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#include <limits>
#include "precomp.hpp"
#define thetaA_VAL 200
#define thetaL_VAL 250
#define epslonGeneric 20
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;
Ainc = 6; // Activity Incrementation;
Bmax = 80; // Upper-bound value for pixel activity
Bth = 20; //70; // Max activity threshold
Binc = 15; //50;
Bdec = 5; //20; // Threshold for pixel-level decision threshold (epslon) adaptation
deltaINC = 20;
deltaDEC = 0.125; // Increment-decrement value for epslon adaptation
epslonMIN = 18;
epslonMAX = 80;
className = "BinWangApr2014";
}
bool MotionSaliencyBinWangApr2014::init()
{
activityControlFlag = false;
Size imgSize( imageWidth, imageHeight );
epslonPixelsValue = Mat( imgSize.height, imgSize.width, CV_32F, Scalar( epslonGeneric ) );
potentialBackground = Mat( imgSize.height, imgSize.width, CV_8UC2, Scalar( 0, 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( std::numeric_limits<float>::quiet_NaN(), 0 ) );
Ptr<Mat> tmp = Ptr<Mat>( tmpm );
backgroundModel[i] = tmp;
}
noisePixelMask.create( imgSize.height, imgSize.width, CV_8U );
noisePixelMask.setTo( Scalar( 0 ) );
activityPixelsValue.create( imgSize.height, imgSize.width, CV_8U );
activityPixelsValue.setTo( Scalar( 0 ) );
return true;
}
MotionSaliencyBinWangApr2014::~MotionSaliencyBinWangApr2014()
{
}
// classification (and adaptation) functions
bool MotionSaliencyBinWangApr2014::fullResolutionDetection( const Mat& image2, Mat& highResBFMask )
{
Mat image = image2.clone();
uchar 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_8U );
highResBFMask.setTo( 1 );
uchar* pImage;
float* pEpslon;
uchar* 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<uchar>( i );
for ( int j = 0; j < image.cols; j++ )
{
/* Pixels with activity greater than Bth are eliminated from the detection result. In this way,
continuously blinking noise-pixels will be eliminated from the detection results,
preventing the generation of false positives.*/
if( activityPixelsValue.at<uchar>( i, j ) < Bth )
{
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 )
break;
}
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
}
}
else
{
pMask[j] = 0;
}
}
} // 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_8U );
lowResBFMask.setTo( 1 );
// Scan all the ROI of original matrices
for ( int i = 0; i < (int)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 < (int)ceil( (float) image.cols / N ); j++ )
{
/* Pixels with activity greater than Bth are eliminated from the detection result. In this way,
continuously blinking noise-pixels will be eliminated from the detection results,
preventing the generation of false positives.*/
if( activityPixelsValue.at<uchar>( i, j ) < Bth )
{
// 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 ) );
}
}
else
{
// The correspondence pixel in the BF mask is set as background ( 0 value)
rectangle( lowResBFMask, roi, Scalar( 0 ), FILLED );
}
}
//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_8U );
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 );
}
bool MotionSaliencyBinWangApr2014::templateOrdering()
{
Mat dstMask, tempMat, dstMask2, dstMask3;
Mat convertMat1, convertMat2;
int backGroundModelSize = (int)backgroundModel.size();
std::vector<std::vector<Mat> > channelSplit( backGroundModelSize );
for ( int i = 0; i < backGroundModelSize; i++ )
{
split( *backgroundModel[i], channelSplit[i] );
}
//Bubble sort : Template T1 - Tk
for ( int i = 1; i < backGroundModelSize - 1; i++ )
{
// compare and order the i-th template with the others
