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#include "precomp.hpp"
//TODO delete highgui include
//#include <opencv2/highgui.hpp>
#define thetaA_VAL 200
#define thetaL_VAL 250
namespace cv
{
namespace saliency
{
void MotionSaliencyBinWangApr2014::setImagesize( int W, int H )
{
imageWidth = W;
imageHeight = H;
}
MotionSaliencyBinWangApr2014::MotionSaliencyBinWangApr2014()
{
N_DS = 2; // Number of template to be downsampled and used in lowResolutionDetection function
K = 3; // Number of background model template
N = 4; // NxN is the size of the block for downsampling in the lowlowResolutionDetection
alpha = (float) 0.01; // Learning rate
L0 = 1000; // Upper-bound values for C0 (efficacy of the first template (matrices) of backgroundModel
L1 = 800; // Upper-bound values for C1 (efficacy of the second template (matrices) of backgroundModel
thetaL = thetaL_VAL; // T0, T1 swap threshold
thetaA = thetaA_VAL;
gamma = 3;
neighborhoodCheck = true;
className = "BinWangApr2014";
}
bool MotionSaliencyBinWangApr2014::init()
{
Size imgSize( imageWidth, imageHeight );
epslonPixelsValue = Mat( imgSize.height, imgSize.width, CV_32F, Scalar( 20 ) );
// Median of range [18, 80] advised in reference paper.
// Since data is even, the median is estimated using two values that occupy
// the position (n / 2) and ((n / 2) +1) (choose their arithmetic mean).
potentialBackground = Mat( imgSize.height, imgSize.width, CV_32FC2, Scalar( NAN, 0 ) );
backgroundModel.resize( K + 1 );
for ( int i = 0; i < K + 1; i++ )
{
Mat* tmpm = new Mat;
tmpm->create( imgSize.height, imgSize.width, CV_32FC2 );
tmpm->setTo( Scalar( NAN, 0 ) );
Ptr<Mat> tmp = Ptr<Mat>( tmpm );
backgroundModel[i] = tmp;
}
return true;
}
MotionSaliencyBinWangApr2014::~MotionSaliencyBinWangApr2014()
{
}
// classification (and adaptation) functions
bool MotionSaliencyBinWangApr2014::fullResolutionDetection( const Mat& image2, Mat& highResBFMask )
{
Mat image = image2.clone();
float currentPixelValue;
float currentEpslonValue;
bool backgFlag = false;
// Initially, all pixels are considered as foreground and then we evaluate with the background model
highResBFMask.create( image.rows, image.cols, CV_32F );
highResBFMask.setTo( 1 );
uchar* pImage;
float* pEpslon;
float* pMask;
// Scan all pixels of image
for ( int i = 0; i < image.rows; i++ )
{
pImage = image.ptr<uchar>( i );
pEpslon = epslonPixelsValue.ptr<float>( i );
pMask = highResBFMask.ptr<float>( i );
for ( int j = 0; j < image.cols; j++ )
{
backgFlag = false;
currentPixelValue = pImage[j];
currentEpslonValue = pEpslon[j];
int counter = 0;
for ( size_t z = 0; z < backgroundModel.size(); z++ )
{
counter += (int) backgroundModel[z]->ptr<Vec2f>( i )[j][1];
}
if( counter != 0 ) //if at least the first template is activated / initialized
{
// scan background model vector
for ( size_t z = 0; z < backgroundModel.size(); z++ )
{
float* currentB;
float* currentC;
currentB = & ( backgroundModel[z]->ptr<Vec2f>( i )[j][0] );
currentC = & ( backgroundModel[z]->ptr<Vec2f>( i )[j][1] );
//continue;
if( ( *currentC ) > 0 ) //The current template is active
{
// If there is a match with a current background template
if( abs( currentPixelValue - ( *currentB ) ) < currentEpslonValue && !backgFlag )
{
// The correspondence pixel in the BF mask is set as background ( 0 value)
pMask[j] = 0;
if( ( *currentC < L0 && z == 0 ) || ( *currentC < L1 && z == 1 ) || ( z > 1 ) )
{
*currentC += 1; // increment the efficacy of this template
}
*currentB = ( ( 1 - alpha ) * ( *currentB ) ) + ( alpha * currentPixelValue ); // Update the template value
backgFlag = true;
}
else
{
