OpenCV  3.2.0
Open Source Computer Vision
Back Projection

Goal

In this tutorial you will learn:

Theory

What is Back Projection?

How does it work?

Code

Explanation

  1. Declare the matrices to store our images and initialize the number of bins to be used by our histogram:
    Mat src; Mat hsv; Mat hue;
    int bins = 25;
  2. Read the input image and transform it to HSV format:
    src = imread( argv[1], 1 );
    cvtColor( src, hsv, COLOR_BGR2HSV );
  3. For this tutorial, we will use only the Hue value for our 1-D histogram (check out the fancier code in the links above if you want to use the more standard H-S histogram, which yields better results):
    hue.create( hsv.size(), hsv.depth() );
    int ch[] = { 0, 0 };
    mixChannels( &hsv, 1, &hue, 1, ch, 1 );
    as you see, we use the function cv::mixChannels to get only the channel 0 (Hue) from the hsv image. It gets the following parameters:
    • **&hsv:** The source array from which the channels will be copied
    • 1: The number of source arrays
    • **&hue:** The destination array of the copied channels
    • 1: The number of destination arrays
    • ch[] = {0,0}: The array of index pairs indicating how the channels are copied. In this case, the Hue(0) channel of &hsv is being copied to the 0 channel of &hue (1-channel)
    • 1: Number of index pairs
  4. Create a Trackbar for the user to enter the bin values. Any change on the Trackbar means a call to the Hist_and_Backproj callback function.
    char* window_image = "Source image";
    namedWindow( window_image, WINDOW_AUTOSIZE );
    createTrackbar("* Hue bins: ", window_image, &bins, 180, Hist_and_Backproj );
    Hist_and_Backproj(0, 0);
  5. Show the image and wait for the user to exit the program:
    imshow( window_image, src );
    return 0;
  6. Hist_and_Backproj function: Initialize the arguments needed for cv::calcHist . The number of bins comes from the Trackbar:
    void Hist_and_Backproj(int, void* )
    {
    MatND hist;
    int histSize = MAX( bins, 2 );
    float hue_range[] = { 0, 180 };
    const float* ranges = { hue_range };
  7. Calculate the Histogram and normalize it to the range \([0,255]\)
    calcHist( &hue, 1, 0, Mat(), hist, 1, &histSize, &ranges, true, false );
    normalize( hist, hist, 0, 255, NORM_MINMAX, -1, Mat() );
  8. Get the Backprojection of the same image by calling the function cv::calcBackProject
    MatND backproj;
    calcBackProject( &hue, 1, 0, hist, backproj, &ranges, 1, true );
    all the arguments are known (the same as used to calculate the histogram), only we add the backproj matrix, which will store the backprojection of the source image (&hue)
  9. Display backproj:
    imshow( "BackProj", backproj );
  10. Draw the 1-D Hue histogram of the image:
    int w = 400; int h = 400;
    int bin_w = cvRound( (double) w / histSize );
    Mat histImg = Mat::zeros( w, h, CV_8UC3 );
    for( int i = 0; i < bins; i ++ )
    { rectangle( histImg, Point( i*bin_w, h ), Point( (i+1)*bin_w, h - cvRound( hist.at<float>(i)*h/255.0 ) ), Scalar( 0, 0, 255 ), -1 ); }
    imshow( "Histogram", histImg );

Results

Here are the output by using a sample image ( guess what? Another hand ). You can play with the bin values and you will observe how it affects the results:

Back_Projection1_Source_Image.jpg
R0
Back_Projection1_Histogram.jpg
R1
Back_Projection1_BackProj.jpg
R2