FastDeploy/examples/vision/matting/rvm/cpp/README.md

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RobustVideoMatting C++ Deployment Example

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 the RobustVideoMatting inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 0.7.0 or above (x.x.x>=0.7.0) is required to support this model.

This directory provides examples that infer.cc fast finishes the deployment of RobustVideoMatting on CPU/GPU and GPU accelerated by TensorRT. The script is as follows

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 RobustVideoMatting model files, test images and videos
## Original ONNX model
wget https://bj.bcebos.com/paddlehub/fastdeploy/rvm_mobilenetv3_fp32.onnx
## The ONNX model is specially processed for loading TRT
wget https://bj.bcebos.com/paddlehub/fastdeploy/rvm_mobilenetv3_trt.onnx
wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_input.jpg
wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_bgr.jpg
wget https://bj.bcebos.com/paddlehub/fastdeploy/video.mp4

# CPU inference
./infer_demo rvm_mobilenetv3_fp32.onnx matting_input.jpg matting_bgr.jpg 0
# GPU inference
./infer_demo rvm_mobilenetv3_fp32.onnx matting_input.jpg matting_bgr.jpg 1
# TRT inference
./infer_demo rvm_mobilenetv3_trt.onnx matting_input.jpg matting_bgr.jpg 2

The visualized result after running is as follows

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

• How to use FastDeploy C++ SDK in Windows

RobustVideoMatting C++ Interface

fastdeploy::vision::matting::RobustVideoMatting(
        const string& model_file,
        const string& params_file = "",
        const RuntimeOption& runtime_option = RuntimeOption(),
        const ModelFormat& model_format = ModelFormat::ONNX)

RobustVideoMatting model loading and initialization, among which model_file is the exported ONNX model format.

Parameter

• model_file(str): Model file path
• params_file(str): Parameter file path. No need to set when the model is in ONNX format
• runtime_option(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
• model_format(ModelFormat): Model format. ONNX format by default

Predict Function

RobustVideoMatting::Predict(cv::Mat* im, MattingResult* result)

Model prediction interface. Input images and output matting results.

Parameter

• im: Input images in HWC or BGR format
• result: Matting result. Refer to Vision Model Prediction Results for the description of MattingResult

Other Documents

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