FastDeploy/examples/vision/matting/modnet/cpp

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MODNet C++ Deployment Example

This directory provides examples that infer.cc fast finishes the deployment of ArcFace on CPU/GPU and GPU accelerated by TensorRT.

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 CPU 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.

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 the official converted MODNet model files and test images 

wget https://bj.bcebos.com/paddlehub/fastdeploy/modnet_photographic_portrait_matting.onnx
wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_input.jpg
wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_bgr.jpg


# CPU inference
./infer_demo modnet_photographic_portrait_matting.onnx matting_input.jpg matting_bgr.jpg 0
# GPU inference
./infer_demo modnet_photographic_portrait_matting.onnx matting_input.jpg matting_bgr.jpg 1
# TensorRT inference on GPU
./infer_demo modnet_photographic_portrait_matting.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

MODNet C++ Interface

MODNet Class

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

MODNet 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. Only passing an empty string 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

MODNet::Predict(cv::Mat* im, MattingResult* result,
                float conf_threshold = 0.25,
                float nms_iou_threshold = 0.5)

Model prediction interface. Input images and output detection results.

Parameter

• im: Input images in HWC or BGR format
• result: Detection results, including detection box and confidence of each box. Refer to Vision Model Prediction Result for MattingResult
• conf_threshold: Filtering threshold of detection box confidence
• nms_iou_threshold: iou threshold during NMS processing

Class Member Variable

Pre-processing Parameter

Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results

• size(vector<int>): This parameter changes the size of the resize used during preprocessing, containing two integer elements for [width, height] with default value [256, 256]
• alpha(vector<float>): Preprocess normalized alpha, and calculated as x'=x*alpha+beta，alpha defaults to [1. / 127.5, 1.f / 127.5, 1. / 127.5]
• beta(vector<float>): Preprocess normalized beta, and calculated as x'=x*alpha+beta，beta defaults to [-1.f, -1.f, -1.f]
• swap_rb(bool): Whether to convert BGR to RGB in pre-processing. Default True

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