198 lines
7.8 KiB
Python
198 lines
7.8 KiB
Python
# python finite_difference_comp.py imgs/tiger.svg
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# python finite_difference_comp.py --use_prefiltering True imgs/tiger.svg
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# python finite_difference_comp.py imgs/boston.svg
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# python finite_difference_comp.py --use_prefiltering True imgs/boston.svg
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# python finite_difference_comp.py imgs/contour.svg
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# python finite_difference_comp.py --use_prefiltering True imgs/contour.svg
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# python finite_difference_comp.py --size_scale 0.5 --clamping_factor 0.05 imgs/hawaii.svg
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# python finite_difference_comp.py --size_scale 0.5 --clamping_factor 0.05 --use_prefiltering True imgs/hawaii.svg
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# python finite_difference_comp.py imgs/mcseem2.svg
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# python finite_difference_comp.py --use_prefiltering True imgs/mcseem2.svg
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# python finite_difference_comp.py imgs/reschart.svg
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# python finite_difference_comp.py --use_prefiltering True imgs/reschart.svg
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import pydiffvg
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import diffvg
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from matplotlib import cm
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import matplotlib.pyplot as plt
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import argparse
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import torch
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pydiffvg.set_print_timing(True)
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#pydiffvg.set_use_gpu(False)
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def normalize(x, min_, max_):
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range = max(abs(min_), abs(max_))
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return (x + range) / (2 * range)
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def main(args):
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canvas_width, canvas_height, shapes, shape_groups = \
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pydiffvg.svg_to_scene(args.svg_file)
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w = int(canvas_width * args.size_scale)
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h = int(canvas_height * args.size_scale)
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print(w, h)
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curve_counts = 0
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for s in shapes:
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if isinstance(s, pydiffvg.Circle):
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curve_counts += 1
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elif isinstance(s, pydiffvg.Ellipse):
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curve_counts += 1
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elif isinstance(s, pydiffvg.Path):
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curve_counts += len(s.num_control_points)
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elif isinstance(s, pydiffvg.Polygon):
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curve_counts += len(s.points) - 1
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if s.is_closed:
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curve_counts += 1
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elif isinstance(s, pydiffvg.Rect):
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curve_counts += 1
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print('curve_counts:', curve_counts)
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pfilter = pydiffvg.PixelFilter(type = diffvg.FilterType.box,
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radius = torch.tensor(0.5))
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use_prefiltering = args.use_prefiltering
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print('use_prefiltering:', use_prefiltering)
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scene_args = pydiffvg.RenderFunction.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups,
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filter = pfilter,
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use_prefiltering = use_prefiltering)
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num_samples_x = args.num_spp
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num_samples_y = args.num_spp
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if (use_prefiltering):
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num_samples_x = 1
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num_samples_y = 1
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render = pydiffvg.RenderFunction.apply
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img = render(w, # width
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h, # height
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num_samples_x, # num_samples_x
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num_samples_y, # num_samples_y
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0, # seed
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None, # background_image
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*scene_args)
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pydiffvg.imwrite(img.cpu(), 'results/finite_difference_comp/img.png', gamma=1.0)
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epsilon = 0.1
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def perturb_scene(axis, epsilon):
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for s in shapes:
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if isinstance(s, pydiffvg.Circle):
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s.center[axis] += epsilon
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elif isinstance(s, pydiffvg.Ellipse):
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s.center[axis] += epsilon
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elif isinstance(s, pydiffvg.Path):
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s.points[:, axis] += epsilon
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elif isinstance(s, pydiffvg.Polygon):
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s.points[:, axis] += epsilon
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elif isinstance(s, pydiffvg.Rect):
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s.p_min[axis] += epsilon
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s.p_max[axis] += epsilon
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for s in shape_groups:
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if isinstance(s.fill_color, pydiffvg.LinearGradient):
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s.fill_color.begin[axis] += epsilon
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s.fill_color.end[axis] += epsilon
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perturb_scene(0, epsilon)
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scene_args = pydiffvg.RenderFunction.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups,
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filter = pfilter,
