// 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());
}
// Return the partner of this TabVector if the vector qualifies as
// being a vertical text line, otherwise NULL.
TabVector* TabVector::VerticalTextlinePartner() {
if (!partners_.singleton())
return NULL;
TabVector_C_IT partner_it(&partners_);
TabVector* partner = partner_it.data();
BLOBNBOX_C_IT box_it1(&boxes_);
BLOBNBOX_C_IT box_it2(&partner->boxes_);
// Count how many boxes are also in the other list.
// At the same time, gather the mean width and median vertical gap.
if (textord_debug_tabfind > 1) {
Print("Testing for vertical text");
partner->Print(" partner");
}
int num_matched = 0;
int num_unmatched = 0;
int total_widths = 0;
int width = startpt().x() - partner->startpt().x();
if (width < 0)
width = -width;
STATS gaps(0, width * 2);
BLOBNBOX* prev_bbox = NULL;
box_it2.mark_cycle_pt();
for (box_it1.mark_cycle_pt(); !box_it1.cycled_list(); box_it1.forward()) {
BLOBNBOX* bbox = box_it1.data();
TBOX box = bbox->bounding_box();
if (prev_bbox != NULL) {
gaps.add(box.bottom() - prev_bbox->bounding_box().top(), 1);
}
while (!box_it2.cycled_list() && box_it2.data() != bbox &&
box_it2.data()->bounding_box().bottom() < box.bottom()) {
box_it2.forward();
}
if (!box_it2.cycled_list() && box_it2.data() == bbox &&
bbox->region_type() >= BRT_UNKNOWN &&
(prev_bbox == NULL || prev_bbox->region_type() >= BRT_UNKNOWN))
++num_matched;
else
++num_unmatched;
total_widths += box.width();
prev_bbox = bbox;
}
if (num_unmatched + num_matched == 0) return NULL;
double avg_width = total_widths * 1.0 / (num_unmatched + num_matched);
double max_gap = textord_tabvector_vertical_gap_fraction * avg_width;
int min_box_match = static_cast<int>((num_matched + num_unmatched) *
textord_tabvector_vertical_box_ratio);
bool is_vertical = (gaps.get_total() > 0 &&
num_matched >= min_box_match &&
gaps.median() <= max_gap);
if (textord_debug_tabfind > 1) {
tprintf("gaps=%d, matched=%d, unmatched=%d, min_match=%d "
"median gap=%.2f, width=%.2f max_gap=%.2f Vertical=%s\n",
gaps.get_total(), num_matched, num_unmatched, min_box_match,
gaps.median(), avg_width, max_gap, is_vertical?"Yes":"No");
}
return (is_vertical) ? partner : NULL;
}
// 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());
}
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());
}
// 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;
}
// Sets up the DENORM to execute a non-linear transformation based on
// preserving an even distribution of stroke edges. The transformation
// operates only within the given box.
// x_coords is a collection of the x-coords of vertical edges for each
// y-coord starting at box.bottom().
// y_coords is a collection of the y-coords of horizontal edges for each
// x-coord starting at box.left().
// Eg x_coords[0] is a collection of the x-coords of edges at y=bottom.
// Eg x_coords[1] is a collection of the x-coords of edges at y=bottom + 1.
// The second-level vectors must all be sorted in ascending order.
// See comments on the helper functions above for more details.
void DENORM::SetupNonLinear(
const DENORM* predecessor, const TBOX& box, float target_width,
float target_height, float final_xshift, float final_yshift,
const GenericVector<GenericVector<int> >& x_coords,
const GenericVector<GenericVector<int> >& y_coords) {
Clear();
predecessor_ = predecessor;
// x_map_ and y_map_ store a mapping from input x and y coordinate to output
// x and y coordinate, based on scaling to the supplied target_width and
// target_height.
x_map_ = new GenericVector<float>;
y_map_ = new GenericVector<float>;
// Set a 2-d image array to the run lengths at each pixel.
int width = box.width();
int height = box.height();
GENERIC_2D_ARRAY<int> minruns(width, height, 0);
ComputeRunlengthImage(box, x_coords, y_coords, &minruns);
// Edge density is the sum of the inverses of the run lengths. Compute
// edge density projection profiles.
ComputeEdgeDensityProfiles(box, minruns, x_map_, y_map_);
// Convert the edge density profiles to the coordinates by multiplying by
// the desired size and accumulating.
