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YOLOv5 C Deployment Example
This directory provides infer.c to finish the deployment of YOLOv5 on CPU/GPU.
Before deployment, two steps require confirmation
-
- Software and hardware should meet the requirements. Please refer to FastDeploy Environment Requirements
-
- Download the precompiled deployment library and samples code according to your development environment. Refer to FastDeploy Precompiled Library
Taking inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 1.0.4 or above (x.x.x>=1.0.4) is required to support this model.
# 1. # Download the YOLOv5 model file and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
# CPU inference
./infer_demo yolov5s.onnx 000000014439.jpg 0
# GPU inference
./infer_demo yolov5s.onnx 000000014439.jpg 1
The above command works for Linux or MacOS. For SDK use-pattern in Windows, refer to:
YOLOv5 C Interface
RuntimeOption
FD_C_RuntimeOptionWrapper* FD_C_CreateRuntimeOptionWrapper()
Create a RuntimeOption object, and return a pointer to manipulate it.
Return
- fd_c_runtime_option_wrapper(FD_C_RuntimeOptionWrapper*): Pointer to manipulate RuntimeOption object.
void FD_C_RuntimeOptionWrapperUseCpu(
FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper)
Enable Cpu inference.
Params
- fd_c_runtime_option_wrapper(FD_C_RuntimeOptionWrapper*): Pointer to manipulate RuntimeOption object.
void FD_C_RuntimeOptionWrapperUseGpu(
FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper,
int gpu_id)
Enable Gpu inference.
Params
- fd_c_runtime_option_wrapper(FD_C_RuntimeOptionWrapper*): Pointer to manipulate RuntimeOption object.
- gpu_id(int): gpu id
Model
FD_C_YOLOv5Wrapper* FD_C_CreateYOLOv5Wrapper(
const char* model_file, const char* params_file, const char* config_file,
FD_C_RuntimeOptionWrapper* runtime_option,
const FD_C_ModelFormat model_format)
Create a YOLOv5 model object, and return a pointer to manipulate it.
Params
- model_file(str): Model file path
- params_file(str): Parameter file path，when model format is onnx，this can be empty string
- runtime_option(FD_C_RuntimeOptionWrapper*): Backend inference configuration. None by default, which is the default configuration
- model_format(FD_C_ModelFormat): Model format.
Return
- fd_c_yolov5_wrapper(FD_C_YOLOv5Wrapper*): Pointer to manipulate YOLOv5 object.
Read and write image
FD_C_Mat FD_C_Imread(const char* imgpath)
Read an image, and return a pointer to cv::Mat.
Params
- imgpath(const char*): image path
Return
- imgmat(FD_C_Mat): pointer to cv::Mat object which holds the image.
FD_C_Bool FD_C_Imwrite(const char* savepath, FD_C_Mat img);
Write image to a file.
Params
- savepath(const char*): save path
- img(FD_C_Mat): pointer to cv::Mat object
Return
- result(FD_C_Bool): bool to indicate success or failure
Prediction
FD_C_Bool FD_C_YOLOv5WrapperPredict(
__fd_take FD_C_YOLOv5Wrapper* fd_c_yolov5_wrapper, FD_C_Mat img,
FD_C_DetectionResult* fd_c_detection_result)
Predict an image, and generate detection result.
Params
- fd_c_yolov5_wrapper(FD_C_YOLOv5Wrapper*): Pointer to manipulate YOLOv5 object.
- img（FD_C_Mat）: pointer to cv::Mat object, which can be obained by FD_C_Imread interface
- fd_c_detection_resultFD_C_DetectionResult*): Detection result, including detection box and confidence of each box. Refer to Vision Model Prediction Result for DetectionResults
Result
FD_C_Mat FD_C_VisDetection(FD_C_Mat im, FD_C_DetectionResult* fd_detection_result,
float score_threshold, int line_size, float font_size);
Visualize detection results and return visualization image.
Params
- im(FD_C_Mat): pointer to input image
- fd_detection_result(FD_C_DetectionResult*): pointer to C DetectionResult structure
- score_threshold(float): score threshold
- line_size(int): line size
- font_size(float): font size
Return
- vis_im(FD_C_Mat): pointer to visualization image.