Contents

1. Backend
2. Project Description (PictoPy)

Backend

Steps to run backend

1. Head over to /backend after cloning
2. Create a virtual env by running: python3 -m venv venv
3. Activate the env by running source venv/bin/activate and install dependancies via pip install requirments.txt
4. Start the server via ./run.sh (for development mode add the flag --test [RECOMMENDED])
5. The server should now be hosted on port 8000 on localhost.

Some pointers for the backend

• Entry point is main.py which calls the routes, currently there is a /test route for developing, to test an image, send a request to the backend (localhost:8000/test/return) with the following Body format:

{
  "path": "<Path to image>"
}

Eg: try it out with path asbackend/tests/inputs/zidane.jpg

• A window should pop up showing the highlighted detections, simply press q to exit, or comment out the cv2 lines in test.py
• Change the type of inference by changing model_path variable in app/test route, by default there are two models available in the app/models directory (face and object deteciton).

PictoPy

An open source image sorting tool designed to organize your image collection efficiently.

Features to be Implemented

1. A dedicated Local Database:

   • A dedicated local database for caching tags, significantly improving the speed and efficiency of image sorting.

2. Image Recognition Library:

   • Uses an advanced image recognition library for evaluating objects in images along with faces, ensuring a smoother and more accurate sorting process.

3. Improved UI/UX:

   • A smooth user interface for a more intuitive and pleasant experience.

4. Multiprocessing Support:

   • Pictopy should supports multiprocessing, optimizing the sorting process and improving overall performance.