(*x_map_)[width] = target_width;
for (int x = width - 1; x >= 0; --x) {
(*x_map_)[x] = (*x_map_)[x + 1] - (*x_map_)[x] * target_width;
}
(*y_map_)[height] = target_height;
for (int y = height - 1; y >= 0; --y) {
(*y_map_)[y] = (*y_map_)[y + 1] - (*y_map_)[y] * target_height;
}
x_origin_ = box.left();
y_origin_ = box.bottom();
final_xshift_ = final_xshift;
final_yshift_ = final_yshift;
}
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;
}
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;
}
// 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);
}
}
}
// 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);
}
// Extract the OCR results, costs (penalty points for uncertainty),
// and the bounding boxes of the characters.
static void extract_result(ELIST_ITERATOR *out,
PAGE_RES* page_res) {
PAGE_RES_IT page_res_it(page_res);
int word_count = 0;
while (page_res_it.word() != NULL) {
WERD_RES *word = page_res_it.word();
const char *str = word->best_choice->string().string();
const char *len = word->best_choice->lengths().string();
if (word_count)
add_space(out);
TBOX bln_rect;
PBLOB_LIST *blobs = word->outword->blob_list();
PBLOB_IT it(blobs);
int n = strlen(len);
TBOX** boxes_to_fix = new TBOX*[n];
for (int i = 0; i < n; i++) {
PBLOB *blob = it.data();
TBOX current = pblob_get_bbox(blob);
bln_rect.bounding_union(current);
TESS_CHAR *tc = new TESS_CHAR(rating_to_cost(word->best_choice->rating()),
str, *len);
tc->box = current;
boxes_to_fix[i] = &tc->box;
out->add_after_then_move(tc);
it.forward();
str += *len;
len++;
}
// Find the word bbox before normalization.
// Here we can't use the C_BLOB bboxes directly,
// since connected letters are not yet cut.
TBOX real_rect = c_blob_list_get_bbox(word->word->cblob_list());
// Denormalize boxes by transforming the bbox of the whole bln word
// into the denorm bbox (`real_rect') of the whole word.
double x_stretch = double(real_rect.width()) / bln_rect.width();
double y_stretch = double(real_rect.height()) / bln_rect.height();
for (int j = 0; j < n; j++) {
TBOX *box = boxes_to_fix[j];
int x0 = int(real_rect.left() +
x_stretch * (box->left() - bln_rect.left()) + 0.5);
int x1 = int(real_rect.left() +
x_stretch * (box->right() - bln_rect.left()) + 0.5);
int y0 = int(real_rect.bottom() +
y_stretch * (box->bottom() - bln_rect.bottom()) + 0.5);
int y1 = int(real_rect.bottom() +
y_stretch * (box->top() - bln_rect.bottom()) + 0.5);
*box = TBOX(ICOORD(x0, y0), ICOORD(x1, y1));
}
delete [] boxes_to_fix;
page_res_it.forward();
word_count++;
}
}
// Creates a box file string from a unichar string, TBOX and page number.
void MakeBoxFileStr(const char* unichar_str, const TBOX& box, int page_num,
STRING* box_str) {
*box_str = unichar_str;
box_str->add_str_int(" ", box.left());
box_str->add_str_int(" ", box.bottom());
box_str->add_str_int(" ", box.right());
box_str->add_str_int(" ", box.top());
box_str->add_str_int(" ", page_num);
}
// Returns true if any cell value in the given rectangle is zero.
bool IntGrid::AnyZeroInRect(const TBOX& rect) const {
int min_x, min_y, max_x, max_y;
GridCoords(rect.left(), rect.bottom(), &min_x, &min_y);
GridCoords(rect.right(), rect.top(), &max_x, &max_y);
for (int y = min_y; y <= max_y; ++y) {
for (int x = min_x; x <= max_x; ++x) {
if (GridCellValue(x, y) == 0)
return true;
}
}
return false;
}
// Setup for a baseline normalization. If there are segs, then they
// are used, otherwise, if there is a row, that is used, otherwise the
// bottom of the word_box is used for the baseline.
