data-manager:自动处理数据集,返回数据集的一些常用属性
作者:互联网
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 h5py
from scipy.misc import imsave
from utils import mkdir_if_missing, write_json, read_json
"""Image ReID"""
class Market1501(object):
"""
Market1501
Reference:
Zheng et al. Scalable Person Re-identification: A Benchmark. ICCV 2015.
URL: http://www.liangzheng.org/Project/project_reid.html
Dataset statistics:
# identities: 1501 (+1 for background)
# images: 12936 (train) + 3368 (query) + 15913 (gallery)
"""
dataset_dir = 'market1501'
def __init__(self, root='data', **kwargs):
self.dataset_dir = osp.join(root, self.dataset_dir)
self.train_dir = osp.join(self.dataset_dir, 'bounding_box_train')
self.query_dir = osp.join(self.dataset_dir, 'query')
self.gallery_dir = osp.join(self.dataset_dir, 'bounding_box_test')
self._check_before_run()
train, num_train_pids, num_train_imgs = self._process_dir(self.train_dir, relabel=True)
query, num_query_pids, num_query_imgs = self._process_dir(self.query_dir, relabel=False)
gallery, num_gallery_pids, num_gallery_imgs = self._process_dir(self.gallery_dir, relabel=False)
num_total_pids = num_train_pids + num_query_pids
num_total_imgs = num_train_imgs + num_query_imgs + num_gallery_imgs
print("=> Market1501 loaded")
print("Dataset statistics:")
print(" ------------------------------")
print(" subset | # ids | # images")
print(" ------------------------------")
print(" train | {:5d} | {:8d}".format(num_train_pids, num_train_imgs))
print(" query | {:5d} | {:8d}".format(num_query_pids, num_query_imgs))
print(" gallery | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_imgs))
print(" ------------------------------")
print(" total | {:5d} | {:8d}".format(num_total_pids, num_total_imgs))
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.dataset_dir):
raise RuntimeError("'{}' is not available".format(self.dataset_dir))
if not osp.exists(self.train_dir):
raise RuntimeError("'{}' is not available".format(self.train_dir))
if not osp.exists(self.query_dir):
raise RuntimeError("'{}' is not available".format(self.query_dir))
if not osp.exists(self.gallery_dir):
raise RuntimeError("'{}' is not available".format(self.gallery_dir))
def _process_dir(self, dir_path, relabel=False):
img_paths = glob.glob(osp.join(dir_path, '*.jpg'))
pattern = re.compile(r'([-\d]+)_c(\d)')
pid_container = set()
for img_path in img_paths:
pid, _ = map(int, pattern.search(img_path).groups())
if pid == -1: continue # junk images are just ignored
pid_container.add(pid)
pid2label = {pid:label for label, pid in enumerate(pid_container)}
dataset = []
for img_path in img_paths:
pid, camid = map(int, pattern.search(img_path).groups())
if pid == -1: continue # junk images are just ignored
assert 0 <= pid <= 1501 # pid == 0 means background
assert 1 <= camid <= 6
camid -= 1 # index starts from 0
if relabel: pid = pid2label[pid]
dataset.append((img_path, pid, camid))
num_pids = len(pid_container)
num_imgs = len(dataset)
return dataset, num_pids, num_imgs
标签:data,self,manager,num,train,query,pids,数据,dir 来源: https://blog.csdn.net/Jane_JU/article/details/122279364