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| import os | |
| import json | |
| common_root = 'd:/Datasets' | |
| depth_root = 'kitti/kitti_depth/depth/data_depth_annotated' | |
| raw_root = 'kitti_raw/kitti_raw' | |
| #print(os.listdir(os.path.join(common_root, raw_root))) | |
| mid = 'proj_depth/groundtruth' | |
| mid_raw = 'data' | |
| test_file_dict = {} | |
| test_file_list = [] | |
| train_file_dict = {} | |
| with open('D:/Datasets/eigen_train.txt') as f: | |
| lines_train = f.readlines() | |
| cnt = 0 | |
| invalid_cnt = 0 | |
| if True: | |
| lines = lines_train | |
| for l in lines: | |
| l_ls = l.split(' ') | |
| scene = l_ls[0] | |
| date = scene.split('/')[0] | |
| scene_no_date = scene.split('/')[1] | |
| frame = l_ls[1] | |
| frame = frame.zfill(10) | |
| if 'l' in l_ls[2]: | |
| cam = 'image_02' | |
| P_str = 'P_rect_02' | |
| elif 'r' in l_ls[2]: | |
| cam = 'image_03' | |
| P_str = 'P_rect_03' | |
| else: | |
| raise NotImplementedError() | |
| depth_train = os.path.join(depth_root, 'train', scene_no_date, mid, cam , frame + '.png') | |
| depth_val = os.path.join(depth_root, 'val', scene_no_date, mid, cam, frame+'.png') | |
| rgb = os.path.join(raw_root, scene, cam, mid_raw, frame+'.png') | |
| with open(os.path.join(common_root, raw_root, date, 'calib_cam_to_cam.txt')) as c: | |
| lines_c = c.readlines() | |
| for l_c in lines_c: | |
| if P_str in l_c: | |
| k_str = l_c.split(':')[1:] | |
| k = k_str[0].split(' ') | |
| cam_in = [float(k[1]), float(k[6]), float(k[3]), float(k[7])] | |
| rgb_path = os.path.join(common_root, rgb) | |
| assert os.path.join(common_root, rgb_path) | |
| if os.path.exists(os.path.join(common_root, depth_train)): | |
| depth_path = os.path.join(common_root, depth_train) | |
| depth_rel = depth_train | |
| else: | |
| depth_path = os.path.join(common_root, depth_val) | |
| depth_rel = depth_val | |
| try: | |
| assert os.path.exists(depth_path) | |
| cnt += 1 | |
| except: | |
| invalid_cnt += 1 | |
| continue | |
| curr_file = [{'rgb':rgb.replace("\\", '/'), 'depth':depth_rel.replace("\\", '/'), 'cam_in':cam_in}] | |
| test_file_list = test_file_list + curr_file | |
| if ((cnt + invalid_cnt) % 1000 == 0): | |
| print(cnt + invalid_cnt) | |
| print(cnt, invalid_cnt) | |
| train_file_dict['files'] = test_file_list | |
| with open('eigen_train.json', 'w') as fj: | |
| json.dump(train_file_dict, fj) |