private void Worker()
{
while (!CancelThreads.Token.IsCancellationRequested && PathImages.TryDequeue(out string image))
{
ResultClassification result = Model.PredictModel(image);
Result.Enqueue(result);
ImageRecognitionCompleted(result);
}
}
public ResultClassification PredictModel(string imageFilePath)
{
DenseTensor <float> TensorImage = OnnxClassifier.PreprocImage(imageFilePath);
var inputs = new List <NamedOnnxValue>
{
NamedOnnxValue.CreateFromTensor(session.InputMetadata.Keys.First(), TensorImage)
};
using IDisposableReadOnlyCollection <DisposableNamedOnnxValue> results = session.Run(inputs);
var output = results.First().AsEnumerable <float>().ToArray();
float sum = output.Sum(x => (float)Math.Exp(x));
var softmax = output.Select(x => (float)Math.Exp(x) / sum).ToList();
string cl = LabelMap.Labels[softmax.IndexOf(softmax.Max())];
ResultClassification result = new ResultClassification(imageFilePath, cl, softmax.Max());
return(result);
}
