# dnnDetector

**Repository Path**: T_Geek/dnnDetector

## Basic Information

- **Project Name**: dnnDetector
- **Description**: Object detection based on Opencv.dnn, easy to use
- **Primary Language**: C++
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-07-06
- **Last Updated**: 2021-07-06

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# dnnDetector
![](https://img.shields.io/badge/TGeek-Projects-blue.svg)

![](https://img.shields.io/badge/linux%20build-pass-green.svg)

Object detection based on Opencv.dnn, easy to use

[English](README.md)

---

作者: Michael.Chen

网站: www.tgeek.tech

联系我: m.c.chen@outlook.com

---

项目在OpenCV4.1编译与测试, 任何 OpenCV>=3.3 版本均可

## 依赖

OpenCV

OpenCV_contrib

## 文件结构

**src/:** 源码，示例程序- demo.cpp	

**include/:** 头文件 - detector.hpp

**lib/:** 库文件 - libdnnDetector.so

**param/:** 配置文件 - param.xml

**dnn_nets/:** 神经网络文件 - 网络结构，模型，标签文件

**video/:** 测试视频

## 使用

### 编译

#### 创建文件夹用来编译项目

```bash
mkdir build
```

#### 编译

如果有多版本OpenCV，去掉 ```CMakeLists.txt```第9行注释并修改

例如, OpenCV目录在 "/home/user/opencv_4.1". 修改 ```CMakeLists.txt``` 

```cmake
set(OpenCV_DIR "/home/user/opencv_4.1/build/")
```

运行 ``cmake``

```
cd build/
cmake ..
```

运行 ```make ``` 进行编译

```bash
make
```

## 运行 

```bash
./dnnDetector
```

## 参数修改

任何参数的修改 **不需要重新编译** 

修改 ```param/param.xml``` 改变参数

### 预测参数

line5 - line7

```xml
<net_type>0</net_type>              <!-->0-ssd 1-yolo<-->
<thresh>0.35</thresh>               <!-->confidence threshold<-->
<nms_thresh>0.25</nms_thresh>       <!-->nms threshold<-->
```

### Yolo 配置

line10 - line14, 可以选择任意yolo网络或模型

```xml
<Yolo_meanVal>1</Yolo_meanVal> 
<Yolo_scaleFactor>0.003921569</Yolo_scaleFactor>
<Yolo_config>../dnn_nets/yolo/yolov3-tiny.cfg</Yolo_config>
<Yolo_model>../dnn_nets/yolo/yolov3-tiny.weights</Yolo_model>
<coco_name>../dnn_nets/yolo/coco.names</coco_name>
```

### SSD 配置

line17 - line22, 可以选择任意SSD网络或模型

```xml
<ssd_meanVal>127.5</ssd_meanVal> 
<ssd_scaleFactor>0.007843</ssd_scaleFactor>
<ssd_config>../dnn_nets/ssd/deploy.prototxt</ssd_config>
<ssd_model>../dnn_nets/ssd/mobilenet_iter_73000.caffemodel</ssd_model>
<ssd_name>../dnn_nets/ssd/ssd.names</ssd_name>
</opencv_storage>
```

 ## Demo

对demo.cpp进行详解

### 头文件

添加头文件

```c++
#include "detector.hpp"
```

使用到的所有头文件均在```include/detector.hpp```中提到

```c++
#include <opencv2/dnn.hpp>
#include <opencv2/opencv.hpp>
#include <iostream>
#include <vector>
```

### 网络读取

实例化 ```Detector``` 类, 并且读取网络

```c++
	Detector detector;
	cv::dnn::Net net = detector.net;
```

网络初始化在 ```include/detector.hpp``` 中

### 视频或摄像头读取

```c++
cv::VideoCapture capture;
capture.open("../video/test.mp4");
if(capture.isOpened())
	std::cout<<"INFO: Video file load sucessfully"<<std::endl;
else
	std::cout<<"ERROR: Cannot find video file"<<std::endl;
cv::Mat frame, output;
while (capture.read(frame))
{
  // 检测
}
```

### 检测

```while``` 循环中写

#### 得到预测结果

执行 ```Detector.thePredictor(cv::Mat frame, cv::dnn::Net net)```

```c++
detector.thePredictor(frame, net);			// do detection
```

#### 画出结果以及显示

```c++
frame.copyTo(output);
// draw output
detector.drawResult(output,detector.out_names,detector.out_boxes,detector.out_confidences,detector.out_centers,true);
// display
cv::imshow("demo",output);
// read keyboard
char c = cv::waitKey(5);
if (c == 27)
{ 
	// ESC to exit
	break;
}
```

循环结束

#### 释放摄像头结束程序

```c++
capture.release();
return 0;
```

##Detector类 公共函数与变量

### 构造函数 Detector()

#### 读取配置文件

```c++
// Read configration file
cv::FileStorage setting_fs("../param/param.xml", cv::FileStorage::READ);       
```

#### 传入预测参数

网络类型，置信度阈值，非极大值抑制阈值

```c++
setting_fs["net_type"]>>net_type;                  
setting_fs["thresh"]>>thresh;
setting_fs["nms_thresh"]>>nms_thresh;
```

#### 如果为YOLO 读取YOLO配置

```c++
// If use YoloV3
if (net_type){
std::cout << "INFO: Found \"net_type==1\", using **YoloV3** network" << std::endl;
width = 416;
height = 416;
setting_fs["Yolo_config"] >> net_structure;
setting_fs["Yolo_model"] >> model;
setting_fs["coco_name"] >> name_file;
setting_fs["Yolo_scaleFactor"]>>scaleFactor;
setting_fs["Yolo_meanVal"]>>meanVal;
}
```

#### 如果为SSD读取SSD配置

```C++
// If use SSD
else{
std::cout << "INFO: Found \"net_type==0\", using **SSD** network" << std::endl;
width = 300;
height = 300;
setting_fs["ssd_config"] >> net_structure;
setting_fs["ssd_model"] >> model;
setting_fs["ssd_name"] >> name_file;
setting_fs["ssd_scaleFactor"]>>scaleFactor;
setting_fs["ssd_meanVal"]>>meanVal;
}
```

#### 读取网络结构

```c++
// Set network
net = cv::dnn::readNet(net_structure, model);
if (net.empty()){
std::cerr << "ERROR: Can't load network by using the following files: " << std::endl;
exit(-1);
}
else    std::cout<<"INFO: Load network sucessfully"<<std::endl;
```

#### 读取标签文件

```c++
// Read lable file
std::ifstream ifs(name_file.c_str());
std::string line;
while (std::getline(ifs, line)) classes.push_back(line);
```

### 公共变量

```c++
std::vectorcv::Rect out_boxes		// Bounding box

std::vectorcv::Point out_centers	// Bounding box 中心坐标

std::vectorstd::string out_names	// 检测到的物体名称

std::vector<float> out_confidences	// 检测到的物体置信度

```

### 公共函数

```c++
// 进行预测
// 输入参数为	-待预测图像
// 				-网络
void thePredictor(cv::Mat frame, cv::dnn::Net net);  
// 画出结果  
// 输入参数为	-输入/输出图像
//				-物体名称向量
//				-物体BBOX向量
//				-置信度向量
//				-物体中心点向量
//				-是否绘制FPS
void drawResult(cv::Mat& frame, std::vector<std::string> out_names, std::vector<cv::Rect> out_boxes,std::vector<float> confidences,std::vector<cv::Point> out_centers,bool if_fps); 
```