FaceNet | Keras implementation of the renowned publication | Computer Vision library

I'm trying to learn python, for detect someone used mask or not.

when i run this code

I have installed a basic python server with deepface library with apache2 on ubuntu.

The library makes a .deepface directory on app initialization but it is unable to do so due to permission denied error as it's hidden in linux by default. I am getting the following error

I am following this guide to build an environment to develop an facial recognition project in.
I've pulled the image provided in the guide as shown here using the command docker pull colemurray/medium-facenet-tutorial in the Desktop Docker.
I am running a container with the image but I do not understand how I can develop an application in it (e.g having access to the modules downloaded in Docker)
The only action I think I can take here is opening the CLI of the container as shown here but I can't find any guide to use it to add the local folder into the environment.

I understand that using anaconda, I'll just have to activate a environment and run jupyter notebook to develop in that environment and I am trying to do the exact same thing with Docker, but I fail make the same connection, any advice will be greatly appreciated.

My DeepFace Implementation

I am using the Facenet algorithm for face recognition. I want to create application based on this, but the problem is the Facenet algorithm returns an array of length 128, which is the face embedding per person.

For person identification, I have to find the Euclidian difference between two persons face embedding, then check that if it is greater than a threshold or not. If it is then the persons are same; if it is less then persons are different.

Let's say If I have to find person x in the database of 10k persons. I have to calculate the difference with each and every person's embeddings, which is not efficient.

Is there any way to store this face embedding efficiently and search for the person with better efficiency?

I guess reading this blog will help the others.

It's in detail and also covers most aspects of implementation.

Face recognition on 330 million faces at 400 images per second

So I've been following this tutorial to detect if person wearing mask or not on camera and got everything to work when using the camera by using the following code:

I am new at tensorflow and models training. I am using the face recognition algorithm based on yolo and facenet. i am now trying to train my own model. But i get an error everytime I do so. I would be very grateful to you if you help me solve it. Thank you in advance. here is the link to the code : https://github.com/AzureWoods/faceRecognition-yolo-facenet/blob/master/train_tripletloss.py

here is t the error:

I am creating an api that will call a python script using python-shell in nodejs.However when I am running the api,the api returns the error of "Cannot set headers after they have been sent to the client".Below is the code for the api.

I am getting a runtime error that says inputs and weights must be on same. However I made sure that my model and input are on the same device yet I cannot get rid of the error. As far as I read, I know that my input data is not on GPU . Since, In this case image is an input so I tried img = torch.from_numpy(img).to(device) and pred = model(img)[0].to(device but no luck. Please let me know what can be done.

Here's the code: