// Computes the center of mass and second moments for the old baseline and
// 2nd moment normalizations. Returns the outline length.
// The input denorm should be the normalizations that have been applied from
// the image to the current state of this TBLOB.
int TBLOB::ComputeMoments(FCOORD* center, FCOORD* second_moments) const {
// Compute 1st and 2nd moments of the original outline.
LLSQ accumulator;
TBOX box = bounding_box();
// Iterate the outlines, accumulating edges relative the box.botleft().
CollectEdges(box, NULL, &accumulator, NULL, NULL);
*center = accumulator.mean_point() + box.botleft();
// The 2nd moments are just the standard deviation of the point positions.
double x2nd = sqrt(accumulator.x_variance());
double y2nd = sqrt(accumulator.y_variance());
if (x2nd < 1.0) x2nd = 1.0;
if (y2nd < 1.0) y2nd = 1.0;
second_moments->set_x(x2nd);
second_moments->set_y(y2nd);
return accumulator.count();
}
// Helper returns the mean direction vector from the given stats. Use the
// mean direction from dirs if there is information available, otherwise, use
// the fit_vector from point_diffs.
static FCOORD MeanDirectionVector(const LLSQ& point_diffs, const LLSQ& dirs,
const FCOORD& start_pt,
const FCOORD& end_pt) {
FCOORD fit_vector;
if (dirs.count() > 0) {
// There were directions, so use them. To avoid wrap-around problems, we
// have 2 accumulators in dirs: x for normal directions and y for
// directions offset by 128. We will use the one with the least variance.
FCOORD mean_pt = dirs.mean_point();
double mean_dir = 0.0;
if (dirs.x_variance() <= dirs.y_variance()) {
mean_dir = mean_pt.x();
} else {
mean_dir = mean_pt.y() + 128;
}
fit_vector.from_direction(Modulo(IntCastRounded(mean_dir), 256));
} else {
// There were no directions, so we rely on the vector_fit to the points.
// Since the vector_fit is 180 degrees ambiguous, we align with the
// supplied feature_dir by making the scalar product non-negative.
FCOORD feature_dir(end_pt - start_pt);
fit_vector = point_diffs.vector_fit();
if (fit_vector.x() == 0.0f && fit_vector.y() == 0.0f) {
// There was only a single point. Use feature_dir directly.
fit_vector = feature_dir;
} else {
// Sometimes the least mean squares fit is wrong, due to the small sample
// of points and scaling. Use a 90 degree rotated vector if that matches
// feature_dir better.
FCOORD fit_vector2 = !fit_vector;
// The fit_vector is 180 degrees ambiguous, so resolve the ambiguity by
// insisting that the scalar product with the feature_dir should be +ve.
if (fit_vector % feature_dir < 0.0)
fit_vector = -fit_vector;
if (fit_vector2 % feature_dir < 0.0)
fit_vector2 = -fit_vector2;
// Even though fit_vector2 has a higher mean squared error, it might be
// a better fit, so use it if the dot product with feature_dir is bigger.
if (fit_vector2 % feature_dir > fit_vector % feature_dir)
fit_vector = fit_vector2;
}
}
return fit_vector;
}
