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());
}
}
public void TestNonMaximumSuppression_IOUGreaterThanThreshold()
{
var observation1 = new Observation(_category, 0.78, new SKRect(1f, 1f, 7f, 7f));
var observation2 = new Observation(_category, 0.9, new SKRect(0f, 0f, 6f, 6f));
var observations = new[] { observation1, observation2 };
var result = PostProcessingService.NonMaximumSuppression(observations, IOUThreshold, ConfidenceThreshold)
.ToList();
AssertUtils.AreEqualIEnumerable(new[] { observation2 }, result);
}
public void TestNonMaximumSuppression_NonOverlapping()
{
var observation1 = new Observation(_category, 0.9, new SKRect(0.1f, 0.2f, 0.3f, 0.4f));
var observation2 = new Observation(_category, 0.78, new SKRect(0.4f, 0.5f, 0.5f, 0.6f));
var observations = new[] { observation1, observation2 };
var result = PostProcessingService.NonMaximumSuppression(observations, IOUThreshold, ConfidenceThreshold)
.ToList();
AssertUtils.AreEqualIEnumerable(observations, result);
}
public void TestNonMaximumSuppression_CompletelyOverlappingIOUEqualToThreshold()
{
var observation1 = new Observation(_category, 0.9, new SKRect(1f, 2f, 5f, 8f));
var observation2 = new Observation(_category, 0.78, new SKRect(2f, 3f, 5f, 7f));
var observations = new[] { observation1, observation2 };
var result = PostProcessingService.NonMaximumSuppression(observations, IOUThreshold, ConfidenceThreshold)
.ToList();
AssertUtils.AreEqualIEnumerable(observations, result);
}
/// <summary>
/// Starts the analysis for the uploaded images.
/// </summary>
private void StartAnalysis()
{
if (ImageEntries == null || ImageEntries.Count == 0)
{
LaunchImageUploadPage();
}
_totalNumberOfChips = ImageEntries.Sum(imageEntry => GetNumberOfChips(imageEntry.Image));
_numberOfProcessedChips = 0;
_task = Task.Factory.StartNew(() =>
{
try
{
foreach (var imageEntry in ImageEntries)
{
var categoryManagers = ImageAnalysisService.AnalyzeImage(imageEntry.Image,
ChipAnalysisService.AnalyzeChip, _ct,
() =>
{
Interlocked.Increment(ref _numberOfProcessedChips);
BeginInvokeOnMainThread(() =>
{
ProgressView.SetProgress((float)_numberOfProcessedChips / _totalNumberOfChips,
true);
});
})
.ToList();
var observations = PostProcessingService.SelectObservations(
categoryManagers,
SharedConstants.DefaultIOUThreshold,
NSUserDefaults.StandardUserDefaults.DoubleForKey(
Constants.ConfidenceThresholdUserDefaultName), _ct)
.ToList();
imageEntry.Observations = observations;
}
if (_ts.IsCancellationRequested)
{
return;
}
Task.Delay(Constants.DelayBeforeResults / _numberOfProcessedChips, _ct).Wait(_ct);
BeginInvokeOnMainThread(LaunchResultsView);
}
catch (OperationCanceledException)
{
// ignored
}
}, _ct);
}