void DENORM::SetupBLNormalize(const BLOCK* block, const ROW* row,
float x_height, const TBOX& word_box,
int num_segs, const DENORM_SEG* segs) {
float scale = kBlnXHeight / x_height;
float x_origin = (word_box.left() + word_box.right()) / 2.0f;
float y_origin = 0.0f;
if (num_segs == 0 && row == NULL) {
y_origin = word_box.bottom();
}
SetupNormalization(block, row, NULL, NULL, segs, num_segs,
x_origin, y_origin, scale, scale,
0.0f, static_cast<float>(kBlnBaselineOffset));
}
void show_point(PAGE_RES* page_res, float x, float y) {
FCOORD pt(x, y);
PAGE_RES_IT pr_it(page_res);
const int kBufsize = 512;
char msg[kBufsize];
char *msg_ptr = msg;
msg_ptr += sprintf(msg_ptr, "Pt:(%0.3f, %0.3f) ", x, y);
for (WERD_RES* word = pr_it.word(); word != NULL; word = pr_it.forward()) {
if (pr_it.row() != pr_it.prev_row() &&
pr_it.row()->row->bounding_box().contains(pt)) {
msg_ptr += sprintf(msg_ptr, "BL(x)=%0.3f ",
pr_it.row()->row->base_line(x));
}
if (word->word->bounding_box().contains(pt)) {
TBOX box = word->word->bounding_box();
msg_ptr += sprintf(msg_ptr, "Wd(%d, %d)/(%d, %d) ",
box.left(), box.bottom(),
box.right(), box.top());
C_BLOB_IT cblob_it(word->word->cblob_list());
for (cblob_it.mark_cycle_pt();
!cblob_it.cycled_list();
cblob_it.forward()) {
C_BLOB* cblob = cblob_it.data();
box = cblob->bounding_box();
if (box.contains(pt)) {
msg_ptr += sprintf(msg_ptr,
"CBlb(%d, %d)/(%d, %d) ",
box.left(), box.bottom(),
box.right(), box.top());
}
}
}
}
image_win->AddMessage(msg);
}
/**
* Returns the bounding rectangle of the current object at the given level in
* the coordinates of the working image that is pix_binary().
* See comment on coordinate system above.
* Returns false if there is no such object at the current position.
*/
bool PageIterator::BoundingBoxInternal(PageIteratorLevel level,
int* left, int* top,
int* right, int* bottom) const {
if (Empty(level))
return false;
TBOX box;
PARA *para = NULL;
switch (level) {
case RIL_BLOCK:
box = it_->block()->block->bounding_box();
break;
case RIL_PARA:
para = it_->row()->row->para();
// explicit fall-through.
case RIL_TEXTLINE:
box = it_->row()->row->bounding_box();
break;
case RIL_WORD:
box = it_->word()->word->bounding_box();
break;
case RIL_SYMBOL:
if (cblob_it_ == NULL)
box = it_->word()->box_word->BlobBox(blob_index_);
else
box = cblob_it_->data()->bounding_box();
}
if (level == RIL_PARA) {
PageIterator other = *this;
other.Begin();
do {
if (other.it_->block() &&
other.it_->block()->block == it_->block()->block &&
other.it_->row() && other.it_->row()->row &&
other.it_->row()->row->para() == para) {
box = box.bounding_union(other.it_->row()->row->bounding_box());
}
} while (other.Next(RIL_TEXTLINE));
}
if (level != RIL_SYMBOL || cblob_it_ != NULL)
box.rotate(it_->block()->block->re_rotation());
// Now we have a box in tesseract coordinates relative to the image rectangle,
// we have to convert the coords to a top-down system.
const int pix_height = pixGetHeight(tesseract_->pix_binary());
const int pix_width = pixGetWidth(tesseract_->pix_binary());
*left = ClipToRange(static_cast<int>(box.left()), 0, pix_width);
*top = ClipToRange(pix_height - box.top(), 0, pix_height);
*right = ClipToRange(static_cast<int>(box.right()), *left, pix_width);
*bottom = ClipToRange(pix_height - box.bottom(), *top, pix_height);
return true;
}
/**********************************************************************
* char_box_to_tbox
*
* Create a TBOX from a character bounding box. If nonzero, the
* x_offset accounts for any additional padding of the word box that
* should be taken into account.
*
**********************************************************************/
TBOX char_box_to_tbox(Box* char_box, TBOX word_box, int x_offset) {
l_int32 left;
l_int32 top;
l_int32 width;
l_int32 height;
l_int32 right;
l_int32 bottom;
boxGetGeometry(char_box, &left, &top, &width, &height);
left += word_box.left() - x_offset;
right = left + width;
top = word_box.bottom() + word_box.height() - top;
bottom = top - height;
return TBOX(left, bottom, right, top);
}
// Returns the bounding box including the desired combination of upper and
// lower noise/diacritic elements.
TBOX WERD::restricted_bounding_box(bool upper_dots, bool lower_dots) const {
TBOX box = true_bounding_box();
int bottom = box.bottom();
int top = box.top();
// This is a read-only iteration of the rejected blobs.
