104 lines
4.1 KiB
Python
104 lines
4.1 KiB
Python
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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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class MSMT17(object):
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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__()
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self.dataset_dir = osp.join(root, self.dataset_dir)
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self.train_dir = osp.join(self.dataset_dir, 'MSMT17_V1/train')
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self.test_dir = osp.join(self.dataset_dir, 'MSMT17_V1/test')
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self.list_train_path = osp.join(self.dataset_dir, 'MSMT17_V1/list_train.txt')
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self.list_val_path = osp.join(self.dataset_dir, 'MSMT17_V1/list_val.txt')
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self.list_query_path = osp.join(self.dataset_dir, 'MSMT17_V1/list_query.txt')
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self.list_gallery_path = osp.join(self.dataset_dir, 'MSMT17_V1/list_gallery.txt')
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self._check_before_run()
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train, num_train_pids, num_train_imgs = self._process_dir(self.train_dir, self.list_train_path)
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#val, num_val_pids, num_val_imgs = self._process_dir(self.train_dir, self.list_val_path)
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query, num_query_pids, num_query_imgs = self._process_dir(self.test_dir, self.list_query_path)
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gallery, num_gallery_pids, num_gallery_imgs = self._process_dir(self.test_dir, self.list_gallery_path)
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#train += val
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#num_train_imgs += num_val_imgs
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num_total_pids = num_train_pids + num_query_pids
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num_total_imgs = num_train_imgs + num_query_imgs + num_gallery_imgs
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if verbose:
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print("=> MSMT17 loaded")
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print("Dataset statistics:")
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print(" ------------------------------")
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print(" subset | # ids | # images")
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print(" ------------------------------")
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print(" train | {:5d} | {:8d}".format(num_train_pids, num_train_imgs))
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print(" query | {:5d} | {:8d}".format(num_query_pids, num_query_imgs))
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print(" gallery | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_imgs))
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print(" ------------------------------")
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print(" total | {:5d} | {:8d}".format(num_total_pids, num_total_imgs))
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print(" ------------------------------")
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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 = num_train_pids
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self.num_query_pids = num_query_pids
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self.num_gallery_pids = num_gallery_pids
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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])
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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_imgs = len(dataset)
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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, num_pids, num_imgs
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