Python 使用 face_recognition 人臉識別
阿新 • • 發佈：2019-01-12
Python 使用 face_recognition 人臉識別
官方說明：https://face-recognition.readthedocs.io/en/latest/readme.html
人臉識別
face_recognition 是世界上最簡單的人臉識別庫。
使用 dlib 最先進的人臉識別功能構建建立深度學習，該模型準確率在99.38%。
Python模組的使用
Python可以安裝匯入 face_recognition 模組輕鬆操作，對於簡單的幾行程式碼來講，再簡單不過了。
Python操作 face_recognition API 文件：https://face-recognition.readthedocs.io/en/latest/face_recognition.html
自動查詢圖片中的所有面部
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!
還可以選擇更準確的給予深度學習的人臉檢測模型
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!
自動定點陣圖像中人物的面部特徵
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.
識別影象中的面部並識別它們是誰
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
要在專案中使用面部識別，首先匯入面部識別庫，沒有則安裝：
import face_recognition
基本思路是首先載入圖片：
# 匯入人臉識別庫
import face_recognition
# 載入圖片
image = face_recognition.load_image_file("1.jpg")
上面這一步會將影象載入到 numpy 陣列中，如果已經有一個 numpy 陣列影象則可以跳過此步驟。
然後對圖片進行操作，例如找出面部、識別面部特徵、查詢面部編碼：
例如對此照片進行操作
# 匯入人臉識別庫
import face_recognition
# 載入圖片
image = face_recognition.load_image_file("1.jpg")
# 查詢面部
face_locations = face_recognition.face_locations(image)
# 查詢面部特徵
face_landmarks_list = face_recognition.face_landmarks(image)
# 查詢面部編碼
list_of_face_encodings = face_recognition.face_encodings(image)
# 列印輸出
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
可以將面部編碼相互比較以檢視面部是否匹配，注意：查詢面部編碼有點慢，因此如果在以後還需對次圖片進行面部分析參考，建議將每個圖片的結果存留快取或儲存進資料庫。
一旦得到面部編碼，便可以比較他們
# 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)
就是這麼簡單！