///////////////////////////////////////////////////////////////////////
// File:        par_control.cpp
// Description: Control code for parallel implementation.
// Author:      Ray Smith
// Created:     Mon Nov 04 13:23:15 PST 2013
//
// (C) Copyright 2013, Google Inc.
// 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 "tesseractclass.h"

namespace tesseract {

struct BlobData {
  BlobData() : blob(NULL), choices(NULL) {}
  BlobData(int index, Tesseract* tess, const WERD_RES& word)
    : blob(word.chopped_word->blobs[index]),
      tesseract(tess),
      choices(&(*word.ratings)(index, index)) {}

  TBLOB* blob;
  Tesseract* tesseract;
  BLOB_CHOICE_LIST** choices;
};

void Tesseract::PrerecAllWordsPar(const GenericVector<WordData>& words) {
  // Prepare all the blobs.
  GenericVector<BlobData> blobs;
  for (int w = 0; w < words.size(); ++w) {
    if (words[w].word->ratings != NULL &&
        words[w].word->ratings->get(0, 0) == NULL) {
      for (int s = 0; s < words[w].lang_words.size(); ++s) {
        Tesseract* sub = s < sub_langs_.size() ? sub_langs_[s] : this;
        const WERD_RES& word = *words[w].lang_words[s];
        for (int b = 0; b < word.chopped_word->NumBlobs(); ++b) {
          blobs.push_back(BlobData(b, sub, word));
        }
      }
    }
  }
  // Pre-classify all the blobs.
  if (tessedit_parallelize > 1) {
    #pragma omp parallel for num_threads(10)
    for (int b = 0; b < blobs.size(); ++b) {
      *blobs[b].choices =
          blobs[b].tesseract->classify_blob(blobs[b].blob, "par", White, NULL);
    }
  } else {
    // TODO(AMD) parallelize this.
    for (int b = 0; b < blobs.size(); ++b) {
      *blobs[b].choices =
          blobs[b].tesseract->classify_blob(blobs[b].blob, "par", White, NULL);
    }
  }
}

}  // namespace tesseract.