public void Generate_SimpleCase()
{
// arrange - instantiate generator
ScanningWindowGenerator generator = new ScanningWindowGenerator
(
2.0f,
1.0f,
1.0f,
1
);
// arrange - define frame size and initial bounding box
Size frameSize = new Size(2, 2);
IBoundingBox initialBb = new BoundingBox(new PointF(0, 0), new SizeF(1, 1));
// define expected
IBoundingBox[] expected = new IBoundingBox[]
{
initialBb.CreateInstance(new PointF(0, 0), new SizeF(1, 1)),
initialBb.CreateInstance(new PointF(1, 0), new SizeF(1, 1)),
initialBb.CreateInstance(new PointF(0, 1), new SizeF(1, 1)),
initialBb.CreateInstance(new PointF(1, 1), new SizeF(1, 1)),
initialBb.CreateInstance(new PointF(0.5f, 0.5f), new SizeF(2, 2)),
};
// get actual
IBoundingBox[] actual = generator.Generate(frameSize, initialBb);
// assert
Assert.IsTrue(BoundingBoxListsAreEqual(expected.ToList(), actual.ToList()));
}
public void SwgAndBaseClassifier()
{
// arrange - define frame
Image <Gray, byte> frame = new Image <Gray, byte>(new Size(4, 3));
// arrange - create scanning window generator
ScanningWindowGenerator swg = new ScanningWindowGenerator(2.0f, 0.1f, 0.1f, 1);
// arrange - generate scanning windows
swg.Generate(frame.Size, new BoundingBox(new PointF(2.5f, 1.5f), new SizeF(2, 1)));
// arrange - create pixel comparison scheduler with for 1 base classifier with 1 comparisons
PixelComparisonSchedulerStub pcs = new PixelComparisonSchedulerStub();
PixelComparisonGroupF pcg = new PixelComparisonGroupF();
pcg.Value = new PixelComparisonF[]
{
new PixelComparisonF(new PointF(0, 0), new PointF(1, 0)),
};
pcs.ScheduledPixelComparisons = new PixelComparisonGroupF[]
{
pcg
};
// arrange - create base classifier
BaseClassifier classifier = new BaseClassifier(swg, 0, pcs);
classifier.PostInstantiation();
classifier.Initialize();
classifier.SetCurrentFrame(frame);
// calculate binary code for every window - this part must not throw exception
for (int i = 0; i < swg.ScanningWindows.Length; i++)
{
classifier.CalculatePosteriorForWindow(i);
}
}
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 void GetPosteriors_InitializationAndOneMethodCall()
{
// arrange - define initialization frame and bb
Image <Gray, byte> initFrame = new Image <Gray, byte>(Path.Combine(_resourceDir, "LP_up_left.jpg"));
IBoundingBox initBb = new BoundingBox(new PointF(170, 150), new SizeF(269, 189));
// arrange - generate positive and negative patches
// -> positive patches
List <Image <Gray, byte> > positivePatches = new List <Image <Gray, byte> >();
int positiveCount = 100;
for (int i = 0; i < positiveCount; i++)
{
Image <Gray, byte> patch = Service.GenerateSimilarPatch(initFrame, initBb, 0, 0, 0);
CvInvoke.cvSmooth(patch, patch, SMOOTH_TYPE.CV_GAUSSIAN, 0, 0, 1.5, 1.5);
positivePatches.Add(patch);
}
// -> negative patches
ScanningWindowGenerator swg = new ScanningWindowGenerator(1.2f, 0.1f, 0.1f, 20);
IBoundingBox[] scanningWindows = swg.Generate(initFrame.Size, initBb);
List <IBoundingBox> sortedScanningWindows = Service.SortScanningWindowsByOverlap(scanningWindows.ToList(), initBb);
List <IBoundingBox> negativeScanningWindows = Service.GetScanningWindowsWithOverlapLessThan(0.2f, initBb, sortedScanningWindows);
List <Image <Gray, byte> > negativePatches = new List <Image <Gray, byte> >();
int negativeCount = 100;
int step = negativeScanningWindows.Count / negativeCount;
for (int i = 0; i < negativeCount; i++)
{
IBoundingBox bb = negativeScanningWindows[i * step];
negativePatches.Add(initFrame.GetPatch(bb.Center, Size.Round(bb.Size)));
}
// arrange - instantiate and initialize base classifier
PixelComparisonGroupF pcg = new PixelComparisonGroupF();
pcg.Value = new PixelComparisonF[]
{
new PixelComparisonF(new PointF(0, 0.5f), new PointF(0.5f, 0.5f)),
new PixelComparisonF(new PointF(0.5f, 0), new PointF(0.5f, 0.5f)),
new PixelComparisonF(new PointF(1, 0.5f), new PointF(0.5f, 0.5f)),
new PixelComparisonF(new PointF(0.5f, 1), new PointF(0.5f, 0.5f))
};
IPixelComparisonScheduler pxs = new PixelComparisonSchedulerStub(
new PixelComparisonGroupF[] {
pcg
}
);
BaseClassifier classifier = new BaseClassifier(swg, 0, pxs);
classifier.PostInstantiation();
classifier.Update(positivePatches, negativePatches);
// arrange - define current frame used scanning windows
Image <Gray, byte> currentFrame = new Image <Gray, byte>(Path.Combine(_resourceDir, "LP_down_right.jpg"));
