This demo will create and compare your face embedding to face embeddings you created beforehand.
Files:
aidemo.py : contains the GUI of the DemoPrerequisite:
ai.py : contains the Neural Network creating the Embeddings. It Returns Euclidian Distance and index of the minimal euclidain distance.
cameraimx8mp.py : contains the gstreamer pipeline to read in the camera image when used on the ARM device
camerapc.py : contains the opencv pipeline to read in the camera image when used on the x86 device
v4l2_2.py : contains the video for Linux settings
After cloning the git repository please add the model file which you can find here (13), here (15), here (15 all int), and here (220 all int) to the demo-data/ models/tflite/ folder
And add the Embeddingsfiles, which you can find here (220) and here (115), to the demo-data/ folder.