GoatMorpho - Automated Goat Morphometric Analysis

🐐 GoatMorpho is a Django-based web application that uses computer vision and AI to automatically extract morphometric measurements from goat images, reducing human errors typically associated with manual tape measurements.

Features

🔍 Automated Measurement Extraction

• Computer Vision Processing: Uses MediaPipe and OpenCV for pose detection
• 17 Morphometric Measurements: Comprehensive body measurements including:
  • Heights: Wither, Back, Sternum, Rump
  • Circumferences: Heart Girth, Chest, Abdominal, Neck
  • Widths: Chest, Hip, Head, Bi-costal Diameter
  • Lengths: Body, Head, Neck, Ear, Tail

📊 Measurement Management

• Goat Database: Store and manage multiple goats with breed, age, sex information
• Measurement History: Track measurements over time
• Confidence Scoring: AI confidence ratings for each measurement
• Manual Corrections: Hybrid AI-assisted + manual correction workflow

🎯 Accuracy Features

• Reference Object Scaling: Use known objects in images for accurate scaling
• Keypoint Detection: Detailed anatomical landmark detection
• Multiple Measurement Methods: Automatic, Manual, or Hybrid approaches

Morphometric Variables Measured

Variable	Code	Description (French)	Description (English)
WH	hauteur_au_garrot	Hauteur au garrot	Wither Height
BH	hauteur_au_dos	Hauteur au dos	Back Height
SH	hauteur_au_sternum	Hauteur au sternum	Sternum Height
RH	hauteur_au_sacrum	Hauteur au Sacrum	Rump Height
HG	tour_de_poitrine	Tour de poitrine	Heart Girth
CC	perimetre_thoracique	Périmètre thoracique	Chest Circumference
AG	tour_abdominal	Tour abdominal	Abdominal Girth
BD	diametre_biscotal	Diamètre biscotal	Bi-costal Diameter
CW	largeur_poitrine	Largeur poitrine	Chest Width
RW	largeur_hanche	Largeur de Hanche/bassin	Rump Width
EL	longueur_oreille	Longueur oreille	Ear Length
HL	longueur_tete	Longueur de la tête	Head Length
HW	largeur_tete	Largeur de la tête	Head Width
BL	body_length	Body length	Body Length
NL	longueur_cou	Longueur du cou	Neck Length
NG	tour_du_cou	Tour du cou	Neck Girth
TL	longueur_queue	Longueur de la queue	Tail Length

Installation

Prerequisites

• Python 3.8+
• pip
• Virtual environment (recommended)

Setup Instructions

1. Clone the repository

   git clone <repository-url>
   cd GoatMorpho

2. Create and activate virtual environment

   python -m venv .venv
   # Windows
   .venv\Scripts\activate
   # Linux/Mac
   source .venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Configure database

   python manage.py makemigrations
   python manage.py migrate

5. Create superuser

   python manage.py createsuperuser

6. Run development server

   python manage.py runserver

7. Access the application

   • Web Interface: http://127.0.0.1:8000/
   • Admin Panel: http://127.0.0.1:8000/admin/
   • API Endpoints: http://127.0.0.1:8000/api/

Usage

Web Interface

1. Dashboard: Overview of your goats and measurements
2. Upload Image: Upload goat images for analysis
3. Admin Panel: Manage goats, measurements, and review results

API Endpoints

Upload and Process Image

POST /api/upload/
Content-Type: multipart/form-data

{
  "image": <image_file>,
  "goat_id": "<optional_goat_uuid>",
  "goat_name": "<optional_new_goat_name>",
  "reference_length_cm": <optional_float>
}

List Goats

GET /api/goats/
Authorization: Token <your_token>

Get Goat Measurements

GET /api/goats/<goat_id>/measurements/
Authorization: Token <your_token>

Update Measurement

PUT /api/measurements/<measurement_id>/update/
Authorization: Token <your_token>
Content-Type: application/json

{
  "hauteur_au_garrot": 75.5,
  "body_length": 120.2,
  "notes": "Manual correction applied"
}

Computer Vision Pipeline

1. Image Preprocessing: Resize, normalize, and prepare image
2. Pose Detection: Use MediaPipe to detect anatomical landmarks
3. Keypoint Extraction: Extract 2D coordinates of key body points
4. Scale Calculation: Determine pixel-to-centimeter ratio
5. Measurement Calculation: Calculate distances and proportions
6. Confidence Assessment: Evaluate reliability of measurements

Best Practices for Image Capture

📸 Image Quality

• Use high-resolution images (minimum 1024x768)
• Ensure good lighting conditions
• Side view preferred for most measurements
• Entire goat should be visible in frame

📏 Measurement Accuracy

• Include a reference object of known length
• Goat should be standing naturally
• Minimize background clutter
• Capture when goat is calm and still

🔄 Workflow

1. Capture image following best practices
2. Upload through web interface or API
3. Review AI-generated measurements
4. Make manual corrections if needed
5. Save final measurements

Technology Stack

• Backend: Django 5.2.4, Django REST Framework
• Computer Vision: OpenCV, MediaPipe
• Machine Learning: TensorFlow (future enhancements)
• Database: SQLite (development), PostgreSQL (production)
• Frontend: Bootstrap 5, Vanilla JavaScript
• Image Processing: Pillow, scikit-image

Database Schema

Models

• Goat: Basic goat information (name, breed, age, sex, weight)
• MorphometricMeasurement: All 17 measurements with metadata
• KeyPoint: Detected anatomical landmarks with coordinates
• MeasurementSession: Batch processing sessions

Development

Project Structure

GoatMorpho/
├── goat_morpho/          # Django project settings
├── measurements/         # Main application
│   ├── models.py        # Database models
│   ├── views.py         # API endpoints and web views
│   ├── cv_processor.py  # Computer vision processing
│   ├── serializers.py   # API serializers
│   └── templates/       # Web interface templates
├── media/               # Uploaded images
├── static/              # Static files
└── requirements.txt     # Python dependencies

Adding New Measurements

1. Add field to MorphometricMeasurement model
2. Update cv_processor.py calculation logic
3. Add to serializers and admin interface
4. Create migration: python manage.py makemigrations

Future Enhancements

• 3D pose estimation for circumference measurements
• Breed-specific measurement standards
• Batch processing of multiple images
• Mobile app for field use
• Integration with livestock management systems
• Machine learning model training on goat-specific datasets
• Export to standard formats (CSV, Excel, PDF reports)

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use GoatMorpho in your research, please cite:

GoatMorpho: Automated Goat Morphometric Analysis System
[DOSSOU KPONGAN LFCY/Animal Breeding and genetics]
[2025]

Support

For questions, issues, or contributions:

• Create an issue on GitHub
• Contact: [dossoukponganfleming@gmail.com]

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Note: This is a research/development tool. Always verify AI measurements with manual checks for critical applications.