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718698a32a
* add yolov5cls * fixed bugs * fixed bugs * fixed preprocess bug * add yolov5cls readme * deal with comments * Add YOLOv5Cls Note * add yolov5cls test * add rvm support * support rvm model * add rvm demo * fixed bugs * add rvm readme * add TRT support * add trt support * add rvm test * add EXPORT.md * rename export.md * rm poros doxyen * deal with comments * deal with comments * add rvm video_mode note Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
93 lines
3.5 KiB
C++
Executable File
93 lines
3.5 KiB
C++
Executable File
// 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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/** \brief All image/video matting model APIs are defined inside this namespace
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*
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*/
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namespace matting {
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/*! @brief RobustVideoMatting model object used when to load a RobustVideoMatting model exported by RobustVideoMatting
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*/
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class FASTDEPLOY_DECL RobustVideoMatting : public FastDeployModel {
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public:
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/** \brief Set path of model file and configuration file, and the configuration of runtime
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*
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* \param[in] model_file Path of model file, e.g rvm/rvm_mobilenetv3_fp32.onnx
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* \param[in] params_file Path of parameter file, 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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RobustVideoMatting(const std::string& model_file,
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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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/// Get model's name
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std::string ModelName() const { return "matting/RobustVideoMatting"; }
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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()
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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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/// Preprocess image size, the default is (1080, 1920)
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std::vector<int> size;
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/// Whether to open the video mode, if there are some irrelevant pictures, set it to fasle, the default is true // NOLINT
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bool video_mode;
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private:
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bool Initialize();
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/// Preprocess an input image, and set the preprocessed results to `outputs`
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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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/// Postprocess the inferenced results, and set the final result to `result`
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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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/// Init dynamic inputs datas
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std::vector<std::vector<float>> dynamic_inputs_datas_ = {
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{0.0f}, // r1i
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{0.0f}, // r2i
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{0.0f}, // r3i
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{0.0f}, // r4i
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{0.25f}, // downsample_ratio
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};
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/// Init dynamic inputs dims
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std::vector<std::vector<int64_t>> dynamic_inputs_dims_ = {
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{1, 1, 1, 1}, // r1i
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{1, 1, 1, 1}, // r2i
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{1, 1, 1, 1}, // r3i
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{1, 1, 1, 1}, // r4i
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{1}, // downsample_ratio
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};
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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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