Basic Face Detection, Opencv 3 Visual Studio 2015

Basic Opencv Face Detection Tutorial

Basic Opencv C++ tutorial how to detect the face from video image and any source you can achieve.
If you have little bit skills with programing you can just achieve the result under 10 minutes. I would like to reccomend also the installation by NUGET package. It is the fastest approach in Visual Studio how to start coding with opencv.. 

Problem with CascadeClassifier detectMultiScale Opencv 3.0.0. Windows prebuild libs

Problem is that in many cases with 32 bit Opencv 3.0.0 Windows version in debug and in release mode. I thing any of haarcascade_xxx return vector<Rect> Faces (testing number of cascades is 7). The length of the vector is incomprehensibly bigger than I can expect with my parameters and experiences with previous Opencv version..

vector<Rect> of detection is to big 

This is some kind of bug that I try to find in opencv source. What is surprising is fact that 64 bit version of Opencv works fine in my case. 

 I am using Visual Studio 2015 with 64 bit Opencv 3.0.0

 Here is my tutorial how to install Opencv 3.0.0 libs for Visual Studio 2015. 

Install Opencv for Visual Studio 2015
You can simple prepare the project inside the Visual Studio 2015 by Nuget Packages. This approach is easy for beginers and better than standard installation with all the environmental variables problems. Just follow the installation steps inside here 

Face detection basic Opencv C++ code example

There is nothing special, but you can also find the example how to replace the face with transparent mask or any pictures. This is at least fun. 

#include <opencv2/core.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/videoio.hpp>
#include <opencv2/objdetect.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>

using namespace cv;
using namespace std;


int main(int argc, char** argv)
{
 // capture from web camera init

 VideoCapture cap(0);
 cap.open(0);

 Mat img;

// Load cascate classifier placed in sulution folder
 CascadeClassifier detector;
 string cascadeName = "haarcascade_frontalface_alt.xml";
 bool loaded = detector.load(cascadeName);
// Parameters of detectMultiscale Cascade Classifier
 int groundThreshold = 2;
 double scaleStep = 1.1;
 Size minimalObjectSize(80, 80);
 Size maximalObjectSize(200, 200);

// Vector of returned faces
 vector found;

 for (;;)
 {
 
      // Image from camera to Mat

      cap >> img;
     // Convert input to greyscale 
      Mat image_grey;
      cvtColor(img, image_grey, CV_BGR2GRAY);

      // why not
      found.clear();
  
      // Detect faces
      detector.detectMultiScale(image_greyHead, found, scaleStep, groundThreshold, 0         | 2, minimalObjectSize,maximalObjectSize);
      // Draw the results into mat retrieved from webcam
      if (found.size() > 0) {
            for (int i = 0; i <= found.size() - 1; i++) {
             rectangle(img, found[i].br(), found[i].tl(), Scalar(0, 0, 0), 1, 8, 0);
             }
      }

    //Show the results

     imshow("wooohooo", img);
     int key2 = waitKey(20);

 }
 return 0;
}