yolov5分割+检测c++ qt 中部署，以opencv方式（详细代码（全）+复制可用）

1：版本说明：

qt 5.12.10

opencv 4.5.3 （yolov5模型部署要求opencv>4.5.0）

2：检测的代码

yolo.h

#pragma once
#include<iostream>
#include<cmath>
#include<vector>
#include <opencv2/opencv.hpp>
#include<opencv2/dnn.hpp>class yolo
{
public:yolo() {}~yolo(){}bool readModel(cv::dnn::Net &net,std::string &netpath, bool isCuda);struct Output{int id;//结果类别idfloat confidence;//结果置信度cv::Rect box;//矩形框int ship_id;//船的idint truck_id;//人的idint person_id;//人的idint  staring_id;//车的idint  forklift_id;//车的idint unload_car_id;//船的idint load_car_id;//人的idint  private_car_id;//车的id};struct Output_max_confidence{int id;//结果类别idfloat confidence;//结果置信度cv::Rect box;//矩形框};int ship_num;//总人数int  car_trucks_num;//总车数int person_num;//总船数int stacking_area_num;//总人数int  car_forklift_num;//总车数int unload_car_num;//总船数int load_car_num;//总人数int  car_private_num;//总车数bool Detect(cv::Mat &SrcImg, cv::dnn::Net &net, std::vector<Output> &output);void drawPred(cv::Mat &img, std::vector<Output> result, std::vector<cv::Scalar> color);//参数为私有参数，当然也可以是设置成公开或者保护。Output_max_confidence get_only_one_max_confidence(std::vector<Output> result);float findMax(std::vector<float> vec);int getPositionOfMax(std::vector<float> vec, int max);
\void drawRect(cv::Mat &img, yolo::Output_max_confidence result);
private://计算归一化函数float Sigmoid(float x) {return static_cast<float>(1.f / (1.f + exp(-x)));}//anchorsconst float netAnchors[3][6] = { { 10.0, 13.0, 16.0, 30.0, 33.0, 23.0 },{ 30.0, 61.0, 62.0, 45.0, 59.0, 119.0 },{ 116.0, 90.0, 156.0, 198.0, 373.0, 326.0 } };//strideconst float netStride[3] = { 8.0, 16.0, 32.0 };const int netWidth = 640; //网络模型输入大小const int netHeight = 640;float nmsThreshold = 0.02;float boxThreshold = 0.05;float classThreshold = 0.45;//类名
//    std::vector<std::string> className = { "car", "person"};
//    std::vector<std::string> className = { "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
//                                           "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
//                                           "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
//                                           "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
//                                           "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
//                                           "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
//                                           "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
//                                           "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
//                                           "hair drier", "toothbrush"};std::vector<std::string> className = { "ship","car_trucks","person","stacking_area","car_forklift","unload_car","load_car","car_private"};//"car","person"};

