# This source is based on https://github.com/AbnerHqC/GaitSet/blob/master/pretreatment.py import argparse import logging import multiprocessing as mp import os import pickle from collections import defaultdict from functools import partial from pathlib import Path from typing import Tuple import cv2 import numpy as np from tqdm import tqdm import json import open3d as o3d def compare_pcd_rgb_timestamp(pcd_file,rgb_file): pcd_time = float(pcd_file.split('/')[-1].replace('.pcd','')) + 0.05 rgb_time = float(rgb_file.split('/')[-1].replace('.jpg','')[:10] + '.' + rgb_file.split('/')[-1].replace('.jpg','')[10:]) return pcd_time, rgb_time def imgs2pickle(img_groups: Tuple, output_path: Path, img_size: int = 64, verbose: bool = False, dataset='CASIAB') -> None: """Reads a group of images and saves the data in pickle format. Args: img_groups (Tuple): Tuple of (sid, seq, view) and list of image paths. output_path (Path): Output path. img_size (int, optional): Image resizing size. Defaults to 64. verbose (bool, optional): Display debug info. Defaults to False. """ sinfo = img_groups[0] img_paths = img_groups[1] # path with modality name to_pickle = [] cnt = 0 pcd_list = [] rgb_list = [] threshold = 0.020 # 20 ms for index, modality_files in enumerate(img_paths): data_files = modality_files[1] modality = modality_files[0] if modality == 'PCDs': data = [np.asarray(o3d.io.read_point_cloud(points).points) for points in data_files] pcd_list = data_files elif modality == 'RGB_raw': imgs = [cv2.imread(rgb) for rgb in data_files] rgb_list = data_files imgs = [cv2.cvtColor(img, cv2.COLOR_BGR2RGB) for img in imgs] HWs = [img.shape[:2] for img in imgs] # transpose to (C, H W) data = [cv2.resize(img, (img_size, img_size), interpolation=cv2.INTER_CUBIC) for img in imgs] imgs = [img.transpose(2, 0, 1) for img in imgs] data = np.asarray(data) HWs = np.asarray(HWs) elif modality == 'Sils_raw': sils = [cv2.imread(sil, cv2.IMREAD_GRAYSCALE) for sil in data_files] data = [cv2.resize(sil, (img_size, img_size), interpolation=cv2.INTER_CUBIC) for sil in sils] data = np.asarray(data) elif modality == 'Sils_aligned': sils = [cv2.imread(sil, cv2.IMREAD_GRAYSCALE) for sil in data_files] data = [cv2.resize(sil, (img_size, img_size), interpolation=cv2.INTER_CUBIC) for sil in sils] data = np.asarray(data) elif modality == 'Pose': data = [json.load(open(pose)) for pose in data_files] data = np.asarray(data) elif modality == 'PCDs_depths': imgs = [cv2.imread(rgb) for rgb in data_files] imgs = [cv2.cvtColor(img, cv2.COLOR_BGR2RGB) for img in imgs] data = [img.transpose(2, 0, 1) for img in imgs] data = np.asarray(data) elif modality == 'PCDs_sils': data = [cv2.imread(sil, cv2.IMREAD_GRAYSCALE) for sil in data_files] data = np.asarray(data) dst_path = os.path.join(output_path, *sinfo) os.makedirs(dst_path, exist_ok=True) if modality == 'RGB_raw': pkl_path = os.path.join(dst_path, f'{cnt:02d}-{sinfo[2]}-Camera-Ratios-HW.pkl') pickle.dump(HWs, open(pkl_path, 'wb')) cnt += 1 if 'PCDs' in modality: pkl_path = os.path.join(dst_path, f'{cnt:02d}-{sinfo[2]}-LiDAR-{modality}.pkl') pickle.dump(data, open(pkl_path, 'wb')) else: pkl_path = os.path.join(dst_path, f'{cnt:02d}-{sinfo[2]}-Camera-{modality}.pkl') pickle.dump(data, open(pkl_path, 'wb')) cnt += 1 pcd_indexs = [] rgb_indexs = [] # print(pcd_list) for pcd_index in range(len(pcd_list)): time_diff = 1 tmp = pcd_index, 0 for rgb_index in range(len(rgb_list)): pcd_t, rgb_t = compare_pcd_rgb_timestamp(pcd_list[pcd_index], rgb_list[rgb_index]) diff = abs(pcd_t - rgb_t) if diff < time_diff: tmp = pcd_index, rgb_index time_diff = diff if time_diff <= threshold: pcd_indexs.append(tmp[0]) rgb_indexs.append(tmp[1]) if len(set(pcd_indexs)) != len(pcd_indexs): print(img_groups[0], pcd_indexs, rgb_indexs, len(pcd_indexs) == len(pcd_indexs)) for index, modality_files in enumerate(img_paths): modality = modality_files[0] data_files = modality_files[1] data_files = [data_files[index] for index in pcd_indexs] if 'PCDs' in modality else [data_files[index] for index in rgb_indexs] if modality == 'PCDs': data = [np.asarray(o3d.io.read_point_cloud(points).points) for points in data_files] pcd_list = data_files elif modality == 'RGB_raw': imgs = [cv2.imread(rgb) for rgb in data_files] rgb_list = data_files imgs = [cv2.cvtColor(img, cv2.COLOR_BGR2RGB) for img in imgs] HWs = [img.shape[:2] for img in imgs] # transpose to (C, H W) data = [cv2.resize(img, (img_size, img_size), interpolation=cv2.INTER_CUBIC) for img in imgs] imgs = [img.transpose(2, 0, 1) for img in imgs] data = np.asarray(data) HWs = np.asarray(HWs) elif modality == 'Sils_raw': sils = [cv2.imread(sil, cv2.IMREAD_GRAYSCALE) for sil in data_files] data = [cv2.resize(sil, (img_size, img_size), interpolation=cv2.INTER_CUBIC) for sil in sils] data = np.asarray(data) elif modality == 'Sils_aligned': sils = [cv2.imread(sil, cv2.IMREAD_GRAYSCALE) for sil in data_files] data = [cv2.resize(sil, (img_size, img_size), interpolation=cv2.INTER_CUBIC) for sil in sils] data = np.asarray(data) elif modality == 'Pose': data = [json.load(open(pose)) for pose in data_files] data = np.asarray(data) elif modality == 'PCDs_depths': imgs = [cv2.imread(rgb) for rgb in data_files] imgs = [cv2.cvtColor(img, cv2.COLOR_BGR2RGB) for img in imgs] data = [img.transpose(2, 0, 1) for img in imgs] data = np.asarray(data) elif modality == 'PCDs_sils': data = [cv2.imread(sil, cv2.IMREAD_GRAYSCALE) for sil in data_files] data = np.asarray(data) dst_path = os.path.join(output_path, *sinfo) os.makedirs(dst_path, exist_ok=True) if modality == 'RGB_raw': pkl_path = os.path.join(dst_path, f'{cnt:02d}-sync-{sinfo[2]}-Camera-Ratios-HW.pkl') pickle.dump(HWs, open(pkl_path, 'wb')) cnt += 1 if 'PCDs' in modality: pkl_path = os.path.join(dst_path, f'{cnt:02d}-sync-{sinfo[2]}-LiDAR-{modality}.pkl') pickle.dump(data, open(pkl_path, 'wb')) else: pkl_path = os.path.join(dst_path, f'{cnt:02d}-sync-{sinfo[2]}-Camera-{modality}.pkl') pickle.dump(data, open(pkl_path, 'wb')) cnt += 1 def pretreat(input_path: Path, output_path: Path, img_size: int = 64, workers: int = 4, verbose: bool = False, dataset: str = 'CASIAB') -> None: """Reads a dataset and saves the data in pickle format. Args: input_path (Path): Dataset root path. output_path (Path): Output path. img_size (int, optional): Image resizing size. Defaults to 64. workers (int, optional): Number of thread workers. Defaults to 4. verbose (bool, optional): Display debug info. Defaults to False. """ img_groups = defaultdict(list) logging.info(f'Listing {input_path}') total_files = 0 for id_ in tqdm(sorted(os.listdir(input_path))): for type_ in os.listdir(os.path.join(input_path,id_)): for view_ in os.listdir(os.path.join(input_path,id_,type_)): for modality in sorted(os.listdir(os.path.join(input_path,id_,type_,view_))): modality_path = os.path.join(input_path,id_,type_,view_,modality) file_names = sorted(os.listdir(modality_path)) file_names = [os.path.join(modality_path, file_name) for file_name in file_names] img_groups[(id_, type_, view_)].append((modality, file_names)) total_files += 1 logging.info(f'Total files listed: {total_files}') progress = tqdm(total=len(img_groups), desc='Pretreating', unit='folder') with mp.Pool(workers) as pool: logging.info(f'Start pretreating {input_path}') for _ in pool.imap_unordered(partial(imgs2pickle, output_path=output_path, img_size=img_size, verbose=verbose, dataset=dataset), img_groups.items()): progress.update(1) logging.info('Done') if __name__ == '__main__': parser = argparse.ArgumentParser(description='OpenGait dataset pretreatment module.') parser.add_argument('-i', '--input_path', default='', type=str, help='Root path of raw dataset.') parser.add_argument('-o', '--output_path', default='', type=str, help='Output path of pickled dataset.') parser.add_argument('-l', '--log_file', default='./pretreatment.log', type=str, help='Log file path. Default: ./pretreatment.log') parser.add_argument('-n', '--n_workers', default=4, type=int, help='Number of thread workers. Default: 4') parser.add_argument('-r', '--img_size', default=64, type=int, help='Image resizing size. Default 64') parser.add_argument('-d', '--dataset', default='CASIAB', type=str, help='Dataset for pretreatment.') parser.add_argument('-v', '--verbose', default=False, action='store_true', help='Display debug info.') args = parser.parse_args() logging.basicConfig(level=logging.INFO, filename=args.log_file, filemode='w', format='[%(asctime)s - %(levelname)s]: %(message)s') if args.verbose: logging.getLogger().setLevel(logging.DEBUG) logging.info('Verbose mode is on.') for k, v in args.__dict__.items(): logging.debug(f'{k}: {v}') pretreat(input_path=Path(args.input_path), output_path=Path(args.output_path), img_size=args.img_size, workers=args.n_workers, verbose=args.verbose, dataset=args.dataset)