// 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 "ultra_infer/ultra_infer_model.h"
#include "ultra_infer/vision/common/processors/transform.h"
#include "ultra_infer/vision/common/result.h"
#include "ultra_infer/vision/detection/ppdet/postprocessor.h"
#include "ultra_infer/vision/detection/ppdet/preprocessor.h"

#include "ultra_infer/vision/utils/utils.h"

namespace ultra_infer {
namespace vision {
/** \brief All object detection model APIs are defined inside this namespace
 *
 */
namespace detection {

/*! @brief Base model object used when to load a model exported by
 * PaddleDetection
 */
class ULTRAINFER_DECL PPDetBase : public UltraInferModel {
public:
  /** \brief Set path of model file and configuration file, and the
   * configuration of runtime
   *
   * \param[in] model_file Path of model file, e.g ppyoloe/model.pdmodel
   * \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
   * ppyoloe/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 Paddle format
   */
  PPDetBase(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);

  /** \brief Clone a new PaddleDetModel with less memory usage when multiple
   * instances of the same model are created
   *
   * \return new PaddleDetModel* type unique pointer
   */
  virtual std::unique_ptr<PPDetBase> Clone() const;

  /// Get model's name
  virtual std::string ModelName() const { return "PaddleDetection/BaseModel"; }

  /** \brief DEPRECATED Predict the 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 detection result
   * \return true if the prediction succeeded, otherwise false
   */
  virtual bool Predict(cv::Mat *im, DetectionResult *result);

  /** \brief Predict the 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 detection result
   * \return true if the prediction succeeded, otherwise false
   */
  virtual bool Predict(const cv::Mat &im, DetectionResult *result);

  /** \brief Predict the detection result for an input image list
   * \param[in] im The input image list, all the elements come from
   * cv::imread(), is a 3-D array with layout HWC, BGR format \param[in] results
   * The output detection result list \return true if the prediction succeeded,
   * otherwise false
   */
  virtual bool BatchPredict(const std::vector<cv::Mat> &imgs,
                            std::vector<DetectionResult> *results);

  PaddleDetPreprocessor &GetPreprocessor() { return preprocessor_; }

  PaddleDetPostprocessor &GetPostprocessor() { return postprocessor_; }
  virtual bool CheckArch();

protected:
  virtual bool Initialize();
  PaddleDetPreprocessor preprocessor_;
  PaddleDetPostprocessor postprocessor_;
};

} // namespace detection
} // namespace vision
} // namespace ultra_infer