text: // Copyright 2010 Google Inc. All Rights Reserved.
  // Author: rays@google.com (Ray Smith)
  //
  // 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.
  //
  ///////////////////////////////////////////////////////////////////////
  
  #define _USE_MATH_DEFINES // for M_PI
  // Include automatically generated configuration file if running autoconf.
  #ifdef HAVE_CONFIG_H
  #  include "config_auto.h"
  #endif
  
  #include "trainingsample.h"
  
  #include "helpers.h"
  #include "intfeaturespace.h"
  #include "normfeat.h"
  #include "shapetable.h"
  
  #include <allheaders.h>
  
  #include <cmath> // for M_PI
  
  namespace tesseract {
  
  ELISTIZE(TrainingSample)
  
  // Center of randomizing operations.
  const int kRandomizingCenter = 128;
  
  // Randomizing factors.
  const int TrainingSample::kYShiftValues[kSampleYShiftSize] = {6, 3, -3, -6, 0};
  const double TrainingSample::kScaleValues[kSampleScaleSize] = {1.0625, 0.9375, 1.0};
  
  TrainingSample::~TrainingSample() {
    delete[] features_;
    delete[] micro_features_;
  }
  
  // WARNING! Serialize/DeSerialize do not save/restore the "cache" data
  // members, which is mostly the mapped features, and the weight.
  // It is assumed these can all be reconstructed from what is saved.
  // Writes to the given file. Returns false in case of error.
  bool TrainingSample::Serialize(FILE *fp) const {
    if (fwrite(&class_id_, sizeof(class_id_), 1, fp) != 1) {
      return false;
    }
    if (fwrite(&font_id_, sizeof(font_id_), 1, fp) != 1) {
      return false;
    }
    if (fwrite(&page_num_, sizeof(page_num_), 1, fp) != 1) {
      return false;
    }
    if (!bounding_box_.Serialize(fp)) {
      return false;
    }
    if (fwrite(&num_features_, sizeof(num_features_), 1, fp) != 1) {
      return false;
    }
    if (fwrite(&num_micro_features_, sizeof(num_micro_features_), 1, fp) != 1) {
      return false;
    }
    if (fwrite(&outline_length_, sizeof(outline_length_), 1, fp) != 1) {
      return false;
    }
    if (fwrite(features_, sizeof(*features_), num_features_, fp) != num_features_) {
      return false;
    }
    if (fwrite(micro_features_, sizeof(*micro_features_), num_micro_features_, fp) !=
        num_micro_features_) {
      return false;
    }
    if (fwrite(cn_feature_, sizeof(*cn_feature_), kNumCNParams, fp) != kNumCNParams) {
      return false;
    }
    if (fwrite(geo_feature_, sizeof(*geo_feature_), GeoCount, fp) != GeoCount) {
      return false;
    }
    return true;
  }
  
  // Creates from the given file. Returns nullptr in case of error.
  // If swap is true, assumes a big/little-endian swap is needed.
  TrainingSample *TrainingSample::DeSerializeCreate(bool swap, FILE *fp) {
    auto *sample = new TrainingSample;
    if (sample->DeSerialize(swap, fp)) {
      return sample;
    }
    delete sample;
    return nullptr;
  }
  
  // Reads from the given file. Returns false in case of error.
  // If swap is true, assumes a big/little-endian swap is needed.
  bool TrainingSample::DeSerialize(bool swap, FILE *fp) {
    if (fread(&class_id_, sizeof(class_id_), 1, fp) != 1) {
      return false;
    }
    if (fread(&font_id_, sizeof(font_id_), 1, fp) != 1) {
      return false;
    }
    if (fread(&page_num_, sizeof(page_num_), 1, fp) != 1) {
      return false;
    }
    if (!bounding_box_.DeSerialize(swap, fp)) {
      return false;
    }
    if (fread(&num_features_, sizeof(num_features_), 1, fp) != 1) {
      return false;
    }
    if (fread(&num_micro_features_, sizeof(num_micro_features_), 1, fp) != 1) {
      return false;
    }
    if (fread(&outline_length_, sizeof(outline_length_), 1, fp) != 1) {
      return false;
    }
    if (swap) {
      ReverseN(&class_id_, sizeof(class_id_));
      ReverseN(&num_features_, sizeof(num_features_));
      ReverseN(&num_micro_features_, sizeof(num_micro_features_));
      ReverseN(&outline_length_, sizeof(outline_length_));
    }
    // Arbitrarily limit the number of elements to protect against bad data.
    if (num_features_ > UINT16_MAX) {
      return false;
    }
    if (num_micro_features_ > UINT16_MAX) {
      return false;
    }
    delete[] features_;
    features_ = new INT_FEATURE_STRUCT[num_features_];
    if (fread(features_, sizeof(*features_), num_features_, fp) != num_features_) {
      return false;
    }
    delete[] micro_features_;
    micro_features_ = new MicroFeature[num_micro_features_];
    if (fread(micro_features_, sizeof(*micro_features_), num_micro_features_, fp) !=
        num_micro_features_) {
      return false;
    }
    if (fread(cn_feature_, sizeof(*cn_feature_), kNumCNParams, fp) != kNumCNParams) {
      return false;
    }
    if (fread(geo_feature_, sizeof(*geo_feature_), GeoCount, fp) != GeoCount) {
      return false;
    }
    return true;
  }
  
