Spaces:
Running
on
Zero
Running
on
Zero
| # | |
| # Copyright (C) 2023, Inria | |
| # GRAPHDECO research group, https://team.inria.fr/graphdeco | |
| # All rights reserved. | |
| # | |
| # This software is free for non-commercial, research and evaluation use | |
| # under the terms of the LICENSE.md file. | |
| # | |
| # For inquiries contact george.drettakis@inria.fr | |
| # | |
| import torch | |
| import math | |
| import numpy as np | |
| from typing import NamedTuple | |
| class BasicPointCloud(NamedTuple): | |
| points : np.array | |
| colors : np.array | |
| normals : np.array | |
| def geom_transform_points(points, transf_matrix): | |
| P, _ = points.shape | |
| ones = torch.ones(P, 1, dtype=points.dtype, device=points.device) | |
| points_hom = torch.cat([points, ones], dim=1) | |
| points_out = torch.matmul(points_hom, transf_matrix.unsqueeze(0)) | |
| denom = points_out[..., 3:] + 0.0000001 | |
| return (points_out[..., :3] / denom).squeeze(dim=0) | |
| def getWorld2View(R, t): | |
| Rt = np.zeros((4, 4)) | |
| Rt[:3, :3] = R.transpose() | |
| Rt[:3, 3] = t | |
| Rt[3, 3] = 1.0 | |
| return np.float32(Rt) | |
| def getWorld2View2(R, t, translate=np.array([.0, .0, .0]), scale=1.0): | |
| Rt = np.zeros((4, 4)) | |
| Rt[:3, :3] = R.transpose() | |
| Rt[:3, 3] = t | |
| Rt[3, 3] = 1.0 | |
| C2W = np.linalg.inv(Rt) | |
| cam_center = C2W[:3, 3] | |
| cam_center = (cam_center + translate) * scale | |
| C2W[:3, 3] = cam_center | |
| Rt = np.linalg.inv(C2W) | |
| return np.float32(Rt) | |
| def getView2World(R, t, translate=np.array([.0, .0, .0]), scale=1.0): | |
| Rt = np.zeros((4, 4)) | |
| Rt[:3, :3] = R.transpose() | |
| Rt[:3, 3] = t | |
| Rt[3, 3] = 1.0 | |
| C2W = np.linalg.inv(Rt) | |
| cam_center = C2W[:3, 3] | |
| cam_center = (cam_center + translate) * scale | |
| C2W[:3, 3] = cam_center | |
| Rt = C2W | |
| return np.float32(Rt) | |
| def getProjectionMatrix(znear, zfar, fovX, fovY): | |
| tanHalfFovY = math.tan((fovY / 2)) | |
| tanHalfFovX = math.tan((fovX / 2)) | |
| top = tanHalfFovY * znear | |
| bottom = -top | |
| right = tanHalfFovX * znear | |
| left = -right | |
| P = torch.zeros(4, 4) | |
| z_sign = 1.0 | |
| P[0, 0] = 2.0 * znear / (right - left) | |
| P[1, 1] = 2.0 * znear / (top - bottom) | |
| P[0, 2] = (right + left) / (right - left) | |
| P[1, 2] = (top + bottom) / (top - bottom) | |
| P[3, 2] = z_sign | |
| P[2, 2] = z_sign * zfar / (zfar - znear) | |
| P[2, 3] = -(zfar * znear) / (zfar - znear) | |
| return P | |
| def fov2focal(fov, pixels): | |
| return pixels / (2 * math.tan(fov / 2)) | |
| def focal2fov(focal, pixels): | |
| return 2*math.atan(pixels/(2*focal)) |