Nasal Depth Detection for Malnourished Children

This project uses facial landmark detection to analyze the nasal depth of children, aiming to contribute to malnutrition detection.

Project Overview

The goal of this project is to assess nasal depth as a potential indicator of malnutrition in children. Facial landmarks are detected using the dlib library, allowing for the calculation of distances and positions related to key points on the face.

How it Works

1. Facial Landmark Detection:

   • Utilizes dlib's pre-trained facial landmark model (shape_predictor_68_face_landmarks.dat) for accurate detection of facial features.

2. Distance Calculation:

   • Defines specific points (e.g., 22, 23, 28) on the face and calculates distances between them, providing insights into nasal depth.

3. Positional Analysis:

   • Analyzes the position of the center point between the eyes and the center point of a triangle formed by specific facial landmarks.

4. Visualization:

   • Draws lines connecting the detected facial landmarks on the image for better visualization.

Requirements

• Python
• OpenCV
• dlib

Installation

1. Clone the Repository:

   git clone https://github.com/aditya2922/nasal-depth-detection.git
   cd nasal-depth-detection

2. Download Facial Landmark Model

To use this project, you need to download the shape_predictor_68_face_landmarks.dat model from dlib's model repository and place it in the project directory.

Follow these steps:

Download Model: - Visit dlib's model repository. - Download the shape_predictor_68_face_landmarks.dat.bz2 file.

Extract Model: - Extract the contents of the downloaded file. You should now have the shape_predictor_68_face_landmarks.dat model file.

Place in Project Directory: - Move the extracted shape_predictor_68_face_landmarks.dat file to the project directory.

Now you're ready to run the script with the facial landmark model properly set up.

Usage

1. Run the Script:

   Execute the Python script in a Python environment.

   python Final/nasal_depth_detection.py

2. Input Image:

   Enter the path of the input image when prompted.

3. Analysis:

   View the output image with lines connecting facial landmarks and receive information on distances and positions related to nasal depth.

Contributing

Contributions to enhance the project or address any issues are welcome. Feel free to submit pull requests or open issues.

License

This project is licensed under the MIT License.

Acknowledgments

Thank you to the dlib library developers for providing a robust facial landmark detection model.

Explore the nasal depth detection project and contribute to advancing malnutrition detection methods for children!