下载Person_reID_baseline_pytorch地址：https://github.com/layumi/Person_reID_baseline_pytorch/tree/master/tutorial

下载Market1501数据集：http://www.liangzheng.org/Project/project_reid.html

Market1501数据集结构：

├── Market/

│   ├── bounding_box_test/          /* Files for testing (candidate images pool)

│   ├── bounding_box_train/         /* Files for training

│   ├── gt_bbox/                    /* We do not use it

│   ├── gt_query/                   /* Files for multiple query testing

│   ├── query/                      /* Files for testing (query images)

│   ├── readme.txt

修改--test_dir路径，执行python prepare.py之后的数据集结构：

├── Market/

│   ├── bounding_box_test/          /* Files for testing (candidate images pool)

│   ├── bounding_box_train/         /* Files for training

│   ├── gt_bbox/                    /* We do not use it

│   ├── gt_query/                   /* Files for multiple query testing

│   ├── query/                      /* Files for testing (query images)

│   ├── readme.txt

│   ├── pytorch/

│       ├── train/                   /* train

│           ├── 0002

|           ├── 0007

|           ...

│       ├── val/                     /* val

│       ├── train_all/               /* train+val

│       ├── query/                   /* query files

│       ├── gallery/                 /* gallery files

训练模型并测试，修改train.py、test.py中的--test_dir路径/home/hylink/eclipse-workspace/reID/Market/pytorch：

python train.py

python test.py

python demo.py --query_index 777

效果展示：

修改test.py（将原gallery和query生成底库，改为只生成gallery底库）

# -*- coding: utf-8 -*-

from __future__ import print_function, division

import argparse

import torch

import torch.nn as nn

import torch.optim as optim

from torch.optim import lr_scheduler

from torch.autograd import Variable

import numpy as np

import torchvision

from torchvision import datasets, models, transforms

import time

import os

import scipy.io

from model import ft_net, ft_net_dense, PCB, PCB_test

######################################################################

# Options

# --------

parser = argparse.ArgumentParser(description='Training')

parser.add_argument('--gpu_ids',default='0', type=str,help='gpu_ids: e.g. 0  0,1,2  0,2')

parser.add_argument('--which_epoch',default='last', type=str, help='0,1,2,3...or last')

parser.add_argument('--test_dir',default='/home/hylink/eclipse-workspace/reID/Market/pytorch',type=str, help='./test_data')

parser.add_argument('--name', default='ft_ResNet50', type=str, help='save model path')

parser.add_argument('--batchsize', default=32, type=int, help='batchsize')

parser.add_argument('--use_dense', action='store_true', help='use densenet121' )

parser.add_argument('--PCB', action='store_true', help='use PCB' )

parser.add_argument('--multi', action='store_true', help='use multiple query' )

opt = parser.parse_args()

str_ids = opt.gpu_ids.split(',')

#which_epoch = opt.which_epoch

name = opt.name

test_dir = opt.test_dir

gpu_ids = []

for str_id in str_ids:

    id = int(str_id)

    if id >=0:

        gpu_ids.append(id)

# set gpu ids

if len(gpu_ids)>0:

    torch.cuda.set_device(gpu_ids[0])

######################################################################

# Load Data

# ---------

#

# We will use torchvision and torch.utils.data packages for loading the

# data.

#

data_transforms = transforms.Compose([

        transforms.Resize((288,144), interpolation=3),

        transforms.ToTensor(),

        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])

############### Ten Crop

        #transforms.TenCrop(224),

        #transforms.Lambda(lambda crops: torch.stack(

         #   [transforms.ToTensor()(crop)

          #      for crop in crops]

           # )),

        #transforms.Lambda(lambda crops: torch.stack(

         #   [transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])(crop)

          #       for crop in crops]

          # ))

])

if opt.PCB:

    data_transforms = transforms.Compose([

        transforms.Resize((384,192), interpolation=3),

        transforms.ToTensor(),

        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])

    ])

data_dir = test_dir

if opt.multi:

    image_datasets = {x: datasets.ImageFolder( os.path.join(data_dir,x) ,data_transforms) for x in ['gallery','query','multi-query']}

    dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=opt.batchsize,

                                             shuffle=False, num_workers=16) for x in ['gallery','query','multi-query']}

else:

    image_datasets = {x: datasets.ImageFolder( os.path.join(data_dir,x) ,data_transforms) for x in ['gallery']}

    dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=opt.batchsize,

                                             shuffle=False, num_workers=16) for x in ['gallery']}

#class_names = image_datasets['query'].classes

use_gpu = torch.cuda.is_available()

