#pragma once
#include "paddle_api.h"
#include "paddle_inference_api.h"
#include <ocr_cls.h>
#include <ANSOCRUtility.h>
namespace ANSCENTER {
    class CRNNRecognizer {
public:
    CRNNRecognizer() {}
    bool Initialize(const std::string &model_dir, const bool &use_gpu,
                          const int &gpu_id, const int &gpu_mem,
                          const int &cpu_math_library_num_threads,
                          const bool &use_mkldnn, const std::string &label_path,
                          const bool &use_tensorrt,
                          const std::string &precision,
                          const int &rec_batch_num, const int &rec_img_h,
                          const int &rec_img_w) {

      try {
          this->use_gpu_ = use_gpu;
          this->gpu_id_ = gpu_id;
          this->gpu_mem_ = gpu_mem;
          this->cpu_math_library_num_threads_ = cpu_math_library_num_threads;
          this->use_mkldnn_ = use_mkldnn;
          this->use_tensorrt_ = use_tensorrt;
          this->precision_ = precision;
          this->rec_batch_num_ = rec_batch_num;
          this->rec_img_h_ = rec_img_h;
          this->rec_img_w_ = rec_img_w;
          std::vector<int> rec_image_shape = { 3, rec_img_h, rec_img_w };
          this->rec_image_shape_ = rec_image_shape;

          this->label_list_ = ANSOCRUtility::ReadDict(label_path);
          this->label_list_.insert(this->label_list_.begin(), "#"); // blank char for ctc
          this->label_list_.push_back(" ");

          return LoadModel(model_dir);
      }
      catch (std::exception& e) {
          std::cout << "CRNNRecognizer::CRNNRecognizer" << e.what() << std::endl;
          return false;
      }
  }

  // Load Paddle inference model
  bool LoadModel(const std::string &model_dir);

  void Run(std::vector<cv::Mat> img_list, std::vector<std::string> &rec_texts,std::vector<float> &rec_text_scores);

private:
  std::shared_ptr<paddle_infer::Predictor> predictor_;
  //paddle_infer::Predictor* predictor_;
  bool use_gpu_ = false;
  int gpu_id_ = 0;
  int gpu_mem_ = 4000;
  int cpu_math_library_num_threads_ = 4;
  bool use_mkldnn_ = false;

  std::vector<std::string> label_list_;

  std::vector<float> mean_ = {0.5f, 0.5f, 0.5f};
  std::vector<float> scale_ = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
  bool is_scale_ = true;
  bool use_tensorrt_ = false;
  std::string precision_ = "fp32";
  int rec_batch_num_ = 6;
  int rec_img_h_ = 32;
  int rec_img_w_ = 320;
  std::vector<int> rec_image_shape_ = {3, rec_img_h_, rec_img_w_};
  // pre-process
  CrnnResizeImg resize_op_;
  Normalize normalize_op_;
  PermuteBatch permute_op_;

}; // class CrnnRecognizer
} // namespace ANSCENTER
