yolov3 opencv教程

#include "pch.h" #include <fstream> #include <sstream> #include <iostream> #include <time.h> #include <opencv2/dnn.hpp> #include <opencv2/imgproc.hpp> #include <opencv2/highgui.hpp> ; using namespace cv; using namespace dnn; using namespace std; // Initialize the parameters 初始参数 // Confidence threshold 置信度阈值 float confThreshold = 0.5; // Non-maximum suppression threshold 非极大性抑制阈值 float nmsThreshold = 0.4; //检测图像宽高 int inpWidth = 416; int inpHeight = 416; //类别参数 vector<string> classes; // Remove the bounding boxes with low confidence using non-maxima suppression // 基于非极大性抑制去除低置信度的检测框 void postprocess(Mat& frame, const vector<Mat>& out); // 画预测框 void drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame); // 提取输出输出层 vector<String> getOutputsNames(const Net& net); int main() { // Give the configuration and weight files for the model 模型参数文件 String modelConfiguration = "./model/yolov3.cfg"; String modelWeights = "./model/yolov3.weights"; // Load names of classes 读取分类类名 string classesFile = "./model/coco.names"; ifstream ifs(classesFile.c_str()); string line; while (getline(ifs, line)) { classes.push_back(line); } // Load the network 导入网络 Net net = readNetFromDarknet(modelConfiguration, modelWeights); net.setPreferableBackend(DNN_BACKEND_OPENCV); //仅仅使用CPU net.setPreferableTarget(DNN_TARGET_CPU); // Open a video file or an image file or a camera stream. string str, outputFile; Mat blob; clock_t start, finish; //读图 Mat frame = imread("bird.jpg"); //根据需求决定是不是重置图像 //resize(frame, frame, Size(300, 300)); start = clock(); // Create a 4D blob from a frame. 创建神经网络输入图像 blobFromImage(frame, blob, 1 / 255.0, cvSize(inpWidth, inpHeight), Scalar(0, 0, 0), true, false); //Sets the input to the network 设置输出 net.setInput(blob); // Runs the forward pass to get output of the output layers 获取输出层结果 vector<Mat> outs; net.forward(outs, getOutputsNames(net)); // Remove the bounding boxes with low confidence postprocess(frame, outs); finish = clock(); cout << "time is " << double(finish - start) / CLOCKS_PER_SEC << endl; // Put efficiency information. The function getPerfProfile returns the overall time for inference(t) and the timings for each of the layers(in layersTimes) //输出前向传播的时间 vector<double> layersTimes; double freq = getTickFrequency() / 1000; double t = net.getPerfProfile(layersTimes) / freq; string label = format("Inference time for a frame : %.2f ms", t); putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 255)); imshow("result", frame); //保存图像 imwrite("result.jpg", frame); waitKey(0); return 0; } /** * @brief Remove the bounding boxes with low confidence using non-maxima suppression 基于非极大性抑制去除边框 * * @param frame 视频图像 * @param outs 输出层结果 */ void postprocess(Mat& frame, const vector<Mat>& outs) { //输出类 vector<int> classIds; //置信度 vector<float> confidences; vector<Rect> boxes; //遍历所有的输出层 for (size_t i = 0; i < outs.size(); ++i) { // Scan through all the bounding boxes output from the network and keep only the // ones with high confidence scores. Assign the box's class label as the class // with the highest score for the box. //扫描所有来自网络的边界框输出，只保留具有高置信度分数的边界框。将框的类标签指定为框得分最高的类。 //读取框 float* data = (float*)outs[i].data; for (int j = 0; j < outs[i].rows; ++j, data += outs[i].cols) { Mat scores = outs[i].row(j).colRange(5, outs[i].cols); Point classIdPoint; double confidence; // Get the value and location of the maximum score 获取置信度和位置参数 minMaxLoc(scores, 0, &confidence, 0, &classIdPoint); //如果大于置信度阈值 if (confidence > confThreshold) { //获取坐标 int centerX = (int)(data[0] * frame.cols); int centerY = (int)(data[1] * frame.rows); int width = (int)(data[2] * frame.cols); int height = (int)(data[3] * frame.rows); int left = cen