/// <summary>
/// Load network file
/// </summary>
/// <param name="ANN"></param>
static public void LoadNeuralNetworkOption(ref ArtificialNeuralNetworkLibrary.ArtificialNeuralNetwork ANN)
{
OpenFileDialog fileDialog = new OpenFileDialog();
fileDialog.Title = "Select a neural network file...";
System.Console.WriteLine("Select a neural network file...");
fileDialog.ShowDialog();
string neuralNetworkFile = fileDialog.FileName;
System.Console.WriteLine("Selected neural network file: " + neuralNetworkFile);
System.Console.WriteLine();
ANN.LoadNetworkParameters(neuralNetworkFile);
}
static void Main(string[] args)
{
// Network Parameters:
#region
int nrOfPixelChannels = 3; // i.e. RGB
int networkInputPatchDim = 64; // 64 in report, Has to be a multiple of the networkOutputDim.
int networkOutputPatchDim = 16; // 16 in report
int nrOfOutputs = networkOutputPatchDim * networkOutputPatchDim;
int filterDim = 12; // 12 in report
int nrOfFilters = 80; // 64 in report
int filterStride = 4; // 4 in report
int featureMapDim = (networkInputPatchDim - filterDim) / filterStride + 1;
int[] weightDimForConvLayer = { nrOfFilters, filterDim, filterDim, nrOfPixelChannels }; // W(f,a,b,ff): (a,b,ff) = filter position, f = feature map index next layer;
int[] weightDimForOutputLayer = { nrOfOutputs, featureMapDim, featureMapDim, nrOfFilters }; // W(k,i,j,f): (i,j,f) = neuron position input layer, k = neuron index this layer;
string activationFunctionConvoluted = "relu"; // relu in report
string activationFunctionOutput = "sigmoid"; // sigmoid in report
string lossFunction = "crossentropy"; // crossentropy in report
#endregion
// Training parameters:
#region
// Due to rotation and edge effects (difference between network input and output dim) the output image will be
// smaller. At least by a factor of sqrt(2)/2 due to the rotation
int nrOfTrainingImagePairs, nrOfTrainingImagePairsInMemory, nrOfValidationImagePairs, nrOfImagePairs;
bool visuallyCheckTraining = true;
// Termination conditions:
int maxNrOfEpochs;
int maxNrOfMinutes;
int miniBatchSize = 100;
int calculateErrorInterval = 1;
int updateMomentumLearningRateInterval = 2;
double initLearningRate = 2.5 * Math.Pow(10, -6);; // constant 0.0005 in report, 0.000005 worked for single image (841) mini batch size
double finalLearningRate = 3.125 * Math.Pow(10, -7);
double initMomentum = 0.6;// constant 0.9 in report
double finalMomentum = 0.9;
double weightDecay = 0.0001; // 0.0002 in report
#endregion
// Other parameters:
#region
double roadProbabilityThreshold = 0.5;
#endregion
// Main menu:
#region
string mainMenuSelection, sel1MenuSelection, sel2MenuSelection, sel2MenuSelection2, sel5MenuSelection, sel5MenuSelection2,
inputImagePath, goldImagePath, networkFolderLocation, inputImageFolder, saveImageFolder, runAnotherImage;
OpenFileDialog fileDialog = new OpenFileDialog();
SaveFileDialog saveDialog = new SaveFileDialog();
FolderBrowserDialog folderDialog = new FolderBrowserDialog();
Bitmap roadMap;
bool displayMenu = true;
string currentDirectory = Directory.GetCurrentDirectory();
//System.Media.SoundPlayer goodbyePlayer = new System.Media.SoundPlayer(currentDirectory + "\\Audio\\" + "t2_hasta_la_vista.wav");
while(displayMenu) {
// Main header:
#region
System.Console.WriteLine("////// ////// //// ////// //////// // // //////////");
System.Console.WriteLine("// // // // // // // // // // // // ");
System.Console.WriteLine("// // // // // // // // // // // // ");
System.Console.WriteLine("///// // // ////////// // // ////// //// // ");
System.Console.WriteLine("// // // // // // // // // // // // ");
System.Console.WriteLine("// // // // // // // // // // // // ");
System.Console.WriteLine("// // ////// // // ////// //////// // // // ");
System.Console.WriteLine("©2015\n");
#endregion
mainMenuSelection = Menu.MainMenu();
switch (mainMenuSelection) {
case "1":
// Training a network:
#region
sel1MenuSelection = Menu.Sel1Menu();
ArtificialNeuralNetworkLibrary.ArtificialNeuralNetwork ANNTrain =
new ArtificialNeuralNetworkLibrary.ArtificialNeuralNetwork(weightDimForConvLayer, weightDimForOutputLayer,
networkInputPatchDim, networkOutputPatchDim);
// Adding layers to the network: (Note in the current state these two layers has to be added!)
