mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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586684daa9
* add c api for yolo serial models * fix bug * update batch predict func * fix * update code * split code
320 lines
11 KiB
C
320 lines
11 KiB
C
// Copyright (c) 2023 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_capi/core/fd_common.h"
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#include "fastdeploy_capi/core/fd_type.h"
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#include "fastdeploy_capi/runtime/runtime_option.h"
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#include "fastdeploy_capi/vision/result.h"
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#include "fastdeploy_capi/vision/detection/contrib/yolo/base_define.h"
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typedef struct FD_C_RuntimeOptionWrapper FD_C_RuntimeOptionWrapper;
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#ifdef __cplusplus
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extern "C" {
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#endif
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// YOLOv5
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typedef struct FD_C_YOLOv5Wrapper FD_C_YOLOv5Wrapper;
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/** \brief Create a new FD_C_YOLOv5Wrapper object
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*
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* \param[in] model_file Path of model file, e.g ./yolov5.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] fd_c_runtime_option_wrapper 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 Paddle format
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*
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* \return Return a pointer to FD_C_YOLOv5Wrapper object
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*/
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YOLO_DECLARE_CREATE_WRAPPER_FUNCTION(YOLOv5);
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/** \brief Destroy a FD_C_YOLOv5Wrapper object
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*
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* \param[in] fd_c_yolov5_wrapper pointer to FD_C_YOLOv5Wrapper object
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*/
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YOLO_DECLARE_DESTROY_WRAPPER_FUNCTION(YOLOv5, fd_c_yolov5_wrapper);
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/** \brief Predict the detection result for an input image
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*
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* \param[in] fd_c_yolov5_wrapper pointer to FD_C_YOLOv5Wrapper object
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* \param[in] img pointer to cv::Mat image
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* \param[in] fd_c_detection_result_wrapper pointer to FD_C_DetectionResultWrapper object, which stores the result.
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*/
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YOLO_DECLARE_PREDICT_FUNCTION(YOLOv5, fd_c_yolov5_wrapper);
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/** \brief Check if the model is initialized successfully
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*
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* \param[in] fd_c_yolov5_wrapper pointer to FD_C_YOLOv5Wrapper object
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*
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* \return Return a bool of value true if initialized successfully
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*/
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YOLO_DECLARE_INITIALIZED_FUNCTION(YOLOv5, fd_c_yolov5_wrapper);
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/** \brief Predict the detection results for a batch of input images
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*
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* \param[in] fd_c_yolov5_wrapper pointer to FD_C_YOLOv5Wrapper object
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* \param[in] imgs, The input image list, each element comes from cv::imread()
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* \param[in] results The output detection result list
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*
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* \return true if the prediction successed, otherwise false
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*/
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YOLO_DECLARE_BATCH_PREDICT_FUNCTION(YOLOv5, fd_c_yolov5_wrapper);
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// YOLOv7
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typedef struct FD_C_YOLOv7Wrapper FD_C_YOLOv7Wrapper;
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/** \brief Create a new FD_C_YOLOv7Wrapper object
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*
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* \param[in] model_file Path of model file, e.g ./yolov7.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] fd_c_runtime_option_wrapper 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 Paddle format
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*
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* \return Return a pointer to FD_C_YOLOv7Wrapper object
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*/
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YOLO_DECLARE_CREATE_WRAPPER_FUNCTION(YOLOv7);
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/** \brief Destroy a FD_C_YOLOv7Wrapper object
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*
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* \param[in] fd_c_yolov7_wrapper pointer to FD_C_YOLOv7Wrapper object
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*/
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YOLO_DECLARE_DESTROY_WRAPPER_FUNCTION(YOLOv7, fd_c_yolov7_wrapper);
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/** \brief Predict the detection result for an input image
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*
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* \param[in] fd_c_yolov7_wrapper pointer to FD_C_YOLOv7Wrapper object
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* \param[in] img pointer to cv::Mat image
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* \param[in] fd_c_detection_result_wrapper pointer to FD_C_DetectionResultWrapper object, which stores the result.
