diff --git a/NeuralDetector/Camera.cpp b/NeuralDetector/Camera.cpp
index 79e08a5..47b3265 100644
--- a/NeuralDetector/Camera.cpp
+++ b/NeuralDetector/Camera.cpp
@@ -8,8 +8,11 @@ Camera::Camera(int port)
 	Camera::port = port;
 	capture = VideoCapture(port);
 
+	cameraAvailable = true;
+
 	if (!capture.isOpened())
 	{
+		cameraAvailable = false;
 		cout << "Failed to open camera on port " << port << endl;
 	}
 
@@ -31,6 +34,9 @@ Camera::~Camera()
 
 bool Camera::Calibrate()
 {
+	cout << "Hold a 9x7 chessboard below the camera" << endl;
+	cout << "Press space bar to take a picture" << endl;
+
 	// The number of boards you want to capture, the number of internal corners horizontally 
 	// and the number of internal corners vertically (That's just how the algorithm works). 
 	int numBoards = 10;
@@ -156,23 +162,43 @@ bool Camera::Calibrate()
 	fs.release();
 	destroyAllWindows();
 
+	cout << "Calibration finished, press enter to exit" << endl;
+	cin.ignore();
+
 	return true;
 }
 
 Mat Camera::getImage()
 {
-	Mat imageUndistorted;
 	Mat image;
-	Mat RGB_img;
 
-	capture >> image;
+	if (!cameraAvailable)
+	{
+		vector<string> files;
+		string dir = "training/";
+		read_directory(dir, files);
 
-	undistort(image, imageUndistorted, intrinsic, distCoeffs);
-	
-	Rect region_of_interest = Rect(10, 10, image.cols - 20, image.rows - 20);
-	Mat image_roi = imageUndistorted(region_of_interest);
+		string file = files[rand() % files.size()];
 
-	return image_roi;
+		cout << file << endl;
+
+		image = imread("training/" + file, CV_LOAD_IMAGE_COLOR);
+		return image;
+	}
+	else
+	{
+		Mat imageUndistorted;
+		Mat RGB_img;
+
+		capture >> image;
+
+		undistort(image, imageUndistorted, intrinsic, distCoeffs);
+
+		Rect region_of_interest = Rect(10, 10, image.cols - 20, image.rows - 20);
+		Mat image_roi = imageUndistorted(region_of_interest);
+
+		return image_roi;
+	}
 }
 
 Mat Camera::takeImage()
@@ -181,9 +207,13 @@ Mat Camera::takeImage()
 
 	bool finished = false;
 
+	if (!cameraAvailable)
+		image = getImage();
+
 	while (!finished)
 	{
-		image = getImage();
+		if(cameraAvailable)
+			image = getImage();
 
 		imshow("Live feed", image);
 
@@ -196,3 +226,18 @@ Mat Camera::takeImage()
 
 	return image;
 }
+
+
+void Camera::read_directory(const string& name, vector<string> &v)
+{
+	string pattern = name;
+	pattern.append("\\*");
+	WIN32_FIND_DATA data;
+	HANDLE hFind;
+	if ((hFind = FindFirstFile(pattern.c_str(), &data)) != INVALID_HANDLE_VALUE) {
+		do {
+			v.push_back(data.cFileName);
+		} while (FindNextFile(hFind, &data) != 0);
+		FindClose(hFind);
+	}
+}
\ No newline at end of file
diff --git a/NeuralDetector/Camera.h b/NeuralDetector/Camera.h
index 0b3d396..522670d 100644
--- a/NeuralDetector/Camera.h
+++ b/NeuralDetector/Camera.h
@@ -5,6 +5,7 @@
 #include <opencv2/calib3d/calib3d.hpp>
 #include <iostream>
 #include <string>
+#include <Windows.h>
 
 #include "avansvisionlib20.h"
 