ANNTrain.InitializeConvolutionalLayer(networkInputPatchDim, nrOfPixelChannels, activationFunctionConvoluted, nrOfFilters, filterDim, filterStride);
ANNTrain.InitializeFullyConnectedOutputLayer(featureMapDim, nrOfFilters, activationFunctionOutput, networkOutputPatchDim, lossFunction);
if (sel1MenuSelection == "1") {
ANNTrain.InitializeRandomWeightAndBiases();
} else if (sel1MenuSelection == "2") {
Dialogs.LoadNeuralNetworkOption(ref ANNTrain);
} else {
System.Console.WriteLine("Wrong input please try again.");
System.Console.WriteLine();
break;
}
System.Console.WriteLine("Select network folder...");
folderDialog.ShowDialog();
networkFolderLocation = folderDialog.SelectedPath + "\\";
System.Console.WriteLine("Selected network folder: " + networkFolderLocation);
System.Console.WriteLine();
Directory.CreateDirectory(networkFolderLocation + "Training set");
Directory.CreateDirectory(networkFolderLocation + "Validation set");
System.Console.WriteLine("Put the corresponding image pairs SatX.bmp and GoldX.bmp X = {1,2...,N} in the");
System.Console.WriteLine("created subfolders \"Training set\" and \"Validation set\" located in the network");
System.Console.WriteLine("folder location.");
System.Console.WriteLine("Press any key to continue when this is done...");
System.Console.ReadKey();
System.Console.WriteLine();
// Get number of image pairs
DirectoryInfo dirInfoTrain = new DirectoryInfo(networkFolderLocation + "Training set");
DirectoryInfo dirInfoValidation = new DirectoryInfo(networkFolderLocation + "Validation set");
nrOfTrainingImagePairs = dirInfoTrain.GetFiles().Length/2;
nrOfValidationImagePairs = dirInfoValidation.GetFiles().Length/2;
System.Console.Write("Enter number of training image pairs which can be loaded in memory: ");
nrOfTrainingImagePairsInMemory = Int32.Parse(System.Console.ReadLine());
System.Console.Write("Enter max number of epochs: ");
maxNrOfEpochs = Int32.Parse(System.Console.ReadLine());
System.Console.Write("Enter max number of minutes: ");
maxNrOfMinutes = Int32.Parse(System.Console.ReadLine());
System.Console.WriteLine();
System.Console.WriteLine("Training algorithm is running...");
System.Console.WriteLine("Press \"esc\" to exit and save network.");
System.Console.WriteLine();
ANNTrain.TrainNetwork(networkFolderLocation, maxNrOfEpochs, maxNrOfMinutes, miniBatchSize, initLearningRate,
finalLearningRate, initMomentum, finalMomentum, weightDecay, calculateErrorInterval, updateMomentumLearningRateInterval,
nrOfTrainingImagePairs, nrOfTrainingImagePairsInMemory, nrOfValidationImagePairs, roadProbabilityThreshold, visuallyCheckTraining);
System.Console.WriteLine();
System.Console.WriteLine("Training complete!");
System.Console.WriteLine("Network parameters saved in: " + networkFolderLocation + "Network parameters");
System.Console.WriteLine();
System.Console.ReadKey();
#endregion
break;
case "2":
// Run saved neural network
#region
ArtificialNeuralNetworkLibrary.ArtificialNeuralNetwork ANNRun =
new ArtificialNeuralNetworkLibrary.ArtificialNeuralNetwork(weightDimForConvLayer, weightDimForOutputLayer,
networkInputPatchDim, networkOutputPatchDim);
// Adding layers to the network: (Note in the current state these two layers has to be added!)
ANNRun.InitializeConvolutionalLayer(networkInputPatchDim, nrOfPixelChannels, activationFunctionConvoluted, nrOfFilters, filterDim, filterStride);
ANNRun.InitializeFullyConnectedOutputLayer(featureMapDim, nrOfFilters, activationFunctionOutput, networkOutputPatchDim, lossFunction);
Dialogs.LoadNeuralNetworkOption(ref ANNRun);
sel2MenuSelection = Menu.Sel2Menu();
sel2MenuSelection2 = Menu.Sel2Menu2();
if ((sel2MenuSelection != "1" && sel2MenuSelection != "2") || (sel2MenuSelection2 != "1" && sel2MenuSelection2 != "2")) {
System.Console.WriteLine("Wrong input please try again.");
System.Console.WriteLine();
break;
}
if (sel2MenuSelection2 == "1") {
runAnotherImage = "1";
while (runAnotherImage == "1") {
inputImagePath = Dialogs.SelectInputImage();
System.Console.WriteLine("Running road detection algorithm...");
if (sel2MenuSelection == "1") {
roadMap = ANNRun.GetRoadBitmap(inputImagePath, roadProbabilityThreshold);
} else {
roadMap = ANNRun.GetRawRoadBitmap(inputImagePath);
}
Dialogs.SaveBitmap(roadMap);
System.Console.WriteLine("Road detection complete!");
System.Console.WriteLine();
runAnotherImage = Menu.RunAnotherImage();
}
} else {
System.Console.WriteLine("Put the images named SatX.bmp X = {1,2...,N} in an input image folder");
inputImageFolder = Dialogs.SelectInputImageFolder();
DirectoryInfo dirInfo = new DirectoryInfo(inputImageFolder);
int nrOfImages = dirInfo.GetFiles("Sat*").Length;
saveImageFolder = Dialogs.SelectSaveImageFolder();
System.Console.WriteLine("Running road detection algorithm...");
for (int i = 1; i <= nrOfImages; ++i) {
if (sel2MenuSelection == "1") {
roadMap = ANNRun.GetRoadBitmap(inputImageFolder + "Sat" + i + ".bmp", roadProbabilityThreshold);
} else {
roadMap = ANNRun.GetRawRoadBitmap(inputImageFolder + "Sat" + i + ".bmp");
}
roadMap.Save(saveImageFolder + "Sat" + i + ".bmp");
}
System.Console.WriteLine("Road detection complete!");
System.Console.WriteLine();
}
System.Console.ReadKey();
#endregion
break;
case "3":
// Run road extraction algorithm
#region
ArtificialNeuralNetworkLibrary.ArtificialNeuralNetwork ANNInit =
new ArtificialNeuralNetworkLibrary.ArtificialNeuralNetwork(weightDimForConvLayer, weightDimForOutputLayer,
networkInputPatchDim, networkOutputPatchDim);
// Adding layers to the network: (Note in the current state these two layers has to be added!)
