FastDeploy/examples/vision/faceid/insightface/cpp

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InsightFace C++ Deployment Example

This direct ory provides examples that infer_xxx.cc fast finishes the deployment of InsighFace, including ArcFace\CosFace\VPL\Partial_FC on CPU/GPU and GPU accelerated by TensorRT. Taking ArcFace as an example, we demonstrate how infer_arcface.cc fast finishes the deployment of InsighFace 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.

mkdir build
cd build
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 ArcFace model files and test images 
wget https://bj.bcebos.com/paddlehub/fastdeploy/ms1mv3_arcface_r100.onnx
wget https://bj.bcebos.com/paddlehub/fastdeploy/rknpu2/face_demo.zip
unzip face_demo.zip

# CPU inference
./infer_arcface_demo ms1mv3_arcface_r100.onnx face_0.jpg face_1.jpg face_2.jpg 0
# GPU inference
./infer_arcface_demo ms1mv3_arcface_r100.onnx face_0.jpg face_1.jpg face_2.jpg 1
# TensorRT inference on GPU
./infer_arcface_demo ms1mv3_arcface_r100.onnx face_0.jpg face_1.jpg face_2.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

InsightFace C++ Interface

ArcFace Class

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

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

CosFace Class

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

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

PartialFC Class

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

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

VPL Class

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

VPL 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

ArcFace::Predict(cv::Mat* im, FaceRecognitionResult* 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 Results for FaceRecognitionResult

Change pre-processing and post-processing parameters

Pre-processing and post-processing parameters can be changed by modifying the member variables of InsightFaceRecognitionPostprocessor and InsightFaceRecognitionPreprocessor

InsightFaceRecognitionPreprocessor member variables (preprocessing parameters)

• size(vector<int>): This parameter changes the size of the resize during preprocessing, containing two integer elements for [width, height] with default value [112, 112]. Revise through InsightFaceRecognitionPreprocessor::SetSize(std::vector& size)
• 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]. Revise through InsightFaceRecognitionPreprocessor::SetAlpha(std::vector& alpha)
• beta(vector<float>): Preprocess normalized beta, and calculated as x'=x*alpha+beta. beta defaults to [-1.f, -1.f, -1.f]. Revise through InsightFaceRecognitionPreprocessor::SetBeta(std::vector& beta)
• permute(bool): Whether to convert BGR to RGB in pre-processing. Default true Revise through InsightFaceRecognitionPreprocessor::SetPermute(bool permute)

InsightFaceRecognitionPostprocessor member variables (post-processing parameters)

• l2_normalize(bool): Whether to perform l2 normalization before outputting the face vector. Default false. Revise through InsightFaceRecognitionPostprocessor::SetL2Normalize(bool& l2_normalize)

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