changed col rows
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@ -31,7 +31,7 @@ bool AbstractionLayer_SURFFeatures::EvaluateQuality (coor constraintCoordinate,
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// Calculate absolute difference between constraints and each piece and safe it
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for( int i = 0; i < qVector.size(); i++ )
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{
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float diff = abs(m_constraintMatrix[constraintCoordinate.row][constraintCoordinate.col].m_numberOfFeaturesDetected - qVector[i].second->m_a4.m_numberOfFeaturesDetected);
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float diff = abs(m_constraintMatrix[constraintCoordinate.col][constraintCoordinate.row].m_numberOfFeaturesDetected - qVector[i].second->m_a4.m_numberOfFeaturesDetected);
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qVector[i].first = 1 - diff;
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//cout << fixed << qVector[i].first << endl;
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}
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@ -81,8 +81,8 @@ bool AbstractionLayer_SURFFeatures::PreProcessingFullImg(coor mySize)
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goodFeaturesToTrack( image, corners, maxCorners, qualityLevel, minDistance, mask, blockSize, useHarrisDetector, k );
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// Empty the matrix
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for( int j = 0; j < mySize.row ; j++ )
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{ for( int i = 0; i < mySize.col; i++ )
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for( int j = 0; j < mySize.col ; j++ )
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{ for( int i = 0; i < mySize.row; i++ )
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{
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m_constraintMatrix[j][i].m_numberOfFeaturesDetected = 0;
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}
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@ -94,15 +94,15 @@ bool AbstractionLayer_SURFFeatures::PreProcessingFullImg(coor mySize)
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for( int i = 0; i < corners.size(); i++ ) // For all found features
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{
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// Increment number of found pieces
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m_constraintMatrix[int(corners[i].y/pieceRowSize)][int(corners[i].x/pieceColSize)].m_numberOfFeaturesDetected++;
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m_constraintMatrix[int(corners[i].x/pieceColSize)][int(corners[i].y/pieceRowSize)].m_numberOfFeaturesDetected++;
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}
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// Get minimal and maximal number of features -> TODO: Do in first loop to safe time?
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int minFeatures = int(m_constraintMatrix[0][0].m_numberOfFeaturesDetected);
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int maxFeatures = int(m_constraintMatrix[0][0].m_numberOfFeaturesDetected);
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for( int j = 0; j < mySize.row ; j++ )
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for( int j = 0; j < mySize.col ; j++ )
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{
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for( int i = 0; i < mySize.col; i++ )
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for( int i = 0; i < mySize.row; i++ )
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{
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if(m_constraintMatrix[j][i].m_numberOfFeaturesDetected < minFeatures) minFeatures = int(m_constraintMatrix[j][i].m_numberOfFeaturesDetected);
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if(m_constraintMatrix[j][i].m_numberOfFeaturesDetected > maxFeatures) maxFeatures = int(m_constraintMatrix[j][i].m_numberOfFeaturesDetected);
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@ -110,9 +110,9 @@ bool AbstractionLayer_SURFFeatures::PreProcessingFullImg(coor mySize)
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}
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// Calculate percentage from 0 to 100% (normalized 0-1) with numberOfFeatures and safe it
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for( int j = 0; j < mySize.row ; j++ )
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for( int j = 0; j < mySize.col ; j++ )
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{
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for( int i = 0; i < mySize.col; i++ )
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for( int i = 0; i < mySize.row; i++ )
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{
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m_constraintMatrix[j][i].m_numberOfFeaturesDetected = (m_constraintMatrix[j][i].m_numberOfFeaturesDetected - minFeatures) / (maxFeatures - minFeatures);
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//cout << fixed << m_constraintMatrix[i][j].m_numberOfFeaturesDetected << " ";
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@ -187,6 +187,7 @@ Mat Puzzle::resultImage( vector<LogEntry>& log){
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sprintf(name, PATH, imageNumber);
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Mat img = imread(name, 1);
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cout << name << endl;
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copyMakeBorder(img,img,200,200,200,200,BORDER_CONSTANT,Scalar(255,255,255));
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Mat invert = Mat::ones(img.size(), CV_8UC3); // invert for rotation to work correctly
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bitwise_not ( img, invert );
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