*currentC -= 1; // decrement the efficacy of this template
}
}
} // end "for" cicle of template vector
}
else
{
pMask[j] = 1; //if the model of the current pixel is not yet initialized, we mark the pixels as foreground
}
}
} // end "for" cicle of all image's pixels
return true;
}
bool MotionSaliencyBinWangApr2014::lowResolutionDetection( const Mat& image, Mat& lowResBFMask )
{
std::vector<Mat> mv;
split( *backgroundModel[0], mv );
//if at least the first template is activated / initialized for all pixels
if( countNonZero( mv[1] ) > ( mv[1].cols * mv[1].rows ) / 2 )
{
float currentPixelValue;
float currentEpslonValue;
float currentB;
float currentC;
// Create a mask to select ROI in the original Image and Backgound model and at the same time compute the mean
Rect roi( Point( 0, 0 ), Size( N, N ) );
Scalar imageROImean;
Scalar backGModelROImean;
Mat currentModel;
// Initially, all pixels are considered as foreground and then we evaluate with the background model
lowResBFMask.create( image.rows, image.cols, CV_32F );
lowResBFMask.setTo( 1 );
// Scan all the ROI of original matrices
for ( int i = 0; i < ceil( (float) image.rows / N ); i++ )
{
if( ( roi.y + ( N - 1 ) ) <= ( image.rows - 1 ) )
{
// Reset original ROI dimension
roi = Rect( Point( roi.x, roi.y ), Size( N, N ) );
}
for ( int j = 0; j < ceil( (float) image.cols / N ); j++ )
{
// Compute the mean of image's block and epslonMatrix's block based on ROI
Mat roiImage = image( roi );
Mat roiEpslon = epslonPixelsValue( roi );
currentPixelValue = (float) mean( roiImage ).val[0];
currentEpslonValue = (float) mean( roiEpslon ).val[0];
// scan background model vector
for ( int z = 0; z < N_DS; z++ )
{
// Select the current template 2 channel matrix, select ROI and compute the mean for each channel separately
Mat roiTemplate = ( * ( backgroundModel[z] ) )( roi );
Scalar templateMean = mean( roiTemplate );
currentB = (float) templateMean[0];
currentC = (float) templateMean[1];
if( ( currentC ) > 0 ) //The current template is active
{
// If there is a match with a current background template
if( abs( currentPixelValue - ( currentB ) ) < currentEpslonValue )
{
// The correspondence pixel in the BF mask is set as background ( 0 value)
rectangle( lowResBFMask, roi, Scalar( 0 ), FILLED );
break;
}
}
}
// Shift the ROI from left to right follow the block dimension
roi = roi + Point( N, 0 );
if( ( roi.x + ( roi.width - 1 ) ) > ( image.cols - 1 ) && ( roi.y + ( N - 1 ) ) <= ( image.rows - 1 ) )
{
roi = Rect( Point( roi.x, roi.y ), Size( abs( ( image.cols - 1 ) - roi.x ) + 1, N ) );
}
else if( ( roi.x + ( roi.width - 1 ) ) > ( image.cols - 1 ) && ( roi.y + ( N - 1 ) ) > ( image.rows - 1 ) )
{
roi = Rect( Point( roi.x, roi.y ), Size( abs( ( image.cols - 1 ) - roi.x ) + 1, abs( ( image.rows - 1 ) - roi.y ) + 1 ) );
}
}
//Shift the ROI from up to down follow the block dimension, also bringing it back to beginning of row
roi.x = 0;
roi.y += N;
if( ( roi.y + ( roi.height - 1 ) ) > ( image.rows - 1 ) )
{
roi = Rect( Point( roi.x, roi.y ), Size( N, abs( ( image.rows - 1 ) - roi.y ) + 1 ) );
}
}
return true;
}
else
{
lowResBFMask.create( image.rows, image.cols, CV_32F );
lowResBFMask.setTo( 1 );
return false;
}
}
bool inline pairCompare( std::pair<float, float> t, std::pair<float, float> t_plusOne )
{
return ( t.second > t_plusOne.second );
}
// Background model maintenance functions
bool MotionSaliencyBinWangApr2014::templateOrdering()
{
std::vector<std::pair<float, float> > pixelTemplates( backgroundModel.size() );
Vec2f* bgModel_0P;
Vec2f* bgModel_1P;
// Scan all pixels of image
for ( int i = 0; i < backgroundModel[0]->rows; i++ )
{
