C++ TBOX::top方法代码示例

// Internal version of EvaluateBox returns the unclipped gradients as well
// as the result of EvaluateBox.
// hgrad1 and hgrad2 are the gradients for the horizontal textline.
int TextlineProjection::EvaluateBoxInternal(const TBOX& box,
                                            const DENORM* denorm, bool debug,
                                            int* hgrad1, int* hgrad2,
                                            int* vgrad1, int* vgrad2) const {
  int top_gradient = BestMeanGradientInRow(denorm, box.left(), box.right(),
                                           box.top(), true);
  int bottom_gradient = -BestMeanGradientInRow(denorm, box.left(), box.right(),
                                               box.bottom(), false);
  int left_gradient = BestMeanGradientInColumn(denorm, box.left(), box.bottom(),
                                               box.top(), true);
  int right_gradient = -BestMeanGradientInColumn(denorm, box.right(),
                                                 box.bottom(), box.top(),
                                                 false);
  int top_clipped = MAX(top_gradient, 0);
  int bottom_clipped = MAX(bottom_gradient, 0);
  int left_clipped = MAX(left_gradient, 0);
  int right_clipped = MAX(right_gradient, 0);
  if (debug) {
    tprintf("Gradients: top = %d, bottom = %d, left= %d, right= %d for box:",
            top_gradient, bottom_gradient, left_gradient, right_gradient);
    box.print();
  }
  int result = MAX(top_clipped, bottom_clipped) -
      MAX(left_clipped, right_clipped);
  if (hgrad1 != NULL && hgrad2 != NULL) {
    *hgrad1 = top_gradient;
    *hgrad2 = bottom_gradient;
  }
  if (vgrad1 != NULL && vgrad2 != NULL) {
    *vgrad1 = left_gradient;
    *vgrad2 = right_gradient;
  }
  return result;
}

// Compute the distance from the from_box to the to_box using curved
// projection space. Separation that involves a decrease in projection
// density (moving from the from_box to the to_box) is weighted more heavily
// than constant density, and an increase is weighted less.
// If horizontal_textline is true, then curved space is used vertically,
// as for a diacritic on the edge of a textline.
// The projection uses original image coords, so denorm is used to get
// back to the image coords from box/part space.
// How the calculation works: Think of a diacritic near a textline.
// Distance is measured from the far side of the from_box to the near side of
// the to_box. Shown is the horizontal textline case.
//          |------^-----|
//          | from | box |
//          |------|-----|
//   perpendicular |
//          <------v-------->|--------------------|
//                  parallel |     to box         |
//                           |--------------------|
// Perpendicular distance uses "curved space" See VerticalDistance below.
// Parallel distance is linear.
// Result is perpendicular_gap + parallel_gap / kParaPerpDistRatio.
int TextlineProjection::DistanceOfBoxFromBox(const TBOX& from_box,
                                             const TBOX& to_box,
                                             bool horizontal_textline,
                                             const DENORM* denorm,
                                             bool debug) const {
  // The parallel_gap is the horizontal gap between a horizontal textline and
  // the box. Analogous for vertical.
  int parallel_gap = 0;
  // start_pt is the box end of the line to be modified for curved space.
  TPOINT start_pt;
  // end_pt is the partition end of the line to be modified for curved space.
  TPOINT end_pt;
  if (horizontal_textline) {
    parallel_gap = from_box.x_gap(to_box) + from_box.width();
    start_pt.x = (from_box.left() + from_box.right()) / 2;
    end_pt.x = start_pt.x;
    if (from_box.top() - to_box.top() >= to_box.bottom() - from_box.bottom()) {
      start_pt.y = from_box.top();
      end_pt.y = MIN(to_box.top(), start_pt.y);
    } else {
      start_pt.y = from_box.bottom();
      end_pt.y = MAX(to_box.bottom(), start_pt.y);
    }
  } else {
    parallel_gap = from_box.y_gap(to_box) + from_box.height();
    if (from_box.right() - to_box.right() >= to_box.left() - from_box.left()) {
      start_pt.x = from_box.right();
      end_pt.x = MIN(to_box.right(), start_pt.x);
    } else {
      start_pt.x = from_box.left();
      end_pt.x = MAX(to_box.left(), start_pt.x);
    }
    start_pt.y = (from_box.bottom() + from_box.top()) / 2;
    end_pt.y = start_pt.y;
  }
  // The perpendicular gap is the max vertical distance gap out of:
  // top of from_box to to_box top and bottom of from_box to to_box bottom.
  // This value is then modified for curved projection space.
  // Analogous for vertical.
  int perpendicular_gap = 0;
  // If start_pt == end_pt, then the from_box lies entirely within the to_box
  // (in the perpendicular direction), so we don't need to calculate the
  // perpendicular_gap.
  if (start_pt.x != end_pt.x || start_pt.y != end_pt.y) {
    if (denorm != NULL) {
      // Denormalize the start and end.
      denorm->DenormTransform(NULL, start_pt, &start_pt);
      denorm->DenormTransform(NULL, end_pt, &end_pt);
    }
    if (abs(start_pt.y - end_pt.y) >= abs(start_pt.x - end_pt.x)) {
      perpendicular_gap = VerticalDistance(debug, start_pt.x, start_pt.y,
                                           end_pt.y);
    } else {
      perpendicular_gap = HorizontalDistance(debug, start_pt.x, end_pt.x,
                                             start_pt.y);
    }
  }
  // The parallel_gap weighs less than the perpendicular_gap.
  return perpendicular_gap + parallel_gap / kParaPerpDistRatio;
}

