private void resetBoxEstimation(CvMat input, ref CvMat hue, ref CvMat normalize)
{
boxEstimationType = BoxEstimationType.NONE;
// this logic is here (and not somewhere else) so that we don't have to calculate hue/normalize twice in a single frame
// that's because hue/normalize are also needed at a later state of frame processing
// this check right here sounds stupid, but I want to easily change priority between hue and normalize
// in the end, one of the 2 will stay at 1st place and the check will be removed...
if (boxEstimationType == BoxEstimationType.NONE)
{
hue = MatOps.BGRtoHue(input);
//MatOps.NewWindowShow( hue, "HUE-processed" );
if (estimateBoxHint(hue, ref floodHueTolerance))
{
boxEstimationType = BoxEstimationType.HUE;
}
}
if (boxEstimationType == BoxEstimationType.NONE)
{
normalize = MatOps.MyNormalize(input);
MatOps.NewWindowShow(normalize, "NORMALIZE-processed");
if (estimateBoxHint(normalize, ref floodNormTolerance))
{
boxEstimationType = BoxEstimationType.NORMALIZE;
}
}
}
// => hue and normalize, if null, may be created and assigned
// <= ROI
private CvMat detectROI(CvMat input, ref CvMat hue, ref CvMat normalize)
{
// TODO : Like I said above, if I get the minimum/maximum values, I have an accurate lowerBound/upperBound pair to work with!!!
CvMat roi;
CvScalar lowerBound;
CvScalar upperBound;
// IDEA 3:
// Determine if I should check for "features" in the "thresholded" image, or in a cropped grayscale version of the original one!!
// For now, lets search the thresholded one...
if (boxEstimationType == BoxEstimationType.HUE)
{
roi = MatOps.CopySize(input, MatrixType.U8C1);
lowerBound = boxEstimatedValue - floodHueTolerance / 1; // TODO : this should be +-(MAX VALUE)
upperBound = boxEstimatedValue + floodHueTolerance / 1;
if (hue == null)
{
hue = MatOps.BGRtoHue(input);
}
hue.InRangeS(lowerBound, upperBound, roi);
}
else if (boxEstimationType == BoxEstimationType.NORMALIZE)
{
// TODO : must investigate, range doesn't return anything
roi = MatOps.CopySize(input, MatrixType.U8C1);
lowerBound = boxEstimatedValue - floodNormTolerance;
upperBound = boxEstimatedValue + floodNormTolerance;
if (normalize == null)
{
normalize = MatOps.MyNormalize(input);
}
normalize.InRangeS(lowerBound, upperBound, roi);
}
else
{
// Couldn't estimate either way? We are off to a bad start, but lets try to see if features can be extracted anyway.
roi = MatOps.ConvertChannels(input); // we are already losing valuable info here!!
}
return(roi);
}
