deep-person-reid/torchreid/datasets/grid.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

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 h5py
from scipy.misc import imsave

from torchreid.utils.iotools import mkdir_if_missing, write_json, read_json
from .bases import BaseImageDataset


class GRID(BaseImageDataset):
    """GRID

    Reference:
    Loy et al. Multi-camera activity correlation analysis. CVPR 2009.

    URL: http://personal.ie.cuhk.edu.hk/~ccloy/downloads_qmul_underground_reid.html
    
    Dataset statistics:
    # identities: 250
    # images: 1275
    # cameras: 8
    """
    dataset_dir = 'grid'

    def __init__(self, root='data', split_id=0, verbose=True, **kwargs):
        super(GRID, self).__init__(root)
        self.dataset_dir = osp.join(self.root, self.dataset_dir)
        self.dataset_url = 'http://personal.ie.cuhk.edu.hk/~ccloy/files/datasets/underground_reid.zip'
        self.probe_path = osp.join(self.dataset_dir, 'underground_reid', 'probe')
        self.gallery_path = osp.join(self.dataset_dir, 'underground_reid', 'gallery')
        self.split_mat_path = osp.join(self.dataset_dir, 'underground_reid', 'features_and_partitions.mat')
        self.split_path = osp.join(self.dataset_dir, 'splits.json')

        self.download_data()
        
        required_files = [
            self.dataset_dir,
            self.probe_path,
            self.gallery_path,
            self.split_mat_path
        ]
        self.check_before_run(required_files)

        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 = split['train']
        query = split['query']
        gallery = split['gallery']

        train = [tuple(item) for item in train]
        query = [tuple(item) for item in query]
        gallery = [tuple(item) for item in gallery]

        self.init_attributes(train, query, gallery)

        if verbose:
            self.print_dataset_statistics(train, query, gallery)

    def download_data(self):
        if osp.exists(self.dataset_dir):
            return

        print('Creating directory {}'.format(self.dataset_dir))
        mkdir_if_missing(self.dataset_dir)
        fpath = osp.join(self.dataset_dir, osp.basename(self.dataset_url))

        print('Downloading GRID dataset')
        urllib.urlretrieve(self.dataset_url, fpath)

        print('Extracting files')
        zip_ref = zipfile.ZipFile(fpath, 'r')
        zip_ref.extractall(self.dataset_dir)
        zip_ref.close()

    def prepare_split(self):
        if not osp.exists(self.split_path):
            print('Creating 10 random splits')
            split_mat = loadmat(self.split_mat_path)
            trainIdxAll = split_mat['trainIdxAll'][0] # length = 10
            probe_img_paths = sorted(glob.glob(osp.join(self.probe_path, '*.jpeg')))
            gallery_img_paths = sorted(glob.glob(osp.join(self.gallery_path, '*.jpeg')))

            splits = []
            for split_idx in range(10):
                train_idxs = trainIdxAll[split_idx][0][0][2][0].tolist()
                assert len(train_idxs) == 125
                idx2label = {idx: label for label, idx in enumerate(train_idxs)}

                train, query, gallery = [], [], []
                
                # processing probe folder
                for img_path in probe_img_paths:
                    img_name = osp.basename(img_path)
                    img_idx = int(img_name.split('_')[0])
                    camid = int(img_name.split('_')[1]) - 1 # index starts from 0
                    if img_idx in train_idxs:
                        train.append((img_path, idx2label[img_idx], camid))
                    else:
                        query.append((img_path, img_idx, camid))
                
                # process gallery folder
                for img_path in gallery_img_paths:
                    img_name = osp.basename(img_path)
                    img_idx = int(img_name.split('_')[0])
                    camid = int(img_name.split('_')[1]) - 1 # index starts from 0
                    if img_idx in train_idxs:
                        train.append((img_path, idx2label[img_idx], camid))
                    else:
                        gallery.append((img_path, img_idx, camid))

                split = {
                    'train': train,
                    'query': query,
                    'gallery': gallery,
                    'num_train_pids': 125,
                    'num_query_pids': 125,
                    'num_gallery_pids': 900
                }
                splits.append(split)
            
            print('Totally {} splits are created'.format(len(splits)))
            write_json(splits, self.split_path)
            print('Split file saved to {}'.format(self.split_path))