 QString printTBOX(TBOX box,int height, bool eol)
 {
     if (eol)
         return QString ("Bounding box=(%1,%2)->(%3,%4)\n").arg(box.left())
             .arg(height - box.top()).arg(box.right()).arg(height - box.bottom());
     else
         return QString ("Bounding box=(%1,%2)->(%3,%4)").arg(box.left())
             .arg(height - box.top()).arg(box.right()).arg(height - box.bottom());
 }

// Tests each blob in the list to see if it is certain non-text using 2
// conditions:
// 1. blob overlaps a cell with high value in noise_density_ (previously set
// by ComputeNoiseDensity).
// OR 2. The blob overlaps more than max_blob_overlaps in *this grid. This
// condition is disabled with max_blob_overlaps == -1.
// If it does, the blob is declared non-text, and is used to mark up the
// nontext_mask. Such blobs are fully deleted, and non-noise blobs have their
// neighbours reset, as they may now point to deleted data.
// WARNING: The blobs list blobs may be in the *this grid, but they are
// not removed. If any deleted blobs might be in *this, then this must be
// Clear()ed immediately after MarkAndDeleteNonTextBlobs is called.
// If the win is not NULL, deleted blobs are drawn on it in red, and kept
// blobs are drawn on it in ok_color.
void CCNonTextDetect::MarkAndDeleteNonTextBlobs(BLOBNBOX_LIST* blobs,
        int max_blob_overlaps,
        ScrollView* win,
        ScrollView::Color ok_color,
        Pix* nontext_mask) {
    int imageheight = tright().y() - bleft().x();
    BLOBNBOX_IT blob_it(blobs);
    BLOBNBOX_LIST dead_blobs;
    BLOBNBOX_IT dead_it(&dead_blobs);
    for (blob_it.mark_cycle_pt(); !blob_it.cycled_list(); blob_it.forward()) {
        BLOBNBOX* blob = blob_it.data();
        TBOX box = blob->bounding_box();
        if (!noise_density_->RectMostlyOverThreshold(box, max_noise_count_) &&
                (max_blob_overlaps < 0 ||
                 !BlobOverlapsTooMuch(blob, max_blob_overlaps))) {
            blob->ClearNeighbours();
#ifndef GRAPHICS_DISABLED
            if (win != NULL)
                blob->plot(win, ok_color, ok_color);
#endif  // GRAPHICS_DISABLED
        } else {
            if (noise_density_->AnyZeroInRect(box)) {
                // There is a danger that the bounding box may overlap real text, so
                // we need to render the outline.
                Pix* blob_pix = blob->cblob()->render_outline();
                pixRasterop(nontext_mask, box.left(), imageheight - box.top(),
                            box.width(), box.height(), PIX_SRC | PIX_DST,
                            blob_pix, 0, 0);
                pixDestroy(&blob_pix);
            } else {
                if (box.area() < gridsize() * gridsize()) {
                    // It is a really bad idea to make lots of small components in the
                    // photo mask, so try to join it to a bigger area by expanding the
                    // box in a way that does not touch any zero noise density cell.
                    box = AttemptBoxExpansion(box, *noise_density_, gridsize());
                }
                // All overlapped cells are non-zero, so just mark the rectangle.
                pixRasterop(nontext_mask, box.left(), imageheight - box.top(),
                            box.width(), box.height(), PIX_SET, NULL, 0, 0);
            }
#ifndef GRAPHICS_DISABLED
            if (win != NULL)
                blob->plot(win, ScrollView::RED, ScrollView::RED);
#endif  // GRAPHICS_DISABLED
            // It is safe to delete the cblob now, as it isn't used by the grid
            // or BlobOverlapsTooMuch, and the BLOBNBOXes will go away with the
            // dead_blobs list.
            // TODO(rays) delete the delete when the BLOBNBOX destructor deletes
            // the cblob.
            delete blob->cblob();
            dead_it.add_to_end(blob_it.extract());
        }
    }
}

