OpenCV Tutorial 6 - Chapter 7

My Vision Tutorials Index

This tutorial assumes the reader:
(1) Has a basic knowledge of Visual C++
(2) Has some familiarity with computer vision concepts
(3) Has read the previous tutorials in this series

The rest of the tutorial is presented as follows:

• Step 1: Histograms and Back Projection
• Step 2: Template Matching
• Final Words

Important Note!

Step 1: Histograms and Back Projection

Original Image, Back Projection, and Histogram

int _tmain(int argc, _TCHAR* argv[])
{
	// Set up images
	IplImage* img = cvLoadImage("MGC.jpg");
	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 );

	return 0;
}

Step 2: Template Matching

Histogram Template Matching

int _tmain(int argc, _TCHAR* argv[])
{
	IplImage *src, *templ, *ftmp[6]; // ftmp will hold results
	int i;

	// Read in the source image to be searched
	src = cvLoadImage("MGC.jpg");

	// Read in the template to be used for matching:
	templ = cvLoadImage("template.jpg");

	// Allocate Output Images:
	int iwidth = src->width - templ->width + 1;
	int iheight = src->height - templ->height + 1;
	for(i = 0; i < 6; ++i){
		ftmp[i]= cvCreateImage( cvSize( iwidth, iheight ), 32, 1 );
	}

	// Do the matching of the template with the image
	for( i = 0; i < 6; ++i ){
		cvMatchTemplate( src, templ, ftmp[i], i );
		cvNormalize( ftmp[i], ftmp[i], 1, 0, CV_MINMAX );
	}

	// DISPLAY
	cvNamedWindow( "Template", 0 );
	cvShowImage( "Template", templ );
	cvNamedWindow( "Image", 0 );
	cvShowImage( "Image", src );
	cvNamedWindow( "SQDIFF", 0 );
	cvShowImage( "SQDIFF", ftmp[0] );
	cvNamedWindow( "SQDIFF_NORMED", 0 );
	cvShowImage( "SQDIFF_NORMED", ftmp[1] );
	cvNamedWindow( "CCORR", 0 );
	cvShowImage( "CCORR", ftmp[2] );
	cvNamedWindow( "CCORR_NORMED", 0 );
	cvShowImage( "CCORR_NORMED", ftmp[3] );
	cvNamedWindow( "COEFF", 0 );
	cvShowImage( "COEFF", ftmp[4] );
	cvNamedWindow( "COEFF_NORMED", 0 );
	cvShowImage( "COEFF_NORMED", ftmp[5] );

	cvWaitKey(0);

	return 0;
}

Final Words

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