private List <Prediction> Classify(MLModel model, UIImage source)
{
if (model == null)
{
throw new ArgumentNullException(nameof(model));
}
if (source == null)
{
throw new ArgumentNullException(nameof(source));
}
var scaled = source.Scale(new CGSize(227, 227));
var pixelBuffer = ToCVPixelBuffer(scaled);
var imageValue = MLFeatureValue.Create(pixelBuffer);
var inputs = new NSDictionary <NSString, NSObject>(new NSString("data"), imageValue);
NSError error, error2;
var inputFp = new MLDictionaryFeatureProvider(inputs, out error);
if (error != null)
{
System.Diagnostics.Debug.WriteLine(error);
throw new InvalidOperationException(error.Description);
}
var outFeatures = model.GetPrediction(inputFp, out error2);
if (error2 != null)
{
System.Diagnostics.Debug.WriteLine(error2);
throw new InvalidOperationException(error2.Description);
}
var predictionsDictionary = outFeatures.GetFeatureValue("loss").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));
}
byProbability.Sort((t1, t2) => t1.Item1.CompareTo(t2.Item1) * -1);
var result = byProbability
.Select(p => new Prediction {
Label = p.Item2, Probability = p.Item1
})
.ToList();
return(result);
}
internal void Classify(UIImage source)
{
var model = models[currentModel];
var pixelBuffer = source.Scale(sizeFor[model]).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 = model.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 = currentModel;
prediction.predictions = byProbability;
PredictionsUpdated(this, new EventArgsT <ImageDescriptionPrediction>(prediction));
}
public ImageClassificationResult RecognizeImage(UIImage imageSource)
{
var pixelBuffer = imageSource.Scale(new CGSize(InputSize, InputSize)).ToCVPixelBuffer();
var imageValue = MLFeatureValue.Create(pixelBuffer);
var inputs = new NSDictionary <NSString, NSObject>(new NSString(InputName), imageValue);
NSError error, error2;
var inputFp = new MLDictionaryFeatureProvider(inputs, out error);
if (error != null)
{
Console.WriteLine("RecognizeImage Error 1: {0}", error);
return(null);
}
var outFeatures = _imageClassifierModel.GetPrediction(inputFp, out error2);
if (error2 != null)
{
Console.WriteLine("RecognizeImage Error 2: {0}", error2);
return(null);
}
var predictionsDictionary = outFeatures.GetFeatureValue(OutputName).DictionaryValue;
var outputs = new List <Tuple <double, string> >();
foreach (var key in predictionsDictionary.Keys)
{
var description = (string)(NSString)key;
var prob = (double)predictionsDictionary[key];
outputs.Add(new Tuple <double, string>(prob, description));
}
var results = outputs
.Select((t1, t2) => new ImageClassificationResult(t1.Item2, t1.Item1))
.ToList();
return(results.OrderByDescending(t => t.Probability).First());
}
