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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 [email protected] | |
| # | |
| import json | |
| import numpy as np | |
| import torch | |
| from scene.cameras import Camera, MiniCam | |
| from utils.general import PILtoTorch | |
| from utils.graphics import fov2focal, focal2fov, getWorld2View, getProjectionMatrix | |
| WARNED = False | |
| def load_json(path, H, W): | |
| cams = [] | |
| with open(path) as json_file: | |
| contents = json.load(json_file) | |
| FoVx = contents["camera_angle_x"] | |
| FoVy = focal2fov(fov2focal(FoVx, W), H) | |
| zfar = 100.0 | |
| znear = 0.01 | |
| frames = contents["frames"] | |
| for idx, frame in enumerate(frames): | |
| # NeRF 'transform_matrix' is a camera-to-world transform | |
| c2w = np.array(frame["transform_matrix"]) | |
| # change from OpenGL/Blender camera axes (Y up, Z back) to COLMAP (Y down, Z forward) | |
| c2w[:3, 1:3] *= -1 | |
| if c2w.shape[0] == 3: | |
| one = np.zeros((1, 4)) | |
| one[0, -1] = 1 | |
| c2w = np.concatenate((c2w, one), axis=0) | |
| # get the world-to-camera transform and set R, T | |
| w2c = np.linalg.inv(c2w) | |
| R = np.transpose(w2c[:3, :3]) # R is stored transposed due to 'glm' in CUDA code | |
| T = w2c[:3, 3] | |
| w2c = torch.as_tensor(getWorld2View(R, T)).T.cuda() | |
| proj = getProjectionMatrix(znear, zfar, FoVx, FoVy).T.cuda() | |
| cams.append(MiniCam(W, H, FoVx, FoVy, znear, zfar, w2c, w2c @ proj)) | |
| return cams | |
| 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 | |