mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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65 lines
2.2 KiB
C++
65 lines
2.2 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/fastdeploy_model.h"
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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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#include "fastdeploy/vision/ocr/ppocr/utils/ocr_postprocess_op.h"
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namespace fastdeploy {
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namespace vision {
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namespace ocr {
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class FASTDEPLOY_DECL Classifier : public FastDeployModel {
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public:
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Classifier();
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// 当model_format为ONNX时，无需指定params_file
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// 当model_format为Paddle时，则需同时指定model_file & params_file
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Classifier(const std::string& model_file, const std::string& params_file = "",
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const RuntimeOption& custom_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::PADDLE);
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// 定义模型的名称
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std::string ModelName() const { return "ppocr/ocr_cls"; }
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// 模型预测接口，即用户调用的接口
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virtual bool Predict(cv::Mat* img, std::tuple<int, float>* result);
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// pre & post parameters
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float cls_thresh;
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std::vector<int> cls_image_shape;
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int cls_batch_num;
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std::vector<float> mean;
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std::vector<float> scale;
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bool is_scale;
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private:
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// 初始化函数，包括初始化后端，以及其它模型推理需要涉及的操作
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bool Initialize();
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// 输入图像预处理操作
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// FDTensor为预处理后的Tensor数据，传给后端进行推理
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bool Preprocess(Mat* img, FDTensor* output);
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// 后端推理结果后处理，输出给用户
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// infer_result 为后端推理后的输出Tensor
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bool Postprocess(FDTensor& infer_result, std::tuple<int, float>* result);
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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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