@@ -21,5 +22,7 @@ private:
 	int port = 0;
 	VideoCapture capture;
 	Mat intrinsic, distCoeffs;
+	bool cameraAvailable;
+	void read_directory(const string& name, vector<string> &v);
 };
 
diff --git a/NeuralDetector/FeatureExtractor.cpp b/NeuralDetector/FeatureExtractor.cpp
index ab6909c..501aef3 100644
--- a/NeuralDetector/FeatureExtractor.cpp
+++ b/NeuralDetector/FeatureExtractor.cpp
@@ -16,6 +16,7 @@ FeatureExtractor::~FeatureExtractor()
 
 void FeatureExtractor::Extract(Mat &ref)
 {
+	//Bepaal verschillende features
 	double ar = AspectRatio();
 	double cr = Circularity();
 	double bendingEnergy = getBendingEnergy();
@@ -24,10 +25,10 @@ void FeatureExtractor::Extract(Mat &ref)
 	double perimeter = getPerimeter();
 	double numDefects = convexDefects();
 
-	//ref = (Mat_<double>(1, 7) << 1.0, convexHullBendingEnergy / 100.0, ar, cr, bendingEnergy / 10000.0, , perimeter / 10000.0);
 	ref = (Mat_<double>(1, 5) << 1.0, ar, cr / 10.0, convexHullBendingEnergy / 100.0, numDefects / 100.0);
 }
 
+//Rotatie onafhankelijke aspect ratio
 double FeatureExtractor::AspectRatio()
 {
 	double ar = 0;
@@ -40,6 +41,8 @@ double FeatureExtractor::AspectRatio()
 	return ar;
 }
 
+
+//Oppervlakte van de minimale circle gedeeld door het oppervlakte van de bounding box
 double FeatureExtractor::Circularity()
 {
 	double radius = getMinEnclosingCircleRadius();
@@ -50,11 +53,13 @@ double FeatureExtractor::Circularity()
 	return cir;
 }
 
+//Rotated bounding box om het object
 RotatedRect FeatureExtractor::Rectangle()
 {
 	return rect;
 }
 
+//De radius van de minimale circle
 double FeatureExtractor::getMinEnclosingCircleRadius()
 {
 	float radius;
@@ -66,6 +71,7 @@ double FeatureExtractor::getMinEnclosingCircleRadius()
 	return rad;
 }
 
+//De lengte van het contour
 double FeatureExtractor::getPerimeter()
 {
 	double per = arcLength(contour, true);
@@ -73,6 +79,7 @@ double FeatureExtractor::getPerimeter()
 	return per;
 }
 
+//De bending energy van de contour
 double FeatureExtractor::getBendingEnergy()
 {
 	double energy = 0;
@@ -92,6 +99,7 @@ double FeatureExtractor::getBendingEnergy()
 	return energy;
 }
 
+//De bending energy van de convex hull
 double FeatureExtractor::getConvexHullBendingEnergy() {
 
 	vector<Point> convex;
@@ -113,6 +121,7 @@ double FeatureExtractor::getConvexHullBendingEnergy() {
 	return energy;
 }
 
+//Bending energy support methode, zoekt naar de hoek tussen twee pixels volgens de freeman code
 int FeatureExtractor::discoverNextRelativeDirection(const cv::Point &pos, const cv::Point &target)
 {
 	for (int i = 0; i < 8; i++) {
@@ -128,6 +137,7 @@ int FeatureExtractor::discoverNextRelativeDirection(const cv::Point &pos, const
 	return -1;
 }
 
+//Het aantal defecten die in de convexhull zitten
 double FeatureExtractor::convexDefects()
 {
 	vector<int> hullsI(contour.size()); // Indices to contour points
@@ -138,6 +148,7 @@ double FeatureExtractor::convexDefects()
 	return (double)defects.size();
 }
 
+//Statische help functie om het contour te bepalen in een plaatje
 void FeatureExtractor::findContour(Mat &image, vector<Point> &contour)
 {
 	Mat canny_output;
diff --git a/NeuralDetector/Main.cpp b/NeuralDetector/Main.cpp
index 051b9bb..1c0bf02 100644
--- a/NeuralDetector/Main.cpp
+++ b/NeuralDetector/Main.cpp
@@ -17,30 +17,41 @@ int main(int argc, char** argv)
 	Training tr;
 	
 	cout << "Welcome to Jarvis" << endl;
-	cout << "Would you like to calibrate the camera (c), take pictures (p), train the network (t) or run the neural network (r)?" << endl;
 
-	char c;
-	cin >> c;
+	bool running = true;
 
