// The relationship between the white and the black part in a traditional

// Haar-like feature could be much more general. Why do they have to

// touch each other? Why do they need to have the same aspect ratio?

// All we require is white area = black area so the subtraction makes sense.

//

// And even that could be lifted by applying a correction factor

// (area white / area black).

//

// [We should generally start normalizing the result between -1

// (maximum inverted match) and 1 (maximum direct match) at this point

// as now we have two different areas in play so we can't really return

// meaningful "pixels" anymore.]

//

// Finally, black and white can even overlap and the formula doesn't even change.

// Although you may want to boost the result value because it's never 

// going to reach -1 or 1 when there is overlap. Not sure how to do

// the actual calculation for that.

//

// So these are... "generalized Haar features"?

//

// Result classification:

//

//   positive = match

//   0        = non-match  (indifferent/not applicable)

//   negative = anti-match (inverted Haar feature found)

srecord Haar(IIntegralImage img, DoubleRect rBlack, DoubleRect rWhite) {

  double get() {

    double blackArea = area(rBlack);

    double whiteArea = area(rWhite);

    //double overlappingArea = area(intersectDoubleRects(rBlack, rWhite));

    //double boost = ?

    double whiteSum = img.pixelSum(rWhite); // this is between 0 and whiteArea*255

    double blackSum = img.pixelSum(rBlack); // this is between 0 and blackArea*255

    ret (doubleRatio(whiteSum, whiteArea) - doubleRatio(blackSum, blackArea))/255.0;

  }

}