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Commit e56f2870 authored by jaco's avatar jaco
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background Model initialization problems fixed

parent c8085a75
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Showing with 145 additions and 72 deletions
modules/saliency/include/opencv2/saliency/saliencySpecializedClasses.hpp
View file @ e56f2870
......@@ -127,7 +127,7 @@ class CV_EXPORTS_W MotionSaliencyBinWangApr2014 : public MotionSaliency
//bool decisionThresholdAdaptation();
// changing structure
vector<Mat> backgroundModel; // The vector represents the background template T0---TK of reference paper.
vector<Ptr<Mat> > backgroundModel; // The vector represents the background template T0---TK of reference paper.
// Matrices are two-channel matrix. In the first layer there are the B (background value)
// for each pixel. In the second layer, there are the C (efficacy) value for each pixel
Mat potentialBackground; // Two channel Matrix. For each pixel, in the first level there are the Ba value (potential background value)
......
modules/saliency/src/motionSaliencyBinWangApr2014.cpp
View file @ e56f2870
......@@ -63,7 +63,7 @@ MotionSaliencyBinWangApr2014::MotionSaliencyBinWangApr2014()
alpha = 0.01; // Learning rate
L0 = 6000; // Upper-bound values for C0 (efficacy of the first template (matrices) of backgroundModel
L1 = 4000; // Upper-bound values for C1 (efficacy of the second template (matrices) of backgroundModel
thetaL = 2500; // T0, T1 swap threshold
thetaL = 50; // T0, T1 swap threshold
thetaA = 200;
gamma = 3;
neighborhoodCheck = true;
......@@ -76,19 +76,24 @@ bool MotionSaliencyBinWangApr2014::init()
epslonPixelsValue = Mat( imgSize->height, imgSize->width, CV_32F );
potentialBackground = Mat( imgSize->height, imgSize->width, CV_32FC2 );
backgroundModel = std::vector<Mat>( K + 1, Mat::zeros( imgSize->height, imgSize->width, CV_32FC2 ) );
//backgroundModel = std::vector<Mat>( K + 1, Mat::zeros( imgSize->height, imgSize->width, CV_32FC2 ) );
//TODO set to nan
potentialBackground.setTo( Scalar(NAN,0) );
potentialBackground.setTo( 0 );
backgroundModel.resize(K+1);
//TODO set to nan
for ( size_t i = 0; i < backgroundModel.size(); i++ )
for ( int i = 0; i < K + 1; i++ )
{
backgroundModel[i].setTo( Scalar(NAN, 0) );
Mat* tmpm = new Mat;
tmpm->create(imgSize->height, imgSize->width, CV_32FC2);
tmpm->setTo(0);
Ptr<Mat> tmp = Ptr<Mat>( tmpm );
backgroundModel[i] = tmp;
}
epslonPixelsValue.setTo( 48.5 ); // 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).
epslonPixelsValue.setTo( 70 ); // 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).
/* epslonPixelsValue = Mat::zeros( imgSize->height, imgSize->width, CV_8U );
potentialBackground = Mat::NAN( imgSize->height, imgSize->width, CV_32FC2 );
......@@ -107,12 +112,28 @@ MotionSaliencyBinWangApr2014::~MotionSaliencyBinWangApr2014()
bool MotionSaliencyBinWangApr2014::fullResolutionDetection( const Mat& image2, Mat& highResBFMask )
{
Mat image = image2.clone();
float* currentB;
float* currentC;
float currentPixelValue;
float currentEpslonValue;
bool backgFlag = false;
Mat test( image.rows, image.cols, CV_8U );
Mat test1( image.rows, image.cols, CV_8U );
test.setTo( 255 );
for ( int i = 0; i < test.rows; i++ )
{