yolo.cpp

#include "yolo.h"
#include<iostream>
#include<cmath>
#include<vector>
#include <opencv2/opencv.hpp>
#include<opencv2/dnn.hpp>
using namespace std;
using namespace cv;
using namespace dnn;
#pragma execution_character_set("utf-8");
bool yolo::readModel(Net &net, string &netPath, bool isCuda = false) {try {net = readNetFromONNX(netPath);cout<<"load net successfull!"<<endl;}catch (const std::exception&) {return false;}//cudaif (isCuda) {net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA);}//cpuelse {net.setPreferableBackend(cv::dnn::DNN_BACKEND_DEFAULT);net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);}return true;
}bool yolo::Detect(Mat &SrcImg, Net &net, vector<Output> &output) {Mat blob;int col = SrcImg.cols;int row = SrcImg.rows;int maxLen = MAX(col, row);Mat netInputImg = SrcImg.clone();if (maxLen > 1.2*col || maxLen > 1.2*row) {Mat resizeImg = Mat::zeros(maxLen, maxLen, CV_8UC3);SrcImg.copyTo(resizeImg(Rect(0, 0, col, row)));netInputImg = resizeImg;}blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(netWidth, netHeight), cv::Scalar(104, 117, 123), true, false);blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(netWidth, netHeight), cv::Scalar(0, 0,0), true, false);//如果训练集未对图片进行减去均值操作，则需要设置为这句//blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(netWidth, netHeight), cv::Scalar(114, 114,114), true, false);net.setInput(blob);std::vector<cv::Mat> netOutputImg;//vector<string> outputLayerName{"345","403", "461","output" };//net.forward(netOutputImg, outputLayerName[3]); //获取output的输出net.forward(netOutputImg, net.getUnconnectedOutLayersNames());//接上面std::vector<int> classIds;//结果id数组std::vector<float> confidences;//结果每个id对应置信度数组std::vector<cv::Rect> boxes;//每个id矩形框std::vector<int> ship_id;//记录下人数的id号码----补充std::vector<int> trucks_id;//记录下车数的id号码----补充std::vector<int> person_id;//记录下人数的id号码----补充std::vector<int> stacking_id;//记录下车数的id号码----补充std::vector<int> forklift_id;//记录下人数的id号码----补充std::vector<int> unload_car_id;//记录下车数的id号码----补充std::vector<int> load_car_id;//记录下人数的id号码----补充std::vector<int> car_private_id;//记录下车数的id号码----补充float ratio_h = (float)netInputImg.rows / netHeight;float ratio_w = (float)netInputImg.cols / netWidth;int net_width = className.size() + 5;  //输出的网络宽度是类别数+5float* pdata = (float*)netOutputImg[0].data;for (int stride = 0; stride < 3; stride++) {    //strideint grid_x = (int)(netWidth / netStride[stride]);int grid_y = (int)(netHeight / netStride[stride]);for (int anchor = 0; anchor < 3; anchor++) { //anchorsconst float anchor_w = netAnchors[stride][anchor * 2];const float anchor_h = netAnchors[stride][anchor * 2 + 1];for (int i = 0; i < grid_y; i++) {for (int j = 0; j < grid_y; j++) {//float box_score = Sigmoid(pdata[4]);//获取每一行的box框中含有某个物体的概率 yolo5.0float box_score = pdata[4];//获取每一行的box框中含有某个物体的概率 yolo6.0if (box_score > boxThreshold) {//为了使用minMaxLoc(),将85长度数组变成Mat对象cv::Mat scores(1, className.size(), CV_32FC1, pdata + 5);Point classIdPoint;double max_class_socre;minMaxLoc(scores, 0, &max_class_socre, 0, &classIdPoint);//max_class_socre = Sigmoid((float)max_class_socre);  //yolo 5.0max_class_socre = (float)max_class_socre;    //yolo 6.0if (max_class_socre > classThreshold) {//rect [x,y,w,h]//yolov5 5.0格式//float x = (Sigmoid(pdata[0]) * 2.f - 0.5f + j) * netStride[stride];  //x//float y = (Sigmoid(pdata[1]) * 2.f - 0.5f + i) * netStride[stride];   //y//float w = powf(Sigmoid(pdata[2]) * 2.f, 2.f) * anchor_w;   //w//float h = powf(Sigmoid(pdata[3]) * 2.f, 2.f) * anchor_h;  //h//yolov5 6.0格式：float x = pdata[0];// (Sigmoid(pdata[0]) * 2.f - 0.5f + j) * netStride[stride];  //xfloat y = pdata[1];// (Sigmoid(pdata[1]) * 2.f - 0.5f + i) * netStride[stride];   //yfloat w = pdata[2];// powf(Sigmoid(pdata[2]) * 2.f, 2.f) * anchor_w;   //wfloat h = pdata[3];// powf(Sigmoid(pdata[3]) * 2.f, 2.f) * anchor_h;  //hint left = (x - 0.5*w)*ratio_w;int top = (y - 0.5*h)*ratio_h;classIds.push_back(classIdPoint.x);confidences.push_back(max_class_socre*box_score);boxes.push_back(Rect(left, top, int(w*ratio_w), int(h*ratio_h)));}}pdata += net_width;//指针移到下一行}}}}//接上面执行非最大抑制以消除具有较低置信度的冗余重叠框（NMS）vector<int> nms_result;int ship_id_ini=0;//初始化人数，int trucks_id_ini=0;//初始化人数，int person_id_ini=0;//初始化车辆数int stacking_id_ini=0;//初始化车辆数int forklift_id_ini=0;//初始化车辆数int unload_car_id_ini=0;//初始化车辆数int