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Merge branch 'Layer_SURFFeatures' of https://github.com/MMRVZ2017/MPK.Puzzle into Layer_SURFFeatures

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Raphael Maenle
2018-01-26 22:30:08 +01:00
parent 7920e73bfb 1787af11ab
commit 9a3661647a
3 changed files with 19 additions and 17 deletions
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29
Source/functions/AbstractionLayers/Layer_SURFFeatures/AbstractionLayer_SURFFeatures.cpp
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@@ -10,14 +10,15 @@
// k – Free parameter of Harris detector
#include <iostream>
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
using namespace cv;
using namespace std;
bool AbstractionLayer_SURFFeatures::PreProcessing(coor mySize, const vector<Part*>* partArray)
{
cout << "Abstraction 4 (Features) Preprocessing... " << flush;
InitialiseConstraintMatrixSize(mySize.col, mySize.row);
if(!PreProcessingFullImg(mySize)) return false;
if(!PreProcessingPieces(mySize, partArray)) return false;
@@ -32,7 +33,7 @@ bool AbstractionLayer_SURFFeatures::EvaluateQuality (coor constraintCoordinate,
{
float diff = abs(m_constraintMatrix[constraintCoordinate.row][constraintCoordinate.col].m_numberOfFeaturesDetected - qVector[i].second->m_a4.m_numberOfFeaturesDetected);
qVector[i].first = 1 - diff;
//cout << fixed << qVector[i].first << " ";
//cout << fixed << qVector[i].first << endl;
}
return true;
@@ -42,6 +43,7 @@ bool AbstractionLayer_SURFFeatures::SetConstraintOnPosition(const coor constrain
{
//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
return true;
}
bool AbstractionLayer_SURFFeatures::RemoveConstraintOnPosition(const coor constraintCoordinate)
@@ -49,6 +51,7 @@ bool AbstractionLayer_SURFFeatures::RemoveConstraintOnPosition(const coor constr
//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.
return true;
}
bool AbstractionLayer_SURFFeatures::PreProcessingFullImg(coor mySize)
@@ -56,26 +59,26 @@ bool AbstractionLayer_SURFFeatures::PreProcessingFullImg(coor mySize)
std::vector< cv::Point2f > corners; // Variable to store corner-positions at
// 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);
Mat image = imread(PATH_FULL_PUZZLE, IMREAD_GRAYSCALE);
if (!image.data) {
cerr << "Problem loading image of complete puzzle!" << endl;
return false;
}
//cout << "PRE: " << image.cols << " x " << image.rows << endl;
cv::resize(image, image, Size(int(ceil(double(image.cols)/mySize.col)*mySize.col), int(ceil(double(image.rows)/mySize.row)*mySize.row)));
resize(image, image, Size(int(ceil(double(image.cols)/mySize.col)*mySize.col), 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;
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 );
goodFeaturesToTrack( image, corners, maxCorners, qualityLevel, minDistance, mask, blockSize, useHarrisDetector, k );
// Empty the matrix
for( int j = 0; j < mySize.row ; j++ )
@@ -138,7 +141,7 @@ bool AbstractionLayer_SURFFeatures::PreProcessingPieces(coor mySize, const vecto
int maxCorners = 500;
double qualityLevel = 0.05;
double minDistance = .5;
cv::Mat mask;
Mat mask;
int blockSize = 3;
bool useHarrisDetector = false;
double k = 0.04;
@@ -148,13 +151,13 @@ bool AbstractionLayer_SURFFeatures::PreProcessingPieces(coor mySize, const vecto
char name[100];
for (unsigned imgID = 0; imgID < mySize.col*mySize.row; imgID++) {
sprintf(name, PATH, imgID);
Mat src = cv::imread(name, IMREAD_GRAYSCALE);
sprintf(name, PATH1, imgID);
Mat src = imread(name, IMREAD_GRAYSCALE);
if (!src.data) {
cerr << "Problem loading image of puzzle piece!" << endl;
return false;
} else {
cv::goodFeaturesToTrack(src, corners, maxCorners, qualityLevel, minDistance, mask, blockSize,
goodFeaturesToTrack(src, corners, maxCorners, qualityLevel, minDistance, mask, blockSize,
useHarrisDetector, k);
if (corners.size() < minFeatures) minFeatures = corners.size();
if (corners.size() > maxFeatures) maxFeatures = corners.size();
@@ -173,8 +176,8 @@ bool AbstractionLayer_SURFFeatures::PreProcessingPieces(coor mySize, const vecto
for( unsigned i = 0; i < mySize.col*mySize.row; i++ )
{
partArray->at(i)->m_a4.m_numberOfFeaturesDetected = (partArray->at(i)->m_a4.m_numberOfFeaturesDetected - minFeatures) / (maxFeatures - minFeatures);
cout << fixed << partArray->at(i)->m_a4.m_numberOfFeaturesDetected << endl;
//cout << fixed << partArray->at(i)->m_a4.m_numberOfFeaturesDetected << endl;
}
cout << endl;
//cout << endl;
return true;
}

6
Source/functions/AbstractionLayers/Layer_SURFFeatures/AbstractionLayer_SURFFeatures.h
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@@ -14,11 +14,11 @@
#endif
#ifdef _WIN32 //TODO: Code duplicate from AbstractionLayer_1.h
#define PATH "..\\..\\..\\pieces\\%04d.jpg"
#define PATH1 "..\\..\\..\\pieces\\%04d.jpg"
#elif defined __unix__
#define PATH "..//..//..//pieces//%04d.jpg"
#define PATH1 "..//..//..//pieces//%04d.jpg"
#elif defined __APPLE__
#define PATH "..//..//..//pieces//%04d.jpg"
#define PATH1 "..//..//..//pieces//%04d.jpg"
#endif

1
Source/functions/solve/structure.cpp
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@@ -86,7 +86,6 @@ void solve(vector<LogEntry>& log,Puzzle& puzzleMat)
puzzleMat.a3.EvaluateQuality(log.back().myCoor,log.back().PieceCollector);
break;
case 4://SURFFeature
break;
case -1://random
cout << endl;