finding the dominate color for each object when there are too many objects using opencv [closed]

edit

Is there a way to find the maximum intensity element of each object in an image that contains multiple objects using opencv? The objects is supposed to be books. I want to find the dominate color for each book. I used contours to do so, but it didn't work. The function that finds the maximum intensity element:

int max_intensity_element(Mat image){

    // allcoate memory for no of pixels for each intensity value
       int histogram[256];

         // initialize all intensity values to 0
        for(int i = 0; i < 255; i++)
           {
           histogram[i] = 0;
         }

          for(int i = 0; i < 255; i++)
                   {
            cout<< histogram[i] ;
                }

               // calculate the no of pixels for each intensity values
              for(int y = 0; y < image.rows; y++)
                    for(int x = 0; x < image.cols; x++)
                       histogram[(int)image.at<uchar>(y,x)]++;

                  // draw the histograms
                 int hist_w = 512; int hist_h = 400;
                 int bin_w = cvRound((double) hist_w/256);

                    Mat histImage(hist_h, hist_w, CV_8UC1, Scalar(255, 255, 255));

                   // find the maximum intensity element from histogram
                    int max = histogram[0];
                 int index;
                 for(int i = 1; i < 256; i++){
                  if(max < histogram[i]){
                   max = histogram[i];
                        index=i;
                        }
                           }



                     return index;
                     }

Code in main:

Mat image = imread("boo2.png");
Mat image2 = imread("boo.png");
 int index, index2;
CvMemStorage* storage = NULL;  
 //////////////////////////////////////////////////////////////////////////  
//Step 1   
//Load Reference Image   
IplImage * R;  
int thresh = 120;
R = cvLoadImage("boo.png", 0);  
//Load Target Image  
IplImage * T;  
T = cvLoadImage("boo2.png", 0);  

 //////////////////////////////////////////////////////////////////////////  
//step 2  
 storage = cvCreateMemStorage(0);  
//R contour  
CvSeq* contours = 0;  
cvThreshold(R, R, thresh, 255, CV_THRESH_BINARY);  
cvFindContours( R, storage, &contours ); 
cvZero(R);  
if( contours ){
cvDrawContours(
    R,
    contours,
    cvScalarAll(255),
    cvScalarAll(255),
    8, CV_FILLED );
 }  
//////////////////////////////////////////////////////////////////////////  
//Step 3  
  CvSeq* contour_ptr2= contours;

 while( (contour_ptr2!= NULL) ) {  
       index2=max_intensity_element(image2);
cout<<index2<<endl;


contour_ptr2= contour_ptr2->h_next;
} 


 ////////////////////////////////////////////////////////////////////////// 
//T contour  
 CvSeq* contours2 = 0;  
  cvThreshold(T, T, thresh, 255, CV_THRESH_BINARY);  
   cvFindContours(T, storage, &contours2 );   
    cvZero(T);  
    if( contours2 ){
cvDrawContours(
    T,
    contours2,
    cvScalarAll(255),
    cvScalarAll(255),
    8, CV_FILLED );
      }   

  //////////////////////////////////////////////////////////////////////////  
  //Step 3  
 CvSeq* contour_ptr = contours2;

 while( (contour_ptr != NULL) ) {  
   index=max_intensity_element(image);
cout<<index <<endl;


contour_ptr = contour_ptr->h_next;
    } 


       //////////////////////////////////////////////////////////////////////////  
 if (index==index2)
 {
     cout<<"match";
 }
 else
      {
     cout<<" not match";
 }




         waitKey();

        return 0;
           }