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Running
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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 [email protected] | |
| # | |
| from scene.cameras import Camera | |
| import numpy as np | |
| from utils.graphics_utils import fov2focal | |
| from PIL import Image | |
| import cv2 | |
| WARNED = False | |
| def loadCam(args, id, cam_info, resolution_scale, is_nerf_synthetic, is_test_dataset): | |
| image = Image.open(cam_info.image_path) | |
| if cam_info.depth_path != "": | |
| try: | |
| if is_nerf_synthetic: | |
| invdepthmap = cv2.imread(cam_info.depth_path, -1).astype(np.float32) / 512 | |
| else: | |
| invdepthmap = cv2.imread(cam_info.depth_path, -1).astype(np.float32) / float(2**16) | |
| except FileNotFoundError: | |
| print(f"Error: The depth file at path '{cam_info.depth_path}' was not found.") | |
| raise | |
| except IOError: | |
| print(f"Error: Unable to open the image file '{cam_info.depth_path}'. It may be corrupted or an unsupported format.") | |
| raise | |
| except Exception as e: | |
| print(f"An unexpected error occurred when trying to read depth at {cam_info.depth_path}: {e}") | |
| raise | |
| else: | |
| invdepthmap = None | |
| orig_w, orig_h = 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)) | |
| return Camera(resolution, colmap_id=cam_info.uid, R=cam_info.R, T=cam_info.T, | |
| FoVx=cam_info.FovX, FoVy=cam_info.FovY, depth_params=cam_info.depth_params, | |
| image=image, invdepthmap=invdepthmap, | |
| image_name=cam_info.image_name, uid=id, data_device=args.data_device, | |
| train_test_exp=args.train_test_exp, is_test_dataset=is_test_dataset, is_test_view=cam_info.is_test) | |
| def cameraList_from_camInfos(cam_infos, resolution_scale, args, is_nerf_synthetic, is_test_dataset): | |
| camera_list = [] | |
| for id, c in enumerate(cam_infos): | |
| camera_list.append(loadCam(args, id, c, resolution_scale, is_nerf_synthetic, is_test_dataset)) | |
| 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 |