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import pydiffvg
import torch
import skimage
# Use GPU if available
pydiffvg.set_use_gpu(torch.cuda.is_available())
canvas_width, canvas_height = 256, 256
num_control_points = torch.tensor([2])
points = torch.tensor([[120.0, 30.0], # base
[150.0, 60.0], # control point
[ 90.0, 198.0], # control point
[ 60.0, 218.0]]) # base
path = pydiffvg.Path(num_control_points = num_control_points,
points = points,
is_closed = False,
stroke_width = torch.tensor(5.0))
shapes = [path]
path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]),
fill_color = None,
stroke_color = torch.tensor([0.6, 0.3, 0.6, 0.8]))
shape_groups = [path_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)
# The output image is in linear RGB space. Do Gamma correction before saving the image.
pydiffvg.imwrite(img.cpu(), 'results/single_open_curve/target.png', gamma=2.2)
target = img.clone()
# Move the path to produce initial guess
# normalize points for easier learning rate
points_n = torch.tensor([[100.0/256.0, 40.0/256.0], # base
[155.0/256.0, 65.0/256.0], # control point
[100.0/256.0, 180.0/256.0], # control point
[ 65.0/256.0, 238.0/256.0]], # base
requires_grad = True)
stroke_color = torch.tensor([0.4, 0.7, 0.5, 0.5], requires_grad=True)
stroke_width_n = torch.tensor(10.0 / 100.0, requires_grad=True)
path.points = points_n * 256
path.stroke_width = stroke_width_n * 100
path_group.stroke_color = stroke_color
scene_args = pydiffvg.RenderFunction.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
img = render(256, # width
256, # height
2, # num_samples_x
2, # num_samples_y
1, # seed
None, # background_image
*scene_args)
pydiffvg.imwrite(img.cpu(), 'results/single_open_curve/init.png', gamma=2.2)
# Optimize
optimizer = torch.optim.Adam([points_n, stroke_color, stroke_width_n], lr=1e-2)
# Run 200 Adam iterations.
for t in range(200):
print('iteration:', t)
optimizer.zero_grad()
# Forward pass: render the image.
path.points = points_n * 256
path.stroke_width = stroke_width_n * 100
path_group.stroke_color = stroke_color
scene_args = pydiffvg.RenderFunction.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
img = render(256, # width
256, # height
2, # num_samples_x
2, # num_samples_y
t+1, # seed
None, # background_image
*scene_args)
# Save the intermediate render.
pydiffvg.imwrite(img.cpu(), 'results/single_open_curve/iter_{}.png'.format(t), gamma=2.2)
# Compute the loss function. Here it is L2.
loss = (img - target).pow(2).sum()
print('loss:', loss.item())
# Backpropagate the gradients.
loss.backward()
# Print the gradients
print('points_n.grad:', points_n.grad)
print('stroke_color.grad:', stroke_color.grad)
print('stroke_width.grad:', stroke_width_n.grad)
# Take a gradient descent step.
optimizer.step()
# Print the current params.
print('points:', path.points)
print('stroke_color:', path_group.stroke_color)
print('stroke_width:', path.stroke_width)
# Render the final result.
path.points = points_n * 256
path.stroke_width = stroke_width_n * 100
path_group.stroke_color = stroke_color
scene_args = pydiffvg.RenderFunction.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
img = render(256, # width
256, # height
2, # num_samples_x
2, # num_samples_y
202, # seed
None, # background_image
*scene_args)
# Save the images and differences.
pydiffvg.imwrite(img.cpu(), 'results/single_open_curve/final.png')
# Convert the intermediate renderings to a video.
from subprocess import call
call(["ffmpeg", "-framerate", "24", "-i",
"results/single_open_curve/iter_%d.png", "-vb", "20M",
"results/single_open_curve/out.mp4"])
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