/******************************************************************************

 ** Filename:    float2int.cpp

 ** Purpose:     Routines for converting float features to int 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 "float2int.h"

#include "classify.h"

#include "mfoutline.h"

#include "normmatch.h"

#include "picofeat.h"

#include "helpers.h"

#define MAX_INT_CHAR_NORM (INT_CHAR_NORM_RANGE - 1)

/*---------------------------------------------------------------------------*/

namespace tesseract {

/**

 * For each class in the unicharset, clears the corresponding

 * entry in char_norm_array.  char_norm_array is indexed by unichar_id.

 *

 * Globals:

 * - none

 *

 * @param char_norm_array array to be cleared

 */

void Classify::ClearCharNormArray(uint8_t *char_norm_array) {

  memset(char_norm_array, 0, sizeof(*char_norm_array) * unicharset.size());

} /* ClearCharNormArray */

/*---------------------------------------------------------------------------*/

/**

 * For each class in unicharset, computes the match between

 * norm_feature and the normalization protos for that class.

 * Converts this number to the range from 0 - 255 and stores it

 * into char_norm_array.  CharNormArray is indexed by unichar_id.

 *

 * Globals:

 * - PreTrainedTemplates current set of built-in templates

 *

 * @param norm_feature character normalization feature

 * @param[out] char_norm_array place to put results of size unicharset.size()

 */

void Classify::ComputeIntCharNormArray(const FEATURE_STRUCT &norm_feature,

                                       uint8_t *char_norm_array) {

  for (unsigned i = 0; i < unicharset.size(); i++) {

    if (i < PreTrainedTemplates->NumClasses) {

      int norm_adjust =

          static_cast<int>(INT_CHAR_NORM_RANGE * ComputeNormMatch(i, norm_feature, false));

      char_norm_array[i] = ClipToRange(norm_adjust, 0, MAX_INT_CHAR_NORM);

    } else {

      // Classes with no templates (eg. ambigs & ligatures) default

      // to worst match.

      char_norm_array[i] = MAX_INT_CHAR_NORM;

    }

  }

} /* ComputeIntCharNormArray */

/*---------------------------------------------------------------------------*/

/**

 * This routine converts each floating point pico-feature

 * in Features into integer format and saves it into

 * IntFeatures.

 *

 * Globals:

 * - none

 *

 * @param Features floating point pico-features to be converted

 * @param[out] IntFeatures array to put converted features into

 */

void Classify::ComputeIntFeatures(FEATURE_SET Features, INT_FEATURE_ARRAY IntFeatures) {

  float YShift;

  if (classify_norm_method == baseline) {

    YShift = BASELINE_Y_SHIFT;

  } else {

    YShift = Y_SHIFT;

  }

  for (int Fid = 0; Fid < Features->NumFeatures; Fid++) {

    FEATURE Feature = Features->Features[Fid];

    IntFeatures[Fid].X = Bucket8For(Feature->Params[PicoFeatX], X_SHIFT, INT_FEAT_RANGE);

    IntFeatures[Fid].Y = Bucket8For(Feature->Params[PicoFeatY], YShift, INT_FEAT_RANGE);

    IntFeatures[Fid].Theta =

        CircBucketFor(Feature->Params[PicoFeatDir], ANGLE_SHIFT, INT_FEAT_RANGE);

    IntFeatures[Fid].CP_misses = 0;

  }

} /* ComputeIntFeatures */

} // namespace tesseract