// 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 "fastdeploy/fastdeploy_model.h"
#include "fastdeploy/vision/common/processors/transform.h"
#include "fastdeploy/vision/common/result.h"

namespace fastdeploy {

namespace vision {
/** \brief All object face detection model APIs are defined inside this namespace
 *
 */
namespace facedet {
/*! @brief RetinaFace model object used when to load a RetinaFace model exported by RetinaFace.
 */
class FASTDEPLOY_DECL RetinaFace : public FastDeployModel {
 public:
  /** \brief  Set path of model file and the configuration of runtime.
   *
   * \param[in] model_file Path of model file, e.g ./retinaface.onnx
   * \param[in] params_file Path of parameter file, e.g ppyoloe/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 ONNX format
   */
  RetinaFace(const std::string& model_file, const std::string& params_file = "",
             const RuntimeOption& custom_option = RuntimeOption(),
             const ModelFormat& model_format = ModelFormat::ONNX);

  std::string ModelName() const { return "Pytorch_Retinaface"; }
  /** \brief Predict the face detection result for an input image
   *
   * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
   * \param[in] result The output face detection result will be writen to this structure
   * \param[in] conf_threshold confidence threashold for postprocessing, default is 0.25
   * \param[in] nms_iou_threshold iou threashold for NMS, default is 0.4
   * \return true if the prediction successed, otherwise false
   */
  virtual bool Predict(cv::Mat* im, FaceDetectionResult* result,
                       float conf_threshold = 0.25f,
                       float nms_iou_threshold = 0.4f);

  /*! @brief
  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize, default (640, 640)
  */
  std::vector<int> size;
  /*! @brief
  Argument for image postprocessing step, variance in RetinaFace's prior-box(anchor) generate process, default (0.1, 0.2)
  */
  std::vector<float> variance;
  /*! @brief
  Argument for image postprocessing step, downsample strides (namely, steps) for RetinaFace to generate anchors, will take (8,16,32) as default values
  */
  std::vector<int> downsample_strides;
  /*! @brief
  Argument for image postprocessing step, min sizes, width and height for each anchor, default min_sizes = {{16, 32}, {64, 128}, {256, 512}}
  */
  std::vector<std::vector<int>> min_sizes;
  /*! @brief
  Argument for image postprocessing step, landmarks_per_face, default 5 in RetinaFace
  */
  int landmarks_per_face;

 private:
  bool Initialize();

  bool Preprocess(Mat* mat, FDTensor* output,
                  std::map<std::string, std::array<float, 2>>* im_info);

  bool Postprocess(std::vector<FDTensor>& infer_result,
                   FaceDetectionResult* result,
                   const std::map<std::string, std::array<float, 2>>& im_info,
                   float conf_threshold, float nms_iou_threshold);

  bool IsDynamicInput() const { return is_dynamic_input_; }

  bool is_dynamic_input_;
};

}  // namespace facedet
}  // namespace vision
}  // namespace fastdeploy