  // Saves the given features into a TrainingSample.
  TrainingSample *TrainingSample::CopyFromFeatures(const INT_FX_RESULT_STRUCT &fx_info,
                                                   const TBOX &bounding_box,
                                                   const INT_FEATURE_STRUCT *features,
                                                   int num_features) {
    auto *sample = new TrainingSample;
    sample->num_features_ = num_features;
    sample->features_ = new INT_FEATURE_STRUCT[num_features];
    sample->outline_length_ = fx_info.Length;
    memcpy(sample->features_, features, num_features * sizeof(features[0]));
    sample->geo_feature_[GeoBottom] = bounding_box.bottom();
    sample->geo_feature_[GeoTop] = bounding_box.top();
    sample->geo_feature_[GeoWidth] = bounding_box.width();
  
    // Generate the cn_feature_ from the fx_info.
    sample->cn_feature_[CharNormY] = MF_SCALE_FACTOR * (fx_info.Ymean - kBlnBaselineOffset);
    sample->cn_feature_[CharNormLength] = MF_SCALE_FACTOR * fx_info.Length / LENGTH_COMPRESSION;
    sample->cn_feature_[CharNormRx] = MF_SCALE_FACTOR * fx_info.Rx;
    sample->cn_feature_[CharNormRy] = MF_SCALE_FACTOR * fx_info.Ry;
  
    sample->features_are_indexed_ = false;
    sample->features_are_mapped_ = false;
    return sample;
  }
  
  // Returns the cn_feature as a FEATURE_STRUCT* needed by cntraining.
  FEATURE_STRUCT *TrainingSample::GetCNFeature() const {
    auto feature = new FEATURE_STRUCT(&CharNormDesc);
    for (int i = 0; i < kNumCNParams; ++i) {
      feature->Params[i] = cn_feature_[i];
    }
    return feature;
  }
  
  // Constructs and returns a copy randomized by the method given by
  // the randomizer index. If index is out of [0, kSampleRandomSize) then
  // an exact copy is returned.
  TrainingSample *TrainingSample::RandomizedCopy(int index) const {
    TrainingSample *sample = Copy();
    if (index >= 0 && index < kSampleRandomSize) {
      ++index; // Remove the first combination.
      const int yshift = kYShiftValues[index / kSampleScaleSize];
      double scaling = kScaleValues[index % kSampleScaleSize];
      for (uint32_t i = 0; i < num_features_; ++i) {
        double result = (features_[i].X - kRandomizingCenter) * scaling;
        result += kRandomizingCenter;
        sample->features_[i].X = ClipToRange<int>(result + 0.5, 0, UINT8_MAX);
        result = (features_[i].Y - kRandomizingCenter) * scaling;
        result += kRandomizingCenter + yshift;
        sample->features_[i].Y = ClipToRange<int>(result + 0.5, 0, UINT8_MAX);
      }
    }
    return sample;
  }
  
  // Constructs and returns an exact copy.
  TrainingSample *TrainingSample::Copy() const {
    auto *sample = new TrainingSample;
    sample->class_id_ = class_id_;
    sample->font_id_ = font_id_;
    sample->weight_ = weight_;
    sample->sample_index_ = sample_index_;
    sample->num_features_ = num_features_;
    if (num_features_ > 0) {
      sample->features_ = new INT_FEATURE_STRUCT[num_features_];
      memcpy(sample->features_, features_, num_features_ * sizeof(features_[0]));
    }
    sample->num_micro_features_ = num_micro_features_;
    if (num_micro_features_ > 0) {
      sample->micro_features_ = new MicroFeature[num_micro_features_];
      memcpy(sample->micro_features_, micro_features_,
             num_micro_features_ * sizeof(micro_features_[0]));
    }
    memcpy(sample->cn_feature_, cn_feature_, sizeof(*cn_feature_) * kNumCNParams);
    memcpy(sample->geo_feature_, geo_feature_, sizeof(*geo_feature_) * GeoCount);
    return sample;
  }
  