-	switch (c) {
-	case 'c':
-		cam.Calibrate();
-		break;
-	case 'p':
-		tr.CreateTrainingSet();
-		break;
-	case 't':
-		tr.LoadTrainingSet();
-		break;
-	case 'r':
-		run();
-		break;
-	default:
-		return 0;
+	while (running)
+	{
+		cout << endl << "What would you like to do?" << endl;
+		cout << "Calibrate the camera (c), take pictures (p), train the network (t), run the neural network (r) or exit (e)?" << endl;
+
+		char c;
+		cin >> c;
+
+		cout << endl;
+
+		switch (c) {
+		case 'c':
+			cam.Calibrate();
+			break;
+		case 'p':
+			tr.CreateTrainingSet();
+			break;
+		case 't':
+			tr.LoadTrainingSet();
+			break;
+		case 'r':
+			run();
+			break;
+		case 'e':
+			running = false;
+			break;
+		default:
+			return 0;
+		}
 	}
 
-	cout << "The program has finished, press enter to exit" << endl;
-	cin.ignore();
+	cout << "The program has finished" << endl;
 
 	return 0;
 }
@@ -51,7 +62,13 @@ int run()
 	NeuralNetwork bpn;
 	bpn.Read();
 
-	while (true)
+	cout << "The neural network is ready to be used" << endl;
+	cout << "Please place an item below the camera and press the spacebar" << endl << endl;
+
+
+	bool running = true;
+
+	while (running)
 	{
 		//Take picture and pre-process
 		Mat image, gray_image, binaryImage;
@@ -92,41 +109,17 @@ int run()
 		vector<vector<Point>> contours;
 		contours.push_back(contour);
 		drawContours(image, contours, -1, CV_RGB(0, 255, 0), 4);
-		putText(image, cls, cvPoint(15, 30),
-			FONT_HERSHEY_COMPLEX, 1.0, cvScalar(0, 0, 0), 1, CV_AA);
+		putText(image, cls, cvPoint(15, 30), FONT_HERSHEY_COMPLEX, 1.0, cvScalar(0, 0, 0), 1, CV_AA);
 
 		imshow("Neural detection", image);
 
-		waitKey(0);
+		auto c = waitKey(0);
+
+		if (c == 27)
+		{
+			running = false;
+		}
 
 		destroyAllWindows();
 	}
-}
-
-
-/*
-// Creeer een witte image
-IplImage* iplimage = cvCreateImage(cvSize(binaryImage.cols, binaryImage.rows), IPL_DEPTH_8U, 3);
-Mat contourImage = cvarrToMat(iplimage);
-contourImage = Scalar(255, 255, 255);
-
-// teken de contouren op de witte image
-vector<vector<Point>> contours;
-contours.push_back(contour);
-
-Point2f vertices2f[4];
-ext.Rectangle().points(vertices2f);
-
-// Convert them so we can use them in a fillConvexPoly
-Point vertices[4];
-for (int i = 0; i < 4; ++i) {
-	vertices[i] = vertices2f[i];
-}
-
-// Now we can fill the rotated rectangle with our specified color
-fillConvexPoly(contourImage, vertices, 4, Scalar(0, 255, 0));
-drawContours(contourImage, contours, -1, CV_RGB(255, 0, 0));
-
-imshow("Features", contourImage);
-waitKey(0);
-*/
\ No newline at end of file
+}
\ No newline at end of file
diff --git a/NeuralDetector/NeuralNetwork.cpp b/NeuralDetector/NeuralNetwork.cpp
index a81442c..a2df8c2 100644
--- a/NeuralDetector/NeuralNetwork.cpp
+++ b/NeuralDetector/NeuralNetwork.cpp
@@ -4,6 +4,8 @@
 
 #include "NeuralNetwork.h"
 
+using namespace chrono;
+
 NeuralNetwork::NeuralNetwork()
 {
 	V0 = Mat();
@@ -40,11 +42,18 @@ Mat NeuralNetwork::Train(Mat& ITset, Mat& OTset)
 	// outputError1: error on output for the current input and new calculated 
 	//               weighfactors, i.e. V1, W1
 	double outputError0, outputError1, sumSqrDiffError = MAX_OUTPUT_ERROR + 1;
+	double lowestError = 10;
+	Mat V0l, W0l;
 	Mat V1, W1;
 
