行人重识别数据集转换--统一为market1501数据集进行多数据集联合训练

2023-11-10

0 前言

常用的reID数据集如图所示

下载好数据集，我的数据集一开始是这样的

第一步创建出来market1501的数据集文件夹格式

market1501数据集的具体介绍可以看看这个 http://blog.fangchengjin.cn/reid-market-1501.html

import os

def make_market_dir(dst_dir='./'):
    market_root = os.path.join(dst_dir, 'market1501')
    train_path = os.path.join(market_root, 'bounding_box_train')
    query_path = os.path.join(market_root, 'query')
    test_path = os.path.join(market_root, 'bounding_box_test')

    if not os.path.exists(train_path):
        os.makedirs(train_path)
    if not os.path.exists(query_path):
        os.makedirs(query_path)
    if not os.path.exists(test_path):
        os.makedirs(test_path)


if __name__ == '__main__':
   make_market_dir(dst_dir='E:/reID')

这样就创建出来我们需要的几个文件夹了

第二步将market1501数据集抽取出来

import re
import os
import shutil

def extract_market(src_path, dst_dir):
    img_names = os.listdir(src_path)
    pattern = re.compile(r'([-\d]+)_c(\d)')
    pid_container = set()
    for img_name in img_names:
        if '.jpg' not in img_name:
            continue
        print(img_name)
        # pid: 每个人的标签编号 1
        # _  : 摄像头号 2
        pid, _ = map(int, pattern.search(img_name).groups())
        # 去掉没用的图片
        if pid == 0 or pid == -1:
            continue
        shutil.copy(os.path.join(src_path, img_name), os.path.join(dst_dir, img_name))

if __name__ == '__main__':
    src_train_path = r'D:\data\market1501\bounding_box_train'
    src_query_path = r'D:\data\market1501\query'
    src_test_path = r'D:\data\market1501\bounding_box_test'
    # 将整个market1501数据集作为训练集
    dst_dir = r'E:\reID\market1501\bounding_box_train'

    extract_market(src_train_path, dst_dir)
    extract_market(src_query_path, dst_dir)
    extract_market(src_test_path, dst_dir)

抽取的结果如图所示，现在一共有 29419 张图片， ID从0001到1501一共1501 个不同ID的行人。

第三步将CUHK数据集抽取出来

具体介绍看这个：http://blog.fangchengjin.cn/reid-cuhk03.html

import glob
import re
import os.path as osp
import shutil

import re
import os
import shutil

def extract_cuhk03(src_path, dst_dir):
    img_names = os.listdir(src_path)
    pattern = re.compile(r'([-\d]+)_c(\d)_([\d]+)')
    pid_container = set()
    for img_name in img_names:
        if '.png' not in img_name and '.jpg' not in img_name:
            continue
        print(img_name)
        # pid: 每个人的标签编号 1
        # camid  : 摄像头号 2
        pid, camid, fname = map(int, pattern.search(img_name).groups())
        # 这里注意需要加上前面的market1501数据集的最后一个ID 1501
        # 在前面数据集的最后那个ID基础上继续往后排
        pid += 1501
        dst_img_name = str(pid).zfill(6) + '_c' + str(camid) + '_CUHK' + str(fname) + '.jpg'
        shutil.copy(os.path.join(src_path, img_name), os.path.join(dst_dir, dst_img_name))

if __name__ == '__main__':
    src_train_path = r'D:\data\cuhk03-np\detected\bounding_box_train'
    src_query_path = r'D:\data\cuhk03-np\detected\query'
    src_test_path = r'D:\data\cuhk03-np\detected\bounding_box_test'
    dst_dir = r'E:\reID\market1501\bounding_box_train'

    extract_cuhk03(src_train_path, dst_dir)
    extract_cuhk03(src_query_path, dst_dir)
    extract_cuhk03(src_test_path, dst_dir)

