public frmVarianceClassifier(VarianceClassifier classifier, frmMain frmMain)
{
InitializeComponent();
_frmMain = frmMain;
_classifier = classifier;
FillFormWithConfiguration();
}
public void RejectPatches_2scanningWindows()
{
// arrange - initialize classifier
Image <Gray, byte> initialFrame = new Image <Gray, byte>(Path.Combine(_resourceDir, "violeta_1.jpg"));
IBoundingBox initialBb = new BoundingBox(new PointF(168, 178), new SizeF(80, 40));
// arrange - instantiate scanning window generator
ScanningWindowGeneratorStub swg = new ScanningWindowGeneratorStub();
IBoundingBox correctBb = initialBb.CreateInstance(new PointF(294, 281), new SizeF(80, 40));
swg.ScanningWindows = new IBoundingBox[]
{
correctBb,
initialBb.CreateInstance(new PointF(133, 222), new SizeF(80, 40))
};
foreach (IBoundingBox bb in swg.ScanningWindows)
{
bb.ScanningWindow = bb.CreateScanningWindow();
}
// arrange - instantiate classifier
VarianceClassifier classifier = new VarianceClassifier(swg, 0.5);
classifier.Initialize(initialFrame, initialBb);
// arrange - define future frame
Image <Gray, byte> futureFrame = new Image <Gray, byte>(Path.Combine(_resourceDir, "violeta_4.jpg"));
// define expected
List <int> expected = new List <int>()
{
0
};
// get actual
List <int> actual = classifier.AcceptedWindows(futureFrame, new List <int>()
{
0, 1
});
// assert
CollectionAssert.AreEqual(expected, actual);
}
private static IDetector LoadDetector(IObjectModel objectModel)
{
// scanning window generator
IScanningWindowGenerator scanningWindowGenerator = new ScanningWindowGenerator
(
1.2f,
0.1f,
0.1f,
100
);
// cascaded classifier
// variance classifier
IClassifier varianceClassifier = new VarianceClassifier(scanningWindowGenerator, 0.5);
// ensemble classifier
int width = objectModel.PatchSize.Width;
int height = objectModel.PatchSize.Height;
IPixelComparisonScheduler pcs = new PixelComparisonScheduler(12, 3, width, height);
IClassifier ensembleClassifier = new EnsembleClassifier(scanningWindowGenerator, pcs, 2);
// nn classifier
NnClassifier nnClassifier = new NnClassifier(scanningWindowGenerator, objectModel, 0.75f, 0.55f);
// instantiate cascaded classifier
IClassifier cascadedClassifier = new CascadedClassifier(varianceClassifier, ensembleClassifier, nnClassifier);
IDetector detector = new Detector
(
scanningWindowGenerator,
cascadedClassifier,
Service.NonMaximalBoundingBoxSuppress
);
return(detector);
}
public Learner(
IObjectModel objectModel,
IDetector detector,
PositivePatchSynthesisInfo initPositivePatchSysthesisInfo,
NegativePatchPickingInfo initNegativePatchPickingInfo,
PositivePatchSynthesisInfo runtimePositivePatchSysthesisInfo,
NegativePatchPickingInfo runtimeNegativePatchPickingInfo,
float sameSimilarityThreshold,
float validConservativeSimilarityThreshold
)
{
_objectModel = objectModel;
_detector = detector;
_initPositivePatchSynthesisInfo = initPositivePatchSysthesisInfo;
_initNegativePatchPickingInfo = initNegativePatchPickingInfo;
_runtimePosPatchSynthesisInfo = runtimePositivePatchSysthesisInfo;
_runtimeNegativePatchPickingInfo = runtimeNegativePatchPickingInfo;
_sameSimilarityThreshold = sameSimilarityThreshold;
_validConservativeSimilarityThreshold = validConservativeSimilarityThreshold;
_varianceClassifier = ((_detector as Detector).CascadedClassifier as CascadedClassifier).VarianceClassifier as VarianceClassifier;
_ensembleClassifier = ((_detector as Detector).CascadedClassifier as CascadedClassifier).EnsembleClassifier as EnsembleClassifier;
_nnClassifier = ((_detector as Detector).CascadedClassifier as CascadedClassifier).NnClassifier as NnClassifier;
}
