private static RecognitionResults RecognizeImage(double[] input, long inputToken, int width, int height, int finalResolution, TrainedRecognizer[] recognizers, LifeEventToVector lifeEvents, ConvolutionBase2D convolution, bool isColor)
{
if (recognizers == null)
{
return new RecognitionResults(inputToken, new double[lifeEvents.Types.Length]);
}
double[] normalized = NormalizeInput(input, width, height, finalResolution, convolution, isColor);
foreach (var recognizer in recognizers)
{
double[] output = recognizer.Network.Compute(normalized);
//TODO: Analyze outputs of all the recognizers to come up with a final result. Can't just take the average -- if they all
//agree, that's great. But disagreement should have a zero output (or at least a very weak output)
return new RecognitionResults(inputToken, output);
}
throw new ApplicationException("finish this");
}
private void FinishedTraining(TrainedRecognizer result)
{
lock (_lock)
{
_trainingTask = null;
if (result != null)
{
//TODO: Keep a few around. If max count is met, throw out the oldest/worst
_recognizers = new[] { result };
_currentTrainingData = Tuple.Create(result.InputRaw, result.InputNormalized);
}
if (_areLifeEventsDirty || result == null) // null means it was unsuccessful. Try again
{
// There is new data, train against it
TrainerInput trainerInput = GetTrainingInput();
if (trainerInput != null)
{
_areLifeEventsDirty = false;
_trainingTask = new Task<TrainedRecognizer>(() => Train(trainerInput, _convolution, _isColor, _finalResolution));
_trainingTask.ContinueWith(r => FinishedTraining(r.Result));
_trainingTask.Start();
}
}
}
}
