Как установить face recognition python на windows
Перейти к содержимому

Как установить face recognition python на windows

  • автор:

Installation¶

To install Face Recognition, run this command in your terminal:

$ pip3 install face_recognition

This is the preferred method to install Face Recognition, as it will always install the most recent stable release.

If you don’t have pip installed, this Python installation guide can guide you through the process.

From sources¶

The sources for Face Recognition can be downloaded from the Github repo.

You can either clone the public repository:

$ git clone git://github.com/ageitgey/face_recognition

Or download the tarball:

$ curl -OL https://github.com/ageitgey/face_recognition/tarball/master

Once you have a copy of the source, you can install it with:

$ python setup.py install

© Copyright 2017, Adam Geitgey Revision 2e2dccea .

Built with Sphinx using a theme provided by Read the Docs.
Read the Docs v: latest

Versions latest stable Downloads pdf html epub On Read the Docs Project Home Builds Free document hosting provided by Read the Docs.

face-recognition 1.3.0

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

like applying digital make-up (think ‘Meitu’):

Identify faces in pictures

Recognize who appears in each photo.

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

See this example 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:

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

pip3 install face_recognition

If you are having trouble with installation, you can also try out a

Installing on Raspberry Pi 2+
Installing on Windows

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

Installing a pre-configured Virtual Machine image
  • Download the pre-configured VM image (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:

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

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:

$ face_recognition ./pictures_of_people_i_know/ ./unknown_pictures/ Obama
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:
$ face_recognition --tolerance  ./pictures_of_people_i_know/ ./unknown_pictures/ Obama
to adjust the tolerance setting, you can use --show-distance true :
$ face_recognition --show-distance  ./pictures_of_people_i_know/ ./unknown_pictures/ Obama,0.378542298956785
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: $ face_recognition ./pictures_of_people_i_know/ ./unknown_pictures/ cut -d -f2 Obama
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 parameter:

$ face_recognition --cpus  ./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!

Automatically find all the faces in an image

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 .
to try it out.
If you have a lot of images and a GPU, you can also

Automatically locate the facial features of a person in an image

to try it out.

Recognize faces in images and identify who they are

to try it out.

Python Code Examples

All the examples are available here.

Face Detection
  • Find faces in a photograph
  • Find faces in a photograph (using deep learning)
  • Find faces in batches of images w/ GPU (using deep learning)
Facial Features
  • Identify specific facial features in a photograph
  • Apply (horribly ugly) digital make-up
Facial Recognition
  • Find and recognize unknown faces in a photograph based on photographs of known people
  • Compare faces by numeric face distance instead of only True/False matches
  • Recognize faces in live video using your webcam - Simple / Slower Version (Requires OpenCV to be installed)
  • Recognize faces in live video using your webcam - Faster Version (Requires OpenCV to be installed)
  • Recognize faces in a video file and write out new video file (Requires OpenCV to be installed)
  • Recognize faces on a Raspberry Pi w/ camera
  • Run a web service to recognize faces via HTTP (Requires Flask to be installed)
  • Recognize faces with a K-nearest neighbors classifier 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.

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 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.

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.

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 (@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.
  • 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 and the audreyr/cookiecutter-pypackage project template for making Python project packaging way more tolerable.

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.

problem in Installing (python Library) face_recognition on windows 10/11

and when I tried to install face_recognition using command pip install face_recognition I got the following:

