FastDeploy/examples/vision/facedet/blazeface/cpp/README.md

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BlazeFace C++ Deployment Example

This directory provides examples that infer.cc fast finishes the deployment of BlazeFace 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 the CPU inference on Linux as an example, the compilation test can be completed by executing the following command in this directory.

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 # x.x.x >= 1.0.4
tar xvf fastdeploy-linux-x64-x.x.x.tgz # x.x.x >= 1.0.4
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x # x.x.x >= 1.0.4
make -j

#Download the official converted YOLOv7Face model files and test images
wget https://raw.githubusercontent.com/DefTruth/lite.ai.toolkit/main/examples/lite/resources/test_lite_face_detector_3.jpg
wget https://bj.bcebos.com/paddlehub/fastdeploy/blzeface-1000e.tgz

#Use blazeface-1000e model
# CPU inference
./infer_demo blazeface-1000e/ test_lite_face_detector_3.jpg 0
# GPU Inference
./infer_demo blazeface-1000e/ test_lite_face_detector_3.jpg 1

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

BlazeFace C++ Interface

BlazeFace Class

fastdeploy::vision::facedet::BlazeFace(
        const string& model_file,
        const string& params_file = "",
        const string& config_file = "",
        const RuntimeOption& runtime_option = RuntimeOption(),
        const ModelFormat& model_format = ModelFormat::PADDLE)

BlazeFace model loading and initialization, among which model_file is the exported PADDLE 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 PADDLE format
• config_file(str): Config file path. Only passing an empty string when the model is in PADDLE format
• runtime_option(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
• model_format(ModelFormat): Model format. PADDLE format by default

Predict Function

BlazeFace::Predict(cv::Mat& im, FaceDetectionResult* result)

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 FaceDetectionResult

• Model Description
• Python Deployment
• Vision Model Prediction Results