112 lines
4.1 KiB
Python
112 lines
4.1 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 torchreid.utils.iotools import mkdir_if_missing
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from .bases import BaseImageDataset
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class DukeMTMCreID(BaseImageDataset):
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"""
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DukeMTMC-reID
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Reference:
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1. Ristani et al. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. ECCVW 2016.
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2. Zheng et al. Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro. ICCV 2017.
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URL: https://github.com/layumi/DukeMTMC-reID_evaluation
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Dataset statistics:
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# identities: 1404 (train + query)
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# images:16522 (train) + 2228 (query) + 17661 (gallery)
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# cameras: 8
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"""
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dataset_dir = 'dukemtmc-reid'
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def __init__(self, root='data', verbose=True, **kwargs):
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super(DukeMTMCreID, self).__init__(root)
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self.dataset_dir = osp.join(self.root, self.dataset_dir)
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self.dataset_url = 'http://vision.cs.duke.edu/DukeMTMC/data/misc/DukeMTMC-reID.zip'
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self.train_dir = osp.join(self.dataset_dir, 'DukeMTMC-reID/bounding_box_train')
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self.query_dir = osp.join(self.dataset_dir, 'DukeMTMC-reID/query')
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self.gallery_dir = osp.join(self.dataset_dir, 'DukeMTMC-reID/bounding_box_test')
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self.download_data()
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self.check_before_run()
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train = self.process_dir(self.train_dir, relabel=True)
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query = self.process_dir(self.query_dir, relabel=False)
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gallery = self.process_dir(self.gallery_dir, relabel=False)
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if verbose:
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print('=> DukeMTMC-reID 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 download_data(self):
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if osp.exists(self.dataset_dir):
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print('This dataset has been downloaded.')
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return
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print('Creating directory {}'.format(self.dataset_dir))
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mkdir_if_missing(self.dataset_dir)
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fpath = osp.join(self.dataset_dir, osp.basename(self.dataset_url))
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print('Downloading DukeMTMC-reID dataset')
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urllib.urlretrieve(self.dataset_url, fpath)
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print('Extracting files')
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zip_ref = zipfile.ZipFile(fpath, 'r')
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zip_ref.extractall(self.dataset_dir)
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zip_ref.close()
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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.query_dir):
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raise RuntimeError('"{}" is not available'.format(self.query_dir))
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if not osp.exists(self.gallery_dir):
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raise RuntimeError('"{}" is not available'.format(self.gallery_dir))
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def process_dir(self, dir_path, relabel=False):
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img_paths = glob.glob(osp.join(dir_path, '*.jpg'))
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pattern = re.compile(r'([-\d]+)_c(\d)')
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pid_container = set()
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for img_path in img_paths:
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pid, _ = map(int, pattern.search(img_path).groups())
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pid_container.add(pid)
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pid2label = {pid:label for label, pid in enumerate(pid_container)}
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dataset = []
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for img_path in img_paths:
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pid, camid = map(int, pattern.search(img_path).groups())
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assert 1 <= camid <= 8
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camid -= 1 # index starts from 0
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if relabel: pid = pid2label[pid]
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dataset.append((img_path, pid, camid))
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return dataset |