Metadata-Version: 2.4
Name: mukh
Version: 0.0.34
Summary: A comprehensive face analysis library that provides unified APIs for various face-related tasks
Home-page: https://github.com/ishandutta0098/mukh
Author: Ishan Dutta
Author-email: duttaishan098@gmail.com
Project-URL: Bug Tracker, https://github.com/ishandutta0098/mukh/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: matplotlib>=3.10.0
Requires-Dist: matplotlib-inline>=0.1.7
Requires-Dist: mediapipe
Requires-Dist: numpy>=1.26.4
Requires-Dist: opencv-contrib-python>=4.11.0.86
Requires-Dist: opencv-python>=4.11.0.86
Requires-Dist: opencv-python-headless
Requires-Dist: scipy==1.13.0
Requires-Dist: torch>=2.5.1
Requires-Dist: torchvision>=0.20.1
Provides-Extra: dev
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Dynamic: license-file

# Mukh

Mukh (मुख, meaning "face" in Sanskrit) is a comprehensive face analysis library that provides unified APIs for various face-related tasks. It simplifies the process of working with multiple face analysis models through a consistent interface.

## Features

- 🎯 **Unified API**: Single, consistent interface for multiple face analysis tasks
- 🔄 **Model Flexibility**: Support for multiple models per task
- 🛠️ **Custom Pipelines**: Optimized preprocessing and model combinations
- 📊 **Evaluator Mode**: Intelligent model recommendations based on your dataset
- 🚀 **Easy to Use**: Simple, intuitive APIs for quick integration

## Currently Supported Tasks

- Face Detection
- Facial Landmark Prediction

## Installation

```bash
pip install mukh
```

## Usage

### Face Detection

```python
import cv2
from mukh.detection import FaceDetector

# Initialize detector
detection_model = "blazeface" # Available models: "blazeface", "mediapipe", "ultralight"
detector = FaceDetector.create(detection_model)

# Detect faces
image_path = "path/to/image.jpg"
faces, annotated_image = detector.detect_with_landmarks(image_path)

# Save output
output_path = "path/to/output.jpg"
cv2.imwrite(output_path, annotated_image)
```

## Contact

For questions and feedback, please open an issue on GitHub.
