// 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.

#pragma once

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

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

#include <include/preprocess_op.h>
#include <include/yaml_config.h>

using namespace paddle_infer;

namespace Feature {

class FeatureExtracter {
public:
  explicit FeatureExtracter(const YAML::Node &config_file) {
    this->use_gpu_ = config_file["Global"]["use_gpu"].as<bool>();
    if (config_file["Global"]["gpu_id"].IsDefined())
      this->gpu_id_ = config_file["Global"]["gpu_id"].as<int>();
    else
      this->gpu_id_ = 0;
    this->gpu_mem_ = config_file["Global"]["gpu_mem"].as<int>();
    this->cpu_math_library_num_threads_ =
        config_file["Global"]["cpu_num_threads"].as<int>();
    this->use_mkldnn_ = config_file["Global"]["enable_mkldnn"].as<bool>();
    this->use_tensorrt_ = config_file["Global"]["use_tensorrt"].as<bool>();
    this->use_fp16_ = config_file["Global"]["use_fp16"].as<bool>();

    this->cls_model_path_ =
        config_file["Global"]["rec_inference_model_dir"].as<std::string>() +
        OS_PATH_SEP + "inference.pdmodel";
    this->cls_params_path_ =
        config_file["Global"]["rec_inference_model_dir"].as<std::string>() +
        OS_PATH_SEP + "inference.pdiparams";
    this->resize_size_ =
        config_file["RecPreProcess"]["transform_ops"][0]["ResizeImage"]["size"]
            .as<int>();
    this->scale_ = config_file["RecPreProcess"]["transform_ops"][1]
                              ["NormalizeImage"]["scale"]
                                  .as<float>();
    this->mean_ = config_file["RecPreProcess"]["transform_ops"][1]
                             ["NormalizeImage"]["mean"]
                                 .as<std::vector<float>>();
    this->std_ = config_file["RecPreProcess"]["transform_ops"][1]
                            ["NormalizeImage"]["std"]
                                .as<std::vector<float>>();
    if (config_file["Global"]["rec_feature_normlize"].IsDefined())
      this->feature_norm =
          config_file["Global"]["rec_feature_normlize"].as<bool>();

    LoadModel(cls_model_path_, cls_params_path_);
  }

  // Load Paddle inference model
  void LoadModel(const std::string &model_path, const std::string &params_path);

  // Run predictor
  void Run(cv::Mat &img, std::vector<float> &out_data,
           std::vector<double> &times);
  void FeatureNorm(std::vector<float> &feature);

  std::shared_ptr<Predictor> predictor_;

private:
  bool use_gpu_ = false;
  int gpu_id_ = 0;
  int gpu_mem_ = 4000;
  int cpu_math_library_num_threads_ = 4;
  bool use_mkldnn_ = false;
  bool use_tensorrt_ = false;
  bool feature_norm = true;
  bool use_fp16_ = false;
  std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
  std::vector<float> std_ = {0.229f, 0.224f, 0.225f};
  float scale_ = 0.00392157;
  int resize_size_ = 224;
  int resize_short_ = 224;
  std::string cls_model_path_;
  std::string cls_params_path_;

  // pre-process
  ResizeImg resize_op_;
  Normalize normalize_op_;
  Permute permute_op_;
};

} // namespace Feature