转换结果如图所示，CUHK03一共有 14097 张图片， ID从001502到002968一共1467个不同ID的行人。

第四步将MSMT17数据集抽取出来

import glob
import re
import os.path as osp
import shutil

def msmt2market(dir_path, list_path, dst_dir, prev_pid):
    with open(list_path, 'r') as txt:
        lines = txt.readlines()
    pid_container = set()
    for img_idx, img_info in enumerate(lines):
        img_path, pid = img_info.split(' ')
        pid = int(pid) + prev_pid + 1 # 2969 5121
        camid = int(img_path.split('_')[2])
        img_path = osp.join(dir_path, img_path)
        name = img_path.split('/')[-1]  # '0001_c2_f0046182.jpg'
        Newdir = osp.join(dst_dir, str(pid).zfill(6) + '_c' + str(camid) + '_' + name)  # 用字符串函数zfill 以0补全所需位数
        shutil.copy(img_path, Newdir)  # 复制一个文件到一个文件或一个目录


    # check if pid starts from 0 and increments with 1
    for idx, pid in enumerate(pid_container):
        assert idx == pid, "See code comment for explanation"

if __name__ == '__main__':
    dataset_dir = r'D:\data\MSMT17_V2'
    train_dir = osp.join(dataset_dir, 'mask_train_v2')
    test_dir = osp.join(dataset_dir, 'mask_test_v2')
    list_train_path = osp.join(dataset_dir, 'list_train.txt')
    list_val_path = osp.join(dataset_dir, 'list_val.txt')
    list_query_path = osp.join(dataset_dir, 'list_query.txt')
    list_gallery_path = osp.join(dataset_dir, 'list_gallery.txt')

    dst_dir = r'E:\reID\market1501\bounding_box_train'
    msmt2market(train_dir, list_train_path, dst_dir, 2968)
    msmt2market(train_dir, list_val_path, dst_dir, 2968)
    msmt2market(test_dir, list_query_path, dst_dir, 4009)
    msmt2market(test_dir, list_gallery_path, dst_dir, 4009)

转换结果如图所示，MSMT17一共有 126441 张图片， ID从002969到007069一共1467个不同ID的行人。

到现在以及完成了除了duke以外的几个大型主流数据集的转换，duke数据集想留作测试，体现出模型的泛化能力。

目前的统计结果如下图所示，训练集现在已经有将近17w的图片，ID一共有7069个。

第五步将viper数据集抽取出来

import re
import os
import shutil

def extract_viper(src_path, dst_dir, camid=1):
    img_names = os.listdir(src_path)
    pattern = re.compile(r'([\d]+)_([\d]+)')
    pid_container = set()
    for img_name in img_names:
        if '.bmp' not in img_name:
            continue
        print(img_name)
        pid, fname = map(int, pattern.search(img_name).groups())
        # 这里注意需要加上前面的数据集的最后一个ID 7069
        # 由于viper数据集ID是从0开始，因此需要+1
        pid += 7069 + 1
        dst_img_name = str(pid).zfill(6) + '_c' + str(camid) + '_viper' + str(fname) + '.jpg'
        shutil.copy(os.path.join(src_path, img_name), os.path.join(dst_dir, dst_img_name))

if __name__ == '__main__':
    src_cam_a = r'D:\data\viper\cam_a'
    src_cam_b = r'D:\data\viper\cam_b'
    dst_dir = r'E:\reID\market1501\bounding_box_train'

    extract_viper(src_cam_a, dst_dir, camid=1)
    extract_viper(src_cam_b, dst_dir, camid=2)

转换后的viper数据集一共有1264张图片， ID从007070到007943一共1467个不同ID的行人。需要注意这里ID不是连续的，不过只要ID跟之前不重复即可。

第六步将SenseReID数据集抽取出来

import re
import os
import shutil

def extract_SenseReID(src_path, dst_dir, fname):
    img_names = os.listdir(src_path)
    pattern = re.compile(r'([\d]+)_([\d]+)')
    pid_container = set()
    for img_name in img_names:
        if '.jpg' not in img_name:
            continue
        print(img_name)
        pid, camid = map(int, pattern.search(img_name).groups())
        pid += 7943 + 1
        dst_img_name = str(pid).zfill(6) + '_c' + str(camid + 1) + '_SenseReID_' + fname + '.jpg'
        shutil.copy(os.path.join(src_path, img_name), os.path.join(dst_dir, dst_img_name))

if __name__ == '__main__':
    src_cam_a = r'D:\data\SenseReID\test_gallery'
    src_cam_b = r'D:\data\SenseReID\test_probe'
    dst_dir = r'E:\reID\market1501\bounding_box_train'

    extract_SenseReID(src_cam_a, dst_dir, 'gallery')
    extract_SenseReID(src_cam_b, dst_dir, 'probe')

