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k mean clustering of hsv histogram of frames of a video

I have calculated the hsv histogram of frames of a video . now i want to cluster frames in using k mean clustering i have searched it and found the in build method

kmeans(InputArray data, int K, InputOutputArray bestLabels, TermCriteria criteria, int attempts, int flags, OutputArray centers=noArray() )

but I don't understand how to use it can anyone explain it. my code is shown below if anyone can tell what i have to pass as arguments

// Set up images
System::String ^ str = path->Text ;
char* str2 = (char*)Marshal::StringToHGlobalAnsi(str).ToPointer();
printf(str2);

String^ P = path->Text;
//const char* t = P.
IplImage* img = cvLoadImage(str2);
IplImage* back_img = cvCreateImage( cvGetSize( img ), IPL_DEPTH_8U, 1 );

// Compute HSV image and separate into colors
IplImage* hsv = cvCreateImage( cvGetSize(img), IPL_DEPTH_8U, 3 );
cvCvtColor( img, hsv, CV_BGR2HSV );

IplImage* h_plane = cvCreateImage( cvGetSize( img ), 8, 1 );
IplImage* s_plane = cvCreateImage( cvGetSize( img ), 8, 1 );
IplImage* v_plane = cvCreateImage( cvGetSize( img ), 8, 1 );
IplImage* planes[] = { h_plane, s_plane };
cvCvtPixToPlane( hsv, h_plane, s_plane, v_plane, 0 );

// Build and fill the histogram int h_bins = 30, s_bins = 32; CvHistogram* hist; { int hist_size[] = { h_bins, s_bins }; float h_ranges[] = { 0, 180 }; float s_ranges[] = { 0, 255 }; float* ranges[] = { h_ranges, s_ranges }; hist = cvCreateHist( 2, hist_size, CV_HIST_ARRAY, ranges, 1 ); } cvCalcHist( planes, hist, 0, 0 ); // Compute histogram //cvNormalizeHist( hist, 20*255 ); // Normalize it

//cvCalcBackProject( planes, back_img, hist );// Calculate back projection
//cvNormalizeHist( hist, 1.0 ); // Normalize it

// Create an image to visualize the histogram
int scale = 10;
IplImage* hist_img = cvCreateImage( cvSize( h_bins * scale, s_bins * scale ), 8, 3 );
cvZero ( hist_img );

// populate the visualization
float max_value = 0;
cvGetMinMaxHistValue( hist, 0, &max_value, 0, 0 );

for( int h = 0; h < h_bins; h++ ){
    for( int s = 0; s < s_bins; s++ ){
        float bin_val = cvQueryHistValue_2D( hist, h, s );
        int intensity = cvRound( bin_val * 255 / max_value );
        cvRectangle( hist_img, cvPoint( h*scale, s*scale ),
                    cvPoint( (h+1)*scale - 1, (s+1)*scale - 1 ),
                    CV_RGB( intensity, intensity, intensity ),
                    CV_FILLED );
    }
}

// Show original
cvNamedWindow( "Source", 1) ;
cvShowImage( "Source", img );

// Show back projection
//cvNamedWindow( "Back Projection", 1) ;
//cvShowImage( "Back Projection", back_img );

// Show histogram equalized
cvNamedWindow( "H-S Histogram", 1) ;
cvShowImage( "H-S Histogram", hist_img );

cvWaitKey(0);

cvReleaseImage( &img );
cvReleaseImage( &back_img );
cvReleaseImage( &hist_img );

     }
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retagged

updated 2013-10-28 13:18:19 -0500

berak gravatar image

k mean clustering of hsv histogram of frames of a video

I have calculated the hsv histogram of frames of a video . now i want to cluster frames in using k mean clustering i have searched it and found the in build method

kmeans(InputArray data, int K, InputOutputArray bestLabels, TermCriteria criteria, int attempts, int flags, OutputArray centers=noArray() )

but I don't understand how to use it can anyone explain it. my code is shown below if anyone can tell what i have to pass as arguments

// Set up images
System::String ^ str = path->Text ;
char* str2 = (char*)Marshal::StringToHGlobalAnsi(str).ToPointer();
printf(str2);

String^ P = path->Text;
//const char* t = P.
IplImage* img = cvLoadImage(str2);
IplImage* back_img = cvCreateImage( cvGetSize( img ), IPL_DEPTH_8U, 1 );

// Compute HSV image and separate into colors
IplImage* hsv = cvCreateImage( cvGetSize(img), IPL_DEPTH_8U, 3 );
cvCvtColor( img, hsv, CV_BGR2HSV );

IplImage* h_plane = cvCreateImage( cvGetSize( img ), 8, 1 );
IplImage* s_plane = cvCreateImage( cvGetSize( img ), 8, 1 );
IplImage* v_plane = cvCreateImage( cvGetSize( img ), 8, 1 );
IplImage* planes[] = { h_plane, s_plane };
cvCvtPixToPlane( hsv, h_plane, s_plane, v_plane, 0 );

// Build and fill the histogram int h_bins = 30, s_bins = 32; CvHistogram* hist; { int hist_size[] = { h_bins, s_bins }; float h_ranges[] = { 0, 180 }; float s_ranges[] = { 0, 255 }; float* ranges[] = { h_ranges, s_ranges }; hist = cvCreateHist( 2, hist_size, CV_HIST_ARRAY, ranges, 1 ); } cvCalcHist( planes, hist, 0, 0 ); // Compute histogram //cvNormalizeHist( hist, 20*255 ); // Normalize it

//cvCalcBackProject( planes, back_img, hist );// Calculate back projection
//cvNormalizeHist( hist, 1.0 ); // Normalize it

// Create an image to visualize the histogram
int scale = 10;
IplImage* hist_img = cvCreateImage( cvSize( h_bins * scale, s_bins * scale ), 8, 3 );
cvZero ( hist_img );

// populate the visualization
float max_value = 0;
cvGetMinMaxHistValue( hist, 0, &max_value, 0, 0 );

for( int h = 0; h < h_bins; h++ ){
    for( int s = 0; s < s_bins; s++ ){
        float bin_val = cvQueryHistValue_2D( hist, h, s );
        int intensity = cvRound( bin_val * 255 / max_value );
        cvRectangle( hist_img, cvPoint( h*scale, s*scale ),
                    cvPoint( (h+1)*scale - 1, (s+1)*scale - 1 ),
                    CV_RGB( intensity, intensity, intensity ),
                    CV_FILLED );
    }
}

// Show original
cvNamedWindow( "Source", 1) ;
cvShowImage( "Source", img );

// Show back projection
//cvNamedWindow( "Back Projection", 1) ;
//cvShowImage( "Back Projection", back_img );

// Show histogram equalized
cvNamedWindow( "H-S Histogram", 1) ;
cvShowImage( "H-S Histogram", hist_img );

cvWaitKey(0);

cvReleaseImage( &img );
cvReleaseImage( &back_img );
cvReleaseImage( &hist_img );

     }