133 lines
5.3 KiB
Python
133 lines
5.3 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 = 256, 256
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num_control_points = torch.tensor([2, 2, 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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path = pydiffvg.Path(num_control_points = num_control_points,
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points = points,
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is_closed = True,
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stroke_width = torch.tensor(5.0))
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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 = torch.tensor([0.3, 0.6, 0.3, 1.0]),
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stroke_color = torch.tensor([0.6, 0.3, 0.6, 0.8]))
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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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render = pydiffvg.RenderFunction.apply
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img = render(256, # width
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256, # height
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2, # num_samples_x
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2, # num_samples_y
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0, # seed
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*scene_args)
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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_outline/target.png', gamma=2.2)
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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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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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fill_color = torch.tensor([0.3, 0.2, 0.8, 1.0], requires_grad=True)
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stroke_color = torch.tensor([0.4, 0.7, 0.5, 0.5], requires_grad=True)
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stroke_width_n = torch.tensor(10.0 / 100.0, requires_grad=True)
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path.points = points_n * 256
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path.stroke_width = stroke_width_n * 100
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path_group.fill_color = fill_color
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path_group.stroke_color = stroke_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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img = render(256, # width
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256, # height
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2, # num_samples_x
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2, # num_samples_y
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1, # seed
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*scene_args)
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pydiffvg.imwrite(img.cpu(), 'results/single_curve_outline/init.png', gamma=2.2)
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# Optimize
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optimizer = torch.optim.Adam([points_n, fill_color, stroke_color, stroke_width_n], lr=1e-2)
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# Run 200 Adam iterations.
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for t in range(200):
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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.stroke_width = stroke_width_n * 100
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path_group.fill_color = fill_color
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path_group.stroke_color = stroke_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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img = render(256, # width
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256, # height
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2, # num_samples_x
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2, # num_samples_y
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t+1, # seed
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*scene_args)
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# Save the intermediate render.
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pydiffvg.imwrite(img.cpu(), 'results/single_curve_outline/iter_{}.png'.format(t), gamma=2.2)
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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('fill_color.grad:', fill_color.grad)
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print('stroke_color.grad:', stroke_color.grad)
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print('stroke_width.grad:', stroke_width_n.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('fill_color:', path_group.fill_color)
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print('stroke_color:', path_group.stroke_color)
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print('stroke_width:', path.stroke_width)
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# Render the final result.
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path.points = points_n * 256
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path.stroke_width = stroke_width_n * 100
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path_group.fill_color = fill_color
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path_group.stroke_color = stroke_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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img = render(256, # width
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256, # height
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2, # num_samples_x
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2, # num_samples_y
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202, # seed
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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_curve_outline/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_curve_outline/iter_%d.png", "-vb", "20M",
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"results/single_curve_outline/out.mp4"])
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