#安裝 git
$ sudo apt-get install -y git
# 安裝 cmake
$ sudo apt-get install -y cmake
# 安裝 python-pip
$ sudo apt-get install -y python-pip

# 編譯dlib前先安裝 boost
$ sudo apt-get install libboost-all-dev
 
# 開始編譯dlib
# 克隆dlib源代碼
$ 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 .（注意中間有個空格）
$ cd ..
$ python setup.py install --yes USE_AVX_INSTRUCTIONS --no   DLIB_USE_CUDA

# 安裝 face_recognition
$ pip install face_recognition
# 安裝face_recognition過程中會自動安裝 numpy、scipy 等

 # filename : find_faces_in_picture.py
 # -*- coding: utf-8 -*-
 # 導(dǎo)入pil模塊 ，可用命令安裝 apt-get install python-Imaging
 from PIL import Image
 # 導(dǎo)入face_recogntion模塊，可用命令安裝 pip install face_recognition
 import face_recognition
 
 # 將jpg文件加載到numpy 數(shù)組中
image = face_recognition.load_image_file("/opt/face/unknown_pic/all_star.jpg")
 
 # 使用默認的給予HOG模型查找圖像中所有人臉
 # 這個方法已經(jīng)相當準確了，但還是不如CNN模型那么準確，因為沒有使用GPU加速
 # 另請參見: 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?。何覐膱D片中找到了 多少 張人臉
print("I found {} face(s) in this photograph.".format(len(face_locations)))
 
 # 循環(huán)找到的所有人臉
 for face_location in face_locations:
 
        # 打印每張臉的位置信息
        top, right, bottom, left = face_location
        print("A face is located at pixel location Top: {}, Left: {}, Bottom: {}, Right: {}".format(top, left, bottom, right))
 
        # 指定人臉的位置信息，然后顯示人臉圖片
        face_image = image[top:bottom, left:right]
        pil_image = Image.fromarray(face_image)
        pil_image.show()

 # 執(zhí)行python文件
$ python find_faces_in_picture.py

 # filename : find_facial_features_in_picture.py
 # -*- coding: utf-8 -*-
 # 導(dǎo)入pil模塊 ，可用命令安裝 apt-get install python-Imaging
from PIL import Image, ImageDraw
 # 導(dǎo)入face_recogntion模塊，可用命令安裝 pip install face_recognition
import face_recognition
 
 # 將jpg文件加載到numpy 數(shù)組中
image = face_recognition.load_image_file("biden.jpg")
 
＃查找圖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:
 
   ＃打印此圖像中每個面部特征的位置
    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]))
 
   ＃讓我們在圖像中描繪出每個人臉特征！
    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 -*-
 # 導(dǎo)入face_recogntion模塊，可用命令安裝 pip install face_recognition
import face_recognition
 
 ＃將jpg文件加載到numpy數(shù)組中
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")
 
 ＃獲取每個圖像文件中每個面部的面部編碼
 ＃由于每個圖像中可能有多個面，所以返回一個編碼列表。
 ＃但是由于我知道每個圖像只有一個臉，我只關(guān)心每個圖像中的第一個編碼，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
]
 
 ＃結(jié)果是True/false的數(shù)組，未知面孔known_faces陣列中的任何人相匹配的結(jié)果
results = face_recognition.compare_faces(known_faces, unknown_face_encoding)
 
print("這個未知面孔是 Babe 嗎? {}".format(results[0]))
print("這個未知面孔是 容祖兒 嗎? {}".format(results[1]))
print("這個未知面孔是 我們從未見過的新面孔嗎? {}".format(not True in results))

 # filename : digital_makeup.py
 # -*- coding: utf-8 -*-
 # 導(dǎo)入pil模塊 ，可用命令安裝 apt-get install python-Imaging
from PIL import Image, ImageDraw
 # 導(dǎo)入face_recogntion模塊，可用命令安裝 pip install face_recognition
import face_recognition
 
＃將jpg文件加載到numpy數(shù)組中
image = face_recognition.load_image_file("biden.jpg")
 
＃查找圖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')
 
    ＃讓眉毛變成了一場噩夢
    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)
 
    ＃光澤的嘴唇
    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)
 
    ＃閃耀眼睛
    d.polygon(face_landmarks['left_eye'], fill=(255, 255, 255, 30))
    d.polygon(face_landmarks['right_eye'], fill=(255, 255, 255, 30))
 
    ＃涂一些眼線
    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()