学习Python3Dlib19.7进行人脸面部识别资源-CSDN文库

dlib

dlib人脸检测

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学习学习Python3 Dlib19.7进行人脸面部识别进行人脸面部识别

0.引言引言

自己在下载dlib官网给的example代码时，一开始不知道怎么使用，在一番摸索之后弄明白怎么使用了；

现分享下 face_detector.py 和 face_landmark_detection.py 这两个py的使用方法；

1.简介简介

python:　　3.6.3

dlib:　　　 19.7

利用dlib的特征提取器，进行人脸矩形框的特征提取：　　

dets = dlib.get_frontal_face_detector(img)

利用dlib的68点特征预测器，进行人脸 68点特征提取：

predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

shape = predictor(img, dets[0])

效果：

(a) face_detector.py

b) face_landmark_detection.py

2.py文件功能介绍文件功能介绍

face_detector.py :

识别出图片文件中一张或多张人脸，并用矩形框框出标识出人脸；

link: http://dlib.net/cnn_face_detector.py.html

face_landmark_detection.py ：在face_detector.py的识别人脸基础上，识别出人脸部的具体特征部位：下巴轮廓、眉毛、眼睛、嘴巴，同样用标记标识出面部特征；

link: http://dlib.net/face_landmark_detection.py.html

2.1. face_detector.py

官网给的face_detector.py

#!/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]))

为了方便理解，修改增加注释之后的 face_detector.py

import dlib

from skimage import io

# 使用特征提取器frontal_face_detector

detector = dlib.get_frontal_face_detector()

# path是图片所在路径

path = "F:/code/python/P_dlib_face/pic/"

img = io.imread(path+"1.jpg")

# 特征提取器的实例化

dets = detector(img)

print("人脸数：", len(dets))

# 输出人脸矩形的四个坐标点

for i, d in enumerate(dets):

print("第", i, "个人脸d的坐标：",

"left:", d.left(),

"right:", d.right(),

"top:", d.top(),

"bottom:", d.bottom())

# 绘制图片

win = dlib.image_window()

# 清除覆盖

#win.clear_overlay()

win.set_image(img)

# 将生成的矩阵覆盖上

win.add_overlay(dets)

# 保持图像

dlib.hit_enter_to_continue()

对test.jpg进行人脸检测：　

结果：

图片窗口结果：

剩余6页未读，继续阅读

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