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