load_car_id_ini=0;//初始化车辆数int car_private_id_ini=0;//初始化车辆数NMSBoxes(boxes, confidences, classThreshold, nmsThreshold, nms_result);for (int i = 0; i < nms_result.size(); i++) {int idx = nms_result[i];Output result;result.id = classIds[idx];result.confidence = confidences[idx];result.box = boxes[idx];output.push_back(result);if(result.id==0)//当类别数等于船的时候{result.ship_id=ship_id_ini;//当船等于ship_id_ini=ship_id_ini+1;}if(result.id==1)//当类别数等于车的时候{result.truck_id=trucks_id_ini;//当车数等于trucks_id_ini=trucks_id_ini+1;}if(result.id==2)//当类别数等于人的时候{result.person_id=person_id_ini;//当人数等于person_id_ini=person_id_ini+1;}if(result.id==3)//当类别数等于人的时候{result.staring_id=stacking_id_ini;//当人数等于stacking_id_ini=stacking_id_ini+1;}if(result.id==4)//当类别数等于人的时候{result.forklift_id=forklift_id_ini;//当人数等于forklift_id_ini=forklift_id_ini+1;}if(result.id==5)//当类别数等于人的时候{result.unload_car_id=unload_car_id_ini;//当人数等于unload_car_id_ini=unload_car_id_ini+1;}if(result.id==6)//当类别数等于人的时候{result.load_car_id=load_car_id_ini;//当人数等于load_car_id_ini=load_car_id_ini+1;}if(result.id==7)//当类别数等于人的时候{result.private_car_id=car_private_id_ini;//当人数等于car_private_id_ini=car_private_id_ini+1;}}ship_num=ship_id_ini;car_trucks_num=trucks_id_ini;person_num=person_id_ini;stacking_area_num=stacking_id_ini;car_forklift_num=forklift_id_ini ;//总车数unload_car_num=unload_car_id_ini;//总船数load_car_num=load_car_id_ini;//总人数car_private_num=car_private_id_ini;//总车数if (output.size())return true;elsereturn false;}//这个括号是最后void yolo::drawPred(Mat &img, vector<Output> result, vector<Scalar> color)
{for (int i = 0; i < result.size(); i++){int left, top;left = result[i].box.x;top = result[i].box.y;int color_num = i;rectangle(img, result[i].box, color[result[i].id], 2, 8);//      string label = className[result[i].id] + ":" + to_string(result[i].confidence)+" id:"+to_string(result[i].person_id);string label;if(result[i].id==0){label = className[result[i].id] ;}if(result[i].id==1){label = className[result[i].id] ;}if(result[i].id==2){label = className[result[i].id] ;}if(result[i].id==3){label = className[result[i].id] ;}if(result[i].id==4){label = className[result[i].id] ;}if(result[i].id==5){label = className[result[i].id] ;}if(result[i].id==6){label = className[result[i].id] ;}if(result[i].id==7){label = className[result[i].id];}int baseLine;Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);top = max(top, labelSize.height);//rectangle(frame, Point(left, top - int(1.5 * labelSize.height)), Point(left + int(1.5 * labelSize.width), top + baseLine), Scalar(0, 255, 0), FILLED);putText(img, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 1, color[result[i].id], 2);}//imshow("res", img);//imwrite("./result.jpg", img);//waitKey();//destroyAllWindows();
}//这个是针对悍马的画框
void yolo::drawRect(cv::Mat &img, yolo::Output_max_confidence result)
{int left, top;left = result.box.x;top = result.box.y;rectangle(img, result.box,Scalar(0,0,255) , 2, 8);//      string label = className[result[i].id] + ":" + to_string(result[i].confidence)+" id:"+to_string(result[i].person_id);string label;if(result.id==0){label = className[result.id];}int baseLine;Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);top = max(top, labelSize.height);//rectangle(frame, Point(left, top - int(1.5 * labelSize.height)), Point(left + int(1.5 * labelSize.width), top + baseLine), Scalar(0, 255, 0), FILLED);putText(img, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 2,Scalar(0,0,255), 2);}float yolo::findMax(std::vector<float> vec) {float max = -999;for (auto v : vec) {if (max < v) max = v;}return max;
}int yolo::getPositionOfMax(std::vector<float> vec, int max) {auto distance = find(vec.begin(), vec.end(), max);return distance - vec.begin();
}//返回一个最大置信度的框
yolo::Output_max_confidence yolo::get_only_one_max_confidence(std::vector<Output> result)
{std::vector<float>confidence;for (int i = 0; i < result.size(); i++){confidence.push_back(result.at(i).confidence);//        cout<<"result.at(i).confidence"<<result.at(i).confidence<<endl;}float maxConfidence=findMax(confidence);int position = result.size()-getPositionOfMax(confidence, maxConfidence);//    cout<<"max_confidengce"<<maxConfidence<<"position:"<<position<<endl;Output_max_confidence o_m_c;o_m_c.confidence=maxConfidence;o_m_c.id=position;o_m_c.box=result.at(position).box;return o_m_c ;}

