Metadata-Version: 2.1
Name: FFEM
Version: 0.2.2
Summary: Easy and Fast Facial Emotion Monitoring
Home-page: https://github.com/WiseGeorge/Fast-Facial-Emotion-Monitoring-FFEM-Package
Author: Jorge Felix Martinez
Author-email: jorgito16040@gmail.com
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: deepface ==0.0.79
Requires-Dist: keras ==2.10.0
Requires-Dist: numpy >=1.23.3
Requires-Dist: opencv-contrib-python >=4.8.1.78
Requires-Dist: opencv-python >=4.6.0.66

# Fast Facial Emotion Monitoring (FFEM)

This package provides a simple and efficient way to perform Facial Emotion Recognition (FER). It leverages advanced techniques and algorithms to detect and classify emotions from facial expressions.

## Main Function

* `MonitorEmotion_From_Video(video_path: str | int, output_path:str) -> None`
  This function takes a video file or a webcam feed as input and performs FER. The results are saved to the specified output path.

## Main Class

* `FaceEmotion_Detection()`
  This class is the backbone of the FER process. It performs face detection and emotion recognition using DeepFace and MediaPipe. The `MonitorEmotion_From_Video` function utilizes this class to carry out its operations.

This package is designed with user-friendliness in mind, making the complex task of FER accessible and straightforward for users. Whether youâ€™re a researcher, a developer, or someone interested in FER, this package can be a valuable tool for your projects.
