# encoding: utf-8 """ @author: liaoxingyu @contact: liaoxingyu2@jd.com """ import json import os.path as osp from fast_reid.fastreid.data.datasets import DATASET_REGISTRY from fast_reid.fastreid.utils.file_io import PathManager from .bases import ImageDataset @DATASET_REGISTRY.register() class CUHK03(ImageDataset): """CUHK03. Reference: Li et al. DeepReID: Deep Filter Pairing Neural Network for Person Re-identification. CVPR 2014. URL: ``_ Dataset statistics: - identities: 1360. - images: 13164. - cameras: 6. - splits: 20 (classic). """ dataset_dir = 'cuhk03' dataset_url = None dataset_name = "cuhk03" def __init__(self, root='datasets', split_id=0, cuhk03_labeled=True, cuhk03_classic_split=False, **kwargs): self.root = root self.dataset_dir = osp.join(self.root, self.dataset_dir) self.data_dir = osp.join(self.dataset_dir, 'cuhk03_release') self.raw_mat_path = osp.join(self.data_dir, 'cuhk-03.mat') self.imgs_detected_dir = osp.join(self.dataset_dir, 'images_detected') self.imgs_labeled_dir = osp.join(self.dataset_dir, 'images_labeled') self.split_classic_det_json_path = osp.join(self.dataset_dir, 'splits_classic_detected.json') self.split_classic_lab_json_path = osp.join(self.dataset_dir, 'splits_classic_labeled.json') self.split_new_det_json_path = osp.join(self.dataset_dir, 'splits_new_detected.json') self.split_new_lab_json_path = osp.join(self.dataset_dir, 'splits_new_labeled.json') self.split_new_det_mat_path = osp.join(self.dataset_dir, 'cuhk03_new_protocol_config_detected.mat') self.split_new_lab_mat_path = osp.join(self.dataset_dir, 'cuhk03_new_protocol_config_labeled.mat') required_files = [ self.dataset_dir, self.data_dir, self.raw_mat_path, self.split_new_det_mat_path, self.split_new_lab_mat_path ] self.check_before_run(required_files) self.preprocess_split() if cuhk03_labeled: split_path = self.split_classic_lab_json_path if cuhk03_classic_split else self.split_new_lab_json_path else: split_path = self.split_classic_det_json_path if cuhk03_classic_split else self.split_new_det_json_path with PathManager.open(split_path) as f: splits = json.load(f) assert split_id < len(splits), 'Condition split_id ({}) < len(splits) ({}) is false'.format(split_id, len(splits)) split = splits[split_id] train = split['train'] tmp_train = [] for img_path, pid, camid in train: new_pid = self.dataset_name + "_" + str(pid) new_camid = self.dataset_name + "_" + str(camid) tmp_train.append((img_path, new_pid, new_camid)) train = tmp_train del tmp_train query = split['query'] gallery = split['gallery'] super(CUHK03, self).__init__(train, query, gallery, **kwargs) def preprocess_split(self): # This function is a bit complex and ugly, what it does is # 1. extract data from cuhk-03.mat and save as png images # 2. create 20 classic splits (Li et al. CVPR'14) # 3. create new split (Zhong et al. CVPR'17) if osp.exists(self.imgs_labeled_dir) \ and osp.exists(self.imgs_detected_dir) \ and osp.exists(self.split_classic_det_json_path) \ and osp.exists(self.split_classic_lab_json_path) \ and osp.exists(self.split_new_det_json_path) \ and osp.exists(self.split_new_lab_json_path): return import h5py from imageio import imwrite from scipy import io PathManager.mkdirs(self.imgs_detected_dir) PathManager.mkdirs(self.imgs_labeled_dir) print('Extract image data from "{}" and save as png'.format(self.raw_mat_path)) mat = h5py.File(self.raw_mat_path, 'r') def _deref(ref): return mat[ref][:].T def _process_images(img_refs, campid, pid, save_dir): img_paths = [] # Note: some persons only have images for one view for imgid, img_ref in enumerate(img_refs): img = _deref(img_ref) if img.size == 0 or img.ndim < 3: continue # skip empty cell # images are saved with the following format, index-1 (ensure uniqueness) # campid: index of camera pair (1-5) # pid: index of person in 'campid'-th camera pair # viewid: index of view, {1, 2} # imgid: index of image, (1-10) viewid = 1 if imgid < 5 else 2 img_name = '{:01d}_{:03d}_{:01d}_{:02d}.png'.format(campid + 1, pid + 1, viewid, imgid + 1) img_path = osp.join(save_dir, img_name) if not osp.isfile(img_path): imwrite(img_path, img) img_paths.append(img_path) return img_paths def _extract_img(image_type): print('Processing {} images ...'