-	cout << endl << "Starting neural training..." << endl;
+	cout << endl << "Starting neural training, this might take a while..." << endl;
 
 	int runs = 0;
+
+	milliseconds lasttime = timestamp();
+	milliseconds starttime = timestamp();
+	bool shown = false;
+
 	while ((sumSqrDiffError > MAX_OUTPUT_ERROR) && (runs < MAXRUNS)) {
 
 		sumSqrDiffError = 0;
@@ -67,16 +76,40 @@ Mat NeuralNetwork::Train(Mat& ITset, Mat& OTset)
 
 			sumSqrDiffError += (outputError1 - outputError0) * (outputError1 - outputError0);
 
+			if (sumSqrDiffError < lowestError)
+			{
+				lowestError = sumSqrDiffError;
+				V0l = V0;
+				W0l = W0;
+			}
+
 			V0 = V1;
 			W0 = W1;
 		}
 		runs++;
 
-		if (runs % 1000 == 0)
-			cout << "Completed " << runs << " runs, still working on it.. (" << sumSqrDiffError << ")" << endl;
+		if (timestamp() > lasttime + milliseconds(15000) && !shown)
+		{
+			cout << "We are still working on it, hold on..." << endl;
+			shown = true;
+		}
+
+		if (runs % 500 == 0)
+		{
+			shown = false;
+			auto runtime = (timestamp() - lasttime) / 1000;
+			cout << "Completed " << runs << " runs, still working on it.. (" << sumSqrDiffError << ")(" << runtime.count() << "s)" << endl;
+
+			lasttime = timestamp();
+		}
 	}
 
-	cout << "Training complete in " << runs << " runs" << endl;
+	V0 = V0l;
+	W0 = W0l;
+
+	auto totalduration = (timestamp() - starttime) / 1000;
+	cout << "Training finished in " << runs << " runs, it took " << totalduration.count() << "s" << endl;
+	cout << "The lowest error rate was " << lowestError << ", we are savind the matching factors" << endl;
 
 	Mat inputVectorTrainingSet, outputVectorTrainingSet, outputVectorBPN;
 
@@ -116,9 +149,6 @@ void NeuralNetwork::Read()
 
 void NeuralNetwork::write()
 {
-	for (pair<int, string> p : classes)
-		cout << p.first << " - " << p.second << endl;
-
 	FileStorage fs("factors.yml", FileStorage::WRITE);
 	fs << "W0" << W0 << "V0" << V0;
 	fs.release();
@@ -218,5 +248,13 @@ void NeuralNetwork::mat_class(Mat& ref, string& name)
 		}
 	}
 
-	name = classes[index] + " " + to_string(maxvalue*100) + "%";
+	name = classes[index] + " - " + to_string(maxvalue*100) + "%";
+}
+
+milliseconds NeuralNetwork::timestamp()
+{
+	milliseconds ms = duration_cast<milliseconds>(
+		system_clock::now().time_since_epoch());
+
+	return ms;
 }
\ No newline at end of file
diff --git a/NeuralDetector/NeuralNetwork.h b/NeuralDetector/NeuralNetwork.h
index 3277282..9563111 100644
--- a/NeuralDetector/NeuralNetwork.h
+++ b/NeuralDetector/NeuralNetwork.h
@@ -6,6 +6,7 @@
 #include <iomanip>
 #include <string>
 #include <map>
+#include <chrono>
 
 #include "avansvisionlib20.h" // versie 2.0 (!)
 
@@ -31,4 +32,6 @@ private:
 	void save_class(const string& name, Mat& ref);
 	void class_mat(int index, Mat& ref);
 	void mat_class(Mat& ref, string& name);
+
+	chrono::milliseconds timestamp();
 };
\ No newline at end of file
diff --git a/NeuralDetector/Training.cpp b/NeuralDetector/Training.cpp
index 218dd2f..62fbe92 100644
--- a/NeuralDetector/Training.cpp
+++ b/NeuralDetector/Training.cpp
@@ -1,7 +1,7 @@
 #include "Training.h"
 
-using namespace cv;
 using namespace std;
+using namespace cv;
 
 
 Training::Training()
@@ -32,6 +32,8 @@ void Training::CreateTrainingSet()
 			break;
 		}
 
+		cout << "Press space to take a picture, fill in a new category (n) or stop (esc)" << endl;
+
 		bool takingPhotos = true;
 		int i = 0;
 
