Function MeanDirectionVector

deps/tesseract/classify/intfx.cpp:187–230

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.

// 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;
}

// Helper computes one or more features corresponding to the given points.
// Emitted features are on the line defined by: