public override void Show()
{
base.Show();
arrivedInTime = true;
imageRecognizer = FindObjectOfType <ImageRecognizer>();
Trip trip = GameManager.Instance.Player.Trip;
fromTo.text = trip.CurrentCity.Name + " - " + trip.CurrentTransport.Option.To.Name;
LoadTransportIcon();
scanMarkerButton.SetActive(false);
SetupEventGroup();
eventGroup.Start();
if (eventGroup.GetRemainingSeconds() > 25)
{
//MinigameController.Instance.LoadGame();
quizScreen.SetActive(true);
LoadQuestion();
minigameSuccess = false;
minigamePlaying = false;
}
else
{
minigamePlaying = false;
minigameSuccess = true;
}
}
/// <summary>
/// Construct the training data for the combo recognizer.
/// </summary>
/// <param name="testData">the data to test on</param>
/// <param name="rubine">the rubine recognizer</param>
/// <param name="dollar">the dollar recognizer</param>
/// <param name="zernike">the zernike recognizer</param>
/// <param name="image">the image recognizer</param>
/// <returns>a list of tuples (s,f) where s is a shape type and f is a set of features</returns>
private static List <KeyValuePair <ShapeType, Dictionary <string, object> > > TrainingDataCombo(
List <Shape> testData,
RubineRecognizerUpdateable rubine,
DollarRecognizer dollar,
ZernikeMomentRecognizerUpdateable zernike,
ImageRecognizer image)
{
List <KeyValuePair <ShapeType, Dictionary <string, object> > > data = new List <KeyValuePair <ShapeType, Dictionary <string, object> > >();
foreach (Shape shape in testData)
{
string z = zernike.Recognize(shape.SubstrokesL);
List <ShapeType> img = image.Recognize(shape.SubstrokesL);
List <string> r = new List <string>();
List <string> dAvg = new List <string>();
List <string> d = new List <string>();
foreach (Substroke s in shape.SubstrokesL)
{
r.Add(rubine.Recognize(s));
dAvg.Add(dollar.RecognizeAverage(s));
d.Add(dollar.Recognize(s));
}
Dictionary <string, object> features = ComboRecognizer.GetFeatures(shape.SubstrokesL.Count, z, img, r, dAvg, d);
data.Add(new KeyValuePair <ShapeType, Dictionary <string, object> >(shape.Type, features));
}
return(data);
}
public async Task <ImageInfoGet> Put([FromBody] object imageForServer)
{
Console.Out.WriteLine("Put");
var des = JsonConvert.DeserializeObject <ImageInfoGet>(imageForServer.ToString());
var tmp = new ImageInfoGet();
var image = dB.GetImage(des.Path, des.JpegCover);
if (image == null)
{
ImageRecognizer imageRecognizer = new ImageRecognizer();
var info = imageRecognizer.RecognizeImage(Convert.FromBase64String(des.JpegCover), des.Path);
tmp.Class = info.className[0];
tmp.Confidence = info.confidence[0];
tmp.Name = des.Name;
tmp.Path = des.Path;
tmp.JpegCover = des.JpegCover;
dB.AddToDB(tmp);
return(tmp);
}
return(image);
}
static void Main(string[] args)
{
DirectoryInfo myDirectory = new DirectoryInfo(args.FirstOrDefault() ?? "images");
ImageRecognizer obj = new ImageRecognizer(myDirectory, new WriteToConsole());
var enterThread = new Thread(new ThreadStart(() =>
{
while (Console.ReadKey().Key != ConsoleKey.Enter)
{
}
ImageRecognizer.cancelTokenSource.Cancel();
}));
enterThread.Start();
obj.GetResults();
// foreach (var item in obj.result)
// {
// Console.WriteLine("Predicting contents of image...");
// Console.Write(item);
// Console.WriteLine();
// }
System.Environment.Exit(0);
}
public string DetectImage(byte[] img_bytes, string path)
{
Tuple <string, string> class_and_counter;
string recognizedClass, counter;
using (var context = new ImageContext())
{
class_and_counter = context.TryGetRecognizedImage(img_bytes);
