OpenCV 实时目标检测_opencv 实时调用detect

0.概述

1.原理介绍

2.代码实现

#include <iostream>
#include <opencv2/opencv.hpp>
 
int main() {
    // Load pre-trained MobileNet SSD model and configuration
    std::string model = "path_to_mobilenet_iter_73000.caffemodel";
    std::string config = "path_to_deploy.prototxt";
    cv::dnn::Net net = cv::dnn::readNetFromCaffe(config, model);
 
    // Use webcam for real-time detection
    cv::VideoCapture cap(0);
    if (!cap.isOpened()) {
        std::cerr << "Error: Couldn't open the webcam." << std::endl;
        return -1;
    }
 
    while (true) {
        cv::Mat frame;
        cap >> frame;
 
        // Prepare the frame for the neural network
        cv::Mat blob = cv::dnn::blobFromImage(frame, 0.007843, cv::Size(300, 300), 127.5);
        net.setInput(blob);
 
        // Forward pass
        cv::Mat detection = net.forward();
 
        // Process the detection
        for (int i = 0; i < detection.size[2]; i++) {
            float confidence = detection.at<float>(0, 0, i, 2);
            if (confidence > 0.2) {  // Threshold for confidence
                int classId = static_cast<int>(detection.at<float>(0, 0, i, 1));
                int left = static_cast<int>(detection.at<float>(0, 0, i, 3) * frame.cols);
                int top = static_cast<int>(detection.at<float>(0, 0, i, 4) * frame.rows);
                int right = static_cast<int>(detection.at<float>(0, 0, i, 5) * frame.cols);
                int bottom = static_cast<int>(detection.at<float>(0, 0, i, 6) * frame.rows);
 
                // Draw bounding box for detected object
                cv::rectangle(frame, cv::Point(left, top), cv::Point(right, bottom), cv::Scalar(0, 255, 0), 2);
            }
        }
 
        // Display the frame with detections
        cv::imshow("Real-time Object Detection", frame);
 
        // Exit on pressing 'q'
        if (cv::waitKey(1) == 'q') break;
    }
 
    cap.release();
    cv::destroyAllWindows();
 
    return 0;
}