检测的调用代码测试案例

这段调用的例子，只要把frame 改成你们自己的图片即可

yolo test;    //创建yolo类
cv::dnn::Net net; //创建yolo网络
vector< cv::Scalar> color;//Bounding Box颜色 
QString Filename_onnx="quanjingbest.onnx";
cv::String filename_onnx=Filename_onnx.toStdString();
vector<yolo::Output>result_video;
test.readModel(net,filename_onnx,false);
for (int i = 0; i < 80; i++) {int b = rand() % 256;int g = rand() % 256;int r = rand() % 256;color.push_back( cv::Scalar(b, g, r));}cv::Mat frame=cv::imread("D://1.jpg");
if(test.Detect(frame, net, result_video))          //调用YOLO模型检测test.drawPred(frame, result_video, color);
cv::imshow("a",frame);
cv::waitKey(1);

4：分割的主要代码

YoloSeg.h

#ifndef YOLO_SEG_H
#define YOLO_SEG_H#pragma once
#include<iostream>
#include<opencv2/opencv.hpp>
#include "yolov5_seg_utils.h"class YoloSeg {
public:YoloSeg() {}~YoloSeg() {}/** \brief Read onnx-model* \param[out] read onnx file into cv::dnn::Net* \param[in] modelPath:onnx-model path* \param[in] isCuda:if true and opencv built with CUDA(cmake),use OpenCV-GPU,else run it on cpu.*/bool ReadModel(cv::dnn::Net& net, std::string& netPath, bool isCuda);/** \brief  detect.* \param[in] srcImg:a 3-channels image.* \param[out] output:detection results of input image.*/bool Detect(cv::Mat& srcImg, cv::dnn::Net& net, std::vector<OutputSeg>& output);#if(defined YOLO_P6 && YOLO_P6==true)const int _netWidth = 1280;  //ONNX图片输入宽度const int _netHeight = 1280; //ONNX图片输入高度const int _segWidth = 320;  //_segWidth=_netWidth/mask_ratioconst int _segHeight = 320;const int _segChannels = 32;
#elseconst int _netWidth = 640;   //ONNX图片输入宽度const int _netHeight = 640;  //ONNX图片输入高度const int _segWidth = 160;    //_segWidth=_netWidth/mask_ratioconst int _segHeight = 160;const int _segChannels = 32;#endif // YOLO_P6float _classThreshold = 0.25;float _nmsThreshold = 0.45;float _maskThreshold = 0.5;public:std::vector<std::string> _className = { "steel"};//类别名，换成自己的模型需要修改此项
};#endif // YOLO_SEG_H