  // Extracts the needed information from the CHAR_DESC_STRUCT.
  void TrainingSample::ExtractCharDesc(int int_feature_type, int micro_type, int cn_type,
                                       int geo_type, CHAR_DESC_STRUCT *char_desc) {
    // Extract the INT features.
    delete[] features_;
    FEATURE_SET_STRUCT *char_features = char_desc->FeatureSets[int_feature_type];
    if (char_features == nullptr) {
      tprintf("Error: no features to train on of type %s\n", kIntFeatureType);
      num_features_ = 0;
      features_ = nullptr;
    } else {
      num_features_ = char_features->NumFeatures;
      features_ = new INT_FEATURE_STRUCT[num_features_];
      for (uint32_t f = 0; f < num_features_; ++f) {
        features_[f].X = static_cast<uint8_t>(char_features->Features[f]->Params[IntX]);
        features_[f].Y = static_cast<uint8_t>(char_features->Features[f]->Params[IntY]);
        features_[f].Theta = static_cast<uint8_t>(char_features->Features[f]->Params[IntDir]);
        features_[f].CP_misses = 0;
      }
    }
    // Extract the Micro features.
    delete[] micro_features_;
    char_features = char_desc->FeatureSets[micro_type];
    if (char_features == nullptr) {
      tprintf("Error: no features to train on of type %s\n", kMicroFeatureType);
      num_micro_features_ = 0;
      micro_features_ = nullptr;
    } else {
      num_micro_features_ = char_features->NumFeatures;
      micro_features_ = new MicroFeature[num_micro_features_];
      for (uint32_t f = 0; f < num_micro_features_; ++f) {
        for (int d = 0; d < MFCount; ++d) {
          micro_features_[f][d] = char_features->Features[f]->Params[d];
        }
      }
    }
    // Extract the CN feature.
    char_features = char_desc->FeatureSets[cn_type];
    if (char_features == nullptr) {
      tprintf("Error: no CN feature to train on.\n");
    } else {
      ASSERT_HOST(char_features->NumFeatures == 1);
      cn_feature_[CharNormY] = char_features->Features[0]->Params[CharNormY];
      cn_feature_[CharNormLength] = char_features->Features[0]->Params[CharNormLength];
      cn_feature_[CharNormRx] = char_features->Features[0]->Params[CharNormRx];
      cn_feature_[CharNormRy] = char_features->Features[0]->Params[CharNormRy];
    }
    // Extract the Geo feature.
    char_features = char_desc->FeatureSets[geo_type];
    if (char_features == nullptr) {
      tprintf("Error: no Geo feature to train on.\n");
    } else {
      ASSERT_HOST(char_features->NumFeatures == 1);
      geo_feature_[GeoBottom] = char_features->Features[0]->Params[GeoBottom];
      geo_feature_[GeoTop] = char_features->Features[0]->Params[GeoTop];
      geo_feature_[GeoWidth] = char_features->Features[0]->Params[GeoWidth];
    }
    features_are_indexed_ = false;
    features_are_mapped_ = false;
  }
  
  // Sets the mapped_features_ from the features_ using the provided
  // feature_space to the indexed versions of the features.
  void TrainingSample::IndexFeatures(const IntFeatureSpace &feature_space) {
    std::vector<int> indexed_features;
    feature_space.IndexAndSortFeatures(features_, num_features_, &mapped_features_);
    features_are_indexed_ = true;
    features_are_mapped_ = false;
  }
  
  // Returns a pix representing the sample. (Int features only.)
  Image TrainingSample::RenderToPix(const UNICHARSET *unicharset) const {
    Image pix = pixCreate(kIntFeatureExtent, kIntFeatureExtent, 1);
    for (uint32_t f = 0; f < num_features_; ++f) {
      int start_x = features_[f].X;
      int start_y = kIntFeatureExtent - features_[f].Y;
      double dx = cos((features_[f].Theta / 256.0) * 2.0 * M_PI - M_PI);
      double dy = -sin((features_[f].Theta / 256.0) * 2.0 * M_PI - M_PI);
      for (int i = 0; i <= 5; ++i) {
        int x = static_cast<int>(start_x + dx * i);
        int y = static_cast<int>(start_y + dy * i);
        if (x >= 0 && x < 256 && y >= 0 && y < 256) {
          pixSetPixel(pix, x, y, 1);
        }
      }
    }
    if (unicharset != nullptr) {
      pixSetText(pix, unicharset->id_to_unichar(class_id_));
    }
    return pix;
  }
  
  #ifndef GRAPHICS_DISABLED
  
  // Displays the features in the given window with the given color.
  void TrainingSample::DisplayFeatures(ScrollView::Color color, ScrollView *window) const {
    for (uint32_t f = 0; f < num_features_; ++f) {
      RenderIntFeature(window, &features_[f], color);
    }
  }
  
  #endif // !GRAPHICS_DISABLED
  
  // Returns a pix of the original sample image. The pix is padded all round
  // by padding wherever possible.
  // The returned Pix must be pixDestroyed after use.
  // If the input page_pix is nullptr, nullptr is returned.
  Image TrainingSample::GetSamplePix(int padding, Image page_pix) const {
    if (page_pix == nullptr) {
      return nullptr;
    }
    int page_width = pixGetWidth(page_pix);
    int page_height = pixGetHeight(page_pix);
    TBOX padded_box = bounding_box();
    padded_box.pad(padding, padding);
    // Clip the padded_box to the limits of the page
    TBOX page_box(0, 0, page_width, page_height);
    padded_box &= page_box;
    Box *box =
        boxCreate(page_box.left(), page_height - page_box.top(), page_box.width(), page_box.height());
    Image sample_pix = pixClipRectangle(page_pix, box, nullptr);
    boxDestroy(&box);
    return sample_pix;
  }
  
  } // namespace tesseract
  