for ( int j = 0; j < test.cols; j++ )
{
if( backgroundModel[0]->at<Vec2f>( i, j )[1] == 0 )
{
test.at<uchar>( i, j ) = 0;
}
test1.at<uchar>( i, j ) = (int) backgroundModel[0]->at<Vec2f>( i, j )[0];
}
}
imshow( "test_T0c", test );
imshow( "test_T0b", test1 );
// 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 );
......@@ -121,14 +142,18 @@ bool MotionSaliencyBinWangApr2014::fullResolutionDetection( const Mat& image2, M
float* pEpslon;
float* pMask;
int countDec = 0;
// 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;
// TODO replace "at" with more efficient matrix access
//currentPixelValue = image.at<uchar>( i, j );
......@@ -136,37 +161,65 @@ bool MotionSaliencyBinWangApr2014::fullResolutionDetection( const Mat& image2, M
currentPixelValue = pImage[j];
currentEpslonValue = pEpslon[j];
if( backgroundModel[0].at<Vec2f>( i, j )[1] != 0 ) //if at least the first template is activated / initialized
if( i == 50 && j == 50 )
cout << "currentPixelValue :" << currentPixelValue << endl << "currentEpslonValue :" << currentEpslonValue << endl;
int counter = 0;
for ( size_t z = 0; z < backgroundModel.size(); z++ )
{
counter += backgroundModel.at(z)->at<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;
// TODO replace "at" with more efficient matrix access
currentB = &backgroundModel[z].at<Vec2f>( i, j )[0];
currentC = &backgroundModel[z].at<Vec2f>( i, j )[1];
currentB = (backgroundModel.at(z)->at<Vec2f>( i, j )[0]);
currentC = (backgroundModel.at(z)->at<Vec2f>( i, j )[1]);
if( *currentC > 0 ) //The current template is active
if( i == 50 && j == 50 )
{
cout << "zeta:" << z << " currentB :" << currentB << endl << "currentC :" << currentC << endl;
}
//continue;
if( currentC > 0 ) //The current template is active
{
//cout<< "DIFFERENCE: "<<abs( currentPixelValue - ( *currentB ) )<<endl;
// If there is a match with a current background template
if( abs( currentPixelValue - * ( currentB ) ) < currentEpslonValue && !backgFlag )
if( abs( currentPixelValue - ( currentB ) ) < currentEpslonValue && !backgFlag )
{
// The correspondence pixel in the BF mask is set as background ( 0 value)
//highResBFMask.at<uchar>( i, j ) = 0;
pMask[j] = 0;
if( ( *currentC < L0 && z == 0 ) || ( *currentC < L1 && z == 1 ) || ( z > 1 ) )
*currentC += 1; // increment the efficacy of this template
//if( ( *currentC < L0 && z == 0 ) || ( *currentC < L1 && z == 1 ) || ( z > 1 ) )
(backgroundModel.at(z)->at<Vec2f>( i, j )[1]) = (backgroundModel.at(z)->at<Vec2f>( i, j )[1]) + 1; // increment the efficacy of this template
*currentB = ( ( 1 - alpha ) * * ( currentB ) ) + ( alpha * currentPixelValue ); // Update the template value
(backgroundModel.at(z)->at<Vec2f>( i, j )[0]) = ( ( 1 - alpha ) * ( currentB ) ) + ( alpha * currentPixelValue ); // Update the template value
backgFlag = true;
//break;
}
else
{
currentC -= 1; // decrement the efficacy of this template
if( z == 0 )
countDec++;
(backgroundModel.at(z)->at<Vec2f>( i, j )[1]) = (backgroundModel.at(z)->at<Vec2f>( i, j )[1]) - 1; // decrement the efficacy of this template
}
}
if( i == 50 && j == 50 )
{
cout << "DOPO IF: " << endl;
cout << "zeta:" << z << " currentB_A :" << &currentB << endl << "currentC_A :" << &currentC<<endl;
cout << "zeta:" << z << " currentB :" << (backgroundModel.at(z)->at<Vec2f>( i, j )[0]) << endl << "currentC :" << (backgroundModel.at(z)->at<Vec2f>( i, j )[1]) << endl<<endl;
