LedMeasurement augmentKeypoint(cv::Mat grayImage,
cv::KeyPoint origKeypoint) {
// Saving this now before we monkey with m_floodFillMask
cv::bitwise_not(m_floodFillMask, m_scratchNotMask);
const int loDiff = 2;
const int upDiff = 2;
auto m_area = cv::floodFill(
grayImage, m_floodFillMask, origKeypoint.pt, 255,
&m_filledBounds, loDiff, upDiff,
CV_FLOODFILL_MASK_ONLY | (/* connectivity 4 or 8 */ 4) |
(/* value to write in to mask */ 255 << 8));
// Now m_floodFillMask contains the mask with both our point
// and all other points so far. We need to split them by ANDing with
// the NOT of the old flood-fill mask we saved earlier.
cv::bitwise_and(m_scratchNotMask, m_floodFillMask,
m_perPointResults);
// OK, now we have the results for just this point in per-point
// results
/// Initialize return value
auto ret = static_cast<LedMeasurement>(origKeypoint);
if (m_area <= 0) {
// strange...
return ret;
}
m_binarySubImage = m_perPointResults(m_filledBounds);
computeContour();
m_moments = haveContour() ? cv::moments(m_contour, false)
: cv::moments(m_binarySubImage, true);
// the first two we are using from the keypoint description, the latter we can't
// always get (contour-based)
#if 0
/// Estimate diameter
ret.diameter = diameter();
ret.area = area();
if (haveContour()) {
ret.circularity = circularity();
}
#endif
ret.knowBoundingBox = true;
ret.boundingBox =
cv::Size2f(m_filledBounds.width, m_filledBounds.height);
return ret;
}
double circularity(Mat& src, int obj_id) {
vector<Point> object = foreground(src, obj_id);
return circularity(object);
}