for ( int j = i + 1; j < backGroundModelSize; j++ )
{
compare( channelSplit[j][1], channelSplit[i][1], dstMask, CMP_GT );
channelSplit[i][0].copyTo( tempMat );
channelSplit[j][0].copyTo( channelSplit[i][0], dstMask );
tempMat.copyTo( channelSplit[j][0], dstMask );
channelSplit[i][1].copyTo( tempMat );
channelSplit[j][1].copyTo( channelSplit[i][1], dstMask );
tempMat.copyTo( channelSplit[j][1], dstMask );
}
}
// SORT Template T0 and T1
Mat M_deltaL( backgroundModel[0]->rows, backgroundModel[0]->cols, CV_32F, Scalar( thetaL ) );
compare( channelSplit[1][1], M_deltaL, dstMask2, CMP_GT );
compare( M_deltaL, channelSplit[0][1], dstMask3, CMP_GT );
threshold( dstMask2, dstMask2, 0, 1, THRESH_BINARY );
threshold( dstMask3, dstMask3, 0, 1, THRESH_BINARY );
bitwise_and( dstMask2, dstMask3, dstMask );
//copy correct B element of T1 inside T0 and swap
channelSplit[0][0].copyTo( tempMat );
channelSplit[1][0].copyTo( channelSplit[0][0], dstMask );
tempMat.copyTo( channelSplit[1][0], dstMask );
//copy correct C element of T0 inside T1
channelSplit[0][1].copyTo( channelSplit[1][1], dstMask );
//set new C0 values as gamma * thetaL
M_deltaL.mul( gamma );
M_deltaL.copyTo( channelSplit[0][1], dstMask );
for ( int i = 0; i < backGroundModelSize; i++ )
{
merge( channelSplit[i], *backgroundModel[i] );
}
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;
/* thetaA = 5;
thetaL = 15;*/
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_8U );
Mat backgroundModelROI( roiSize, roiSize, CV_32F );
Mat diffResult( roiSize, roiSize, CV_8U );
// Scan all pixels of finalBFMask and all pixels of others models (the dimension are the same)
const uchar* finalBFMaskP;
Vec2b* pbgP;
const uchar* imageP;
float* epslonP;
for ( int i = 0; i < finalBFMask.rows; i++ )
{
finalBFMaskP = finalBFMask.ptr<uchar>( i );
pbgP = potentialBackground.ptr<Vec2b>( 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] = 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((float) roiSize / 2 ), i - (int) floor((float) roiSize / 2 ), roiSize, roiSize ) );
}
else if( i == 0 && j == 0 ) // upper leftt
{
split( *backgroundModel[z], mv );
backgroundModelROI = mv[0]( Rect( j, i, (int) ceil((float) roiSize / 2 ), (int) ceil((float) 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((float) roiSize / 2 ), (int) ceil((float) 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((float) roiSize / 2 ), (int) ceil((float) roiSize / 2 ), (int) ceil((float) 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((float) roiSize / 2 ) ), i, roiSize, (int) ceil((float) 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((float) roiSize / 2 ), i - (int) floor((float) roiSize / 2 ), roiSize, (int) ceil((float) roiSize / 2 ) ) );
}
else if( i == 0 && j == ( backgroundModel[z]->cols - 1 ) ) // upper right
{
split( *backgroundModel[z], mv );
backgroundModelROI = mv[0]( Rect( j - (int) floor((float) roiSize / 2 ), i, (int) ceil((float) roiSize / 2 ), (int) ceil((float) 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((float) roiSize / 2 ), i - (int) floor((float) roiSize / 2 ), (int) ceil((float) 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((float) roiSize / 2 ), i - (int) floor((float) roiSize / 2 ), (int) ceil((float) roiSize / 2 ), (int) ceil((float) 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_EXACT );
resize( diffResult, diffResult, Size( backgroundModelROI.cols, backgroundModelROI.rows ), 0, 0, INTER_LINEAR_EXACT );
backgroundModelROI.convertTo( backgroundModelROI, CV_8U );
