158 lines
5.6 KiB
Python
158 lines
5.6 KiB
Python
from __future__ import print_function, absolute_import
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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 utils.iotools import mkdir_if_missing, write_json, read_json
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from .base import BaseImgDataset
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class CUHK01(BaseImgDataset):
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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, use_lmdb=False, **kwargs):
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super(CUHK01, self).__init__()
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self.dataset_dir = osp.join(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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num_train_pids = split['num_train_pids']
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num_query_pids = split['num_query_pids']
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num_gallery_pids = split['num_gallery_pids']
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num_train_imgs = len(train)
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num_query_imgs = len(query)
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num_gallery_imgs = len(gallery)
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num_total_pids = num_train_pids + num_query_pids
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num_total_imgs = num_train_imgs + num_query_imgs
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if verbose:
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print("=> CUHK01 loaded")
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print("Dataset statistics:")
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print(" ------------------------------")
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print(" subset | # ids | # images")
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print(" ------------------------------")
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print(" train | {:5d} | {:8d}".format(num_train_pids, num_train_imgs))
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print(" query | {:5d} | {:8d}".format(num_query_pids, num_query_imgs))
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print(" gallery | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_imgs))
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print(" ------------------------------")
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print(" total | {:5d} | {:8d}".format(num_total_pids, num_total_imgs))
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print(" ------------------------------")
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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 = num_train_pids
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self.num_query_pids = num_query_pids
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self.num_gallery_pids = num_gallery_pids
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if use_lmdb:
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self.generate_lmdb()
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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")
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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
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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 = [], []
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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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test.append((img_path, pid, camid))
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split = {'train': train, 'query': test, 'gallery': test,
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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") |