Selfie-AI

This is a selfie camera app that incorporates Face Recognition capabilities. The app allows users to capture selfies using their device's camera and apply face recognition to identify faces in the images.

Features

The SelfieCamera-with-FaceRecognition app offers the following features:

1. Enable Recognition: This mode enables face recognition functionality. When capturing a selfie, the app will detect faces in the image and display the recognized faces along with their names (if known).

2. Disable Recognition: This mode disables face recognition. Users can still capture selfies, but the app will not perform any face recognition.

3. Enable Date: This mode enables the display of the current date and time on the captured selfie.

4. Disable Date: This mode disables the display of the date and time on the captured selfie.

Installation and Setup

To use the SelfieCamera-with-FaceRecognition app, follow these steps:

1. Clone the repository to your local machine:

   git clone https://github.com/Armen-Jean-Andreasian/Selfie-AI.git

2. Install the required dependencies. The app requires the following packages:

• cv2
• streamlit

  You can install these packages using the following command:

  pip install cv2 streamlit

  Run the app using the following command:

  streamlit run main.py

This will start the app, and you can access it in your web browser at http://localhost:8501.

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Deployment Notes

Notes:

• If you want to deploy this app not on Heroku but on Streamlit:
  • Streamlit support suggests to include opencv-python-headless to the requirements.txt
  • However, if you want to completely overcome the issues you can try to use streamlit-webrtc

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Contributing

I welcome contributions to the Selfie AI app! To contribute, please follow these steps:

1. Fork the repository on GitHub.

2. Clone your forked repository to your local machine:

   git clone https://github.com/Armen-Jean-Andreasian/Selfie-AI.git

3. Create a new branch for your contribution:

   git checkout -b new-feature

4. Make your desired changes to the codebase.

5. Commit your changes with descriptive commit messages:

   git commit -m "Add new feature"

6. Push your branch to your forked repository:

   git push origin new-feature

7. Open a pull request on the original repository, explaining your changes and the purpose of the pull request.