Skip to main content

Histogram computation on a video

Histograms are collected counts of data organized into a set of predefined bins


 refracted class for histogram: given an input image, output the histogram image


class atsHistogram
{
public:
    cv::Mat DrawHistogram(Mat src)
    {
        /// Separate the image in 3 places ( R, G and B )
         vector<Mat> rgb_planes;
         split( src, rgb_planes );

         /// Establish the number of bins
         int histSize = 255;

         /// Set the ranges ( for R,G,B) )
         float range[] = { 0, 255 } ;
         const float* histRange = { range };

         bool uniform = true; bool accumulate = false;

         Mat r_hist, g_hist, b_hist;

         /// Compute the histograms:
         calcHist( &rgb_planes[0], 1, 0, Mat(), r_hist, 1, &histSize, &histRange, uniform, accumulate );
         calcHist( &rgb_planes[1], 1, 0, Mat(), g_hist, 1, &histSize, &histRange, uniform, accumulate );
         calcHist( &rgb_planes[2], 1, 0, Mat(), b_hist, 1, &histSize, &histRange, uniform, accumulate );

         // Draw the histograms for R, G and B
         int hist_w = 400; int hist_h = 400;
         int bin_w = cvRound( (double) hist_w/histSize );

         Mat histImage( hist_w, hist_h, CV_8UC3, Scalar( 0,0,0) );

         /// Normalize the result to [ 0, histImage.rows ]
         normalize(r_hist, r_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat() );
         normalize(g_hist, g_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat() );
         normalize(b_hist, b_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat() );

         /// Draw for each channel
         for( int i = 1; i < histSize; i++ )
           {
             line( histImage, Point( bin_w*(i-1), hist_h - cvRound(r_hist.at<float>(i-1)) ) ,
                              Point( bin_w*(i), hist_h - cvRound(r_hist.at<float>(i)) ),
                              Scalar( 0, 0, 255), 2, 8, 0  );
             line( histImage, Point( bin_w*(i-1), hist_h - cvRound(g_hist.at<float>(i-1)) ) ,
                              Point( bin_w*(i), hist_h - cvRound(g_hist.at<float>(i)) ),
                              Scalar( 0, 255, 0), 2, 8, 0  );
             line( histImage, Point( bin_w*(i-1), hist_h - cvRound(b_hist.at<float>(i-1)) ) ,
                              Point( bin_w*(i), hist_h - cvRound(b_hist.at<float>(i)) ),
                              Scalar( 255, 0, 0), 2, 8, 0  );
            }
         return histImage;
    }
private:
};





Labels:

Comments

Post a Comment

Popular posts from this blog

Computing Entropy of an image (CORRECTED)

entropy is a measure of the uncertainty associated with a random variable. basically i want to get a single value representing the entropy of an image. 1. Assign 255 bins for the range of values between 0-255 2. separate the image into its 3 channels 3. compute histogram for each channel 4. normalize all 3 channels unifirmely 5. for each channel get the bin value (Hc) and use its absolute value (negative log is infinity) 6. compute Hc*log10(Hc) 7. add to entropy and continue with 5 until a single value converges 5. get the frequency of each channel - add all the values of the bin 6. for each bin get a probability - if bin 1 = 20 bin 2 = 30 then frequency is 50 and probability is 20/50 and 30/50 then compute using shannon formula  REFERENCE: http://people.revoledu.com/kardi/tutorial/DecisionTree/how-to-measure-impurity.htm class atsHistogram { public:     cv::Mat DrawHistogram(Mat src)     {      ...
6 comments
Read more

Blob Detection, Connected Component (Pure Opencv)

Connected-component labeling (alternatively connected-component analysis, blob extraction, region labeling, blob discovery, or region extraction) is an algorithmic application of graph theory, where subsets of connected components are uniquely labeled based on a given heuristic. Connected-component labeling is not to be confused with segmentation. i got the initial code from this URL: http://nghiaho.com/?p=1102 However the code did not compile with my setup of OpenCV 2.2, im guessing it was an older version. so a refactored and corrected the errors to come up with this Class class atsBlobFinder     {     public:         atsBlobFinder()         {         }         ///Original Code by http://nghiaho.com/?p=1102         ///Changed and added commments. Removed Errors     ...
2 comments
Read more

Region of interest selection ROI

#include <stdlib.h> #include <stdio.h> #include <math.h> #include <string.h> #include<opencv2\opencv.hpp> #include <opencv2\highgui\highgui.hpp> int main(int argc, char *argv[]) { CvCapture *capture = 0; IplImage *frame = 0; int key = 0; /* initialize camera */ capture = cvCaptureFromCAM( 0 ); /* always check */ if ( !capture ) { printf("Cannot open initialize webcam!\n" ); exit(0); } /* create a window for the video */ cvNamedWindow( "result", CV_WINDOW_AUTOSIZE ); while( key != 'q' ) { /* get a frame */ frame = cvQueryFrame( capture ); /* always check */ if( !frame ) break; /* sets the Region of Interest*/ cvSetImageROI(frame, cvRect(150, 50, 150, 250)); /* create destination image */ IplImage *img2 = cvCreateImage(cvGetSize(frame), frame->depth, frame->nChannels); /* * do the main processing with subimage here. * in this example, we simply invert the subimage ...
Post a Comment
Read more