Spaces:
Sleeping
Sleeping
File size: 4,609 Bytes
31726e5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 |
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
num_control_points = torch.tensor([2, 2, 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
[ 90.0, 180.0], # control point
[200.0, 65.0], # control point
[210.0, 98.0], # base
[220.0, 70.0], # control point
[130.0, 55.0]]) # control point
path = pydiffvg.Path(num_control_points = num_control_points,
points = points,
is_closed = True)
shapes = [path]
path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]),
fill_color = torch.tensor([0.3, 0.6, 0.3, 1.0]))
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,
*scene_args)
# The output image is in linear RGB space. Do Gamma correction before saving the image.
pydiffvg.imwrite(img.cpu(), 'results/single_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
[100.0/256.0, 200.0/256.0], # control point
[170.0/256.0, 55.0/256.0], # control point
[220.0/256.0, 100.0/256.0], # base
[210.0/256.0, 80.0/256.0], # control point
[140.0/256.0, 60.0/256.0]], # control point
requires_grad = True)
color = torch.tensor([0.3, 0.2, 0.5, 1.0], requires_grad=True)
path.points = points_n * 256
path_group.fill_color = 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,
*scene_args)
pydiffvg.imwrite(img.cpu(), 'results/single_curve/init.png', gamma=2.2)
# Optimize
optimizer = torch.optim.Adam([points_n, color], lr=1e-2)
# Run 100 Adam iterations.
for t in range(100):
print('iteration:', t)
optimizer.zero_grad()
# Forward pass: render the image.
path.points = points_n * 256
path_group.fill_color = 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,
*scene_args)
# Save the intermediate render.
pydiffvg.imwrite(img.cpu(), 'results/single_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('color.grad:', color.grad)
# Take a gradient descent step.
optimizer.step()
# Print the current params.
print('points:', path.points)
print('color:', path_group.fill_color)
# Render the final result.
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
102, # seed
None,
*scene_args)
# Save the images and differences.
pydiffvg.imwrite(img.cpu(), 'results/single_curve/final.png')
# Convert the intermediate renderings to a video.
from subprocess import call
call(["ffmpeg", "-framerate", "24", "-i",
"results/single_curve/iter_%d.png", "-vb", "20M",
"results/single_curve/out.mp4"])
|