// 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 {

namespace facedet {
/*! @brief YOLOv5Face model object used when to load a YOLOv5Face model exported by YOLOv5Face.
 */
class FASTDEPLOY_DECL YOLOv5Face : public FastDeployModel {
 public:
  /** \brief  Set path of model file and the configuration of runtime.
   *
   * \param[in] model_file Path of model file, e.g ./yolov5face.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
   */
  YOLOv5Face(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 "yolov5-face"; }
  /** \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 0.5
   * \return true if the prediction successed, otherwise false
   */
  virtual bool Predict(cv::Mat* im, FaceDetectionResult* result,
                       float conf_threshold = 0.25,
                       float nms_iou_threshold = 0.5);

  /*! @brief
  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize, default size = {640, 640}
  */
  std::vector<int> size;
  // padding value, size should be the same as channels

  std::vector<float> padding_value;
  // only pad to the minimum rectange which height and width is times of stride
  bool is_mini_pad;
  // while is_mini_pad = false and is_no_pad = true,
  // will resize the image to the set size

  bool is_no_pad;
  // if is_scale_up is false, the input image only can be zoom out,
  // the maximum resize scale cannot exceed 1.0

  bool is_scale_up;
  // padding stride, for is_mini_pad
  int stride;
  /*! @brief
    Argument for image postprocessing step, setup the number of landmarks for per face (if have), default 5 in
    official yolov5face note that, the outupt tensor's shape must be:
    (1,n,4+1+2*landmarks_per_face+1=box+obj+landmarks+cls), default 5
  */
  int landmarks_per_face;

 private:
  bool Initialize();

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

  bool Postprocess(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