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<Vec2b>( i, j );
potentialBackground.at<Vec2b>( i, j )[0] = 0;
potentialBackground.at<Vec2b>( i, j )[1] = 0;
break;
}
} // end for backgroundModel size
}
else
{
backgroundModel[backgroundModel.size() - 1]->at<Vec2f>( i, j ) = potentialBackground.at<Vec2b>( i, j );
potentialBackground.at<Vec2b>( i, j )[0] = 0;
potentialBackground.at<Vec2b>( 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::activityControl( const Mat& current_noisePixelsMask )
{
Mat discordanceFramesNoise, not_current_noisePixelsMask;
Mat nonZeroIndexes, not_discordanceFramesNoise; //u_current_noisePixelsMask;
//current_noisePixelsMask.convertTo( u_current_noisePixelsMask, CV_8UC1 );
// Derive the discrepancy between noise in the frame n-1 and frame n
//threshold( u_current_noisePixelsMask, not_current_noisePixelsMask, 0.5, 1.0, THRESH_BINARY_INV );
threshold( current_noisePixelsMask, not_current_noisePixelsMask, 0.5, 1.0, THRESH_BINARY_INV );
bitwise_and( noisePixelMask, not_current_noisePixelsMask, discordanceFramesNoise );
// indices in which the pixel at frame n-1 was the noise (or not) and now no (or yes) (blinking pixels)
findNonZero( discordanceFramesNoise, nonZeroIndexes );
Vec2i temp;
// we increase the activity value of these pixels
for ( int i = 0; i < nonZeroIndexes.rows; i++ )
{
//TODO check rows, cols inside at
temp = nonZeroIndexes.at<Vec2i>( i );
if( activityPixelsValue.at<uchar>( temp.val[1], temp.val[0] ) < Bmax )
{
activityPixelsValue.at<uchar>( temp.val[1], temp.val[0] ) += Ainc;
}
}
// decrement other pixels that have not changed (not blinking)
threshold( discordanceFramesNoise, not_discordanceFramesNoise, 0.5, 1.0, THRESH_BINARY_INV );
findNonZero( not_discordanceFramesNoise, nonZeroIndexes );
Vec2i temp2;
for ( int j = 0; j < nonZeroIndexes.rows; j++ )
{
temp2 = nonZeroIndexes.at<Vec2i>( j );
if( activityPixelsValue.at<uchar>( temp2.val[1], temp2.val[0] ) > 0 )
{
activityPixelsValue.at<uchar>( temp2.val[1], temp2.val[0] ) -= 1;
}
}
// update the noisePixelsMask
current_noisePixelsMask.copyTo( noisePixelMask );
return true;
}
bool MotionSaliencyBinWangApr2014::decisionThresholdAdaptation()
{
for ( int i = 0; i < activityPixelsValue.rows; i++ )
{
for ( int j = 0; j < activityPixelsValue.cols; j++ )
{
if( activityPixelsValue.at<uchar>( i, j ) > Binc && ( epslonPixelsValue.at<float>( i, j ) + deltaINC ) < epslonMAX )
{
epslonPixelsValue.at<float>( i, j ) += deltaINC;
}
else if( activityPixelsValue.at<uchar>( i, j ) < Bdec && ( epslonPixelsValue.at<float>( i, j ) - deltaDEC ) > epslonMIN )
{
epslonPixelsValue.at<float>( i, j ) -= deltaDEC;
}
}
}
return true;
}
bool MotionSaliencyBinWangApr2014::computeSaliencyImpl( InputArray image, OutputArray saliencyMap )
{
CV_Assert(image.channels() == 1);
Mat highResBFMask, u_highResBFMask;
Mat lowResBFMask, u_lowResBFMask;
Mat not_lowResBFMask;
Mat current_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 );
if( activityControlFlag )
{
// Detect the noise pixels (i.e. for a given pixel, fullRes(pixel) = foreground and lowRes(pixel)= background)
threshold( lowResBFMask, not_lowResBFMask, 0.5, 1.0, THRESH_BINARY_INV );
bitwise_and( highResBFMask, not_lowResBFMask, current_noisePixelsMask );
activityControl( current_noisePixelsMask );
decisionThresholdAdaptation();
}
templateOrdering();
templateReplacement( saliencyMap.getMat(), image.getMat() );
templateOrdering();
activityControlFlag = true;
return true;
}
} // namespace saliency
} // namespace cv