// Copyright (c) 2020 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.

#include "glog/logging.h"
#include "omp.h"
#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
#include <chrono>
#include <iomanip>
#include <iostream>
#include <ostream>
#include <vector>

#include <cstring>
#include <fstream>
#include <numeric>

#include <glog/logging.h>
#include <include/config.h>
#include <include/ocr_det.h>
#include <include/ocr_rec.h>
#include <include/utility.h>
#include <sys/stat.h>

using namespace std;
using namespace cv;
using namespace PaddleOCR;

int main(int argc, char **argv) {
  if (argc < 3) {
    std::cerr << "[ERROR] usage: " << argv[0]
              << " configure_filepath image_path\n";
    exit(1);
  }

  OCRConfig config(argv[1]);

  config.PrintConfigInfo();

  std::string img_path(argv[2]);
  std::vector<std::string> all_img_names;
  // cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
  Utility::GetAllFiles((char *)img_path.c_str(), all_img_names);

  DBDetector det(config.det_model_dir, config.use_gpu, config.gpu_id,
                 config.gpu_mem, config.cpu_math_library_num_threads,
                 config.use_mkldnn, config.max_side_len, config.det_db_thresh,
                 config.det_db_box_thresh, config.det_db_unclip_ratio,
                 config.use_polygon_score, config.visualize,
                 config.use_tensorrt, config.use_fp16);

  Classifier *cls = nullptr;
  if (config.use_angle_cls == true) {
    cls = new Classifier(config.cls_model_dir, config.use_gpu, config.gpu_id,
                         config.gpu_mem, config.cpu_math_library_num_threads,
                         config.use_mkldnn, config.cls_thresh,
                         config.use_tensorrt, config.use_fp16);
  }

  CRNNRecognizer rec(config.rec_model_dir, config.use_gpu, config.gpu_id,
                     config.gpu_mem, config.cpu_math_library_num_threads,
                     config.use_mkldnn, config.char_list_file,
                     config.use_tensorrt, config.use_fp16);

  auto start = std::chrono::system_clock::now();

  for (auto img_dir : all_img_names) {
    LOG(INFO) << "The predict img: " << img_dir;

    cv::Mat srcimg = cv::imread(img_dir, cv::IMREAD_COLOR);
    if (!srcimg.data) {
      std::cerr << "[ERROR] image read failed! image path: " << img_path
                << "\n";
      exit(1);
    }
    std::vector<std::vector<std::vector<int>>> boxes;

    det.Run(srcimg, boxes);
    // for (auto box : boxes){
    //   std::cout << "box: " << box[0][0] << "  " << box[0][1] << "  "
    //             << box[1][0] << "  " << box[1][1] << "  "
    //             << box[2][0] << "  " << box[2][1] << "  "
    //             << box[3][0] << "  " << box[3][1] << "  " << std::endl;
    // }
    rec.Run(boxes, srcimg, cls);
    auto end = std::chrono::system_clock::now();
    auto duration =
        std::chrono::duration_cast<std::chrono::microseconds>(end - start);
    std::cout << "Cost  "
              << double(duration.count()) *
                     std::chrono::microseconds::period::num /
                     std::chrono::microseconds::period::den
              << "s" << std::endl;
  }

  return 0;
}