ANNInit.InitializeConvolutionalLayer(networkInputPatchDim, nrOfPixelChannels, activationFunctionConvoluted, nrOfFilters, filterDim, filterStride);
ANNInit.InitializeFullyConnectedOutputLayer(featureMapDim, nrOfFilters, activationFunctionOutput, networkOutputPatchDim, lossFunction);
ArtificialNeuralNetworkLibrary.ArtificialNeuralNetwork ANNStructure =
new ArtificialNeuralNetworkLibrary.ArtificialNeuralNetwork(weightDimForConvLayer, weightDimForOutputLayer,
networkInputPatchDim, networkOutputPatchDim);
// Adding layers to the network: (Note in the current state these two layers has to be added!)
ANNStructure.InitializeConvolutionalLayer(networkInputPatchDim, nrOfPixelChannels, activationFunctionConvoluted, nrOfFilters, filterDim, filterStride);
ANNStructure.InitializeFullyConnectedOutputLayer(featureMapDim, nrOfFilters, activationFunctionOutput, networkOutputPatchDim, lossFunction);
Dialogs.LoadNeuralNetworkOption(ref ANNInit);
Dialogs.LoadNeuralNetworkOption(ref ANNStructure);
runAnotherImage = "1";
while (runAnotherImage == "1") {
inputImagePath = Dialogs.SelectInputImage();
System.Console.WriteLine("Running road extraction algorithm...");
Bitmap rawRoadMap = ANNInit.GetRawRoadBitmap(inputImagePath);
roadMap = ANNStructure.GetRoadBitmap(rawRoadMap, roadProbabilityThreshold);
System.Console.WriteLine("Road extraction complete!");
System.Console.WriteLine();
Dialogs.SaveBitmap(roadMap);
System.Console.ReadKey();
runAnotherImage = Menu.RunAnotherImage();
}
#endregion
break;
case "4":
// Testing scripts:
#region
ArtificialNeuralNetworkLibrary.ArtificialNeuralNetwork ANNTest =
new ArtificialNeuralNetworkLibrary.ArtificialNeuralNetwork(weightDimForConvLayer, weightDimForOutputLayer,
networkInputPatchDim, networkOutputPatchDim);
// Adding layers to the network: (Note in the current state these two layers has to be added!)
ANNTest.InitializeConvolutionalLayer(networkInputPatchDim, nrOfPixelChannels, activationFunctionConvoluted, nrOfFilters, filterDim, filterStride);
ANNTest.InitializeFullyConnectedOutputLayer(featureMapDim, nrOfFilters, activationFunctionOutput, networkOutputPatchDim, lossFunction);
System.Console.WriteLine("Testing the backpropagation of the network using finite difference calculus on \none random input patch.");
System.Console.WriteLine("A relative error less then 1e-04 is good as epsilon gets smaller.");
System.Console.WriteLine();
inputImagePath = Dialogs.SelectInputImage();
goldImagePath = Dialogs.SelectGoldImage();
ANNTest.InitializeRandomWeightAndBiases();
ANNTest.TestNetwork(inputImagePath, goldImagePath);
System.Console.WriteLine("Testing complete!");
System.Console.WriteLine();
System.Console.ReadKey();
#endregion
break;
case "5":
//Data processing:
#region
sel5MenuSelection = Menu.Sel5Menu();
if (sel5MenuSelection == "1") {
// Preprocess google images
#region
System.Console.WriteLine("Put the Google image pairs named SatX.bmp and GoldX.bmp X = {1,2...,N}");
System.Console.WriteLine("in an input folder.");
System.Console.WriteLine("Press any key to continue when this is done...");
System.Console.ReadKey();
System.Console.WriteLine();
inputImageFolder = Dialogs.SelectInputImageFolder();
saveImageFolder = Dialogs.SelectSaveImageFolder();
DirectoryInfo dirInfo = new DirectoryInfo(inputImageFolder);
nrOfImagePairs = dirInfo.GetFiles().Length / 2;
System.Console.Write("Enter number of boarder pixels to remove for the Google logo: ");
int googleLogoCropSize = Int32.Parse(System.Console.ReadLine());
System.Console.Write("Do you want to randomly rotate the images (y/n): ");
bool rotateImages = (System.Console.ReadLine() == "y") ? true : false;
MiscLibrary.ImageProcessor.PreprocessGoogleMapImages(inputImageFolder, saveImageFolder, nrOfImagePairs,
googleLogoCropSize, networkOutputPatchDim, rotateImages);
System.Console.WriteLine("Preprocessing complete!");
System.Console.WriteLine();
#endregion
} else if (sel5MenuSelection == "2") {
// Crop images
#region
sel5MenuSelection2 = Menu.Sel5Menu2();
System.Console.Write("Enter crop height: ");
int cropWidth = Int32.Parse(System.Console.ReadLine());
System.Console.Write("Enter crop width: ");
int cropHeight = Int32.Parse(System.Console.ReadLine());
if (sel5MenuSelection2 == "1") {
runAnotherImage = "1";
while (runAnotherImage == "1") {
inputImagePath = Dialogs.SelectInputImage();
Bitmap cropBitmap = new Bitmap(inputImagePath);
cropBitmap = MiscLibrary.ImageProcessor.CropBitmap(cropBitmap, cropWidth, cropHeight);
Dialogs.SaveBitmap(cropBitmap);
System.Console.WriteLine("Cropping complete!");
System.Console.WriteLine();
runAnotherImage = Menu.RunAnotherImage();
}
} else if (sel5MenuSelection2 == "2") {