YoloSeg.cpp

#include "yolo_seg.h"#include"yolo_seg.h"
using namespace std;
using namespace cv;
using namespace cv::dnn;bool YoloSeg::ReadModel(Net& net, string& netPath, bool isCuda = false) {try {net = readNet(netPath);
#if CV_VERSION_MAJOR==4 &&CV_VERSION_MINOR==7&&CV_VERSION_REVISION==0net.enableWinograd(false);  //bug of opencv4.7.x in AVX only platform ,https://github.com/opencv/opencv/pull/23112 and https://github.com/opencv/opencv/issues/23080//net.enableWinograd(true);		//If your CPU supports AVX2, you can set it true to speed up
#endif}catch (const std::exception&) {return false;}if (isCuda) {//cudanet.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA); //or DNN_TARGET_CUDA_FP16}else {//cpucout << "Inference device: CPU" << endl;net.setPreferableBackend(cv::dnn::DNN_BACKEND_DEFAULT);net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);}return true;
}bool YoloSeg::Detect(Mat& srcImg, Net& net, vector<OutputSeg>& output) {Mat blob;output.clear();int col = srcImg.cols;int row = srcImg.rows;Mat netInputImg;Vec4d params;LetterBox(srcImg, netInputImg, params, cv::Size(_netWidth, _netHeight));blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(_netWidth, _netHeight), cv::Scalar(0, 0, 0), true, false);//**************************************************************************************************************************************************///如果在其他设置没有问题的情况下但是结果偏差很大，可以尝试下用下面两句语句// If there is no problem with other settings, but results are a lot different from  Python-onnx , you can try to use the following two sentences////$ blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(_netWidth, _netHeight), cv::Scalar(104, 117, 123), true, false);//$ blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(_netWidth, _netHeight), cv::Scalar(114, 114,114), true, false);//****************************************************************************************************************************************************/net.setInput(blob);std::vector<cv::Mat> net_output_img;//*********************************************************************************************************************************//net.forward(net_output_img, net.getUnconnectedOutLayersNames());//opencv4.5.x和4.6.x这里输出不一致，推荐使用下面的固定名称输出// 如果使用net.forward(net_output_img, net.getUnconnectedOutLayersNames())，需要确认下net.getUnconnectedOutLayersNames()返回值中output0在前，output1在后，否者出错//// The outputs of opencv4.5.x and 4.6.x are inconsistent.Please make sure "output0" is in front of "output1" if you use net.forward(net_output_img, net.getUnconnectedOutLayersNames())//*********************************************************************************************************************************vector<string> output_layer_names{ "output0","output1" };net.forward(net_output_img, output_layer_names); //获取output的输出std::vector<int> class_ids;//结果id数组std::vector<float> confidences;//结果每个id对应置信度数组std::vector<cv::Rect> boxes;//每个id矩形框std::vector<vector<float>> picked_proposals;  //output0[:,:, 5 + _className.size():net_width]===> for maskint net_width = _className.size() + 5 + _segChannels;// 80 + 5 + 32 = 117int out0_width= net_output_img[0].size[2];//    assert(net_width == out0_width, "Error Wrong number of _className or _segChannels");  //模型类别数目不对或者_segChannels设置错误int net_height = net_output_img[0].size[1];// 25200float* pdata = (float*)net_output_img[0].data;for (int r = 0; r < net_height; r++) {    //linesfloat box_score = pdata[4];if (box_score >= _classThreshold) {cv::Mat scores(1, _className.size(), CV_32FC1, pdata + 5); //  可是 后面不只是有80个类别的概率；Point classIdPoint;double max_class_socre;minMaxLoc(scores, 0, &max_class_socre, 0, &classIdPoint);max_class_socre = (float)max_class_socre;if (max_class_socre >= _classThreshold) {vector<float> temp_proto(pdata + 5 + _className.size(), pdata + net_width); // Mask Coeffcients,mask的掩码系数picked_proposals.push_back(temp_proto);//rect [x,y,w,h]float x = (pdata[0] - params[2]) / params[0];  //xfloat y = (pdata[1] - params[3]) / params[1];  //yfloat w = pdata[2] / params[0];  //wfloat h = pdata[3] / params[1];  //hint left = MAX(int(x - 0.5 * w + 0.5), 0);int top = MAX(int(y - 0.5 * h + 0.5), 0);class_ids.push_back(classIdPoint.x);confidences.push_back(max_class_socre * box_score);boxes.push_back(Rect(left, top, int(w + 0.5), int(h + 0.5)));}}pdata += net_width;//下一行}//NMSvector<int> nms_result;cv::dnn::NMSBoxes(boxes, confidences, _classThreshold, _nmsThreshold, nms_result);std::vector<vector<float>> temp_mask_proposals;Rect holeImgRect(0, 0, srcImg.cols, srcImg.rows);for (int i = 0; i < nms_result.size(); ++i) {int idx = nms_result[i];OutputSeg result;result.id = class_ids[idx];result.confidence = confidences[idx];result.box = boxes[idx] & holeImgRect;temp_mask_proposals.push_back(picked_proposals[idx]);output.push_back(result);}MaskParams mask_params;mask_params.params = params;mask_params.srcImgShape = srcImg.size();for (int i = 0; i < temp_mask_proposals.size(); ++i) {GetMask2(Mat(temp_mask_proposals[i]).t(), net_output_img[1], output[i], mask_params); // 注意这里是net_output_img[1]，为原型mask}//******************** ****************// 老版本的方案，如果上面GetMask2出错，建议使用这个。// If the GetMask2() still reports errors , it is recommended to use GetMask().// Mat mask_proposals;//for (int i = 0; i < temp_mask_proposals.size(); ++i)//	mask_proposals.push_back(Mat(temp_mask_proposals[i]).t());//GetMask(mask_proposals, net_output_img[1], output, mask_params);//*****************************************************/if (output.size())return true;elsereturn false;
}

