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Environmental requirements:
Environment to build:
1. Installation Ubuntu17.10 > installation steps here
2. Install Python2.7.14 (Ubuntu17.10 default Python version 2.7.14)
3. Install git, cmake, python-pip
# 安装 git
$ sudo apt-get install -y git
# 安装 cmake
$ sudo apt-get install -y cmake
# 安装 python-pip
$ sudo apt-get install -y python-pip
4. Install compiled dlib
Before the installation face_recognition need to install the compiler dlib
# 编译dlib前先安装 boost
$ sudo apt-get install libboost-all-dev
# 开始编译dlib
# 克隆dlib源代码
$ git clone https://github.com/davisking/dlib.git
$ cd dlib
$ mkdir build
$ cd build
$ cmake .. -DDLIB_USE_CUDA=0 -DUSE_AVX_INSTRUCTIONS=1
$ cmake --build .（注意中间有个空格）
$ cd ..
$ python setup.py install --yes USE_AVX_INSTRUCTIONS --no DLIB_USE_CUDA
5. Installation face_recognition
# 安装 face_recognition
$ pip install face_recognition
# 安装face_recognition过程中会自动安装 numpy、scipy 等
After the completion of setting up the environment, enter the command in a terminal to see if face_recognition success
Face recognition:
Example a (face recognition line 1):
1. First you need to provide a folder, which is all you want to know the system of people's pictures. Where each person a picture, the picture of man's named after:
Photo babe, Jackie Chan, Joey Yung folder under known_people
2. Next, you need to prepare another folder, which is you have to identify the picture:
Under unknown_pic folder is to identify the pictures, which Han did not know machine
3. Then you can run the command face_recognition, just the two documents prepared folder as a parameter, the command will return needs identified in the picture who have emerged:
Recognition Success! ! !
Example Two (identify all faces in the picture and displayed):
# filename : find_faces_in_picture.py
# -*- coding: utf-8 -*-
# 导入pil模块 ，可用命令安装 apt-get install python-Imaging
from PIL import Image
# 导入face_recogntion模块，可用命令安装 pip install face_recognition
import face_recognition
# 将jpg文件加载到numpy 数组中
image = face_recognition.load_image_file("/opt/face/unknown_pic/all_star.jpg")
# 使用默认的给予HOG模型查找图像中所有人脸
# 这个方法已经相当准确了，但还是不如CNN模型那么准确，因为没有使用GPU加速
# 另请参见: find_faces_in_picture_cnn.py
face_locations = face_recognition.face_locations(image)
# 使用CNN模型
# face_locations = face_recognition.face_locations(image, number_of_times_to_upsample=0, model="cnn")
# 打印：我从图片中找到了 多少 张人脸
print("I found {} face(s) in this photograph.".format(len(face_locations)))
# 循环找到的所有人脸
for face_location in face_locations:
# 打印每张脸的位置信息
top, right, bottom, left = face_location
print("A face is located at pixel location Top: {}, Left: {}, Bottom: {}, Right: {}".format(top, left, bottom, right))
# 指定人脸的位置信息，然后显示人脸图片
face_image = image[top:bottom, left:right]
pil_image = Image.fromarray(face_image)
pil_image.show()
The picture shows the picture for recognizing the following
# 执行python文件
$ python find_faces_in_picture.py
Identified from the picture face 7, and displayed, as in FIG.
