🐢 Turtle AI — Smart Turtle Monitoring System

Turtle AI is an AI-powered wildlife monitoring system designed for rehabilitation environments. It runs locally, capturing snapshots from live RTSP camera feeds and using an open-source LLM (Gemma 3 via Ollama) to detect signs of turtles in distress. When distress is detected, it sends alerts via Twilio (SMS/WhatsApp).

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✨ Features

• Automated Monitoring: Captures frames from RTSP-enabled cameras at configurable intervals.
• AI Analysis: Uses local LLMs (via Ollama) to analyze images for specific distress indicators:
  • Carapace-up positioning (flipped over)
  • Entrapment
  • Unusual inactivity
  • Aggressive interactions
• Instant Alerts: Sends notifications via Twilio when distress is detected.
• Privacy First: All processing happens locally on your machine; images are not sent to the cloud for analysis.

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🔧 Setup & Installation

Prerequisites

1. Hardware:

   • An RTSP-enabled camera (e.g., Ubiquity).
   • A computer capable of running Docker and Ollama (with sufficient RAM for the LLM).

2. Software:

   • Docker and Docker Compose v2
   • Ollama (must be running locally on the host)

3. Services:

   • Twilio Account: You need an Account SID, Auth Token, and a sender phone number.

1. Prepare the AI Model

Ensure Ollama is running and pull the model you intend to use. We recommend gemma2 or gemma:3n depending on your hardware capabilities.

# Example: Pulling Gemma 2 (9B)
ollama pull gemma2

# Or Gemma 3n if available/preferred
ollama pull gemma:3n

2. Clone the Repository

git clone https://github.com/lynnaloo/turtle-ai.git
cd turtle-ai

3. Set up Virtual Environment (Optional)

If you plan to run scripts locally or contribute to development, it's recommended to use a virtual environment:

# Create virtual environment
python3 -m venv venv

# Activate it
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r capture/requirements.txt
pip install -r scheduler/requirements.txt

4. Configuration

Create a docker-compose.override.yml file in the root directory to set your private environment variables. This file is git-ignored and will override the defaults in docker-compose.yml.

docker-compose.override.yml example:

services:
  capture:
    environment:
      - CAMERA_URL=rtsp://username:[email protected]:554/stream

  scheduler:
    environment:
      - TWILIO_ACCOUNT_SID=your_sid_here
      - TWILIO_AUTH_TOKEN=your_auth_token_here
      - TWILIO_PHONE_NUMBER=+15551234567
      - RECIPIENT_PHONE_NUMBER=+15559876543
      - OLLAMA_MODEL=gemma2:latest  # Match the model you pulled
      - INTERVAL=10                 # Minutes between checks

Note: If you are running Docker on Linux, you might need to adjust OLLAMA_HOST in docker-compose.yml to point to your host's IP address instead of host.docker.internal.

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🏃‍♀️ Run the Application

Start the system with Docker Compose:

docker compose up --build

To view logs and see what's happening:

docker compose logs -f

What happens next?

1. The Scheduler service waits for the configured INTERVAL.
2. It triggers the Capture service to grab a frame from the RTSP stream.
3. The image is saved to the shared ./images directory.
4. The Scheduler sends the image to your local Ollama instance for analysis.
5. If the LLM detects "distress", a Twilio message is sent to your phone.

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🛠 Troubleshooting

Testing Camera Feed

If you are unsure if your CAMERA_URL is correct, you can test it using ffmpeg.

Option 1: Using Docker (Recommended) Run this command to attempt a capture from inside the container (replace the URL with your actual RTSP URL):

docker compose exec capture ffmpeg -rtsp_transport tcp -i "rtsp://192.168.1.x:554/stream" -vframes 1 -q:v 2 /images/test_manual.jpg

Check the images/ folder for test_manual.jpg.

Option 2: Running Locally If you have ffmpeg installed on your machine:

ffmpeg -rtsp_transport tcp -i "rtsp://192.168.1.x:554/stream" -vframes 1 -q:v 2 test_manual.jpg

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📷 System Architecture

📰 Presentation

Check out the project presentation at the TSA symposium on YouTube.