@@ -78,6 +80,7 @@ void Training::LoadTrainingSet()
 	read_directory(dir, files);
 
 	cout << "Found " << files.size()-2 << " files in " << dir << endl;
+	cout << "Please wait while they are being processed..." << endl;
 
 	random_shuffle(files.begin(), files.end());
 
@@ -91,7 +94,7 @@ void Training::LoadTrainingSet()
 		if (!image.data)
 			continue;
 
-		cout << "Loaded " << file << endl;
+		//cout << "Loaded " << file << endl;
 
 		string classname;
 		class_name(file, classname);
@@ -120,8 +123,6 @@ void Training::LoadTrainingSet()
 
 	bpn.Train(ITset, OTset);
 
-	cout << "Training complete" << endl;
-
 	cin.ignore();
 }
 
diff --git a/NeuralDetector/calibration.yml b/NeuralDetector/calibration.yml
index 905b231..e965e24 100644
--- a/NeuralDetector/calibration.yml
+++ b/NeuralDetector/calibration.yml
@@ -4,12 +4,12 @@ intrinsic: !!opencv-matrix
    rows: 3
    cols: 3
    dt: d
-   data: [ 7.7102786562030786e+04, 0., 3.2013752810898518e+02, 0.,
-       1.1313075108780187e+05, 2.4041984682542122e+02, 0., 0., 1. ]
+   data: [ 1.9751044074930709e+04, 0., 3.1998804311606534e+02, 0.,
+       1.8502329761413108e+04, 2.3996853716247819e+02, 0., 0., 1. ]
 distCoeffs: !!opencv-matrix
    rows: 1
    cols: 5
    dt: d
-   data: [ -1.6033489811253014e+02, -3.6422783919263818e-02,
-       -2.9072693275266626e-02, -3.5312598551078556e-01,
-       -6.9187392701624002e-07 ]
+   data: [ -8.1460191784170419e+00, -3.9067690989523931e+03,
+       1.4768943058283386e-02, 1.2753528858688477e-03,
+       -2.8023335779025366e-01 ]
diff --git a/NeuralDetector/factors.yml b/NeuralDetector/factors.yml
index cbe62c7..fd375a5 100644
--- a/NeuralDetector/factors.yml
+++ b/NeuralDetector/factors.yml
@@ -4,67 +4,67 @@ W0: !!opencv-matrix
    rows: 8
    cols: 10
    dt: d
-   data: [ -4.8813774957793505e+00, -1.4851964864258784e+01,
-       -4.5483958882918500e+00, 2.3423976029083846e+01,
-       -2.7036296416086123e+00, -5.9289881679629190e+01,
-       -3.6879635531940997e+00, -3.3153545804030564e+01,
-       -1.4020142561256883e+00, -7.0336731756450614e+00,
-       8.8357037082684666e+00, 1.2314510070259121e+01,
-       -3.4472596296194356e+00, -1.1923745474347557e+01,
-       -4.4282540281508034e+00, 2.4457028473557347e+01,
-       3.6136991577659492e+00, -2.5827572663987759e+01,
-       -1.3892967734397374e+01, -8.4284128215643772e-01,
-       -1.5656959650996116e+01, -4.7792720274921949e+00,
-       2.2975802354552862e+01, -6.2055876498801723e+00,
-       -5.7482437173820735e+01, -4.6866402386216075e+00,
-       -3.0466706366866589e+01, -7.0842282146302944e+00,
-       -6.2597171792574171e+00, -7.2087129758066659e+00,
-       -1.5425205894698555e+01, 4.3120273767387673e+01,
-       -5.5453766803160338e+00, -3.9096843912958307e-01,
-       -1.0403409583426930e+01, 2.5070656273447195e+01,
-       -1.2109893602230631e+01, 2.9945425250036301e+01,
-       -5.6642091074608834e+00, -3.0840158943557448e+01,
-       2.5513648309284322e+00, -1.7087631494600185e+01,
-       -1.4015650118577094e+01, -3.3031333903438806e+01,
-       -1.7294407183796434e+00, -3.2643739919895147e+01,
-       -1.9333188145064401e+01, -6.3934782960318515e+00,
-       -1.8871662520667233e+01, -1.5080559598577183e+01,
-       5.2874934032701137e-01, 2.5826625216358101e+00,
-       8.3597510472828951e+00, -2.0854574479392202e-01,
