#include <stdlib.h>
#include <stdio.h>
#include <math.h>
#include <string.h>
#include<opencv2\opencv.hpp>
#include <opencv2\highgui\highgui.hpp>
IplImage* frame, * img1;
CvPoint point;
int drag = 0;
CvCapture *capture = 0;
int key = 0;
void mouseHandler(int event, int x, int y, int flags, void* param)
{
/* user press left button */
if (event == CV_EVENT_LBUTTONDOWN && !drag)
{
point = cvPoint(x, y);
drag = 1;
}
/* user drag the mouse */
if (event == CV_EVENT_MOUSEMOVE && drag)
{
img1 = cvCloneImage(frame);
cvRectangle(
img1,
point,
cvPoint(x, y),
CV_RGB(255, 0, 0),
1, 8, 0
);
cvCopy(img1,frame, NULL);
cvShowImage("result", img1);
}
/* user release left button */
if (event == CV_EVENT_LBUTTONUP && drag)
{
img1 = cvCloneImage(frame);
cvSetImageROI(
img1,
cvRect(
point.x,
point.y,
x - point.x,
y - point.y
)
);
cvNot(img1, img1); // or do whatever with the ROI
cvResetImageROI(img1);
cvCopy(img1,frame, NULL);
cvShowImage("result", img1);
drag = 0;
}
/* user click right button: reset all */
if (event == CV_EVENT_RBUTTONUP)
{
cvShowImage("result", frame);
drag = 0;
}
}
int main(int argc, char *argv[])
{
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 );
cvSetMouseCallback("result", mouseHandler, NULL);
while( key != 'q' ) {
frame = cvQueryFrame( capture );
cvShowImage("result", frame);
key = cvWaitKey( 1 );
}
cvDestroyWindow("result");
cvReleaseImage(&frame);
cvReleaseImage(&img1);
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) { ...
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