bgModel_0P = backgroundModel[0]->ptr<Vec2f>( i );
bgModel_1P = backgroundModel[1]->ptr<Vec2f>( i );
for ( int j = 0; j < backgroundModel[0]->cols; j++ )
{
// scan background model vector from T1 to Tk
for ( size_t z = 1; z < backgroundModel.size(); z++ )
{
Vec2f bgModel_zP = backgroundModel[z]->ptr<Vec2f>( i )[j];
// Fill vector of pairs
pixelTemplates[z - 1].first = bgModel_zP[0]; // Current B (background value)
pixelTemplates[z - 1].second = bgModel_zP[1]; // Current C (efficacy value)
}
//SORT template from T1 to Tk
std::sort( pixelTemplates.begin(), pixelTemplates.end(), pairCompare );
//REFILL CURRENT MODEL ( T1...Tk)
for ( size_t zz = 1; zz < backgroundModel.size(); zz++ )
{
backgroundModel[zz]->ptr<Vec2f>( i )[j][0] = pixelTemplates[zz - 1].first; // Replace previous B with new ordered B value
backgroundModel[zz]->ptr<Vec2f>( i )[j][1] = pixelTemplates[zz - 1].second; // Replace previous C with new ordered C value
}
// SORT Template T0 and T1
if( bgModel_1P[j][1] > thetaL && bgModel_0P[j][1] < thetaL )
{
// swap B value of T0 with B value of T1 (for current model)
swap( bgModel_0P[j][0], bgModel_1P[j][0] );
// set new C0 value for current model)
swap( bgModel_0P[j][1], bgModel_1P[j][1] );
bgModel_0P[j][1] = (float) gamma * thetaL;
}
}
}
return true;
}
bool MotionSaliencyBinWangApr2014::templateReplacement( const Mat& finalBFMask, const Mat& image )
{
std::vector<Mat> temp;
split( *backgroundModel[0], temp );
//if at least the first template is activated / initialized for all pixels
if( countNonZero( temp[1] ) <= ( temp[1].cols * temp[1].rows ) / 2 )
{
thetaA = 50;
thetaL = 150;
neighborhoodCheck = false;
}
else
{
thetaA = thetaA_VAL;
thetaL = thetaL_VAL;
neighborhoodCheck = true;
}
int roiSize = 3; // FIXED ROI SIZE, not change until you first appropriately adjust the following controls in the EVALUATION section!
int countNonZeroElements = 0;
std::vector<Mat> mv;
Mat replicateCurrentBAMat( roiSize, roiSize, CV_32F );
Mat backgroundModelROI( roiSize, roiSize, CV_32F );
Mat diffResult( roiSize, roiSize, CV_32F );
// Scan all pixels of finalBFMask and all pixels of others models (the dimension are the same)
const float* finalBFMaskP;
Vec2f* pbgP;
const uchar* imageP;
float* epslonP;
for ( int i = 0; i < finalBFMask.rows; i++ )
{
finalBFMaskP = finalBFMask.ptr<float>( i );
pbgP = potentialBackground.ptr<Vec2f>( i );
imageP = image.ptr<uchar>( i );
epslonP = epslonPixelsValue.ptr<float>( i );
for ( int j = 0; j < finalBFMask.cols; j++ )
{
/////////////////// MAINTENANCE of potentialBackground model ///////////////////
if( finalBFMaskP[j] == 1 ) // i.e. the corresponding frame pixel has been market as foreground
{
/* For the pixels with CA= 0, if the current frame pixel has been classified as foreground, its value
* will be loaded into BA and CA will be set to 1*/
if( pbgP[j][1] == 0 )
{
pbgP[j][0] = (float) imageP[j];
pbgP[j][1] = 1;
}
/*the distance between this pixel value and BA is calculated, and if this distance is smaller than
the decision threshold epslon, then CA is increased by 1, otherwise is decreased by 1*/
else if( abs( (float) imageP[j] - pbgP[j][0] ) < epslonP[j] )
{
pbgP[j][1] += 1;
}
else
{
pbgP[j][1] -= 1;
}
/*}*/ /////////////////// END of potentialBackground model MAINTENANCE///////////////////
/////////////////// EVALUATION of potentialBackground values ///////////////////
if( pbgP[j][1] > thetaA )
{
if( neighborhoodCheck )
{
// replicate currentBA value
replicateCurrentBAMat.setTo( pbgP[j][0] );
for ( size_t z = 0; z < backgroundModel.size(); z++ )
{
// Neighborhood of current pixel in the current background model template.