yolov5_seg_utils.h

#ifndef YOLOV5_SEG_UTILS_H
#define YOLOV5_SEG_UTILS_H#pragma once
#include<iostream>
#include <numeric>
#include<opencv2/opencv.hpp>#define YOLO_P6 false //是否使用P6模型
#define ORT_OLD_VISON 12  //ort1.12.0 之前的版本为旧版本APIstruct OutputSeg {int id;             //结果类别idfloat confidence;   //结果置信度cv::Rect box;       //矩形框cv::Mat boxMask;       //矩形框内mask，节省内存空间和加快速度
};
struct MaskParams {int segChannels = 32;int segWidth = 160;int segHeight = 160;int netWidth = 640;int netHeight = 640;float maskThreshold = 0.5;cv::Size srcImgShape;cv::Vec4d params;};
bool CheckParams(int netHeight, int netWidth, const int* netStride, int strideSize);
void DrawPred(cv::Mat& img, std::vector<OutputSeg> result, std::vector<std::string> classNames, std::vector<cv::Scalar> color);
void LetterBox(const cv::Mat& image, cv::Mat& outImage,cv::Vec4d& params, //[ratio_x,ratio_y,dw,dh]const cv::Size& newShape = cv::Size(640, 640),bool autoShape = false,bool scaleFill = false,bool scaleUp = true,int stride = 32,const cv::Scalar& color = cv::Scalar(114, 114, 114));
void GetMask(const cv::Mat& maskProposals, const cv::Mat& maskProtos, std::vector<OutputSeg>& output, const MaskParams& maskParams);
void GetMask2(const cv::Mat& maskProposals, const cv::Mat& maskProtos, OutputSeg& output, const MaskParams& maskParams);#endif // YOLOV5_SEG_UTILS_H