转换后的SenseReID数据集一共有4428张图片， ID从007944到009661。

第七步将prid数据集抽取出来

import re
import os
import shutil

def extract_prid(src_path, dst_dir, prevID, camid=1):
    pattern = re.compile(r'person_([\d]+)')
    pid_container = set()

    sub_dir_names = os.listdir(src_path) # ['person_0001', 'person_0002',...

    for sub_dir_name in sub_dir_names: # 'person_0001'
        img_names_all = os.listdir(os.path.join(src_path, sub_dir_name))
        # 这里我就只取首尾两张，防止重复太多了
        img_names = [img_names_all[0], img_names_all[-1]]
        for img_name in img_names: # '0001.png'
            if '.png' not in img_name:
                continue
            print(img_name)
            # parent.split('\\')[-1] : person_0001
            pid = int(pattern.search(sub_dir_name).group(1))
            pid += prevID
            dst_img_name = str(pid).zfill(6) + '_c' + str(camid) + '_prid' + img_name.replace('.png', '.jpg')
            shutil.copy(os.path.join(src_path, sub_dir_name, img_name), os.path.join(dst_dir, dst_img_name))

if __name__ == '__main__':
    src_cam_a = r'D:\data\prid2011\multi_shot\cam_a'
    src_cam_b = r'D:\data\prid2011\multi_shot\cam_b'
    dst_dir = r'E:\reID\market1501\bounding_box_train'

    extract_prid(src_cam_a, dst_dir, 9661)
    extract_prid(src_cam_b, dst_dir, 10046)

转换后的prid数据集一共有2268张图片， ID从009662到010795。

第八步将ilids数据集抽取出来

import re
import os
import shutil

def extract_ilids(src_path, dst_dir, prevID, camid):
    pattern = re.compile(r'person([\d]+)')
    pid_container = set()

    sub_dir_names = os.listdir(src_path)

    for sub_dir_name in sub_dir_names:
        img_names = os.listdir(os.path.join(src_path, sub_dir_name))
        for img_name in img_names:
            if '.png' not in img_name:
                continue
            print(img_name)
            pid = int(pattern.search(sub_dir_name).group(1))
            pid += prevID
            dst_img_name = str(pid).zfill(6) + '_c' + str(camid) + '_ilids' + '.jpg'
            shutil.copy(os.path.join(src_path, sub_dir_name, img_name), os.path.join(dst_dir, dst_img_name))

if __name__ == '__main__':
    src_cam_a = r'D:\data\ilids\i-LIDS-VID\images\cam1'
    src_cam_b = r'D:\data\ilids\i-LIDS-VID\images\cam2'
    dst_dir = r'E:\reID\market1501\bounding_box_train'

    extract_ilids(src_cam_a, dst_dir, 10795, 1)
    extract_ilids(src_cam_b, dst_dir, 10795, 2)

转换后的ilids数据集一共有600张图片， ID从010796到011114。

第九步将grid数据集抽取出来

import re
import os
import shutil

def extract_grid(src_path, dst_dir, camid=1):
    img_names = os.listdir(src_path)
    pattern = re.compile(r'([\d]+)_')
    pid_container = set()
    for img_name in img_names:
        if '.jpeg' not in img_name:
            continue
        print(img_name)
        pid = int(pattern.search(img_name).group(1))
        if pid == 0:
            continue
        pid += 11114
        dst_img_name = str(pid).zfill(6) + '_c' + str(camid) + '_grid' + '.jpg'
        shutil.copy(os.path.join(src_path, img_name), os.path.join(dst_dir, dst_img_name))

if __name__ == '__main__':
    src_cam_a = r'D:\data\grid\probe'
    src_cam_b = r'D:\data\grid\gallery'
    dst_dir = r'E:\reID\market1501\bounding_box_train'

    extract_grid(src_cam_a, dst_dir, camid=1)
    extract_grid(src_cam_b, dst_dir, camid=2)

转换后的grid数据集一共有500张图片， ID从011115到011364

最终的数据集统计结果如下图所示，一共有将近十八万张图片和11103个不同ID的行人：

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