OpenCV/DNN 人脸（目标）检测

使用深度学习模块进行人脸检测
关于 opencv-python: ​​​使用OpenCV的dnn模块实时目标检测​​

头文件 ​​quick_opencv.h​​：声明类与公共函数

#pragma once
#include <opencv2\opencv.hpp>
using namespace cv;

class QuickDemo {
public:
    ...
    void face_detect_Demo();
};

主函数调用该类的公共成员函数

#include <opencv2\opencv.hpp>
#include <quick_opencv.h>
#include <iostream>
using namespace cv;


int main(int argc, char** argv) {
  Mat src = imread("D:\\Desktop\\pandas.jpg");
  if (src.empty()) {
    printf("Could not load images...\n");
    return -1;
  }
  namedWindow("input", WINDOW_NORMAL);
  imshow("input", src);

  QuickDemo qk;

  ...
  qk.face_detect_Demo();
  waitKey(0);
  destroyAllWindows();
  return 0;
}

使用深度学习模块进行人脸检测

源文件 ​​quick_demo.cpp​​：实现类与公共函数

void QuickDemo::face_detect_Demo() {
  std::string pb_path = "D:\\opencv\\sources\\samples\\dnn\\face_detector\\opencv_face_detector_uint8.pb";
  std::string pbtxt_path = "D:\\opencv\\sources\\samples\\dnn\\face_detector\\opencv_face_detector.pbtxt";
  
  cv::dnn::Net net = cv::dnn::readNetFromTensorflow(pb_path , pbtxt_path);
  //VideoCapture captrue("D:\\opencv-c++\\images\\two_red_line.mp4"); // 推理视频
  VideoCapture captrue(0);
  Mat frame, hsv;
  while (true)
  {
    captrue.read(frame);
    if (frame.empty()) {
      break;
    }

    Mat blob = dnn::blobFromImage(frame, 1.0, Size(300, 300), Scalar(104, 177, 123), false, false);
    net.setInput(blob);      //NCWH
    Mat probs = net.forward();  //推理
    Mat detectionMat(probs.size[2], probs.size[3], CV_32F, probs.ptr<float>());

    // 解析结果
    for (int i = 0; i < detectionMat.rows; i++) {
      float confidence = detectionMat.at<float>(i, 2);
      if (confidence > 0.5) {
        int x1 = static_cast<int>(detectionMat.at<float>(i, 3) * frame.cols);
        int y1 = static_cast<int>(detectionMat.at<float>(i, 4) * frame.rows);
        int x2 = static_cast<int>(detectionMat.at<float>(i, 5) * frame.cols);
        int y2 = static_cast<int>(detectionMat.at<float>(i, 6) * frame.rows);
        Rect box(x1, y1, x2 - x1, y2 - y1);
        rectangle(frame, box, Scalar(0, 0, 255), 2, 8, 0);
      }
    }
    imshow("frame", frame);
    int c = waitKey(10);
    if (c == 27) {
      break;
    }
  }
}