Files
FastDeploy/fastdeploy/vision/ocr/ppocr/rec_preprocessor.h
yunyaoXYY d45382e3cc [Backend] Add Huawei Ascend NPU deploy using PaddleLite CANN. (#757)
* 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
2022-12-26 10:18:34 +08:00

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1.7 KiB
C++

// 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/vision/common/processors/transform.h"
#include "fastdeploy/vision/common/result.h"
namespace fastdeploy {
namespace vision {
namespace ocr {
/*! @brief Preprocessor object for PaddleClas serials model.
*/
class FASTDEPLOY_DECL RecognizerPreprocessor {
public:
/** \brief Process the input image and prepare input tensors for runtime
*
* \param[in] images The input data list, all the elements are FDMat
* \param[in] outputs The output tensors which will be fed into runtime
* \return true if the preprocess successed, otherwise false
*/
bool Run(std::vector<FDMat>* images, std::vector<FDTensor>* outputs);
bool Run(std::vector<FDMat>* images, std::vector<FDTensor>* outputs,
size_t start_index, size_t end_index,
const std::vector<int>& indices);
std::vector<int> rec_image_shape_ = {3, 48, 320};
std::vector<float> mean_ = {0.5f, 0.5f, 0.5f};
std::vector<float> scale_ = {0.5f, 0.5f, 0.5f};
bool is_scale_ = true;
bool static_shape_ = false;
};
} // namespace ocr
} // namespace vision
} // namespace fastdeploy