}
} // end "for" cicle of template vector
}
......@@ -178,13 +231,15 @@ bool MotionSaliencyBinWangApr2014::fullResolutionDetection( const Mat& image2, M
}
} // end "for" cicle of all image's pixels
//cout<<" STATISTICA :"<<countDec<<"/"<< image.rows*image.cols<< " = "<<(float)countDec/(float)(image.rows*image.cols)*100<<" %"<<endl;
return true;
}
bool MotionSaliencyBinWangApr2014::lowResolutionDetection( const Mat& image, Mat& lowResBFMask )
{
std::vector<Mat> mv;
split( backgroundModel[0], mv );
split( *backgroundModel[0], mv );
//if at least the first template is activated / initialized for all pixels
if( countNonZero( mv.at( 1 ) ) > ( mv.at( 1 ).cols * mv.at( 1 ).rows ) / 2 )
......@@ -226,7 +281,7 @@ bool MotionSaliencyBinWangApr2014::lowResolutionDetection( const Mat& image, Mat
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 );
Mat roiTemplate = (*(backgroundModel[z]))( roi );
Scalar templateMean = mean( roiTemplate );
currentB = templateMean[0];
currentC = templateMean[1];
......@@ -270,7 +325,7 @@ bool MotionSaliencyBinWangApr2014::lowResolutionDetection( const Mat& image, Mat
else
{
lowResBFMask.create( image.rows, image.cols, CV_32F );
lowResBFMask.setTo( NAN );
lowResBFMask.setTo( 1 );
return false;
}
......@@ -290,16 +345,16 @@ bool MotionSaliencyBinWangApr2014::templateOrdering()
float temp;
// Scan all pixels of image
for ( int i = 0; i < backgroundModel[0].rows; i++ )
for ( int i = 0; i < backgroundModel[0]->rows; i++ )
{
for ( int j = 0; j < backgroundModel[0].cols; j++ )
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++ )
{
// Fill vector of pairs
pixelTemplates[z - 1].first = backgroundModel[z].at<Vec2f>( i, j )[0]; // Current B (background value)
pixelTemplates[z - 1].second = backgroundModel[z].at<Vec2f>( i, j )[1]; // Current C (efficacy value)
pixelTemplates[z - 1].first = backgroundModel[z]->at<Vec2f>( i, j )[0]; // Current B (background value)
pixelTemplates[z - 1].second = backgroundModel[z]->at<Vec2f>( i, j )[1]; // Current C (efficacy value)
}
//SORT template from T1 to Tk
......@@ -308,23 +363,23 @@ bool MotionSaliencyBinWangApr2014::templateOrdering()
//REFILL CURRENT MODEL ( T1...Tk)
for ( size_t zz = 1; zz < backgroundModel.size(); zz++ )
{
backgroundModel[zz].at<Vec2f>( i, j )[0] = pixelTemplates[zz - 1].first; // Replace previous B with new ordered B value
backgroundModel[zz].at<Vec2f>( i, j )[1] = pixelTemplates[zz - 1].second; // Replace previous C with new ordered C value
backgroundModel[zz]->at<Vec2f>( i, j )[0] = pixelTemplates[zz - 1].first; // Replace previous B with new ordered B value
backgroundModel[zz]->at<Vec2f>( i, j )[1] = pixelTemplates[zz - 1].second; // Replace previous C with new ordered C value
}
// SORT Template T0 and T1
if( backgroundModel[1].at<Vec2f>( i, j )[1] > thetaL && backgroundModel[0].at<Vec2f>( i, j )[1] < thetaL )
if( backgroundModel[1]->at<Vec2f>( i, j )[1] > thetaL && backgroundModel[0]->at<Vec2f>( i, j )[1] < thetaL )
{
// swap B value of T0 with B value of T1 (for current model)
temp = backgroundModel[0].at<Vec2f>( i, j )[0];
backgroundModel[0].at<Vec2f>( i, j )[0] = backgroundModel[1].at<Vec2f>( i, j )[0];
backgroundModel[1].at<Vec2f>( i, j )[0] = temp;
