本文例項為大家分享了OpenCV實現人臉檢測功能的具體程式碼，供大家參考，具體內容如下

1、HAAR級聯檢測

#include <opencv2/opencv.hpp>
#include <iostream>
 
using namespace cv;
 
#include <iostream>
#include <cstdlib>
using namespace std;
 
int main(int artc,char** argv) {
 face_detect_haar();
 waitKey(0);
 return 0;
}
 
void face_detect_haar() {
 CascadeClassifier faceDetector;
 std::string haar_data_file = "./models/haarcascades/haarcascade_frontalface_alt_tree.xml";
 faceDetector.load(haar_data_file); 
 vector<Rect> faces;
 //VideoCapture capture(0);
 VideoCapture capture("./video/test.mp4");
 Mat frame,gray;
 int count=0;
 while (capture.read(frame)) {
 int64 start = getTickCount();
 if (frame.empty())
 {
  break;
 }
 // 水平映象調整
 // flip(frame,frame,1);
 imshow("input",frame);
 if (frame.channels() == 4)
  cvtColor(frame,COLOR_BGRA2BGR);
 cvtColor(frame,gray,COLOR_BGR2GRAY);
 equalizeHist(gray,gray);
 faceDetector.detectMultiScale(gray,faces,1.2,1,Size(30,30),Size(400,400));
 for (size_t t = 0; t < faces.size(); t++) {
  count++;
  rectangle(frame,faces[t],Scalar(0,255,0),2,8,0);
 }
 float fps = getTickFrequency() / (getTickCount() - start);
 ostringstream ss;ss.str("");
 ss << "FPS: " << fps << " ; inference time: " << time << " ms";
 putText(frame,ss.str(),Point(20,20),0.75,255),8);
 imshow("haar_face_detection",frame);
 if (waitKey(1) >= 0) break;
 }
 
  printf("total face: %d\n",count);
}

2、DNN人臉檢測

#include <opencv2/dnn.hpp>
#include <opencv2/opencv.hpp>
 
using namespace cv;
using namespace cv::dnn;
 
#include <iostream>
#include <cstdlib>
using namespace std;
 
const size_t inWidth = 300;
const size_t inHeight = 300;
const double inScaleFactor = 1.0;
const Scalar meanVal(104.0,177.0,123.0);
const float confidenceThreshold = 0.7;
void face_detect_dnn();
void mtcnn_demo();
int main(int argc,char** argv)
{
  face_detect_dnn();
  waitKey(0);
  return 0;
}
 
void face_detect_dnn() {
  //這裡採用tensorflow模型
  std::string modelBinary = "./models/dnn/face_detector/opencv_face_detector_uint8.pb";
  std::string modelDesc = "./models/dnn/face_detector/opencv_face_detector.pbtxt";
  // 初始化網路
  dnn::Net net = readNetFromTensorflow(modelBinary,modelDesc);
 
  net.setPreferableBackend(DNN_BACKEND_OPENCV);
  net.setPreferableTarget(DNN_TARGET_CPU);
  if (net.empty())
  {
    printf("Load models fail...\n");
    return;
  }
 
  // 開啟攝像頭
  // VideoCapture capture(0);
  VideoCapture capture("./video/test.mp4");
  if (!capture.isOpened()) {
    printf("Don't find video...\n");
    return;
  }
 
  Mat frame;
  int count=0;
  while (capture.read(frame)) {
    int64 start = getTickCount();
    if (frame.empty())
    {
      break;
    }
    // 水平映象調整
    // flip(frame,1);
    imshow("input",frame);
    if (frame.channels() == 4)
      cvtColor(frame,COLOR_BGRA2BGR);
 
    // 輸入資料調整
    Mat inputBlob = blobFromImage(frame,inScaleFactor,Size(inWidth,inHeight),meanVal,false,false);
    net.setInput(inputBlob,"data");
 
    // 人臉檢測
    Mat detection = net.forward("detection_out");
    vector<double> layersTimings;
    double freq = getTickFrequency() / 1000;
    double time = net.getPerfProfile(layersTimings) / freq;
    Mat detectionMat(detection.size[2],detection.size[3],CV_32F,detection.ptr<float>());
 
    ostringstream ss;
    for (int i = 0; i < detectionMat.rows; i++)
    {
      // 置信度 0～1之間
      float confidence = detectionMat.at<float>(i,2);
      if (confidence > confidenceThreshold)
      {
        count++;
        int xLeftBottom = static_cast<int>(detectionMat.at<float>(i,3) * frame.cols);
        int yLeftBottom = static_cast<int>(detectionMat.at<float>(i,4) * frame.rows);
        int xRightTop = static_cast<int>(detectionMat.at<float>(i,5) * frame.cols);
        int yRightTop = static_cast<int>(detectionMat.at<float>(i,6) * frame.rows);
 
        Rect object((int)xLeftBottom,(int)yLeftBottom,(int)(xRightTop - xLeftBottom),(int)(yRightTop - yLeftBottom));
 
        rectangle(frame,object,0));
 
        ss << confidence;
        std::string conf(ss.str());
        std::string label = "Face: " + conf;
        int baseLine = 0;
        Size labelSize = getTextSize(label,FONT_HERSHEY_SIMPLEX,0.5,&baseLine);
        rectangle(frame,Rect(Point(xLeftBottom,yLeftBottom - labelSize.height),Size(labelSize.width,labelSize.height + baseLine)),Scalar(255,FILLED);
        putText(frame,label,Point(xLeftBottom,yLeftBottom),0));
      }
    }
    float fps = getTickFrequency() / (getTickCount() - start);
    ss.str("");
    ss << "FPS: " << fps << " ; inference time: " << time << " ms";
    putText(frame,8);
    imshow("dnn_face_detection",frame);
    if (waitKey(1) >= 0) break;
  }
  printf("total face: %d\n",count);
}

以上就是本文的全部內容，希望對大家的學習有所幫助，也希望大家多多支援我們。