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| import pydiffvg | |
| import torch | |
| import skimage | |
| # Use GPU if available | |
| pydiffvg.set_use_gpu(torch.cuda.is_available()) | |
| canvas_width, canvas_height = 510, 510 | |
| # https://www.flaticon.com/free-icon/black-plane_61212#term=airplane&page=1&position=8 | |
| 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') | |
| path_group = pydiffvg.ShapeGroup(shape_ids = torch.tensor([0]), | |
| fill_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(510, # width | |
| 510, # height | |
| 1, # num_samples_x | |
| 1, # num_samples_y | |
| 0, # seed | |
| None, # background_image | |
| *scene_args) | |
| img = img / 510 # Normalize SDF to [0, 1] | |
| pydiffvg.imwrite(img.cpu(), 'results/single_path_sdf/target.png', gamma=1.0) | |
| target = img.clone() | |
| # Move the path to produce initial guess | |
| # normalize points for easier learning rate | |
| noise = torch.FloatTensor(shapes[0].points.shape).uniform_(0.0, 1.0) | |
| points_n = (shapes[0].points.clone() + (noise * 60 - 30)) / 510.0 | |
| points_n.requires_grad = True | |
| color = torch.tensor([0.3, 0.2, 0.5, 1.0], requires_grad=True) | |
| shapes[0].points = points_n * 510 | |
| 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(510, # width | |
| 510, # height | |
| 1, # num_samples_x | |
| 1, # num_samples_y | |
| 1, # seed | |
| None, # background_image | |
| *scene_args) | |
| img = img / 510 # Normalize SDF to [0, 1] | |
| pydiffvg.imwrite(img.cpu(), 'results/single_path_sdf/init.png', gamma=1.0) | |
| # Optimize | |
| optimizer = torch.optim.Adam([points_n, color], lr=1e-2) | |
| # Run 100 Adam iterations. | |
| for t in range(100): | |
| print('iteration:', t) | |
| optimizer.zero_grad() | |
| # Forward pass: render the image. | |
| shapes[0].points = points_n * 510 | |
| 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(510, # width | |
| 510, # height | |
| 1, # num_samples_x | |
| 1, # num_samples_y | |
| t+1, # seed | |
| None, # background_image | |
| *scene_args) | |
| img = img / 510 # Normalize SDF to [0, 1] | |
| # Save the intermediate render. | |
| pydiffvg.imwrite(img.cpu(), 'results/single_path_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:', shapes[0].points) | |
| print('color:', path_group.fill_color) | |
| # Render the final result. | |
| shapes[0].points = points_n * 510 | |
| 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(510, # width | |
| 510, # height | |
| 1, # num_samples_x | |
| 1, # num_samples_y | |
| 102, # seed | |
| None, # background_image | |
| *scene_args) | |
| # Save the images and differences. | |
| pydiffvg.imwrite(img.cpu(), 'results/single_path_sdf/final.png') | |
| # Convert the intermediate renderings to a video. | |
| from subprocess import call | |
| call(["ffmpeg", "-framerate", "24", "-i", | |
| "results/single_path_sdf/iter_%d.png", "-vb", "20M", | |
| "results/single_path_sdf/out.mp4"]) | |