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This is the second part of the project for the course CSE 546 - Cloud Computing.

High level overview: The Raspberry Pi records the videos using its attached camera. The cloud performs face recognition on the collected videos, looks up the recognized students in the database, and returns the relevant academic information of each recognized student back to the user.

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• Python
• Boto3

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This section contains instructions on setting up the project locally. To get a local copy up and running follow these simple steps.

There are two folders, Image recognition training & validation, and lambda 2 docker image.

To train the custom model, run

pip3 install -r install_requirements.txt

For training the model, we have to upload images of 160x160 resolution taken from the PI camera, stored in real_images folder inside the training & validation folder. The images must follow the same structure as the images in test_me directory.

python3 train_face_recognition.py –data_dir "data/real_images/" –num_epochs 100

For evaluating the model, run the command:

python3 eval_face_recognition.py –img_path “data/real_images/val/<name_of_student>/<name_of_file>.png”

For running the code in the first lambda function, directly copy the lambda_1.py file to the lambda function code and deploy it.

For setting up the docker image for the second lambda function run:

docker build -t <folder_name> .
docker push <user_id><region>.amazonaws.com/<folder_name>:latest

For running the Raspberry PI script to push videos to S3, run:

pip3 install boto3
python3 push.py <time_in_minutes>

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Distributed under the MIT License. See LICENSE.md for more information.

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• Choose an Open Source License
• AWS SDK for Python (boto3)

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