コード例 #1
0
ファイル: blobs.cpp プロジェクト: 11110101/tess-two
// Normalize in-place using the DENORM.
void TESSLINE::Normalize(const DENORM& denorm) {
  EDGEPT* pt = loop;
  do {
    denorm.LocalNormTransform(pt->pos, &pt->pos);
    pt = pt->next;
  } while (pt != loop);
  SetupFromPos();
}
コード例 #2
0
ファイル: intfx.cpp プロジェクト: dqsoft/tesseract
// Extracts Tesseract features and appends them to the features vector.
// Startpt to lastpt, inclusive, MUST have the same src_outline member,
// which may be nullptr. The vector from lastpt to its next is included in
// the feature extraction. Hidden edges should be excluded by the caller.
// If force_poly is true, the features will be extracted from the polygonal
// approximation even if more accurate data is available.
static void ExtractFeaturesFromRun(
    const EDGEPT* startpt, const EDGEPT* lastpt,
    const DENORM& denorm, double feature_length, bool force_poly,
    GenericVector<INT_FEATURE_STRUCT>* features) {
  const EDGEPT* endpt = lastpt->next;
  const C_OUTLINE* outline = startpt->src_outline;
  if (outline != nullptr && !force_poly) {
    // Detailed information is available. We have to normalize only from
    // the root_denorm to denorm.
    const DENORM* root_denorm = denorm.RootDenorm();
    int total_features = 0;
    // Get the features from the outline.
    int step_length = outline->pathlength();
    int start_index = startpt->start_step;
    // pos is the integer coordinates of the binary image steps.
    ICOORD pos = outline->position_at_index(start_index);
    // We use an end_index that allows us to use a positive increment, but that
    // may be beyond the bounds of the outline steps/ due to wrap-around, to
    // so we use % step_length everywhere, except for start_index.
    int end_index = lastpt->start_step + lastpt->step_count;
    if (end_index <= start_index)
      end_index += step_length;
    LLSQ prev_points;
    LLSQ prev_dirs;
    FCOORD prev_normed_pos = outline->sub_pixel_pos_at_index(pos, start_index);
    denorm.NormTransform(root_denorm, prev_normed_pos, &prev_normed_pos);
    LLSQ points;
    LLSQ dirs;
    FCOORD normed_pos(0.0f, 0.0f);
    int index = GatherPoints(outline, feature_length, denorm, root_denorm,
                             start_index, end_index, &pos, &normed_pos,
                             &points, &dirs);
    while (index <= end_index) {
      // At each iteration we nominally have 3 accumulated sets of points and
      // dirs: prev_points/dirs, points/dirs, next_points/dirs and sum them
      // into sum_points/dirs, but we don't necessarily get any features out,
      // so if that is the case, we keep accumulating instead of rotating the
      // accumulators.
      LLSQ next_points;
      LLSQ next_dirs;
      FCOORD next_normed_pos(0.0f, 0.0f);
      index = GatherPoints(outline, feature_length, denorm, root_denorm,
                           index, end_index, &pos, &next_normed_pos,
                           &next_points, &next_dirs);
      LLSQ sum_points(prev_points);
      // TODO(rays) find out why it is better to use just dirs and next_dirs
      // in sum_dirs, instead of using prev_dirs as well.
      LLSQ sum_dirs(dirs);
      sum_points.add(points);
      sum_points.add(next_points);
      sum_dirs.add(next_dirs);
      bool made_features = false;
      // If we have some points, we can try making some features.
      if (sum_points.count() > 0) {
        // We have gone far enough from the start. Make a feature and restart.
        FCOORD fit_pt = sum_points.mean_point();
        FCOORD fit_vector = MeanDirectionVector(sum_points, sum_dirs,
                                                prev_normed_pos, normed_pos);
        // The segment to which we fit features is the line passing through
        // fit_pt in direction of fit_vector that starts nearest to
        // prev_normed_pos and ends nearest to normed_pos.
        FCOORD start_pos = prev_normed_pos.nearest_pt_on_line(fit_pt,
                                                              fit_vector);
        FCOORD end_pos = normed_pos.nearest_pt_on_line(fit_pt, fit_vector);
        // Possible correction to match the adjacent polygon segment.
        if (total_features == 0 && startpt != endpt) {
          FCOORD poly_pos(startpt->pos.x, startpt->pos.y);
          denorm.LocalNormTransform(poly_pos, &start_pos);
        }
        if (index > end_index && startpt != endpt) {
          FCOORD poly_pos(endpt->pos.x, endpt->pos.y);
          denorm.LocalNormTransform(poly_pos, &end_pos);
        }
        int num_features = ComputeFeatures(start_pos, end_pos, feature_length,
                                           features);
        if (num_features > 0) {
          // We made some features so shuffle the accumulators.
          prev_points = points;
          prev_dirs = dirs;
          prev_normed_pos = normed_pos;
          points = next_points;
          dirs = next_dirs;
          made_features = true;
          total_features += num_features;
        }
        // The end of the next set becomes the end next time around.
        normed_pos = next_normed_pos;
      }
      if (!made_features) {
        // We didn't make any features, so keep the prev accumulators and
        // add the next ones into the current.
        points.add(next_points);
        dirs.add(next_dirs);
      }
    }
  } else {
    // There is no outline, so we are forced to use the polygonal approximation.
    const EDGEPT* pt = startpt;
    do {
      FCOORD start_pos(pt->pos.x, pt->pos.y);
      FCOORD end_pos(pt->next->pos.x, pt->next->pos.y);
      denorm.LocalNormTransform(start_pos, &start_pos);
      denorm.LocalNormTransform(end_pos, &end_pos);
      ComputeFeatures(start_pos, end_pos, feature_length, features);
    } while ((pt = pt->next) != endpt);
  }
}