}
if (class_and_counter != null)
{
recognizedClass = class_and_counter.Item1;
counter = class_and_counter.Item2;
}
else
{
using (var ms = new MemoryStream(img_bytes))
{
Image img = Image.FromStream(ms);
var recognizer = new ImageRecognizer();
recognizedClass = recognizer.DetectImageClassSync(img, modelPath);
counter = "0";
}
using (var context = new ImageContext())
{
context.SaveRecognizedImage(img_bytes, recognizedClass);
}
}
var response = new JsonObject();
response.Add("class", recognizedClass);
response.Add("db_queries", counter);
response.Add("image_path", path);
return(response.ToString());
}
private static ComboRecognizer CreateCombo(string user, string dir)
{
string dollarStr = dir + "\\Dollar" + user + ".dr";
bool dollarBool = File.Exists(dollarStr);
string rubineStr = dir + "\\RubineLite" + user + ".rr";
bool rubineBool = File.Exists(rubineStr);
string zernikeStr = dir + "\\ZernikeLite" + user + ".zr";
bool zernikeBool = File.Exists(zernikeStr);
string imageStr = dir + "\\Image" + user + ".ir";
bool imageBool = File.Exists(imageStr);
if (!(dollarBool && rubineBool && zernikeBool && imageBool))
{
return(null);
}
DollarRecognizer dr = DollarRecognizer.Load(dollarStr);
RubineRecognizer rr = RubineRecognizer.Load(rubineStr);
ZernikeMomentRecognizer zr = ZernikeMomentRecognizer.Load(zernikeStr);
ImageRecognizer ir = ImageRecognizer.Load(imageStr);
ComboRecognizer combo = new ComboRecognizer(rr, dr, zr, ir);
return(combo);
}
/// <summary>
/// Constructor which takes in pre-trained recognizers, as well
/// as a pre-trained Classifier (Naive Bayes)
/// </summary>
/// <param name="rubine">Pre-Trained Rubine Recognizer</param>
/// <param name="dollar">Pre-Trained Dollar Recognizer</param>
/// <param name="zernike">Pre-Trained Zernike Recognizer</param>
/// <param name="image">Pre-Trained Image Recognizer</param>
/// <param name="classifier">Pre-Trained NaiveBayes Classifier</param>
public ComboRecognizer(RubineRecognizer rubine, DollarRecognizer dollar, ZernikeMomentRecognizer zernike, ImageRecognizer image, NaiveBayesUpdateable classifier)
{
_rubine = rubine;
_dollar = dollar;
_zernike = zernike;
_image = image;
_comboClassifier = classifier;
}
public ImageSaver(Activity activity, Context context, Camera2Fragment owner, Image image)
{
this.owner = owner;
this.context = context;
this.activity = activity;
imageRecognizer = new ImageRecognizer(context, activity);
mImage = image ?? throw new System.ArgumentNullException("image");
}
/// <summary>
/// Deserialization Constructor
/// </summary>
/// <param name="info"></param>
/// <param name="ctxt"></param>
public ComboRecognizer(SerializationInfo info, StreamingContext ctxt)
{
//Get the values from info and assign them to the appropriate properties
_dollar = (DollarRecognizer)info.GetValue("dollar", typeof(DollarRecognizer));
_image = (ImageRecognizer)info.GetValue("image", typeof(ImageRecognizer));
_rubine = (RubineRecognizer)info.GetValue("rubine", typeof(RubineRecognizer));
_zernike = (ZernikeMomentRecognizer)info.GetValue("zernike", typeof(ZernikeMomentRecognizer));
_comboClassifier = (NaiveBayesUpdateable)info.GetValue("comboClassifier", typeof(NaiveBayesUpdateable));
}
public override void OnInspectorGUI()
{
DrawDefaultInspector();
ImageRecognizer script = (ImageRecognizer)target;
if (GUILayout.Button("UploadImage"))
{
script.UpdloadScreen();
}
}
private void button2_Click(object sender, EventArgs e)
{
image = new ImageRecognizer((Bitmap)pictureBox1.Image, (byte)separateValue.Value, 0);
