# 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