94 lines
3.4 KiB
Python
94 lines
3.4 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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from torchreid.utils.iotools import mkdir_if_missing, write_json, read_json
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from .bases import BaseVideoDataset
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class PRID2011(BaseVideoDataset):
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"""
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PRID2011
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Reference:
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Hirzer et al. Person Re-Identification by Descriptive and Discriminative Classification. SCIA 2011.
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URL: https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/PRID11/
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Dataset statistics:
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# identities: 200
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# tracklets: 400
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# cameras: 2
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"""
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dataset_dir = 'prid2011'
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def __init__(self, root='data', split_id=0, min_seq_len=0, verbose=True, **kwargs):
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self.dataset_dir = osp.join(root, self.dataset_dir)
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self.split_path = osp.join(self.dataset_dir, 'splits_prid2011.json')
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self.cam_a_path = osp.join(self.dataset_dir, 'prid_2011', 'multi_shot', 'cam_a')
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self.cam_b_path = osp.join(self.dataset_dir, 'prid_2011', 'multi_shot', 'cam_b')
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self._check_before_run()
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splits = read_json(self.split_path)
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if split_id >= len(splits):
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raise ValueError("split_id exceeds range, received {}, but expected between 0 and {}".format(split_id, len(splits)-1))
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split = splits[split_id]
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train_dirs, test_dirs = split['train'], split['test']
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print("# train identites: {}, # test identites {}".format(len(train_dirs), len(test_dirs)))
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train = self._process_data(train_dirs, cam1=True, cam2=True)
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query = self._process_data(test_dirs, cam1=True, cam2=False)
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gallery = self._process_data(test_dirs, cam1=False, cam2=True)
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if verbose:
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print("=> PRID2011 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_cams = self.get_videodata_info(self.train)
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self.num_query_pids, _, self.num_query_cams = self.get_videodata_info(self.query)
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self.num_gallery_pids, _, self.num_gallery_cams = self.get_videodata_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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def _process_data(self, dirnames, cam1=True, cam2=True):
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tracklets = []
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dirname2pid = {dirname:i for i, dirname in enumerate(dirnames)}
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for dirname in dirnames:
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if cam1:
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person_dir = osp.join(self.cam_a_path, dirname)
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img_names = glob.glob(osp.join(person_dir, '*.png'))
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assert len(img_names) > 0
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img_names = tuple(img_names)
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pid = dirname2pid[dirname]
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tracklets.append((img_names, pid, 0))
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if cam2:
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person_dir = osp.join(self.cam_b_path, dirname)
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img_names = glob.glob(osp.join(person_dir, '*.png'))
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assert len(img_names) > 0
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img_names = tuple(img_names)
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pid = dirname2pid[dirname]
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tracklets.append((img_names, pid, 1))
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return tracklets
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