temp = backgroundModel[0]->at<Vec2f>( i, j )[0];
backgroundModel[0]->at<Vec2f>( i, j )[0] = backgroundModel[1]->at<Vec2f>( i, j )[0];
backgroundModel[1]->at<Vec2f>( i, j )[0] = temp;
// set new C0 value for current model)
temp = backgroundModel[0].at<Vec2f>( i, j )[1];
backgroundModel[0].at<Vec2f>( i, j )[1] = gamma * thetaL;
backgroundModel[1].at<Vec2f>( i, j )[1] = temp;
temp = backgroundModel[0]->at<Vec2f>( i, j )[1];
backgroundModel[0]->at<Vec2f>( i, j )[1] = gamma * thetaL;
backgroundModel[1]->at<Vec2f>( i, j )[1] = temp;
}
......@@ -335,11 +390,22 @@ bool MotionSaliencyBinWangApr2014::templateOrdering()
}
bool MotionSaliencyBinWangApr2014::templateReplacement( const Mat& finalBFMask, const Mat& image )
{
Mat test( image.rows, image.cols, CV_8U );
for ( int i = 0; i < test.rows; i++ )
{
for ( int j = 0; j < test.cols; j++ )
{
test.at<uchar>( i, j ) = (int) potentialBackground.at<Vec2f>( i, j )[0];
}
}
imshow( "test_BA", test );
std::vector<Mat> temp;
split( backgroundModel[0], temp );
split( *backgroundModel[0], temp );
//if at least the first template is activated / initialized for all pixels
if( countNonZero( temp.at( 1 ) ) <= ( temp.at( 1 ).cols * temp.at( 1 ).rows )/2 )
if( countNonZero( temp.at( 1 ) ) <= ( temp.at( 1 ).cols * temp.at( 1 ).rows ) / 2 )
{
thetaA = 2;
neighborhoodCheck = false;
......@@ -348,11 +414,11 @@ bool MotionSaliencyBinWangApr2014::templateReplacement( const Mat& finalBFMask,
else
{
thetaA = 200;
neighborhoodCheck = true;
neighborhoodCheck = false;
}
float roiSize = 3; // FIXED ROI SIZE, not change until you first appropriately adjust the following controls in the EVALUATION section!
int countNonZeroElements = NAN;
int countNonZeroElements = 0;
std::vector<Mat> mv;
Mat replicateCurrentBAMat( roiSize, roiSize, CV_32FC1 );
Mat backgroundModelROI( roiSize, roiSize, CV_32FC1 );
......@@ -370,13 +436,13 @@ bool MotionSaliencyBinWangApr2014::templateReplacement( const Mat& finalBFMask,
* will be loaded into BA and CA will be set to 1*/
if( potentialBackground.at<Vec2f>( i, j )[1] == 0 )
{
potentialBackground.at<Vec2f>( i, j )[0] = image.at<uchar>( i, j );
potentialBackground.at<Vec2f>( i, j )[0] = (float) image.at<uchar>( i, j );
potentialBackground.at<Vec2f>( i, 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( image.at<uchar>( i, j ) - potentialBackground.at<Vec2f>( i, j )[0] ) < epslonPixelsValue.at<float>( i, j ) )
else if( abs( (float) image.at<uchar>( i, j ) - potentialBackground.at<Vec2f>( i, j )[0] ) < epslonPixelsValue.at<float>( i, j ) )
{
potentialBackground.at<Vec2f>( i, j )[1] += 1;
}
......@@ -401,49 +467,49 @@ bool MotionSaliencyBinWangApr2014::templateReplacement( const Mat& finalBFMask,
/*if( ( i - floor( roiSize / 2 ) >= 0 ) && ( j - floor( roiSize / 2 ) >= 0 )
&& ( i + floor( roiSize / 2 ) <= ( backgroundModel[z].rows - 1 ) )
&& ( j + floor( roiSize / 2 ) <= ( backgroundModel[z].cols - 1 ) ) ) */
if( i > 0 && j > 0 && i < ( backgroundModel[z].rows - 1 ) && j < ( backgroundModel[z].cols - 1 ) )
if( i > 0 && j > 0 && i < ( backgroundModel[z]->rows - 1 ) && j < ( backgroundModel[z]->cols - 1 ) )
{
split( backgroundModel[z], mv );
split( *backgroundModel[z], mv );
backgroundModelROI = mv.at( 0 )( Rect( j - floor( roiSize / 2 ), i - floor( roiSize / 2 ), roiSize, roiSize ) );
}
else if( i == 0 && j == 0 ) // upper left
