FastDeploy/fastdeploy/vision/ocr/ppocr/classifier.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 "fastdeploy/fastdeploy_model.h"
#include "fastdeploy/vision/common/processors/transform.h"
#include "fastdeploy/vision/common/result.h"
#include "fastdeploy/vision/ocr/ppocr/utils/ocr_postprocess_op.h"

namespace fastdeploy {
namespace vision {
/** \brief All OCR series model APIs are defined inside this namespace
 *
 */
namespace ocr {
/*! @brief Classifier object is used to load the classification model provided by PaddleOCR.
 */
class FASTDEPLOY_DECL Classifier : public FastDeployModel {
 public:
  Classifier();
  /** \brief Set path of model file, and the configuration of runtime
   *
   * \param[in] model_file Path of model file, e.g ./ch_ppocr_mobile_v2.0_cls_infer/model.pdmodel.
   * \param[in] params_file Path of parameter file, e.g ./ch_ppocr_mobile_v2.0_cls_infer/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 Paddle format.
   */
  Classifier(const std::string& model_file, const std::string& params_file = "",
             const RuntimeOption& custom_option = RuntimeOption(),
             const ModelFormat& model_format = ModelFormat::PADDLE);
  /// Get model's name
  std::string ModelName() const { return "ppocr/ocr_cls"; }
  /** \brief Predict the input image and get OCR classification model result.
   *
   * \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 of OCR classification model result will be writen to this structure.
   * \return true if the prediction is successed, otherwise false.
   */
  virtual bool Predict(cv::Mat* img, std::tuple<int, float>* result);

  // Pre & Post parameters
  float cls_thresh;
  std::vector<int> cls_image_shape;
  int cls_batch_num;

  std::vector<float> mean;
  std::vector<float> scale;
  bool is_scale;

 private:
  bool Initialize();
  /// Preprocess the input data, and set the preprocessed results to `outputs`
  bool Preprocess(Mat* img, FDTensor* output);
  /// Postprocess the inferenced results, and set the final result to `result`
  bool Postprocess(FDTensor& infer_result, std::tuple<int, float>* result);
};

}  // namespace ocr
}  // namespace vision
}  // namespace fastdeploy