face_detection_opencv_dnn.cpp

#include <iostream>
#include <string>
#include <vector>
#include <stdlib.h>
#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/dnn.hpp>

using namespace cv;
using namespace std;
using namespace cv::dnn;

const size_t inWidth = 300;
const size_t inHeight = 300;
const double inScaleFactor = 1.0;
const float confidenceThreshold = 0.7;
const cv::Scalar meanVal(104.0, 177.0, 123.0);

#define CAFFE

const std::string caffeConfigFile = "./models/deploy.prototxt";
const std::string caffeWeightFile = "./models/res10_300x300_ssd_iter_140000_fp16.caffemodel";

const std::string tensorflowConfigFile = "./models/opencv_face_detector.pbtxt";
const std::string tensorflowWeightFile = "./models/opencv_face_detector_uint8.pb";

void detectFaceOpenCVDNN(Net net, Mat &frameOpenCVDNN)
{
    int frameHeight = frameOpenCVDNN.rows;
    int frameWidth = frameOpenCVDNN.cols;
#ifdef CAFFE
        cv::Mat inputBlob = cv::dnn::blobFromImage(frameOpenCVDNN, inScaleFactor, cv::Size(inWidth, inHeight), meanVal, false, false);
#else
        cv::Mat inputBlob = cv::dnn::blobFromImage(frameOpenCVDNN, inScaleFactor, cv::Size(inWidth, inHeight), meanVal, true, false);
#endif

    net.setInput(inputBlob, "data");
    cv::Mat detection = net.forward("detection_out");

    cv::Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());

    for(int i = 0; i < detectionMat.rows; i++)
    {
        float confidence = detectionMat.at<float>(i, 2);

        if(confidence > confidenceThreshold)
        {
            int x1 = static_cast<int>(detectionMat.at<float>(i, 3) * frameWidth);
            int y1 = static_cast<int>(detectionMat.at<float>(i, 4) * frameHeight);
            int x2 = static_cast<int>(detectionMat.at<float>(i, 5) * frameWidth);
            int y2 = static_cast<int>(detectionMat.at<float>(i, 6) * frameHeight);

            cv::rectangle(frameOpenCVDNN, cv::Point(x1, y1), cv::Point(x2, y2), cv::Scalar(0, 255, 0),2, 4);
        }
    }

}


int main( int argc, const char** argv )
{
#ifdef CAFFE
  Net net = cv::dnn::readNetFromCaffe(caffeConfigFile, caffeWeightFile);
#else
  Net net = cv::dnn::readNetFromTensorflow(tensorflowWeightFile, tensorflowConfigFile);
#endif

  VideoCapture source;
  if (argc == 1)
      source.open(0);
  else
      source.open(argv[1]);
  Mat frame;

  double tt_opencvDNN = 0;
  double fpsOpencvDNN = 0;
  while(1)
  {
      source >> frame;
      if(frame.empty())
          break;
      double t = cv::getTickCount();
      detectFaceOpenCVDNN ( net, frame );
      tt_opencvDNN = ((double)cv::getTickCount() - t)/cv::getTickFrequency();
      fpsOpencvDNN = 1/tt_opencvDNN;
      putText(frame, format("OpenCV DNN ; FPS = %.2f",fpsOpencvDNN), Point(10, 50), FONT_HERSHEY_SIMPLEX, 1.4, Scalar(0, 0, 255), 4);
      imshow( "OpenCV - DNN Face Detection", frame );
      int k = waitKey(5);
      if(k == 27)
      {
        destroyAllWindows();
        break;
      }
    }
}