initial commit

apps/single_path_sdf.py

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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
+             *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
+             *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
+                 *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
+             *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"])