99 lines
4.0 KiB
C++
99 lines
4.0 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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namespace fastdeploy {
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namespace vision {
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namespace detection {
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/*! @brief NanoDetPlus model object used when to load a NanoDetPlus model exported by NanoDet.
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*/
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class FASTDEPLOY_DECL NanoDetPlus : 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 ./nanodet_plus_320.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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NanoDetPlus(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 "nanodet"; }
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/** \brief Predict the detection 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 detection result will be writen to this structure
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* \param[in] conf_threshold confidence threashold for postprocessing, default is 0.35
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* \param[in] nms_iou_threshold iou threashold for NMS, default is 0.5
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* \return true if the prediction successed, otherwise false
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*/
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virtual bool Predict(cv::Mat* im, DetectionResult* result,
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float conf_threshold = 0.35f,
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float nms_iou_threshold = 0.5f);
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/*! @brief
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Argument for image preprocessing step, tuple of input size (width, height), default (320, 320)
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*/
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std::vector<int> size;
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// padding value, size should be the same as channels
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std::vector<float> padding_value;
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// keep aspect ratio or not when perform resize operation.
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// This option is set as `false` by default in NanoDet-Plus
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bool keep_ratio;
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// downsample strides for NanoDet-Plus to generate anchors,
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// will take (8, 16, 32, 64) as default values
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std::vector<int> downsample_strides;
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// for offseting the boxes by classes when using NMS, default 4096
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float max_wh;
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/*! @brief
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Argument for image postprocessing step, reg_max for GFL regression, default 7
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*/
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int reg_max;
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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<float, 2>>* im_info);
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bool Postprocess(FDTensor& infer_result, DetectionResult* result,
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const std::map<std::string, std::array<float, 2>>& im_info,
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float conf_threshold, float nms_iou_threshold);
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bool IsDynamicInput() const { return is_dynamic_input_; }
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// whether to inference with dynamic shape (e.g ONNX export with dynamic shape
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// or not.)
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// RangiLyu/nanodet official 'export_onnx.py' script will export static ONNX
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// by default.
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// This value will auto check by fastdeploy after the internal Runtime
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// initialized.
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bool is_dynamic_input_;
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};
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} // namespace detection
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} // namespace vision
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} // namespace fastdeploy
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