fr/fr_env/lib/python3.8/site-packages/face_recognition-1.3.0.dist-info/METADATA

Metadata-Version: 2.1
Name: face-recognition
Version: 1.3.0
Summary: Recognize faces from Python or from the command line
Home-page: https://github.com/ageitgey/face_recognition
Author: Adam Geitgey
Author-email: ageitgey@gmail.com
License: MIT license
Keywords: face_recognition
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Dist: face-recognition-models (>=0.3.0)
Requires-Dist: Click (>=6.0)
Requires-Dist: dlib (>=19.7)
Requires-Dist: numpy
Requires-Dist: Pillow

Face Recognition
================

| Recognize and manipulate faces from Python or from the command line
  with
| the world's simplest face recognition library.

| Built using `dlib <http://dlib.net/>`__'s state-of-the-art face
  recognition
| built with deep learning. The model has an accuracy of 99.38% on the
| `Labeled Faces in the Wild <http://vis-www.cs.umass.edu/lfw/>`__
  benchmark.

| This also provides a simple ``face_recognition`` command line tool
  that lets
| you do face recognition on a folder of images from the command line!

| |PyPI|
| |Build Status|
| |Documentation Status|

Features
--------

Find faces in pictures
^^^^^^^^^^^^^^^^^^^^^^

Find all the faces that appear in a picture:

|image3|

.. code:: python

    import face_recognition
    image = face_recognition.load_image_file("your_file.jpg")
    face_locations = face_recognition.face_locations(image)

Find and manipulate facial features in pictures
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Get the locations and outlines of each person's eyes, nose, mouth and
chin.

|image4|

.. code:: python

    import face_recognition
    image = face_recognition.load_image_file("your_file.jpg")
    face_landmarks_list = face_recognition.face_landmarks(image)

| Finding facial features is super useful for lots of important stuff.
  But you can also use for really stupid stuff
| like applying `digital
  make-up <https://github.com/ageitgey/face_recognition/blob/master/examples/digital_makeup.py>`__
  (think 'Meitu'):

|image5|

Identify faces in pictures
^^^^^^^^^^^^^^^^^^^^^^^^^^

Recognize who appears in each photo.

|image6|

.. code:: python

    import face_recognition
    known_image = face_recognition.load_image_file("biden.jpg")
    unknown_image = face_recognition.load_image_file("unknown.jpg")

    biden_encoding = face_recognition.face_encodings(known_image)[0]
    unknown_encoding = face_recognition.face_encodings(unknown_image)[0]

    results = face_recognition.compare_faces([biden_encoding], unknown_encoding)

You can even use this library with other Python libraries to do
real-time face recognition:

|image7|

See `this
example <https://github.com/ageitgey/face_recognition/blob/master/examples/facerec_from_webcam_faster.py>`__
for the code.

Installation
------------

Requirements
^^^^^^^^^^^^

-  Python 3.3+ or Python 2.7
-  macOS or Linux (Windows not officially supported, but might work)

Installing on Mac or Linux
^^^^^^^^^^^^^^^^^^^^^^^^^^

First, make sure you have dlib already installed with Python bindings:

-  `How to install dlib from source on macOS or
   Ubuntu <https://gist.github.com/ageitgey/629d75c1baac34dfa5ca2a1928a7aeaf>`__

Then, install this module from pypi using ``pip3`` (or ``pip2`` for
Python 2):

.. code:: bash

    pip3 install face_recognition

| If you are having trouble with installation, you can also try out a
| `pre-configured
  VM <https://medium.com/@ageitgey/try-deep-learning-in-python-now-with-a-fully-pre-configured-vm-1d97d4c3e9b>`__.

Installing on Raspberry Pi 2+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

-  `Raspberry Pi 2+ installation
   instructions <https://gist.github.com/ageitgey/1ac8dbe8572f3f533df6269dab35df65>`__

Installing on Windows
^^^^^^^^^^^^^^^^^^^^^

While Windows isn't officially supported, helpful users have posted
instructions on how to install this library:

-  `@masoudr's Windows 10 installation guide (dlib +
   face\_recognition) <https://github.com/ageitgey/face_recognition/issues/175#issue-257710508>`__

Installing a pre-configured Virtual Machine image
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

-  `Download the pre-configured VM
   image <https://medium.com/@ageitgey/try-deep-learning-in-python-now-with-a-fully-pre-configured-vm-1d97d4c3e9b>`__
   (for VMware Player or VirtualBox).