image.TransformToBlackAndWhite();
pictureBox2.Image = image.OutputImage;
image.RecognizePixelsToGroups();
image.PaintFromGroupMap();
pictureBox3.Image = image.OutputImage;
DrawHistogram(zedGraphControl2, pictureBox1);
}
static async Task Main(string[] args)
{
string imagesPath = (args.Length == 0) ? @"D:\Pictires\Desktop" : args[0];
ImageRecognizer.onnxModelPath = (args.Length == 2) ? args[1]: "resnet34-v1-7.onnx";
ImageRecognizer.ImageRecognizerResultUpdate += OutputRecognitionHandler;
await ImageRecognizer.RecognitionAsync(imagesPath);
}
private void button2_Click(object sender, EventArgs e)
{
image = new ImageRecognizer((Bitmap)pictureBox1.Image, (byte)separateValue.Value, (int)MinSquare.Value, (int)KMedianNumber.Value, (double)areaCoefficient.Value,
(double)perimeterCoefficient.Value, (double)elongationCoefficient.Value, (double)DensityCoefficient.Value, (double)massCenterCoefficient.Value);
image.TransformToBlackAndWhite();
pictureBox2.Image = image.OutputImage;
image.RecognizePixelsToGroups();
image.SetPixelsToObjectGroupsWithFilter();
image.SetObjectsToGroups();
image.PaintFromGroupMap();
pictureBox3.Image = image.OutputImage;
DrawHistogram(zedGraphControl2, pictureBox1);
}
public async Task <string> Recognize()
{
ImageObject[] images = new ImageObject[1] {
_targObject
};
var objLists = await ImageRecognizer.RecognizeAsync(_mvSceneSource, images);
foreach (var ResultRecog in objLists)
{
_success = ResultRecog.Success;
_recognizedTarget = ConvertToPoints(ResultRecog.Region);
}
return(_sceneImagePath);
}
static void Main(string[] args)
{
string modelPath = @"C:\csharp\MnistImageRecognizer\model\model.onnx";
// string data_path = Console.ReadLine();
string dataPath = @"C:\csharp\MnistImageRecognizer\mnist_data_large\";
ImageRecognizer recognizer = new ImageRecognizer();
var results = recognizer.RunModelInference(modelPath, dataPath);
string result;
while (!results.IsAddingCompleted)
{
while (results.TryTake(out result))
{
Console.WriteLine(result);
}
}
}
private void DrawTheImage()
{
ImageRecognizer imageRecongizer = new ImageRecognizer(this.image, 128, 0);
imageRecongizer.TransformToBlackAndWhite();
this.image = imageRecongizer.OutputImage;
for (int i = 0; i < this.width; i++)
{
for (int j = 0; j < this.height; j++)
{
if (this.image.GetPixel(i, j).R == 0)
{
this.draws[j * this.width + i] = 1;
}
else
{
this.draws[j * this.width + i] = -1;
}
}
}
this.panel1.Refresh();
}
public void SelectDirectory()
{
string stream = folderDialog.OpenFolder();
if (!stream.Equals(String.Empty))
{
Progress = 0;
ClassVMs = new ObservableCollection <ImageClassVM>();
db = new Database.ApplicationContext();
var dir = Directory.GetFiles(stream);
Images = new ObservableCollection <ImageVM>();
List <string> fileNames = new List <string>();
foreach (var file in dir)
{
bool flag = false;
var fileInfo = new FileInfo(file);
foreach (var img in db.Images)
{
if (fileInfo.FullName == img.Path)
{
var code1 = ConvertImageToByteArray(fileInfo.FullName);
IStructuralEquatable equ = code1;
var code2 = img.Details.Image;
if (equ.Equals(code2, EqualityComparer <object> .Default))
{
img.count++;
db.SaveChanges();
Images.Add(new ImageVM(fileInfo.FullName, fileInfo.Name, img.Confidence, img.ClassName));
flag = true;
Progress++;
dispatcher.BeginInvoke(new Action(() =>
{
if (ClassVMs.Count() > 0)
{
bool flag1 = false;
foreach (var imgClass in ClassVMs)
{
if (imgClass.Type == img.ClassName)
{
imgClass.Count++;
flag1 = true;
break;
}
}
if (!flag1)
{
