public void RelativeSimilarity()
{
// arrange - create object model
IObjectModel objectModel = new ObjectModel(new Size(15, 15));
objectModel.PostInstantiation();
// -> create 2 positive and 2 negative patches
Image <Gray, byte> helpImage = new Image <Gray, byte>(Path.Combine(_resourceDir, "violeta_1.jpg"));
Image <Gray, byte> posPatch1 = helpImage.GetPatch(new PointF(168, 178), new Size(80, 40)).Resize(objectModel.PatchSize.Width, objectModel.PatchSize.Height, INTER.CV_INTER_LINEAR);
Image <Gray, byte> posPatch2 = helpImage.GetPatch(new PointF(169, 179), new Size(81, 41)).Resize(objectModel.PatchSize.Width, objectModel.PatchSize.Height, INTER.CV_INTER_LINEAR);
Image <Gray, byte> negPatch1 = helpImage.GetPatch(new PointF(300, 200), new Size(50, 50)).Resize(objectModel.PatchSize.Width, objectModel.PatchSize.Height, INTER.CV_INTER_LINEAR);
Image <Gray, byte> negPatch2 = helpImage.GetPatch(new PointF(200, 300), new Size(50, 50)).Resize(objectModel.PatchSize.Width, objectModel.PatchSize.Height, INTER.CV_INTER_LINEAR);
// -> add patches to the object model
objectModel.AddPositivePatch(posPatch1);
objectModel.AddPositivePatch(posPatch2);
objectModel.AddNegativePatch(negPatch1);
objectModel.AddNegativePatch(negPatch2);
// arrange - create 2 test patches, 1 should be positive and 1 negative
Image <Gray, byte> posPatch = helpImage.GetPatch(new PointF(167, 177), new Size(79, 39)).Resize(objectModel.PatchSize.Width, objectModel.PatchSize.Height, INTER.CV_INTER_LINEAR);
Image <Gray, byte> negPatch = helpImage.GetPatch(new PointF(300, 300), new Size(80, 40)).Resize(objectModel.PatchSize.Width, objectModel.PatchSize.Height, INTER.CV_INTER_LINEAR);
// get actual relative similarities with the object model
float actual1 = objectModel.RelativeSimilarity(posPatch);
float actual2 = objectModel.RelativeSimilarity(negPatch);
// assert
Assert.IsTrue(actual1 > 0.5f && actual2 <= 1.0f);
Assert.IsTrue(actual2 >= 0.0f && actual2 <= 0.5f);
}
public void RejectPatches()
{
// arrange - create object model
IObjectModel objectModel = new ObjectModel(new Size(15, 15));
objectModel.PostInstantiation();
// -> create 2 positive and 2 negative patches
Image <Gray, byte> helpImage = new Image <Gray, byte>(Path.Combine(_resourceDir, "violeta_1.jpg"));
Image <Gray, byte> posPatch1 = helpImage.GetPatch(new PointF(168, 178), new Size(80, 40)).Resize(objectModel.PatchSize.Width, objectModel.PatchSize.Height, INTER.CV_INTER_LINEAR);
Image <Gray, byte> posPatch2 = helpImage.GetPatch(new PointF(169, 179), new Size(81, 41)).Resize(objectModel.PatchSize.Width, objectModel.PatchSize.Height, INTER.CV_INTER_LINEAR);
Image <Gray, byte> negPatch1 = helpImage.GetPatch(new PointF(300, 200), new Size(50, 50)).Resize(objectModel.PatchSize.Width, objectModel.PatchSize.Height, INTER.CV_INTER_LINEAR);
Image <Gray, byte> negPatch2 = helpImage.GetPatch(new PointF(200, 300), new Size(50, 50)).Resize(objectModel.PatchSize.Width, objectModel.PatchSize.Height, INTER.CV_INTER_LINEAR);
// -> add patches to the object model
objectModel.AddPositivePatch(posPatch1);
objectModel.AddPositivePatch(posPatch2);
objectModel.AddNegativePatch(negPatch1);
objectModel.AddNegativePatch(negPatch2);
// arrange - create scanning window generator with stub windows
ScanningWindowGeneratorStub swg = new ScanningWindowGeneratorStub();
swg.ScanningWindows = new IBoundingBox[]
{
new BoundingBox(new PointF(167, 177), new SizeF(79, 39)),
new BoundingBox(new PointF(300, 300), new SizeF(80, 40))
};
// arrange - create classifier
NnClassifier classifier = new NnClassifier(swg, objectModel, 0.75f, 0.6f);
classifier.PostInstantiation();
classifier.Initialize(new Image <Gray, byte>(new Size(0, 0)), new BoundingBox(new PointF(), new SizeF()));
// define expected accepted patches
List <int> expected = new List <int>()
{
0
};
// get actual accepted patches
List <int> actual = classifier.AcceptedWindows(helpImage, new List <int>()
{
0, 1
});
// assert
CollectionAssert.AreEqual(expected, actual);
}
public void RejectPatches_FrameRoiIsNotSetAfterProcessing()
{
// arrange - create object model
IObjectModel objectModel = new ObjectModel(new Size(15, 15));
objectModel.PostInstantiation();
// -> create 1 patch
Image <Gray, byte> helpImage = new Image <Gray, byte>(Path.Combine(_resourceDir, "violeta_1.jpg"));
Image <Gray, byte> patch = helpImage.GetPatch(new PointF(168, 178), new Size(80, 40)).Resize(objectModel.PatchSize.Width, objectModel.PatchSize.Height, INTER.CV_INTER_LINEAR);
// -> add patch to the object model
objectModel.AddPositivePatch(patch);
// arrange - create scanning window generator with stub windows
ScanningWindowGeneratorStub swg = new ScanningWindowGeneratorStub();
swg.ScanningWindows = new IBoundingBox[]
{
new BoundingBox(new PointF(167, 177), new SizeF(79, 39))
};
// arrange - create classifier
NnClassifier classifier = new NnClassifier(swg, objectModel, 0.75f, 0.6f);
classifier.PostInstantiation();
classifier.Initialize(new Image <Gray, byte>(new Size(0, 0)), new BoundingBox(new PointF(), new SizeF()));
// define expected frame ROI after processing
Rectangle expected = new Rectangle(new Point(), helpImage.Size);
// get actual frame ROI after processing
classifier.AcceptedWindows(helpImage, new List <int>()
{
0
});
Rectangle actual = helpImage.ROI;
// assert
Assert.AreEqual(expected, actual);
Assert.IsTrue(!helpImage.IsROISet); // just in case
}
