githubmirror/tesseract

src/classify/picofeat.cpp
256 строк9 KB
Stefan Weil
Modernize and optimize list_rec a little bit
07 апр 2021, 18:30
07 апр 2021, 18:302fa96b7
/******************************************************************************
 ** Filename:    picofeat.c
 ** Purpose:     Definition of pico-features.
 ** Author:      Dan Johnson
 **
 ** (c) Copyright Hewlett-Packard Company, 1988.
 ** Licensed under the Apache License, Version 2.0 (the "License");
 ** you may not use this file except in compliance with the License.
 ** You may obtain a copy of the License at
 ** http://www.apache.org/licenses/LICENSE-2.0
 ** Unless required by applicable law or agreed to in writing, software
 ** distributed under the License is distributed on an "AS IS" BASIS,
 ** WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 ** See the License for the specific language governing permissions and
 ** limitations under the License.
 ******************************************************************************/

#include "picofeat.h"

#include "classify.h"
#include "featdefs.h"
#include "fpoint.h"
#include "mfoutline.h"
#include "ocrfeatures.h"
#include "params.h"
#include "trainingsample.h"

#include <cmath>
#include <cstdio>

namespace tesseract {

/*---------------------------------------------------------------------------
          Variables
----------------------------------------------------------------------------*/

double_VAR(classify_pico_feature_length, 0.05, "Pico Feature Length");

/*---------------------------------------------------------------------------
          Private Function Prototypes
----------------------------------------------------------------------------*/
void ConvertSegmentToPicoFeat(FPOINT *Start, FPOINT *End, FEATURE_SET FeatureSet);

void ConvertToPicoFeatures2(MFOUTLINE Outline, FEATURE_SET FeatureSet);

void NormalizePicoX(FEATURE_SET FeatureSet);

/*----------------------------------------------------------------------------
              Public Code
----------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
/**
 * Operation: Dummy for now.
 *
 * Globals:
 * - classify_norm_method normalization method currently specified
 * @param Blob blob to extract pico-features from
 * @return Pico-features for Blob.
 */
FEATURE_SET Classify::ExtractPicoFeatures(TBLOB *Blob) {
  auto FeatureSet = new FEATURE_SET_STRUCT(MAX_PICO_FEATURES);
  auto Outlines = ConvertBlob(Blob);
  float XScale, YScale;
  NormalizeOutlines(Outlines, &XScale, &YScale);
  auto RemainingOutlines = Outlines;
  iterate(RemainingOutlines) {
    auto Outline = static_cast<MFOUTLINE>(RemainingOutlines->first_node());
    ConvertToPicoFeatures2(Outline, FeatureSet);
  }
  if (classify_norm_method == baseline) {
    NormalizePicoX(FeatureSet);
  }
  FreeOutlines(Outlines);
  return (FeatureSet);

} /* ExtractPicoFeatures */

/*----------------------------------------------------------------------------
              Private Code
----------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
/**
 * This routine converts an entire segment of an outline
 * into a set of pico features which are added to
 * FeatureSet.  The length of the segment is rounded to the
 * nearest whole number of pico-features.  The pico-features
 * are spaced evenly over the entire segment.
 * Results are placed in FeatureSet.
 * Globals:
 * - classify_pico_feature_length length of a single pico-feature
 * @param Start starting point of pico-feature
 * @param End ending point of pico-feature
 * @param FeatureSet set to add pico-feature to
 */
void ConvertSegmentToPicoFeat(FPOINT *Start, FPOINT *End, FEATURE_SET FeatureSet) {
  float Angle;
  float Length;
  int NumFeatures;
  FPOINT Center;
  FPOINT Delta;
  int i;

  Angle = NormalizedAngleFrom(Start, End, 1.0);
  Length = DistanceBetween(*Start, *End);
  NumFeatures = static_cast<int>(floor(Length / classify_pico_feature_length + 0.5));
  if (NumFeatures < 1) {
    NumFeatures = 1;
  }

  /* compute vector for one pico feature */
  Delta.x = XDelta(*Start, *End) / NumFeatures;
  Delta.y = YDelta(*Start, *End) / NumFeatures;

  /* compute position of first pico feature */
  Center.x = Start->x + Delta.x / 2.0;
  Center.y = Start->y + Delta.y / 2.0;

  /* compute each pico feature in segment and add to feature set */
  for (i = 0; i < NumFeatures; i++) {
    auto Feature = new FEATURE_STRUCT(&PicoFeatDesc);
    Feature->Params[PicoFeatDir] = Angle;
    Feature->Params[PicoFeatX] = Center.x;
    Feature->Params[PicoFeatY] = Center.y;
    AddFeature(FeatureSet, Feature);

    Center.x += Delta.x;
    Center.y += Delta.y;
  }
} /* ConvertSegmentToPicoFeat */

