public Device(IFezHat hat, IDataService dataService)
{
_hat = hat;
_dataService = dataService;
Controls = new Controller();
_mlService = new MachineLearningService(_dataService);
CurrentState = State.Idle;
}
[TestCaseSource("TestCases")] // passing through testCaseSource, as NUnit does not allow passing of Enumerables
public void TestSoftmaxOutput(IEnumerable <double> input)
{
var actualOutput = MachineLearningService.Softmax(input);
var softmaxSum = actualOutput.Sum();
// check if the softmax output sum to 1
Assert.AreEqual(softmaxSum, 1.0f, Epsilon);
// check if the softmax results respect the original ordinal information
var originalSorted = input.Select((x, i) => new KeyValuePair <double, double>(x, i))
.OrderBy(x => x.Key);
var originalIdx = originalSorted.Select(x => x.Value).ToList(); // the index of items that ranked in increasing value
var answerSorted = actualOutput.Select((x, i) => new KeyValuePair <double, double>(x, i))
.OrderBy(x => x.Key);
var answerIdx = answerSorted.Select(x => x.Value).ToList(); // the index of items that ranked in increasing value
for (int i = 0; i < originalIdx.Count; i++)
{
Assert.AreEqual(originalIdx[i], answerIdx[i]);
}
}
/// <summary>
/// Runs CoreML on the image in SKBitmap format .
/// </summary>
/// <returns>A <see cref="CategoryManager"/> that contains all observations detected from the specified
/// <see cref="MLMultiArray"/>.</returns>
/// <param name="input">The array representation of image after pre-processing </param>
public CategoryManager Predict(MLMultiArray input)
{
MLMultiArray output1;
MLMultiArray output2;
(output1, output2) = RawPredict(input);
// checking for expected dimensions
if (output1.Shape == new nint[] { 4, NumAnchorBox, 1 })
{
// one anchoring box is represented by 4 points, no duplicating anchoring box
return(null);
}
if (output2.Shape == new nint[] { 1, 1, NumClasses, 1, NumAnchorBox })
{
return(null);
}
// conversion from MLMultiArray to double readable content
var outputFloats1 = new double[output1.Count]; // box prediction encoding 4 x 1917 x 1
for (var i = 0; i < output1.Count; i++)
{
outputFloats1[i] = (double)output1[i];
}
var outputFloats2 = new double[output2.Count]; // confidence score output matrix : 1 x 1 x 23 x 1 x 1917
for (var i = 0; i < output2.Count; i++)
{
outputFloats2[i] = (double)output2[i];
}
output1.Dispose();
output2.Dispose();
var cm = new CategoryManager();
for (var ibox = 0; ibox < NumAnchorBox; ibox++)
{
var scoresOfClasses = new List <double>();
for (var iclass = 0; iclass < NumClasses; iclass++)
{
var offsetIndex = iclass * NumAnchorBox + ibox;
var logit = outputFloats2[offsetIndex];
scoresOfClasses.Add(logit);
}
scoresOfClasses = MachineLearningService.Softmax(scoresOfClasses).ToList();
for (var iclass = 0; iclass < NumClasses; iclass++)
{
var confidence = scoresOfClasses[iclass];
var box = GenerateSKRect(
outputFloats1[BoxIndexOffset(0, ibox)],
outputFloats1[BoxIndexOffset(1, ibox)],
outputFloats1[BoxIndexOffset(2, ibox)],
outputFloats1[BoxIndexOffset(3, ibox)],
ibox);
if (iclass != 0 && IsWithinFrame(box))
{
var _ = new Observation(cm.GetOrCreate(iclass, ModelClassLabelEncoding.LookUpLabel(iclass)),
confidence, box);
}
}
}
return(cm);
}
public async Task Analyze()
{
MachineLearningService service = new MachineLearningService();
await service.Analyze();
}
public ImagePrediction Get(string nomeImagem)
{
var retorno = new MachineLearningService().ClassificarImagem(nomeImagem);
return(retorno); //
}