Python几行代码即可实现人脸识别

#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

# 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

# 安装 face_recognition
$ pip install face_recognition
# 安装face_recognition过程中会自动安装 numpy、scipy 等

 # 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()

 # 执行python文件
$ python find_faces_in_picture.py

 # 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()

 # 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))

 # 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()