/*---------------------------------------------------------------------------*/
/**
 * This routine steps through the specified outline and cuts it
 * up into pieces of equal length.  These pieces become the
 * desired pico-features.  Each segment in the outline
 * is converted into an integral number of pico-features.
 * Results are returned in FeatureSet.
 *
 * Globals:
 * - classify_pico_feature_length length of features to be extracted
 * @param Outline outline to extract micro-features from
 * @param FeatureSet set of features to add pico-features to
 */
void ConvertToPicoFeatures2(MFOUTLINE Outline, FEATURE_SET FeatureSet) {
  MFOUTLINE Next;
  MFOUTLINE First;
  MFOUTLINE Current;

  if (DegenerateOutline(Outline)) {
    return;
  }

  First = Outline;
  Current = First;
  Next = NextPointAfter(Current);
  do {
    /* note that an edge is hidden if the ending point of the edge is
   marked as hidden.  This situation happens because the order of
   the outlines is reversed when they are converted from the old
   format.  In the old format, a hidden edge is marked by the
   starting point for that edge. */
    if (!(PointAt(Next)->Hidden)) {
      ConvertSegmentToPicoFeat(&(PointAt(Current)->Point), &(PointAt(Next)->Point), FeatureSet);
    }

    Current = Next;
    Next = NextPointAfter(Current);
  } while (Current != First);

} /* ConvertToPicoFeatures2 */

/*---------------------------------------------------------------------------*/
/**
 * This routine computes the average x position over all
 * of the pico-features in FeatureSet and then renormalizes
 * the pico-features to force this average to be the x origin
 * (i.e. x=0).
 * FeatureSet is changed.
 * @param FeatureSet pico-features to be normalized
 */
void NormalizePicoX(FEATURE_SET FeatureSet) {
  int i;
  FEATURE Feature;
  float Origin = 0.0;

  for (i = 0; i < FeatureSet->NumFeatures; i++) {
    Feature = FeatureSet->Features[i];
    Origin += Feature->Params[PicoFeatX];
  }
  Origin /= FeatureSet->NumFeatures;

  for (i = 0; i < FeatureSet->NumFeatures; i++) {
    Feature = FeatureSet->Features[i];
    Feature->Params[PicoFeatX] -= Origin;
  }
} /* NormalizePicoX */

/*---------------------------------------------------------------------------*/
/**
 * @param blob blob to extract features from
 * @param fx_info
 * @return Integer character-normalized features for blob.
 */
FEATURE_SET Classify::ExtractIntCNFeatures(const TBLOB &blob, const INT_FX_RESULT_STRUCT &fx_info) {
  INT_FX_RESULT_STRUCT local_fx_info(fx_info);
  std::vector<INT_FEATURE_STRUCT> bl_features;
  tesseract::TrainingSample *sample =
      tesseract::BlobToTrainingSample(blob, false, &local_fx_info, &bl_features);
  if (sample == nullptr) {
    return nullptr;
  }

  uint32_t num_features = sample->num_features();
  const INT_FEATURE_STRUCT *features = sample->features();
  auto feature_set = new FEATURE_SET_STRUCT(num_features);
  for (uint32_t f = 0; f < num_features; ++f) {
    auto feature = new FEATURE_STRUCT(&IntFeatDesc);
    feature->Params[IntX] = features[f].X;
    feature->Params[IntY] = features[f].Y;
    feature->Params[IntDir] = features[f].Theta;
    AddFeature(feature_set, feature);
  }
  delete sample;

  return feature_set;
} /* ExtractIntCNFeatures */

/*---------------------------------------------------------------------------*/
/**
 * @param blob blob to extract features from
 * @param fx_info
 * @return Geometric (top/bottom/width) features for blob.
 */
FEATURE_SET Classify::ExtractIntGeoFeatures(const TBLOB &blob,
                                            const INT_FX_RESULT_STRUCT &fx_info) {
  INT_FX_RESULT_STRUCT local_fx_info(fx_info);
  std::vector<INT_FEATURE_STRUCT> bl_features;
  tesseract::TrainingSample *sample =
      tesseract::BlobToTrainingSample(blob, false, &local_fx_info, &bl_features);
  if (sample == nullptr) {
    return nullptr;
  }

  auto feature_set = new FEATURE_SET_STRUCT(1);
  auto feature = new FEATURE_STRUCT(&IntFeatDesc);

  feature->Params[GeoBottom] = sample->geo_feature(GeoBottom);
  feature->Params[GeoTop] = sample->geo_feature(GeoTop);
  feature->Params[GeoWidth] = sample->geo_feature(GeoWidth);
  AddFeature(feature_set, feature);
  delete sample;

  return feature_set;
} /* ExtractIntGeoFeatures */

} // namespace tesseract.