1つ：公式ウェブサイトからダウンロード：
http ：//dlib.net/
2：
1。最初に公式ウェブサイトdlib
から最新バージョンのdlibをダウンロードします。dlibは最初はC ++ライブラリであるため、Python用のサードパーティライブラリとしてインストールし、BoostをダウンロードしてC ++をPythonにコンパイルし、ダウンロードする必要があります。 cmake。
sudo apt-get install libboost-python-dev cmake
2.インストール：
setup.py（python2.7パッケージにインストールされている）と同じレベルのディレクトリに切り替えます。
sudo python setup.py install
インストール後の印刷：
Installed /usr/local/lib/python2.7/dist-packages/dlib-19.19.0-py2.7-linux-i686.egg
Processing dependencies for dlib==19.19.0
Finished processing dependencies for dlib==19.19.0
3.検証、エラーなし：
aston@ubuntu:~/workplace/dlib/dlib-19.19$ python
Python 2.7.6 (default, Nov 12 2018, 20:00:40)
[GCC 4.8.4] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import dlib
>>>
4.顔検出を実行します：
pipをインストールします
sudo apt-get install python-pip python-dev build-essential
sudo pip install --upgrade pip
sudo pip install --upgrade virtualenv
最後のステップでエラーが報告されます。
Attempting uninstall: six
Found existing installation: six 1.5.2
ERROR: Cannot uninstall 'six'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.
/usr/local/lib/python2.7/dist-packages/pip/_vendor/urllib3/util/ssl_.py:139: InsecurePlatformWarning: A true SSLContext object is not available. This prevents urllib3 from configuring SSL appropriately and may cause certain SSL connections to fail. You can upgrade to a newer version of Python to solve this. For more information, see https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings
InsecurePlatformWarning,
解決する：
sudo pip install six --upgrade --ignore-installed six
5.次に、skimage.ioモジュールをインストールします。
sudo pip install scikit-image //过程很漫长，要下载好几个包
sudo apt-get install python-skimage
6.デモコード：
#!/usr/bin/python
# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
#
# This example program shows how to find frontal human faces in an image. In
# particular, it shows how you can take a list of images from the command
# line and display each on the screen with red boxes overlaid on each human
# face.
#
# The examples/faces folder contains some jpg images of people. You can run
# this program on them and see the detections by executing the
# following command:
# ./face_detector.py ../examples/faces/*.jpg
#
# This face detector is made using the now classic Histogram of Oriented
# Gradients (HOG) feature combined with a linear classifier, an image
# pyramid, and sliding window detection scheme. This type of object detector
# is fairly general and capable of detecting many types of semi-rigid objects
# in addition to human faces. Therefore, if you are interested in making
# your own object detectors then read the train_object_detector.py example
# program.
#
#
# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
# You can install dlib using the command:
# pip install dlib
#
# Alternatively, if you want to compile dlib yourself then go into the dlib
# root folder and run:
# python setup.py install
# or
# python setup.py install --yes USE_AVX_INSTRUCTIONS
# if you have a CPU that supports AVX instructions, since this makes some
# things run faster.
#
# Compiling dlib should work on any operating system so long as you have
# CMake and boost-python installed. On Ubuntu, this can be done easily by
# running the command:
# sudo apt-get install libboost-python-dev cmake
#
# Also note that this example requires scikit-image which can be installed
# via the command:
# pip install scikit-image
# Or downloaded from http://scikit-image.org/download.html.
import sys
import dlib
from skimage import io
detector = dlib.get_frontal_face_detector()
win = dlib.image_window()
for f in sys.argv[1:]:
print("Processing file: {}".format(f))
img = io.imread(f)
# The 1 in the second argument indicates that we should upsample the image
# 1 time. This will make everything bigger and allow us to detect more
# faces.
dets = detector(img, 1)
print("Number of faces detected: {}".format(len(dets)))
for i, d in enumerate(dets):
print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
i, d.left(), d.top(), d.right(), d.bottom()))
win.clear_overlay()
win.set_image(img)
win.add_overlay(dets)
dlib.hit_enter_to_continue()
# Finally, if you really want to you can ask the detector to tell you the score
# for each detection. The score is bigger for more confident detections.
# The third argument to run is an optional adjustment to the detection threshold,
# where a negative value will return more detections and a positive value fewer.
# Also, the idx tells you which of the face sub-detectors matched. This can be
# used to broadly identify faces in different orientations.
if (len(sys.argv[1:]) > 0):
img = io.imread(sys.argv[1])
dets, scores, idx = detector.run(img, 1, -1)
for i, d in enumerate(dets):
print("Detection {}, score: {}, face_type:{}".format(d, scores[i], idx[i]))
7.デモンストレーションは成功しました：
aston@ubuntu:/mnt/hgfs/share/source_insight/main_119$ python test2.py many.jpg
図1dlib検出効果図
