🏆 JAIA Hackathon 2023 Winner - Joel, Douglas, David

See more info attached -https://www.linkedin.com/posts/douglasbyfield_this-weekend-i-had-the-pleasure-of-participating-activity-7112239121237991424-g2xa?utm_source=share&utm_medium=member_desktop

📣 Synopsis - Gunshot Detection and Classification System

This project won the first-ever JAIA Hackathon. It's designed to detect and classify gunshots in real-time. When a gunshot is detected, the audio is parsed, processed, and sent through two machine learning models. The first model identifies whether the sound is a gunshot or not. If it is, the second model classifies the type of gun the shot originated from. All the data, along with geolocation and timestamp, is then displayed on a real-time map interface built with React. 🛠️ Tech Stack

• Machine Learning Models: Python, Torch
• Audio Preprocessing: Python scripts to convert .wav to MFCCs
• Backend: Django
• Frontend: React
• Communication: Webhooks, Django channel server

🧑‍💻 Team Roles

🤖 Model Training and Prediction (Team)

• Model Selection: CNN for audio classification.

1. Train Model: Used preprocessed audio data to train the model.
2. Prediction: Made predictions and sent data to David's script.

🎧 Douglas: Data Collection and Preprocessing

• Collected audio data of common gunshots in Kingston.
• Preprocessed the raw audio into MFCCs.

mfccs = librosa.feature.mfcc(audio_data, sr)

Performed data augmentation for model robustness.

predictions = model.predict(preprocessed_audio)

🚨 David: Alerting and Backend-Frontend Integration

• Created a system to alert authorities in real-time.

• Ensured seamless communication between the Django backend, ml models, and React frontend.

def initiate_client(data):
    ws = create_connection("ws://localhost:8000/ws/chat/gunsession/")
    ws.send(json.dumps({"message": data}))
    ws.close()

🗺️ Joel: User Interface

• Built a real-time dashboard using React and Leaflet.

ws.onmessage = (event) => {
      const data = JSON.parse(event.data);
      const message = data.message;
      console.log('Received:', message);
    
      if (message && message.geo) {
        const [x, y] = message.geo;
        setGunShot(message)
        // console.log(gunShot)
        flyMap(x, y, 11);
      }
    
      setGunShots(prevGunshots => [...prevGunshots, message]);
      console.log(gunShot,gunShots)
    };

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🚀 Steps to Run the Project

🐍 Django Backend Setup

1. Create a Python Virtual Environment

   • Use Python 3.10.

2. Activate the Virtual Environment

   • Activate the virtual environment that you just created.

3. Install Required Packages

   • Run the command pip install -r requirements.txt in the root of the project.

4. Make Migrations

   • Navigate to the gun_shot_detector folder.
   • Run the command python manage.py makemigrations. (This file should already exist in most cases).

5. Apply Migrations

   • Run the command python manage.py migrate.

🛢️ Redis Setup

General Steps

• Download the Redis server for your respective environment.

macOS

• Run the command brew install redis.

Windows

• Follow the instructions outlined in this article or install using Docker.

Ubuntu

sudo apt install lsb-release curl gpg
curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg
echo "deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main" | sudo tee /etc/apt/sources.list.d/redis.list
sudo apt-get update
sudo apt-get install redis

🏃‍♂️ Running the Applications

1. Start Redis Server

   • Run the command redis-server --port 6379.

2. Start Django Server

   • Navigate to the gun_shot_detector folder.
   • Make sure the virtual environment is activated.
   • Run the command python manage.py runserver.

🌐 Frontend Setup

1. Navigate to Frontend Directory

   • Go to the /frontend folder.

2. Install Node Modules

   • Run the command npm install to install modules from package.json.

3. Start the Frontend

   • Run the command npm start.
   • Make sure the webhook address matches what you have in the Django channel configs.

   // Example from line 89
   const ws = new WebSocket('ws://***.***.**.**:8000/ws/chat/gunsession/');

🎧 Machine Learning Models

• Run main.py in each ml folder to train model.

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