System.Console.Write("Enter image prefixes: ");
string imgPrefix = System.Console.ReadLine();
System.Console.WriteLine("Put the images that are to be cropped in an input image folder.");
System.Console.WriteLine("Name the images as " + imgPrefix + "X.bmp where X = {1,2...,N}.");
inputImageFolder = Dialogs.SelectInputImageFolder();
DirectoryInfo dirInfo = new DirectoryInfo(inputImageFolder);
int nrOfImages = dirInfo.GetFiles(imgPrefix + "*").Length;
saveImageFolder = Dialogs.SelectSaveImageFolder();
for (int i = 1; i <= nrOfImages; ++i) {
Bitmap cropBitmap = new Bitmap(inputImageFolder + imgPrefix + i + ".bmp");
cropBitmap = MiscLibrary.ImageProcessor.CropBitmap(cropBitmap, cropWidth, cropHeight);
cropBitmap.Save(saveImageFolder + imgPrefix + i + ".bmp");
}
System.Console.WriteLine("Cropping complete!");
System.Console.WriteLine();
} else {
System.Console.WriteLine("Wrong menu input please try again.");
System.Console.WriteLine();
}
#endregion
} else if (sel5MenuSelection == "3") {
// Rename bitmaps
#region
System.Console.Write("Enter image prefixes: ");
string imgPrefix = System.Console.ReadLine();
System.Console.WriteLine("Put the images that are to be renamed in an input image folder.");
inputImageFolder = Dialogs.SelectInputImageFolder();
saveImageFolder = Dialogs.SelectSaveImageFolder();
System.Console.WriteLine("The images will be named as " + imgPrefix + "X.bmp where X = {1,2...,N}");
DirectoryInfo dirInfo = new DirectoryInfo(inputImageFolder);
FileInfo[] fileInfo = dirInfo.GetFiles(imgPrefix + "*");
int counter = 1;
foreach(FileInfo f in fileInfo) {
File.Move(f.FullName, saveImageFolder + imgPrefix + counter + ".bmp");
++counter;
}
System.Console.WriteLine("Bitmap renaming complete!");
#endregion
} else {
System.Console.WriteLine("Wrong input please try again.");
System.Console.WriteLine();
}
System.Console.ReadKey();
#endregion
break;
case "6":
//Evaluate road map
#region
inputImagePath = Dialogs.SelectInputImage();
goldImagePath = Dialogs.SelectGoldImage();
Bitmap inputImage = new Bitmap(inputImagePath);
int classificationError = MiscLibrary.ImageProcessor.CalculateClassificationError(inputImagePath, goldImagePath);
int[] evaluationArray = MiscLibrary.ImageProcessor.EvaluateRoadMap(inputImagePath, goldImagePath);
// evaluationArray = {totNrOfRoadPixels, correctRoadPixels, incorrectRoadPixels,
// totNrOfNonRoadPixels, correctNonRoadPixels, incorrectNonRoadPixels}
System.Console.WriteLine("Evaluation:");
System.Console.WriteLine("Total number of pixels: {0}", evaluationArray[0] + evaluationArray[3]);
System.Console.WriteLine("Classification error: {0}, {1}%", classificationError, 100 * (double)classificationError / (evaluationArray[0] + evaluationArray[3]));
System.Console.WriteLine("Total number of road pixels: {0}", evaluationArray[0]);
System.Console.WriteLine("Correct classified road pixels: {0}, {1}%", evaluationArray[1], 100 * (double)evaluationArray[1] / evaluationArray[0]);
System.Console.WriteLine("Incorrect classified road pixels: {0}", evaluationArray[2]);
System.Console.WriteLine("Total number of non road pixels: {0}", evaluationArray[3]);
System.Console.WriteLine("Correct classified non road pixels: {0}, {1}%", evaluationArray[4], 100 * (double)evaluationArray[4] / evaluationArray[3]);
System.Console.WriteLine("Incorrect classified non road pixels: {0}%", evaluationArray[5]);
System.Console.WriteLine();
double precision = (double)evaluationArray[1] / (evaluationArray[1] + evaluationArray[5]);
double recall = (double)evaluationArray[1] / (evaluationArray[1] + evaluationArray[2]);
double quality = (double)evaluationArray[1] / (evaluationArray[1] + evaluationArray[2] + evaluationArray[5]);
double accuracy = (double)(evaluationArray[1] + evaluationArray[4]) / (evaluationArray[0] + evaluationArray[3]);
System.Console.WriteLine("Precision: {0}", precision);
System.Console.WriteLine("Recall: {0}", recall);
System.Console.WriteLine("Quality: {0}", quality);
System.Console.WriteLine("Accuracy: {0}", accuracy);
System.Console.WriteLine("Evaluation complete!");
System.Console.ReadKey();
#endregion
break;
case "7":
// Bye Bye
#region
System.Console.WriteLine("Hasta la vista, baby!");
//goodbyePlayer.Play();
System.Console.ReadKey();
#endregion
return;
default:
// Input error
#region
System.Console.WriteLine("Wrong input, please try again.");
System.Console.WriteLine();
System.Console.ReadKey();
#endregion
break;
}
}
#endregion
}
static void Main(string[] args)
{
// Network Parameters:
#region
int nrOfPixelChannels = 3; // i.e. RGB
int networkInputPatchDim = 64; // 64 in report, Has to be a multiple of the networkOutputDim.
