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