// 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 {

// 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 < (int)MicroFeatureParameter::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