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OpenCV實現(xiàn)人臉檢測功能

更新時間：2020年02月14日 14:07:14 作者：老狼主

這篇文章主要為大家詳細介紹了OpenCV實現(xiàn)人臉檢測功能，文中示例代碼介紹的非常詳細，具有一定的參考價值，感興趣的小伙伴們可以參考一下

本文實例為大家分享了OpenCV實現(xiàn)人臉檢測功能的具體代碼，供大家參考，具體內(nèi)容如下

1、HAAR級聯(lián)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;
 }
 // 水平鏡像調(diào)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";
 // 初始化網(wǎng)絡(luò)
 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;
 }
 
 // 打開攝像頭
 // 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;
  }
  // 水平鏡像調(diào)整
  // flip(frame, frame, 1);
  imshow("input", frame);
  if (frame.channels() == 4)
   cvtColor(frame, frame, COLOR_BGRA2BGR);
 
  // 輸入數(shù)據(jù)調(diào)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, 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內(nèi)容，希望對大家的學(xué)習(xí)有所幫助，也希望大家多多支持腳本之家。

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