diff --git a/Source/functions/AbstractionLayers/Layer_SURFFeatures/AbstractionLayer_SURFFeatures.cpp b/Source/functions/AbstractionLayers/Layer_SURFFeatures/AbstractionLayer_SURFFeatures.cpp
index e4922a0..35271e2 100644
--- a/Source/functions/AbstractionLayers/Layer_SURFFeatures/AbstractionLayer_SURFFeatures.cpp
+++ b/Source/functions/AbstractionLayers/Layer_SURFFeatures/AbstractionLayer_SURFFeatures.cpp
@@ -1,15 +1,94 @@
 #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
 
-    //TODO: Gesamtbild mit OpenCV einlesen
-    //TODO: Gesamtbild anhand der berechneten Spalten und Zeilen auseinander schneiden (Sind in der puzzleKlasse gespeichert)
-    //TODO: Features der einzelnen Felder des ausgeschnittenen Gesamtbildes in der m_constraintMatrix speichern
+    // -- 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;
 
-    // Speichert die Features der linken oberen Ecke des Gesamtpuzzles in die constraintMatrix
-    m_constraintMatrix[0][0].m_numberOfFeaturesDetected = 50;
+    // 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))
diff --git a/Source/functions/AbstractionLayers/Layer_SURFFeatures/AbstractionLayer_SURFFeatures_Properties.h b/Source/functions/AbstractionLayers/Layer_SURFFeatures/AbstractionLayer_SURFFeatures_Properties.h
index 46164ce..3173149 100644
--- a/Source/functions/AbstractionLayers/Layer_SURFFeatures/AbstractionLayer_SURFFeatures_Properties.h
+++ b/Source/functions/AbstractionLayers/Layer_SURFFeatures/AbstractionLayer_SURFFeatures_Properties.h
@@ -9,7 +9,7 @@ public:
     AbstractionLayer_SURFFeatures_Properties() {}
 
 private:
-    uint16_t m_numberOfFeaturesDetected;
+    float m_numberOfFeaturesDetected;
     friend class AbstractionLayer_SURFFeatures;
 };