Usage
-----

Command-Line Interface
^^^^^^^^^^^^^^^^^^^^^^

| When you install ``face_recognition``, you get a simple command-line
  program
| called ``face_recognition`` that you can use to recognize faces in a
| photograph or folder full for photographs.

| First, you need to provide a folder with one picture of each person
  you
| already know. There should be one image file for each person with the
| files named according to who is in the picture:

|known|

Next, you need a second folder with the files you want to identify:

|unknown|

| Then in you simply run the command ``face_recognition``, passing in
| the folder of known people and the folder (or single image) with
  unknown
| people and it tells you who is in each image:

.. code:: bash

    $ face_recognition ./pictures_of_people_i_know/ ./unknown_pictures/

    /unknown_pictures/unknown.jpg,Barack Obama
    /face_recognition_test/unknown_pictures/unknown.jpg,unknown_person

| There's one line in the output for each face. The data is
  comma-separated
| with the filename and the name of the person found.

| An ``unknown_person`` is a face in the image that didn't match anyone
  in
| your folder of known people.

Adjusting Tolerance / Sensitivity
'''''''''''''''''''''''''''''''''

| If you are getting multiple matches for the same person, it might be
  that
| the people in your photos look very similar and a lower tolerance
  value
| is needed to make face comparisons more strict.

| You can do that with the ``--tolerance`` parameter. The default
  tolerance
| value is 0.6 and lower numbers make face comparisons more strict:

.. code:: bash

    $ face_recognition --tolerance 0.54 ./pictures_of_people_i_know/ ./unknown_pictures/

    /unknown_pictures/unknown.jpg,Barack Obama
    /face_recognition_test/unknown_pictures/unknown.jpg,unknown_person

| If you want to see the face distance calculated for each match in
  order
| to adjust the tolerance setting, you can use ``--show-distance true``:

.. code:: bash

    $ face_recognition --show-distance true ./pictures_of_people_i_know/ ./unknown_pictures/

    /unknown_pictures/unknown.jpg,Barack Obama,0.378542298956785
    /face_recognition_test/unknown_pictures/unknown.jpg,unknown_person,None

More Examples
'''''''''''''

| If you simply want to know the names of the people in each photograph
  but don't
| care about file names, you could do this:

.. code:: bash

    $ face_recognition ./pictures_of_people_i_know/ ./unknown_pictures/ | cut -d ',' -f2

    Barack Obama
    unknown_person

Speeding up Face Recognition
''''''''''''''''''''''''''''

| Face recognition can be done in parallel if you have a computer with
| multiple CPU cores. For example if your system has 4 CPU cores, you
  can
| process about 4 times as many images in the same amount of time by
  using
| all your CPU cores in parallel.

If you are using Python 3.4 or newer, pass in a
``--cpus <number_of_cpu_cores_to_use>`` parameter:

.. code:: bash

    $ face_recognition --cpus 4 ./pictures_of_people_i_know/ ./unknown_pictures/

You can also pass in ``--cpus -1`` to use all CPU cores in your system.

Python Module
^^^^^^^^^^^^^

| You can import the ``face_recognition`` module and then easily
  manipulate
| faces with just a couple of lines of code. It's super easy!

API Docs:
`https://face-recognition.readthedocs.io <https://face-recognition.readthedocs.io/en/latest/face_recognition.html>`__.

Automatically find all the faces in an image
''''''''''''''''''''''''''''''''''''''''''''

.. code:: python

    import face_recognition

    image = face_recognition.load_image_file("my_picture.jpg")
    face_locations = face_recognition.face_locations(image)

    # face_locations is now an array listing the co-ordinates of each face!

| See `this
  example <https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_picture.py>`__
| to try it out.

