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https://github.com/niths7/facial-points-estimation-along-with-andromorphic-distance-measurement-
This project uses facial landmark detection to calculate anthropometric measurements from images, such as eye width and mouth width, by converting pixel distances to real-world units (centimeters). A custom reference scale is set by the user, allowing for accurate measurements of facial features. The project leverages OpenCV, dlib, and matplotlib.
https://github.com/niths7/facial-points-estimation-along-with-andromorphic-distance-measurement-
computer-vision jupyter-notebook opencv python3
Last synced: 19 days ago
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This project uses facial landmark detection to calculate anthropometric measurements from images, such as eye width and mouth width, by converting pixel distances to real-world units (centimeters). A custom reference scale is set by the user, allowing for accurate measurements of facial features. The project leverages OpenCV, dlib, and matplotlib.
- Host: GitHub
- URL: https://github.com/niths7/facial-points-estimation-along-with-andromorphic-distance-measurement-
- Owner: niths7
- License: mit
- Created: 2024-09-18T06:57:59.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-09-18T07:12:58.000Z (2 months ago)
- Last Synced: 2024-10-16T19:51:42.905Z (about 1 month ago)
- Topics: computer-vision, jupyter-notebook, opencv, python3
- Language: Jupyter Notebook
- Homepage:
- Size: 292 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Facial-Points-estimation-along-with-andromorphic-distance-measurement
This project detects facial landmarks and calculates anthropological distances (like eye width, mouth width, philtrum length, etc.) using a reference scale of 30 cm. It utilizes OpenCV and Dlib to identify key facial features and estimate measurements in real-world dimensions (centimeters).
## Table of Contents
- [About](#about)
- [Features](#features)
- [Requirements](#requirements)
- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
## About
This tool uses facial landmarks detected from an image and a manually selected reference scale to calculate various anthropological distances such as:
- Eye width
- Mouth width
- Philtrum length
- Upper face breadth
- Middle face length
It allows users to manually select a reference scale in an image (assumed to be 30 cm) to calibrate the measurements, converting pixel distances into real-world centimeters.
## Features
- Detects facial landmarks using Dlib's 68-point facial landmark predictor.
- Calculates anthropological distances such as eye width, mouth width, and philtrum length.
- Allows users to define a reference scale manually (in the image) for pixel-to-centimeter conversion.
- Draws the selected reference scale and landmark points on the image.
- Displays the calculated distances on the image.
## Requirements
- Python 3.x
- OpenCV (`cv2`)
- Dlib (`dlib`)
- NumPy (`numpy`)
- Matplotlib (`matplotlib`)
Make sure you have the pre-trained model file `shape_predictor_68_face_landmarks.dat`, which can be downloaded from [Dlib's website](http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2).
## Installation
1. Clone the repository:
```bash
git clone https://github.com/your-username/your-repository.git
cd your-repository
```
2. Install the required dependencies:
```bash
pip install opencv-python dlib numpy matplotlib
```
3. Download the `shape_predictor_68_face_landmarks.dat` file from [here](http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2), extract it, and place it in the project directory.
## Usage
1. Prepare your image and note the location of the reference scale (assumed to be 30 cm).
2. Run the script with your image file path:
```python
python anthropological_distance.py
```
3. When the image appears:
- Click to select two points on the image that define the reference scale (30 cm in real life).
- The program will calculate the conversion factor based on this scale and detect facial landmarks to compute anthropological distances.
- The distances will be drawn and printed on the image.
### Example Code Snippet
```python
image_path = r"C:\Users\WELCOME\Downloads\kutty_paiyan.jpg" # Replace with the path to your image
image = cv2.imread(image_path)
fig, ax = plt.subplots()
ax.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
ax.set_title('Select two points for reference scale (30 cm)')
selected_points = []
```
4. You will see printed output and visual annotations for:
- Eye width
- Mouth width
- Philtrum length
- Upper face breadth
- Middle face length
-
## Contributing
Contributions are welcome! To contribute:
1. Fork the repository.
2. Create a new feature branch (`git checkout -b feature/new-feature`).
3. Make your changes and commit (`git commit -m 'Add some feature'`).
4. Push to the branch (`git push origin feature/new-feature`).
5. Open a pull request.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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