/// <summary>
/// Make a prediction using the standard interface
/// </summary>
/// <param name="input">an instance of SqueezeNetInput to predict from</param>
/// <param name="error">If an error occurs, upon return contains an NSError object that describes the problem.</param>
public SqueezeNetOutput GetPrediction(SqueezeNetInput input, out NSError error)
{
var prediction = model.GetPrediction(input, out error);
if (prediction == null)
{
return(null);
}
var classLabelProbs = prediction.GetFeatureValue("classLabelProbs").DictionaryValue;
var classLabel = prediction.GetFeatureValue("classLabel").StringValue;
return(new SqueezeNetOutput(classLabelProbs, classLabel));
}
/// <summary>
/// Make a prediction using the convenience interface
/// </summary>
/// <param name="image">Input image to be classified as color (kCVPixelFormatType_32BGRA) image buffer, 227 pizels wide by 227 pixels high</param>
/// <param name="error">If an error occurs, upon return contains an NSError object that describes the problem.</param>
public SqueezeNetOutput GetPrediction(CVPixelBuffer image, out NSError error)
{
var input = new SqueezeNetInput(image);
return(GetPrediction(input, out error));
}
