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Face Recognition Edge2 Demo - 7
This demo integrates RetinaFace and FaceNet. Please refer RetinaFace PyTorch Edge2 Demo - 5 and FaceNet PyTorch Edge2 Demo - 6 to convert model. Here only run inference on the NPU.
Run NPU
Get source code
Clone the source code from our khadas/edge2-npu.
$ git clone https://github.com/khadas/edge2-npu
Install dependencies
$ sudo apt update $ sudo apt install cmake libopencv-dev
Compile and run
Picture input demo
Like facenet, there are also two modes of this demo. One is converting face images into feature vectors and saving vectors in the face library. Another is comparing input face image with faces in the library and outputting Euclidean distance and cosine similarity.
Put retinaface.rknn and facenet.rknn in edge2-npu/C++/retinaface/data/model.
# Compile $ bash build.sh # Run mode 1 $ cd install/face_recognition $ ./face_recognition data/model/retinaface.rknn data/model/facenet.rknn 1
After running mode 1, a file named face_feature_lib will generate in edge2-npu/C++/face_recognition/install/face_recognition/data. With this file generated, you can run mode 2.
# Run mode 2
$ ./face_recognition data/model/retinaface.rknn data/model/facenet.rknn data/img/lin_1.jpg
Camera input demo
Put retinaface.rknn and facenet.rknn in edge2-npu/C++/face_recognition_cap/data/model.
# Compile
$ bash build.sh
Put your full face photo into the edge2-npu/C++/face_recognition/data/img. Recompile and run mode 1 to generate face_feature_lib. Put face_feature_lib in edge2-npu/C++/face_recognition_cap/install/face_recognition_cap/data.
# Run $ cd install/retinaface_cap $ ./face_recognition data/model/retinaface.rknn data/model/facenet.rknn 33
33 is camera device index.