yolov5_seg_utils.cpp

#include "yolov5_seg_utils.h"#pragma once
#include "yolov5_seg_utils.h"
using namespace cv;
using namespace std;
bool CheckParams(int netHeight, int netWidth, const int* netStride, int strideSize) {if (netHeight % netStride[strideSize - 1] != 0 || netWidth % netStride[strideSize - 1] != 0){cout << "Error:_netHeight and _netWidth must be multiple of max stride " << netStride[strideSize - 1] << "!" << endl;return false;}return true;
}void 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)
{if (false) {int maxLen = MAX(image.rows, image.cols);outImage = Mat::zeros(Size(maxLen, maxLen), CV_8UC3);image.copyTo(outImage(Rect(0, 0, image.cols, image.rows)));params[0] = 1;params[1] = 1;params[3] = 0;params[2] = 0;}cv::Size shape = image.size();float r = std::min((float)newShape.height / (float)shape.height,(float)newShape.width / (float)shape.width);if (!scaleUp)r = std::min(r, 1.0f);float ratio[2]{ r, r };int new_un_pad[2] = { (int)std::round((float)shape.width * r),(int)std::round((float)shape.height * r) };auto dw = (float)(newShape.width - new_un_pad[0]);auto dh = (float)(newShape.height - new_un_pad[1]);if (autoShape){dw = (float)((int)dw % stride);dh = (float)((int)dh % stride);}else if (scaleFill){dw = 0.0f;dh = 0.0f;new_un_pad[0] = newShape.width;new_un_pad[1] = newShape.height;ratio[0] = (float)newShape.width / (float)shape.width;ratio[1] = (float)newShape.height / (float)shape.height;}dw /= 2.0f;dh /= 2.0f;if (shape.width != new_un_pad[0] && shape.height != new_un_pad[1]){cv::resize(image, outImage, cv::Size(new_un_pad[0], new_un_pad[1]));}else {outImage = image.clone();}int top = int(std::round(dh - 0.1f));int bottom = int(std::round(dh + 0.1f));int left = int(std::round(dw - 0.1f));int right = int(std::round(dw + 0.1f));params[0] = ratio[0];params[1] = ratio[1];params[2] = left;params[3] = top;cv::copyMakeBorder(outImage, outImage, top, bottom, left, right, cv::BORDER_CONSTANT, color);
}void GetMask(const cv::Mat& maskProposals, const cv::Mat& maskProtos, std::vector<OutputSeg>& output, const MaskParams& maskParams) {//cout << maskProtos.size << endl;int seg_channels = maskParams.segChannels;int net_width = maskParams.netWidth;int seg_width = maskParams.segWidth;int net_height = maskParams.netHeight;int seg_height = maskParams.segHeight;float mask_threshold = maskParams.maskThreshold;Vec4f params = maskParams.params;Size src_img_shape = maskParams.srcImgShape;Mat protos = maskProtos.reshape(0, { seg_channels,seg_width * seg_height });Mat matmul_res = (maskProposals * protos).t();Mat masks = matmul_res.reshape(output.size(), { seg_width,seg_height });vector<Mat> maskChannels;split(masks, maskChannels);for (int i = 0; i < output.size(); ++i) {Mat dest, mask;//sigmoidcv::exp(-maskChannels[i], dest);dest = 1.0 / (1.0 + dest);Rect roi(int(params[2] / net_width * seg_width), int(params[3] / net_height * seg_height), int(seg_width - params[2] / 2), int(seg_height - params[3] / 2));dest = dest(roi);resize(dest, mask, src_img_shape, INTER_NEAREST);//cropRect temp_rect = output[i].box;mask = mask(temp_rect) > mask_threshold;output[i].boxMask = mask;}
}
void GetMask2(const Mat& maskProposals, const Mat& mask_protos, OutputSeg& output, const MaskParams& maskParams) {int seg_channels = maskParams.segChannels;int net_width = maskParams.netWidth;int seg_width = maskParams.segWidth;int net_height = maskParams.netHeight;int seg_height = maskParams.segHeight;float mask_threshold = maskParams.maskThreshold;Vec4f params = maskParams.params;Size src_img_shape = maskParams.srcImgShape;Rect temp_rect = output.box;// 把已经到原图的检测框坐标信息  映射到  获得mask原型分支的输入尺寸上【160, 160】int rang_x = floor((temp_rect.x * params[0] + params[2]) / net_width * seg_width);int rang_y = floor((temp_rect.y * params[1] + params[3]) / net_height * seg_height);int rang_w = ceil(((temp_rect.x + temp_rect.width) * params[0] + params[2]) / net_width * seg_width) - rang_x;int rang_h = ceil(((temp_rect.y  + temp_rect.height) * params[0] + params[3]) / net_width * seg_height) - rang_y;//rang_w = MAX(rang_w, 1);rang_h = MAX(rang_h, 1);if (rang_x + rang_w > seg_width){if (seg_width - rang_x > 0)rang_w =seg_width -rang_x;elserang_x -= 1;}if (rang_y + rang_h > seg_height) {if (seg_height - rang_y > 0)rang_h = seg_height - rang_y;elserang_y -= 1;}vector<Range> roi_ranges;roi_ranges.push_back(Range(0,1));roi_ranges.push_back(Range::all());roi_ranges.push_back(Range(rang_y, rang_h+rang_y));roi_ranges.push_back(Range(rang_x, rang_w+rang_x));// 裁剪mask原型Mat temp_mask_protos = mask_protos(roi_ranges).clone(); // 剪裁原型，保存检测框内部的原型，其余位置清零,  以此来获得感兴趣区域(roi)Mat protos = temp_mask_protos.reshape(0, { seg_channels, rang_w*rang_h});// 检测至检测框大小？// mask系数与mask原型做矩阵乘法Mat matmul_res = (maskProposals * protos).t(); // mask系数【1,32】 与 mask原型【32, h*w】进行矩阵相称Mat masks_feature = matmul_res.reshape(1,{rang_h, rang_w}); //【1,h,w】Mat dest, mask;// sigmodcv::exp(-masks_feature, dest);dest = 1.0 / (1.0 + dest);// 检测框坐标 映射到 原图尺寸int left = floor((net_width / seg_width * rang_x - params[2]) / params[0]);int top = floor((net_width / seg_height * rang_y - params[3]) / params[1]);int width = ceil(net_width / seg_height * rang_w / params[0]);int height = ceil(net_height / seg_height * rang_h / params[1]);// 检测框mask缩放到原图尺寸resize(dest, mask, Size(width, height), INTER_NEAREST);// 阈值化mask = mask(temp_rect - Point(left, top)) > mask_threshold;output.boxMask = mask;
}void DrawPred(Mat& img, vector<OutputSeg> result, std::vector<std::string> classNames, vector<Scalar> color) {Mat mask = img.clone();for (int i=0; i< result.size(); i++){int left, top;left = result[i].box.x;top = result[i].box.y;int color_num =i;// 目标画框rectangle(img, result[i].box, color[result[i].id], 2, 8);// 目标mask，这里非目标像素值为0mask(result[i].box).setTo(color[result[i].id], result[i].boxMask);string label = classNames[result[i].id] + ":" + to_string(result[i].confidence);// 框左上角打印信息int baseLine;Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);top = max(top, labelSize.height);putText(img, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.5, color[result[i].id], 2);}addWeighted(img, 0.5, mask, 0.5, 0, img);
}