ClassVMs.Add(new ImageClassVM(img.ClassName, 1));
}
}
else
{
ClassVMs.Add(new ImageClassVM(img.ClassName, 1));
}
}));
break;
}
}
}
if (!flag)
{
Images.Add(new ImageVM(fileInfo.FullName, fileInfo.Name));
fileNames.Add(fileInfo.FullName);
}
}
imageRecognizer = new ImageRecognizer(fileNames, new ForResults(this));
}
}
/// <summary>
/// Constructor which takes in pre-trained recognizers
/// </summary>
/// <param name="rubine">Pre-Trained Rubine Recognizer</param>
/// <param name="dollar">Pre-Trained Dollar Recognizer</param>
/// <param name="zernike">Pre-Trained Zernike Recognizer</param>
/// <param name="image">Pre-Trained Image Recognizer</param>
public ComboRecognizer(RubineRecognizer rubine, DollarRecognizer dollar, ZernikeMomentRecognizer zernike, ImageRecognizer image)
: this(rubine, dollar, zernike, image, new NaiveBayesUpdateable())
{
}
void Start()
{
imageRecognizer = FindObjectOfType <ImageRecognizer>();
}
/// <summary>
/// Arguments
/// 0: the directory to find files
/// 1: a string to filter filenames (e.g., "*.xml")
/// </summary>
/// <param name="args"></param>
static void Main(string[] args)
{
if (args.Length < 2)
{
return;
}
// Get the list of files
Console.WriteLine("Finding sketch files...");
List <string> allSketches = new List <string>(System.IO.Directory.GetFiles(args[0], args[1]));
Console.WriteLine(" found " + allSketches.Count + " sketches");
// Load all the shapes in all the sketches
Console.WriteLine("Loading full data set...");
List <Shape> shapeData = GetShapeData(allSketches);
Console.WriteLine(" found " + shapeData.Count + " gates");
// Print classes found
HashSet <ShapeType> types = new HashSet <ShapeType>();
foreach (Shape shape in shapeData)
{
types.Add(shape.Type);
}
Console.WriteLine("Found " + types.Count + " types:");
foreach (ShapeType type in types)
{
Console.WriteLine(" " + type);
}
// Save all the shapes to images in the "sketches" folder
string outputPath = @"sketches\";
Console.WriteLine("Saving gates to '" + outputPath + "'...");
if (!System.IO.Directory.Exists(outputPath))
{
System.IO.Directory.CreateDirectory(outputPath);
}
foreach (Shape shape in shapeData)
{
System.Drawing.Bitmap b = ToBitmap.createFromShape(shape, 30, 30, true);
shape.bitmap = b;
string filename = String.Format(outputPath + shape.Type + "-{0:x}.png", shape.GetHashCode());
b.Save(filename);
}
Console.WriteLine(" finished saving gates");
// Train the base recognizers on all the data
Console.WriteLine("Training recognizers on all data...");
#if false
Console.WriteLine(" rubine");
RubineRecognizerUpdateable rubine = new RubineRecognizerUpdateable(shapeData);
rubine.Save("Rubine.rru");
rubine.LiteRecognizer.Save("RubineLite.rr");
Console.WriteLine(" dollar");
DollarRecognizer dollar = new DollarRecognizer(shapeData);
dollar.Save("Dollar.dr");
#else
RubineRecognizerUpdateable rubine = new RubineRecognizerUpdateable();
rubine.Save("Rubine.rru");
rubine.LiteRecognizer.Save("RubineLite.rr");
DollarRecognizer dollar = new DollarRecognizer();
dollar.Save("Dollar.dr");
#endif
Console.WriteLine(" zernike");
ZernikeMomentRecognizerUpdateable zernike = new ZernikeMomentRecognizerUpdateable(shapeData);
zernike.Save("Zernike.zru");
zernike.LiteRecognizer.Save("ZernikeLite.zr");
Console.WriteLine(" image");
ImageRecognizer image = new ImageRecognizer(shapeData);
image.Save("Image.ir");
Console.WriteLine(" finished training recognizers");
RubineRecognizer fullRubine = rubine.LiteRecognizer;
DollarRecognizer fullDollar = dollar;
