public ColourSegment(Image<Bgr, byte> rgbCrop, Image<Gray, byte> binaryCrop, Point[] contour, SignColour colour)
{
this.rgbCrop = rgbCrop;
this.binaryCrop = binaryCrop;
this.contour = contour;
this.colour = colour;
}
public SignType recognizeSign(BGRImage BGRimage, GrayImage grayImage, SignShape shape, SignColour color, List<DebugImage> aDebugImage = null)
{
if (shape == SignShape.Garbage && aDebugImage == null)
return SignType.Garbage;
debugImages = aDebugImage;
GrayImage preprosessedImage = preprocess(BGRimage, grayImage);
Matrix<float> parameter = calculateParameters(preprosessedImage);
SignType classifiedSign = classifySign(parameter, shape);
bool boolColor;
if (color == SignColour.RED)
boolColor = false;
else boolColor = true;
if (isTrained && shape != SignShape.Garbage)
{
if (boolColor != SignTypeColor[(int)classifiedSign])
//Console.WriteLine("Color Mishaps");
return SignType.Garbage;
return classifiedSign;
}
else
return SignType.Garbage;
}
//-> function returning the binary image with white the segment and black not
private Image<Gray, Byte> GetPixelMask(String ColourSpace, SignColour Colour, Image<Bgr, byte> image)
{
int StartRangeH = 0;
int EndRangeH = 0;
int StartRangeV = 0;
int StartRangeS = 0;
if (Colour == SignColour.BLUE)
{
StartRangeH = 100;
EndRangeH = 135;
StartRangeS = 110;
StartRangeV = 30;
}
if (Colour == SignColour.RED)
{
StartRangeH = 12;
EndRangeH = 170;
StartRangeS = 50;
StartRangeV = 30;
}
using (Image<Hsv, Byte> hsv = image.Convert<Hsv, Byte>())
{
Image<Gray, Byte>[] channels = hsv.Split();
try
{
CvInvoke.cvInRangeS(channels[0], new MCvScalar(StartRangeH), new MCvScalar(EndRangeH), channels[0]);
if (Colour == SignColour.RED)
{
channels[0]._Not();
}
channels[1]._ThresholdBinary(new Gray(StartRangeS), new Gray(255.0));
channels[2]._ThresholdBinary(new Gray(StartRangeV), new Gray(255.0));
CvInvoke.cvAnd(channels[0], channels[1], channels[0], IntPtr.Zero);
CvInvoke.cvAnd(channels[0], channels[2], channels[0], IntPtr.Zero);
}
finally
{
channels[1].Dispose();
channels[2].Dispose();
}
return channels[0];
}
}
public SignShape classify(GrayImage binaryImage, SignColour colour, List<DebugImage> debugImages = null)
{
int[] featureVector = extractDtbFeatures(binaryImage, debugImages);
if (isTrained)
{
Matrix<float> data = new Matrix<float>(Array.ConvertAll<int, float>(featureVector, Convert.ToSingle));
return (SignShape)svms[(int)colour].model.Predict(data);
}
else
{
return SignShape.Garbage;
}
}