// Initialize from box coordinates.
POLY_BLOCK::POLY_BLOCK(const TBOX& box, PolyBlockType t) {
  vertices.clear();
  ICOORDELT_IT v = &vertices;
  v.move_to_first();
  v.add_to_end(new ICOORDELT(box.left(), box.top()));
  v.add_to_end(new ICOORDELT(box.left(), box.bottom()));
  v.add_to_end(new ICOORDELT(box.right(), box.bottom()));
  v.add_to_end(new ICOORDELT(box.right(), box.top()));
  compute_bb();
  type = t;
}

/**
 * @name start_seam_list
 *
 * Initialize a list of seams that match the original number of blobs
 * present in the starting segmentation.  Each of the seams created
 * by this routine have location information only.
 */
void start_seam_list(TWERD *word, GenericVector<SEAM*>* seam_array) {
  seam_array->truncate(0);
  TPOINT location;

  for (int b = 1; b < word->NumBlobs(); ++b) {
    TBOX bbox = word->blobs[b - 1]->bounding_box();
    TBOX nbox = word->blobs[b]->bounding_box();
    location.x = (bbox.right() + nbox.left()) / 2;
    location.y = (bbox.bottom() + bbox.top() + nbox.bottom() + nbox.top()) / 4;
    seam_array->push_back(new SEAM(0.0f, location, NULL, NULL, NULL));
  }
}

bool PIXROW::bad_box(  //return true if box exceeds image
                     int xsize,
                     int ysize) const {
  TBOX bbox = bounding_box ();
  if (bbox.left () < 0 || bbox.right () > xsize
  || bbox.top () > ysize || bbox.bottom () < 0) {
    tprintf("Box (%d,%d)->(%d,%d) bad compared to %d,%d\n",
            bbox.left(),bbox.bottom(), bbox.right(), bbox.top(),
            xsize, ysize);
    return true;
  }
  return false;
}

/**********************************************************************
 * render_segmentation
 *
 * Create a list of line segments that represent the list of chunks
 * using the correct segmentation that was supplied as input.
 **********************************************************************/
void render_segmentation(ScrollView *window,
                         TBLOB *chunks,
                         SEARCH_STATE segmentation) {
  TBLOB *blob;
  C_COL color = Black;
  int char_num = -1;
  int chunks_left = 0;

  TBOX bbox;
  if (chunks) bbox = chunks->bounding_box();

  for (blob = chunks; blob != NULL; blob = blob->next) {
    bbox += blob->bounding_box();
    if (chunks_left-- == 0) {
      color = color_list[++char_num % NUM_COLORS];

      if (char_num < segmentation[0])
        chunks_left = segmentation[char_num + 1];
      else
        chunks_left = MAX_INT32;
    }
    render_outline(window, blob->outlines, color);
  }
  window->ZoomToRectangle(bbox.left(), bbox.top(),
                          bbox.right(), bbox.bottom());
}

float blob_noise_score(PBLOB *blob) {
  OUTLINE_IT outline_it;
  TBOX box;                       //BB of outline
  inT16 outline_count = 0;
  inT16 max_dimension;
  inT16 largest_outline_dimension = 0;

  outline_it.set_to_list (blob->out_list ());
  for (outline_it.mark_cycle_pt ();
  !outline_it.cycled_list (); outline_it.forward ()) {
    outline_count++;
    box = outline_it.data ()->bounding_box ();
    if (box.height () > box.width ())
      max_dimension = box.height ();
    else
      max_dimension = box.width ();

    if (largest_outline_dimension < max_dimension)
      largest_outline_dimension = max_dimension;
  }

  if (fixsp_noise_score_fixing) {
    if (outline_count > 5)
                                 //penalise LOTS of blobs
      largest_outline_dimension *= 2;