分割的调用代码测试案例


void frmMain::test_yoloseg()
{string model_path = "20231001segquanjing.onnx";YoloSeg test;Net net;if (test.ReadModel(net, model_path, true)) {cout << "read net ok!" << endl;}else {return ;}vector<OutputSeg> result;cv::Mat img_seg_test=cv::imread("E:/data_seg/quanjingCameraPicture2/1/segdata2278.jpg");bool find = test.Detect(img_seg_test, net, result);if (find) {DrawPred(img_seg_test, result, test._className, color);}else {cout << "Detect Failed!"<<endl;}string label = "steel:" ; //msputText(img_seg_test, label, Point(30,30), FONT_HERSHEY_SIMPLEX,0.5, Scalar(0,0,255), 2, 8);QPixmap img_test_xmap= my_publiction.cvMatToQPixmap(img_seg_test);// 设置QLabel的最小尺寸ui->lab1->setMinimumSize(600, 600);ui->lab1->setScaledContents(true);img_test_xmap = img_test_xmap.scaled( ui->lab1->width(),  ui->lab1->height(), Qt::KeepAspectRatio,Qt::SmoothTransformation); // 将图像缩放到QLabel的大小ui->lab1->setPixmap(img_test_xmap);
//    imshow("result", img_seg_test);}

分割的效果图如下：