yolov5 OpenCV DNN c++ 部署
一、转onnx格式
yolov5-6.2版本可以用export.py导出onnx格式的模型:
python export.py --weights yolov5n.pt yolov5n.onnx
yolov5n.pt我是直接在这里下载的：https://github.com/ultralytics/yolov5/releases/tag/v6.2
二、部署代码
yolo.hpp
// https://github.com/UNeedCryDear/yolov5-opencv-dnn-cpp
#pragma once
#include
#include
#define YOLO_P6 false //是否使用P6模型
struct Output {
int id; //结果类别id
float confidence; //结果置信度
cv::Rect box; //矩形框
};
class Yolov5 {
public:
Yolov5() {
}
~Yolov5() {}
bool readModel(cv::dnn::Net& net, std::string& netPath, bool isCuda);
bool Detect(cv::Mat& SrcImg, cv::dnn::Net& net, std::vector yolo.cpp
#include "yolo.h"
using namespace std;
using namespace cv;
using namespace cv::dnn;
void Yolov5::LetterBox(const cv::Mat& image, cv::Mat& outImage, cv::Vec4d& params, const cv::Size& newShape,
bool autoShape, bool scaleFill, bool scaleUp, int stride, const cv::Scalar& color)
{
Size shape = image.size();
float r = std::min((float)newShape.width / (float)shape.width,
(float)newShape.height / (float)shape.height); // 选出较小的缩放比，否则会超过
float ratio[2]{r, r};
int new_up_pad[2] ={(int)round((float)shape.width * r),
(int)round((float)shape.height * r)}; // 缩放后与目标长宽可能还差一点
auto dw = (float)(newShape.width - new_up_pad[0]);// 算出与目标长宽差多少
auto dh = (float)(newShape.height - new_up_pad[1]);
dw /= 2.0f;
dh /= 2.0f;
if (shape.width != new_up_pad[0] && shape.height != new_up_pad[1])//等比例缩放
{
resize(image, outImage, Size(new_up_pad[0], new_up_pad[1]));
}
else {
outImage = image.clone();
}
int top = int(round(dh - 0.1f)); // 四周用0来填充
int bottom = int(round(dh + 0.1f));
int left = int(round(dw - 0.1f));
int right = int(round(dw + 0.1f));
params[0] = ratio[0];
params[1] = ratio[1];
params[2] = left;
params[3] = top;
copyMakeBorder(outImage, outImage, top, bottom, left, right,BORDER_CONSTANT,color);
}
bool Yolov5::readModel(Net &net, string &netPath, bool isCuda)
{
try {
// net = readNet(netPath);
net = readNetFromONNX(netPath);
} catch (const std::exception&) {
return false;
}
if (isCuda)
{
net.setPreferableBackend(DNN_BACKEND_CUDA);
net.setPreferableTarget(DNN_TARGET_CUDA);
}
else
{
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(DNN_TARGET_CPU);
}
return true;
}
bool Yolov5::Detect(Mat &SrcImg, Net &net, vectormain.cpp
#include "yolo.h"
#include
#include
using namespace std;
using namespace cv;
using namespace cv::dnn;
#include "yolo.h"
#include
//#include
#include
using namespace std;
using namespace cv;
using namespace dnn;
int main()
{
string img_path = "/home/jason/work/01-img/dog2.png";
string model_path = "/home/jason/PycharmProjects/pytorch_learn/yolov5-6.2/yolov5n.onnx";
//int num_devices = cv::cuda::getCudaEnabledDeviceCount();
//if (num_devices <= 0) {
//cerr << "There is no cuda." << endl;
//return -1;
//}
//else {
//cout << num_devices << endl;
//}
Yolov5 test;
Net net;
if (test.readModel(net, model_path, false)) {
cout << "read net ok!" << endl;
}
else {
return -1;
}
//生成随机颜色
vector color;
srand(time(0));
for (int i = 0; i < 80; i++) {
int b = rand() % 256;
int g = rand() % 256;
int r = rand() % 256;
color.push_back(Scalar(b, g, r));
}
vector 三、总结：
我用摄像头直接读取处理，发现yolov5n 运行起来有点卡，AMD R5300H CPU 的耗时是150ms左右，而 同样设备Pytorch Python 推理 只有几十ms耗时！！
欢迎留言、欢迎交流！！
参考：
GitHub - UNeedCryDear/yolov5-opencv-dnn-cpp: 使用opencv模块部署yolov5-6.0版本
2021.11.01 c++下 opencv部署yolov5-6.0版本 (四)_怎么查看yolov5版本_爱晚乏客游的博客-CSDN博客
！