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;
}
private List <Image <Gray, byte> > PickNegativePatches(Image <Gray, byte> frame, List <IBoundingBox> negativeScanningWindows, NegativePatchPickingInfo pickingInfo)
{
List <Image <Gray, byte> > patches = new List <Image <Gray, byte> >();
float step = negativeScanningWindows.Count / pickingInfo.EnsembleCount;
for (int i = 0; i < pickingInfo.EnsembleCount; i++)
{
int index = (int)(i * step);
IBoundingBox bb = negativeScanningWindows[index];
Image <Gray, byte> patch = frame.GetPatch(bb.Center, Size.Round(bb.Size));
patches.Add(patch);
}
return(patches);
}
