private ClassificationResults UseClassifier(Image frame)
{
var classificationResults = frame != null?_classifier?.Classify(frame.CloneAs <Rgb24>()) : null;
var results = classificationResults?.Prediction?.Confidence > Threshold ? classificationResults : null;
return(results);
}
public static Bitmap CustomRun(string path)
{
string classificationResult = imageClassifier.Classify(new Bitmap(path));
string fileName = Path.GetFileNameWithoutExtension(path);
string[] nameParts = fileName.Split(new[] { "_" }, StringSplitOptions.RemoveEmptyEntries);
fileName = nameParts[0];
if (classificationResult.Equals(fileName, StringComparison.CurrentCulture))
{
return(BinaryTransformer.BoolToBitmap(imageClassifier.LastPattern));
}
return(new Bitmap(10, 10));
}
public static string Run(string DataDirectoryName)
{
var statistics = new Dictionary <string, int>();
IImageBinarizer binarizer = new ImageBinarizer();
var directoryInfo = new DirectoryInfo(Environment.CurrentDirectory).Parent.Parent.Parent;
var path = Path.Combine(directoryInfo.FullName, DataDirectoryName);
directoryInfo = new DirectoryInfo(path);
imageClassifier = new ImageClassifier(binarizer, Size * Size);
//Train
foreach (DirectoryInfo directory in directoryInfo.EnumerateDirectories())
{
foreach (var file in directory.EnumerateFiles("*.bmp"))
{
string fileName = Path.GetFileNameWithoutExtension(file.FullName);
imageClassifier.Train(new ImageUnit {
Id = fileName, Image = new Bitmap(file.FullName)
});
}
}
foreach (DirectoryInfo directory in directoryInfo.EnumerateDirectories())
{
foreach (var file in directory.EnumerateFiles("*.bmp"))
{
string fileName = Path.GetFileNameWithoutExtension(file.FullName);
var patternDirectory = directory.EnumerateDirectories().FirstOrDefault(p => p.Name == ("Randomized_" + fileName));
foreach (var patternFile in patternDirectory.EnumerateFiles("*.bmp"))
{
string patternFileName = Path.GetFileNameWithoutExtension(patternFile.FullName);
string[] nameParts = patternFileName.Split(new[] { "_" }, StringSplitOptions.RemoveEmptyEntries);
string percentValue = nameParts[1];
if (statistics.ContainsKey(percentValue) == false)
{
statistics.Add(percentValue, 0);
}
string classificationResult = imageClassifier.Classify(new Bitmap(patternFile.FullName));
if (classificationResult.Equals(fileName, StringComparison.CurrentCulture))
{
statistics[percentValue] += 1;
}
}
}
}
return(statistics.Aggregate("Classification result: \n\n", (current, statistic) => current + String.Format("Noise percent: {0}\n Matched: {1}\n\n", statistic.Key, statistic.Value)));
}
public static ClassificationResults Classify <TPixel>(this ImageClassifier classifier,
Image <TPixel> image) where TPixel : unmanaged, IPixel <TPixel>
{
return(classifier.Classify(image.ToFlatArrayMatchingInputShape(classifier.Signature, "Image")));
}
static void Main(string[] args)
{
//LogEvents.Subscribe(i =>
//{
// i.Operation.Id = "";
// Console.WriteLine(i.ToLogString());
//}, new[]
//{
// typeof(PiTop4Board).Assembly,
// typeof(FoundationPlate).Assembly,
// typeof(ExpansionPlate).Assembly,
// typeof(RoverRobot).Assembly,
// typeof(StreamingCamera).Assembly,
// typeof(Program).Assembly,
//});
ImageClassifier.Register("onnx", () => new OnnxImageClassifier());
ImageClassifier classifier = null;
var js = new XBoxController();
// using ` mjpg_streamer -i "input_uvc.so -d /dev/video0" -o output_http.so`
// ==> http://pi-top.local:8080/?action=stream
PiTop4Board.Instance.UseCamera();
using var rover = new RoverRobot(PiTop4Board.Instance.GetOrCreateExpansionPlate(), PiTop4Board.Instance.GetOrCreateCamera <StreamingCamera>(0),
RoverRobotConfiguration.Default);
var camControl = rover.TiltController;
var motorControl = rover.MotionComponent as SteeringMotorController;
rover.AllLightsOn();
rover.BlinkAllLights();
Observable.Interval(TimeSpan.FromMilliseconds(10))
.Select(_ => (X: js.LeftStick.X, Y: js.LeftStick.Y))
.DistinctUntilChanged()
.Subscribe(stick =>
{
var left = stick.X.WithDeadZone(-.5, .5, .3);
var forward = stick.Y;
motorControl.SetPower((forward + left) / 1.5, (forward - left) / 1.5);
});
js.Events.OfType <ButtonEvent>().Where(e => e.Button == Button.A)
.Subscribe(e =>
{
if (e.Pressed)
{
rover.AllLightsOn();
}
else
{
rover.AllLightsOff();
}
});
js.Events.OfType <ButtonEvent>().Where(e => e.Button == Button.B && e.Pressed)
.Subscribe(e =>
{
rover.Camera.GetFrame().Save("/home/pi/shot.jpg");
});
js.Events.OfType <ButtonEvent>().Where(e => e.Button == Button.Y && e.Pressed)
.Subscribe(e =>
{
classifier?.Dispose();
var signatureFile = new FileInfo("/home/pi/models/pics/signature.json");
classifier = ImageClassifier.CreateFromSignatureFile(signatureFile);
Console.WriteLine($"Loaded model from {signatureFile.FullName}");
});
js.Events.OfType <ButtonEvent>().Where(e => e.Button == Button.X && e.Pressed)
.Subscribe(e =>
{
var frame = rover.Camera.GetFrame().Focus();
var result = classifier?.Classify(frame.CloneAs <Rgb24>());
if (result is { })
{
Console.WriteLine($"{result.Prediction.Label}");
}
});
public static ClassificationResults Classify(this ImageClassifier classifier,
Mat image)
{
return(classifier.Classify(image.ToFlatArrayMatchingInputShape(classifier.Signature, "Image")));
}