// The ROI is centered in the pixel coordinates
if( i > 0 && j > 0 && i < ( backgroundModel[z]->rows - 1 ) && j < ( backgroundModel[z]->cols - 1 ) )
{
split( *backgroundModel[z], mv );
backgroundModelROI = mv[0]( Rect( j - (int) floor( roiSize / 2 ), i - (int) floor( roiSize / 2 ), roiSize, roiSize ) );
}
else if( i == 0 && j == 0 ) // upper leftt
{
split( *backgroundModel[z], mv );
backgroundModelROI = mv[0]( Rect( j, i, (int) ceil( roiSize / 2 ), (int) ceil( roiSize / 2 ) ) );
}
else if( j == 0 && i > 0 && i < ( backgroundModel[z]->rows - 1 ) ) // middle left
{
split( *backgroundModel[z], mv );
backgroundModelROI = mv[0]( Rect( j, i - (int) floor( roiSize / 2 ), (int) ceil( roiSize / 2 ), roiSize ) );
}
else if( i == ( backgroundModel[z]->rows - 1 ) && j == 0 ) //down left
{
split( *backgroundModel[z], mv );
backgroundModelROI = mv[0]( Rect( j, i - (int) floor( roiSize / 2 ), (int) ceil( roiSize / 2 ), (int) ceil( roiSize / 2 ) ) );
}
else if( i == 0 && j > 0 && j < ( backgroundModel[z]->cols - 1 ) ) // upper - middle
{
split( *backgroundModel[z], mv );
backgroundModelROI = mv[0]( Rect( ( j - (int) floor( roiSize / 2 ) ), i, roiSize, (int) ceil( roiSize / 2 ) ) );
}
else if( i == ( backgroundModel[z]->rows - 1 ) && j > 0 && j < ( backgroundModel[z]->cols - 1 ) ) //down middle
{
split( *backgroundModel[z], mv );
backgroundModelROI = mv[0](
Rect( j - (int) floor( roiSize / 2 ), i - (int) floor( roiSize / 2 ), roiSize, (int) ceil( roiSize / 2 ) ) );
}
else if( i == 0 && j == ( backgroundModel[z]->cols - 1 ) ) // upper right
{
split( *backgroundModel[z], mv );
backgroundModelROI = mv[0]( Rect( j - (int) floor( roiSize / 2 ), i, (int) ceil( roiSize / 2 ), (int) ceil( roiSize / 2 ) ) );
}
else if( j == ( backgroundModel[z]->cols - 1 ) && i > 0 && i < ( backgroundModel[z]->rows - 1 ) ) // middle - right
{
split( *backgroundModel[z], mv );
backgroundModelROI = mv[0](
Rect( j - (int) floor( roiSize / 2 ), i - (int) floor( roiSize / 2 ), (int) ceil( roiSize / 2 ), roiSize ) );
}
else if( i == ( backgroundModel[z]->rows - 1 ) && j == ( backgroundModel[z]->cols - 1 ) ) // down right
{
split( *backgroundModel[z], mv );
backgroundModelROI = mv[0](
Rect( j - (int) floor( roiSize / 2 ), i - (int) floor( roiSize / 2 ), (int) ceil( roiSize / 2 ), (int) ceil( roiSize / 2 ) ) );
}
/* Check if the value of current pixel BA in potentialBackground model is already contained in at least one of its neighbors'
* background model
*/
resize( replicateCurrentBAMat, replicateCurrentBAMat, Size( backgroundModelROI.cols, backgroundModelROI.rows ), 0, 0, INTER_LINEAR );
resize( diffResult, diffResult, Size( backgroundModelROI.cols, backgroundModelROI.rows ), 0, 0, INTER_LINEAR );
absdiff( replicateCurrentBAMat, backgroundModelROI, diffResult );
threshold( diffResult, diffResult, epslonP[j], 255, THRESH_BINARY_INV );
countNonZeroElements = countNonZero( diffResult );
if( countNonZeroElements > 0 )
{
/////////////////// REPLACEMENT of backgroundModel template ///////////////////
//replace TA with current TK
backgroundModel[backgroundModel.size() - 1]->at<Vec2f>( i, j ) = potentialBackground.at<Vec2f>( i, j );
potentialBackground.at<Vec2f>( i, j )[0] = NAN;
potentialBackground.at<Vec2f>( i, j )[1] = 0;
break;
}
} // end for backgroundModel size
}
else
{
backgroundModel[backgroundModel.size() - 1]->at<Vec2f>( i, j ) = potentialBackground.at<Vec2f>( i, j );
potentialBackground.at<Vec2f>( i, j )[0] = NAN;
potentialBackground.at<Vec2f>( i, j )[1] = 0;
}
} // close if of EVALUATION
} // end of if( finalBFMask.at<uchar>( i, j ) == 1 ) // i.e. the corresponding frame pixel has been market as foreground
} // end of second for
} // end of first for
return true;
}
bool MotionSaliencyBinWangApr2014::computeSaliencyImpl( const InputArray image, OutputArray saliencyMap )
{
Mat highResBFMask;
Mat lowResBFMask;
Mat not_lowResBFMask;
Mat noisePixelsMask;
fullResolutionDetection( image.getMat(), highResBFMask );
lowResolutionDetection( image.getMat(), lowResBFMask );
// Compute the final background-foreground mask. One pixel is marked as foreground if and only if it is
// foreground in both masks (full and low)
bitwise_and( highResBFMask, lowResBFMask, saliencyMap );
templateOrdering();
templateReplacement( saliencyMap.getMat(), image.getMat() );
templateOrdering();
return true;
}
} // namespace saliency
} // namespace cv