######################################################################

# Load model

#---------------------------

def load_network(network):

    save_path = os.path.join('./model',name,'net_%s.pth'%opt.which_epoch)

    network.load_state_dict(torch.load(save_path))

    return network

######################################################################

# Extract feature

# ----------------------

#

# Extract feature from  a trained model.

#

def fliplr(img):

    '''flip horizontal'''

    inv_idx = torch.arange(img.size(3)-1,-1,-1).long()  # N x C x H x W

    img_flip = img.index_select(3,inv_idx)

    return img_flip

def extract_feature(model,dataloaders):

    features = torch.FloatTensor()

    count = 0

    for data in dataloaders:

        img, label = data

        n, c, h, w = img.size()

        count += n

        print(count)

        if opt.use_dense:

            ff = torch.FloatTensor(n,1024).zero_()

        else:

            ff = torch.FloatTensor(n,2048).zero_()

        if opt.PCB:

            ff = torch.FloatTensor(n,2048,6).zero_() # we have six parts

        for i in range(2):

            if(i==1):

                img = fliplr(img)

            input_img = Variable(img.cuda())

            outputs = model(input_img)

            f = outputs.data.cpu()

            ff = ff+f

        # norm feature

        if opt.PCB:

            # feature size (n,2048,6)

            # 1. To treat every part equally, I calculate the norm for every 2048-dim part feature.

            # 2. To keep the cosine score==1, sqrt(6) is added to norm the whole feature (2048*6).

            fnorm = torch.norm(ff, p=2, dim=1, keepdim=True) * np.sqrt(6)

            ff = ff.div(fnorm.expand_as(ff))

            ff = ff.view(ff.size(0), -1)

        else:

            fnorm = torch.norm(ff, p=2, dim=1, keepdim=True)

            ff = ff.div(fnorm.expand_as(ff))

        features = torch.cat((features,ff), 0)

    return features

def get_id(img_path):

    camera_id = []

    labels = []

    for path, v in img_path:

        #filename = path.split('/')[-1]

        filename = os.path.basename(path)

        label = filename[0:4]

        camera = filename.split('c')[1]

        if label[0:2]=='-1':

            labels.append(-1)

        else:

            labels.append(int(label))

        camera_id.append(int(camera[0]))

    return camera_id, labels

gallery_path = image_datasets['gallery'].imgs

#query_path = image_datasets['query'].imgs

gallery_cam,gallery_label = get_id(gallery_path)

#query_cam,query_label = get_id(query_path)

if opt.multi:

    mquery_path = image_datasets['multi-query'].imgs

    mquery_cam,mquery_label = get_id(mquery_path)

######################################################################

# Load Collected data Trained model

print('-------test-----------')

if opt.use_dense:

    model_structure = ft_net_dense(751)

else:

    model_structure = ft_net(751)

if opt.PCB:

    model_structure = PCB(751)

model = load_network(model_structure)

# Remove the final fc layer and classifier layer

if not opt.PCB:

    model.model.fc = nn.Sequential()

    model.classifier = nn.Sequential()

else:

    model = PCB_test(model)

# Change to test mode

model = model.eval()

if use_gpu:

    model = model.cuda()

# Extract feature

gallery_feature = extract_feature(model,dataloaders['gallery'])

#query_feature = extract_feature(model,dataloaders['query'])

if opt.multi:

    mquery_feature = extract_feature(model,dataloaders['multi-query'])

# Save to Matlab for check

#result = {'gallery_f':gallery_feature.numpy(),'gallery_label':gallery_label,'gallery_cam':gallery_cam,'query_f':query_feature.numpy(),'query_label':query_label,'query_cam':query_cam}

result = {'gallery_f':gallery_feature.numpy(),'gallery_label':gallery_label,'gallery_cam':gallery_cam}

scipy.io.savemat('pytorch_result.mat',result)

if opt.multi:

    result = {'mquery_f':mquery_feature.numpy(),'mquery_label':mquery_label,'mquery_cam':mquery_cam}

    scipy.io.savemat('multi_query.mat',result)