.format(image_type)) meta_data = [] imgs_dir = self.imgs_detected_dir if image_type == 'detected' else self.imgs_labeled_dir for campid, camp_ref in enumerate(mat[image_type][0]): camp = _deref(camp_ref) num_pids = camp.shape[0] for pid in range(num_pids): img_paths = _process_images(camp[pid, :], campid, pid, imgs_dir) assert len(img_paths) > 0, 'campid{}-pid{} has no images'.format(campid, pid) meta_data.append((campid + 1, pid + 1, img_paths)) print('- done camera pair {} with {} identities'.format(campid + 1, num_pids)) return meta_data meta_detected = _extract_img('detected') meta_labeled = _extract_img('labeled') def _extract_classic_split(meta_data, test_split): train, test = [], [] num_train_pids, num_test_pids = 0, 0 num_train_imgs, num_test_imgs = 0, 0 for i, (campid, pid, img_paths) in enumerate(meta_data): if [campid, pid] in test_split: for img_path in img_paths: camid = int(osp.basename(img_path).split('_')[2]) - 1 # make it 0-based test.append((img_path, num_test_pids, camid)) num_test_pids += 1 num_test_imgs += len(img_paths) else: for img_path in img_paths: camid = int(osp.basename(img_path).split('_')[2]) - 1 # make it 0-based train.append((img_path, num_train_pids, camid)) num_train_pids += 1 num_train_imgs += len(img_paths) return train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs print('Creating classic splits (# = 20) ...') splits_classic_det, splits_classic_lab = [], [] for split_ref in mat['testsets'][0]: test_split = _deref(split_ref).tolist() # create split for detected images train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \ _extract_classic_split(meta_detected, test_split) splits_classic_det.append({ 'train': train, 'query': test, 'gallery': test, 'num_train_pids': num_train_pids, 'num_train_imgs': num_train_imgs, 'num_query_pids': num_test_pids, 'num_query_imgs': num_test_imgs, 'num_gallery_pids': num_test_pids, 'num_gallery_imgs': num_test_imgs }) # create split for labeled images train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \ _extract_classic_split(meta_labeled, test_split) splits_classic_lab.append({ 'train': train, 'query': test, 'gallery': test, 'num_train_pids': num_train_pids, 'num_train_imgs': num_train_imgs, 'num_query_pids': num_test_pids, 'num_query_imgs': num_test_imgs, 'num_gallery_pids': num_test_pids, 'num_gallery_imgs': num_test_imgs }) with PathManager.open(self.split_classic_det_json_path, 'w') as f: json.dump(splits_classic_det, f, indent=4, separators=(',', ': ')) with PathManager.open(self.split_classic_lab_json_path, 'w') as f: json.dump(splits_classic_lab, f, indent=4, separators=(',', ': ')) def _extract_set(filelist, pids, pid2label, idxs, img_dir, relabel): tmp_set = [] unique_pids = set() for idx in idxs: img_name = filelist[idx][0] camid = int(img_name.split('_')[2]) - 1 # make it 0-based pid = pids[idx] if relabel: pid = pid2label[pid] img_path = osp.join(img_dir, img_name) tmp_set.append((img_path, int(pid), camid)) unique_pids.add(pid) return tmp_set, len(unique_pids), len(idxs) def _extract_new_split(split_dict, img_dir): train_idxs = split_dict['train_idx'].flatten() - 1 # index-0 pids = split_dict['labels'].flatten() train_pids = set(pids[train_idxs]) pid2label = {pid: label for label, pid in enumerate(train_pids)} query_idxs = split_dict['query_idx'].flatten() - 1 gallery_idxs = split_dict['gallery_idx'].flatten() - 1 filelist = split_dict['filelist'].flatten() train_info = _extract_set(filelist, pids, pid2label, train_idxs, img_dir, relabel=True) query_info = _extract_set(filelist, pids, pid2label, query_idxs, img_dir, relabel=False) gallery_info = _extract_set(filelist, pids, pid2label, gallery_idxs, img_dir, relabel=False) return train_info, query_info, gallery_info print('Creating new split for detected images (767/700) ...') train_info, query_info, gallery_info = _extract_new_split( io.loadmat(self.split_new_det_mat_path), self.imgs_detected_dir ) split = [{ 'train': train_info[0], 'query': query_info[0], 'gallery': gallery_info[0], 'num_train_pids': train_info[1], 'num_train_imgs': train_info[2], 'num_query_pids': query_info[1], 'num_query_imgs': query_info[2], 'num_gallery_pids': gallery_info[1], 'num_gallery_imgs': gallery_info[2] }] with PathManager.open(self.split_new_det_json_path, 'w') as f: json.dump(split, f, indent=4, separators=(',', ': ')) print('Creating new split for labeled images (767/700) ...') train_info, query_info, gallery_info = _extract_new_split( io.loadmat(self.split_new_lab_mat_path), self.imgs_labeled_dir ) split = [{ 'train': train_info[0], 'query': query_info[0], 'gallery': gallery_info[0], 'num_train_pids': train_info[1], 'num_train_imgs': train_info[2], 'num_query_pids': query_info[1], 'num_query_imgs': query_info[2], 'num_gallery_pids': gallery_info[1], 'num_gallery_imgs': gallery_info[2] }] with PathManager.open(self.split_new_lab_json_path, 'w') as f: json.dump(split, f, indent=4, separators=(',', ': '))