Python 使用 face_recognition 人脸识别
官方说明:https://face-recognition.readthedocs.io/en/latest/readme.html
人脸识别
face_recognition 是世界上最简单的人脸识别库。
使用 dlib 最先进的人脸识别功能构建建立深度学习,该模型准确率在99.38%。
Python模块的使用
Python可以安装导入 face_recognition 模块轻松操作,对于简单的几行代码来讲,再简单不过了。
Python操作 face_recognition API 文档:https://face-recognition.readthedocs.io/en/latest/face_recognition.html
自动查找图片中的所有面部
import face_recognition image = face_recognition.load_image_file("my_picture.jpg") face_locations = face_recognition.face_locations(image) # face_locations is now an array listing the co-ordinates of each face!
还可以选择更准确的给予深度学习的人脸检测模型
import face_recognition image = face_recognition.load_image_file("my_picture.jpg") face_locations = face_recognition.face_locations(image, model="cnn") # face_locations is now an array listing the co-ordinates of each face!
自动定位图像中人物的面部特征
import face_recognition image = face_recognition.load_image_file("my_picture.jpg") face_landmarks_list = face_recognition.face_landmarks(image) # face_landmarks_list is now an array with the locations of each facial feature in each face. # face_landmarks_list[0]['left_eye'] would be the location and outline of the first person's left eye.
识别图像中的面部并识别它们是谁
import face_recognition picture_of_me = face_recognition.load_image_file("me.jpg") my_face_encoding = face_recognition.face_encodings(picture_of_me)[0] # my_face_encoding now contains a universal 'encoding' of my facial features that can be compared to any other picture of a face! unknown_picture = face_recognition.load_image_file("unknown.jpg") unknown_face_encoding = face_recognition.face_encodings(unknown_picture)[0] # Now we can see the two face encodings are of the same person with `compare_faces`! results = face_recognition.compare_faces([my_face_encoding], unknown_face_encoding) if results[0] == True: print("It's a picture of me!") else: print("It's not a picture of me!")
face_recognition 用法
要在项目中使用面部识别,首先导入面部识别库,没有则安装:
import face_recognition
基本思路是首先加載圖片:
# 导入人脸识别库 import face_recognition # 加载图片 image = face_recognition.load_image_file("1.jpg")
上面这一步会将图像加载到 numpy 数组中,如果已经有一个 numpy 数组图像则可以跳过此步骤。
然后对图片进行操作,例如找出面部、识别面部特征、查找面部编码:
例如对此照片进行操作
# 导入人脸识别库 import face_recognition # 加载图片 image = face_recognition.load_image_file("1.jpg") # 查找面部 face_locations = face_recognition.face_locations(image) # 查找面部特征 face_landmarks_list = face_recognition.face_landmarks(image) # 查找面部编码 list_of_face_encodings = face_recognition.face_encodings(image) # 打印输出 print(face_locations) print(face_landmarks_list) print(list_of_face_encodings)
/usr/bin/python3.6 /home/wjw/PycharmProjects/face_study/face0112/find_face.py [(297, 759, 759, 297)] [{'chin': [(280, 439), (282, 493), (283, 547), (290, 603), (308, 654), (340, 698), (380, 733), (427, 760), (485, 770), (544, 766), (592, 738), (634, 704), (668, 661), (689, 613), (701, 563), (712, 514), (722, 466)], 'left_eyebrow': [(327, 373), (354, 340), (395, 323), (442, 324), (487, 337)], 'right_eyebrow': [(560, 344), (603, 340), (647, 348), (682, 372), (698, 410)], 'nose_bridge': [(519, 410), (517, 444), (515, 477), (513, 512)], 'nose_tip': [(461, 548), (485, 554), (508, 561), (532, 558), (555, 556)], 'left_eye': [(372, 424), (399, 420), (426, 420), (451, 429), (424, 433), (397, 432)], 'right_eye': [(577, 440), (605, 437), (631, 442), (655, 451), (628, 454), (601, 449)], 'top_lip': [(415, 617), (452, 600), (484, 593), (506, 600), (525, 598), (551, 610), (579, 634), (566, 630), (524, 620), (504, 619), (482, 616), (428, 616)], 'bottom_lip': [(579, 634), (546, 636), (518, 636), (498, 635), (475, 632), (447, 626), (415, 617), (428, 616), (479, 605), (500, 610), (520, 610), (566, 630)]}] [array([-0.14088562, 0.00503807, 0.00270613, -0.07196694, -0.13449337, -0.07765003, -0.03745099, -0.09381913, 0.12006464, -0.14438102, 0.13404925, -0.06327219, -0.17859964, -0.05488868, -0.02019649, 0.1671212 , -0.1643257 , -0.12276072, -0.03441665, -0.05535197, 0.10760178, 0.04479133, -0.06407147, 0.0689199 , -0.11934121, -0.32660219, -0.07756624, -0.06931646, 0.04064362, -0.05714978, -0.0353414 , 0.0762421 , -0.18261658, -0.07098956, 0.02025999, 0.13947421, -0.00086442, -0.05380288, 0.17013952, 0.03612047, -0.24374251, 0.02234841, 0.06126914, 0.25475574, 0.11198805, 0.01954928, 0.01119124, -0.10833667, 0.14647615, -0.14495029, -0.00890255, 0.12340544, 0.05062022, 0.07525564, 0.0184714 , -0.0970083 , 0.07874238, 0.09881058, -0.15751837, 0.02846039, 0.0963228 , -0.07531998, -0.0176545 , -0.07000162, 0.25344211, 0.03867894, -0.09201257, -0.1658347 , 0.12261658, -0.1535762 , -0.15940444, 0.04406216, -0.12239387, -0.10966937, -0.30615237, -0.00739088, 0.39348996, 0.108335 , -0.20034787, 0.08009379, -0.05592394, -0.0375729 , 0.23610245, 0.16506384, 0.03575533, 0.04828007, -0.04044699, 0.01277492, 0.25646573, -0.00142263, -0.04078939, 0.18071812, 0.0617944 , 0.12697747, 0.02988701, -0.00425877, -0.07669616, 0.00568433, -0.10959606, -0.03289849, 0.08964096, -0.00859835, 0.00752143, 0.14310959, -0.14807181, 0.18848835, 0.03889544, 0.0564449 , 0.03094865, 0.05897319, -0.11886788, -0.03628988, 0.09417973, -0.20971358, 0.22439443, 0.18054837, 0.0444049 , 0.06860743, 0.1211487 , 0.02242998, -0.01343671, -0.00214755, -0.24110457, -0.03643485, 0.13142672, -0.05264375, 0.09808614, 0.00694137])] Process finished with exit code 0
可以将面部编码相互比较以查看面部是否匹配,注意:查找面部编码有点慢,因此如果在以后还需对次图片进行面部分析参考,建议将每个图片的结果存留缓存或存储进数据库。
一旦得到面部编码,便可以比较他们
# results is an array of True/False telling if the unknown face matched anyone in the known_faces array results = face_recognition.compare_faces(known_face_encodings, a_single_unknown_face_encoding)
就是这么简单!