientTransform'])
begin = transform @ torch.cat((begin, torch.ones([1])))
begin = begin / begin[2]
begin = begin[:2]
end = transform @ torch.cat((end, torch.ones([1])))
end = end / end[2]
end = end[:2]
for child in node:
tag = remove_namespaces(child.tag)
if tag == 'stop':
offset = float(child.attrib['offset'])
color = [0.0, 0.0, 0.0, 1.0]
if 'stop-color' in child.attrib:
c = parse_color(child.attrib['stop-color'], defs)
color[:3] = [c[0], c[1], c[2]]
if 'stop-opacity' in child.attrib:
color[3] = float(child.attrib['stop-opacity'])
if 'style' in child.attrib:
style = parse_style(child.attrib['style'], defs)
if 'stop-color' in style:
c = parse_color(style['stop-color'], defs)
color[:3] = [c[0], c[1], c[2]]
if 'stop-opacity' in style:
color[3] = float(style['stop-opacity'])
offsets.append(offset)
stop_colors.append(color)
if isinstance(offsets, list):
offsets = torch.tensor(offsets)
if isinstance(stop_colors, list):
stop_colors = torch.tensor(stop_colors)
return pydiffvg.LinearGradient(begin, end, offsets, stop_colors)
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
color = pydiffvg.LinearGradient(\
begin = torch.tensor([50.0, 50.0]),
end = torch.tensor([200.0, 200.0]),
offsets = torch.tensor([0.0, 1.0]),
stop_colors = torch.tensor([[0.2, 0.5, 0.7, 1.0],
[0.7, 0.2, 0.5, 1.0]]))
circle = pydiffvg.Circle(radius = torch.tensor(40.0),
center = torch.tensor([128.0, 128.0]))
shapes = [circle]
circle_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]), fill_color = color)
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, # background_image
*scene_args)