public void Colorize(string sourceImagePath, string greyImagePath, string destinationImagePath, IProgress <int> progressReporter = null)
{
List <PixelYUV> trainingData;
List <PixelYUV> validationData;
Console.WriteLine("Processing image data and splitting into Training and Validation Sets");
Bitmap sourceBMP = (Bitmap)Image.FromFile(sourceImagePath);
List <PixelYUV> allData = GenerateYUVCRDataForColorImage(sourceBMP);
sourceBMP.Dispose();
int splitValue = (allData.Count / 100 * 80);
Shuffle(ref allData);
trainingData = allData.Take(splitValue).ToList();
validationData = allData.TakeLast(allData.Count - splitValue).ToList();
MLContext mlContext = new MLContext(seed: 0);
/************/
/* Training */
/************/
IDataView trainingDataView = mlContext.Data.LoadFromEnumerable(trainingData);
Console.WriteLine("Training U Channel Model");
var u_model = MLFunctions.Train(mlContext, trainingDataView, "U");
Console.WriteLine("Training V Channel Model");
var v_model = MLFunctions.Train(mlContext, trainingDataView, "V");
Common.Utilities.SetProgress(progressReporter, 25);
/**************/
/* Evaluating */
/**************/
IDataView testDataView = mlContext.Data.LoadFromEnumerable(validationData);
Console.WriteLine("Evaluating U Channel Model:");
MLFunctions.Evaluate(mlContext, u_model, testDataView);
Console.WriteLine("Evaluating U Channel Model:");
MLFunctions.Evaluate(mlContext, v_model, testDataView);
Common.Utilities.SetProgress(progressReporter, 50);
/*********************************/
/* Using Model to Predict Colors */
/*********************************/
Console.WriteLine("Loading Greyscale image");
Bitmap bmp2 = (Bitmap)Image.FromFile(greyImagePath);
List <PixelYUV> realData = GenerateYUVPCRDataForGreyImage(bmp2);
CImage greyImage = new CImage(bmp2.Width, bmp2.Height, 24);
bmp2.Dispose();
IDataView realDataView = mlContext.Data.LoadFromEnumerable(realData);
Console.WriteLine("Predicting U Channel values");
float[] uData = MLFunctions.MakeColorPredictions(mlContext, u_model, realDataView);
Console.WriteLine("Predicting V Channel values");
float[] vData = MLFunctions.MakeColorPredictions(mlContext, v_model, realDataView);
Common.Utilities.SetProgress(progressReporter, 75);
Console.WriteLine("Populating RGB Values");
int pixelColorIndex = -1;
for (int y3 = 0; y3 < greyImage.Height; y3++)
{
for (int x3 = 0; x3 < greyImage.Width; x3++)
{
pixelColorIndex += 1;
int i3 = (x3 + (greyImage.Width * y3));
// Convert our YUV values to RGB
var rgb = new YUV(
realData[pixelColorIndex].Y,
Convert.ToDouble(uData[pixelColorIndex]),
Convert.ToDouble(vData[pixelColorIndex])
).ToRGB();
greyImage.Grid[3 * i3 + 2] = rgb.R;
greyImage.Grid[3 * i3 + 1] = rgb.G;
greyImage.Grid[3 * i3 + 0] = rgb.B;
}
}
/*********************/
/* Outputing results */
/*********************/
Console.WriteLine("Saving Colorized Image");
Bitmap greyColored = greyImage.ToBitmap();
greyColored.Save(destinationImagePath);
greyColored.Dispose();
Common.Utilities.SetProgress(progressReporter, 100);
}
public bool Equals(YUV yuv)
{
return((Y == yuv.Y) && (U == yuv.U) && (V == yuv.V));
}