You can also opt-in to a somewhat more accurate deep-learning-based face
detection model.

| Note: GPU acceleration (via nvidia's CUDA library) is required for
  good
| performance with this model. You'll also want to enable CUDA support
| when compliling ``dlib``.

.. code:: python

    import face_recognition

    image = face_recognition.load_image_file("my_picture.jpg")
    face_locations = face_recognition.face_locations(image, model="cnn")

    # face_locations is now an array listing the co-ordinates of each face!

| See `this
  example <https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_picture_cnn.py>`__
| to try it out.

| If you have a lot of images and a GPU, you can also
| `find faces in
  batches <https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_batches.py>`__.

Automatically locate the facial features of a person in an image
''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''

.. code:: python

    import face_recognition

    image = face_recognition.load_image_file("my_picture.jpg")
    face_landmarks_list = face_recognition.face_landmarks(image)

    # face_landmarks_list is now an array with the locations of each facial feature in each face.
    # face_landmarks_list[0]['left_eye'] would be the location and outline of the first person's left eye.

| See `this
  example <https://github.com/ageitgey/face_recognition/blob/master/examples/find_facial_features_in_picture.py>`__
| to try it out.

Recognize faces in images and identify who they are
'''''''''''''''''''''''''''''''''''''''''''''''''''

.. code:: python

    import face_recognition

    picture_of_me = face_recognition.load_image_file("me.jpg")
    my_face_encoding = face_recognition.face_encodings(picture_of_me)[0]

    # my_face_encoding now contains a universal 'encoding' of my facial features that can be compared to any other picture of a face!

    unknown_picture = face_recognition.load_image_file("unknown.jpg")
    unknown_face_encoding = face_recognition.face_encodings(unknown_picture)[0]

    # Now we can see the two face encodings are of the same person with `compare_faces`!

    results = face_recognition.compare_faces([my_face_encoding], unknown_face_encoding)

    if results[0] == True:
        print("It's a picture of me!")
    else:
        print("It's not a picture of me!")

| See `this
  example <https://github.com/ageitgey/face_recognition/blob/master/examples/recognize_faces_in_pictures.py>`__
| to try it out.

Python Code Examples
--------------------

All the examples are available
`here <https://github.com/ageitgey/face_recognition/tree/master/examples>`__.

Face Detection
^^^^^^^^^^^^^^

-  `Find faces in a
   photograph <https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_picture.py>`__
-  `Find faces in a photograph (using deep
   learning) <https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_picture_cnn.py>`__
-  `Find faces in batches of images w/ GPU (using deep
   learning) <https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_batches.py>`__

Facial Features
^^^^^^^^^^^^^^^

-  `Identify specific facial features in a
   photograph <https://github.com/ageitgey/face_recognition/blob/master/examples/find_facial_features_in_picture.py>`__
-  `Apply (horribly ugly) digital
   make-up <https://github.com/ageitgey/face_recognition/blob/master/examples/digital_makeup.py>`__

Facial Recognition
^^^^^^^^^^^^^^^^^^

-  `Find and recognize unknown faces in a photograph based on
   photographs of known
   people <https://github.com/ageitgey/face_recognition/blob/master/examples/recognize_faces_in_pictures.py>`__
-  `Compare faces by numeric face distance instead of only True/False
   matches <https://github.com/ageitgey/face_recognition/blob/master/examples/face_distance.py>`__
-  `Recognize faces in live video using your webcam - Simple / Slower
   Version (Requires OpenCV to be
   installed) <https://github.com/ageitgey/face_recognition/blob/master/examples/facerec_from_webcam.py>`__
-  `Recognize faces in live video using your webcam - Faster Version
   (Requires OpenCV to be
   installed) <https://github.com/ageitgey/face_recognition/blob/master/examples/facerec_from_webcam_faster.py>`__
-  `Recognize faces in a video file and write out new video file
   (Requires OpenCV to be
   installed) <https://github.com/ageitgey/face_recognition/blob/master/examples/facerec_from_video_file.py>`__
-  `Recognize faces on a Raspberry Pi w/
   camera <https://github.com/ageitgey/face_recognition/blob/master/examples/facerec_on_raspberry_pi.py>`__
-  `Run a web service to recognize faces via HTTP (Requires Flask to be
   installed) <https://github.com/ageitgey/face_recognition/blob/master/examples/web_service_example.py>`__
-  `Recognize faces with a K-nearest neighbors
   classifier <https://github.com/ageitgey/face_recognition/blob/master/examples/face_recognition_knn.py>`__

   .. rubric:: How Face Recognition Works
      :name: how-face-recognition-works

| If you want to learn how face location and recognition work instead of
| depending on a black box library, `read my
  article <https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78>`__.