-       -1.0790385094292049e+01, -4.8203290529982112e+00,
-       2.3059664039490269e+01, -1.5713643646359962e+01,
-       -2.1086899521790972e+01, -6.1737512017926734e+00,
-       -1.6040305572556772e+01, -3.1420416546297179e+01,
-       -9.6651877009273299e+00, -9.3255536054811716e-01,
-       -8.7417385800329424e+00, 1.4584107173942127e+00,
-       -1.9611038941263057e+01, -6.9351624096848574e-01,
-       -3.8496653692305358e+01, 3.1842200474091559e+01,
-       -1.2903254663541592e+01, -1.4547573929553105e+01,
-       -9.2829295697875889e+00, -1.0087144923614213e+01,
-       2.0960410041424829e+01, -2.1442616606862913e+01,
-       2.0254590030822840e+01, -2.9413976475212888e+00,
-       3.4350620310034941e+01, -2.0392084844460520e+01 ]
+   data: [ 1.1336092031696420e+01, 4.5907964608485772e+00,
+       -9.8170853293097267e+00, 2.8290762007773935e+00,
+       -1.7922242361752613e+01, 1.4612031964113379e+01,
+       -4.0479882311598461e+00, -2.7888173455852517e+01,
+       -1.0401357548127409e+01, -1.9997698224895021e+01,
+       1.6526407101734485e+01, -2.5048019194677259e+01,
+       -1.4599545059796050e+01, -1.0705176170531191e+01,
+       1.5591483974571350e+01, -6.2472867926201886e+01,
+       2.3716769265461965e-01, -2.4142327639797266e+01,
+       4.8956994474556854e+00, -2.0633207709322686e+01,
+       -1.1954114270472544e+01, -2.1070255286769200e+01,
+       -5.8841854904753843e+00, 1.1692359784513217e+01,
+       -3.4386970253466039e+01, -1.2121202433389728e+01,
+       1.8539709079086894e+01, -6.6924667544118632e-01,
+       2.9549921775562402e+00, -2.8001963902932808e+01,
+       -1.8422012716016877e+01, -5.1406650430909764e+00,
+       2.1360238044988655e+01, -6.1169687658900331e+00,
+       -4.0230184634597009e+01, -4.5605232444954469e+00,
+       -3.2840261827923982e+01, -5.4461743522092156e+00,
+       2.2158992201314216e+01, -1.0335417208286332e+01,
+       -1.9828009946150743e+01, 1.4979778939442804e+01,
+       -5.8343261113808511e+00, 5.7316262652977938e+00,
+       -6.9994299506424893e+00, -2.6043598644624605e+01,
+       -4.5790456193267133e+00, -2.7580122152065578e+00,
+       -4.1716120383476198e+00, -5.7935627689640441e+01,
+       -3.0543934912013349e+00, -5.0496516841592891e+00,
+       -1.4040028354202876e+01, -6.6120192400826427e+00,
+       -3.2379386343661118e+01, 1.6893970327319945e+00,
+       -2.1038153994851378e+01, 1.3627286069725903e+01,
+       -5.5299790069242924e+01, 2.0892336998499403e+01,
+       -9.6072131281893702e-01, 4.2199808448514640e+00,
+       8.5745421903391374e+00, -4.7419793494694629e+00,
+       -1.5332908558713877e+01, -1.2415689661794444e+01,
+       1.7861523825076929e+01, -9.3359096106336636e+00,
+       -2.5532297643346457e+01, -1.6237550846084872e+01,
+       -1.8541642217263320e+01, -8.4468379830802434e+00,
+       -2.6841963906955240e+00, -2.9503409603204791e+01,
+       3.3671318957004903e+00, 2.8247645228394909e+00,
+       -7.7324538168255534e+00, 9.5172262300931063e+00,
+       -9.6252020838806605e+00, 1.2226400714330984e+01 ]
 V0: !!opencv-matrix
    rows: 5
    cols: 8
    dt: d
-   data: [ -6.5636286541558171e+00, 4.5330404221912382e+01,
-       -1.1149842067895429e+01, 7.5086888359720900e+00,
-       2.8132961939742511e+00, -3.2716268982804380e+01,
-       2.4073267678846292e+01, -1.5426805055872450e+01,
