1.安裝

首先,必須提前安裝cmake、numpy、dlib，其中，由於博主所用的python版本是3.6.4(為了防止不相容,所以用之前的版本),只能安裝19.7.0及之前版本的dlib，所以直接pip install dlib會報錯，需要pip install dlib==19.7.0

安裝完預備庫之後就可以直接pip install face_recognition

2.應用

(1)提取人臉

import face_recognition
from PIL import Image
image = face_recognition.load_image_file("1.jpg")
face_locations 
 
= face_recognition.face_locations(image) # top, right, bottom, left
#以下展示提取的人臉
for face_location in face_locations:
    # Print the location of each face in this image
    top, right, bottom, left = face_location
    # You can access the actual face itself like this:
    face_image = image[top:bottom, left:right]
    pil_image 
 
= Image.fromarray(face_image)
    pil_image.show()

(2)查詢面部特徵輪廓線

import face_recognition
from PIL import Image,ImageDraw
image = face_recognition.load_image_file("1.jpg")
face_landmarks_list = face_recognition.face_landmarks(image)
#以下為展示輪廓線
pil_image = Image.fromarray(image)
d = ImageDraw.Draw(pil_image)
 
for face_landmarks in face_landmarks_list:
    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:
        d.line(face_landmarks[facial_feature], width=5)
del d
pil_image.show()

(3)比較人臉

import face_recognition
known_image = face_recognition.load_image_file("known_person.jpg")
unknown_image = face_recognition.load_image_file("unknown.jpg")

biden_encoding = face_recognition.face_encodings(known_image)[0]
unknown_encoding = face_recognition.face_encodings(unknown_image)[0]

results = face_recognition.compare_faces([biden_encoding], unknown_encoding)

(4)辨別人臉

#可以設定多張已知人臉
import face_recognition
import cv2,time

# 人物名稱的集合
known_face_names = ["first","second"]
face_locations = []
face_encodings = []
demo_names = []
process_this_demo = True

# 本地影象一
first_image = face_recognition.load_image_file("first_person.jpg")
first_encoding = face_recognition.face_encodings(first_image)[0]
# 本地影象二
second_image = face_recognition.load_image_file("second_person.jpg")
second_encoding = face_recognition.face_encodings(second_image)[0]

known_face_encodings = [first_encoding,second_encoding]

# demo
path = "unknown.jpg"
demo = cv2.imread(path)
demo_image = face_recognition.load_image_file(path)
demo_encodings = face_recognition.face_encodings(demo_image)
small_demo = cv2.resize(demo, (0, 0), fx=0.25, fy=0.25)
rgb_small_demo = small_demo[:, :, ::-1]
demo_face_locations = face_recognition.face_locations(rgb_small_demo)

for demo_encoding in demo_encodings:
    # 預設為unknown
    matches = face_recognition.compare_faces(known_face_encodings, demo_encoding)
    name = "unknown"
    if True in matches:
        first_match_index = matches.index(True)
        name = known_face_names[first_match_index]
    demo_names.append(name)

# 將捕捉到的人臉顯示出來
for (top, right, bottom, left), name in zip(demo_face_locations, demo_names):
    # Scale back up face locations since the demo we detected in was scaled to 1/4 size
    top *= 4
    right *= 4
    bottom *= 4
    left *= 4
    # 矩形框
    cv2.rectangle(demo, (left, top), (right, bottom), (0, 0, 255), 2)
    #加上標籤
    cv2.rectangle(demo, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
    font = cv2.FONT_HERSHEY_DUPLEX
    cv2.putText(demo, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
    # Display
cv2.imshow('face_recognition', demo)
cv2.waitKey(0)
cv2.destroyAllWindows()

(5)攝像頭實時辨別人臉

import face_recognition
import cv2,time

video_capture = cv2.VideoCapture(0)
# 本地影象一
first_image = face_recognition.load_image_file("1.jpg")
first_face_encoding = face_recognition.face_encodings(first_image)[0]

# 本地影象二
second_image = face_recognition.load_image_file("3.jpg")
second_face_encoding = face_recognition.face_encodings(second_image)[0]

# 本地圖片三
third_image = face_recognition.load_image_file("5.jpg")
third_face_encoding = face_recognition.face_encodings(third_image)[0]

# Create arrays of known face encodings and their names
# 臉部特徵資料的集合
known_face_encodings = [
    first_face_encoding,
    second_face_encoding,
    third_face_encoding
]
# 人物名稱的集合
known_face_names = [
    "first",
    "second",
    "third"
]
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
while True:
    # 讀取攝像頭畫面
    ret, frame = video_capture.read()
    # 改變攝像頭影象的大小，影象小，所做的計算就少
    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
    # opencv的影象是BGR格式的，而我們需要是的RGB格式的，因此需要進行一個轉換。
    rgb_small_frame = small_frame[:, :, ::-1]
    # Only process every other frame of video to save time
    if process_this_frame:
        # 根據encoding來判斷是不是同一個人，是就輸出true，不是為flase
        face_locations = face_recognition.face_locations(rgb_small_frame)
        face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
        face_names = []
        for face_encoding in face_encodings:
            # 預設為unknown
            matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
            name = "Unknown"
            if True in matches:
                first_match_index = matches.index(True)
                name = known_face_names[first_match_index]
            face_names.append(name)
    process_this_frame = not process_this_frame
    # 將捕捉到的人臉顯示出來
    for (top, right, bottom, left), name in zip(face_locations, face_names):
        # Scale back up face locations since the frame we detected in was scaled to 1/4 size
        top *= 4
        right *= 4
        bottom *= 4
        left *= 4
        # 矩形框
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
        #加上標籤
        cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
        font = cv2.FONT_HERSHEY_DUPLEX
        cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
    # Display
    cv2.imshow('monitor', frame)
    # 按Q退出
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
video_capture.release()
cv2.destroyAllWindows()