110 lines
4.1 KiB
Python
110 lines
4.1 KiB
Python
import pydiffvg
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import torch
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import skimage
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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 = 510, 510
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# https://www.flaticon.com/free-icon/black-plane_61212#term=airplane&page=1&position=8
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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')
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path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]),
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fill_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(510, # width
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510, # 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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None, # background_image
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*scene_args)
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img = img / 510 # Normalize SDF to [0, 1]
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pydiffvg.imwrite(img.cpu(), 'results/single_path_sdf/target.png', gamma=1.0)
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target = img.clone()
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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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noise = torch.FloatTensor(shapes[0].points.shape).uniform_(0.0, 1.0)
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points_n = (shapes[0].points.clone() + (noise * 60 - 30)) / 510.0
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points_n.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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shapes[0].points = points_n * 510
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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(510, # width
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510, # 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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None, # background_image
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*scene_args)
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img = img / 510 # Normalize SDF to [0, 1]
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pydiffvg.imwrite(img.cpu(), 'results/single_path_sdf/init.png', gamma=1.0)
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# Optimize
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optimizer = torch.optim.Adam([points_n, color], lr=1e-2)
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# Run 100 Adam iterations.
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for t in range(100):
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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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shapes[0].points = points_n * 510
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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(510, # width
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510, # 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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None, # background_image
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*scene_args)
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img = img / 510 # Normalize SDF to [0, 1]
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# Save the intermediate render.
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pydiffvg.imwrite(img.cpu(), 'results/single_path_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:', shapes[0].points)
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print('color:', path_group.fill_color)
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# Render the final result.
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shapes[0].points = points_n * 510
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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(510, # width
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510, # 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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None, # background_image
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*scene_args)
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# Save the images and differences.
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pydiffvg.imwrite(img.cpu(), 'results/single_path_sdf/final.png')
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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_path_sdf/iter_%d.png", "-vb", "20M",
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"results/single_path_sdf/out.mp4"])
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