// 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"
#include "fastdeploy/vision/facedet/ppdet/blazeface/preprocessor.h"
#include "fastdeploy/vision/facedet/ppdet/blazeface/postprocessor.h"

namespace fastdeploy {

namespace vision {

namespace facedet {
/*! @brief BlazeFace model object used when to load a BlazeFace model exported by BlazeFace.
 */ 
class FASTDEPLOY_DECL BlazeFace: public FastDeployModel{ 
 public:
  /** \brief  Set path of model file and the configuration of runtime.
   *
   * \param[in] model_file Path of model file, e.g ./blazeface.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] config_file Path of configuration file for deployment, e.g resnet/infer_cfg.yml
   * \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
   */
  BlazeFace(const std::string& model_file, const std::string& params_file = "",
            const std::string& config_file = "",
            const RuntimeOption& custom_option = RuntimeOption(),
            const ModelFormat& model_format = ModelFormat::PADDLE);

  std::string ModelName() {return "blaze-face";}

  /** \brief Predict the detection result for an input image
   *
   * \param[in] img The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
   * \param[in] result The output detection result will be writen to this structure
   * \return true if the prediction successed, otherwise false
   */
  bool Predict(const cv::Mat& im, FaceDetectionResult* result);

  /** \brief Predict the detection results for a batch of input images
   *
   * \param[in] imgs, The input image list, each element comes from cv::imread()
   * \param[in] results The output detection result list
   * \return true if the prediction successed, otherwise false
   */
  virtual bool BatchPredict(const std::vector<cv::Mat>& images,
                            std::vector<FaceDetectionResult>* results);

  /// Get preprocessor reference of BlazeFace
  virtual BlazeFacePreprocessor& GetPreprocessor() {
    return preprocessor_;
  }

  /// Get postprocessor reference of BlazeFace
  virtual BlazeFacePostprocessor& GetPostprocessor() {
    return postprocessor_;
  }

 protected:
  bool Initialize();
  BlazeFacePreprocessor preprocessor_;
  BlazeFacePostprocessor postprocessor_;
};

}  // namespace facedet

}  // namespace vision

}  // namespace fastdeploy