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