int networkOutputPatchDim = 16; // 16 in report
int nrOfOutputs = networkOutputPatchDim * networkOutputPatchDim;
int filterDim = 12; // 12 in report
int nrOfFilters = 80; // 64 in report
int filterStride = 4; // 4 in report
int featureMapDim = (networkInputPatchDim - filterDim) / filterStride + 1;
int[] weightDimForConvLayer = { nrOfFilters, filterDim, filterDim, nrOfPixelChannels }; // W(f,a,b,ff): (a,b,ff) = filter position, f = feature map index next layer;
int[] weightDimForOutputLayer = { nrOfOutputs, featureMapDim, featureMapDim, nrOfFilters }; // W(k,i,j,f): (i,j,f) = neuron position input layer, k = neuron index this layer;
string activationFunctionConvoluted = "relu"; // relu in report
string activationFunctionOutput = "sigmoid"; // sigmoid in report
string lossFunction = "crossentropy"; // crossentropy in report
#endregion
// Training parameters:
#region
// Due to rotation and edge effects (difference between network input and output dim) the output image will be
// smaller. At least by a factor of sqrt(2)/2 due to the rotation
int nrOfTrainingImagePairs, nrOfTrainingImagePairsInMemory, nrOfValidationImagePairs, nrOfImagePairs;
bool visuallyCheckTraining = true;
// Termination conditions:
int maxNrOfEpochs;
int maxNrOfMinutes;
int miniBatchSize = 100;
int calculateErrorInterval = 1;
int updateMomentumLearningRateInterval = 2;
double initLearningRate = 2.5 * Math.Pow(10, -6);; // constant 0.0005 in report, 0.000005 worked for single image (841) mini batch size
double finalLearningRate = 3.125 * Math.Pow(10, -7);
double initMomentum = 0.6; // constant 0.9 in report
double finalMomentum = 0.9;
double weightDecay = 0.0001; // 0.0002 in report
#endregion
// Other parameters:
#region
double roadProbabilityThreshold = 0.5;
#endregion
// Main menu:
#region
string mainMenuSelection, sel1MenuSelection, sel2MenuSelection, sel2MenuSelection2, sel5MenuSelection, sel5MenuSelection2,
inputImagePath, goldImagePath, networkFolderLocation, inputImageFolder, saveImageFolder, runAnotherImage;
OpenFileDialog fileDialog = new OpenFileDialog();
SaveFileDialog saveDialog = new SaveFileDialog();
FolderBrowserDialog folderDialog = new FolderBrowserDialog();
Bitmap roadMap;
bool displayMenu = true;
string currentDirectory = Directory.GetCurrentDirectory();
//System.Media.SoundPlayer goodbyePlayer = new System.Media.SoundPlayer(currentDirectory + "\\Audio\\" + "t2_hasta_la_vista.wav");
while (displayMenu)
{
// Main header:
#region
System.Console.WriteLine("////// ////// //// ////// //////// // // //////////");
System.Console.WriteLine("// // // // // // // // // // // // ");
System.Console.WriteLine("// // // // // // // // // // // // ");
System.Console.WriteLine("///// // // ////////// // // ////// //// // ");
System.Console.WriteLine("// // // // // // // // // // // // ");
System.Console.WriteLine("// // // // // // // // // // // // ");
System.Console.WriteLine("// // ////// // // ////// //////// // // // ");
System.Console.WriteLine("©2015\n");
#endregion
mainMenuSelection = Menu.MainMenu();
switch (mainMenuSelection)
{
case "1":
// Training a network:
#region
sel1MenuSelection = Menu.Sel1Menu();
ArtificialNeuralNetworkLibrary.ArtificialNeuralNetwork ANNTrain =
new ArtificialNeuralNetworkLibrary.ArtificialNeuralNetwork(weightDimForConvLayer, weightDimForOutputLayer,
networkInputPatchDim, networkOutputPatchDim);
// Adding layers to the network: (Note in the current state these two layers has to be added!)
