149 lines
5.5 KiB
Python
149 lines
5.5 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, write_json, read_json
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from .bases import BaseImageDataset
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class CUHK01(BaseImageDataset):
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"""
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CUHK01
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Reference:
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Li et al. Human Reidentification with Transferred Metric Learning. ACCV 2012.
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URL: http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html
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Dataset statistics:
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# identities: 971
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# images: 3884
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# cameras: 4
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"""
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dataset_dir = 'cuhk01'
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def __init__(self, root='data', split_id=0, verbose=True, **kwargs):
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super(CUHK01, self).__init__(root)
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self.dataset_dir = osp.join(self.root, self.dataset_dir)
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self.zip_path = osp.join(self.dataset_dir, 'CUHK01.zip')
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self.campus_dir = osp.join(self.dataset_dir, 'campus')
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self.split_path = osp.join(self.dataset_dir, 'splits.json')
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self.extract_file()
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self.check_before_run()
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self.prepare_split()
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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 = split['train']
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query = split['query']
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gallery = split['gallery']
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train = [tuple(item) for item in train]
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query = [tuple(item) for item in query]
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gallery = [tuple(item) for item in gallery]
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if verbose:
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print('=> CUHK01 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 extract_file(self):
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if not osp.exists(self.campus_dir):
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print('Extracting files')
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zip_ref = zipfile.ZipFile(self.zip_path, 'r')
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zip_ref.extractall(self.dataset_dir)
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zip_ref.close()
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print('Files extracted')
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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.campus_dir):
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raise RuntimeError('"{}" is not available'.format(self.campus_dir))
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def prepare_split(self):
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"""
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Image name format: 0001001.png, where first four digits represent identity
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and last four digits represent cameras. Camera 1&2 are considered the same
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view and camera 3&4 are considered the same view.
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"""
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if not osp.exists(self.split_path):
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print('Creating 10 random splits of train ids and test ids')
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img_paths = sorted(glob.glob(osp.join(self.campus_dir, '*.png')))
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img_list = []
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pid_container = set()
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for img_path in img_paths:
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img_name = osp.basename(img_path)
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pid = int(img_name[:4]) - 1
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camid = (int(img_name[4:7]) - 1) // 2 # result is either 0 or 1
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img_list.append((img_path, pid, camid))
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pid_container.add(pid)
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num_pids = len(pid_container)
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num_train_pids = num_pids // 2
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splits = []
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for _ in range(10):
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order = np.arange(num_pids)
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np.random.shuffle(order)
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train_idxs = order[:num_train_pids]
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train_idxs = np.sort(train_idxs)
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idx2label = {idx: label for label, idx in enumerate(train_idxs)}
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train, test_a, test_b = [], [], []
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for img_path, pid, camid in img_list:
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if pid in train_idxs:
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train.append((img_path, idx2label[pid], camid))
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else:
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if camid == 0:
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test_a.append((img_path, pid, camid))
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else:
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test_b.append((img_path, pid, camid))
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# use cameraA as query and cameraB as gallery
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split = {'train': train, 'query': test_a, 'gallery': test_b,
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'num_train_pids': num_train_pids,
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'num_query_pids': num_pids - num_train_pids,
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'num_gallery_pids': num_pids - num_train_pids,
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}
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splits.append(split)
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# use cameraB as query and cameraA as gallery
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split = {'train': train, 'query': test_b, 'gallery': test_a,
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'num_train_pids': num_train_pids,
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'num_query_pids': num_pids - num_train_pids,
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'num_gallery_pids': num_pids - num_train_pids,
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}
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splits.append(split)
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print('Totally {} splits are created'.format(len(splits)))
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write_json(splits, self.split_path)
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print('Split file saved to {}'.format(self.split_path))
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print('Splits created') |