Spaces:
Sleeping
Sleeping
File size: 3,911 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 |
import pydiffvg
import torch
import skimage
# Use GPU if available
pydiffvg.set_use_gpu(torch.cuda.is_available())
canvas_width, canvas_height = 510, 510
# https://www.flaticon.com/free-icon/black-plane_61212#term=airplane&page=1&position=8
shapes = pydiffvg.from_svg_path('M510,255c0-20.4-17.85-38.25-38.25-38.25H331.5L204,12.75h-51l63.75,204H76.5l-38.25-51H0L25.5,255L0,344.25h38.25l38.25-51h140.25l-63.75,204h51l127.5-204h140.25C492.15,293.25,510,275.4,510,255z')
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(510, # width
510, # 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_path/target.png', gamma=2.2)
target = img.clone()
# Move the path to produce initial guess
# normalize points for easier learning rate
noise = torch.FloatTensor(shapes[0].points.shape).uniform_(0.0, 1.0)
points_n = (shapes[0].points.clone() + (noise * 60 - 30)) / 510.0
points_n.requires_grad = True
color = torch.tensor([0.3, 0.2, 0.5, 1.0], requires_grad=True)
shapes[0].points = points_n * 510
path_group.fill_color = color
scene_args = pydiffvg.RenderFunction.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
img = render(510, # width
510, # height
2, # num_samples_x
2, # num_samples_y
1, # seed
None, # background_image
*scene_args)
pydiffvg.imwrite(img.cpu(), 'results/single_path/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.
shapes[0].points = points_n * 510
path_group.fill_color = color
scene_args = pydiffvg.RenderFunction.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
img = render(510, # width
510, # 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_path/iter_{:02}.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:', shapes[0].points)
print('color:', path_group.fill_color)
# Render the final result.
shapes[0].points = points_n * 510
path_group.fill_color = color
scene_args = pydiffvg.RenderFunction.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups)
img = render(510, # width
510, # height
2, # num_samples_x
2, # num_samples_y
102, # seed
None, # background_image
*scene_args)
# Save the images and differences.
pydiffvg.imwrite(img.cpu(), 'results/single_path/final.png')
# Convert the intermediate renderings to a video.
from subprocess import call
call(["ffmpeg", "-framerate", "20", "-i",
"results/single_path/iter_%02d.png", "-vb", "20M",
"results/single_path/out.mp4"])
|