data_manager.py

from __future__ import print_function, absolute_import
import os
import glob
import re
import sys
import urllib
import tarfile
import zipfile
import os.path as osp
from scipy.io import loadmat
import numpy as np
import random

from utils import mkdir_if_missing, write_json, read_json

"""Dataset classes"""


class Mars(object):
    """
    MARS

    Reference:
    Zheng et al. MARS: A Video Benchmark for Large-Scale Person Re-identification. ECCV 2016.
    
    Dataset statistics:
    # identities: 1261
    # tracklets: 8298 (train) + 1980 (query) + 9330 (gallery)
    # cameras: 6

    Args:
        min_seq_len (int): tracklet with length shorter than this value will be discarded (default: 0).
    """
    root = './data/mars'
    train_name_path = osp.join(root, 'info/train_name.txt')
    test_name_path = osp.join(root, 'info/test_name.txt')
    track_train_info_path = osp.join(root, 'info/tracks_train_info.mat')
    track_test_info_path = osp.join(root, 'info/tracks_test_info.mat')
    query_IDX_path = osp.join(root, 'info/query_IDX.mat')

    def __init__(self, min_seq_len=0):
        self._check_before_run()

        # prepare meta data
        train_names = self._get_names(self.train_name_path)
        test_names = self._get_names(self.test_name_path)
        track_train = loadmat(self.track_train_info_path)['track_train_info'] # numpy.ndarray (8298, 4)
        track_test = loadmat(self.track_test_info_path)['track_test_info'] # numpy.ndarray (12180, 4)
        query_IDX = loadmat(self.query_IDX_path)['query_IDX'].squeeze() # numpy.ndarray (1980,)
        query_IDX -= 1 # index from 0
        track_query = track_test[query_IDX,:]
        gallery_IDX = [i for i in range(track_test.shape[0]) if i not in query_IDX]
        track_gallery = track_test[gallery_IDX,:]

        train, num_train_tracklets, num_train_pids, num_train_imgs = \
          self._process_data(train_names, track_train, home_dir='bbox_train', relabel=True, min_seq_len=min_seq_len)

        query, num_query_tracklets, num_query_pids, num_query_imgs = \
          self._process_data(test_names, track_query, home_dir='bbox_test', relabel=False, min_seq_len=min_seq_len)

        gallery, num_gallery_tracklets, num_gallery_pids, num_gallery_imgs = \
          self._process_data(test_names, track_gallery, home_dir='bbox_test', relabel=False, min_seq_len=min_seq_len)

        num_imgs_per_tracklet = num_train_imgs + num_query_imgs + num_gallery_imgs
        min_num = np.min(num_imgs_per_tracklet)
        max_num = np.max(num_imgs_per_tracklet)
        avg_num = np.mean(num_imgs_per_tracklet)

        num_total_pids = num_train_pids + num_query_pids
        num_total_tracklets = num_train_tracklets + num_query_tracklets + num_gallery_tracklets

        print("=> MARS loaded")
        print("Dataset statistics:")
        print("  ------------------------------")
        print("  subset   | # ids | # tracklets")
        print("  ------------------------------")
        print("  train    | {:5d} | {:8d}".format(num_train_pids, num_train_tracklets))
        print("  query    | {:5d} | {:8d}".format(num_query_pids, num_query_tracklets))
        print("  gallery  | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_tracklets))
        print("  ------------------------------")
        print("  total    | {:5d} | {:8d}".format(num_total_pids, num_total_tracklets))
        print("  number of images per tracklet: {} ~ {}, average {:.1f}".format(min_num, max_num, avg_num))
        print("  ------------------------------")

        self.train = train
        self.query = query
        self.gallery = gallery

        self.num_train_pids = num_train_pids
        self.num_query_pids = num_query_pids
        self.num_gallery_pids = num_gallery_pids

    def _check_before_run(self):
        """Check if all files are available before going deeper"""
        if not osp.exists(self.root):
            raise RuntimeError("'{}' is not available".format(self.root))
        if not osp.exists(self.train_name_path):
            raise RuntimeError("'{}' is not available".format(self.train_name_path))
        if not osp.exists(self.test_name_path):
            raise RuntimeError("'{}' is not available".format(self.test_name_path))
        if not osp.exists(self.track_train_info_path):
            raise RuntimeError("'{}' is not available".format(self.track_train_info_path))
        if not osp.exists(self.track_test_info_path):
            raise RuntimeError("'{}' is not available".format(self.track_test_info_path))
        if not osp.exists(self.query_IDX_path):
            raise RuntimeError("'{}' is not available".format(self.query_IDX_path))

    def _get_names(self, fpath):
        names = []
        with open(fpath, 'r') as f:
            for line in f:
                new_line = line.rstrip()
                names.append(new_line)
        return names

    def _process_data(self, names, meta_data, home_dir=None, relabel=False, min_seq_len=0):
        assert home_dir in ['bbox_train', 'bbox_test']
        num_tracklets = meta_data.shape[0]
        pid_list = list(set(meta_data[:,2].tolist()))
        num_pids = len(pid_list)

        if relabel: pid2label = {pid:label for label, pid in enumerate(pid_list)}
        tracklets = []
        num_imgs_per_tracklet = []

