mirror of https://github.com/JDAI-CV/fast-reid.git
115 lines
3.8 KiB
Python
115 lines
3.8 KiB
Python
# encoding: utf-8
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"""
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@author: l1aoxingyu
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@contact: sherlockliao01@gmail.com
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"""
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import sys
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import os
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import os.path as osp
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from .bases import ImageDataset
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from ..datasets import DATASET_REGISTRY
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##### Log #####
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# 22.01.2019
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# - add v2
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# - v1 and v2 differ in dir names
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# - note that faces in v2 are blurred
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TRAIN_DIR_KEY = 'train_dir'
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TEST_DIR_KEY = 'test_dir'
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VERSION_DICT = {
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'MSMT17_V1': {
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TRAIN_DIR_KEY: 'train',
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TEST_DIR_KEY: 'test',
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},
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'MSMT17_V2': {
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TRAIN_DIR_KEY: 'mask_train_v2',
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TEST_DIR_KEY: 'mask_test_v2',
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}
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}
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@DATASET_REGISTRY.register()
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class MSMT17(ImageDataset):
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"""MSMT17.
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Reference:
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Wei et al. Person Transfer GAN to Bridge Domain Gap for Person Re-Identification. CVPR 2018.
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URL: `<http://www.pkuvmc.com/publications/msmt17.html>`_
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Dataset statistics:
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- identities: 4101.
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- images: 32621 (train) + 11659 (query) + 82161 (gallery).
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- cameras: 15.
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"""
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# dataset_dir = 'MSMT17_V2'
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dataset_url = None
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dataset_name = 'msmt17'
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def __init__(self, root='datasets', **kwargs):
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self.dataset_dir = root
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has_main_dir = False
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for main_dir in VERSION_DICT:
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if osp.exists(osp.join(self.dataset_dir, main_dir)):
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train_dir = VERSION_DICT[main_dir][TRAIN_DIR_KEY]
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test_dir = VERSION_DICT[main_dir][TEST_DIR_KEY]
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has_main_dir = True
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break
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assert has_main_dir, 'Dataset folder not found'
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self.train_dir = osp.join(self.dataset_dir, main_dir, train_dir)
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self.test_dir = osp.join(self.dataset_dir, main_dir, test_dir)
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self.list_train_path = osp.join(self.dataset_dir, main_dir, 'list_train.txt')
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self.list_val_path = osp.join(self.dataset_dir, main_dir, 'list_val.txt')
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self.list_query_path = osp.join(self.dataset_dir, main_dir, 'list_query.txt')
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self.list_gallery_path = osp.join(self.dataset_dir, main_dir, 'list_gallery.txt')
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required_files = [
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self.dataset_dir,
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self.train_dir,
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self.test_dir
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]
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self.check_before_run(required_files)
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train = self.process_dir(self.train_dir, self.list_train_path)
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val = self.process_dir(self.train_dir, self.list_val_path)
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query = self.process_dir(self.test_dir, self.list_query_path, is_train=False)
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gallery = self.process_dir(self.test_dir, self.list_gallery_path, is_train=False)
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num_train_pids = self.get_num_pids(train)
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query_tmp = []
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for img_path, pid, camid in query:
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query_tmp.append((img_path, pid+num_train_pids, camid))
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del query
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query = query_tmp
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gallery_temp = []
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for img_path, pid, camid in gallery:
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gallery_temp.append((img_path, pid+num_train_pids, camid))
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del gallery
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gallery = gallery_temp
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# Note: to fairly compare with published methods on the conventional ReID setting,
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# do not add val images to the training set.
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if 'combineall' in kwargs and kwargs['combineall']:
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train += val
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super(MSMT17, self).__init__(train, query, gallery, **kwargs)
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def process_dir(self, dir_path, list_path, is_train=True):
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with open(list_path, 'r') as txt:
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lines = txt.readlines()
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data = []
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for img_idx, img_info in enumerate(lines):
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img_path, pid = img_info.split(' ')
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pid = int(pid) # no need to relabel
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camid = int(img_path.split('_')[2]) - 1 # index starts from 0
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img_path = osp.join(dir_path, img_path)
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if is_train:
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pid = self.dataset_name + "_" + str(pid)
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camid = self.dataset_name + "_" + str(camid)
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data.append((img_path, pid, camid))
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return data
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