This project is a face recognition web application built using FastAPI. It includes functionalities for managing cameras, users, training models, making predictions, and viewing logs.

• Face Recognition Web Application
  • Table of Contents
  • Installation
    • Prerequisites
    • Steps
  • Usage
  • API Endpoints
    • Camera
    • User Management
    • Training
    • Prediction
    • Logs
  • Logging
  • Configuration
  • Folder Structure
  • Contributing
  • License

• Anaconda
• CUDA (for GPU support)

1. Clone the repository:

   git clone https://github.com/yourusername/FaceRecog.git
   cd FaceRecog/WebApp/apiApp

2. Run the installation script:

   ./install.sh

This script will:

• Download and install Anaconda.
• Create a new conda environment with Python 3.10.
• Install dependencies from requirements.txt.
• Install torch, torchvision, and torchaudio with CUDA support.
• Set the environment as the default in .bashrc.

1. Activate the conda environment:

   conda activate myenv

2. Run the FastAPI application:

   uvicorn app.main:app --reload

3. Access the API documentation at http://localhost:8000/docs.

• Get Camera URLs

  • GET /camera/
  • Fetches the list of camera URLs.

• Update Camera URLs

  • POST /camera/
  • Updates the list of camera URLs.

• Add User

  • POST /user/add_user/{user_name}
  • Adds a new user.

• Remove User

  • DELETE /user/remove_user/{user_name}
  • Removes an existing user.

• Add Image

  • POST /user/add_image/{user_name}
  • Adds images for a user.

• Remove Image

  • DELETE /user/remove_image/{user_name}
  • Removes images for a user.

• Start Training

  • POST /train/
  • Starts the training process.

• Get Training Status

  • GET /train/status
  • Fetches the training status.

• Start Prediction

  • POST /predict/
  • Starts the prediction process.

• Get Prediction Status

  • GET /predict/status
  • Fetches the prediction status.

• Stop Prediction

  • POST /predict/stop
  • Stops the prediction process.

• List Logs

  • GET /logs/{log_type}/logs
  • Lists all log files for a specific log type.

• Get Log

  • GET /logs/{log_type}/logs/{log_file}
  • Fetches a specific log file.

• Delete Log

  • DELETE /logs/{log_type}/logs/{log_file}
  • Deletes a specific log file.

Logs are stored in the logs directory with separate subdirectories for application, inference, and training logs.

Configuration settings are stored in the .env file and various configuration files in the machine_learning/config directory.

The project directory structure is as follows:

FaceRecog/
├── WebApp/
│   ├── apiApp/
│   │   ├── app/
│   │   ├── logs/
│   │   ├── machine_learning/
│   │   ├── .env
│   │   ├── install.sh
│   │   ├── requirements.txt
│   │   ├── postman_collection.json
│   │   ├── README.md
│   │   └── .gitignore
│   └── ...
└── ...

Contributions are welcome! Please fork the repository and submit a pull request.

This project is licensed under the MIT License.

This project is owned by PT Sentra Teknologi.