private AppendedMediaSequences datasetsOfInterest()
{
AppendedMediaSequences allMs = new AppendedMediaSequences(new List <IMediaSequences> {
PrivateDataSet.CASIA_IRIS_DISTANCE
});
// AppendedMediaSequences allMs = new AppendedMediaSequences( new List<IMediaSequences> { PrivateDataSet.PUT_FACE_DATABASE, PrivateDataSet.POLYU_NIR_FACE } );
return(allMs);
}
public void BinocularDetectionTest()
{
Settings.Instance.Processing.TrackingMode = TrackingModeEnum.Binocular;
GazeTrackingLibrary.Detection.Eye.Eyetracker et = new GazeTrackingLibrary.Detection.Eye.Eyetracker();
Assert.IsTrue(et.IsReady);
List <double> rightEyeProportionalErrors = new List <double>();
List <double> leftEyeProportionalErrors = new List <double>();
long countEyesNotFound = 0;
long countNoEyeCentreLandmarks = 0;
long countEyesFoundCorrectly = 0;
long countEyesFoundIncorrectly = 0;
long imageCounter = 0;
AppendedMediaSequences allMs = datasetsOfInterest();
foreach (IMediaSequence ms in allMs.getMediaSequences())
{
foreach (IMediaFrame mf in ms.getMediaFrames())
{
Image img = mf.getImage();
imageCounter = imageCounter + 1;
ILabelledFeature eyeR = mf.getLabelledFeature(DataSetEnums.FeatureName.RIGHT_EYE_CENTRE);
ILabelledFeature eyeL = mf.getLabelledFeature(DataSetEnums.FeatureName.LEFT_EYE_CENTRE);
if ((eyeR != null) && (eyeL != null))
{
Point rightEyeCentre = eyeR.getPath().getPoints()[0];
Point leftEyeCentre = eyeL.getPath().getPoints()[0];
Bitmap bmp = new Bitmap(img);
Image <Gray, byte> gray = new Image <Gray, byte>(bmp);
TrackData trackData = new TrackData();
bool eyesFound = et.DetectEyes(gray, trackData);
if (eyesFound)
{
// check more things about the location of the eyes that were found
// Record the detection information alongside the MediaFrame so that a little later we can compare the distance that the
// landmark eye centre is from the centre of the detected region, and the corresponding distance between the eyes for each sample
if (trackData.RightROI.Contains(leftEyeCentre) && trackData.LeftROI.Contains(rightEyeCentre))
{
countEyesFoundCorrectly++;
renderEyeDetection(imageCounter, bmp, gray, trackData, SearchResult.FOUND_OK);
}
else
{
countEyesFoundIncorrectly++;
renderEyeDetection(imageCounter, bmp, gray, trackData, SearchResult.FOUND_WRONG);
}
// Create a measure for the error
Point roiCentreLeftEye = new Point((trackData.RightROI.Right + trackData.RightROI.Left) / 2, (trackData.RightROI.Top + trackData.RightROI.Bottom) / 2);
Point roiCentreRightEye = new Point((trackData.LeftROI.Right + trackData.LeftROI.Left) / 2, (trackData.LeftROI.Top + trackData.LeftROI.Bottom) / 2);
roiCentreRightEye.Offset(-rightEyeCentre.X, -rightEyeCentre.Y);
roiCentreLeftEye.Offset(-leftEyeCentre.X, -leftEyeCentre.Y);
rightEyeCentre.Offset(-leftEyeCentre.X, -leftEyeCentre.Y);
double radialProportionRight = radialDistance(roiCentreRightEye) / radialDistance(rightEyeCentre);
double radialProportionLeft = radialDistance(roiCentreLeftEye) / radialDistance(rightEyeCentre);
rightEyeProportionalErrors.Add(radialProportionRight);
leftEyeProportionalErrors.Add(radialProportionLeft);
}
else
{
countEyesNotFound++;
renderEyeDetection(imageCounter, bmp, gray, trackData, SearchResult.NOT_FOUND);
}
bmp.Dispose();
gray.Dispose();
}
else
{
countNoEyeCentreLandmarks++;
}
img.Dispose();
}
}
double reMean = calculateMean(rightEyeProportionalErrors);
double leMean = calculateMean(leftEyeProportionalErrors);
double reStdDev = calculateStdDev(rightEyeProportionalErrors);
double leStdDev = calculateStdDev(leftEyeProportionalErrors);
// For combined POLY_U NIR FACE Database & PUT Face Database
Assert.True(reMean < 0.15D);
Assert.True(leMean < 0.15D);
Assert.True(( double )countEyesFoundCorrectly / (( double )countEyesFoundCorrectly + ( double )countEyesFoundIncorrectly + ( double )countEyesNotFound) > 0.6);
Assert.True(( double )countEyesFoundCorrectly / (( double )countEyesFoundCorrectly + ( double )countEyesFoundIncorrectly) > 0.74);
Assert.AreEqual(615, countNoEyeCentreLandmarks);
// For POLY_U NIR FACE Database
// Only 24.3% success at the moment (POLYU NIR FACE)
Assert.True(reMean < 0.15D);
Assert.True(leMean < 0.15D);
Assert.True(( double )countEyesFoundCorrectly / (( double )countEyesFoundCorrectly + ( double )countEyesFoundIncorrectly + ( double )countEyesNotFound) > 0.2);
Assert.True(( double )countEyesFoundCorrectly / (( double )countEyesFoundCorrectly + ( double )countEyesFoundIncorrectly) > 0.85);
Assert.AreEqual(0, countNoEyeCentreLandmarks);
// For PUT Face Database
Assert.True(reMean < 0.15D); // actual is only slightly under this
Assert.True(leMean < 0.14D); // actual is only slightly under this
Assert.True(countEyesFoundCorrectly >= 6014); // We get 6014 as baseline
Assert.True(countEyesFoundIncorrectly <= 2048); // We get 2048 as baseline
Assert.True(countEyesNotFound <= 1199); // We get 1199 as baseline
Assert.True(countNoEyeCentreLandmarks <= 615); // We get 615as baseline
Assert.AreEqual(615, countNoEyeCentreLandmarks); // Should not suddenly be getting more landmarks!
}