-       -2.3447511567066414e+01, -7.1071961568313483e+01,
-       2.4376431377296260e+01, -5.2835959692878383e+01,
-       1.5962714728971601e+01, 3.5537070580189244e+01,
-       -2.9387489075397973e+01, 2.7081013867840458e+01,
-       4.3302753434844213e+01, -1.0978817742962482e+02,
-       -4.2383027486121954e+01, 6.8440729304108856e+01,
-       -9.6243156066355802e+00, 2.9979769994501602e+01,
-       -3.7724179920359870e+00, 9.9776059105303023e+00,
-       5.0426955715121640e+00, -5.7015329865766313e+00,
-       7.6021476153135108e+00, 1.4119669046625256e+00,
-       -1.3832537537011591e+01, -7.8448492545467206e+00,
-       -1.8009846302065957e+00, 4.3426637435861783e+00,
-       1.6086554490069808e+01, 4.4710323658171866e+01,
-       -8.9095095711932455e+00, -4.1369158011178691e+00,
-       -9.9794096446219829e+00, 2.2801143768856548e+01,
-       -4.2606068131615935e+01, -1.7282938175700217e+01 ]
+   data: [ 4.0645841804918390e+01, -2.6309456335199283e+01,
+       -3.2021561992823422e+01, -1.1361589664601377e+01,
+       -6.2952730506159709e+00, 3.1551765345114354e+01,
+       -3.3894177485144908e+01, 8.0393818637955405e+00,
+       -7.0086508010827856e+01, 7.3142485753157672e+01,
+       3.3192664589631597e+01, 2.6415090050447283e+01,
+       -2.8098333741102422e+01, -5.2361784379137518e+01,
+       3.4116941209466439e+01, 9.9507452703944796e+00,
+       -8.1604249037583784e+01, -2.2549578961533932e+01,
+       8.5279452974783325e+01, -4.4229283926043621e+01,
+       8.1355987392899749e+01, -9.4980076038844263e+00,
+       3.6541118219404311e+01, -2.2631072532220497e+01,
+       -1.5721950954653123e+00, -1.7752668479393374e+00,
+       2.6087120719774060e+00, 7.7168135306142345e+00,
+       -1.9471502823906410e+00, -5.8323043064098918e+00,
+       -1.0878110969855500e+01, -1.4068980041580041e+01,
+       3.5184273457694218e+01, 8.2166133609647263e-01,
+       -1.7608154171677800e+01, -1.3846806695752736e+01,
+       -2.9004559923798547e+00, -2.1241811956562779e+00,
+       2.6836608163694095e+01, -1.0923774621771331e+01 ]
diff --git a/calibration.yml b/calibration.yml
new file mode 100644
index 0000000..905b231
--- /dev/null
+++ b/calibration.yml
@@ -0,0 +1,15 @@
+%YAML:1.0
+---
+intrinsic: !!opencv-matrix
+   rows: 3
+   cols: 3
+   dt: d
+   data: [ 7.7102786562030786e+04, 0., 3.2013752810898518e+02, 0.,
+       1.1313075108780187e+05, 2.4041984682542122e+02, 0., 0., 1. ]
+distCoeffs: !!opencv-matrix
+   rows: 1
+   cols: 5
+   dt: d
+   data: [ -1.6033489811253014e+02, -3.6422783919263818e-02,
+       -2.9072693275266626e-02, -3.5312598551078556e-01,
+       -6.9187392701624002e-07 ]
diff --git a/classes.yml b/classes.yml
new file mode 100644
index 0000000..482918c
--- /dev/null
+++ b/classes.yml
@@ -0,0 +1,4 @@
+%YAML:1.0
+---
+classes: { e0:weerstand, e1:irtrans, e2:switch, e3:reflsens, e4:irsens,
+    e5:reedsens, e6:card, e7:led, e8:button, e9:rgbled }
diff --git a/factors.yml b/factors.yml
new file mode 100644
index 0000000..cbe62c7
--- /dev/null
+++ b/factors.yml
@@ -0,0 +1,70 @@
+%YAML:1.0
+---
+W0: !!opencv-matrix
+   rows: 8
+   cols: 10
+   dt: d
+   data: [ -4.8813774957793505e+00, -1.4851964864258784e+01,
+       -4.5483958882918500e+00, 2.3423976029083846e+01,
+       -2.7036296416086123e+00, -5.9289881679629190e+01,
