text stringlengths 1 93.6k |
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vec2 = vec2 / np.sqrt((vec2 ** 2).sum())
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return np.arccos(np.dot(vec1, vec2))
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def rotatevec(vec, theta):
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x = vec[0] * torch.cos(theta) - vec[1] * torch.sin(theta)
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y = vec[0] * torch.sin(theta) + vec[1] * torch.cos(theta)
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return torch.cat([x, y])
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def pts_linspace(pa, pb, pts=300):
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pa = pa.view(1, 2)
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pb = pb.view(1, 2)
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w = torch.arange(0, pts + 1, dtype=pa.dtype).view(-1, 1)
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return (pa * (pts - w) + pb * w) / pts
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def xyz2uv(xy, z=-1):
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c = torch.sqrt((xy ** 2).sum(1))
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u = torch.atan2(xy[:, 1], xy[:, 0]).view(-1, 1)
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v = torch.atan2(torch.zeros_like(c) + z, c).view(-1, 1)
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return torch.cat([u, v], dim=1)
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def uv2idx(uv, w, h):
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col = (uv[:, 0] / (2 * np.pi) + 0.5) * w - 0.5
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row = (uv[:, 1] / np.pi + 0.5) * h - 0.5
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return torch.cat([col.view(-1, 1), row.view(-1, 1)], dim=1)
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def wallidx(xy, w, h, z1, z2):
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col = (torch.atan2(xy[1], xy[0]) / (2 * np.pi) + 0.5) * w - 0.5
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c = torch.sqrt((xy ** 2).sum())
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row_s = (torch.atan2(torch.zeros_like(c) + z1, c) / np.pi + 0.5) * h - 0.5
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row_t = (torch.atan2(torch.zeros_like(c) + z2, c) / np.pi + 0.5) * h - 0.5
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pa = torch.cat([col.view(1), row_s.view(1)])
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pb = torch.cat([col.view(1), row_t.view(1)])
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return pts_linspace(pa, pb)
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def map_coordinates(input, coordinates):
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''' PyTorch version of scipy.ndimage.interpolation.map_coordinates
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input: (H, W)
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coordinates: (2, ...)
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'''
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h = input.shape[0]
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w = input.shape[1]
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def _coordinates_pad_wrap(h, w, coordinates):
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coordinates[0] = coordinates[0] % h
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coordinates[1] = coordinates[1] % w
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return coordinates
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co_floor = torch.floor(coordinates).long()
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co_ceil = torch.ceil(coordinates).long()
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d1 = (coordinates[1] - co_floor[1].float())
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d2 = (coordinates[0] - co_floor[0].float())
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co_floor = _coordinates_pad_wrap(h, w, co_floor)
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co_ceil = _coordinates_pad_wrap(h, w, co_ceil)
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f00 = input[co_floor[0], co_floor[1]]
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f10 = input[co_floor[0], co_ceil[1]]
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f01 = input[co_ceil[0], co_floor[1]]
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f11 = input[co_ceil[0], co_ceil[1]]
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fx1 = f00 + d1 * (f10 - f00)
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fx2 = f01 + d1 * (f11 - f01)
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return fx1 + d2 * (fx2 - fx1)
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def pc2cor_id(pc, pc_vec, pc_theta, pc_height):
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ps = torch.stack([
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(pc + pc_vec),
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(pc + rotatevec(pc_vec, pc_theta)),
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(pc - pc_vec),
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(pc + rotatevec(pc_vec, pc_theta - np.pi))
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])
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return torch.cat([
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uv2idx(xyz2uv(ps, z=-1), 1024, 512),
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uv2idx(xyz2uv(ps, z=pc_height), 1024, 512),
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], dim=0)
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def project2sphere_score(pc, pc_vec, pc_theta, pc_height, scoreedg, scorecor, i_step=None):
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# Sample corner loss
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corid = pc2cor_id(pc, pc_vec, pc_theta, pc_height)
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corid_coordinates = torch.stack([corid[:, 1], corid[:, 0]])
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loss_cor = -map_coordinates(scorecor, corid_coordinates).mean()
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# Sample boundary loss
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p1 = pc + pc_vec
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p2 = pc + rotatevec(pc_vec, pc_theta)
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p3 = pc - pc_vec
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p4 = pc + rotatevec(pc_vec, pc_theta - np.pi)
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segs = [
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pts_linspace(p1, p2),
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pts_linspace(p2, p3),
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pts_linspace(p3, p4),
|
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