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