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*/
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YOLO_DECLARE_PREDICT_FUNCTION(YOLOv7, fd_c_yolov7_wrapper);
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/** \brief Check if the model is initialized successfully
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*
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* \param[in] fd_c_yolov7_wrapper pointer to FD_C_YOLOv7Wrapper object
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*
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* \return Return a bool of value true if initialized successfully
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*/
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YOLO_DECLARE_INITIALIZED_FUNCTION(YOLOv7, fd_c_yolov7_wrapper);
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/** \brief Predict the detection results for a batch of input images
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*
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* \param[in] fd_c_yolov7_wrapper pointer to FD_C_YOLOv7Wrapper object
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* \param[in] imgs, The input image list, each element comes from cv::imread()
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* \param[in] results The output detection result list
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*
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* \return true if the prediction successed, otherwise false
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*/
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YOLO_DECLARE_BATCH_PREDICT_FUNCTION(YOLOv7, fd_c_yolov7_wrapper);
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// YOLOv8
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typedef struct FD_C_YOLOv8Wrapper FD_C_YOLOv8Wrapper;
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/** \brief Create a new FD_C_YOLOv8Wrapper object
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*
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* \param[in] model_file Path of model file, e.g ./yolov8.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] fd_c_runtime_option_wrapper 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 Paddle format
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*
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* \return Return a pointer to FD_C_YOLOv8Wrapper object
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*/
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YOLO_DECLARE_CREATE_WRAPPER_FUNCTION(YOLOv8);
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/** \brief Destroy a FD_C_YOLOv8Wrapper object
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*
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* \param[in] fd_c_yolov8_wrapper pointer to FD_C_YOLOv8Wrapper object
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*/
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YOLO_DECLARE_DESTROY_WRAPPER_FUNCTION(YOLOv8, fd_c_yolov8_wrapper);
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/** \brief Predict the detection result for an input image
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*
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* \param[in] fd_c_yolov8_wrapper pointer to FD_C_YOLOv8Wrapper object
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* \param[in] img pointer to cv::Mat image
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* \param[in] fd_c_detection_result_wrapper pointer to FD_C_DetectionResultWrapper object, which stores the result.
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*/
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YOLO_DECLARE_PREDICT_FUNCTION(YOLOv8, fd_c_yolov8_wrapper);
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/** \brief Check if the model is initialized successfully
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*
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* \param[in] fd_c_yolov8_wrapper pointer to FD_C_YOLOv8Wrapper object
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*
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* \return Return a bool of value true if initialized successfully
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*/
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YOLO_DECLARE_INITIALIZED_FUNCTION(YOLOv8, fd_c_yolov8_wrapper);
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/** \brief Predict the detection results for a batch of input images
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*
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* \param[in] fd_c_yolov8_wrapper pointer to FD_C_YOLOv8Wrapper object
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* \param[in] imgs, The input image list, each element comes from cv::imread()
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* \param[in] results The output detection result list
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*
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* \return true if the prediction successed, otherwise false
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*/
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YOLO_DECLARE_BATCH_PREDICT_FUNCTION(YOLOv8, fd_c_yolov8_wrapper);
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// YOLOv6
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typedef struct FD_C_YOLOv6Wrapper FD_C_YOLOv6Wrapper;
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/** \brief Create a new FD_C_YOLOv6Wrapper object
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*
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* \param[in] model_file Path of model file, e.g ./yolov6.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] fd_c_runtime_option_wrapper 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 Paddle format
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*
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* \return Return a pointer to FD_C_YOLOv6Wrapper object
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*/
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YOLO_DECLARE_CREATE_WRAPPER_FUNCTION(YOLOv6);
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/** \brief Destroy a FD_C_YOLOv6Wrapper object
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*
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* \param[in] fd_c_yolov6_wrapper pointer to FD_C_YOLOv6Wrapper object
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*/
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YOLO_DECLARE_DESTROY_WRAPPER_FUNCTION(YOLOv6, fd_c_yolov6_wrapper);
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/** \brief Predict the detection result for an input image
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*
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* \param[in] fd_c_yolov6_wrapper pointer to FD_C_YOLOv6Wrapper object
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* \param[in] img pointer to cv::Mat image
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* \param[in] conf_threshold confidence threashold for postprocessing
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* \param[in] nms_iou_threshold iou threashold for NMS
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* \param[in] fd_c_detection_result_wrapper pointer to FD_C_DetectionResultWrapper object, which stores the result.
