initial commit

apps/gaussian_blur.py

@@ -0,0 +1,93 @@
+"""
+"""
+import os
+import pydiffvg
+import torch as th
+import scipy.ndimage.filters as F
+
+
+def render(canvas_width, canvas_height, shapes, shape_groups):
+    _render = pydiffvg.RenderFunction.apply
+    scene_args = pydiffvg.RenderFunction.serialize_scene(\
+        canvas_width, canvas_height, shapes, shape_groups)
+    img = _render(canvas_width, # width
+                 canvas_height, # height
+                 2,   # num_samples_x
+                 2,   # num_samples_y
+                 0,   # seed
+                 None,
+                 *scene_args)
+    return img
+
+
+def main():
+    pydiffvg.set_device(th.device('cuda:1'))
+
+    # Load SVG
+    svg = os.path.join("imgs", "peppers.svg")
+    canvas_width, canvas_height, shapes, shape_groups = \
+        pydiffvg.svg_to_scene(svg)
+
+    # Save initial state
+    ref = render(canvas_width, canvas_height, shapes, shape_groups)
+    pydiffvg.imwrite(ref.cpu(), 'results/gaussian_blur/init.png', gamma=2.2)
+
+    target = F.gaussian_filter(ref.cpu().numpy(), [10, 10, 0])
+    target = th.from_numpy(target).to(ref.device)
+    pydiffvg.imwrite(target.cpu(), 'results/gaussian_blur/target.png', gamma=2.2)
+
+    # Collect variables to optimize
+    points_vars = []
+    width_vars = []
+    for path in shapes:
+        path.points.requires_grad = True
+        points_vars.append(path.points)
+        path.stroke_width.requires_grad = True
+        width_vars.append(path.stroke_width)
+    color_vars = []
+    for group in shape_groups:
+        # do not optimize alpha
+        group.fill_color[..., :3].requires_grad = True
+        color_vars.append(group.fill_color)
+
+    # Optimize
+    points_optim = th.optim.Adam(points_vars, lr=1.0)
+    width_optim = th.optim.Adam(width_vars, lr=1.0)
+    color_optim = th.optim.Adam(color_vars, lr=0.01)
+
+    for t in range(20):
+        print('\niteration:', t)
+        points_optim.zero_grad()
+        width_optim.zero_grad()
+        color_optim.zero_grad()
+        # Forward pass: render the image.
+        img = render(canvas_width, canvas_height, shapes, shape_groups)
+        # Save the intermediate render.
+        pydiffvg.imwrite(img.cpu(), 'results/gaussian_blur/iter_{}.png'.format(t), gamma=2.2)
+        loss = (img - target)[..., :3].pow(2).mean()
+
+        print('alpha:', img[..., 3].mean().item())
+        print('render loss:', loss.item())
+    
+        # Backpropagate the gradients.
+        loss.backward()
+    
+        # Take a gradient descent step.
+        points_optim.step()
+        width_optim.step()
+        color_optim.step()
+        for group in shape_groups:
+            group.fill_color.data.clamp_(0.0, 1.0)
+
+    # Final render
+    img = render(canvas_width, canvas_height, shapes, shape_groups)
+    pydiffvg.imwrite(img.cpu(), 'results/gaussian_blur/final.png', gamma=2.2)
+
+    # Convert the intermediate renderings to a video.
+    from subprocess import call
+    call(["ffmpeg", "-framerate", "24", "-i",
+        "results/gaussian_blur/iter_%d.png", "-vb", "20M",
+        "results/gaussian_blur/out.mp4"])
+
+if __name__ == "__main__":
+    main()