Face Recognition 是一个基于 Python 的人脸识别库,它还提供了一个命令行工具,让你通过命令行对任意文件夹中的图像进行人脸识别操作。

该库使用 dlib 顶尖的深度学习人脸识别技术构建,在户外脸部检测数据库基准(Labeled Faces in the Wild benchmark)上的准确率高达 99.38%。

在网上找到了很多关于face_recognition的有趣程序,这里进行一下汇总。

安装:

* 人脸检测基于dlib,dlib依赖Boost和cmake
* 在windows中如果要使用dlib还是比较麻烦的,最好使用anaconda中安装,这样可以减少很多麻烦 
执行:pip install face_recognition

这是安装好的face_recognition,可以看见所依赖的库!

如果安装的过程遇到缺少库的话,缺少哪个就安装哪个!!!

 

应用1:

检测给定图像中的所有人脸
# -*- coding: utf-8 -*- # 检测人脸 import face_recognition import cv2 # 读取图片并识别人脸
img = face_recognition.load_image_file("1.png") face_locations =
face_recognition.face_locations(img) print (face_locations) # 调用opencv函数显示图片
img = cv2.imread("1.png") cv2.namedWindow("原图") cv2.imshow("原图", img) #
遍历每个人脸,并标注 faceNum = len(face_locations) for i in range(0, faceNum): top =
face_locations[i][0] right = face_locations[i][1] bottom = face_locations[i][2]
left = face_locations[i][3] start = (left, top) end = (right, bottom) color =
(55,255,155) thickness = 3 cv2.rectangle(img, start, end, color, thickness) #
显示识别结果 cv2.namedWindow("识别") cv2.imshow("识别", img) cv2.waitKey(0)
cv2.destroyAllWindows()
用到的图片1.png

运行结果:

 

应用2:

识别图像中的人脸

文件夹结构:

images文件夹中的文件 

my_image.jpg

 

代码 :faceRecognition.py
# 导入库 import os import face_recognition # 制作所有可用图像的列表 images =
os.listdir('images') # 加载图像 image_to_be_matched =
face_recognition.load_image_file('my_image.jpg') # 将加载图像编码为特征向量
image_to_be_matched_encoded = face_recognition.face_encodings(
image_to_be_matched)[0] # 遍历每张图像 for image in images: # 加载图像 current_image =
face_recognition.load_image_file("images/" + image) # 将加载图像编码为特征向量
current_image_encoded = face_recognition.face_encodings(current_image)[0] #
将你的图像和图像对比,看是否为同一人 result = face_recognition.compare_faces(
[image_to_be_matched_encoded], current_image_encoded) # 检查是否一致 if result[0] ==
True: print ("Matched: " + image) else: print ("Not matched: " + image)
运行结果:

代码中利用face_recognition将要查看的图片加载,并将图片编码为特征向量。然后遍历images文件中的每一张图片都加载为特征向量,并进行比较,输出结果。

 

应用3:

实时人脸识别

 

代码:
# -*- coding: utf-8 -*- import face_recognition import cv2 video_capture =
cv2.VideoCapture(0) obama_img = face_recognition.load_image_file("lq.jpg")
obama_face_encoding = face_recognition.face_encodings(obama_img)[0]
face_locations = [] face_encodings = [] face_names = [] process_this_frame =
True while True: ret, frame = video_capture.read() small_frame =
cv2.resize(frame,(0,0),fx=0.25, fy=0.25) if process_this_frame: face_locations
= face_recognition.face_locations(small_frame) face_encodings =
face_recognition.face_encodings(small_frame, face_locations) face_names = []
for face_encoding in face_encodings: match =
face_recognition.compare_faces([obama_face_encoding], face_encoding) if
match[0]: name = "lq" else: name = "unkonwn" face_names.append(name)
process_this_frame = not process_this_frame for (top, right, bottom, left),
name in zip(face_locations, face_names): top *= 4 right *= 4 bottom *= 4 left
*= 4 cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), 2) font
= cv2.FONT_HERSHEY_DUPLEX cv2.putText(frame, name, (left+6, bottom-6), font,
1.0, (255, 255, 255), 1) cv2.imshow('Video', frame) #按Q退出,结束程序 if
cv2.waitKey(1) & 0xFF == ord('q'): break video_capture.release()
cv2.destroyAllWindows()
 

运行结果:

 

应用4:

检测和标记图像中的人脸特征:

代码:
# -*- coding: utf-8 -*- # 自动识别人脸特征 from PIL import Image, ImageDraw import
face_recognition # 将jpg文件加载到numpy 数组中 image =
face_recognition.load_image_file("my_image.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) #打印发现的脸张数 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()
 

结果:

如果用上文中的1.png,就会发现5张脸,会标记每一张脸的特征。

 

 

应用5:

识别人脸并美颜

代码 :
# -*- coding: utf-8 -*- from PIL import Image, ImageDraw import
face_recognition #将jpg文件加载到numpy数组中 image =
face_recognition.load_image_file("3.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()
这个就不放运行的截图了,哈哈,感兴趣可以自己找一张图片运行!!!

 

技术
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