{
split( backgroundModel[z], mv );
split( *backgroundModel[z], mv );
backgroundModelROI = mv.at( 0 )( Rect( j, i, ceil( roiSize / 2 ), ceil( roiSize / 2 ) ) );
}
else if( j == 0 && i > 0 && i < ( backgroundModel[z].rows - 1 ) ) // middle left
else if( j == 0 && i > 0 && i < ( backgroundModel[z]->rows - 1 ) ) // middle left
{
split( backgroundModel[z], mv );
split( *backgroundModel[z], mv );
backgroundModelROI = mv.at( 0 )( Rect( j, i - floor( roiSize / 2 ), ceil( roiSize / 2 ), roiSize ) );
}
else if( i == ( backgroundModel[z].rows - 1 ) && j == 0 ) //down left
else if( i == ( backgroundModel[z]->rows - 1 ) && j == 0 ) //down left
{
split( backgroundModel[z], mv );
split( *backgroundModel[z], mv );
backgroundModelROI = mv.at( 0 )( Rect( j, i - floor( roiSize / 2 ), ceil( roiSize / 2 ), ceil( roiSize / 2 ) ) );
}
else if( i == 0 && j > 0 && j < ( backgroundModel[z].cols - 1 ) ) // upper - middle
else if( i == 0 && j > 0 && j < ( backgroundModel[z]->cols - 1 ) ) // upper - middle
{
split( backgroundModel[z], mv );
split( *backgroundModel[z], mv );
backgroundModelROI = mv.at( 0 )( Rect( ( j - floor( roiSize / 2 ) ), i, roiSize, ceil( roiSize / 2 ) ) );
}
else if( i == ( backgroundModel[z].rows - 1 ) && j > 0 && j < ( backgroundModel[z].cols - 1 ) ) //down middle
else if( i == ( backgroundModel[z]->rows - 1 ) && j > 0 && j < ( backgroundModel[z]->cols - 1 ) ) //down middle
{
split( backgroundModel[z], mv );
split( *backgroundModel[z], mv );
backgroundModelROI = mv.at( 0 )( Rect( j - floor( roiSize / 2 ), i - floor( roiSize / 2 ), roiSize, ceil( roiSize / 2 ) ) );
}
else if( i == 0 && j == ( backgroundModel[z].cols - 1 ) ) // upper right
else if( i == 0 && j == ( backgroundModel[z]->cols - 1 ) ) // upper right
{
split( backgroundModel[z], mv );
split( *backgroundModel[z], mv );
backgroundModelROI = mv.at( 0 )( Rect( j - floor( roiSize / 2 ), i, ceil( roiSize / 2 ), ceil( roiSize / 2 ) ) );
}
else if( j == ( backgroundModel[z].cols - 1 ) && i > 0 && i < ( backgroundModel[z].rows - 1 ) ) // middle - right
else if( j == ( backgroundModel[z]->cols - 1 ) && i > 0 && i < ( backgroundModel[z]->rows - 1 ) ) // middle - right
{
split( backgroundModel[z], mv );
split( *backgroundModel[z], mv );
backgroundModelROI = mv.at( 0 )( Rect( j - floor( roiSize / 2 ), i - floor( roiSize / 2 ), ceil( roiSize / 2 ), roiSize ) );
}
else if( i == ( backgroundModel[z].rows - 1 ) && j == ( backgroundModel[z].cols - 1 ) ) // down right
else if( i == ( backgroundModel[z]->rows - 1 ) && j == ( backgroundModel[z]->cols - 1 ) ) // down right
{
split( backgroundModel[z], mv );
split( *backgroundModel[z], mv );
backgroundModelROI = mv.at( 0 )(
Rect( j - floor( roiSize / 2 ), i - floor( roiSize / 2 ), ceil( roiSize / 2 ), ceil( roiSize / 2 ) ) );
}
......@@ -466,9 +532,9 @@ bool MotionSaliencyBinWangApr2014::templateReplacement( const Mat& finalBFMask,
//TODO CHECK BACKGROUND MODEL COUNTER ASSIGNEMENT
//backgroundModel[backgroundModel.size()-1].at<Vec2f>( i, j )[0]=potentialBackground.at<Vec2f>( i, j )[0];
//backgroundModel[backgroundModel.size()-1].at<Vec2f>( i, j )[1]= potentialBackground.at<Vec2f>( i, j )[1];
backgroundModel[backgroundModel.size() - 1].at<Vec2f>( i, j ) = potentialBackground.at<Vec2f>( i, j );
//potentialBackground.at<Vec2f>( i, j )[0]=-255;
