PaddleX/deploy/ultra-infer/ultra_infer/vision/matting/contrib/modnet.h

// 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"

namespace ultra_infer {

namespace vision {

namespace matting {
/*! @brief MODNet model object used when to load a MODNet model exported by
 * MODNet.
 */
class ULTRAINFER_DECL MODNet : public UltraInferModel {
public:
  /** \brief  Set path of model file and the configuration of runtime.
   *
   * \param[in] model_file Path of model file, e.g ./modnet.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
   */
  MODNet(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 "matting/MODNet"; }

  /*! @brief
  Argument for image preprocessing step, tuple of (width, height), decide the
  target size after resize, default (256, 256)
  */
  std::vector<int> size;
  /*! @brief
  Argument for image preprocessing step, parameters for normalization, size
  should be the the same as channels, default alpha = {1.f / 127.5f, 1.f /
  127.5f, 1.f / 127.5f}
  */
  std::vector<float> alpha;
  /*! @brief
  Argument for image preprocessing step, parameters for normalization, size
  should be the the same as channels, default beta = {-1.f, -1.f, -1.f}
  */
  std::vector<float> beta;
  /*! @brief
  Argument for image preprocessing step, whether to swap the B and R channel,
  such as BGR->RGB, default true.
  */
  bool swap_rb;
  /** \brief Predict the matting 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 matting result
   * will be written to this structure \return true if the prediction succeeded,
   * otherwise false
   */
  bool Predict(cv::Mat *im, MattingResult *result);

private:
  bool Initialize();

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

  bool Postprocess(std::vector<FDTensor> &infer_result, MattingResult *result,
                   const std::map<std::string, std::array<int, 2>> &im_info);
};

} // namespace matting
} // namespace vision
} // namespace ultra_infer