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https://github.com/PaddlePaddle/FastDeploy.git
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f601d076e4
* Fix links in readme * Fix links in readme * Update PPOCRv2/v3 examples * Update auto compression configs * Add neww quantization support for paddleclas model * Update quantized Yolov6s model download link * Improve PPOCR comments
47 lines
1.6 KiB
C++
47 lines
1.6 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 DBDetector serials model.
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*/
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class FASTDEPLOY_DECL DBDetectorPreprocessor {
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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 feed in runtime
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* \param[in] batch_det_img_info_ptr The output of preprocess
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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,
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std::vector<FDTensor>* outputs,
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std::vector<std::array<int, 4>>* batch_det_img_info_ptr);
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int max_side_len_ = 960;
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std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
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std::vector<float> scale_ = {0.229f, 0.224f, 0.225f};
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bool is_scale_ = true;
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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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