        #txt_name = self.root + home_dir + str(len(meta_data)) + '.txt'
        #fid = open(txt_name, "w")

        for tracklet_idx in range(num_tracklets):
            data = meta_data[tracklet_idx,...]
            start_index, end_index, pid, camid = data
            if pid == -1: continue # junk images are just ignored
            assert 1 <= camid <= 6
            if relabel: pid = pid2label[pid]
            camid -= 1 # index starts from 0
            img_names = names[start_index-1:end_index]

            # make sure image names correspond to the same person
            pnames = [img_name[:4] for img_name in img_names]
            assert len(set(pnames)) == 1, "Error: a single tracklet contains different person images"

            # make sure all images are captured under the same camera
            camnames = [img_name[5] for img_name in img_names]
            assert len(set(camnames)) == 1, "Error: images are captured under different cameras!"

            # append image names with directory information
            img_paths = [osp.join(self.root, home_dir, img_name[:4], img_name) for img_name in img_names]
            if len(img_paths) >= min_seq_len:
                img_paths = tuple(img_paths)
                tracklets.append((img_paths, pid, camid))
                num_imgs_per_tracklet.append(len(img_paths))
                #fid.write(img_names[0] + '\n')
 
        #fid.close()
        num_tracklets = len(tracklets)

        return tracklets, num_tracklets, num_pids, num_imgs_per_tracklet

class iLIDSVID(object):
    """
    iLIDS-VID

    Reference:
    Wang et al. Person Re-Identification by Video Ranking. ECCV 2014.
    
    Dataset statistics:
    # identities: 300
    # tracklets: 600
    # cameras: 2

    Args:
        split_id (int): indicates which split to use. There are totally 10 splits.
    """
    root = './data/ilids-vid'
    dataset_url = 'http://www.eecs.qmul.ac.uk/~xiatian/iLIDS-VID/iLIDS-VID.tar'
    data_dir = osp.join(root, 'i-LIDS-VID')
    split_dir = osp.join(root, 'train-test people splits')
    split_mat_path = osp.join(split_dir, 'train_test_splits_ilidsvid.mat')
    split_path = osp.join(root, 'splits.json')
    cam_1_path = osp.join(root, 'i-LIDS-VID/sequences/cam1')
    cam_2_path = osp.join(root, 'i-LIDS-VID/sequences/cam2')

    def __init__(self, split_id=0):
        self._download_data()
        self._check_before_run()

        self._prepare_split()
        splits = read_json(self.split_path)
        if split_id >= len(splits):
            raise ValueError("split_id exceeds range, received {}, but expected between 0 and {}".format(split_id, len(splits)-1))
        split = splits[split_id]
        train_dirs, test_dirs = split['train'], split['test']
        print("# train identites: {}, # test identites {}".format(len(train_dirs), len(test_dirs)))

        train, num_train_tracklets, num_train_pids, num_imgs_train = \
          self._process_data(train_dirs, cam1=True, cam2=True)
        query, num_query_tracklets, num_query_pids, num_imgs_query = \
          self._process_data(test_dirs, cam1=True, cam2=False)
        gallery, num_gallery_tracklets, num_gallery_pids, num_imgs_gallery = \
          self._process_data(test_dirs, cam1=False, cam2=True)

        num_imgs_per_tracklet = num_imgs_train + num_imgs_query + num_imgs_gallery
        min_num = np.min(num_imgs_per_tracklet)
        max_num = np.max(num_imgs_per_tracklet)
        avg_num = np.mean(num_imgs_per_tracklet)

        num_total_pids = num_train_pids + num_query_pids
        num_total_tracklets = num_train_tracklets + num_query_tracklets + num_gallery_tracklets

        print("=> iLIDS-VID loaded")
        print("Dataset statistics:")
        print("  ------------------------------")
        print("  subset   | # ids | # tracklets")
        print("  ------------------------------")
        print("  train    | {:5d} | {:8d}".format(num_train_pids, num_train_tracklets))
        print("  query    | {:5d} | {:8d}".format(num_query_pids, num_query_tracklets))
        print("  gallery  | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_tracklets))
        print("  ------------------------------")
        print("  total    | {:5d} | {:8d}".format(num_total_pids, num_total_tracklets))
        print("  number of images per tracklet: {} ~ {}, average {:.1f}".format(min_num, max_num, avg_num))
        print("  ------------------------------")