    box = blob->bounding_box ();

    if ((box.bottom () > bln_baseline_offset * 4) ||
      (box.top () < bln_baseline_offset / 2))
                                 //Lax blob is if high or low
      largest_outline_dimension /= 2;
  }
  return largest_outline_dimension;
}

static void PrintBoxWidths(BLOBNBOX* neighbour) {
  TBOX nbox = neighbour->bounding_box();
  tprintf("Box (%d,%d)->(%d,%d): h-width=%.1f, v-width=%.1f p-width=%1.f\n",
          nbox.left(), nbox.bottom(), nbox.right(), nbox.top(),
          neighbour->horz_stroke_width(), neighbour->vert_stroke_width(),
          2.0 * neighbour->cblob()->area()/neighbour->cblob()->perimeter());
}

// Inserts a list of blobs into the projection.
// Rotation is a multiple of 90 degrees to get from blob coords to
// nontext_map coords, nontext_map_box is the bounds of the nontext_map.
// Blobs are spread horizontally or vertically according to their internal
// flags, but the spreading is truncated by set pixels in the nontext_map
// and also by the horizontal rule line limits on the blobs.
void TextlineProjection::ProjectBlobs(BLOBNBOX_LIST* blobs,
                                      const FCOORD& rotation,
                                      const TBOX& nontext_map_box,
                                      Pix* nontext_map) {
  BLOBNBOX_IT blob_it(blobs);
  for (blob_it.mark_cycle_pt(); !blob_it.cycled_list(); blob_it.forward()) {
    BLOBNBOX* blob = blob_it.data();
    TBOX bbox = blob->bounding_box();
    ICOORD middle((bbox.left() + bbox.right()) / 2,
                  (bbox.bottom() + bbox.top()) / 2);
    bool spreading_horizontally = PadBlobBox(blob, &bbox);
    // Rotate to match the nontext_map.
    bbox.rotate(rotation);
    middle.rotate(rotation);
    if (rotation.x() == 0.0f)
      spreading_horizontally = !spreading_horizontally;
    // Clip to the image before applying the increments.
    bbox &= nontext_map_box;  // This is in-place box intersection.
    // Check for image pixels before spreading.
    TruncateBoxToMissNonText(middle.x(), middle.y(), spreading_horizontally,
                             nontext_map, &bbox);
    if (bbox.area() > 0) {
      IncrementRectangle8Bit(bbox);
    }
  }
}

void OL_BUCKETS::extract_children(                     // recursive count
                                  C_OUTLINE *outline,  // parent outline
                                  C_OUTLINE_IT *it     // destination iterator
                                 ) {
  inT16 xmin, xmax;              // coord limits
  inT16 ymin, ymax;
  inT16 xindex, yindex;          // current bucket
  TBOX olbox;
  C_OUTLINE_IT child_it;         // search iterator

  olbox = outline->bounding_box();
  xmin =(olbox.left() - bl.x()) / BUCKETSIZE;
  xmax =(olbox.right() - bl.x()) / BUCKETSIZE;
  ymin =(olbox.bottom() - bl.y()) / BUCKETSIZE;
  ymax =(olbox.top() - bl.y()) / BUCKETSIZE;
  for (yindex = ymin; yindex <= ymax; yindex++) {
    for (xindex = xmin; xindex <= xmax; xindex++) {
      child_it.set_to_list(&buckets[yindex * bxdim + xindex]);
      for (child_it.mark_cycle_pt(); !child_it.cycled_list();
           child_it.forward()) {
        if (*child_it.data() < *outline) {
          it->add_after_then_move(child_it.extract());
        }
      }
    }
  }
}

float Tesseract::blob_noise_score(TBLOB *blob) {
  TBOX box;                       // BB of outline
  inT16 outline_count = 0;
  inT16 max_dimension;
  inT16 largest_outline_dimension = 0;

  for (TESSLINE* ol = blob->outlines; ol != NULL; ol= ol->next) {
    outline_count++;
    box = ol->bounding_box();
    if (box.height() > box.width()) {
      max_dimension = box.height();
    } else {
      max_dimension = box.width();
    }

    if (largest_outline_dimension < max_dimension)
      largest_outline_dimension = max_dimension;
  }

  if (outline_count > 5) {
    // penalise LOTS of blobs
    largest_outline_dimension *= 2;
  }

