// 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 <include/args.h>
#include <include/paddleocr.h>

#include "auto_log/autolog.h"
#include <numeric>
namespace PaddleOCR {

PPOCR::PPOCR() {
  if (FLAGS_det) {
    this->detector_ = new DBDetector(
        FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, FLAGS_gpu_mem,
        FLAGS_cpu_threads, FLAGS_enable_mkldnn, FLAGS_limit_type,
        FLAGS_limit_side_len, FLAGS_det_db_thresh, FLAGS_det_db_box_thresh,
        FLAGS_det_db_unclip_ratio, FLAGS_det_db_score_mode, FLAGS_use_dilation,
        FLAGS_use_tensorrt, FLAGS_precision);
  }

  if (FLAGS_cls && FLAGS_use_angle_cls) {
    this->classifier_ = new Classifier(
        FLAGS_cls_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, FLAGS_gpu_mem,
        FLAGS_cpu_threads, FLAGS_enable_mkldnn, FLAGS_cls_thresh,
        FLAGS_use_tensorrt, FLAGS_precision, FLAGS_cls_batch_num);
  }
  if (FLAGS_rec) {
    this->recognizer_ = new CRNNRecognizer(
        FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, FLAGS_gpu_mem,
        FLAGS_cpu_threads, FLAGS_enable_mkldnn, FLAGS_rec_char_dict_path,
        FLAGS_use_tensorrt, FLAGS_precision, FLAGS_rec_batch_num,
        FLAGS_rec_img_h, FLAGS_rec_img_w);
  }
};

void PPOCR::det(cv::Mat img, std::vector<OCRPredictResult> &ocr_results,
                std::vector<double> &times) {
  std::vector<std::vector<std::vector<int>>> boxes;
  std::vector<double> det_times;

  this->detector_->Run(img, boxes, det_times);

  for (int i = 0; i < boxes.size(); i++) {
    OCRPredictResult res;
    res.box = boxes[i];
    ocr_results.push_back(res);
  }
  // sort boex from top to bottom, from left to right
  Utility::sorted_boxes(ocr_results);
  times[0] += det_times[0];
  times[1] += det_times[1];
  times[2] += det_times[2];
}

void PPOCR::rec(std::vector<cv::Mat> img_list,
                std::vector<OCRPredictResult> &ocr_results,
                std::vector<double> &times) {
  std::vector<std::string> rec_texts(img_list.size(), "");
  std::vector<float> rec_text_scores(img_list.size(), 0);
  std::vector<double> rec_times;
  this->recognizer_->Run(img_list, rec_texts, rec_text_scores, rec_times);
  // output rec results
  for (int i = 0; i < rec_texts.size(); i++) {
    ocr_results[i].text = rec_texts[i];
    ocr_results[i].score = rec_text_scores[i];
  }
  times[0] += rec_times[0];
  times[1] += rec_times[1];
  times[2] += rec_times[2];
}

void PPOCR::cls(std::vector<cv::Mat> img_list,
                std::vector<OCRPredictResult> &ocr_results,
                std::vector<double> &times) {
  std::vector<int> cls_labels(img_list.size(), 0);
  std::vector<float> cls_scores(img_list.size(), 0);
  std::vector<double> cls_times;
  this->classifier_->Run(img_list, cls_labels, cls_scores, cls_times);
  // output cls results
  for (int i = 0; i < cls_labels.size(); i++) {
    ocr_results[i].cls_label = cls_labels[i];
    ocr_results[i].cls_score = cls_scores[i];
  }
  times[0] += cls_times[0];
  times[1] += cls_times[1];
  times[2] += cls_times[2];
}

std::vector<std::vector<OCRPredictResult>>
PPOCR::ocr(std::vector<cv::String> cv_all_img_names, bool det, bool rec,
           bool cls) {
  std::vector<double> time_info_det = {0, 0, 0};
  std::vector<double> time_info_rec = {0, 0, 0};
  std::vector<double> time_info_cls = {0, 0, 0};
  std::vector<std::vector<OCRPredictResult>> ocr_results;

