// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// 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.

#pragma once
#include "ultra_infer/ultra_infer_model.h"
#include "ultra_infer/utils/unique_ptr.h"
#include "ultra_infer/vision/common/processors/transform.h"
#include "ultra_infer/vision/common/result.h"
#include "ultra_infer/vision/ocr/ppocr/structurev2_table_postprocessor.h"
#include "ultra_infer/vision/ocr/ppocr/structurev2_table_preprocessor.h"
#include "ultra_infer/vision/ocr/ppocr/utils/ocr_postprocess_op.h"

namespace ultra_infer {
namespace vision {
/** \brief All OCR series model APIs are defined inside this namespace
 *
 */
namespace ocr {

/*! @brief DBDetector object is used to load the detection model provided by
 * PaddleOCR.
 */
class ULTRAINFER_DECL StructureV2Table : public UltraInferModel {
public:
  StructureV2Table();
  /** \brief Set path of model file, and the configuration of runtime
   *
   * \param[in] model_file Path of model file, e.g
   * ./en_ppstructure_mobile_v2.0_SLANet_infer/model.pdmodel. \param[in]
   * params_file Path of parameter file, e.g
   * ./en_ppstructure_mobile_v2.0_SLANet_infer/model.pdiparams, if the model
   * format is ONNX, this parameter will be ignored. \param[in] custom_option
   * RuntimeOption for inference, the default will use cpu, and choose the
   * backend defined in `valid_cpu_backends`. \param[in] model_format Model
   * format of the loaded model, default is Paddle format. \param[in] box_shape
   * Type of output box, default is ori.
   */
  StructureV2Table(const std::string &model_file,
                   const std::string &params_file = "",
                   const std::string &table_char_dict_path = "",
                   const std::string &box_shape = "ori",
                   const RuntimeOption &custom_option = RuntimeOption(),
                   const ModelFormat &model_format = ModelFormat::PADDLE);

  /** \brief Clone a new StructureV2Table Recognizer with less memory usage when
   * multiple instances of the same model are created
   *
   * \return new StructureV2Table* type unique pointer
   */
  virtual std::unique_ptr<StructureV2Table> Clone() const;

  /// Get model's name
  std::string ModelName() const { return "ppocr/ocr_table"; }

  /** \brief Predict the input image and get OCR detection model result.
   *
   * \param[in] img The input image data, comes from cv::imread(), is a 3-D
   * array with layout HWC, BGR format. \param[in] boxes_result The output of
   * OCR detection model result will be written to this structure. \return true
   * if the prediction is succeeded, otherwise false.
   */
  virtual bool Predict(const cv::Mat &img,
                       std::vector<std::array<int, 8>> *boxes_result,
                       std::vector<std::string> *structure_result);

  /** \brief Predict the input image and get OCR detection model result.
   *
   * \param[in] img The input image data, comes from cv::imread(), is a 3-D
   * array with layout HWC, BGR format. \param[in] ocr_result The output of OCR
   * detection model result will be written to this structure. \return true if
   * the prediction is succeeded, otherwise false.
   */
  virtual bool Predict(const cv::Mat &img, vision::OCRResult *ocr_result);

  /** \brief BatchPredict the input image and get OCR detection model result.
   *
   * \param[in] images The list input of image data, comes from cv::imread(), is
   * a 3-D array with layout HWC, BGR format. \param[in] det_results The output
   * of OCR detection model result will be written to this structure. \return
   * true if the prediction is succeeded, otherwise false.
   */
  virtual bool
  BatchPredict(const std::vector<cv::Mat> &images,
               std::vector<std::vector<std::array<int, 8>>> *det_results,
               std::vector<std::vector<std::string>> *structure_results);

  /** \brief BatchPredict the input image and get OCR detection model result.
   *
   * \param[in] images The list input of image data, comes from cv::imread(), is
   * a 3-D array with layout HWC, BGR format. \param[in] ocr_results The output
   * of OCR detection model result will be written to this structure. \return
   * true if the prediction is succeeded, otherwise false.
   */
  virtual bool BatchPredict(const std::vector<cv::Mat> &images,
                            std::vector<vision::OCRResult> *ocr_results);

  /// Get preprocessor reference of StructureV2TablePreprocessor
  virtual StructureV2TablePreprocessor &GetPreprocessor() {
    return preprocessor_;
  }

  /// Get postprocessor reference of StructureV2TablePostprocessor
  virtual StructureV2TablePostprocessor &GetPostprocessor() {
    return postprocessor_;
  }

private:
  bool Initialize();
  StructureV2TablePreprocessor preprocessor_;
  StructureV2TablePostprocessor postprocessor_;
};

} // namespace ocr
} // namespace vision
} // namespace ultra_infer