facereg

.. image:: https://img.shields.io/pypi/v/facereg.svg :target: https://pypi.org/pypi/facereg/

.. image:: https://img.shields.io/pypi/pyversions/facereg.svg :target: https://pypi.org/project/facereg

.. image:: https://github.com/verifid/facereg/workflows/facereg%20ci/badge.svg :target: https://github.com/verifid/facereg/actions

.. image:: https://codecov.io/gh/verifid/facereg/branch/master/graph/badge.svg :target: https://codecov.io/gh/verifid/facereg

.. image:: https://pepy.tech/badge/facereg :target: https://pepy.tech/project/facereg

facereg is a module for face recognition with OpenCV and Deep Learning.

For now it can be used for just images. It is easy to use with a handy feature which downloads images from Google for you with given keywords to create dataset/s.

Uses two different technics CNN and HoG for recognition based on dlib <http://dlib.net/>'s face recognition system with using face_recognition <https://github.com/ageitgey/face_recognition>. facereg has totally three different layers and only recognizer has connection on encoder.

|image_layers|

Prerequisites

• CMake <https://cmake.org>_
• All dependencies are listed on requirements.txt and will be installed when you install with pip.

Installation

• Install module using pip::

  $ pip install facereg

• Download the latest facereg library from: https://github.com/verifid/facereg and install module using pip::

  $ pip install -e .

• Extract the source distribution and run::

  $ python setup.py build $ python setup.py install

Usage

• google_images:

.. code:: python

import os
from facereg import google_images

output_directory = os.getcwd() + '/datasets' # directory path where you want to save photos
image_paths = google_images.download('michael jordan', limit=3)

• face_encoder:

.. code:: python

import os
from facereg import face_encoder

datasets_path = os.getcwd() + '/datasets'
encodings_path = os.path.dirname(os.path.realpath(__file__)) + '/encodings.pickle'
# these are default values for this method
face_encoder.encode_faces(datasets=datasets_path, encodings=encodings_path, detection_method='cnn')

• recognize_faces:

.. code:: python

from facereg import recognize_faces

image_path = 'DIRECTORY PATH OF YOUR_IMAGE'
names = recognize_faces.recognize(image_path)
# returns found names from your datasets

CLI Usage

• Download images

.. code::

# -d: keyword, -l: limit
$ python -m facereg -d 'michael jordan'
$ python -m facereg -d 'michael jordan' -l 5

• Recognition

.. code::

# -i: Directory path for image
$ python -m facereg -i tests/resources/michael_jordan.jpeg

Sample Result

|image_sample|

.. |image_layers| image:: https://raw.githubusercontent.com/verifid/facereg/master/resources/layers.png .. |image_sample| image:: https://raw.githubusercontent.com/verifid/facereg/master/resources/mj&sp.png