PaddleX/libs/ultra-infer/ultra_infer/vision/perception/paddle3d/petr/preprocessor.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/vision/common/processors/manager.h"
#include "ultra_infer/vision/common/processors/transform.h"
#include "ultra_infer/vision/common/result.h"

namespace ultra_infer {
namespace vision {

namespace perception {
/*! @brief Preprocessor object for Petr serials model.
 */
class ULTRAINFER_DECL PetrPreprocessor : public ProcessorManager {
public:
  PetrPreprocessor() = default;
  /** \brief Create a preprocessor instance for Petr model
   *
   * \param[in] config_file Path of configuration file for deployment, e.g
   * smoke/infer_cfg.yml
   */
  explicit PetrPreprocessor(const std::string &config_file);

  /** \brief Process the input image and prepare input tensors for runtime
   *
   * \param[in] images The input image data list, all the elements are returned
   * by cv::imread() \param[in] outputs The output tensors which will feed in
   * runtime \param[in] ims_info The shape info list, record input_shape and
   * output_shape \return true if the preprocess succeeded, otherwise false
   */
  bool Apply(FDMatBatch *image_batch, std::vector<FDTensor> *outputs);

protected:
  bool BuildPreprocessPipelineFromConfig();
  std::vector<std::shared_ptr<Processor>> processors_;

  bool disable_permute_ = false;

  bool initialized_ = false;

  std::string config_file_;

  float scale_ = 1.0f;
  std::vector<float> mean_;
  std::vector<float> std_;

  std::vector<float> input_k_data_{
      -1.40307297e-03, 9.07780395e-06,  4.84838307e-01,  -5.43047376e-02,
      -1.40780103e-04, 1.25770375e-05,  1.04126692e+00,  7.67668605e-01,
      -1.02884378e-05, -1.41007011e-03, 1.02823459e-01,  -3.07415128e-01,
      0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  1.00000000e+00,
      -9.39000631e-04, -7.65239349e-07, 1.14073277e+00,  4.46270645e-01,
      1.04998052e-03,  1.91798881e-05,  2.06218868e-01,  7.42717385e-01,
      1.48074005e-05,  -1.40855671e-03, 7.45946690e-02,  -3.16081315e-01,
      0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  1.00000000e+00,
      -7.0699735e-04,  4.2389297e-07,   -5.5183989e-01,  -5.3276348e-01,
      -1.2281288e-03,  2.5626015e-05,   1.0212017e+00,   6.1102939e-01,
      -2.2421273e-05,  -1.4170362e-03,  9.3639769e-02,   -3.0863306e-01,
      0.0000000e+00,   0.0000000e+00,   0.0000000e+00,   1.0000000e+00,
      2.2227580e-03,   2.5312484e-06,   -9.7261822e-01,  9.0684637e-02,
      1.9360810e-04,   2.1347081e-05,   -1.0779887e+00,  -7.9227984e-01,
      4.3742721e-06,   -2.2310747e-03,  1.0842450e-01,   -2.9406491e-01,
      0.0000000e+00,   0.0000000e+00,   0.0000000e+00,   1.0000000e+00,
      5.97175560e-04,  -5.88774265e-06, -1.15893924e+00, -4.49921310e-01,
      -1.28312141e-03, 3.58297058e-07,  1.48300052e-01,  1.14334166e-01,
      -2.80917516e-06, -1.41527120e-03, 8.37693438e-02,  -2.36765608e-01,
      0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  1.00000000e+00,
      3.6048229e-04,   3.8333174e-06,   7.9871160e-01,   4.3321830e-01,
      1.3671946e-03,   6.7484652e-06,   -8.4722507e-01,  1.9411178e-01,
      7.5027779e-06,   -1.4139183e-03,  8.2083985e-02,   -2.4505949e-01,
      0.0000000e+00,   0.0000000e+00,   0.0000000e+00,   1.0000000e+00};
};

} // namespace perception
} // namespace vision
} // namespace ultra_infer