+       -3.6879635531940997e+00, -3.3153545804030564e+01,
+       -1.4020142561256883e+00, -7.0336731756450614e+00,
+       8.8357037082684666e+00, 1.2314510070259121e+01,
+       -3.4472596296194356e+00, -1.1923745474347557e+01,
+       -4.4282540281508034e+00, 2.4457028473557347e+01,
+       3.6136991577659492e+00, -2.5827572663987759e+01,
+       -1.3892967734397374e+01, -8.4284128215643772e-01,
+       -1.5656959650996116e+01, -4.7792720274921949e+00,
+       2.2975802354552862e+01, -6.2055876498801723e+00,
+       -5.7482437173820735e+01, -4.6866402386216075e+00,
+       -3.0466706366866589e+01, -7.0842282146302944e+00,
+       -6.2597171792574171e+00, -7.2087129758066659e+00,
+       -1.5425205894698555e+01, 4.3120273767387673e+01,
+       -5.5453766803160338e+00, -3.9096843912958307e-01,
+       -1.0403409583426930e+01, 2.5070656273447195e+01,
+       -1.2109893602230631e+01, 2.9945425250036301e+01,
+       -5.6642091074608834e+00, -3.0840158943557448e+01,
+       2.5513648309284322e+00, -1.7087631494600185e+01,
+       -1.4015650118577094e+01, -3.3031333903438806e+01,
+       -1.7294407183796434e+00, -3.2643739919895147e+01,
+       -1.9333188145064401e+01, -6.3934782960318515e+00,
+       -1.8871662520667233e+01, -1.5080559598577183e+01,
+       5.2874934032701137e-01, 2.5826625216358101e+00,
+       8.3597510472828951e+00, -2.0854574479392202e-01,
+       -1.0790385094292049e+01, -4.8203290529982112e+00,
+       2.3059664039490269e+01, -1.5713643646359962e+01,
+       -2.1086899521790972e+01, -6.1737512017926734e+00,
+       -1.6040305572556772e+01, -3.1420416546297179e+01,
+       -9.6651877009273299e+00, -9.3255536054811716e-01,
+       -8.7417385800329424e+00, 1.4584107173942127e+00,
+       -1.9611038941263057e+01, -6.9351624096848574e-01,
+       -3.8496653692305358e+01, 3.1842200474091559e+01,
+       -1.2903254663541592e+01, -1.4547573929553105e+01,
+       -9.2829295697875889e+00, -1.0087144923614213e+01,
+       2.0960410041424829e+01, -2.1442616606862913e+01,
+       2.0254590030822840e+01, -2.9413976475212888e+00,
+       3.4350620310034941e+01, -2.0392084844460520e+01 ]
+V0: !!opencv-matrix
+   rows: 5
+   cols: 8
+   dt: d
+   data: [ -6.5636286541558171e+00, 4.5330404221912382e+01,
+       -1.1149842067895429e+01, 7.5086888359720900e+00,
+       2.8132961939742511e+00, -3.2716268982804380e+01,
+       2.4073267678846292e+01, -1.5426805055872450e+01,
+       -2.3447511567066414e+01, -7.1071961568313483e+01,
+       2.4376431377296260e+01, -5.2835959692878383e+01,
+       1.5962714728971601e+01, 3.5537070580189244e+01,
+       -2.9387489075397973e+01, 2.7081013867840458e+01,
+       4.3302753434844213e+01, -1.0978817742962482e+02,
+       -4.2383027486121954e+01, 6.8440729304108856e+01,
+       -9.6243156066355802e+00, 2.9979769994501602e+01,
+       -3.7724179920359870e+00, 9.9776059105303023e+00,
+       5.0426955715121640e+00, -5.7015329865766313e+00,
+       7.6021476153135108e+00, 1.4119669046625256e+00,
+       -1.3832537537011591e+01, -7.8448492545467206e+00,
+       -1.8009846302065957e+00, 4.3426637435861783e+00,
+       1.6086554490069808e+01, 4.4710323658171866e+01,
+       -8.9095095711932455e+00, -4.1369158011178691e+00,
+       -9.9794096446219829e+00, 2.2801143768856548e+01,
+       -4.2606068131615935e+01, -1.7282938175700217e+01 ]