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6d0261e9e4
* [Model] init pp-structurev2-layout code * [Model] init pp-structurev2-layout code * [Model] init pp-structurev2-layout code * [Model] add structurev2_layout_preprocessor * [PP-StructureV2] add postprocessor and layout detector class * [PP-StructureV2] add postprocessor and layout detector class * [PP-StructureV2] add postprocessor and layout detector class * [PP-StructureV2] add postprocessor and layout detector class * [PP-StructureV2] add postprocessor and layout detector class * [pybind] add pp-structurev2-layout model pybind * [pybind] add pp-structurev2-layout model pybind * [Bug Fix] fixed code style * [examples] add pp-structurev2-layout c++ examples * [PP-StructureV2] add python example and docs * [benchmark] add pp-structurev2-layout benchmark support
81 lines
3.7 KiB
C++
81 lines
3.7 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include "fastdeploy/vision/common/processors/transform.h"
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#include "fastdeploy/vision/common/result.h"
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namespace fastdeploy {
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namespace vision {
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namespace ocr {
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/*! @brief Postprocessor object for PaddleDet serials model.
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*/
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class FASTDEPLOY_DECL StructureV2LayoutPostprocessor {
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public:
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StructureV2LayoutPostprocessor() {}
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/** \brief Process the result of runtime and fill to batch DetectionResult
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*
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* \param[in] tensors The inference result from runtime
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* \param[in] results The output result of layout detection
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* \param[in] batch_layout_img_info The image info of input images,
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* {{image width, image height, resize width, resize height},...}
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* \return true if the postprocess successed, otherwise false
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*/
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bool Run(const std::vector<FDTensor>& tensors,
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std::vector<DetectionResult>* results,
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const std::vector<std::array<int, 4>>& batch_layout_img_info);
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/// Set score_threshold_ for layout detection postprocess, default is 0.4
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void SetScoreThreshold(float score_threshold) { score_threshold_ = score_threshold; }
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/// Set nms_threshold_ for layout detection postprocess, default is 0.5
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void SetNMSThreshold(float nms_threshold) { nms_threshold_ = nms_threshold; }
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/// Set num_class_ for layout detection postprocess, default is 5
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void SetNumClass(int num_class) { num_class_ = num_class; }
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/// Set fpn_stride_ for layout detection postprocess, default is {8, 16, 32, 64}
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void SetFPNStride(const std::vector<int>& fpn_stride) { fpn_stride_ = fpn_stride; }
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/// Set reg_max_ for layout detection postprocess, default is 8
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void SetRegMax(int reg_max) { reg_max_ = reg_max; } // should private ?
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/// Get score_threshold_ of layout detection postprocess, default is 0.4
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float GetScoreThreshold() const { return score_threshold_; }
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/// Get nms_threshold_ of layout detection postprocess, default is 0.5
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float GetNMSThreshold() const { return nms_threshold_; }
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/// Get num_class_ of layout detection postprocess, default is 5
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int GetNumClass() const { return num_class_; }
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/// Get fpn_stride_ of layout detection postprocess, default is {8, 16, 32, 64}
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std::vector<int> GetFPNStride() const { return fpn_stride_; }
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/// Get reg_max_ of layout detection postprocess, default is 8
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int GetRegMax() const { return reg_max_; }
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private:
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std::array<float, 4> DisPred2Bbox(const std::vector<float>& bbox_pred, int x, int y,
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int stride, int resize_w, int resize_h, int reg_max);
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bool SingleBatchPostprocessor(const std::vector<FDTensor>& single_batch_tensors,
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const std::array<int, 4>& layout_img_info,
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DetectionResult* result);
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void SetSingleBatchExternalData(const std::vector<FDTensor>& tensors,
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std::vector<FDTensor>& single_batch_tensors,
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size_t batch_idx);
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std::vector<int> fpn_stride_ = {8, 16, 32, 64};
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float score_threshold_ = 0.4;
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float nms_threshold_ = 0.5;
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int num_class_ = 5;
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int reg_max_ = 8;
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};
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} // namespace ocr
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} // namespace vision
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} // namespace fastdeploy
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