FastDeploy/examples/vision/segmentation/paddleseg/cpu-gpu/c/README.md

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PaddleSeg C Deployment Example

This directory provides infer.c to finish the deployment of PaddleSeg on CPU/GPU.

Before deployment, two steps require confirmation

• 1. Software and hardware should meet the requirements. Please refer to FastDeploy Environment Requirements
• 2. 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.

mkdir build
cd build
# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
tar xvf fastdeploy-linux-x64-x.x.x.tgz
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
make -j

# Download model, image files
wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz
tar -xvf PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz
wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png


# CPU inference
./infer_demo PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer cityscapes_demo.png 0
# GPU inference
./infer_demo PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer cityscapes_demo.png 1

The above command works for Linux or MacOS. For SDK in Windows, refer to:

• How to use FastDeploy C++ SDK in Windows

The visualized result after running is as follows

PaddleSeg 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_PaddleSegWrapper* FD_C_CreatePaddleSegWrapper(
    const char* model_file, const char* params_file, const char* config_file,
    FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper,
    const FD_C_ModelFormat model_format
)

Create a PaddleSeg model object, and return a pointer to manipulate it.

Params

• model_file(const char*): Model file path
• params_file(const char*): Parameter file path
• config_file(const char*): config file path
• fd_c_runtime_option_wrapper(FD_C_RuntimeOptionWrapper*): Backend inference configuration. None by default, which is the default configuration
• model_format(FD_C_ModelFormat): Model format

Return

• fd_c_ppseg_wrapper(FD_C_PaddleSegWrapper*): Pointer to manipulate PaddleSeg 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_PaddleSegWrapperPredict(
    FD_C_PaddleSegWrapper* fd_c_ppseg_wrapper,
    FD_C_Mat img,
    FD_C_SegmentationResult* result)

Predict an image, and generate result.

Params

• fd_c_ppseg_wrapper(FD_C_PaddleSegWrapper*): Pointer to manipulate PaddleSeg object.
• img（FD_C_Mat）: pointer to cv::Mat object, which can be obained by FD_C_Imread interface
• result(FD_C_SegmentationResult*): Segmentation prediction results, Refer to Vision Model Prediction Results for SegmentationResult

Result

FD_C_Mat FD_C_VisSegmentation(FD_C_Mat im,
                              FD_C_SegmentationResult* result,
                              float weight)

Visualize segmentation results and return visualization image.

Params

• im(FD_C_Mat): pointer to input image
• segmentation_result(FD_C_SegmentationResult*): pointer to C FD_C_SegmentationResult structure
• weight(float): weight transparent weight of visualized result image

Return

• vis_im(FD_C_Mat): pointer to visualization image.

Other Documents

• PPSegmentation Model Description
• PaddleSeg Python Deployment
• Model Prediction Results
• How to switch the model inference backend engine