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. HTML5解决大文件断点续传

    一.概述 所谓断点续传,其实只是指下载,也就是要从文件已经下载的地方开始继续下载.在以前版本的HTTP协议是不支持断点的,HTTP/1.1开始就支持了.一般断点下载时才用到Range和Content- ...

  2. POJ 3691 DNA repair ( Trie图 && DP )

    题意 : 给出 n 个病毒串,最后再给出一个主串,问你最少改变主串中的多少个单词才能使得主串中不包含任何一个病毒串 分析 : 做多了AC自动机的题,就会发现这些题有些都是很套路的题目.在构建 Trie ...

  3. 特征提取算法（3）——SIFT特征提取算子

    目录: 前言 1.高斯尺度空间GSS 2.高斯差分DOG 用DoG检测特征点 GSS尺度选择 3.图像金字塔建立 用前一个octave中的倒数第三幅图像生成下一octave中的第一幅图像 每层octa ...

  4. 【CF1251E】Voting（贪心）

    题意:有n个人,需要搞到全部n个人的票,搞到第i个人的票有两种方式:之前已经搞到mi个人的票,或者直接花费pi 问最小的搞到所有票的总代价 n<=2e5,1<=p[i]<=1e9,0 ...

  5. Numpy基础(数组创建，切片，通用函数）

    1.创建ndarray 数组的创建函数: array:将输入的数据(列表,元组,数组,或者其他序列类型)转换为ndarray.要么推断出dtype,要么显式给定dtype asarray:将输入转换为 ...

  6. 170906-MyBatis续

    ===============================================Dynamic SQL========================================== ...

  7. [CSP-S模拟测试]:简单的序列（DP）

    题目描述 从前有个括号序列$s$,满足$|s|=m$.你需要统计括号序列对$(p,q)$的数量. 其中$(p,q)$满足$|p|+|s|+|q|=n$,且$p+s+q$是一个合法的括号序列. 输入格式 ...

  8. css简单实现带箭头的边框

    原文地址 https://tianshengjie.cn/artic... css简单实现带箭头的边框 普通边框 <style> .border { width: 100px; heigh ...

  9. scrapy-splash常用设置

    # Splash服务器地址 SPLASH_URL = 'http://localhost:8050' # 开启Splash的两个下载中间件并调整HttpCompressionMiddleware的次序 ...

  10. (转) intellij idea部署web项目时的位置(Tomcat)

    这篇文章说的比较好: 原文地址:https://blog.csdn.net/zmx729618/article/details/78340566 1.当你项目启动的时候console能看到项目运行的位 ...