ZernikeMomentRecognizer fullZernike = zernike.LiteRecognizer;
ImageRecognizer fullImage = image;
// Split the data up per-user
Console.WriteLine("Loading per-user data...");
Dictionary <string, List <Shape>[]> user2data = GetSketchesPerUser(allSketches);
Console.WriteLine(" found " + user2data.Count + " users");
// Foreach user: train each of the recognizers and accumulate training data
// for the combo recognizer
List <KeyValuePair <ShapeType, Dictionary <string, object> > > data = new List <KeyValuePair <ShapeType, Dictionary <string, object> > >();
foreach (KeyValuePair <string, List <Shape>[]> pair in user2data)
{
string user = pair.Key;
////////////////////////////////////////
//////////// Train /////////////////
////////////////////////////////////////
Console.WriteLine("User: "******" rubine");
rubine = new RubineRecognizerUpdateable(trainingSet);
rubine.Save("Rubine" + user + ".rru");
rubine.LiteRecognizer.Save("RubineLite" + user + ".rr");
Console.WriteLine(" dollar");
dollar = new DollarRecognizer(trainingSet);
dollar.Save("Dollar" + user + ".dr");
#else
rubine = new RubineRecognizerUpdateable();
rubine.Save("Rubine" + user + ".rru");
rubine.LiteRecognizer.Save("RubineLite" + user + ".rr");
dollar = new DollarRecognizer();
dollar.Save("Dollar" + user + ".dr");
#endif
Console.WriteLine(" zernike");
zernike = new ZernikeMomentRecognizerUpdateable(trainingSet);
zernike.Save("Zernike" + user + ".zru");
zernike.LiteRecognizer.Save("ZernikeLite" + user + ".zr");
Console.WriteLine(" image");
image = new ImageRecognizer(trainingSet);
image.Save("Image" + user + ".ir");
fullImage = image;
////////////////////////////////////////
//////////// Evaluate //////////////////
////////////////////////////////////////
List <Shape> testingSet = pair.Value[1];
// Create the training data for the combo recognizer
List <KeyValuePair <ShapeType, Dictionary <string, object> > > comboTrainingData = TrainingDataCombo(testingSet, rubine, dollar, zernike, image);
foreach (KeyValuePair <ShapeType, Dictionary <string, object> > pair2 in comboTrainingData)
{
data.Add(pair2);
}
}
if (data.Count == 0)
{
throw new Exception("no data!");
}
List <string> features = new List <string>();
foreach (KeyValuePair <ShapeType, Dictionary <string, object> > instance in data)
{
foreach (string feature in instance.Value.Keys)
{
if (!features.Contains(feature))
{
features.Add(feature);
}
}
}
Console.WriteLine("Found " + data.Count + " data points and " + features.Count + " features.");
ComboRecognizer combo = new ComboRecognizer(fullRubine, fullDollar, fullZernike, fullImage);
combo.TrainCombo(features, data);
combo.Save("Combo.cru");
Console.WriteLine("Naive bayes updatable has " + combo.ComboClassifier.Examples.Count + " examples.");
Console.WriteLine("Naive bayes updatable has " + combo.ComboClassifier.Classifier.Classes.Count + " classes:");
foreach (ShapeType cls in combo.ComboClassifier.Classifier.Classes)
{
Console.WriteLine(" " + cls);
}
Console.WriteLine("Press ENTER to continue...");
Console.ReadLine();
}
/// <summary>
/// Arguments
/// 0: the directory to find files
/// 1: directory to find real-world data (recursive)
/// </summary>
/// <param name="args"></param>
static void Main(string[] args)
{
if (args.Length < 2)
{
return;
}
// Get the list of files
Console.WriteLine("Finding sketch files...");
List <string> allSketches = new List <string>(System.IO.Directory.GetFiles(args[0], "*.xml"));
Console.WriteLine(" found " + allSketches.Count + " sketches");
// Load all the shapes in all the sketches