  if (!det) {
    std::vector<OCRPredictResult> ocr_result;
    // read image
    std::vector<cv::Mat> img_list;
    for (int i = 0; i < cv_all_img_names.size(); ++i) {
      cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
      if (!srcimg.data) {
        std::cerr << "[ERROR] image read failed! image path: "
                  << cv_all_img_names[i] << endl;
        exit(1);
      }
      img_list.push_back(srcimg);
      OCRPredictResult res;
      ocr_result.push_back(res);
    }
    if (cls && this->classifier_ != nullptr) {
      this->cls(img_list, ocr_result, time_info_cls);
      for (int i = 0; i < img_list.size(); i++) {
        if (ocr_result[i].cls_label % 2 == 1 &&
            ocr_result[i].cls_score > this->classifier_->cls_thresh) {
          cv::rotate(img_list[i], img_list[i], 1);
        }
      }
    }
    if (rec) {
      this->rec(img_list, ocr_result, time_info_rec);
    }
    for (int i = 0; i < cv_all_img_names.size(); ++i) {
      std::vector<OCRPredictResult> ocr_result_tmp;
      ocr_result_tmp.push_back(ocr_result[i]);
      ocr_results.push_back(ocr_result_tmp);
    }
  } else {
    if (!Utility::PathExists(FLAGS_output) && FLAGS_det) {
      Utility::CreateDir(FLAGS_output);
    }

    for (int i = 0; i < cv_all_img_names.size(); ++i) {
      std::vector<OCRPredictResult> ocr_result;
      if (!FLAGS_benchmark) {
        cout << "predict img: " << cv_all_img_names[i] << endl;
      }

      cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
      if (!srcimg.data) {
        std::cerr << "[ERROR] image read failed! image path: "
                  << cv_all_img_names[i] << endl;
        exit(1);
      }
      // det
      this->det(srcimg, ocr_result, time_info_det);
      // crop image
      std::vector<cv::Mat> img_list;
      for (int j = 0; j < ocr_result.size(); j++) {
        cv::Mat crop_img;
        crop_img = Utility::GetRotateCropImage(srcimg, ocr_result[j].box);
        img_list.push_back(crop_img);
      }

      // cls
      if (cls && this->classifier_ != nullptr) {
        this->cls(img_list, ocr_result, time_info_cls);
        for (int i = 0; i < img_list.size(); i++) {
          if (ocr_result[i].cls_label % 2 == 1 &&
              ocr_result[i].cls_score > this->classifier_->cls_thresh) {
            cv::rotate(img_list[i], img_list[i], 1);
          }
        }
      }
      // rec
      if (rec) {
        this->rec(img_list, ocr_result, time_info_rec);
      }
      ocr_results.push_back(ocr_result);
    }
  }
  if (FLAGS_benchmark) {
    this->log(time_info_det, time_info_rec, time_info_cls,
              cv_all_img_names.size());
  }
  return ocr_results;
} // namespace PaddleOCR

void PPOCR::log(std::vector<double> &det_times, std::vector<double> &rec_times,
                std::vector<double> &cls_times, int img_num) {
  if (det_times[0] + det_times[1] + det_times[2] > 0) {
    AutoLogger autolog_det("ocr_det", FLAGS_use_gpu, FLAGS_use_tensorrt,
                           FLAGS_enable_mkldnn, FLAGS_cpu_threads, 1, "dynamic",
                           FLAGS_precision, det_times, img_num);
    autolog_det.report();
  }
  if (rec_times[0] + rec_times[1] + rec_times[2] > 0) {
    AutoLogger autolog_rec("ocr_rec", FLAGS_use_gpu, FLAGS_use_tensorrt,
                           FLAGS_enable_mkldnn, FLAGS_cpu_threads,
                           FLAGS_rec_batch_num, "dynamic", FLAGS_precision,
                           rec_times, img_num);
    autolog_rec.report();
  }
  if (cls_times[0] + cls_times[1] + cls_times[2] > 0) {
    AutoLogger autolog_cls("ocr_cls", FLAGS_use_gpu, FLAGS_use_tensorrt,
                           FLAGS_enable_mkldnn, FLAGS_cpu_threads,
                           FLAGS_cls_batch_num, "dynamic", FLAGS_precision,
                           cls_times, img_num);
    autolog_cls.report();
  }
}
PPOCR::~PPOCR() {
  if (this->detector_ != nullptr) {
    delete this->detector_;
  }
  if (this->classifier_ != nullptr) {
    delete this->classifier_;
  }
  if (this->recognizer_ != nullptr) {
    delete this->recognizer_;
  }
};

} // namespace PaddleOCR