109 lines
3.8 KiB
Python
109 lines
3.8 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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ellipse = pydiffvg.Ellipse(radius = torch.tensor([60.0, 30.0]),
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center = torch.tensor([128.0, 128.0]))
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shapes = [ellipse]
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ellipse_group = pydiffvg.ShapeGroup(\
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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_to_canvas = torch.eye(3, 3))
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shape_groups = [ellipse_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_ellipse_transform/target.png', gamma=2.2)
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target = img.clone()
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# Affine transform the ellipse to produce initial guess
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color = torch.tensor([0.3, 0.2, 0.8, 1.0], requires_grad=True)
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affine = torch.zeros(2, 3)
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affine[0, 0] = 1.3
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affine[0, 1] = 0.2
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affine[0, 2] = 0.1
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affine[1, 0] = 0.2
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affine[1, 1] = 0.6
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affine[1, 2] = 0.3
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affine.requires_grad = True
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shape_to_canvas = torch.cat((affine, torch.tensor([[0.0, 0.0, 1.0]])), axis=0)
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ellipse_group.fill_color = color
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ellipse_group.shape_to_canvas = shape_to_canvas
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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_ellipse_transform/init.png', gamma=2.2)
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# Optimize for radius & center
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optimizer = torch.optim.Adam([color, affine], lr=1e-2)
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# Run 150 Adam iterations.
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for t in range(150):
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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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ellipse_group.fill_color = color
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ellipse_group.shape_to_canvas = torch.cat((affine, torch.tensor([[0.0, 0.0, 1.0]])), axis=0)
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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_ellipse_transform/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('color.grad:', color.grad)
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print('affine.grad:', affine.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('color:', ellipse_group.fill_color)
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print('affine:', affine)
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# Render the final result.
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ellipse_group.fill_color = color
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ellipse_group.shape_to_canvas = torch.cat((affine, torch.tensor([[0.0, 0.0, 1.0]])), axis=0)
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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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52, # 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_ellipse_transform/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_ellipse_transform/iter_%d.png", "-vb", "20M",
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"results/single_ellipse_transform/out.mp4"])
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