Console.WriteLine("Loading full data set...");
List <Shape> shapeData = GetShapeData(allSketches);
Console.WriteLine(" found " + shapeData.Count + " gates");
// Print classes found
HashSet <ShapeType> types = new HashSet <ShapeType>();
foreach (Shape shape in shapeData)
{
types.Add(shape.Type);
}
Console.WriteLine("Found " + types.Count + " types:");
foreach (ShapeType type in types)
{
Console.WriteLine(" " + type);
}
// Save all the shapes to images in the "sketches" folder
string outputPath = @"shapes\";
Console.WriteLine("Saving gates to '" + outputPath + "'...");
if (!System.IO.Directory.Exists(outputPath))
{
System.IO.Directory.CreateDirectory(outputPath);
}
foreach (Shape shape in shapeData)
{
System.Drawing.Bitmap b = shape.createBitmap(100, 100, true);
shape.TemplateDrawing = b;
string filename = String.Format(outputPath + shape.Type + "-{0:x}.png", shape.GetHashCode());
b.Save(filename);
}
Console.WriteLine(" finished saving gates");
// Train the base recognizers on all the data
Console.WriteLine("Training recognizers on all data...");
#if false
Console.WriteLine(" rubine");
RubineRecognizerUpdateable rubine = new RubineRecognizerUpdateable(shapeData);
rubine.Save("Rubine.rru");
rubine.LiteRecognizer.Save("RubineLite.rr");
Console.WriteLine(" dollar");
DollarRecognizer dollar = new DollarRecognizer(shapeData);
dollar.Save("Dollar.dr");
RubineRecognizerUpdateable rubine = new RubineRecognizerUpdateable();
rubine.Save("Rubine.rru");
rubine.LiteRecognizer.Save("RubineLite.rr");
DollarRecognizer dollar = new DollarRecognizer();
dollar.Save("Dollar.dr");
Console.WriteLine(" zernike");
ZernikeMomentRecognizerUpdateable zernike = new ZernikeMomentRecognizerUpdateable(shapeData);
zernike.Save("Zernike.zru");
zernike.LiteRecognizer.Save("ZernikeLite.zr");
#endif
Console.WriteLine(" adaptive image");
AdaptiveImageRecognizer adaptiveimage = new AdaptiveImageRecognizer(shapeData);
adaptiveimage.Save("AdaptiveImage.air");
Console.WriteLine(" image");
ImageRecognizer image = new ImageRecognizer(shapeData);
image.Save("Image.ir");
Console.WriteLine(" finished training recognizers");
#if false
RubineRecognizer fullRubine = rubine.LiteRecognizer;
DollarRecognizer fullDollar = dollar;
ZernikeMomentRecognizer fullZernike = zernike.LiteRecognizer;
ImageRecognizer fullImage = image;
// Split the data up per-user
Console.WriteLine("Loading per-user data...");
Dictionary <string, List <Shape>[]> user2data = GetSketchesPerUser(allSketches);
Console.WriteLine(" found " + user2data.Count + " users");
// Foreach user: train each of the recognizers and accumulate training data
// for the combo recognizer
List <KeyValuePair <ShapeType, Dictionary <string, object> > > data = new List <KeyValuePair <ShapeType, Dictionary <string, object> > >();
foreach (KeyValuePair <string, List <Shape>[]> pair in user2data)
{
string user = pair.Key;
////////////////////////////////////////
//////////// Train /////////////////
////////////////////////////////////////
Console.WriteLine("User: "******" rubine");
rubine = new RubineRecognizerUpdateable(trainingSet);
rubine.Save("Rubine" + user + ".rru");
rubine.LiteRecognizer.Save("RubineLite" + user + ".rr");
Console.WriteLine(" dollar");
dollar = new DollarRecognizer(trainingSet);
dollar.Save("Dollar" + user + ".dr");
#else
rubine = new RubineRecognizerUpdateable();
rubine.Save("Rubine" + user + ".rru");
rubine.LiteRecognizer.Save("RubineLite" + user + ".rr");
dollar = new DollarRecognizer();
