122 lines
5.5 KiB
C++
122 lines
5.5 KiB
C++
#include "AbstractionLayer_SURFFeatures.h"
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// Parameters for algorithm:
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// maxCorners – The maximum number of corners to return. If there are more corners than that will be found, the strongest of them will be returned
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// qualityLevel – Characterizes the minimal accepted quality of image corners;
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// minDistance – The minimum possible Euclidean distance between the returned corners
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// mask – The optional region of interest. It will specify the region in which the corners are detected
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// blockSize – Size of the averaging block for computing derivative covariation
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// useHarrisDetector – Indicates, whether to use operator or cornerMinEigenVal()
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// k – Free parameter of Harris detector
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#include <iostream>
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#include "opencv2/highgui.hpp"
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#include "opencv2/imgproc.hpp"
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#ifdef _WIN32
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#define PATH_FULL_PUZZLE "..\\..\\..\\puzzle_img\\puzzle1.jpg"
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#elif defined __unix__
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#define PATH_FULL_PUZZLE "..//..//..//puzzle_img//puzzle1.jpg"
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#elif defined __APPLE__
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#define PATH_FULL_PUZZLE "..//..//..//puzzle_img//puzzle1.jpg"
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#endif
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using namespace cv;
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using namespace std;
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bool AbstractionLayer_SURFFeatures::PreProcessing(coor mySize, const vector<Part*>* partArray)
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{
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InitialiseConstraintMatrixSize(mySize.col, mySize.row);
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std::vector< cv::Point2f > corners; // Variable to store corner-positions at
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// -- Complete puzzle image processing --
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// Load and resize image, so that number of parts in row and col fit in
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cv::Mat image = cv::imread(PATH_FULL_PUZZLE, IMREAD_GRAYSCALE);
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//cout << "PRE: " << image.cols << " x " << image.rows << endl;
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cv::resize(image, image, Size(int(ceil(double(image.cols)/mySize.col)*mySize.row), int(ceil(double(image.rows)/mySize.row)*mySize.row)));
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//cout << "POST: " << image.cols << " x " << image.rows << endl;
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// PARAMETERS (for description see top of file)
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int maxCorners = 10000;
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double qualityLevel = 0.01;
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double minDistance = .5;
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cv::Mat mask;
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int blockSize = 3;
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bool useHarrisDetector = false;
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double k = 0.04;
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// Detect features - this is where the magic happens
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cv::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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{
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m_constraintMatrix[j][i].m_numberOfFeaturesDetected = 0;
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}
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}
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int pieceColSize = image.cols/mySize.col;
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int pieceRowSize = image.rows/mySize.row;
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// Calculate number of features for each piece-position
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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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}
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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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{
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for( int i = 0; i < mySize.col; 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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}
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}
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// Calculate percentage from 0 to 100% with numberOfFeatures and safe it
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for( int j = 0; j < mySize.row ; j++ )
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{
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for( int i = 0; i < mySize.col; 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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}
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//cout << endl;
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}
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// DEBUG - Display image
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/*for( size_t i = 0; i < corners.size(); i++ )
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{
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cv::circle( image, corners[i], 2, cv::Scalar( 255. ), -1 );
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}
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cv::namedWindow( "Output", CV_WINDOW_AUTOSIZE );
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cv::imshow( "Output", image );
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cv::waitKey(0);*/
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//TODO: Alle Bilder mit OpenCV öffnen und deren erkannten Features in SURFFeature_Properties der Part-Klasse speichern
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// Speichert die erkannten Features des jeweiligen Bilds im partArray an der Stelle (->at(xxx))
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partArray->at(0)->m_a4.m_numberOfFeaturesDetected = 40;
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}
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bool AbstractionLayer_SURFFeatures::EvaluateQuality (coor constraintCoordinate, qualityVector& qVector)
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{
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//TODO: Vergleichen, welche der in qualityVector erhaltenen ähnlich viele Features besitzen, wie an der jeweiligen constraintCoordinate in der m_constraintMatrix gespeichert sind
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}
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bool AbstractionLayer_SURFFeatures::SetConstraintOnPosition(const coor constraintCoordinate,const AbstractionLayer_SURFFeatures_Properties constraint)
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{
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//TODO: Benötigen wir nicht unbedint.
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//TODO: Hier erhalten wir vom Dispatcher welches Teil an welche Position gesetzt wird und wir könnten hier die Features des Bilds in die m_constraintMatrix speichern
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}
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bool AbstractionLayer_SURFFeatures::RemoveConstraintOnPosition(const coor constraintCoordinate)
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{
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//TODO: Wie auch beim SetConstraint sollte uns das hier nicht wirklich interessieren.
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//TODO: Außer wir setzen etwas in die Contraintmatrix.
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//TODO: Dann ruft uns der Dispatcher beim Backtrack hier auf und wir müssten das jeweilige PuzzlePart hier wieder rauslöschen.
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} |