OpenCV实现人脸检测功能

本文实例为大家分享了OpenCV实现人脸检测功能的具体代码，供大家参考，具体内容如下

1、HAAR级联检测

#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;
 }
 // 水平镜像调整
 // flip(frame, frame, 1);
 imshow("input", frame);
 if (frame.channels() == 4)
  cvtColor(frame, frame, COLOR_BGRA2BGR);
 cvtColor(frame, gray, COLOR_BGR2GRAY);
 equalizeHist(gray, gray);
 faceDetector.detectMultiScale(gray, faces, 1.2, 1, 0, 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, 0.75, Scalar(0, 0, 255), 2, 8);
 imshow("haar_face_detection", frame);
 if (waitKey(1) >= 0) break;
 }

  printf("total face: %d\n", count);
}

2、 DNN人脸检测

#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() {
  //这里采用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";
  // 初始化网络
  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;
  }

  // 打开摄像头
  // 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;
    }
    // 水平镜像调整
    // flip(frame, frame, 1);
    imshow("input", frame);
    if (frame.channels() == 4)
      cvtColor(frame, frame, COLOR_BGRA2BGR);

    // 输入数据调整
    Mat inputBlob = blobFromImage(frame, inScaleFactor,
      Size(inWidth, inHeight), meanVal, false, false);
    net.setInput(inputBlob, "data");

    // 人脸检测
    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之间
      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, Scalar(0, 255, 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, 1, &baseLine);
        rectangle(frame, Rect(Point(xLeftBottom, yLeftBottom - labelSize.height),
          Size(labelSize.width, labelSize.height + baseLine)),
          Scalar(255, 255, 255), FILLED);
        putText(frame, label, Point(xLeftBottom, yLeftBottom),
          FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 0));
      }
    }
    float fps = getTickFrequency() / (getTickCount() - start);
    ss.str("");
    ss << "FPS: " << fps << " ; inference time: " << time << " ms";
    putText(frame, ss.str(), Point(20, 20), 0, 0.75, Scalar(0, 0, 255), 2, 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内容，希望对大家的学习有所帮助，也希望大家多多支持我们。