• What is Back Projection and why it is useful
• How to use the OpenCV function calcBackProject to calculate Back Projection
• How to mix different channels of an image by using the OpenCV function mixChannels

Theory

Code

#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"#include <iostream>using namespace cv;
using namespace std;/// Global Variables
Mat src; Mat hsv; Mat hue;
int bins = 25;/// Function Headers
void Hist_and_Backproj(int, void* );/** @function main */
int main( int argc, char** argv )
{/// Read the imagesrc = imread( argv[1], 1 );/// Transform it to HSVcvtColor( src, hsv, CV_BGR2HSV );/// Use only the Hue valuehue.create( hsv.size(), hsv.depth() );int ch[] = { 0, 0 };mixChannels( &hsv, 1, &hue, 1, ch, 1 );/// Create Trackbar to enter the number of binschar* window_image = "Source image";namedWindow( window_image, CV_WINDOW_AUTOSIZE );createTrackbar("* Hue  bins: ", window_image, &bins, 180, Hist_and_Backproj );Hist_and_Backproj(0, 0);/// Show the imageimshow( window_image, src );/// Wait until user exits the programwaitKey(0);return 0;
}/**
 * @function Hist_and_Backproj
 * @brief Callback to Trackbar
 */
void Hist_and_Backproj(int, void* )
{MatND hist;int histSize = MAX( bins, 2 );float hue_range[] = { 0, 180 };const float* ranges = { hue_range };/// Get the Histogram and normalize itcalcHist( &hue, 1, 0, Mat(), hist, 1, &histSize, &ranges, true, false );normalize( hist, hist, 0, 255, NORM_MINMAX, -1, Mat() );/// Get BackprojectionMatND backproj;calcBackProject( &hue, 1, 0, hist, backproj, &ranges, 1, true );/// Draw the backprojimshow( "BackProj", backproj );/// Draw the histogramint 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 );
}

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, CV_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 http://docs.opencv.org/modules/core/doc/operations_on_arrays.html?highlight=mixchannels#mixchannelsmixChannels 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, CV_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 );waitKey(0);
   return 0;
   

6. Hist_and_Backproj function: Initialize the arguments needed for 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

   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 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 );
    

normalize

Normalizes the norm or value range of an array.

C++: void normalize(InputArray src, OutputArray dst, double alpha=1, double beta=0, int norm_type=NORM_L2, int dtype=-1, InputArray mask=noArray() )

C++: void normalize(const SparseMat& src, SparseMat& dst, double alpha, int normType)

Python: cv2.normalize(src[, dst[, alpha[, beta[, norm_type[, dtype[, mask]]]]]]) → dst

Parameters:

src – input array. dst – output array of the same size as src . alpha – norm value to normalize to or the lower range boundary in case of the range normalization. beta – upper range boundary in case of the range normalization; it is not used for the norm normalization. normType – normalization type (see the details below). dtype – when negative, the output array has the same type as src; otherwise, it has the same number of channels as src and the depth =CV_MAT_DEPTH(dtype). mask – optional operation mask.

The functions normalize scale and shift the input array elements so that

(where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that

when normType=NORM_MINMAX (for dense arrays only).The optional mask specifies a sub-array to be normalized. This means that the norm or min-n-max are calculated over the sub-array, and then this sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or min-max but modify the whole array, you can usenorm() andMat::convertTo().

In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this, the range transformation for sparse matrices is not allowed since it can shift the zero level.