156 lines
5.7 KiB
Plaintext
156 lines
5.7 KiB
Plaintext
Metadata-Version: 2.1
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Name: mtcnn
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Version: 1.0.0
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Summary: Multitask Cascaded Convolutional Networks for face detection and alignment (MTCNN) in Python >= 3.10 and TensorFlow >= 2.12
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Home-page: https://github.com/ipazc/mtcnn
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Author: Iván de Paz Centeno
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Author-email: ipazc@unileon.es
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License: MIT
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Project-URL: Documentation, https://github.com/ipazc/mtcnn/docs
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Project-URL: Source, https://github.com/ipazc/mtcnn
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Project-URL: Tracker, https://github.com/ipazc/mtcnn/issues
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Classifier: Development Status :: 4 - Beta
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Classifier: Intended Audience :: Developers
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Classifier: Intended Audience :: Science/Research
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Classifier: License :: OSI Approved :: MIT License
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Classifier: Programming Language :: Python :: 3.10
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Classifier: Programming Language :: Python :: 3.11
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Classifier: Programming Language :: Python :: 3.12
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Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
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Requires-Python: >=3.10
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Description-Content-Type: text/markdown
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License-File: LICENSE
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Requires-Dist: joblib>=1.4.2
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Requires-Dist: lz4>=4.3.3
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Provides-Extra: dev
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Requires-Dist: pytest>=8.3.3; extra == "dev"
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Requires-Dist: pytest-cov>=5.0.0; extra == "dev"
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Requires-Dist: mkdocs>=1.6.1; extra == "dev"
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Requires-Dist: mkdocs-material>=9.5.39; extra == "dev"
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Requires-Dist: mkdocs-jupyter>=0.25.0; extra == "dev"
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Provides-Extra: tensorflow
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Requires-Dist: tensorflow>=2.12.0; extra == "tensorflow"
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# MTCNN - Multitask Cascaded Convolutional Networks for Face Detection and Alignment
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[](https://badge.fury.io/py/mtcnn)
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[](https://mtcnn.readthedocs.io/en/latest/?badge=latest)
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## Overview
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MTCNN is a robust face detection and alignment library implemented for Python >= 3.10 and TensorFlow >= 2.12, designed to detect faces and their landmarks using a multitask cascaded convolutional network. This library improves on the original implementation by offering a complete refactor, simplifying usage, improving performance, and providing support for batch processing.
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This library is ideal for applications requiring face detection and alignment, with support for both bounding box and landmark prediction.
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## Installation
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MTCNN can be installed via pip:
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```bash
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pip install mtcnn
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```
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MTCNN requires Tensorflow >= 2.12. This external dependency can be installed manually or automatically along with MTCNN via:
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```bash
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pip install mtcnn[tensorflow]
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```
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## Usage Example
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```python
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from mtcnn import MTCNN
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from mtcnn.utils.images import load_image
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# Create a detector instance
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detector = MTCNN(device="CPU:0")
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# Load an image
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image = load_image("ivan.jpg")
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# Detect faces in the image
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result = detector.detect_faces(image)
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# Display the result
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print(result)
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```
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Output example:
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```json
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[
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{
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"box": [277, 90, 48, 63],
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"keypoints": {
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"nose": (303, 131),
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"mouth_right": (313, 141),
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"right_eye": (314, 114),
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"left_eye": (291, 117),
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"mouth_left": (296, 143)
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},
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"confidence": 0.9985
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}
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]
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```
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## Models Overview
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MTCNN uses a cascade of three networks to detect faces and facial landmarks:
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- **PNet (Proposal Network)**: Scans the image and proposes candidate face regions.
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- **RNet (Refine Network)**: Refines the face proposals from PNet.
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- **ONet (Output Network)**: Detects facial landmarks (eyes, nose, mouth) and provides a final refinement of the bounding boxes.
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All networks are implemented using TensorFlow’s functional API and optimized to avoid unnecessary operations, such as transpositions, ensuring faster and more efficient execution.
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# Documentation
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The full documentation for this project is available at [Read the Docs](http://mtcnn.readthedocs.io/).
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## Citation
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If you use this library for your research or projects, please consider citing the original work:
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```
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@article{7553523,
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author={K. Zhang and Z. Zhang and Z. Li and Y. Qiao},
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journal={IEEE Signal Processing Letters},
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title={Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks},
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year={2016},
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volume={23},
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number={10},
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pages={1499-1503},
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keywords={Benchmark testing;Computer architecture;Convolution;Detectors;Face;Face detection;Training;Cascaded convolutional neural network (CNN);face alignment;face detection},
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doi={10.1109/LSP.2016.2603342},
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ISSN={1070-9908},
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month={Oct}
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}
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```
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You may also reference the original GitHub repository that this project was based on (including the networks weights):
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[Original MTCNN Implementation by Kaipeng Zhang](https://github.com/kpzhang93/MTCNN_face_detection_alignment/tree/master/code)
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And the FaceNet's implementation that served as inspiration:
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[Facenet's MTCNN implementation](https://github.com/davidsandberg/facenet/tree/master/src/align)
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## About this project
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The code for this project was created to standardize face detection and provide an easy-to-use framework that helps the research community push the boundaries of AI knowledge. Learn more about the author of this code on [Iván de Paz Centeno's website](https://ipazc.com)
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If you find this project useful, please consider supporting it through GitHub Sponsors.
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[](https://github.com/sponsors/ipazc)
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Your support will help cover costs related to improving the codebase, adding new features, and providing better documentation.
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## License
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This project is licensed under the [MIT License](LICENSE).
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