        self.train = train
        self.query = query
        self.gallery = gallery

        self.num_train_pids = num_train_pids
        self.num_query_pids = num_query_pids
        self.num_gallery_pids = num_gallery_pids

    def _download_data(self):
        if osp.exists(self.root):
            print("This dataset has been downloaded.")
            return

        mkdir_if_missing(self.root)
        fpath = osp.join(self.root, osp.basename(self.dataset_url))

        print("Downloading iLIDS-VID dataset")
        url_opener = urllib.URLopener()
        url_opener.retrieve(self.dataset_url, fpath)

        print("Extracting files")
        tar = tarfile.open(fpath)
        tar.extractall(path=self.root)
        tar.close()

    def _check_before_run(self):
        """Check if all files are available before going deeper"""
        if not osp.exists(self.root):
            raise RuntimeError("'{}' is not available".format(self.root))
        if not osp.exists(self.data_dir):
            raise RuntimeError("'{}' is not available".format(self.data_dir))
        if not osp.exists(self.split_dir):
            raise RuntimeError("'{}' is not available".format(self.split_dir))

    def _prepare_split(self):
        if not osp.exists(self.split_path):
            print("Creating splits")
            mat_split_data = loadmat(self.split_mat_path)['ls_set']
            
            num_splits = mat_split_data.shape[0]
            num_total_ids = mat_split_data.shape[1]
            assert num_splits == 10
            assert num_total_ids == 300
            num_ids_each = int(num_total_ids/2)

            # pids in mat_split_data are indices, so we need to transform them
            # to real pids
            person_cam1_dirs = os.listdir(self.cam_1_path)
            person_cam2_dirs = os.listdir(self.cam_2_path)

            # make sure persons in one camera view can be found in the other camera view
            assert set(person_cam1_dirs) == set(person_cam2_dirs)

            splits = []
            for i_split in range(num_splits):
                # first 50% for testing and the remaining for training, following Wang et al. ECCV'14.
                train_idxs = sorted(list(mat_split_data[i_split,num_ids_each:]))
                test_idxs = sorted(list(mat_split_data[i_split,:num_ids_each]))
                
                train_idxs = [int(i)-1 for i in train_idxs]
                test_idxs = [int(i)-1 for i in test_idxs]
                
                # transform pids to person dir names
                train_dirs = [person_cam1_dirs[i] for i in train_idxs]
                test_dirs = [person_cam1_dirs[i] for i in test_idxs]
                
                split = {'train': train_dirs, 'test': test_dirs}
                splits.append(split)

            print("Totally {} splits are created, following Wang et al. ECCV'14".format(len(splits)))
            print("Split file is saved to {}".format(self.split_path))
            write_json(splits, self.split_path)

        print("Splits created")

    def _process_data(self, dirnames, cam1=True, cam2=True):
        tracklets = []
        num_imgs_per_tracklet = []
        dirname2pid = {dirname:i for i, dirname in enumerate(dirnames)}

        #txt_name = 'ilids' + str(cam1) + str(cam2) + '.txt'
        #fid = open(txt_name, "w")    

        for dirname in dirnames:
            if cam1:
                person_dir = osp.join(self.cam_1_path, dirname)
                img_names = glob.glob(osp.join(person_dir, '*.png'))
                assert len(img_names) > 0
                img_names = tuple(img_names)
                pid = dirname2pid[dirname]
                tracklets.append((img_names, pid, 0))
                num_imgs_per_tracklet.append(len(img_names))
                #fid.write("cam1_" + dirname + '\n')
            if cam2:
                person_dir = osp.join(self.cam_2_path, dirname)
                img_names = glob.glob(osp.join(person_dir, '*.png'))
                assert len(img_names) > 0
                img_names = tuple(img_names)
                pid = dirname2pid[dirname]
                tracklets.append((img_names, pid, 1))
                num_imgs_per_tracklet.append(len(img_names))
                #fid.write("cam2_" + dirname + '\n')

        num_tracklets = len(tracklets)
        num_pids = len(dirnames)

        return tracklets, num_tracklets, num_pids, num_imgs_per_tracklet

class PRID(object):
    """
    PRID

    Reference:
    Hirzer et al. Person Re-Identification by Descriptive and Discriminative Classification. SCIA 2011.
    
