前言
dlib毕竟是一个很有名的库了，有c++、Python的接口。使用dlib可以大大简化开发，比如人脸识别，特征点检测之类的工作都可以很轻松实现。同时也有很多基于dlib开发的应用和开源库，比如face_recogintion库（应用一个基于Python的开源人脸识别库，face_recognition）等等。
环境安装
不算复杂，我只在Linux和win下跑过。安装配置不算难，直接贴链接了。
Linux下的安装在这篇博客中介绍了（应用一个基于Python的开源人脸识别库，face_recognition），不做赘述。
win下安装教程：
python 安装dlib和boost
Windows环境 安装dlib（python） 总结
程序
注：程序中使用了python-opencv、dlib，使用前请配置好环境。
程序中已有注释。
# -*- coding: utf-8 -*-
import sys
import dlib
import cv2
detector = dlib.get_frontal_face_detector() #获取人脸分类器
# 传入的命令行参数
for f in sys.argv[1:]:
# opencv 读取图片，并显示
img = cv2.imread(f, cv2.IMREAD_COLOR)
# 摘自官方文档：
# image is a numpy ndarray containing either an 8bit grayscale or RGB image.
# opencv读入的图片默认是bgr格式，我们需要将其转换为rgb格式；都是numpy的ndarray类。
b, g, r = cv2.split(img) # 分离三个颜色通道
img2 = cv2.merge([r, g, b]) # 融合三个颜色通道生成新图片
dets = detector(img, 1) #使用detector进行人脸检测 dets为返回的结果
print("Number of faces detected: {}".format(len(dets))) # 打印识别到的人脸个数
# enumerate是一个Python的内置方法，用于遍历索引
# index是序号；face是dets中取出的dlib.rectangle类的对象，包含了人脸的区域等信息
# left()、top()、right()、bottom()都是dlib.rectangle类的方法，对应矩形四条边的位置
for index, face in enumerate(dets):
print('face {}; left {}; top {}; right {}; bottom {}'.format(index, face.left(), face.top(), face.right(), face.bottom()))
# 在图片中标注人脸，并显示
left = face.left()
top = face.top()
right = face.right()
bottom = face.bottom()
cv2.rectangle(img, (left, top), (right, bottom), (0, 255, 0), 3)
cv2.namedWindow(f, cv2.WINDOW_AUTOSIZE)
cv2.imshow(f, img)
# 等待按键，随后退出，销毁窗口
k = cv2.waitKey(0)
cv2.destroyAllWindows()
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运行结果
运行程序，后缀是图片的名称。
官方例程：
最后附上官方程序：
#!/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, idx))
---------------------
【转载】
作者：hongbin_xu
原文：https://blog.csdn.net/hongbin_xu/article/details/78347484
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