173 lines
6.6 KiB
Python
173 lines
6.6 KiB
Python
import pydiffvg
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import torch
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import skimage
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import numpy as np
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# Use GPU if available
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pydiffvg.set_use_gpu(torch.cuda.is_available())
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canvas_width, canvas_height = 256, 256
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num_control_points = torch.tensor([2])
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# points = torch.tensor([[120.0, 30.0], # base
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# [150.0, 60.0], # control point
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# [ 90.0, 198.0], # control point
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# [ 60.0, 218.0], # base
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# [ 90.0, 180.0], # control point
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# [200.0, 65.0], # control point
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# [210.0, 98.0], # base
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# [220.0, 70.0], # control point
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# [130.0, 55.0]]) # control point
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points = torch.tensor([[ 20.0, 128.0], # base
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[ 50.0, 128.0], # control point
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[170.0, 128.0], # control point
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[200.0, 128.0]]) # base
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path = pydiffvg.Path(num_control_points = num_control_points,
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points = points,
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is_closed = False,
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stroke_width = torch.tensor(10.0))
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shapes = [path]
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path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]),
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fill_color = None,
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stroke_color = torch.tensor([0.3, 0.6, 0.3, 1.0]))
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shape_groups = [path_group]
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scene_args = pydiffvg.RenderFunction.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups,
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output_type = pydiffvg.OutputType.sdf)
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render = pydiffvg.RenderFunction.apply
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img = render(256, # width
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256, # height
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1, # num_samples_x
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1, # num_samples_y
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0, # seed
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*scene_args)
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path.points[:, 1] += 1e-3
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scene_args = pydiffvg.RenderFunction.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups,
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output_type = pydiffvg.OutputType.sdf)
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img2 = render(256, # width
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256, # height
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1, # num_samples_x
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1, # num_samples_y
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0, # seed
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*scene_args)
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# diff = img2 - img
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# diff = diff[:, :, 0] / 1e-3
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# import matplotlib.pyplot as plt
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# plt.imshow(diff)
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# plt.show()
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# # The output image is in linear RGB space. Do Gamma correction before saving the image.
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# pydiffvg.imwrite(img.cpu(), 'results/single_curve_sdf/target.png', gamma=1.0)
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# target = img.clone()
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render_grad = pydiffvg.RenderFunction.render_grad
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img = render_grad(torch.ones(256, 256, 1), # grad_img
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256, # width
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256, # height
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1, # num_samples_x
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1, # num_samples_y
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0, # seed
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*scene_args)
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img = img[:, :, 0]
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import matplotlib.pyplot as plt
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plt.imshow(img)
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plt.show()
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# # Move the path to produce initial guess
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# # normalize points for easier learning rate
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# # points_n = torch.tensor([[100.0/256.0, 40.0/256.0], # base
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# # [155.0/256.0, 65.0/256.0], # control point
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# # [100.0/256.0, 180.0/256.0], # control point
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# # [ 65.0/256.0, 238.0/256.0], # base
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# # [100.0/256.0, 200.0/256.0], # control point
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# # [170.0/256.0, 55.0/256.0], # control point
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# # [220.0/256.0, 100.0/256.0], # base
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# # [210.0/256.0, 80.0/256.0], # control point
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# # [140.0/256.0, 60.0/256.0]], # control point
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# # requires_grad = True)
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# points_n = torch.tensor([[118.4274/256.0, 32.0159/256.0],
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# [174.9657/256.0, 28.1877/256.0],
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# [ 87.6629/256.0, 175.1049/256.0],
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# [ 57.8093/256.0, 232.8987/256.0],
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# [ 80.1829/256.0, 165.4280/256.0],
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# [197.3640/256.0, 83.4058/256.0],
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# [209.3676/256.0, 97.9176/256.0],
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# [219.1048/256.0, 72.0000/256.0],
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# [143.1226/256.0, 57.0636/256.0]],
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# requires_grad = True)
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# color = torch.tensor([0.3, 0.2, 0.5, 1.0], requires_grad=True)
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# path.points = points_n * 256
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# path_group.fill_color = color
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# scene_args = pydiffvg.RenderFunction.serialize_scene(\
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# canvas_width, canvas_height, shapes, shape_groups,
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# output_type = pydiffvg.OutputType.sdf)
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# img = render(256, # width
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# 256, # height
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# 1, # num_samples_x
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# 1, # num_samples_y
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# 1, # seed
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# *scene_args)
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# img /= 256.0
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# pydiffvg.imwrite(img.cpu(), 'results/single_curve_sdf/init.png', gamma=1.0)
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# # Optimize
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# optimizer = torch.optim.Adam([points_n, color], lr=1e-3)
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# # Run 100 Adam iterations.
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# for t in range(2):
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# print('iteration:', t)
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# optimizer.zero_grad()
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# # Forward pass: render the image.
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# path.points = points_n * 256
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# path_group.fill_color = color
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# scene_args = pydiffvg.RenderFunction.serialize_scene(\
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# canvas_width, canvas_height, shapes, shape_groups,
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# output_type = pydiffvg.OutputType.sdf)
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# img = render(256, # width
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# 256, # height
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# 1, # num_samples_x
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# 1, # num_samples_y
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# t+1, # seed
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# *scene_args)
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# img /= 256.0
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# # Save the intermediate render.
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# pydiffvg.imwrite(img.cpu(), 'results/single_curve_sdf/iter_{}.png'.format(t), gamma=1.0)
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# # Compute the loss function. Here it is L2.
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# loss = (img - target).pow(2).sum()
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# print('loss:', loss.item())
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# # Backpropagate the gradients.
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# loss.backward()
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# # Print the gradients
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# print('points_n.grad:', points_n.grad)
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# print('color.grad:', color.grad)
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# # Take a gradient descent step.
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# optimizer.step()
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# # Print the current params.
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# print('points:', path.points)
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# print('color:', path_group.fill_color)
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# exit()
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# # Render the final result.
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# scene_args = pydiffvg.RenderFunction.serialize_scene(\
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# canvas_width, canvas_height, shapes, shape_groups,
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# output_type = pydiffvg.OutputType.sdf)
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# img = render(256, # width
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# 256, # height
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# 1, # num_samples_x
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# 1, # num_samples_y
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# 102, # seed
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# *scene_args)
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# img /= 256.0
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# # Save the images and differences.
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# pydiffvg.imwrite(img.cpu(), 'results/single_curve_sdf/final.png', gamma=1.0)
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# # Convert the intermediate renderings to a video.
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# from subprocess import call
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# call(["ffmpeg", "-framerate", "24", "-i",
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# "results/single_curve_sdf/iter_%d.png", "-vb", "20M",
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# "results/single_curve_sdf/out.mp4"])
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