1、环境准备：
1.1、下载Yolov3（darknet）
下载链接：https://github.com/AlexeyAB/darknet1.2、下载权重文件(yolov3.weights)
下载链接：https://link.csdn.net/?target=https%3A%2F%2Fpjreddie.com%2Fmedia%2Ffiles%2Fyolov3.weights 下载完成后，将yolov3.weights文件复制到E:\yolo\darknet-master\darknet-master\build\darknet\x64目录下。1.3、下载CUDA 9.0
下载链接：链接：https://pan.baidu.com/s/1LLMmFVOCSLvaY4GzTuVYcg 提取码：kkvb 安装过程网上搜教程就行了，挺简单的（PS：CUDA版本需根据自己电脑配置选择，我电脑显卡是：NVIDIA GTX 950M的，一开始我装CUDA10.0和11.0都不行，后来改为9.0可以了，也是一波三折）1.4、下载CUDNN 9.0
下载链接：链接：https://pan.baidu.com/s/1d-yigqIoy9q6Ha5-fdJkSA 提取码：74kw1.4、opencv3.4.9和VS2015
网上搜索安装即可并将opencv配置到VS2015中2、编译Yolo
2.1、编译darknet.exe
2.1.1、找到darknet.vcxproj用EditPlus打开CTRL+F搜索CUDA 11.1全部改为9.0然后保存。共2处
2.1.2、打开darknet.sln
修改为Release X64模式，然后生成即可。3、运行yolov3
双击darknet_yolo_v3.cmd识别图片实例；而darknet_web_cam_voc.cmd是打开笔记本摄像头动态检测识别。4、编译Yolov3动态链接库
打开yolo_cpp_dll.sln在Release X64模式下生成（也要修改yolo_cpp_dll.vcxproj中的cuda版本）5、使用yolo的动态链接库进行开发
#pragma once
//这段代码一定要加，这是在yolo_v2_class.hpp文件中使用opencv函数
#ifdef _WIN32
#define OPENCV
#define GPU
#endif
#include <QtWidgets/QMainWindow>
#include "ui_mianwindow.h"
#include "yolo_v2_class.hpp"
#include "opencv\highgui.h"
#include "opencv2\opencv.hpp"
#include "opencv2\core\core.hpp"
#include "opencv2\highgui\highgui.hpp"
#include <iostream>
#include <stdio.h>
#pragma comment(lib, "yolo_cpp_dll.lib")//引入yolo链接库
#pragma execution_character_set("utf-8")
using namespace cv;
class mianwindow : public QMainWindow
{
Q_OBJECT
public:
mianwindow(QWidget *parent = Q_NULLPTR);
void draw_boxes(Mat mat_img, std::vector<bbox_t> result_vec, std::vector<std::string> obj_names);
std::vector<std::string> objects_names_from_file(std::string const filename);
QImage cvMat2QImage(const Mat& mat);
Mat QImage2cvMat(QImage image);
private:
Ui::mianwindowClass ui;
};
mainwindow.cpp
#include "mianwindow.h"
#include "yolo_v2_class.hpp"
#include <QDebug>
mianwindow::mianwindow(QWidget *parent)
: QMainWindow(parent)
{
ui.setupUi(this);
QImage q_image(QString("E:/TX_workCode/DataInfo/images/dog.jpg"));
ui.label_img->setPixmap(QPixmap::fromImage(q_image));
std::string names_file = "coco.names";
std::string cfg_file = "yolov3.cfg";
std::string weights_file = "yolov3.weights";
Detector detector(cfg_file, weights_file); //初始化Detector
std::vector<std::string> obj_names;
obj_names = objects_names_from_file(names_file);
//测试是否成功读入分类对象文件
for (int i = 0; i < obj_names.size(); i++)
{
qDebug() << "第i个:" << QString(QString::fromLocal8Bit(obj_names[i].c_str()));
}
Mat frame = QImage2cvMat(q_image);
std::vector<bbox_t> result_vec = detector.detect(frame);
draw_boxes(frame, result_vec, obj_names);
QImage det_image = cvMat2QImage(frame);
ui.label_detectorImg->setPixmap(QPixmap::fromImage(det_image));
}
//以下draw_boxes和objects_names_from_file两段代码来自yolo_console_dll.sln
//给Mat类型的图像画出检测出对象框
void mianwindow::draw_boxes(cv::Mat mat_img, std::vector<bbox_t> result_vec, std::vector<std::string> obj_names)
{
int const colors[6][3] = { { 1,0,1 },{ 0,0,1 },{ 0,1,1 },{ 0,1,0 },{ 1,1,0 },{ 1,0,0 } };
for (auto &i : result_vec) {
cv::Scalar color = obj_id_to_color(i.obj_id);
