源代码
#include<opencv2/opencv.hpp>
#include<opencv2/dnn.hpp>
#include <iostream>
#include<string>using namespace std;
using namespace cv;
using namespace dnn;int main()
{String modelConfiguration = "/home/oliver/darknet-master/cfg/yolov2.cfg";String modelBinary = "/home/oliver/darknet-master/yolov2.weights";dnn::Net net = readNetFromDarknet(modelConfiguration, modelBinary);if (net.empty()){printf("Could not load net...\n");return 0;}vector<string> classNamesVec;ifstream classNamesFile("/home/oliver/darknet-master/data/coco.names");if (classNamesFile.is_open()){string className = "";while (std::getline(classNamesFile, className))classNamesVec.push_back(className);}// 加载图像VideoCapture capture(2);// VideoCapture:OENCV中新增的类，捕获视频并显示出来while (1){Mat frame;capture >> frame;Mat inputBlob = blobFromImage(frame, 1 / 255.F, Size(608, 608), Scalar(), true, false);net.setInput(inputBlob, "data");// 检测Mat detectionMat = net.forward("detection_out");vector<double> layersTimings;double freq = getTickFrequency() / 1000;double time = net.getPerfProfile(layersTimings) / freq;ostringstream ss;ss << "detection time: " << time << " ms";putText(frame, ss.str(), Point(20, 20), 0, 0.5, Scalar(0, 0, 255));// 输出结果for (int i = 0; i < detectionMat.rows; i++){const int probability_index = 5;const int probability_size = detectionMat.cols - probability_index;float *prob_array_ptr = &detectionMat.at<float>(i, probability_index);size_t objectClass = max_element(prob_array_ptr, prob_array_ptr + probability_size) - prob_array_ptr;float confidence = detectionMat.at<float>(i, (int)objectClass + probability_index);if (confidence > 0.5){float x = detectionMat.at<float>(i, 0);float y = detectionMat.at<float>(i, 1);float width = detectionMat.at<float>(i, 2);float height = detectionMat.at<float>(i, 3);int xLeftBottom = static_cast<int>((x - width / 2) * frame.cols);int yLeftBottom = static_cast<int>((y - height / 2) * frame.rows);int xRightTop = static_cast<int>((x + width / 2) * frame.cols);int yRightTop = static_cast<int>((y + height / 2) * frame.rows);Rect object(xLeftBottom, yLeftBottom,xRightTop - xLeftBottom,yRightTop - yLeftBottom);rectangle(frame, object, Scalar(0, 0, 255), 2, 8);if (objectClass < classNamesVec.size()){ss.str("");ss << confidence;String conf(ss.str());String label = String(classNamesVec[objectClass]) + ": " + conf;int baseLine = 0;Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);rectangle(frame, Rect(Point(xLeftBottom, yLeftBottom),Size(labelSize.width, labelSize.height + baseLine)),Scalar(255, 255, 255), CV_FILLED);putText(frame, label, Point(xLeftBottom, yLeftBottom + labelSize.height),FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 0));}}}cv::namedWindow("YOLO-Detections",0);cv::imshow("YOLO-Detections", frame);waitKey(30);}return 0;
}
实验结果