Collecting face_recognition Using cached face_recognition-1.3.0-py2.py3-none-any.whl (15 kB) Requirement already satisfied: face-recognition-models>=0.3.0 in c:\users\aenas\appdata\local\packages\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\localcache\local-packages\python39\site-packages (from face_recognition) (0.3.0) Collecting dlib>=19.7 Using cached dlib-19.22.1.tar.gz (7.4 MB) Preparing metadata (setup.py) . done Collecting Click>=6.0 Using cached click-8.0.3-py3-none-any.whl (97 kB) Requirement already satisfied: numpy in c:\users\aenas\appdata\local\packages\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\localcache\local-packages\python39\site-packages (from face_recognition) (1.21.4) Requirement already satisfied: Pillow in c:\users\aenas\appdata\local\packages\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\localcache\local-packages\python39\site-packages (from face_recognition) (8.4.0) Requirement already satisfied: colorama in c:\users\aenas\appdata\local\packages\pythonsoftwarefoundation.python.3.9_qbz5n2kfra8p0\localcache\local-packages\python39\site-packages (from Click>=6.0->face_recognition) (0.4.4) Building wheels for collected packages: dlib Building wheel for dlib (setup.py) . error ERROR: Command errored out with exit status 1: command: 'C:\Users\aenas\AppData\Local\Microsoft\WindowsApps\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\aenas\\AppData\\Local\\Temp\\pip-install-cy9bzkon\\dlib_8e45a10b6067402ab676a29d5bd742c8\\setup.py'"'"'; __file__='"'"'C:\\Users\\aenas\\AppData\\Local\\Temp\\pip-install-cy9bzkon\\dlib_8e45a10b6067402ab676a29d5bd742c8\\setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\aenas\AppData\Local\Temp\pip-wheel-elyfs9nv' cwd: C:\Users\aenas\AppData\Local\Temp\pip-install-cy9bzkon\dlib_8e45a10b6067402ab676a29d5bd742c8\ Complete output (8 lines): running bdist_wheel running build running build_py package init file 'tools\python\dlib\__init__.py' not found (or not a regular file) running build_ext ERROR: CMake must be installed to build dlib ---------------------------------------- ERROR: Failed building wheel for dlib Running setup.py clean for dlib Failed to build dlib Installing collected packages: dlib, Click, face-recognition Running setup.py install for dlib . error ERROR: Command errored out with exit status 1: command: 'C:\Users\aenas\AppData\Local\Microsoft\WindowsApps\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\aenas\\AppData\\Local\\Temp\\pip-install-cy9bzkon\\dlib_8e45a10b6067402ab676a29d5bd742c8\\setup.py'"'"'; __file__='"'"'C:\\Users\\aenas\\AppData\\Local\\Temp\\pip-install-cy9bzkon\\dlib_8e45a10b6067402ab676a29d5bd742c8\\setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\aenas\AppData\Local\Temp\pip-record-oojov4ij\install-record.txt' --single-version-externally-managed --user --prefix= --compile --install-headers 'C:\Users\aenas\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\Include\dlib' cwd: C:\Users\aenas\AppData\Local\Temp\pip-install-cy9bzkon\dlib_8e45a10b6067402ab676a29d5bd742c8\ Complete output (8 lines): running install running build running build_py package init file 'tools\python\dlib\__init__.py' not found (or not a regular file) running build_ext ERROR: CMake must be installed to build dlib ---------------------------------------- ERROR: Command errored out with exit status 1: 'C:\Users\aenas\AppData\Local\Microsoft\WindowsApps\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\aenas\\AppData\\Local\\Temp\\pip-install-cy9bzkon\\dlib_8e45a10b6067402ab676a29d5bd742c8\\setup.py'"'"'; __file__='"'"'C:\\Users\\aenas\\AppData\\Local\\Temp\\pip-install-cy9bzkon\\dlib_8e45a10b6067402ab676a29d5bd742c8\\setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\aenas\AppData\Local\Temp\pip-record-oojov4ij\install-record.txt' --single-version-externally-managed --user --prefix= --compile --install-headers 'C:\Users\aenas\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\Include\dlib' Check the logs for full command output. 

Ошибка при установке python библиотеки face-recognition

Вам ошибка говорит что нужно сделать прямым текстом: "Для сборки дополнения python на windows необходимо использовать Visual Studio. Если вы получаете эту ошибку, это означает, что вы не установили Visual C++. Обратите внимание что существует много разновидностей Visual Studio, например Visual Studio для C# разработки. Вам необходимо установить Visual Studio для C++." - (перевод.)

Данная библиотека распознавания лиц, должна работать как можно шустрее, значит большая часть её кода на более быстром языке чем python (в разы), поэтому она написана на С++, но что бы собрать её под python, нужен компилятор С++, для Windows обычно используется компилятор от Microsoft, у вас как раз его и не хватает.

Решение: Установи Visual Studio C++

Отслеживать
ответ дан 26 дек 2021 в 3:15
123 6 6 бронзовых знаков
Спасибо большое
26 дек 2021 в 11:12

    Важное на Мете
Похожие

Подписаться на ленту

Лента вопроса

Для подписки на ленту скопируйте и вставьте эту ссылку в вашу программу для чтения RSS.

Дизайн сайта / логотип © 2024 Stack Exchange Inc; пользовательские материалы лицензированы в соответствии с CC BY-SA . rev 2024.2.16.5008

Добавить комментарий

Ваш адрес email не будет опубликован. Обязательные поля помечены *