dollar.Save("Dollar" + user + ".dr");
#endif
Console.WriteLine(" zernike");
zernike = new ZernikeMomentRecognizerUpdateable(trainingSet);
zernike.Save("Zernike" + user + ".zru");
zernike.LiteRecognizer.Save("ZernikeLite" + user + ".zr");
Console.WriteLine(" image");
image = new ImageRecognizer(trainingSet);
image.Save("Image" + user + ".ir");
fullImage = image;
////////////////////////////////////////
//////////// Evaluate //////////////////
////////////////////////////////////////
List <Shape> testingSet = pair.Value[1];
// Create the training data for the combo recognizer
List <KeyValuePair <ShapeType, Dictionary <string, object> > > comboTrainingData = TrainingDataCombo(testingSet, rubine, dollar, zernike, image);
foreach (KeyValuePair <ShapeType, Dictionary <string, object> > pair2 in comboTrainingData)
{
data.Add(pair2);
}
}
if (data.Count == 0)
{
throw new Exception("no data!");
}
List <string> features = new List <string>();
foreach (KeyValuePair <ShapeType, Dictionary <string, object> > instance in data)
{
foreach (string feature in instance.Value.Keys)
{
if (!features.Contains(feature))
{
features.Add(feature);
}
}
}
Console.WriteLine("Found " + data.Count + " data points and " + features.Count + " features.");
ComboRecognizer combo = new ComboRecognizer(fullRubine, fullDollar, fullZernike, fullImage);
combo.TrainCombo(features, data);
combo.Save("Combo.cru");
Console.WriteLine("Naive bayes updatable has " + combo.ComboClassifier.Examples.Count + " examples.");
Console.WriteLine("Naive bayes updatable has " + combo.ComboClassifier.Classifier.Classes.Count + " classes:");
foreach (ShapeType cls in combo.ComboClassifier.Classifier.Classes)
{
Console.WriteLine(" " + cls);
}
#endif
Console.WriteLine("Training neural image recognizer on real-world data...");
List <Shape> goodGates; // list of correctly-identified gates
List <Shape> badGates; // list of shapes grouped as gates that aren't
Dictionary <Shape, string> shapeSources; // map of shapes to source filename
string cacheFile = outputPath + "goodAndBadGates.data";
if (!System.IO.File.Exists(cacheFile))
{
goodGates = new List <Shape>();
badGates = new List <Shape>();
shapeSources = new Dictionary <Shape, string>();
Grouper grouper = RecognitionPipeline.createDefaultGrouper();
Classifier classifier = RecognitionPipeline.createDefaultClassifier();
RecognitionPipeline pipeline = new RecognitionPipeline(classifier, grouper);
var files = Files.FUtil.AllFiles(args[1], Files.Filetype.XML, true);
Console.WriteLine(" Found " + files.Count() + " real-world sketches");
int i = 1;
foreach (string file in files)
{
Console.WriteLine(" Sketch " + i + " / " + files.Count());
i++;
Sketch.Sketch sketch = new ReadXML(file).Sketch;
Sketch.Sketch original = sketch.Clone();
sketch.RemoveLabels();
sketch.resetShapes();
pipeline.process(sketch);
foreach (Sketch.Shape shape in sketch.Shapes)
{
if (shape.Classification != LogicDomain.GATE_CLASS)
{
continue;
}
Shape originalGate = original.ShapesL.Find(delegate(Shape s) { return(s.GeometricEquals(shape)); });
if (originalGate != null && originalGate.Classification == LogicDomain.GATE_CLASS)
{
goodGates.Add(shape);
}
else
{
// We can't just say "this is a bad gate." If it wasn't found,
// the shape might be an XOR gate missing the back, or a NAND
// gate missing a bubble. We will apply the following heuristic:
// if all the strokes in the shape are part of the same
// shape in the original sketch and that shape in the
// original sketch is a gate, this is not a bad gate.
