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// 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).
//
// 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"?
//
// The general unit returned by a Haar match should be pixels*brightness
// (value can be negative too).
//
// 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) {
  // Let's return a value between -1 (maximum anti-match) and
  // 1 (maximum match). Seems most elegant.
  
  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);
  }
}