ANNTrain.InitializeConvolutionalLayer(networkInputPatchDim, nrOfPixelChannels, activationFunctionConvoluted, nrOfFilters, filterDim, filterStride);
ANNTrain.InitializeFullyConnectedOutputLayer(featureMapDim, nrOfFilters, activationFunctionOutput, networkOutputPatchDim, lossFunction);
if (sel1MenuSelection == "1")
{
ANNTrain.InitializeRandomWeightAndBiases();
}
else if (sel1MenuSelection == "2")
{
Dialogs.LoadNeuralNetworkOption(ref ANNTrain);
}
else
{
System.Console.WriteLine("Wrong input please try again.");
System.Console.WriteLine();
break;
}
System.Console.WriteLine("Select network folder...");
folderDialog.ShowDialog();
networkFolderLocation = folderDialog.SelectedPath + "\\";
System.Console.WriteLine("Selected network folder: " + networkFolderLocation);
System.Console.WriteLine();
Directory.CreateDirectory(networkFolderLocation + "Training set");
Directory.CreateDirectory(networkFolderLocation + "Validation set");
System.Console.WriteLine("Put the corresponding image pairs SatX.bmp and GoldX.bmp X = {1,2...,N} in the");
System.Console.WriteLine("created subfolders \"Training set\" and \"Validation set\" located in the network");
System.Console.WriteLine("folder location.");
System.Console.WriteLine("Press any key to continue when this is done...");
System.Console.ReadKey();
System.Console.WriteLine();
// Get number of image pairs
DirectoryInfo dirInfoTrain = new DirectoryInfo(networkFolderLocation + "Training set");
DirectoryInfo dirInfoValidation = new DirectoryInfo(networkFolderLocation + "Validation set");
nrOfTrainingImagePairs = dirInfoTrain.GetFiles().Length / 2;
nrOfValidationImagePairs = dirInfoValidation.GetFiles().Length / 2;
System.Console.Write("Enter number of training image pairs which can be loaded in memory: ");
nrOfTrainingImagePairsInMemory = Int32.Parse(System.Console.ReadLine());
System.Console.Write("Enter max number of epochs: ");
maxNrOfEpochs = Int32.Parse(System.Console.ReadLine());
System.Console.Write("Enter max number of minutes: ");
maxNrOfMinutes = Int32.Parse(System.Console.ReadLine());
System.Console.WriteLine();
System.Console.WriteLine("Training algorithm is running...");
System.Console.WriteLine("Press \"esc\" to exit and save network.");
System.Console.WriteLine();
ANNTrain.TrainNetwork(networkFolderLocation, maxNrOfEpochs, maxNrOfMinutes, miniBatchSize, initLearningRate,
finalLearningRate, initMomentum, finalMomentum, weightDecay, calculateErrorInterval, updateMomentumLearningRateInterval,
nrOfTrainingImagePairs, nrOfTrainingImagePairsInMemory, nrOfValidationImagePairs, roadProbabilityThreshold, visuallyCheckTraining);
System.Console.WriteLine();
System.Console.WriteLine("Training complete!");
System.Console.WriteLine("Network parameters saved in: " + networkFolderLocation + "Network parameters");
System.Console.WriteLine();
System.Console.ReadKey();
#endregion
break;
case "2":
// Run saved neural network
#region
ArtificialNeuralNetworkLibrary.ArtificialNeuralNetwork ANNRun =
new ArtificialNeuralNetworkLibrary.ArtificialNeuralNetwork(weightDimForConvLayer, weightDimForOutputLayer,
networkInputPatchDim, networkOutputPatchDim);
// Adding layers to the network: (Note in the current state these two layers has to be added!)
ANNRun.InitializeConvolutionalLayer(networkInputPatchDim, nrOfPixelChannels, activationFunctionConvoluted, nrOfFilters, filterDim, filterStride);
ANNRun.InitializeFullyConnectedOutputLayer(featureMapDim, nrOfFilters, activationFunctionOutput, networkOutputPatchDim, lossFunction);
Dialogs.LoadNeuralNetworkOption(ref ANNRun);
sel2MenuSelection = Menu.Sel2Menu();
sel2MenuSelection2 = Menu.Sel2Menu2();
if ((sel2MenuSelection != "1" && sel2MenuSelection != "2") || (sel2MenuSelection2 != "1" && sel2MenuSelection2 != "2"))
{
System.Console.WriteLine("Wrong input please try again.");
System.Console.WriteLine();
break;
}
if (sel2MenuSelection2 == "1")
{
runAnotherImage = "1";
while (runAnotherImage == "1")
{
inputImagePath = Dialogs.SelectInputImage();
System.Console.WriteLine("Running road detection algorithm...");
if (sel2MenuSelection == "1")
{
roadMap = ANNRun.GetRoadBitmap(inputImagePath, roadProbabilityThreshold);
}
else
{
roadMap = ANNRun.GetRawRoadBitmap(inputImagePath);
}
Dialogs.SaveBitmap(roadMap);
System.Console.WriteLine("Road detection complete!");
System.Console.WriteLine();
runAnotherImage = Menu.RunAnotherImage();
}
}
else
{
System.Console.WriteLine("Put the images named SatX.bmp X = {1,2...,N} in an input image folder");
inputImageFolder = Dialogs.SelectInputImageFolder();
DirectoryInfo dirInfo = new DirectoryInfo(inputImageFolder);
int nrOfImages = dirInfo.GetFiles("Sat*").Length;
saveImageFolder = Dialogs.SelectSaveImageFolder();
System.Console.WriteLine("Running road detection algorithm...");
for (int i = 1; i <= nrOfImages; ++i)
{
if (sel2MenuSelection == "1")
{
roadMap = ANNRun.GetRoadBitmap(inputImageFolder + "Sat" + i + ".bmp", roadProbabilityThreshold);
}
else
{
roadMap = ANNRun.GetRawRoadBitmap(inputImageFolder + "Sat" + i + ".bmp");
}
roadMap.Save(saveImageFolder + "Sat" + i + ".bmp");
}
System.Console.WriteLine("Road detection complete!");
System.Console.WriteLine();
}
System.Console.ReadKey();
#endregion
break;
case "3":
// Run road extraction algorithm
#region
ArtificialNeuralNetworkLibrary.ArtificialNeuralNetwork ANNInit =
new ArtificialNeuralNetworkLibrary.ArtificialNeuralNetwork(weightDimForConvLayer, weightDimForOutputLayer,
networkInputPatchDim, networkOutputPatchDim);
// Adding layers to the network: (Note in the current state these two layers has to be added!)