Example Three (automatically recognize facial features):
# filename : find_facial_features_in_picture.py
# -*- coding: utf-8 -*-
# 导入pil模块 ，可用命令安装 apt-get install python-Imaging
from PIL import Image, ImageDraw
# 导入face_recogntion模块，可用命令安装 pip install face_recognition
import face_recognition
# 将jpg文件加载到numpy 数组中
image = face_recognition.load_image_file("biden.jpg")
＃查找图像中所有面部的所有面部特征
face_landmarks_list = face_recognition.face_landmarks(image)
print("I found {} face(s) in this photograph.".format(len(face_landmarks_list)))
for face_landmarks in face_landmarks_list:
＃打印此图像中每个面部特征的位置
facial_features = [
'chin',
'left_eyebrow',
'right_eyebrow',
'nose_bridge',
'nose_tip',
'left_eye',
'right_eye',
'top_lip',
'bottom_lip'
]
for facial_feature in facial_features:
print("The {} in this face has the following points: {}".format(facial_feature, face_landmarks[facial_feature]))
＃让我们在图像中描绘出每个人脸特征！
pil_image = Image.fromarray(image)
d = ImageDraw.Draw(pil_image)
for facial_feature in facial_features:
d.line(face_landmarks[facial_feature], width=5)
pil_image.show()
Automatically recognize facial features (profile)
Example Four (facial recognition to identify which person):
# filename : recognize_faces_in_pictures.py
# -*- conding: utf-8 -*-
# 导入face_recogntion模块，可用命令安装 pip install face_recognition
import face_recognition
＃将jpg文件加载到numpy数组中
babe_image = face_recognition.load_image_file("/opt/face/known_people/babe.jpeg")
Rong_zhu_er_image = face_recognition.load_image_file("/opt/face/known_people/Rong zhu er.jpg")
unknown_image = face_recognition.load_image_file("/opt/face/unknown_pic/babe2.jpg")
＃获取每个图像文件中每个面部的面部编码
＃由于每个图像中可能有多个面，所以返回一个编码列表。
＃但是由于我知道每个图像只有一个脸，我只关心每个图像中的第一个编码，所以我取索引0。
babe_face_encoding = face_recognition.face_encodings(babe_image)[0]
Rong_zhu_er_face_encoding = face_recognition.face_encodings(Rong_zhu_er_image)[0]
unknown_face_encoding = face_recognition.face_encodings(unknown_image)[0]
known_faces = [
babe_face_encoding,
Rong_zhu_er_face_encoding
]
＃结果是True/false的数组，未知面孔known_faces阵列中的任何人相匹配的结果
results = face_recognition.compare_faces(known_faces, unknown_face_encoding)
print("这个未知面孔是 Babe 吗? {}".format(results[0]))
print("这个未知面孔是 容祖儿 吗? {}".format(results[1]))
print("这个未知面孔是 我们从未见过的新面孔吗? {}".format(not True in results))
Results in the display in FIG.
Example V (facial feature identification and beauty):
# filename : digital_makeup.py
# -*- coding: utf-8 -*-
# 导入pil模块 ，可用命令安装 apt-get install python-Imaging
from PIL import Image, ImageDraw
# 导入face_recogntion模块，可用命令安装 pip install face_recognition
import face_recognition
＃将jpg文件加载到numpy数组中
image = face_recognition.load_image_file("biden.jpg")
＃查找图像中所有面部的所有面部特征
face_landmarks_list = face_recognition.face_landmarks(image)
for face_landmarks in face_landmarks_list:
pil_image = Image.fromarray(image)
d = ImageDraw.Draw(pil_image, 'RGBA')
＃让眉毛变成了一场噩梦
d.polygon(face_landmarks['left_eyebrow'], fill=(68, 54, 39, 128))
d.polygon(face_landmarks['right_eyebrow'], fill=(68, 54, 39, 128))
d.line(face_landmarks['left_eyebrow'], fill=(68, 54, 39, 150), width=5)
d.line(face_landmarks['right_eyebrow'], fill=(68, 54, 39, 150), width=5)
＃光泽的嘴唇
d.polygon(face_landmarks['top_lip'], fill=(150, 0, 0, 128))
d.polygon(face_landmarks['bottom_lip'], fill=(150, 0, 0, 128))
d.line(face_landmarks['top_lip'], fill=(150, 0, 0, 64), width=8)
d.line(face_landmarks['bottom_lip'], fill=(150, 0, 0, 64), width=8)
＃闪耀眼睛
d.polygon(face_landmarks['left_eye'], fill=(255, 255, 255, 30))
d.polygon(face_landmarks['right_eye'], fill=(255, 255, 255, 30))
＃涂一些眼线
d.line(face_landmarks['left_eye'] + [face_landmarks['left_eye'][0]], fill=(0, 0, 0, 110), width=6)
d.line(face_landmarks['right_eye'] + [face_landmarks['right_eye'][0]], fill=(0, 0, 0, 110), width=6)
pil_image.show()
Make-up before and after contrast below
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