Analizing an image histogram, it is possible to observe several aspects of an image or a video, such as motion detection. In this example, we use the correlation between two frames of an video to determine if there was motion or not in it.

To achive this goal, the OpenCV CalcHist function was used to get the histogram of the frame, and the comparison between two frames was perfomed by the compareHist function, through the correlation comparison.;

Create a VideoCapture object
Capture frame-by-frame
Calculate histogram by using the OpenCV function 
Display the source and equalized images in a window.

Calculates a histogram of a set of arrays: C++: void calcHist(const Mat* images, int nimages, const int* channels, InputArray mask, OutputArray hist, int dims, const int* histSize, const float** ranges, bool uniform=true, bool accumulate=false ) OpenCV implements the function compareHist to perform a comparison. It also offers 4 different metrics to compute the matching, in this program we used the correlation as an metric. compareHist(histR, old_hist, CV_COMP_CORREL);

This example was based on the histogram.cpp algorithm and answers to the proposed exercises at agostinhobritojr.github.io.

#include <iostream>
#include <opencv2/opencv.hpp>

using namespace cv;
using namespace std;

int main(int argc, char** argv){
  Mat image;
  int width, height;
  VideoCapture cap;
  vector<Mat> planes;
  Mat histR, motion, old_hist;
  int nbins = 64;
  float range[] = {0, 256};
  const float *histrange = { range };
  bool uniform = true;
  bool acummulate = false;

  // Start Camera
  cap.open(0);
  
  if(!cap.isOpened()){
    cout << "cameras indisponiveis";
    return -1;
  }
  
  // image size
  width  = cap.get(CV_CAP_PROP_FRAME_WIDTH);
  height = cap.get(CV_CAP_PROP_FRAME_HEIGHT);

  cout << "largura = " << width << endl;
  cout << "altura  = " << height << endl;

  // mat for histogram - analyzing only red here.
  int histw = nbins, histh = nbins/2;
  Mat histImgR(histh, histw, CV_8UC3, Scalar(0,0,0));

  // Motion alert
  motion = imread("img/motion_detected.png",CV_LOAD_IMAGE_COLOR);
  if(!motion.data)
    cout << "couldn't load image" << endl;

  namedWindow("window",WINDOW_AUTOSIZE);

  // faz uma vez para poder, calcular a primeira correlação, dar continuidade dentro do loop
  cap >> image;
  split (image, planes);
  calcHist(&planes[0], 1, 0, Mat(), histR, 1,
             &nbins, &histrange,
             uniform, acummulate);
  normalize(histR, histR, 0, histImgR.rows, NORM_MINMAX, -1, Mat());
  
  histR.copyTo(old_hist);

  while(1){
    histR.copyTo(old_hist);
    cap >> image;
    split (image, planes);
    calcHist(&planes[0], 1, 0, Mat(), histR, 1,
             &nbins, &histrange,
             uniform, acummulate);

    normalize(histR, histR, 0, histImgR.rows, NORM_MINMAX, -1, Mat());

    histImgR.setTo(Scalar(0));
    
    double compare = compareHist(histR, old_hist, CV_COMP_CORREL);
    cout << compare << endl;

    if ( compare < 0.01)
    {
      cout << "Motion detected\n";
      motion.copyTo(image(Rect(0, histh, motion.size().width, motion.size().height)));
    }

    for(int i=0; i<nbins; i++){
      line(histImgR,
           Point(i, histh),
           Point(i, histh-cvRound(histR.at<float>(i))),
           Scalar(0, 0, 255), 1, 8, 0);
    }
    histImgR.copyTo(image(Rect(0, 0       ,nbins, histh)));
    imshow("image", image);
    if(waitKey(30) >= 0) break;
    old_hist.copyTo(histR);

  }
  return 0;
}