CvInvoke.cvSmooth(currentFrame, currentFrame, SMOOTH_TYPE.CV_GAUSSIAN, 0, 0, 1.5, 1.5);
List <IBoundingBox> usedScanningWindows = new List <IBoundingBox>()
{
new BoundingBox(new PointF(163, 132), new SizeF(288, 220)),
new BoundingBox(new PointF(478, 127), new SizeF(300, 199)),
new BoundingBox(new PointF(150, 355), new SizeF(308, 214)),
new BoundingBox(new PointF(480, 364), new SizeF(277, 217)),
};
// get actual
List <double> posteriors = new List <double>();
foreach (IBoundingBox bb in usedScanningWindows)
{
posteriors.Add(classifier.GetPosterior(currentFrame, bb));
}
// assert
Assert.AreEqual(posteriors.Count, usedScanningWindows.Count);
Assert.IsTrue(posteriors[0] < 0.5);
Assert.IsTrue(posteriors[1] < 0.5);
Assert.IsTrue(posteriors[2] < 0.5);
Assert.IsTrue(posteriors[3] > 0.5);
}
public void GetPosteriors_InitializationAndOneMethodCall()
{
// arrange - generate pixel comparisons
PixelComparisonGroupF pcg1 = new PixelComparisonGroupF();
pcg1.Value = new PixelComparisonF[]
{
new PixelComparisonF(new PointF(0, 0.5f), new PointF(0.5f, 0.5f)),
new PixelComparisonF(new PointF(0.5f, 0), new PointF(0.5f, 0.5f)),
new PixelComparisonF(new PointF(1, 0.5f), new PointF(0.5f, 0.5f)),
new PixelComparisonF(new PointF(0.5f, 1), new PointF(0.5f, 0.5f))
};
PixelComparisonGroupF pcg2 = new PixelComparisonGroupF();
pcg2.Value = new PixelComparisonF[]
{
new PixelComparisonF(new PointF(0, 0.5f), new PointF(0.5f, 0.5f)),
new PixelComparisonF(new PointF(0.5f, 0), new PointF(0.5f, 0.5f)),
new PixelComparisonF(new PointF(1, 0.5f), new PointF(0.5f, 0.5f)),
new PixelComparisonF(new PointF(0.5f, 1), new PointF(0.5f, 0.5f))
};
IPixelComparisonScheduler pxs = new PixelComparisonSchedulerStub(
new PixelComparisonGroupF[] {
pcg1,
pcg2
}
);
// arrange - instantiate ensamble classifier
ScanningWindowGeneratorStub swgEnsemble = new ScanningWindowGeneratorStub();
IBoundingBox positiveScanningWindow = new BoundingBox(new PointF(480, 364), new SizeF(277, 217));
swgEnsemble.ScanningWindows = new IBoundingBox[]
{
new BoundingBox(new PointF(163, 132), new SizeF(288, 220)),
new BoundingBox(new PointF(478, 127), new SizeF(300, 199)),
new BoundingBox(new PointF(150, 355), new SizeF(290, 214)),
positiveScanningWindow,
};
foreach (IBoundingBox bb in swgEnsemble.ScanningWindows)
{
bb.ScanningWindow = bb.CreateScanningWindow();
}
EnsembleClassifier classifier = new EnsembleClassifier(swgEnsemble, pxs, 1.5);
classifier.PostInstantiation();
// arrange - define initialization frame and bb
Image <Gray, byte> initFrame = new Image <Gray, byte>(Path.Combine(_resourceDir, "LP_up_left.jpg"));
IBoundingBox initBb = new BoundingBox(new PointF(170, 150), new SizeF(269, 189));
// arrange - initialize ensemble classifier
classifier.Initialize(initFrame, initBb);
// arrange - generate positive and negative patches
// -> positive patches
List <Image <Gray, byte> > positivePatches = new List <Image <Gray, byte> >();
int positiveCount = 100;
for (int i = 0; i < positiveCount; i++)
{
Image <Gray, byte> patch = Service.GenerateSimilarPatch(initFrame, initBb, 0, 0, 0);
CvInvoke.cvSmooth(patch, patch, SMOOTH_TYPE.CV_GAUSSIAN, 0, 0, 1.5, 1.5);
positivePatches.Add(patch);
}
// -> negative patches
IScanningWindowGenerator swg = new ScanningWindowGenerator(1.5f, 0.7f, 0.7f, 30);
List <IBoundingBox> scanningWindows = swg.Generate(initFrame.Size, initBb).ToList();
List <IBoundingBox> sortedScanningWindows = Service.SortScanningWindowsByOverlap(scanningWindows, initBb);
List <IBoundingBox> negativeScanningWindows = Service.GetScanningWindowsWithOverlapLessThan(0.2f, initBb, sortedScanningWindows);
List <Image <Gray, byte> > negativePatches = new List <Image <Gray, byte> >();
int negativeCount = 100;
int step = negativeScanningWindows.Count / negativeCount;
for (int i = 0; i < negativeCount; i++)
{
IBoundingBox bb = negativeScanningWindows[i * step];
negativePatches.Add(initFrame.GetPatch(bb.Center, Size.Round(bb.Size)));
}
// update classifier with new positive and negative patches
classifier.Update(positivePatches, negativePatches);
// arrange - define current frame
Image <Gray, byte> currentFrame = new Image <Gray, byte>(Path.Combine(_resourceDir, "LP_down_right.jpg"));
// define expected
List <int> expected = new List <int>()
{
3
};
// get actual
List <int> actual = classifier.AcceptedWindows(currentFrame, new List <int> {
0, 1, 2, 3
});
// assert
CollectionAssert.AreEqual(expected, actual);
}
