initial commit

apps/single_ellipse_transform.py

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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
+ellipse = pydiffvg.Ellipse(radius = torch.tensor([60.0, 30.0]),
+                           center = torch.tensor([128.0, 128.0]))
+shapes = [ellipse]
+ellipse_group = pydiffvg.ShapeGroup(\
+    shape_ids = torch.tensor([0]),
+    fill_color = torch.tensor([0.3, 0.6, 0.3, 1.0]),
+    shape_to_canvas = torch.eye(3, 3))
+shape_groups = [ellipse_group]
+scene_args = pydiffvg.RenderFunction.serialize_scene(\
+    canvas_width, canvas_height, shapes, shape_groups)
+
+render = pydiffvg.RenderFunction.apply
+img = render(256, # width
+             256, # height
+             2,   # num_samples_x
+             2,   # num_samples_y
+             0,   # seed
+             *scene_args)
+# The output image is in linear RGB space. Do Gamma correction before saving the image.
+pydiffvg.imwrite(img.cpu(), 'results/single_ellipse_transform/target.png', gamma=2.2)
+target = img.clone()
+
+# Affine transform the ellipse to produce initial guess
+color = torch.tensor([0.3, 0.2, 0.8, 1.0], requires_grad=True)
+affine = torch.zeros(2, 3)
+affine[0, 0] = 1.3
+affine[0, 1] = 0.2
+affine[0, 2] = 0.1
+affine[1, 0] = 0.2
+affine[1, 1] = 0.6
+affine[1, 2] = 0.3
+affine.requires_grad = True
+shape_to_canvas = torch.cat((affine, torch.tensor([[0.0, 0.0, 1.0]])), axis=0)
+ellipse_group.fill_color = color
+ellipse_group.shape_to_canvas = shape_to_canvas
+scene_args = pydiffvg.RenderFunction.serialize_scene(\
+    canvas_width, canvas_height, shapes, shape_groups)
+img = render(256, # width
+             256, # height
+             2,   # num_samples_x
+             2,   # num_samples_y
+             1,   # seed
+             *scene_args)
+pydiffvg.imwrite(img.cpu(), 'results/single_ellipse_transform/init.png', gamma=2.2)
+
+# Optimize for radius & center
+optimizer = torch.optim.Adam([color, affine], lr=1e-2)
+# Run 150 Adam iterations.
+for t in range(150):
+    print('iteration:', t)
+    optimizer.zero_grad()
+    # Forward pass: render the image.
+    ellipse_group.fill_color = color
+    ellipse_group.shape_to_canvas = torch.cat((affine, torch.tensor([[0.0, 0.0, 1.0]])), axis=0)
+    scene_args = pydiffvg.RenderFunction.serialize_scene(\
+        canvas_width, canvas_height, shapes, shape_groups)
+    img = render(256,   # width
+                 256,   # height
+                 2,     # num_samples_x
+                 2,     # num_samples_y
+                 t+1,   # seed
+                 *scene_args)
+    # Save the intermediate render.
+    pydiffvg.imwrite(img.cpu(), 'results/single_ellipse_transform/iter_{}.png'.format(t), gamma=2.2)
+    # 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('color.grad:', color.grad)
+    print('affine.grad:', affine.grad)
+
+    # Take a gradient descent step.
+    optimizer.step()
+    # Print the current params.
+    print('color:', ellipse_group.fill_color)
+    print('affine:', affine)
+
+# Render the final result.
+ellipse_group.fill_color = color
+ellipse_group.shape_to_canvas = torch.cat((affine, torch.tensor([[0.0, 0.0, 1.0]])), axis=0)
+scene_args = pydiffvg.RenderFunction.serialize_scene(\
+    canvas_width, canvas_height, shapes, shape_groups)
+img = render(256,   # width
+             256,   # height
+             2,     # num_samples_x
+             2,     # num_samples_y
+             52,    # seed
+             *scene_args)
+# Save the images and differences.
+pydiffvg.imwrite(img.cpu(), 'results/single_ellipse_transform/final.png')
+
+# Convert the intermediate renderings to a video.
+from subprocess import call
+call(["ffmpeg", "-framerate", "24", "-i",
+    "results/single_ellipse_transform/iter_%d.png", "-vb", "20M",
+    "results/single_ellipse_transform/out.mp4"])