Learning OpenCV, 2nd Edition

16BBackground differencing: Finding foreground objects

We’ve seen how to create a background codebook model and how to clear it of seldom-used entries. Next

we turn to background_diff(), where we use the learned model to segment foreground pixels from

the previously learned background:

// Given a pixel and a codebook, determine if the pixel is

// covered by the codebook

//

// NOTES:

// minMod and maxMod must have length numChannels,

// e.g. 3 channels => minMod[3], maxMod[3]. There is one min and

// one max threshold per channel.

//

uchar backgroundDiff( // return 0 => background, 255 => foreground

cv::Vec3b& p, // Pixel (YUV)

CodeBook& c, // Codebook

int numChannels, // Number of channels we are testing

int* minMod, // Add this (possibly negative) number onto max level

// when determining if new pixel is foreground

int* maxMod // Subtract this (possibly negative) number from min level

// when determining if new pixel is foreground

) {

int matchChannel;

// SEE IF THIS FITS AN EXISTING CODEWORD

//

for( int i=0; i<c.size(); i++ ) {

matchChannel = 0;

for( int n=0; n<numChannels; n++ ) {

if(

(c[i]->min[n] - minMod[n] <= p[n] ) && (p[n] <= c[i]->max[n] + maxMod[n])

) {

matchChannel++; // Found an entry for this channel

} else {

break;

}

}

if(matchChannel == numChannels) {

break; // Found an entry that matched all channels

}

}

if( i >= c.size() ) return ...