// a shape consists of one or more substrokes from shapes in the
// original, correct sketch
HashSet <Shape> originalShapes = new HashSet <Shape>();
foreach (Substroke substroke in shape.Substrokes)
{
Substroke originalSubstroke = original.SubstrokesL.Find(delegate(Substroke s) { return(s.GeometricEquals(substroke)); });
if (originalSubstroke == null)
{
throw new Exception("A substroke is missing in the original sketch???");
}
if (originalSubstroke.ParentShape != null)
{
originalShapes.Add(originalSubstroke.ParentShape);
}
}
List <Shape> originalShapesL = originalShapes.ToList();
if (originalShapesL.Count != 1 || originalShapesL[0].Classification != LogicDomain.GATE_CLASS)
{
badGates.Add(shape);
}
}
shapeSources.Add(shape, file);
}
}
Console.WriteLine("Saving found gates to " + cacheFile);
var stream = System.IO.File.Open(cacheFile, System.IO.FileMode.Create);
var bformatter = new System.Runtime.Serialization.Formatters.Binary.BinaryFormatter();
bformatter.Serialize(stream, Tuple.Create(goodGates, badGates, shapeSources));
stream.Close();
}
else
{
Console.WriteLine("Loading good and bad gates from " + cacheFile);
var stream = System.IO.File.Open(cacheFile, System.IO.FileMode.Open);
var bformatter = new System.Runtime.Serialization.Formatters.Binary.BinaryFormatter();
var data = (Tuple <List <Shape>, List <Shape>, Dictionary <Shape, string> >)bformatter.Deserialize(stream);
stream.Close();
goodGates = data.Item1;
badGates = data.Item2;
shapeSources = data.Item3;
}
Console.WriteLine(" Found " + goodGates.Count + " good gates, " + badGates.Count + " bad gates");
ImageRecognizer innerNeuralRecgonizer = image;
string neuralPath = @"neuralResults\";
string arffFilename = "data.arff";
if (!System.IO.Directory.Exists(neuralPath))
{
System.IO.Directory.CreateDirectory(neuralPath);
}
Console.WriteLine(" Writing ARFF file '" + neuralPath + arffFilename + "'...");
TextWriter arffWriter = new StreamWriter(neuralPath + arffFilename);
NeuralImageRecognizer.WriteARFF(arffWriter, innerNeuralRecgonizer, goodGates, badGates);
arffWriter.Close();
Console.WriteLine(" Training the network...");
// Network settings -- determined empircally
NeuralNetworkInfo info = new NeuralNetworkInfo();
info.Layers = new int[] { 8, 1 };
info.NumTrainingEpochs = 1000;
info.LearningRate = 0.05;
info.Momentum = 0.2;
NeuralImageRecognizer neuralImage = new NeuralImageRecognizer(innerNeuralRecgonizer, goodGates, badGates, info);
neuralImage.Save("NeuralImage.nir");
Console.WriteLine(" Testing the network (results in " + neuralPath + ")...");
neuralImage = NeuralImageRecognizer.Load("NeuralImage.nir");
TextWriter writer = new StreamWriter(neuralPath + "info.csv");
writer.WriteLine("Sketch File, Shape Bitmap, Good?, Tanimoto, Yule, Partial Hausdorff, Modified Hausdorff, Output Confidence");
int falseNegatives = 0;
foreach (Shape gate in goodGates)
{
ImageRecognitionResult result = (ImageRecognitionResult)neuralImage.recognize(gate, null);
if (result.Confidence < 0.5)
{
falseNegatives++;
}
System.Drawing.Bitmap b = gate.createBitmap(100, 100, true);
string filename = String.Format("good-" + "-{0:x}.png", gate.GetHashCode());
b.Save(neuralPath + filename);
writer.WriteLine(shapeSources[gate] + "," + filename +
", 1, " +
result.Tanimoto + ", " +
result.Yule + ", " +
result.PartialHausdorff + ", " +
result.ModifiedHausdorff + ", " +
result.Confidence);
}
Console.WriteLine(" Good gates with low confidence: " + falseNegatives + "/" + (goodGates.Count));
int falsePositives = 0;
foreach (Shape gate in badGates)
{
ImageRecognitionResult result = (ImageRecognitionResult)neuralImage.recognize(gate, null);
if (result.Confidence > 0.5)
{
falsePositives++;
}
System.Drawing.Bitmap b = gate.createBitmap(100, 100, true);
string filename = String.Format("bad-" + "-{0:x}.png", gate.GetHashCode());
b.Save(neuralPath + filename);
writer.WriteLine(shapeSources[gate] + "," + filename +
", 0, " +
result.Tanimoto + ", " +
result.Yule + ", " +
result.PartialHausdorff + ", " +
result.ModifiedHausdorff + ", " +
result.Confidence);
}
Console.WriteLine(" Bad gates with high confidence: " + falsePositives + "/" + (badGates.Count));
writer.Close();
Console.WriteLine("Press ENTER to continue...");
Console.ReadLine();
}