picofeat.cpp

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/******************************************************************************
 ** 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
----------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
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
----------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
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 */
 
/*---------------------------------------------------------------------------*/
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 */
 
/*---------------------------------------------------------------------------*/
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 */
 
/*---------------------------------------------------------------------------*/
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 */
 
/*---------------------------------------------------------------------------*/
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.