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use_prefiltering = use_prefiltering)
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render = pydiffvg.RenderFunction.apply
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img0 = render(w, # width
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h, # height
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num_samples_x, # num_samples_x
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num_samples_y, # num_samples_y
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0, # seed
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None, # background_image
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*scene_args)
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perturb_scene(0, -2 * epsilon)
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scene_args = pydiffvg.RenderFunction.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups,
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filter = pfilter,
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use_prefiltering = use_prefiltering)
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img1 = render(w, # width
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h, # height
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num_samples_x, # num_samples_x
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num_samples_y, # num_samples_y
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0, # seed
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None, # background_image
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*scene_args)
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x_diff = (img0 - img1) / (2 * epsilon)
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x_diff = x_diff.sum(axis = 2)
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x_diff_max = x_diff.max() * args.clamping_factor
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x_diff_min = x_diff.min() * args.clamping_factor
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print(x_diff.max())
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print(x_diff.min())
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x_diff = cm.viridis(normalize(x_diff, x_diff_min, x_diff_max).cpu().numpy())
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pydiffvg.imwrite(x_diff, 'results/finite_difference_comp/finite_x_diff.png', gamma=1.0)
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perturb_scene(0, epsilon)
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perturb_scene(1, epsilon)
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scene_args = pydiffvg.RenderFunction.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups,
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filter = pfilter,
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use_prefiltering = use_prefiltering)
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render = pydiffvg.RenderFunction.apply
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img0 = render(w, # width
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h, # height
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num_samples_x, # num_samples_x
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num_samples_y, # num_samples_y
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0, # seed
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None, # background_image
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*scene_args)
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perturb_scene(1, -2 * epsilon)
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scene_args = pydiffvg.RenderFunction.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups,
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filter = pfilter,
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use_prefiltering = use_prefiltering)
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img1 = render(w, # width
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h, # height
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num_samples_x, # num_samples_x
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num_samples_y, # num_samples_y
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0, # seed
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None, # background_image
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*scene_args)
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y_diff = (img0 - img1) / (2 * epsilon)
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y_diff = y_diff.sum(axis = 2)
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y_diff_max = y_diff.max() * args.clamping_factor
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y_diff_min = y_diff.min() * args.clamping_factor
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y_diff = cm.viridis(normalize(y_diff, y_diff_min, y_diff_max).cpu().numpy())
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pydiffvg.imwrite(y_diff, 'results/finite_difference_comp/finite_y_diff.png', gamma=1.0)
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perturb_scene(1, epsilon)
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scene_args = pydiffvg.RenderFunction.serialize_scene(\
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canvas_width, canvas_height, shapes, shape_groups,
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filter = pfilter,
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use_prefiltering = use_prefiltering)
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render_grad = pydiffvg.RenderFunction.render_grad
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img_grad = render_grad(torch.ones(h, w, 4, device = pydiffvg.get_device()),
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w, # width
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h, # height
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num_samples_x, # num_samples_x
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num_samples_y, # num_samples_y
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0, # seed
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None, # background_image
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*scene_args)
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print(img_grad[:, :, 0].max())
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print(img_grad[:, :, 0].min())
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x_diff = cm.viridis(normalize(img_grad[:, :, 0], x_diff_min, x_diff_max).cpu().numpy())
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y_diff = cm.viridis(normalize(img_grad[:, :, 1], y_diff_min, y_diff_max).cpu().numpy())
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pydiffvg.imwrite(x_diff, 'results/finite_difference_comp/ours_x_diff.png', gamma=1.0)
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pydiffvg.imwrite(y_diff, 'results/finite_difference_comp/ours_y_diff.png', gamma=1.0)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("svg_file", help="source SVG path")
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parser.add_argument("--size_scale", type=float, default=1.0)
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parser.add_argument("--clamping_factor", type=float, default=0.1)
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parser.add_argument("--num_spp", type=int, default=4)
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parser.add_argument("--use_prefiltering", type=bool, default=False)
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args = parser.parse_args()
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main(args)
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