public void TestProcessChipResults()
{
var offset = new SKPointI(300, 600);
var observations = new[]
{
new Observation(_categoryManager.GetOrCreate(1, "Car"), 0.9, new SKRect(0.1f, 0.2f, 0.3f, 0.4f)),
new Observation(_categoryManager.GetOrCreate(2, "Building"), 0.78, new SKRect(.2f, 0.3f, 0.5f, 0.8f))
};
PostProcessingService.ProcessChipResults(offset, observations);
AssertUtils.AreEqualIEnumerable(new[]
{
new Observation(
_categoryManager.GetOrCreate(1, "Car"),
0.9,
new SKRect(offset.X + 0.1f * SharedConstants.DefaultChipWidth,
offset.Y + 0.2f * SharedConstants.DefaultChipHeight,
offset.X + 0.3f * SharedConstants.DefaultChipWidth,
offset.Y + 0.4f * SharedConstants.DefaultChipHeight)),
new Observation(
_categoryManager.GetOrCreate(2, "Building"),
0.78,
new SKRect(offset.X + 0.2f * SharedConstants.DefaultChipWidth,
offset.Y + 0.3f * SharedConstants.DefaultChipHeight,
offset.X + 0.5f * SharedConstants.DefaultChipWidth,
offset.Y + 0.8f * SharedConstants.DefaultChipHeight)),
},
observations,
AssertUtils.AreEqualObservation);
}
/// <summary>
/// Analyzes the specified <see cref="Chip"/> using the machine learning model.
/// </summary>
/// <param name="chip">The specified <see cref="Chip"/> of default size.</param>
/// <returns>A <see cref="CategoryManager"/> that contains all <see cref="Category"/>s and <see cref="Observation"/>s detected in the specified <see cref="Chip"/>.</returns>
public static CategoryManager AnalyzeChip(Chip chip)
{
if (chip == null)
{
throw new ArgumentNullException();
}
try
{
using (var mlMultiArray = chip.ToMLMultiArray())
using (var model = new CoreMLModel())
{
var categoryManager = model.Predict(mlMultiArray);
var observations = categoryManager.Observations;
var observationList = observations.ToList();
PostProcessingService.ProcessChipResults(chip.Region.Location, observationList);
return(categoryManager);
}
}
catch (FileNotFoundException)
{
return(new CategoryManager());
}
}