C_BLOB_IT it(const_cast<C_BLOB_LIST*>(&rej_cblobs));
for (it.mark_cycle_pt(); !it.cycled_list(); it.forward()) {
TBOX dot_box = it.data()->bounding_box();
if ((upper_dots || dot_box.bottom() <= top) &&
(lower_dots || dot_box.top() >= bottom)) {
box += dot_box;
}
}
return box;
}
// Helper function to add 1 to a rectangle in source image coords to the
// internal projection pix_.
void TextlineProjection::IncrementRectangle8Bit(const TBOX& box) {
int scaled_left = ImageXToProjectionX(box.left());
int scaled_top = ImageYToProjectionY(box.top());
int scaled_right = ImageXToProjectionX(box.right());
int scaled_bottom = ImageYToProjectionY(box.bottom());
int wpl = pixGetWpl(pix_);
uint32_t* data = pixGetData(pix_) + scaled_top * wpl;
for (int y = scaled_top; y <= scaled_bottom; ++y) {
for (int x = scaled_left; x <= scaled_right; ++x) {
int pixel = GET_DATA_BYTE(data, x);
if (pixel < 255)
SET_DATA_BYTE(data, x, pixel + 1);
}
data += wpl;
}
}
// Returns true if more than half the area of the rect is covered by grid
// cells that are over the threshold.
bool IntGrid::RectMostlyOverThreshold(const TBOX& rect, int threshold) const {
int min_x, min_y, max_x, max_y;
GridCoords(rect.left(), rect.bottom(), &min_x, &min_y);
GridCoords(rect.right(), rect.top(), &max_x, &max_y);
int total_area = 0;
for (int y = min_y; y <= max_y; ++y) {
for (int x = min_x; x <= max_x; ++x) {
int value = GridCellValue(x, y);
if (value > threshold) {
TBOX cell_box(x * gridsize_, y * gridsize_,
(x + 1) * gridsize_, (y + 1) * gridsize_);
cell_box &= rect; // This is in-place box intersection.
total_area += cell_box.area();
}
}
}
return total_area * 2 > rect.area();
}
// Display the tab codes of the BLOBNBOXes in this grid.
ScrollView* AlignedBlob::DisplayTabs(const char* window_name,
ScrollView* tab_win) {
#ifndef GRAPHICS_DISABLED
if (tab_win == NULL)
tab_win = MakeWindow(0, 50, window_name);
// For every tab in the grid, display it.
GridSearch<BLOBNBOX, BLOBNBOX_CLIST, BLOBNBOX_C_IT> gsearch(this);
gsearch.StartFullSearch();
BLOBNBOX* bbox;
while ((bbox = gsearch.NextFullSearch()) != NULL) {
TBOX box = bbox->bounding_box();
int left_x = box.left();
int right_x = box.right();
int top_y = box.top();
int bottom_y = box.bottom();
TabType tabtype = bbox->left_tab_type();
if (tabtype != TT_NONE) {
if (tabtype == TT_UNCONFIRMED)
tab_win->Pen(ScrollView::BLUE);
else if (tabtype == TT_CONFIRMED)
tab_win->Pen(ScrollView::GREEN);
else if (tabtype == TT_FAKE)
tab_win->Pen(ScrollView::YELLOW);
else
tab_win->Pen(ScrollView::GREY);
tab_win->Line(left_x, top_y, left_x, bottom_y);
}
tabtype = bbox->right_tab_type();
if (tabtype != TT_NONE) {
if (tabtype == TT_UNCONFIRMED)
tab_win->Pen(ScrollView::MAGENTA);
else if (tabtype == TT_CONFIRMED)
tab_win->Pen(ScrollView::RED);
else if (tabtype == TT_FAKE)
tab_win->Pen(ScrollView::ORANGE);
else
tab_win->Pen(ScrollView::GREY);
tab_win->Line(right_x, top_y, right_x, bottom_y);
}
}
tab_win->Update();
#endif
return tab_win;
}
/**
* Given a recognized blob, see if a contiguous collection of sub-pieces
* (chopped blobs) starting at its left might qualify as being a subscript
* or superscript letter based only on y position. Also do this for the
* right side.