ANNInit.InitializeConvolutionalLayer(networkInputPatchDim, nrOfPixelChannels, activationFunctionConvoluted, nrOfFilters, filterDim, filterStride);
ANNInit.InitializeFullyConnectedOutputLayer(featureMapDim, nrOfFilters, activationFunctionOutput, networkOutputPatchDim, lossFunction);
ArtificialNeuralNetworkLibrary.ArtificialNeuralNetwork ANNStructure =
new ArtificialNeuralNetworkLibrary.ArtificialNeuralNetwork(weightDimForConvLayer, weightDimForOutputLayer,
networkInputPatchDim, networkOutputPatchDim);
// Adding layers to the network: (Note in the current state these two layers has to be added!)
ANNStructure.InitializeConvolutionalLayer(networkInputPatchDim, nrOfPixelChannels, activationFunctionConvoluted, nrOfFilters, filterDim, filterStride);
ANNStructure.InitializeFullyConnectedOutputLayer(featureMapDim, nrOfFilters, activationFunctionOutput, networkOutputPatchDim, lossFunction);
Dialogs.LoadNeuralNetworkOption(ref ANNInit);
Dialogs.LoadNeuralNetworkOption(ref ANNStructure);
runAnotherImage = "1";
while (runAnotherImage == "1")
{
inputImagePath = Dialogs.SelectInputImage();
System.Console.WriteLine("Running road extraction algorithm...");
Bitmap rawRoadMap = ANNInit.GetRawRoadBitmap(inputImagePath);
roadMap = ANNStructure.GetRoadBitmap(rawRoadMap, roadProbabilityThreshold);
System.Console.WriteLine("Road extraction complete!");
System.Console.WriteLine();
Dialogs.SaveBitmap(roadMap);
System.Console.ReadKey();
runAnotherImage = Menu.RunAnotherImage();
}
#endregion
break;
case "4":
// Testing scripts:
#region
ArtificialNeuralNetworkLibrary.ArtificialNeuralNetwork ANNTest =
new ArtificialNeuralNetworkLibrary.ArtificialNeuralNetwork(weightDimForConvLayer, weightDimForOutputLayer,
networkInputPatchDim, networkOutputPatchDim);
// Adding layers to the network: (Note in the current state these two layers has to be added!)
ANNTest.InitializeConvolutionalLayer(networkInputPatchDim, nrOfPixelChannels, activationFunctionConvoluted, nrOfFilters, filterDim, filterStride);
ANNTest.InitializeFullyConnectedOutputLayer(featureMapDim, nrOfFilters, activationFunctionOutput, networkOutputPatchDim, lossFunction);
System.Console.WriteLine("Testing the backpropagation of the network using finite difference calculus on \none random input patch.");
System.Console.WriteLine("A relative error less then 1e-04 is good as epsilon gets smaller.");
System.Console.WriteLine();
inputImagePath = Dialogs.SelectInputImage();
goldImagePath = Dialogs.SelectGoldImage();
ANNTest.InitializeRandomWeightAndBiases();
ANNTest.TestNetwork(inputImagePath, goldImagePath);
System.Console.WriteLine("Testing complete!");
System.Console.WriteLine();
System.Console.ReadKey();
#endregion
break;
case "5":
//Data processing:
#region
sel5MenuSelection = Menu.Sel5Menu();
if (sel5MenuSelection == "1")
{
// Preprocess google images
#region
System.Console.WriteLine("Put the Google image pairs named SatX.bmp and GoldX.bmp X = {1,2...,N}");
System.Console.WriteLine("in an input folder.");
System.Console.WriteLine("Press any key to continue when this is done...");
System.Console.ReadKey();
System.Console.WriteLine();
inputImageFolder = Dialogs.SelectInputImageFolder();
saveImageFolder = Dialogs.SelectSaveImageFolder();
DirectoryInfo dirInfo = new DirectoryInfo(inputImageFolder);
nrOfImagePairs = dirInfo.GetFiles().Length / 2;
System.Console.Write("Enter number of boarder pixels to remove for the Google logo: ");
int googleLogoCropSize = Int32.Parse(System.Console.ReadLine());
System.Console.Write("Do you want to randomly rotate the images (y/n): ");
bool rotateImages = (System.Console.ReadLine() == "y") ? true : false;
MiscLibrary.ImageProcessor.PreprocessGoogleMapImages(inputImageFolder, saveImageFolder, nrOfImagePairs,
googleLogoCropSize, networkOutputPatchDim, rotateImages);
System.Console.WriteLine("Preprocessing complete!");
System.Console.WriteLine();
#endregion
}
else if (sel5MenuSelection == "2")
{
// Crop images
#region
sel5MenuSelection2 = Menu.Sel5Menu2();
System.Console.Write("Enter crop height: ");
int cropWidth = Int32.Parse(System.Console.ReadLine());
System.Console.Write("Enter crop width: ");
int cropHeight = Int32.Parse(System.Console.ReadLine());
if (sel5MenuSelection2 == "1")
{
runAnotherImage = "1";
while (runAnotherImage == "1")
{
inputImagePath = Dialogs.SelectInputImage();
Bitmap cropBitmap = new Bitmap(inputImagePath);
cropBitmap = MiscLibrary.ImageProcessor.CropBitmap(cropBitmap, cropWidth, cropHeight);
Dialogs.SaveBitmap(cropBitmap);
System.Console.WriteLine("Cropping complete!");