Caveats
-------

-  The face recognition model is trained on adults and does not work
   very well on children. It tends to mix
   up children quite easy using the default comparison threshold of 0.6.

Deployment to Cloud Hosts (Heroku, AWS, etc)
--------------------------------------------

| Since ``face_recognition`` depends on ``dlib`` which is written in
  C++, it can be tricky to deploy an app
| using it to a cloud hosting provider like Heroku or AWS.

| To make things easier, there's an example Dockerfile in this repo that
  shows how to run an app built with
| ``face_recognition`` in a `Docker <https://www.docker.com/>`__
  container. With that, you should be able to deploy
| to any service that supports Docker images.

Common Issues
-------------

Issue: ``Illegal instruction (core dumped)`` when using
face\_recognition or running examples.

| Solution: ``dlib`` is compiled with SSE4 or AVX support, but your CPU
  is too old and doesn't support that.
| You'll need to recompile ``dlib`` after `making the code change
  outlined
  here <https://github.com/ageitgey/face_recognition/issues/11#issuecomment-287398611>`__.

Issue:
``RuntimeError: Unsupported image type, must be 8bit gray or RGB image.``
when running the webcam examples.

Solution: Your webcam probably isn't set up correctly with OpenCV. `Look
here for
more <https://github.com/ageitgey/face_recognition/issues/21#issuecomment-287779524>`__.

Issue: ``MemoryError`` when running ``pip2 install face_recognition``

| Solution: The face\_recognition\_models file is too big for your
  available pip cache memory. Instead,
| try ``pip2 --no-cache-dir install face_recognition`` to avoid the
  issue.

Issue:
``AttributeError: 'module' object has no attribute 'face_recognition_model_v1'``

Solution: The version of ``dlib`` you have installed is too old. You
need version 19.7 or newer. Upgrade ``dlib``.

Issue:
``Attribute Error: 'Module' object has no attribute 'cnn_face_detection_model_v1'``

Solution: The version of ``dlib`` you have installed is too old. You
need version 19.7 or newer. Upgrade ``dlib``.

Issue: ``TypeError: imread() got an unexpected keyword argument 'mode'``

Solution: The version of ``scipy`` you have installed is too old. You
need version 0.17 or newer. Upgrade ``scipy``.

Thanks
------

-  Many, many thanks to `Davis King <https://github.com/davisking>`__
   (`@nulhom <https://twitter.com/nulhom>`__)
   for creating dlib and for providing the trained facial feature
   detection and face encoding models
   used in this library. For more information on the ResNet that powers
   the face encodings, check out
   his `blog
   post <http://blog.dlib.net/2017/02/high-quality-face-recognition-with-deep.html>`__.
-  Thanks to everyone who works on all the awesome Python data science
   libraries like numpy, scipy, scikit-image,
   pillow, etc, etc that makes this kind of stuff so easy and fun in
   Python.
-  Thanks to `Cookiecutter <https://github.com/audreyr/cookiecutter>`__
   and the
   `audreyr/cookiecutter-pypackage <https://github.com/audreyr/cookiecutter-pypackage>`__
   project template
   for making Python project packaging way more tolerable.

