行人重识别(ReID) ——基于Person_reID_baseline_pytorch修改业务流程

电路设计

行人重识别(ReID) ——基于Person_reID_baseline_pytorch修改业务流程

2019-07-14 11:54发布生成海报

站内文章 / 电路设计

11251 0

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,


                         
                         
     
  
      Ta的文章  更多 >> 
             
      


                    
          行人重识别(ReID) ——基于Person_reID_baseline_pytorch修改业务流程
               
                0 个评论
               
              
                              
          图像处理之快速均值模糊(Box Blur)
               
                0 个评论
               
              
                              
          DSP6000的上电及供电
               
                0 个评论
               
              
                               

              
                  
  

  
  
    
         
  
      热门文章


      



  
    
      
      举报内容
    
    






检举类型


检举内容


检举用户




检举原因



广告推广


恶意灌水


回答内容与提问无关



抄袭答案


其他





检举说明(必填)






    

                
                 
      



    

  


 打开微信“扫一扫”，打开网页后点击屏幕右上角分享按钮
 

 





   


  
    
    



      
      
       
  
      




  
             
            
                    Copyright © 2024 平头弟 京ICP备15032243号-1
                         投诉举报邮箱：nettui@qq.com

行人重识别(ReID) ——基于Person_reID_baseline_pytorch修改业务流程

Ta的文章 更多 >>

热门文章

举报内容

检举类型

检举原因

检举说明(必填)

打开微信“扫一扫”，打开网页后点击屏幕右上角分享按钮

Ta的文章更多 >>