Face Recognition Engine
Project description
FaceEngine is a lightweight python library that provides an easy interface to work with face recognition tasks.
>>> from face_engine import FaceEngine
>>> engine = FaceEngine()
>>> engine.fit(['bubbles1.jpg', 'drive.jpg'], [1, 2])
>>> engine.make_prediction('bubbles2.jpg')
([(270, 75, 406, 211)], [1])
Installation
It is distributed on PyPi, and can be installed with pip:
$ pip install face-engine
FaceEngine is supported only on Python 3.6 and above.
Models
FaceEngine is built on top of three model interfaces Detector, Embedder and Estimator (see models), and leans on user provided implementations of these models.
Installation provides optional dlib models.
These implementations are using dlib python api and dlib provided pre-trained model files.
To work with your own custom models you have to implement required models and import it. FaceEngine models are used to register all inheriting imported subclasses (subclass registration PEP 487).
For more information read the full documentation.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file face-engine-2.1.0.tar.gz
.
File metadata
- Download URL: face-engine-2.1.0.tar.gz
- Upload date:
- Size: 15.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.8.10
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | facfed4a954a8b7cb8372008227a0ee22218eaa563b014dfcc31cc82e90a06a0 |
|
MD5 | 7136aeb27a7ff24043daa23d8f7a9e1a |
|
BLAKE2b-256 | d9d6752eb5321766dfb21515353730f309aacd72f8048434105e7f9bfcb5f1b3 |
File details
Details for the file face_engine-2.1.0-py3-none-any.whl
.
File metadata
- Download URL: face_engine-2.1.0-py3-none-any.whl
- Upload date:
- Size: 17.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.8.10
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | e20378cdfeba943c96ebb646d3551b84f726d6972e3e171cd98cd6c422e76736 |
|
MD5 | 91809178cd80d259058f48732cf80d1e |
|
BLAKE2b-256 | 301b983e80ebb99ebbb92c613d10ec1a90161b835107ae2d8eb72a600f80da3c |