自從YOLOv5更新成7.0版本，YOLOv8推出以后，OpenCV4.6以前的版本都無法再加載導(dǎo)出ONNX格式模型了，只有OpenCV4.7以上版本才可以支持最新版本YOLOv5與YOLOv8模型的推理部署。首先看一下最新版本的YOLOv5與YOLOv8的輸入與輸出格式：

ni hao

std::stringonnxpath="D:/python/yolov5-7.0/yolov5s.onnx";
autonet=cv::readNetFromONNX(onnxpath);

net.setPreferableBackend(cv::DNN_BACKEND_OPENCV);
net.setPreferableTarget(cv::DNN_TARGET_CPU);

cv::VideoCapturecapture("D:/images/video/sample.mp4");
cv::Matframe;
while(true){
boolret=capture.read(frame);
if(frame.empty()){
break;
}
int64start=cv::getTickCount();
//圖象預(yù)處理-格式化操作
intw=frame.cols;
inth=frame.rows;
int_max=std::max(h,w);
cv::Matimage=cv::Size(_max,_max),CV_8UC3);
cv::Rectroi(0,0,w,h);
frame.copyTo(image(roi));

floatx_factor=image.cols/640.0f;
floaty_factor=image.rows/640.0f;

//推理
cv::Matblob=cv::blobFromImage(image,1/255.0,cv::Size(640,640),cv::Scalar(0,0,0),true,false);
net.setInput(blob);
cv::Matpreds=net.forward();

//后處理,1x25200x85
cv::Matdet_output(preds.size[1],preds.size[2],CV_32F,preds.ptr());
floatconfidence_threshold=0.5;
std::vectorboxes;
std::vectorclassIds;
std::vectorconfidences;
for(inti=0;i(i,4);
if(confidence0.25)
{
floatcx=det_output.at(i,0);
floatcy=det_output.at(i,1);
floatow=det_output.at(i,2);
floatoh=det_output.at(i,3);
intx=static_cast((cx-0.5*ow)*x_factor);
inty=static_cast((cy-0.5*oh)*y_factor);
intwidth=static_cast(ow*x_factor);
intheight=static_cast(oh*y_factor);
cv::Rectbox;
box.x=x;
box.y=y;
box.width=width;
box.height=height;

boxes.push_back(box);
classIds.push_back(classIdPoint.x);
confidences.push_back(score);
}
}

//NMS
std::vectorindexes;
cv::NMSBoxes(boxes,confidences,0.25,0.50,indexes);
for(size_ti=0;i(cv::getTickFrequency());
putText(frame,cv::format("FPS:%.2f",1.0/t),cv::Point(20,40),cv::FONT_HERSHEY_PLAIN,2.0,cv::Scalar(255,0,0),2,8);

charc=cv::waitKey(1);
if(c==27){
break;
}
cv::imshow("OpenCV4.8+YOLOv5",frame);
}

審核編輯：湯梓紅