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Tzu-Mao Li
2020-09-03 22:30:30 -04:00
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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])
# 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
points = torch.tensor([[ 20.0, 128.0], # base
[ 50.0, 128.0], # control point
[170.0, 128.0], # control point
[200.0, 128.0]]) # base
path = pydiffvg.Path(num_control_points = num_control_points,
points = points,
is_closed = False,
stroke_width = torch.tensor(10.0))
shapes = [path]
path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]),
fill_color = None,
stroke_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,
output_type = pydiffvg.OutputType.sdf)
render = pydiffvg.RenderFunction.apply
img = render(256, # width
256, # height
1, # num_samples_x
1, # num_samples_y
0, # seed
*scene_args)
path.points[:, 1] += 1e-3
scene_args = pydiffvg.RenderFunction.serialize_scene(\
canvas_width, canvas_height, shapes, shape_groups,
output_type = pydiffvg.OutputType.sdf)
img2 = render(256, # width
256, # height
1, # num_samples_x
1, # num_samples_y
0, # seed
*scene_args)
# diff = img2 - img
# diff = diff[:, :, 0] / 1e-3
# import matplotlib.pyplot as plt
# plt.imshow(diff)
# plt.show()
# # The output image is in linear RGB space. Do Gamma correction before saving the image.
# pydiffvg.imwrite(img.cpu(), 'results/single_curve_sdf/target.png', gamma=1.0)
# target = img.clone()
render_grad = pydiffvg.RenderFunction.render_grad
img = render_grad(torch.ones(256, 256, 1), # grad_img
256, # width
256, # height
1, # num_samples_x
1, # num_samples_y
0, # seed
*scene_args)
img = img[:, :, 0]
import matplotlib.pyplot as plt
plt.imshow(img)
plt.show()
# # 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)
# points_n = torch.tensor([[118.4274/256.0, 32.0159/256.0],
# [174.9657/256.0, 28.1877/256.0],
# [ 87.6629/256.0, 175.1049/256.0],
# [ 57.8093/256.0, 232.8987/256.0],
# [ 80.1829/256.0, 165.4280/256.0],
# [197.3640/256.0, 83.4058/256.0],
# [209.3676/256.0, 97.9176/256.0],
# [219.1048/256.0, 72.0000/256.0],
# [143.1226/256.0, 57.0636/256.0]],
# 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,
# output_type = pydiffvg.OutputType.sdf)
# img = render(256, # width
# 256, # height
# 1, # num_samples_x
# 1, # num_samples_y
# 1, # seed
# *scene_args)
# img /= 256.0
# pydiffvg.imwrite(img.cpu(), 'results/single_curve_sdf/init.png', gamma=1.0)
# # Optimize
# optimizer = torch.optim.Adam([points_n, color], lr=1e-3)
# # Run 100 Adam iterations.
# for t in range(2):
# 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,
# output_type = pydiffvg.OutputType.sdf)
# img = render(256, # width
# 256, # height
# 1, # num_samples_x
# 1, # num_samples_y
# t+1, # seed
# *scene_args)
# img /= 256.0
# # Save the intermediate render.
# pydiffvg.imwrite(img.cpu(), 'results/single_curve_sdf/iter_{}.png'.format(t), gamma=1.0)
# # 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)
# exit()
# # Render the final result.
# scene_args = pydiffvg.RenderFunction.serialize_scene(\
# canvas_width, canvas_height, shapes, shape_groups,
# output_type = pydiffvg.OutputType.sdf)
# img = render(256, # width
# 256, # height
# 1, # num_samples_x
# 1, # num_samples_y
# 102, # seed
# *scene_args)
# img /= 256.0
# # Save the images and differences.
# pydiffvg.imwrite(img.cpu(), 'results/single_curve_sdf/final.png', gamma=1.0)
# # Convert the intermediate renderings to a video.
# from subprocess import call
# call(["ffmpeg", "-framerate", "24", "-i",
# "results/single_curve_sdf/iter_%d.png", "-vb", "20M",
# "results/single_curve_sdf/out.mp4"])