cv::rectangle(mat_img, cv::Rect(i.x, i.y, i.w, i.h), color, 2);
if (obj_names.size() > i.obj_id) {
std::string obj_name = obj_names[i.obj_id];
if (i.track_id > 0) obj_name += " - " + std::to_string(i.track_id);
cv::Size const text_size = getTextSize(obj_name, cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, 2, 0);
int const max_width = (text_size.width > i.w + 2) ? text_size.width : (i.w + 2);
cv::rectangle(mat_img, cv::Point2f(std::max((int)i.x - 1, 0), std::max((int)i.y - 30, 0)),
cv::Point2f(std::min((int)i.x + max_width, mat_img.cols - 1), std::min((int)i.y, mat_img.rows - 1)),
color, CV_FILLED, 8, 0);
putText(mat_img, obj_name, cv::Point2f(i.x, i.y - 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, cv::Scalar(0, 0, 0), 2);
}
}
}
//读names文件中对象类别名称保存在vector<std::string>容器中
std::vector<std::string> mianwindow::objects_names_from_file(std::string const filename) {
std::ifstream file(filename);
std::vector<std::string> file_lines;
if (!file.is_open()) return file_lines;
for (std::string line; getline(file, line);) file_lines.push_back(line);
std::cout << "object names loaded \n";
return file_lines;
}
//---------QImage与Mat类型相互转换----------------------------------------------------------
QImage mianwindow::cvMat2QImage(const cv::Mat& mat)
{
// 8-bits unsigned, NO. OF CHANNELS = 1
if (mat.type() == CV_8UC1)
{
QImage image(mat.cols, mat.rows, QImage::Format_Indexed8);
// Set the color table (used to translate colour indexes to qRgb values)
image.setColorCount(256);
for (int i = 0; i < 256; i++)
{
image.setColor(i, qRgb(i, i, i));
}
// Copy input Mat
uchar *pSrc = mat.data;
for (int row = 0; row < mat.rows; row++)
{
uchar *pDest = image.scanLine(row);
memcpy(pDest, pSrc, mat.cols);
pSrc += mat.step;
}
return image;
}
// 8-bits unsigned, NO. OF CHANNELS = 3
else if (mat.type() == CV_8UC3)
{
// Copy input Mat
const uchar *pSrc = (const uchar*)mat.data;
// Create QImage with same dimensions as input Mat
QImage image(pSrc, mat.cols, mat.rows, mat.step, QImage::Format_RGB888);
return image.rgbSwapped();
}
else if (mat.type() == CV_8UC4)
{
qDebug() << "CV_8UC4";
// Copy input Mat
const uchar *pSrc = (const uchar*)mat.data;
// Create QImage with same dimensions as input Mat
QImage image(pSrc, mat.cols, mat.rows, mat.step, QImage::Format_ARGB32);
return image.copy();
}
else
{
qDebug() << "ERROR: Mat could not be converted to QImage.";
return QImage();
}
}
cv::Mat mianwindow::QImage2cvMat(QImage image)
{
cv::Mat mat;
qDebug() << image.format();
switch (image.format())
{
case QImage::Format_ARGB32:
case QImage::Format_RGB32:
case QImage::Format_ARGB32_Premultiplied:
mat = cv::Mat(image.height(), image.width(), CV_8UC4, (void*)image.constBits(), image.bytesPerLine());
break;
case QImage::Format_RGB888:
mat = cv::Mat(image.height(), image.width(), CV_8UC3, (void*)image.constBits(), image.bytesPerLine());
cv::cvtColor(mat, mat, CV_BGR2RGB);
break;
case QImage::Format_Indexed8:
mat = cv::Mat(image.height(), image.width(), CV_8UC1, (void*)image.constBits(), image.bytesPerLine());
break;
}
return mat;
}