修改demo.py（将query路径下的图片生成特征并于gallery底库进行比对并展示）

# -*- coding: utf-8 -*-

from __future__ import print_function, division

import argparse

import torch

import torch.nn as nn

import torch.optim as optim

from torch.optim import lr_scheduler

from torch.autograd import Variable

import numpy as np

import torchvision

from torchvision import datasets, models, transforms

import time

import os

import scipy.io

import matplotlib.pyplot as plt

from model import ft_net, ft_net_dense, PCB, PCB_test

######################################################################

# Options

# --------

parser = argparse.ArgumentParser(description='Training')

parser.add_argument('--gpu_ids',default='0', type=str,help='gpu_ids: e.g. 0  0,1,2  0,2')

parser.add_argument('--which_epoch',default='last', type=str, help='0,1,2,3...or last')

parser.add_argument('--test_dir',default='/home/hylink/eclipse-workspace/reID/Market/pytorch',type=str, help='./test_data')

parser.add_argument('--name', default='ft_ResNet50', type=str, help='save model path')

parser.add_argument('--batchsize', default=32, type=int, help='batchsize')

parser.add_argument('--use_dense', action='store_true', help='use densenet121' )

parser.add_argument('--PCB', action='store_true', help='use PCB' )

parser.add_argument('--multi', action='store_true', help='use multiple query' )

parser.add_argument('--query_index', default=3, type=int, help='test_image_index')

opt = parser.parse_args()

str_ids = opt.gpu_ids.split(',')

#which_epoch = opt.which_epoch

name = opt.name

test_dir = opt.test_dir

gpu_ids = []

for str_id in str_ids:

    id = int(str_id)

    if id >=0:

        gpu_ids.append(id)

# set gpu ids

if len(gpu_ids)>0:

    torch.cuda.set_device(gpu_ids[0])

######################################################################

# Load Data

# ---------

#

# We will use torchvision and torch.utils.data packages for loading the

# data.

#

data_transforms = transforms.Compose([

        transforms.Resize((288,144), interpolation=3),

        transforms.ToTensor(),

        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])

############### Ten Crop

        #transforms.TenCrop(224),

        #transforms.Lambda(lambda crops: torch.stack(

         #   [transforms.ToTensor()(crop)

          #      for crop in crops]

           # )),

        #transforms.Lambda(lambda crops: torch.stack(

         #   [transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])(crop)

          #       for crop in crops]

          # ))

])

if opt.PCB:

    data_transforms = transforms.Compose([

        transforms.Resize((384,192), interpolation=3),

        transforms.ToTensor(),

        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])

    ])

data_dir = test_dir

if opt.multi:

    image_datasets = {x: datasets.ImageFolder( os.path.join(data_dir,x) ,data_transforms) for x in ['gallery','query','multi-query']}

    dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=opt.batchsize,

                                             shuffle=False, num_workers=16) for x in ['gallery','query','multi-query']}

else:

    image_datasets = {x: datasets.ImageFolder( os.path.join(data_dir,x) ,data_transforms) for x in ['gallery','query']}

    dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=opt.batchsize,

                                             shuffle=False, num_workers=16) for x in ['gallery','query']}

class_names = image_datasets['query'].classes

use_gpu = torch.cuda.is_available()

######################################################################

# Load model

#---------------------------

def load_network(network):

    save_path = os.path.join('./model',name,'net_%s.pth'%opt.which_epoch)

    network.load_state_dict(torch.load(save_path))

    return network

######################################################################

# Extract feature

# ----------------------

#

# Extract feature from  a trained model.

#

def fliplr(img):

    '''flip horizontal'''

    inv_idx = torch.arange(img.size(3)-1,-1,-1).long()  # N x C x H x W

    img_flip = img.index_select(3,inv_idx)

    return img_flip

def extract_feature(model,dataloaders):

    features = torch.FloatTensor()

    count = 0

    for data in dataloaders:

        img, label = data

        n, c, h, w = img.size()

        count += n

        print(count)

        if opt.use_dense:

            ff = torch.FloatTensor(n,1024).zero_()

        else:

            ff = torch.FloatTensor(n,2048).zero_()

        if opt.PCB:

            ff = torch.FloatTensor(n,2048,6).zero_() # we have six parts

        for i in range(2):

            if(i==1):

                img = fliplr(img)

            input_img = Variable(img.cuda())

            outputs = model(input_img)

            f = outputs.data.cpu()

            ff = ff+f

        # norm feature

        if opt.PCB:

            # feature size (n,2048,6)

            # 1. To treat every part equally, I calculate the norm for every 2048-dim part feature.

            # 2. To keep the cosine score==1, sqrt(6) is added to norm the whole feature (2048*6).

            fnorm = torch.norm(ff, p=2, dim=1, keepdim=True) * np.sqrt(6)

            ff = ff.div(fnorm.expand_as(ff))

            ff = ff.view(ff.size(0), -1)

        else:

            fnorm = torch.norm(ff, p=2, dim=1, keepdim=True)

            ff = ff.div(fnorm.expand_as(ff))

        features = torch.cat((features,ff), 0)

    return features

def get_id(img_path):

    camera_id = []

    labels = []

    for path, v in img_path:

        #filename = path.split('/')[-1]

        filename = os.path.basename(path)

        label = filename[0:4]

        camera = filename.split('c')[1]

        if label[0:2]=='-1':

            labels.append(-1)

        else:

            labels.append(int(label))

        camera_id.append(int(camera[0]))

    return camera_id, labels

query_path = image_datasets['query'].imgs

query_cam,query_label = get_id(query_path)

if opt.multi:

    mquery_path = image_datasets['multi-query'].imgs

    mquery_cam,mquery_label = get_id(mquery_path)

######################################################################

# Load Collected data Trained model

print('-------test-----------')

if opt.use_dense:

    model_structure = ft_net_dense(751)

else:

    model_structure = ft_net(751)

if opt.PCB:

    model_structure = PCB(751)

model = load_network(model_structure)

# Remove the final fc layer and classifier layer

if not opt.PCB:

    model.model.fc = nn.Sequential()

    model.classifier = nn.Sequential()

else:

    model = PCB_test(model)

# Change to test mode

model = model.eval()

if use_gpu:

    model = model.cuda()

# Extract feature

query_feature = extract_feature(model,dataloaders['query'])

######################################################################

######################################################################

def imshow(path, title=None):

    """Imshow for Tensor."""

    im = plt.imread(path)

    plt.imshow(im)

    if title is not None:

        plt.title(title)

    plt.pause(0.001)  # pause a bit so that plots are updated

######################################################################

result = scipy.io.loadmat('pytorch_result.mat')

gallery_feature = torch.FloatTensor(result['gallery_f'])

gallery_cam = result['gallery_cam'][0]

gallery_label = result['gallery_label'][0]

query_feature = query_feature.cuda()

gallery_feature = gallery_feature.cuda()

#######################################################################

# sort the images

def sort_img(qf, ql, qc, gf, gl, gc):

    query = qf.view(-1,1)

    # print(query.shape)

    score = torch.mm(gf,query)

    score = score.squeeze(1).cpu()

    score = score.numpy()

    # predict index

    index = np.argsort(score)  #from small to large

    index = index[::-1]

    # index = index[0:2000]

    # good index

    query_index = np.argwhere(gl==ql)

    #same camera

    camera_index = np.argwhere(gc==qc)

    junk_index1 = np.argwhere(gl==-1)

    junk_index2 = np.intersect1d(query_index, camera_index)

    junk_index = np.append(junk_index2, junk_index1) 

    mask = np.in1d(index, junk_index, invert=True)

    index = index[mask]

    return index

i = opt.query_index

index = sort_img(query_feature[i],query_label[i],query_cam[i],gallery_feature,gallery_label,gallery_cam)

########################################################################

# Visualize the rank result

query_path, _ = image_datasets['query'].imgs[i]

query_label = query_label[i]

print(query_path)

print('Top 10 images are as follow:')

try: # Visualize Ranking Result

    # Graphical User Interface is needed

    fig = plt.figure(figsize=(16,4))

    ax = plt.subplot(1,11,1)

    ax.axis('off')

    imshow(query_path,'query')

    for i in range(10):

        ax = plt.subplot(1,11,i+2)

        ax.axis('off')

        img_path, _ = image_datasets['gallery'].imgs[index[i]]

        label = gallery_label[index[i]]

        imshow(img_path)

        if label == query_label:

            ax.set_title('%d'%(i+1), color='green')

        else:

            ax.set_title('%d'%(i+1), color='red')

        print(img_path)

except RuntimeError:

    for i in range(10):

        img_path = image_datasets.imgs[index[i]]

        print(img_path[0])

    print('If you want to see the visualization of the ranking result, graphical user interface is needed.')

fig.savefig("show.png")

自定义底库放置在pytorch/gallery/



自定义查询库放置在pytorch/query/



效果展示

行人重识别(ReID) ——基于Person_reID_baseline_pytorch修改业务流程的更多相关文章

  1. 行人重识别(ReID) ——基于深度学习的行人重识别研究综述

    转自:https://zhuanlan.zhihu.com/p/31921944 前言:行人重识别(Person Re-identification)也称行人再识别,本文简称为ReID,是利用计算机视 ...

