使用C++版本的opencv dnn 部署onnx模型
使用OpenCV的DNN模块在C++中部署ONNX模型涉及几个步骤，包括加载模型、预处理输入数据、进行推理以及处理输出。
构建了yolo类，方便调用
yolo.h 文件
#ifndef YOLO_H
#define YOLO_H
#include <fstream>
#include <sstream>
#include <iostream>
#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>struct yoloDetectionResult_detection_thread
{cv::Point2f DetectionResultLocation; // 目标中心点像素位置cv::Point2d DetectionResultClassAndConf; //类型、置信度cv::Rect DetectionResultRect; //目标矩形框cv::Mat DetectionResultIMG; //目标像素unsigned char object_no = -1; //目标序号unsigned char object_mission = -1; //目标任务状态int frame_no; //图像帧号
};
class detect_result
{
public:int classId;float confidence;cv::Rect_<float> box;};class YOLO
{
public:YOLO();~YOLO();void init(std::string onnxpath);void detect(cv::Mat& frame, std::vector<detect_result>& result);void draw_frame(cv::Mat& frame, std::vector<detect_result>& results);private:cv::dnn::Net net;const float confidence_threshold_ = 0.4f;const float nms_threshold_ = 0.4f;const int model_input_width_ = 640;const int model_input_height_ = 640;double HighWidthHeightRatio = 25;double LowWidthHeightRatio = 0.05;
};#endif // !YOLO_Hyolo.cpp
#include "yolo.h"YOLO::YOLO()
{}YOLO::~YOLO()
{}void YOLO::init(std::string onnxpath)
{this->net = cv::dnn::readNetFromONNX(onnxpath);
}void YOLO::detect(cv::Mat& frame, std::vector<detect_result>& results)
{int w = frame.cols;int h = frame.rows;int _max = std::max(h, w);cv::Mat image = cv::Mat::zeros(cv::Size(_max, _max), CV_8UC3);if(frame.channels()==1){cv::cvtColor(frame, frame, cv::COLOR_GRAY2BGR);} cv::Rect roi(0, 0, w, h);frame.copyTo(image(cv::Rect(0, 0, w, h)));float x_factor = static_cast<float>(image.cols) / model_input_width_;float y_factor = static_cast<float>(image.rows) / model_input_height_;cv::Mat blob = cv::dnn::blobFromImage(image, 1 / 255.0, cv::Size(model_input_width_, model_input_height_), cv::Scalar(0, 0, 0), true, false);this->net.setInput(blob);cv::Mat preds = this->net.forward("output0");//outputname，使用Netron看一下输出的名字，一般为output0或者outputcv::Mat det_output(preds.size[1], preds.size[2], CV_32F, preds.ptr<float>());std::vector<cv::Rect> boxes;std::vector<int> classIds;std::vector<float> confidences;for (int i = 0; i < det_output.rows; i++){float box_conf = det_output.at<float>(i, 4);if (box_conf < nms_threshold_){continue;}cv::Mat classes_confidences = det_output.row(i).colRange(5, 6);cv::Point classIdPoint;double cls_conf;cv::minMaxLoc(classes_confidences, 0, &cls_conf, 0, &classIdPoint);if (cls_conf > confidence_threshold_){float cx = det_output.at<float>(i, 0);float cy = det_output.at<float>(i, 1);float ow = det_output.at<float>(i, 2);float oh = det_output.at<float>(i, 3);int x = static_cast<int>((cx - 0.5 * ow) * x_factor);int y = static_cast<int>((cy - 0.5 * oh) * y_factor);int width = static_cast<int>(ow * x_factor);int height = static_cast<int>(oh * y_factor);cv::Rect box;box.x = x;box.y = y;box.width = width;box.height = height;boxes.push_back(box);classIds.push_back(classIdPoint.x);confidences.push_back(cls_conf * box_conf);}}std::vector<int> indexes;cv::dnn::NMSBoxes(boxes, confidences, confidence_threshold_, nms_threshold_, indexes);for (size_t i = 0; i < indexes.size(); i++){detect_result dr;int index = indexes[i];int idx = classIds[index];dr.box = boxes[index];dr.classId = idx;dr.confidence = confidences[index];results.push_back(dr);}std::vector<cv::Rect>().swap(boxes);std::vector<int>().swap( classIds);std::vector<float>().swap( confidences);std::vector<int>().swap( indexes);
}void YOLO::draw_frame(cv::Mat& frame, std::vector<detect_result>& results)
{for (auto dr : results){cv::rectangle(frame, dr.box, cv::Scalar(0, 0, 255), 2, 8);cv::rectangle(frame, cv::Point(dr.box.tl().x, dr.box.tl().y - 20), cv::Point(dr.box.br().x, dr.box.tl().y), cv::Scalar(255, 0, 0), -1);std::string label = cv::format("%.2f", dr.confidence);label = dr.classId + ":" + label;cv::putText(frame, label, cv::Point(dr.box.x, dr.box.y + 6), 1, 2, cv::Scalar(0, 255, 0), 2);}}
下面是调用函数编写部分
#include<string>
#include"yolo.h"
#include<opencv2\opencv.hpp>
#include<iostream>
int main(){YOLO* yolo = new YOLO;std::string modelPath = "C:\\Resource\\model\\XXX.onnx";//模型的地址std::string imgPath= "C:\\Resource\\model\\XXX.jpg";//模型的地址//clock_t start_times{},end_times{};yolo->init(modelPath);std::vector<detect_result> output;cv::Mat yoloImages = cv::imread(imgPath);if(!yoloImages.empty()){ //start_times= clock();yolo->detect(yoloImages, output);yolo->draw_frame(yoloImages, output);//end_times = clock();//double FPS = 1 / ((double)(end_times - start_times) / CLOCKS_PER_SEC);cv::imshow("images", yoloImages);cv::waitKey(1);std::vector<detect_result>().swap(output);std::string().swap(model);if(yolo!=NULL){delete yolo;yolo =NULL;}}
}