119 lines
4.3 KiB
Python
119 lines
4.3 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import glob
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import re
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import sys
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import urllib
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import tarfile
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import zipfile
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import os.path as osp
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from scipy.io import loadmat
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import numpy as np
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import h5py
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from scipy.misc import imsave
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from .bases import BaseImageDataset
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# To adapt to different versions
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# Log:
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# 22.01.2019: v1 and v2 only differ in dir names
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_TRAIN_DIR_KEY = 'train_dir'
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_TEST_DIR_KEY = 'test_dir'
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_VERSION = {
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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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class MSMT17(BaseImageDataset):
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"""
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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'
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def __init__(self, root='data', verbose=True, **kwargs):
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super(MSMT17, self).__init__(root)
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self.dataset_dir = osp.join(self.root, self.dataset_dir)
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has_main_dir = False
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for main_dir in _VERSION:
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if osp.exists(osp.join(self.dataset_dir, main_dir)):
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train_dir = _VERSION[main_dir][_TRAIN_DIR_KEY]
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test_dir = _VERSION[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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self._check_before_run()
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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)
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gallery = self._process_dir(self.test_dir, self.list_gallery_path)
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# To fairly compare with published methods, don't use val images for training
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#train += val
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#num_train_imgs += num_val_imgs
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if verbose:
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print("=> MSMT17 loaded")
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self.print_dataset_statistics(train, query, gallery)
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self.train = train
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self.query = query
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self.gallery = gallery
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self.num_train_pids, self.num_train_imgs, self.num_train_cams = self.get_imagedata_info(self.train)
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self.num_query_pids, self.num_query_imgs, self.num_query_cams = self.get_imagedata_info(self.query)
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self.num_gallery_pids, self.num_gallery_imgs, self.num_gallery_cams = self.get_imagedata_info(self.gallery)
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def _check_before_run(self):
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"""Check if all files are available before going deeper"""
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if not osp.exists(self.dataset_dir):
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raise RuntimeError("'{}' is not available".format(self.dataset_dir))
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if not osp.exists(self.train_dir):
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raise RuntimeError("'{}' is not available".format(self.train_dir))
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if not osp.exists(self.test_dir):
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raise RuntimeError("'{}' is not available".format(self.test_dir))
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def _process_dir(self, dir_path, list_path):
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with open(list_path, 'r') as txt:
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lines = txt.readlines()
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dataset = []
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pid_container = set()
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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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dataset.append((img_path, pid, camid))
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pid_container.add(pid)
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num_pids = len(pid_container)
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# check if pid starts from 0 and increments with 1
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for idx, pid in enumerate(pid_container):
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assert idx == pid, "See code comment for explanation"
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return dataset |