    Dataset statistics:
    # identities: 200
    # tracklets: 400
    # cameras: 2

    Args:
        split_id (int): indicates which split to use. There are totally 10 splits.
        min_seq_len (int): tracklet with length shorter than this value will be discarded (default: 0).
    """
    root = './data/prid2011'
    dataset_url = 'https://files.icg.tugraz.at/f/6ab7e8ce8f/?raw=1'
    split_path = osp.join(root, 'splits_prid2011.json')
    cam_a_path = osp.join(root, 'prid_2011', 'multi_shot', 'cam_a')
    cam_b_path = osp.join(root, 'prid_2011', 'multi_shot', 'cam_b')

    def __init__(self, split_id=0, min_seq_len=0):
        self._check_before_run()
        splits = read_json(self.split_path)
        if split_id >=  len(splits):
            raise ValueError("split_id exceeds range, received {}, but expected between 0 and {}".format(split_id, len(splits)-1))
        split = splits[split_id]
        train_dirs, test_dirs = split['train'], split['test']
        print("# train identites: {}, # test identites {}".format(len(train_dirs), len(test_dirs)))

        train, num_train_tracklets, num_train_pids, num_imgs_train = \
          self._process_data(train_dirs, cam1=True, cam2=True)
        query, num_query_tracklets, num_query_pids, num_imgs_query = \
          self._process_data(test_dirs, cam1=True, cam2=False)
        gallery, num_gallery_tracklets, num_gallery_pids, num_imgs_gallery = \
          self._process_data(test_dirs, cam1=False, cam2=True)

        num_imgs_per_tracklet = num_imgs_train + num_imgs_query + num_imgs_gallery
        min_num = np.min(num_imgs_per_tracklet)
        max_num = np.max(num_imgs_per_tracklet)
        avg_num = np.mean(num_imgs_per_tracklet)

        num_total_pids = num_train_pids + num_query_pids
        num_total_tracklets = num_train_tracklets + num_query_tracklets + num_gallery_tracklets

        print("=> PRID-2011 loaded")
        print("Dataset statistics:")
        print("  ------------------------------")
        print("  subset   | # ids | # tracklets")
        print("  ------------------------------")
        print("  train    | {:5d} | {:8d}".format(num_train_pids, num_train_tracklets))
        print("  query    | {:5d} | {:8d}".format(num_query_pids, num_query_tracklets))
        print("  gallery  | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_tracklets))
        print("  ------------------------------")
        print("  total    | {:5d} | {:8d}".format(num_total_pids, num_total_tracklets))
        print("  number of images per tracklet: {} ~ {}, average {:.1f}".format(min_num, max_num, avg_num))
        print("  ------------------------------")

        self.train = train
        self.query = query
        self.gallery = gallery

        self.num_train_pids = num_train_pids
        self.num_query_pids = num_query_pids
        self.num_gallery_pids = num_gallery_pids

    def _check_before_run(self):
        """Check if all files are available before going deeper"""
        if not osp.exists(self.root):
            raise RuntimeError("'{}' is not available".format(self.root))

    def _process_data(self, dirnames, cam1=True, cam2=True):
        tracklets = []
        num_imgs_per_tracklet = []
        dirname2pid = {dirname:i for i, dirname in enumerate(dirnames)}

        #txt_name = 'prid' + str(cam1) + str(cam2) + '.txt'
        #fid = open(txt_name, "w")
    
        for dirname in dirnames:
            if cam1:
                person_dir = osp.join(self.cam_a_path, dirname)
                img_names = glob.glob(osp.join(person_dir, '*.png'))
                assert len(img_names) > 0
                img_names = tuple(img_names)
                pid = dirname2pid[dirname]
                tracklets.append((img_names, pid, 0))
                num_imgs_per_tracklet.append(len(img_names))
                #fid.write("cama_" + dirname + '\n')
            if cam2:
                person_dir = osp.join(self.cam_b_path, dirname)
                img_names = glob.glob(osp.join(person_dir, '*.png'))
                assert len(img_names) > 0
                img_names = tuple(img_names)
                pid = dirname2pid[dirname]
                tracklets.append((img_names, pid, 1))
                num_imgs_per_tracklet.append(len(img_names))
                #fid.write("camb_" + dirname + '\n')

        num_tracklets = len(tracklets)
        num_pids = len(dirnames)

        return tracklets, num_tracklets, num_pids, num_imgs_per_tracklet

"""Create dataset"""

__factory = {
    'mars': Mars,
    'ilidsvid': iLIDSVID,
    'prid': PRID,
}

def get_names():
    return __factory.keys()

def init_dataset(name, *args, **kwargs):
    if name not in __factory.keys():
        raise KeyError("Unknown dataset: {}".format(name))
    return __factory[name](*args, **kwargs)

if __name__ == '__main__':
    # test
    #dataset = Market1501()
    #dataset = Mars()
    dataset = iLIDSVID()
    dataset = PRID()