orofacIAnalysis 0.1.2

A library for analyzing chewing patterns using computer vision

orofacIAnalysis

orofacIAnalysis is a Python library for analyzing chewing patterns using computer vision and facial landmark detection. It uses MediaPipe to track jaw movements and provides tools for cycle detection and analysis.

Installation

pip install orofacIAnalysis

Features

• Jaw movement tracking using facial landmarks
• Chewing cycle detection and analysis
• Various signal smoothing methods
• Utilities for data visualization and analysis

Usage

from orofacIAnalysis import ChewAnnotator

# Analyze a video file
annotator = ChewAnnotator(video_path="path/to/video.mp4")
cycles = annotator.analyze_chewing()

# Print cycle information
for cycle in cycles:
    print(f"Chew count: {len(cycle['directions'])}")
    print(f"Left: {cycle['left']}, Right: {cycle['right']}, Middle: {cycle['middle']}")

Advanced Usage

from orofacIAnalysis import Cycle, SmoothingMethods
import numpy as np

# Create a cycle manually
cycle = Cycle(start_frame=10)
cycle.jaw_movements = np.array([...])  # Your jaw movement data
cycle.fit()

# Print cycle stats
print(cycle)

# Apply different smoothing methods
from orofacIAnalysis.smoothing import apply_smoothing

smoothed_signal = apply_smoothing(jaw_movements, "butterworth")

License

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