.. |PyPI| image:: https://img.shields.io/pypi/v/face_recognition.svg
   :target: https://pypi.python.org/pypi/face_recognition
.. |Build Status| image:: https://travis-ci.org/ageitgey/face_recognition.svg?branch=master
   :target: https://travis-ci.org/ageitgey/face_recognition
.. |Documentation Status| image:: https://readthedocs.org/projects/face-recognition/badge/?version=latest
   :target: http://face-recognition.readthedocs.io/en/latest/?badge=latest
.. |image3| image:: https://cloud.githubusercontent.com/assets/896692/23625227/42c65360-025d-11e7-94ea-b12f28cb34b4.png
.. |image4| image:: https://cloud.githubusercontent.com/assets/896692/23625282/7f2d79dc-025d-11e7-8728-d8924596f8fa.png
.. |image5| image:: https://cloud.githubusercontent.com/assets/896692/23625283/80638760-025d-11e7-80a2-1d2779f7ccab.png
.. |image6| image:: https://cloud.githubusercontent.com/assets/896692/23625229/45e049b6-025d-11e7-89cc-8a71cf89e713.png
.. |image7| image:: https://cloud.githubusercontent.com/assets/896692/24430398/36f0e3f0-13cb-11e7-8258-4d0c9ce1e419.gif
.. |known| image:: https://cloud.githubusercontent.com/assets/896692/23582466/8324810e-00df-11e7-82cf-41515eba704d.png
.. |unknown| image:: https://cloud.githubusercontent.com/assets/896692/23582465/81f422f8-00df-11e7-8b0d-75364f641f58.png



History
=======

1.2.3 (2018-08-21)
------------------

* You can now pass model="small" to face_landmarks() to use the 5-point face model instead of the 68-point model.
* Now officially supporting Python 3.7
* New example of using this library in a Jupyter Notebook

1.2.2 (2018-04-02)
------------------

* Added the face_detection CLI command
* Removed dependencies on scipy to make installation easier
* Cleaned up KNN example and fixed a bug with drawing fonts to label detected faces in the demo


1.2.1 (2018-02-01)
------------------

* Fixed version numbering inside of module code.


1.2.0 (2018-02-01)
------------------

* Fixed a bug where batch size parameter didn't work correctly when doing batch face detections on GPU.
* Updated OpenCV examples to do proper BGR -> RGB conversion
* Updated webcam examples to avoid common mistakes and reduce support questions
* Added a KNN classification example
* Added an example of automatically blurring faces in images or videos
* Updated Dockerfile example to use dlib v19.9 which removes the boost dependency.


1.1.0 (2017-09-23)
------------------

* Will use dlib's 5-point face pose estimator when possible for speed (instead of 68-point face pose esimator)
* dlib v19.7 is now the minimum required version
* face_recognition_models v0.3.0 is now the minimum required version


1.0.0 (2017-08-29)
------------------

* Added support for dlib's CNN face detection model via model="cnn" parameter on face detecion call
* Added support for GPU batched face detections using dlib's CNN face detector model
* Added find_faces_in_picture_cnn.py to examples
* Added find_faces_in_batches.py to examples
* Added face_rec_from_video_file.py to examples
* dlib v19.5 is now the minimum required version
* face_recognition_models v0.2.0 is now the minimum required version


0.2.2 (2017-07-07)
------------------

* Added --show-distance to cli
* Fixed a bug where --tolerance was ignored in cli if testing a single image
* Added benchmark.py to examples


0.2.1 (2017-07-03)
------------------

* Added --tolerance to cli


0.2.0 (2017-06-03)
------------------

* The CLI can now take advantage of multiple CPUs. Just pass in the -cpus X parameter where X is the number of CPUs to use.
* Added face_distance.py example
* Improved CLI tests to actually test the CLI functionality
* Updated facerec_on_raspberry_pi.py to capture in rgb (not bgr) format.


0.1.14 (2017-04-22)
-------------------

* Fixed a ValueError crash when using the CLI on Python 2.7


0.1.13 (2017-04-20)
-------------------

* Raspberry Pi support.


0.1.12 (2017-04-13)
-------------------

* Fixed: Face landmarks wasn't returning all chin points.


0.1.11 (2017-03-30)
-------------------

* Fixed a minor bug in the command-line interface.


0.1.10 (2017-03-21)
-------------------

* Minor pref improvements with face comparisons.
* Test updates.


0.1.9 (2017-03-16)
------------------

* Fix minimum scipy version required.


0.1.8 (2017-03-16)
------------------

* Fix missing Pillow dependency.


0.1.7 (2017-03-13)
------------------

* First working release.