*/
void YOutlierPieces(WERD_RES *word, int rebuilt_blob_index,
int super_y_bottom, int sub_y_top,
ScriptPos *leading_pos, int *num_leading_outliers,
ScriptPos *trailing_pos, int *num_trailing_outliers) {
ScriptPos sp_unused1, sp_unused2;
int unused1, unused2;
if (!leading_pos) leading_pos = &sp_unused1;
if (!num_leading_outliers) num_leading_outliers = &unused1;
if (!trailing_pos) trailing_pos = &sp_unused2;
if (!num_trailing_outliers) num_trailing_outliers = &unused2;
*num_leading_outliers = *num_trailing_outliers = 0;
*leading_pos = *trailing_pos = SP_NORMAL;
int chopped_start = LeadingUnicharsToChopped(word, rebuilt_blob_index);
int num_chopped_pieces = word->best_state[rebuilt_blob_index];
ScriptPos last_pos = SP_NORMAL;
int trailing_outliers = 0;
for (int i = 0; i < num_chopped_pieces; i++) {
TBOX box = word->chopped_word->blobs[chopped_start + i]->bounding_box();
ScriptPos pos = SP_NORMAL;
if (box.bottom() >= super_y_bottom) {
pos = SP_SUPERSCRIPT;
} else if (box.top() <= sub_y_top) {
pos = SP_SUBSCRIPT;
}
if (pos == SP_NORMAL) {
if (trailing_outliers == i) {
*num_leading_outliers = trailing_outliers;
*leading_pos = last_pos;
}
trailing_outliers = 0;
} else {
if (pos == last_pos) {
trailing_outliers++;
} else {
trailing_outliers = 1;
}
}
last_pos = pos;
}
*num_trailing_outliers = trailing_outliers;
*trailing_pos = last_pos;
}
// Find a set of blobs that are aligned in the given vertical
// direction with the given blob. Returns a list of aligned
// blobs and the number in the list.
// For other parameters see FindAlignedBlob below.
int AlignedBlob::AlignTabs(const AlignedBlobParams& params,
bool top_to_bottom, BLOBNBOX* bbox,
BLOBNBOX_CLIST* good_points, int* end_y) {
int ptcount = 0;
BLOBNBOX_C_IT it(good_points);
TBOX box = bbox->bounding_box();
bool debug = WithinTestRegion(2, box.left(), box.bottom());
if (debug) {
tprintf("Starting alignment run at blob:");
box.print();
}
int x_start = params.right_tab ? box.right() : box.left();
while (bbox != nullptr) {
// Add the blob to the list if the appropriate side is a tab candidate,
// or if we are working on a ragged tab.
TabType type = params.right_tab ? bbox->right_tab_type()
: bbox->left_tab_type();
if (((type != TT_NONE && type != TT_MAYBE_RAGGED) || params.ragged) &&
(it.empty() || it.data() != bbox)) {
if (top_to_bottom)
it.add_before_then_move(bbox);
else
it.add_after_then_move(bbox);
++ptcount;
}
// Find the next blob that is aligned with the current one.
// FindAlignedBlob guarantees that forward progress will be made in the
// top_to_bottom direction, and therefore eventually it will return nullptr,
// making this while (bbox != nullptr) loop safe.
bbox = FindAlignedBlob(params, top_to_bottom, bbox, x_start, end_y);
if (bbox != nullptr) {
box = bbox->bounding_box();
if (!params.ragged)
x_start = params.right_tab ? box.right() : box.left();
}
}
if (debug) {
tprintf("Alignment run ended with %d pts at blob:", ptcount);
box.print();
}
return ptcount;
}
// Displays the segmentation state of *this (if not the same as the last
// one displayed) and waits for a click in the window.
void WERD_CHOICE::DisplaySegmentation(TWERD* word) {
#ifndef GRAPHICS_DISABLED
// Number of different colors to draw with.
const int kNumColors = 6;
static ScrollView *segm_window = NULL;
// Check the state against the static prev_drawn_state.
static GenericVector<int> prev_drawn_state;
bool already_done = prev_drawn_state.size() == length_;
if (!already_done) prev_drawn_state.init_to_size(length_, 0);
for (int i = 0; i < length_; ++i) {
if (prev_drawn_state[i] != state_[i]) {
already_done = false;
}
prev_drawn_state[i] = state_[i];
}
if (already_done || word->blobs.empty()) return;
// Create the window if needed.
if (segm_window == NULL) {
segm_window = new ScrollView("Segmentation", 5, 10, 500, 256,
2000.0, 256.0, true);
} else {
segm_window->Clear();
}
TBOX bbox;
int blob_index = 0;
for (int c = 0; c < length_; ++c) {
ScrollView::Color color =
static_cast<ScrollView::Color>(c % kNumColors + 3);
for (int i = 0; i < state_[c]; ++i, ++blob_index) {
TBLOB* blob = word->blobs[blob_index];
bbox += blob->bounding_box();
blob->plot(segm_window, color, color);
}
}
segm_window->ZoomToRectangle(bbox.left(), bbox.top(),
bbox.right(), bbox.bottom());
segm_window->Update();
window_wait(segm_window);
#endif
}
