private void ProcessImage()
{
Bitmap imageDetection = BitmapImage2Bitmap(imageSource);
// If ML Engine Fails to Load
if (cancerDetectionPredictionEngine == null)
{
Debug.WriteLine("ML Engine Failed");
txtOutput.Text = "ML Engine Failed";
return;
}
var frame = new ImageInputData {
Image = imageDetection
};
Debug.WriteLine("Detect Objects Using Model");
var prediction = DetectUsingModel(frame);
// Text output to textblock
// Note: Label 1 means having metastasis and Label 0 means not having metastasis
txtOutput.Text = "Having metastasis: \n";
txtOutput.Text += Math.Round(prediction[1] * 100, 4) + "%\n\n";
txtOutput.Text += "Not having metastasis: \n";
txtOutput.Text += Math.Round(prediction[0] * 100, 4) + "%\n";
// Debug: print each predicted values
// foreach (var result in prediction){ Debug.WriteLine($"BoxDescription: {result}");}
}
private void ProcessWholeSlide(int ResolutionX, int ResolutionY)
{
Bitmap imageDetection = BitmapImage2Bitmap(imageSource);
// Initialize an array and index count to save the processed prediction[1] (prob of having metasasis) output
float[] haveCancerPrediction = new float[ResolutionX * ResolutionY];
int count = 0;
// If ML Engine Fails to Load
if (cancerDetectionPredictionEngine == null)
{
Debug.WriteLine("ML Engine Failed");
txtOutput.Text = "ML Engine Failed";
return;
}
// Note: Have better condition handling for resolution input
// Given the resolution a crop of the whole slide image of size (ImageSettings.imageWidth by ImageSettings.imageHeight) will be processed using the ML model to return a float (prob of having metastasis) appended to the float[]
for (float x = ImageSettings.imageWidth / 2; x <= imageDetection.Width - ImageSettings.imageWidth / 2; x += (float)(imageDetection.Width - ImageSettings.imageWidth) / (float)(ResolutionX - 1))
{
for (float y = ImageSettings.imageHeight / 2; y <= imageDetection.Height - ImageSettings.imageHeight / 2; y += (float)(imageDetection.Height - ImageSettings.imageHeight) / (float)(ResolutionY - 1))
{
// Crop whole slide image
Int32Rect rect = new Int32Rect((int)(x - ImageSettings.imageWidth / 2), (int)(y - ImageSettings.imageHeight / 2), ImageSettings.imageWidth, ImageSettings.imageHeight);
CroppedBitmap cb = new CroppedBitmap(GetBitmapSource(imageDetection), rect);
// Predict outcome and set up the float[] to generate the overlay mask
var frame = new ImageInputData {
Image = BitmapImage2Bitmap(Bitmap2BitmapImage(cb))
};
var prediction = DetectUsingModel(frame);
haveCancerPrediction.SetValue(prediction[1], count);
count++;
}
}
Bitmap overlayBitmap = new Bitmap(ResolutionX, ResolutionY);
for (int pixel = 0; pixel < haveCancerPrediction.Length; pixel++)
{
int r_value = 255 - (int)(haveCancerPrediction[pixel] * 255 * 100000); // The intensity multiplier is 100000
if (r_value < 0)
{
r_value = 0;
}
overlayBitmap.SetPixel(pixel % ResolutionX, (int)Math.Floor((float)pixel / (float)ResolutionX), System.Drawing.Color.FromArgb(r_value, 0, 0));
overlayBitmap.SetPixel((int)Math.Floor((float)pixel / (float)ResolutionX), pixel % ResolutionX, System.Drawing.Color.FromArgb(r_value, 0, 0));
}
// Scale the Bitmap mask to cover the whole slide image
var targetBitmapImage = new TransformedBitmap(GetBitmapSource(overlayBitmap), new ScaleTransform((imageDetection.Width) / ResolutionX, (imageDetection.Height) / ResolutionY));
// Set the bitmap to display
imgMask.Source = targetBitmapImage;
}
// Use Model to Predict Feedback from ImageInputData (a single image)
public float[] DetectUsingModel(ImageInputData imageInputData)
{
var labels = cancerDetectionPredictionEngine?.Predict(imageInputData).PredictedLabels;
return(labels);
}