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*/
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YOLO_DECLARE_PREDICT_FUNCTION_WITH_THRESHOLD(YOLOv6, fd_c_yolov6_wrapper);
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/** \brief Check if the model is initialized successfully
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*
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* \param[in] fd_c_yolov6_wrapper pointer to FD_C_YOLOv6Wrapper object
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*
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* \return Return a bool of value true if initialized successfully
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*/
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YOLO_DECLARE_INITIALIZED_FUNCTION(YOLOv6, fd_c_yolov6_wrapper);
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// YOLOR
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typedef struct FD_C_YOLORWrapper FD_C_YOLORWrapper;
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/** \brief Create a new FD_C_YOLORWrapper object
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*
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* \param[in] model_file Path of model file, e.g ./yolor.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] fd_c_runtime_option_wrapper 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 Paddle format
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*
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* \return Return a pointer to FD_C_YOLORWrapper object
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*/
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YOLO_DECLARE_CREATE_WRAPPER_FUNCTION(YOLOR);
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/** \brief Destroy a FD_C_YOLORWrapper object
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*
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* \param[in] fd_c_yolor_wrapper pointer to FD_C_YOLORWrapper object
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*/
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YOLO_DECLARE_DESTROY_WRAPPER_FUNCTION(YOLOR, fd_c_yolor_wrapper);
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/** \brief Predict the detection result for an input image
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*
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* \param[in] fd_c_yolor_wrapper pointer to FD_C_YOLORWrapper object
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* \param[in] img pointer to cv::Mat image
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* \param[in] conf_threshold confidence threashold for postprocessing
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* \param[in] nms_iou_threshold iou threashold for NMS
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* \param[in] fd_c_detection_result_wrapper pointer to FD_C_DetectionResultWrapper object, which stores the result.
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*/
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YOLO_DECLARE_PREDICT_FUNCTION_WITH_THRESHOLD(YOLOR, fd_c_yolor_wrapper);
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/** \brief Check if the model is initialized successfully
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*
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* \param[in] fd_c_yolor_wrapper pointer to FD_C_YOLORWrapper object
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*
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* \return Return a bool of value true if initialized successfully
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*/
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YOLO_DECLARE_INITIALIZED_FUNCTION(YOLOR, fd_c_yolor_wrapper);
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// YOLOX
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typedef struct FD_C_YOLOXWrapper FD_C_YOLOXWrapper;
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/** \brief Create a new FD_C_YOLOXWrapper object
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*
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* \param[in] model_file Path of model file, e.g ./yolox.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] fd_c_runtime_option_wrapper 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 Paddle format
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*
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* \return Return a pointer to FD_C_YOLOXWrapper object
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*/
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YOLO_DECLARE_CREATE_WRAPPER_FUNCTION(YOLOX);
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/** \brief Destroy a FD_C_YOLOXWrapper object
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*
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* \param[in] fd_c_yolox_wrapper pointer to FD_C_YOLOXWrapper object
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*/
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YOLO_DECLARE_DESTROY_WRAPPER_FUNCTION(YOLOX, fd_c_yolox_wrapper);
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/** \brief Predict the detection result for an input image
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*
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* \param[in] fd_c_yolox_wrapper pointer to FD_C_YOLOXWrapper object
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* \param[in] img pointer to cv::Mat image
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* \param[in] conf_threshold confidence threashold for postprocessing
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* \param[in] nms_iou_threshold iou threashold for NMS
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* \param[in] fd_c_detection_result_wrapper pointer to FD_C_DetectionResultWrapper object, which stores the result.
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*/
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YOLO_DECLARE_PREDICT_FUNCTION_WITH_THRESHOLD(YOLOX, fd_c_yolox_wrapper);
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/** \brief Check if the model is initialized successfully
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*
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* \param[in] fd_c_yolox_wrapper pointer to FD_C_YOLOXWrapper object
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*
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* \return Return a bool of value true if initialized successfully
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*/
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YOLO_DECLARE_INITIALIZED_FUNCTION(YOLOX, fd_c_yolox_wrapper);
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#ifdef __cplusplus
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} // extern "C"
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#endif
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