initial commit

2020-09-03 22:30:30 -04:00
commit 413a3e5cee
148 changed files with 138536 additions and 0 deletions
--- a/apps/shared_edge_compare.py
+++ b/apps/shared_edge_compare.py
@@ -0,0 +1,127 @@
+import pydiffvg
+import diffvg
+from matplotlib import cm
+import matplotlib.pyplot as plt
+import argparse
+import torch
+
+def normalize(x, min_, max_):
+    range = max(abs(min_), abs(max_))
+    return (x + range) / (2 * range)
+
+def main(args):
+    canvas_width, canvas_height, shapes, shape_groups = \
+        pydiffvg.svg_to_scene(args.svg_file)
+
+    w = int(canvas_width * args.size_scale)
+    h = int(canvas_height * args.size_scale)
+
+    pfilter = pydiffvg.PixelFilter(type = diffvg.FilterType.box,
+                                   radius = torch.tensor(0.5))
+
+    use_prefiltering = False
+    scene_args = pydiffvg.RenderFunction.serialize_scene(\
+        canvas_width, canvas_height, shapes, shape_groups,
+        filter = pfilter,
+        use_prefiltering = use_prefiltering)
+
+    num_samples_x = 16
+    num_samples_y = 16
+    render = pydiffvg.RenderFunction.apply
+    img = render(w, # width
+                 h, # height
+                 num_samples_x, # num_samples_x
+                 num_samples_y, # num_samples_y
+                 0, # seed
+                 None,
+                 *scene_args)
+    pydiffvg.imwrite(img.cpu(), 'results/finite_difference_comp/img.png', gamma=1.0)
+
+    epsilon = 0.1
+    def perturb_scene(axis, epsilon):
+        shapes[2].points[:, axis] += epsilon
+        # for s in shapes:
+        #     if isinstance(s, pydiffvg.Circle):
+        #         s.center[axis] += epsilon
+        #     elif isinstance(s, pydiffvg.Ellipse):
+        #         s.center[axis] += epsilon
+        #     elif isinstance(s, pydiffvg.Path):
+        #         s.points[:, axis] += epsilon
+        #     elif isinstance(s, pydiffvg.Polygon):
+        #         s.points[:, axis] += epsilon
+        #     elif isinstance(s, pydiffvg.Rect):
+        #         s.p_min[axis] += epsilon
+        #         s.p_max[axis] += epsilon
+        # for s in shape_groups:
+        #     if isinstance(s.fill_color, pydiffvg.LinearGradient):
+        #         s.fill_color.begin[axis] += epsilon
+        #         s.fill_color.end[axis] += epsilon
+
+    perturb_scene(0, epsilon)
+    scene_args = pydiffvg.RenderFunction.serialize_scene(\
+        canvas_width, canvas_height, shapes, shape_groups,
+        filter = pfilter,
+        use_prefiltering = use_prefiltering)
+    render = pydiffvg.RenderFunction.apply
+    img0 = render(w, # width
+                  h, # height
+                  num_samples_x,   # num_samples_x
+                  num_samples_y,   # num_samples_y
+                  0,   # seed
+                  None,
+                  *scene_args)
+
+    forward_diff = (img0 - img) / (epsilon)
+    forward_diff = forward_diff.sum(axis = 2)
+    x_diff_max = 1.5
+    x_diff_min = -1.5
+    print(forward_diff.max())
+    print(forward_diff.min())
+    forward_diff = cm.viridis(normalize(forward_diff, x_diff_min, x_diff_max).cpu().numpy())
+    pydiffvg.imwrite(forward_diff, 'results/finite_difference_comp/shared_edge_forward_diff.png', gamma=1.0)
+
+    perturb_scene(0, -2 * epsilon)
+    scene_args = pydiffvg.RenderFunction.serialize_scene(\
+        canvas_width, canvas_height, shapes, shape_groups,
+        filter = pfilter,
+        use_prefiltering = use_prefiltering)
+    img1 = render(w, # width
+                  h, # height
+                  num_samples_x,   # num_samples_x
+                  num_samples_y,   # num_samples_y
+                  0,   # seed
+                  None,
+                  *scene_args)
+    backward_diff = (img - img1) / (epsilon)
+    backward_diff = backward_diff.sum(axis = 2)
+    print(backward_diff.max())
+    print(backward_diff.min())
+    backward_diff = cm.viridis(normalize(backward_diff, x_diff_min, x_diff_max).cpu().numpy())
+    pydiffvg.imwrite(backward_diff, 'results/finite_difference_comp/shared_edge_backward_diff.png', gamma=1.0)
+    perturb_scene(0, epsilon)
+
+    num_samples_x = 4
+    num_samples_y = 4
+    scene_args = pydiffvg.RenderFunction.serialize_scene(\
+        canvas_width, canvas_height, shapes, shape_groups,
+        filter = pfilter,
+        use_prefiltering = use_prefiltering)
+    render_grad = pydiffvg.RenderFunction.render_grad
+    img_grad = render_grad(torch.ones(h, w, 4),
+                           w, # width
+                           h, # height
+                           num_samples_x, # num_samples_x
+                           num_samples_y, # num_samples_y
+                           0, # seed
+                           *scene_args)
+    print(img_grad[:, :, 0].max())
+    print(img_grad[:, :, 0].min())
+    x_diff = cm.viridis(normalize(img_grad[:, :, 0], x_diff_min, x_diff_max).cpu().numpy())
+    pydiffvg.imwrite(x_diff, 'results/finite_difference_comp/ours_x_diff.png', gamma=1.0)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("svg_file", help="source SVG path")
+    parser.add_argument("--size_scale", type=float, default=1.0)
+    args = parser.parse_args()
+    main(args)