代码描述

代码来源： https://github.com/layumi/Person_reID_baseline_pytorch

详细信息可见README.md

prepare.py 文件简单讲解

文件代码：

import os
from shutil import copyfile

# You only need to change this line to your dataset download path
download_path = 'C:/Users/葛宇/学习/ReId/数据集/Market-1501-v15.09.15'

if not os.path.isdir(download_path):
    print('please change the download_path')

save_path = download_path + '/pytorch'
if not os.path.isdir(save_path):
    os.mkdir(save_path)
#-----------------------------------------
#query
query_path = download_path + '/query'
query_save_path = download_path + '/pytorch/query'
if not os.path.isdir(query_save_path):
    os.mkdir(query_save_path)

for root, dirs, files in os.walk(query_path, topdown=True):
    for name in files:
        if not name[-3:]=='jpg':
            continue
        ID  = name.split('_')
        src_path = query_path + '/' + name
        dst_path = query_save_path + '/' + ID[0] 
        if not os.path.isdir(dst_path):
            os.mkdir(dst_path)
        copyfile(src_path, dst_path + '/' + name)

#-----------------------------------------
#multi-query
query_path = download_path + '/gt_bbox'
# for dukemtmc-reid, we do not need multi-query
if os.path.isdir(query_path):
    query_save_path = download_path + '/pytorch/multi-query'
    if not os.path.isdir(query_save_path):
        os.mkdir(query_save_path)

    for root, dirs, files in os.walk(query_path, topdown=True):
        for name in files:
            if not name[-3:]=='jpg':
                continue
            ID  = name.split('_')
            src_path = query_path + '/' + name
            dst_path = query_save_path + '/' + ID[0]
            if not os.path.isdir(dst_path):
                os.mkdir(dst_path)
            copyfile(src_path, dst_path + '/' + name)

#-----------------------------------------
#gallery
gallery_path = download_path + '/bounding_box_test'
gallery_save_path = download_path + '/pytorch/gallery'
if not os.path.isdir(gallery_save_path):
    os.mkdir(gallery_save_path)

for root, dirs, files in os.walk(gallery_path, topdown=True):
    for name in files:
        if not name[-3:]=='jpg':
            continue
        ID  = name.split('_')
        src_path = gallery_path + '/' + name
        dst_path = gallery_save_path + '/' + ID[0]
        if not os.path.isdir(dst_path):
            os.mkdir(dst_path)
        copyfile(src_path, dst_path + '/' + name)

#---------------------------------------
#train_all
train_path = download_path + '/bounding_box_train'
train_save_path = download_path + '/pytorch/train_all'
if not os.path.isdir(train_save_path):
    os.mkdir(train_save_path)

for root, dirs, files in os.walk(train_path, topdown=True):
    for name in files:
        if not name[-3:]=='jpg':
            continue
        ID  = name.split('_')
        src_path = train_path + '/' + name
        dst_path = train_save_path + '/' + ID[0]
        if not os.path.isdir(dst_path):
            os.mkdir(dst_path)
        copyfile(src_path, dst_path + '/' + name)


#---------------------------------------
#train_val
train_path = download_path + '/bounding_box_train'
train_save_path = download_path + '/pytorch/train'
val_save_path = download_path + '/pytorch/val'
if not os.path.isdir(train_save_path):
    os.mkdir(train_save_path)
    os.mkdir(val_save_path)

for root, dirs, files in os.walk(train_path, topdown=True):
    for name in files:
        if not name[-3:]=='jpg':
            continue
        ID  = name.split('_')
        src_path = train_path + '/' + name
        dst_path = train_save_path + '/' + ID[0]
        if not os.path.isdir(dst_path):
            os.mkdir(dst_path)
            dst_path = val_save_path + '/' + ID[0]  #first image is used as val image
            os.mkdir(dst_path)
        copyfile(src_path, dst_path + '/' + name)

代码比较冗长，我总结一下。
这个脚本的作用是将原始数据集重新按图片ID分类。主要操作是通过调用系统函数加之必要的循环判断来实现。可不必细究。运行之前先将第五行download_path = 'C:/Users/葛宇/学习/ReId/数据集/Market-1501-v15.09.15'改为你自己的地址。
按照原作者的使用教程，第一步要做的就是运行prepare.py文件。
运行之后会发现在原先的Market1501数据集中（详见之前的博客）多出了一个文件夹“pytorch”,打开看看：

里面是6个文件夹：

1. gallery中包含752个文件夹项目，均按ID排序：
2. multi中是1051个文件夹项目，从0001到1501
3. query中包含750个文件项目，不含0000和-1
4. train中包含751个文件项目，不含0000和-1
5. train all中包含751个文件项目，不含0000和-1
6. val中包含751个文件项目，不含0000和-1

具体情况可以自己尝试打开观察。