FastDeploy/examples/vision/faceid/insightface/rknpu2/cpp/README.md

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InsightFace C++ Deployment Example

FastDeploy supports the deployment of InsightFace models like ArcFace\CosFace\VPL\Partial_FC on RKNPU.

This directoty provides the example that infer_arcface.cc fast finishes the deployment of InsighFace models like ArcFace on CPU/RKNPU.

Two steps before deployment:

1. Software and hardware should meet the requirements.
2. Download the precompiled deployment library or deploy FastDeploy repository from scratch according to your development environment.

Refer to RK2 generation NPU deployment library compilation for the above steps

The compilation can be completed by executing the following command in this directory.

mkdir build
cd build
# FastDeploy version need >=1.0.3
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_r18.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
# RKNPU inference
./infer_arcface_demo ms1mv3_arcface_r100.onnx face_0.jpg face_1.jpg face_2.jpg 1

The visualized result is as follows

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

• How to use FastDeploy C++ SDK in Windows

InsightFace C++ Interface

ArcFace

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

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

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

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. Merely 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(const 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 the description of FaceRecognitionResult(../../../../../../docs/api/vision_results/)

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

Member variables of InsightFaceRecognitionPreprocessor (preprocessing parameters)

• size(vector<int>): This parameter changes 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. Alpha defaults to [-1.f, -1.f, -1.f], Revise through InsightFaceRecognitionPreprocessor::SetBeta(std::vector& beta)

Member variables of InsightFaceRecognitionPostprocessor(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 Deployemnt
• Vision Model Prediction Results
• How to switch the backend engine