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| # | |
| # 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 | |
| # | |
| from pathlib import Path | |
| from mediapy import read_video, write_video | |
| from scene.cameras import Camera | |
| import numpy as np | |
| from utils.general_utils import PILtoTorch | |
| from utils.graphics_utils import fov2focal | |
| WARNED = False | |
| def loadCam(args, id, cam_info, resolution_scale): | |
| orig_w, orig_h = cam_info.image.size | |
| if args.resolution in [1, 2, 4, 8]: | |
| resolution = round(orig_w / (resolution_scale * args.resolution)), round( | |
| orig_h / (resolution_scale * args.resolution) | |
| ) | |
| else: # should be a type that converts to float | |
| if args.resolution == -1: | |
| if orig_w > 1600: | |
| global WARNED | |
| if not WARNED: | |
| print( | |
| "[ INFO ] Encountered quite large input images (>1.6K pixels width), rescaling to 1.6K.\n " | |
| "If this is not desired, please explicitly specify '--resolution/-r' as 1" | |
| ) | |
| WARNED = True | |
| global_down = orig_w / 1600 | |
| else: | |
| global_down = 1 | |
| else: | |
| global_down = orig_w / args.resolution | |
| scale = float(global_down) * float(resolution_scale) | |
| resolution = (int(orig_w / scale), int(orig_h / scale)) | |
| resized_image_rgb = PILtoTorch(cam_info.image, resolution) | |
| gt_image = resized_image_rgb[:3, ...] | |
| loaded_mask = None | |
| if resized_image_rgb.shape[1] == 4: | |
| loaded_mask = resized_image_rgb[3:4, ...] | |
| return Camera( | |
| colmap_id=cam_info.uid, | |
| R=cam_info.R, | |
| T=cam_info.T, | |
| FoVx=cam_info.FovX, | |
| FoVy=cam_info.FovY, | |
| image=gt_image, | |
| gt_alpha_mask=loaded_mask, | |
| image_name=cam_info.image_name, | |
| uid=id, | |
| data_device=args.data_device, | |
| ) | |
| def cameraList_from_camInfos(cam_infos, resolution_scale, args): | |
| camera_list = [] | |
| for id, c in enumerate(cam_infos): | |
| camera_list.append(loadCam(args, id, c, resolution_scale)) | |
| return camera_list | |
| def camera_to_JSON(id, camera: Camera): | |
| Rt = np.zeros((4, 4)) | |
| Rt[:3, :3] = camera.R.transpose() | |
| Rt[:3, 3] = camera.T | |
| Rt[3, 3] = 1.0 | |
| W2C = np.linalg.inv(Rt) | |
| pos = W2C[:3, 3] | |
| rot = W2C[:3, :3] | |
| serializable_array_2d = [x.tolist() for x in rot] | |
| camera_entry = { | |
| "id": id, | |
| "img_name": camera.image_name, | |
| "width": camera.width, | |
| "height": camera.height, | |
| "position": pos.tolist(), | |
| "rotation": serializable_array_2d, | |
| "fy": fov2focal(camera.FovY, camera.height), | |
| "fx": fov2focal(camera.FovX, camera.width), | |
| } | |
| return camera_entry | |
| def get_c2w_from_up_and_look_at( | |
| up, | |
| look_at, | |
| pos, | |
| opengl=False, | |
| ): | |
| up = up / np.linalg.norm(up) | |
| z = look_at - pos | |
| z = z / np.linalg.norm(z) | |
| y = -up | |
| x = np.cross(y, z) | |
| x /= np.linalg.norm(x) | |
| y = np.cross(z, x) | |
| c2w = np.zeros([4, 4], dtype=np.float32) | |
| c2w[:3, 0] = x | |
| c2w[:3, 1] = y | |
| c2w[:3, 2] = z | |
| c2w[:3, 3] = pos | |
| c2w[3, 3] = 1.0 | |
| # opencv to opengl | |
| if opengl: | |
| c2w[..., 1:3] *= -1 | |
| return c2w | |
| def get_uniform_poses(num_frames, radius, elevation, opengl=False): | |
| T = num_frames | |
| azimuths = np.deg2rad(np.linspace(0, 360, T + 1)[:T]) | |
| elevations = np.full_like(azimuths, np.deg2rad(elevation)) | |
| cam_dists = np.full_like(azimuths, radius) | |
| campos = np.stack( | |
| [ | |
| cam_dists * np.cos(elevations) * np.cos(azimuths), | |
| cam_dists * np.cos(elevations) * np.sin(azimuths), | |
| cam_dists * np.sin(elevations), | |
| ], | |
| axis=-1, | |
| ) | |
| center = np.array([0, 0, 0], dtype=np.float32) | |
| up = np.array([0, 0, 1], dtype=np.float32) | |
| poses = [] | |
| for t in range(T): | |
| poses.append(get_c2w_from_up_and_look_at(up, center, campos[t], opengl=opengl)) | |
| return np.stack(poses, axis=0) | |