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https://github.com/PaddlePaddle/FastDeploy.git
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58d63f3e90
* Add Huawei Ascend NPU deploy through PaddleLite CANN * Add NNAdapter interface for paddlelite * Modify Huawei Ascend Cmake * Update way for compiling Huawei Ascend NPU deployment * remove UseLiteBackend in UseCANN * Support compile python whlee * Change names of nnadapter API * Add nnadapter pybind and remove useless API * Support Python deployment on Huawei Ascend NPU * Add models suppor for ascend * Add PPOCR rec reszie for ascend * fix conflict for ascend * Rename CANN to Ascend * Rename CANN to Ascend * Improve ascend * fix ascend bug * improve ascend docs * improve ascend docs * improve ascend docs * Improve Ascend * Improve Ascend * Move ascend python demo * Imporve ascend * Improve ascend * Improve ascend * Improve ascend * Improve ascend * Imporve ascend * Imporve ascend * Improve ascend * acc eval script * acc eval * remove acc_eval from branch huawei * Add detection and segmentation examples for Ascend deployment * Add detection and segmentation examples for Ascend deployment * Add PPOCR example for ascend deploy * Imporve paddle lite compiliation * Add FlyCV doc * Add FlyCV doc * Add FlyCV doc * Imporve Ascend docs * Imporve Ascend docs
67 lines
2.7 KiB
C++
67 lines
2.7 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 Preprocessor object for Classifier serials model.
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*/
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class FASTDEPLOY_DECL ClassifierPreprocessor {
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public:
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/** \brief Process the input image and prepare input tensors for runtime
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*
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* \param[in] images The input data list, all the elements are FDMat
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* \param[in] outputs The output tensors which will be fed into runtime
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* \return true if the preprocess successed, otherwise false
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*/
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bool Run(std::vector<FDMat>* images, std::vector<FDTensor>* outputs);
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bool Run(std::vector<FDMat>* images, std::vector<FDTensor>* outputs,
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size_t start_index, size_t end_index);
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/// Set mean value for the image normalization in classification preprocess
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void SetMean(std::vector<float> mean) { mean_ = mean; }
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/// Get mean value of the image normalization in classification preprocess
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std::vector<float> GetMean() const { return mean_; }
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/// Set scale value for the image normalization in classification preprocess
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void SetScale(std::vector<float> scale) { scale_ = scale; }
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/// Get scale value of the image normalization in classification preprocess
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std::vector<float> GetScale() const { return scale_; }
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/// Set is_scale for the image normalization in classification preprocess
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void SetIsScale(bool is_scale) { is_scale_ = is_scale; }
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/// Get is_scale of the image normalization in classification preprocess
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bool GetIsScale() const { return is_scale_; }
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/// Set cls_image_shape for the classification preprocess
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void SetClsImageShape(std::vector<int> cls_image_shape)
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{ cls_image_shape_ = cls_image_shape; }
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/// Get cls_image_shape for the classification preprocess
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std::vector<int> GetClsImageShape() const { return cls_image_shape_; }
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std::vector<float> mean_ = {0.5f, 0.5f, 0.5f};
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std::vector<float> scale_ = {0.5f, 0.5f, 0.5f};
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bool is_scale_ = true;
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std::vector<int> cls_image_shape_ = {3, 48, 192};
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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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