#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;
/* display current frame */
cvShowImage( "result", frame );
/* exit if user press 'q' */
key = cvWaitKey( 1 );
}
/* free memory */
cvDestroyWindow( "result" );
cvReleaseCapture( &capture );
return 0;
}
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) { /// Separate the image in 3 places ( R, G and B )
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