void ShowPrediction(ImageDescriptionPrediction imageDescriptionPrediction)
{
//Grab the first 5 predictions, format them for display, and show 'em
InvokeOnMainThread(() =>
{
var message = $"{imageDescriptionPrediction.ModelName} thinks:\n";
var topFive = imageDescriptionPrediction.predictions.Take(5);
foreach (var prediction in topFive)
{
var prob = prediction.Item1;
var desc = prediction.Item2;
message += $"{desc} : {prob.ToString("P") }\n";
}
ShowMessage(message);
});
}
internal void Classify(UIImage source)
{
var pixelBuffer = source.Scale(sizeForModel).ToCVPixelBuffer();
var imageValue = MLFeatureValue.Create(pixelBuffer);
var inputs = new NSDictionary <NSString, NSObject>(new NSString("image"), imageValue);
NSError error, error2;
var inputFp = new MLDictionaryFeatureProvider(inputs, out error);
if (error != null)
{
ErrorOccurred(this, new EventArgsT <string>(error.ToString()));
return;
}
var outFeatures = currentModel.GetPrediction(inputFp, out error2);
if (error2 != null)
{
ErrorOccurred(this, new EventArgsT <string>(error2.ToString()));
return;
}
var predictionsDictionary = outFeatures.GetFeatureValue("classLabelProbs").DictionaryValue;
var byProbability = new List <Tuple <double, string> >();
foreach (var key in predictionsDictionary.Keys)
{
var description = (string)(NSString)key;
var prob = (double)predictionsDictionary[key];
byProbability.Add(new Tuple <double, string>(prob, description));
}
//Sort descending
byProbability.Sort((t1, t2) => t1.Item1.CompareTo(t2.Item1) * -1);
var prediction = new ImageDescriptionPrediction();
prediction.ModelName = "VGG16";
prediction.predictions = byProbability;
PredictionsUpdated(this, new EventArgsT <ImageDescriptionPrediction>(prediction));
}