How do I determine means of image blocks using opencv histogram - visual-c++

Question

I'd like to determine the mean block of my image using histogram. Let's say my image has 64 by 64 dimension, I need to divide it into 4 by 4 block then determine each block mean (in other word now I will have 4 blocks).
Using opencv, How do I can utilize my IplImage to determine block mean using histogram bins?
The code below is opencv histogram in order to determine whole image mean:
int i, hist_size = 256;
float max_value,min_value;
float min_idx,max_idx;
float bin_w;
float mean =0, low_mean =0, high_mean =0, variance =0;
float range_0[]={0,256};
float *ranges[]={range_0};
IplImage* im = cvLoadImage("killerbee.jpg");
//Create a single planed image of the same size as the original
IplImage* grayImage = cvCreateImage(cvSize(im->width,im->height),IPL_DEPTH_8U, 1);
//convert the original image to gray
cvCvtColor(im, grayImage, CV_BGR2GRAY);
/* Remark this, since wanna evaluate whole area.
//create a rectangular area to evaluate
CvRect rect = cvRect(0, 0, 500, 600 );
//apply the rectangle to the image and establish a region of interest
cvSetImageROI(grayImage, rect);
End remark*/
//create an image to hold the histogram
IplImage* histImage = cvCreateImage(cvSize(320,200), 8, 1);
//create a histogram to store the information from the image
CvHistogram* hist = cvCreateHist(1, &hist_size, CV_HIST_ARRAY, ranges, 1);
//calculate the histogram and apply to hist
cvCalcHist( &grayImage, hist, 0, NULL );
//grab the min and max values and their indeces
cvGetMinMaxHistValue( hist, &min_value, &max_value, 0, 0);
//scale the bin values so that they will fit in the image representation
cvScale( hist->bins, hist->bins, ((double)histImage->height)/max_value, 0 );
//set all histogram values to 255
cvSet( histImage, cvScalarAll(255), 0 );
//create a factor for scaling along the width
bin_w = cvRound((double)histImage->width/hist_size);
for( i = 0; i < hist_size; i++ ) {
//draw the histogram data onto the histogram image
cvRectangle( histImage, cvPoint(i*bin_w, histImage->height),cvPoint((i+1)*bin_w,histImage->height - cvRound(cvGetReal1D(hist->bins,i))),cvScalarAll(0), -1, 8, 0 );
//get the value at the current histogram bucket
float* bins = cvGetHistValue_1D(hist,i);
//increment the mean value
mean += bins[0];
}
//finish mean calculation
mean /= hist_size;
//display mean value onto output window
cout<<"MEAN VALUE of THIS IMAGE : "<<mean<<"\n";
//go back through now that mean has been calculated in order to calculate variance
for( i = 0; i < hist_size; i++ ) {
float* bins = cvGetHistValue_1D(hist,i);
variance += pow((bins[0] - mean),2);
}
//finish variance calculation
variance /= hist_size;
cvNamedWindow("Original", 0);
cvShowImage("Original", im );
cvNamedWindow("Gray", 0);
cvShowImage("Gray", grayImage );
cvNamedWindow("Histogram", 0);
cvShowImage("Histogram", histImage );
//hold the images until a key is pressed
cvWaitKey(0);
//clean up images
cvReleaseImage(&histImage);
cvReleaseImage(&grayImage);
cvReleaseImage(&im);
//remove windows
cvDestroyWindow("Original");
cvDestroyWindow("Gray");
cvDestroyWindow("Histogram");
Really thanks in advance.

Answer 1

You can do that by histograms, but a much more effective way to do it is an integral image, which does almost what you want.
Read here http://en.wikipedia.org/wiki/Summed_area_table and then use it to calculate the sum of all the pixels in every block. Then divide by the number of pixels in each block (4x4=16). Isn't it nice?
OpenCV has a function to calculate the integral image, with the difficult name cv::integral()
And an even easier way to do it is the humble resize().
Call resize(image64_64, image_16_16, Size(16, 16), INTER_AREA), and the result will be a smaller image whose pixel values have exactly the values you're looking for. Isn't it great?
Just do not forget the INTER_AREA flag. It determines the correct algorithm to be used.