  box = blob->bounding_box();
  if (box.bottom() > kBlnBaselineOffset * 4 ||
      box.top() < kBlnBaselineOffset / 2) {
    // Lax blob is if high or low
    largest_outline_dimension /= 2;
  }

  return largest_outline_dimension;
}

// Generates a TrainingSample from a TBLOB. Extracts features and sets
// the bounding box, so classifiers that operate on the image can work.
// TODO(rays) Make BlobToTrainingSample a member of Classify now that
// the FlexFx and FeatureDescription code have been removed and LearnBlob
// is now a member of Classify.
TrainingSample* BlobToTrainingSample(
    const TBLOB& blob, bool nonlinear_norm, INT_FX_RESULT_STRUCT* fx_info,
    GenericVector<INT_FEATURE_STRUCT>* bl_features) {
  GenericVector<INT_FEATURE_STRUCT> cn_features;
  Classify::ExtractFeatures(blob, nonlinear_norm, bl_features,
                            &cn_features, fx_info, nullptr);
  // TODO(rays) Use blob->PreciseBoundingBox() instead.
  TBOX box = blob.bounding_box();
  TrainingSample* sample = nullptr;
  int num_features = fx_info->NumCN;
  if (num_features > 0) {
    sample = TrainingSample::CopyFromFeatures(*fx_info, box, &cn_features[0],
                                              num_features);
  }
  if (sample != nullptr) {
    // Set the bounding box (in original image coordinates) in the sample.
    TPOINT topleft, botright;
    topleft.x = box.left();
    topleft.y = box.top();
    botright.x = box.right();
    botright.y = box.bottom();
    TPOINT original_topleft, original_botright;
    blob.denorm().DenormTransform(nullptr, topleft, &original_topleft);
    blob.denorm().DenormTransform(nullptr, botright, &original_botright);
    sample->set_bounding_box(TBOX(original_topleft.x, original_botright.y,
                                  original_botright.x, original_topleft.y));
  }
  return sample;
}

// This function takes tif/box pair of files and runs recognition on the image,
// while making sure that the word bounds that tesseract identified roughly
// match to those specified by the input box file. For each word (ngram in a
// single bounding box from the input box file) it outputs the ocred result,
// the correct label, rating and certainty.
void Tesseract::recog_training_segmented(const STRING &fname,
                                         PAGE_RES *page_res,
                                         volatile ETEXT_DESC *monitor,
                                         FILE *output_file) {
  STRING box_fname = fname;
  const char *lastdot = strrchr(box_fname.string(), '.');
  if (lastdot != NULL) box_fname[lastdot - box_fname.string()] = '\0';
  box_fname += ".box";
  // read_next_box() will close box_file
  FILE *box_file = open_file(box_fname.string(), "r");

  PAGE_RES_IT page_res_it;
  page_res_it.page_res = page_res;
  page_res_it.restart_page();
  char label[kBoxReadBufSize];

  // Process all the words on this page.
  TBOX tbox;  // tesseract-identified box
  TBOX bbox;  // box from the box file
  bool keep_going;
  int line_number = 0;
  do {
    keep_going = read_t(&page_res_it, &tbox);
    keep_going &= read_b(applybox_page, &line_number, box_file, label, &bbox);
    // Align bottom left points of the TBOXes.
    while (keep_going &&
           !NearlyEqual<int>(tbox.bottom(), bbox.bottom(), kMaxBoxEdgeDiff)) {
      keep_going = (bbox.bottom() < tbox.bottom()) ?
          read_t(&page_res_it, &tbox) :
            read_b(applybox_page, &line_number, box_file, label, &bbox);
    }
    while (keep_going &&
           !NearlyEqual<int>(tbox.left(), bbox.left(), kMaxBoxEdgeDiff)) {
      keep_going = (bbox.left() > tbox.left()) ? read_t(&page_res_it, &tbox) :
        read_b(applybox_page, &line_number, box_file, label, &bbox);
    }
    // OCR the word if top right points of the TBOXes are similar.
    if (keep_going &&
        NearlyEqual<int>(tbox.right(), bbox.right(), kMaxBoxEdgeDiff) &&
        NearlyEqual<int>(tbox.top(), bbox.top(), kMaxBoxEdgeDiff)) {
        ambigs_classify_and_output(page_res_it.prev_word(),
                                   page_res_it.prev_row(),
                                   page_res_it.prev_block(),
                                   label, output_file);
    }
  } while (keep_going);
}