  2. 行人重识别(ReID) ——基于MGN-pytorch进行可视化展示

    下载MGN-pytorch:https://github.com/seathiefwang/MGN-pytorch 下载Market1501数据集:http://www.liangzheng.org/ ...

  3. 行人重识别(ReID) ——技术实现及应用场景

    导读 跨镜追踪(Person Re-Identification,简称 ReID)技术是现在计算机视觉研究的热门方向,主要解决跨摄像头跨场景下行人的识别与检索.该技术能够根据行人的穿着.体态.发型等信 ...

  4. 行人重识别(ReID) ——数据集描述 DukeMTMC-reID

    数据集简介 DukeMTMC 数据集是一个大规模标记的多目标多摄像机行人跟踪数据集.它提供了一个由 8 个同步摄像机记录的新型大型高清视频数据集,具有 7,000 多个单摄像机轨迹和超过 2,700 ...

  5. 行人重识别(ReID) ——数据集描述 Market-1501

    数据集简介 Market-1501 数据集在清华大学校园中采集,夏天拍摄,在 2015 年构建并公开.它包括由6个摄像头(其中5个高清摄像头和1个低清摄像头)拍摄到的 1501 个行人.32668 个 ...

  6. 行人重识别(ReID) ——概述

    什么是Re-ID? 行人重识别(Person re-identification,简称Re-ID)也称行人再识别,是利用计算机视觉技术判断图像或者视频序列中是否存在特定行人的技术.广泛被认为是一个图像 ...

  7. 行人重识别(ReID) ——数据集描述 CUHK03

    数据集简介 CUHK03是第一个足以进行深度学习的大规模行人重识别数据集,该数据集的图像采集于香港中文大学(CUHK)校园.数据以"cuhk-03.mat"的 MAT 文件格式存储 ...

  8. CVPR2020行人重识别算法论文解读

    CVPR2020行人重识别算法论文解读 Cross-modalityPersonre-identificationwithShared-SpecificFeatureTransfer 具有特定共享特征变换 ...

  9. 行人重识别和车辆重识别（ReID）中的评测指标——mAP和Rank-k

    1.mAP mAP的全称是mean Average Precision,意为平均精度均值(如果按照原来的顺利翻译就是平均均值精度).这个指标是多目标检测和多标签图像分类中长常用的评测指标,因为这类任务 ...

随机推荐

  1. Linux GDB 程序调试工具使用详解

    转自    http://www.codeceo.com/article/linux-gdb-tools.html 整理的挺全的 GDB概述 GDB是GNU开源组织发布的一个强大的UNIX下的程序调试 ...

  2. PHP入门培训教程　一个漂亮的PHP验证码

    如何写一个漂亮的PHP验证码?兄弟连PHP培训 小编分享一段代码给大家: <?php class Imagecode{ private $width ; private $height; pri ...

  3. PHP获取数组最大值下标的方法

    <?php $hots = array('8213'=> 0,'8212'=> 100,'8172'=> 10008); $key = array_search(max($ho ...

  4. AC自动机再加强版

    AC自动机 拓扑排序优化,注意拓扑排序前要把所有入度为零的点都加进去 #include<bits/stdc++.h> using namespace std; #define maxn 1 ...

  5. IDEA 创建spring boot 的Hello World 项目

    1.Open IDEA,choose "New-->Project" 2.Choose "Spring Initializr" 3. Choose jav ...

  6. loadrunner性能测试巧匠训练营-controller

    1.设置集合点 现在脚本添加集合点的函数,集合点不能添加到事务里面,负责统计事务的时候会把时间计算进去 2.IP欺骗 前言 https://www.cnblogs.com/danbing/p/7459 ...

  7. rf-idf的java实现

    还存在的问题是,对于其中分词借助的库还存在问题 参考此篇链接 http://www.cnblogs.com/ywl925/archive/2013/08/26/3275878.html 具体代码部分: ...

  8. 设计 4 个线程，其中两个线程每次对 j 增加 1，另外两个线程对 j 每次减少 1。写出程序。

    先设计一个类处理加减这一行为: public class ManyThread { private int j = 0; public synchronized void inc(){ j++; Sy ...

  9. 如何删除由Automater创建的服务

    想要设置两个实用的快捷设置(如何设置): 1.复制当前文件或者文件夹路径 2.在终端打开文件夹 然后想到可以用mac自带的自动操作这款软件,英文叫Automater.接着发现,显示路径栏后,直接就提供 ...

  10. So the type system doesn’t feel so static.

    object wb{ def main(args:Array[String]){ println("Happy everyday!DATA-CENTER!") println(ne ...