79 lines
3.2 KiB
C
79 lines
3.2 KiB
C
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// 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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namespace fastdeploy {
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namespace vision {
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namespace matting {
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/*! @brief MODNet model object used when to load a MODNet model exported by MODNet.
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*/
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class FASTDEPLOY_DECL MODNet : public FastDeployModel {
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public:
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/** \brief Set path of model file and the configuration of runtime.
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*
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* \param[in] model_file Path of model file, e.g ./modnet.onnx
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* \param[in] params_file Path of parameter file, e.g ppyoloe/model.pdiparams, if the model format is ONNX, this parameter will be ignored
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* \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in "valid_cpu_backends"
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* \param[in] model_format Model format of the loaded model, default is ONNX format
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*/
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MODNet(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::ONNX);
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std::string ModelName() const { return "matting/MODNet"; }
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/*! @brief
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Argument for image preprocessing step, tuple of (width, height), decide the target size after resize, default (256, 256)
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*/
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std::vector<int> size;
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/*! @brief
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Argument for image preprocessing step, parameters for normalization, size should be the the same as channels, default alpha = {1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f}
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*/
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std::vector<float> alpha;
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/*! @brief
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Argument for image preprocessing step, parameters for normalization, size should be the the same as channels, default beta = {-1.f, -1.f, -1.f}
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*/
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std::vector<float> beta;
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/*! @brief
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Argument for image preprocessing step, whether to swap the B and R channel, such as BGR->RGB, default true.
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*/
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bool swap_rb;
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/** \brief Predict the matting result for an input image
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*
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* \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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* \param[in] result The output matting result will be writen to this structure
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* \return true if the prediction successed, otherwise false
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*/
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bool Predict(cv::Mat* im, MattingResult* result);
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private:
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bool Initialize();
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bool Preprocess(Mat* mat, FDTensor* output,
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std::map<std::string, std::array<int, 2>>* im_info);
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bool Postprocess(std::vector<FDTensor>& infer_result, MattingResult* result,
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const std::map<std::string, std::array<int, 2>>& im_info);
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};
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} // namespace matting
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} // namespace vision
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} // namespace fastdeploy
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