99 lines
3.6 KiB
Python
Executable File
99 lines
3.6 KiB
Python
Executable File
# MIT License
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# Copyright (c) 2022 Intelligent Systems Lab Org
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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# File author: Shariq Farooq Bhat
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import numpy as np
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def get_intrinsics(H,W):
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"""
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Intrinsics for a pinhole camera model.
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Assume fov of 55 degrees and central principal point.
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"""
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f = 0.5 * W / np.tan(0.5 * 55 * np.pi / 180.0)
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cx = 0.5 * W
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cy = 0.5 * H
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return np.array([[f, 0, cx],
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[0, f, cy],
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[0, 0, 1]])
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def depth_to_points(depth, R=None, t=None):
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K = get_intrinsics(depth.shape[1], depth.shape[2])
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Kinv = np.linalg.inv(K)
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if R is None:
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R = np.eye(3)
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if t is None:
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t = np.zeros(3)
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# M converts from your coordinate to PyTorch3D's coordinate system
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M = np.eye(3)
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M[0, 0] = -1.0
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M[1, 1] = -1.0
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height, width = depth.shape[1:3]
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x = np.arange(width)
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y = np.arange(height)
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coord = np.stack(np.meshgrid(x, y), -1)
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coord = np.concatenate((coord, np.ones_like(coord)[:, :, [0]]), -1) # z=1
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coord = coord.astype(np.float32)
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# coord = torch.as_tensor(coord, dtype=torch.float32, device=device)
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coord = coord[None] # bs, h, w, 3
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D = depth[:, :, :, None, None]
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# print(D.shape, Kinv[None, None, None, ...].shape, coord[:, :, :, :, None].shape )
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pts3D_1 = D * Kinv[None, None, None, ...] @ coord[:, :, :, :, None]
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# pts3D_1 live in your coordinate system. Convert them to Py3D's
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pts3D_1 = M[None, None, None, ...] @ pts3D_1
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# from reference to targe tviewpoint
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pts3D_2 = R[None, None, None, ...] @ pts3D_1 + t[None, None, None, :, None]
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# pts3D_2 = pts3D_1
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# depth_2 = pts3D_2[:, :, :, 2, :] # b,1,h,w
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return pts3D_2[:, :, :, :3, 0][0]
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def create_triangles(h, w, mask=None):
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"""
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Reference: https://github.com/google-research/google-research/blob/e96197de06613f1b027d20328e06d69829fa5a89/infinite_nature/render_utils.py#L68
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Creates mesh triangle indices from a given pixel grid size.
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This function is not and need not be differentiable as triangle indices are
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fixed.
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Args:
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h: (int) denoting the height of the image.
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w: (int) denoting the width of the image.
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Returns:
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triangles: 2D numpy array of indices (int) with shape (2(W-1)(H-1) x 3)
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"""
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x, y = np.meshgrid(range(w - 1), range(h - 1))
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tl = y * w + x
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tr = y * w + x + 1
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bl = (y + 1) * w + x
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br = (y + 1) * w + x + 1
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triangles = np.array([tl, bl, tr, br, tr, bl])
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triangles = np.transpose(triangles, (1, 2, 0)).reshape(
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((w - 1) * (h - 1) * 2, 3))
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if mask is not None:
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mask = mask.reshape(-1)
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triangles = triangles[mask[triangles].all(1)]
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return triangles
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