System.Console.WriteLine();
runAnotherImage = Menu.RunAnotherImage();
}
}
else if (sel5MenuSelection2 == "2")
{
System.Console.Write("Enter image prefixes: ");
string imgPrefix = System.Console.ReadLine();
System.Console.WriteLine("Put the images that are to be cropped in an input image folder.");
System.Console.WriteLine("Name the images as " + imgPrefix + "X.bmp where X = {1,2...,N}.");
inputImageFolder = Dialogs.SelectInputImageFolder();
DirectoryInfo dirInfo = new DirectoryInfo(inputImageFolder);
int nrOfImages = dirInfo.GetFiles(imgPrefix + "*").Length;
saveImageFolder = Dialogs.SelectSaveImageFolder();
for (int i = 1; i <= nrOfImages; ++i)
{
Bitmap cropBitmap = new Bitmap(inputImageFolder + imgPrefix + i + ".bmp");
cropBitmap = MiscLibrary.ImageProcessor.CropBitmap(cropBitmap, cropWidth, cropHeight);
cropBitmap.Save(saveImageFolder + imgPrefix + i + ".bmp");
}
System.Console.WriteLine("Cropping complete!");
System.Console.WriteLine();
}
else
{
System.Console.WriteLine("Wrong menu input please try again.");
System.Console.WriteLine();
}
#endregion
}
else if (sel5MenuSelection == "3")
{
// Rename bitmaps
#region
System.Console.Write("Enter image prefixes: ");
string imgPrefix = System.Console.ReadLine();
System.Console.WriteLine("Put the images that are to be renamed in an input image folder.");
inputImageFolder = Dialogs.SelectInputImageFolder();
saveImageFolder = Dialogs.SelectSaveImageFolder();
System.Console.WriteLine("The images will be named as " + imgPrefix + "X.bmp where X = {1,2...,N}");
DirectoryInfo dirInfo = new DirectoryInfo(inputImageFolder);
FileInfo[] fileInfo = dirInfo.GetFiles(imgPrefix + "*");
int counter = 1;
foreach (FileInfo f in fileInfo)
{
File.Move(f.FullName, saveImageFolder + imgPrefix + counter + ".bmp");
++counter;
}
System.Console.WriteLine("Bitmap renaming complete!");
#endregion
}
else
{
System.Console.WriteLine("Wrong input please try again.");
System.Console.WriteLine();
}
System.Console.ReadKey();
#endregion
break;
case "6":
//Evaluate road map
#region
inputImagePath = Dialogs.SelectInputImage();
goldImagePath = Dialogs.SelectGoldImage();
Bitmap inputImage = new Bitmap(inputImagePath);
int classificationError = MiscLibrary.ImageProcessor.CalculateClassificationError(inputImagePath, goldImagePath);
int[] evaluationArray = MiscLibrary.ImageProcessor.EvaluateRoadMap(inputImagePath, goldImagePath);
// evaluationArray = {totNrOfRoadPixels, correctRoadPixels, incorrectRoadPixels,
// totNrOfNonRoadPixels, correctNonRoadPixels, incorrectNonRoadPixels}
System.Console.WriteLine("Evaluation:");
System.Console.WriteLine("Total number of pixels: {0}", evaluationArray[0] + evaluationArray[3]);
System.Console.WriteLine("Classification error: {0}, {1}%", classificationError, 100 * (double)classificationError / (evaluationArray[0] + evaluationArray[3]));
System.Console.WriteLine("Total number of road pixels: {0}", evaluationArray[0]);
System.Console.WriteLine("Correct classified road pixels: {0}, {1}%", evaluationArray[1], 100 * (double)evaluationArray[1] / evaluationArray[0]);
System.Console.WriteLine("Incorrect classified road pixels: {0}", evaluationArray[2]);
System.Console.WriteLine("Total number of non road pixels: {0}", evaluationArray[3]);
System.Console.WriteLine("Correct classified non road pixels: {0}, {1}%", evaluationArray[4], 100 * (double)evaluationArray[4] / evaluationArray[3]);
System.Console.WriteLine("Incorrect classified non road pixels: {0}%", evaluationArray[5]);
System.Console.WriteLine();
double precision = (double)evaluationArray[1] / (evaluationArray[1] + evaluationArray[5]);
double recall = (double)evaluationArray[1] / (evaluationArray[1] + evaluationArray[2]);
double quality = (double)evaluationArray[1] / (evaluationArray[1] + evaluationArray[2] + evaluationArray[5]);
double accuracy = (double)(evaluationArray[1] + evaluationArray[4]) / (evaluationArray[0] + evaluationArray[3]);
System.Console.WriteLine("Precision: {0}", precision);
System.Console.WriteLine("Recall: {0}", recall);
System.Console.WriteLine("Quality: {0}", quality);
System.Console.WriteLine("Accuracy: {0}", accuracy);
System.Console.WriteLine("Evaluation complete!");
System.Console.ReadKey();
#endregion
break;
case "7":
// Bye Bye
#region
System.Console.WriteLine("Hasta la vista, baby!");
//goodbyePlayer.Play();
System.Console.ReadKey();
#endregion
return;
default:
// Input error
#region
System.Console.WriteLine("Wrong input, please try again.");
System.Console.WriteLine();
System.Console.ReadKey();
#endregion
break;
}
}
#endregion
}