//potentialBackground.at<Vec2f>( i, j )[1]=0;
backgroundModel[backgroundModel.size() - 1]->at<Vec2f>( i, j ) = potentialBackground.at<Vec2f>( i, j );
potentialBackground.at<Vec2f>( i, j )[0] = 0;
potentialBackground.at<Vec2f>( i, j )[1] = 0;
break;
}
......@@ -476,7 +542,11 @@ bool MotionSaliencyBinWangApr2014::templateReplacement( const Mat& finalBFMask,
}
else
{
backgroundModel[backgroundModel.size() - 1].at<Vec2f>( i, j ) = potentialBackground.at<Vec2f>( i, j );
((backgroundModel.at(backgroundModel.size() - 1))->at<Vec2f>( i, j ))[0] = potentialBackground.at<Vec2f>( i, j )[0];
((backgroundModel.at(backgroundModel.size() - 1))->at<Vec2f>( i, j ))[1] = potentialBackground.at<Vec2f>( i, j )[1];
potentialBackground.at<Vec2f>( i, j )[0] = 0;
potentialBackground.at<Vec2f>( i, j )[1] = 0;
//((backgroundModel.at(0))->at<Vec2f>( i, j ))[1] = 3;
}
} // close if of EVALUATION
} // end of if( finalBFMask.at<uchar>( i, j ) == 1 ) // i.e. the corresponding frame pixel has been market as foreground
......@@ -502,13 +572,13 @@ bool MotionSaliencyBinWangApr2014::computeSaliencyImpl( const InputArray image,
std::ofstream ofs4;
ofs4.open( "TEMPLATE_0_B.txt", std::ofstream::out );
for ( int i = 0; i < backgroundModel[0].rows; i++ )
for ( int i = 0; i < backgroundModel[0]->rows; i++ )
{
for ( int j = 0; j < backgroundModel[0].cols; j++ )
for ( int j = 0; j < backgroundModel[0]->cols; j++ )
{
//highResBFMask.at<int>( i, j ) = i + j;
stringstream str;
str << backgroundModel[0].at<Vec2f>( i, j )[0] << " ";
str << backgroundModel[0]->at<Vec2f>( i, j )[0] << " ";
ofs4 << str.str();
}
stringstream str2;
......@@ -520,13 +590,13 @@ bool MotionSaliencyBinWangApr2014::computeSaliencyImpl( const InputArray image,
std::ofstream ofs5;
ofs5.open( "TEMPLATE_0_C.txt", std::ofstream::out );
for ( int i = 0; i < backgroundModel[0].rows; i++ )
for ( int i = 0; i < backgroundModel[0]->rows; i++ )
{
for ( int j = 0; j < backgroundModel[0].cols; j++ )
for ( int j = 0; j < backgroundModel[0]->cols; j++ )
{
//highResBFMask.at<int>( i, j ) = i + j;
stringstream str;
str << backgroundModel[0].at<Vec2f>( i, j )[1] << " ";
str << backgroundModel[0]->at<Vec2f>( i, j )[1] << " ";
ofs5 << str.str();
}
stringstream str2;
......@@ -538,20 +608,23 @@ bool MotionSaliencyBinWangApr2014::computeSaliencyImpl( const InputArray image,
fullResolutionDetection( image.getMat(), highResBFMask );
lowResolutionDetection( image.getMat(), lowResBFMask );
imshow( "highResBFMask", highResBFMask * 255 );
imshow( "lowResBFMask", lowResBFMask * 255 );
// 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 );
// Detect the noise pixels (i.e. for a given pixel, fullRes(pixel) = foreground and lowRes(pixel)= background)
bitwise_not( lowResBFMask, not_lowResBFMask );
bitwise_and( highResBFMask, not_lowResBFMask, noisePixelsMask );
//bitwise_not( lowResBFMask, not_lowResBFMask );
//bitwise_and( highResBFMask, not_lowResBFMask, noisePixelsMask );
templateOrdering();
templateReplacement( saliencyMap.getMat(), image.getMat() );
//templateReplacement( highResBFMask, image.getMat() );
templateOrdering();
//highResBFMask.copyTo(saliencyMap);
//highResBFMask.copyTo( saliencyMap );
std::ofstream ofs;
ofs.open( "highResBFMask.txt", std::ofstream::out );
......
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