#導(dǎo)入face_recognition模塊

import face_recognition

#將jpg文件加載到numpy數(shù)組中

image = face_recognition.load_image_file(“your_file.jpg”)

#查找圖片中人臉（上下左右）的位置，圖像中可能有多個(gè)人臉 

#face_locations的值類(lèi)似[(135，536，198，474),()]

Face_locations = face_recognition.face_locations(image);

# 使用CNN模型 準(zhǔn)確率高

face_locations = face_recognition.face_locations(image, number_of_times_to_upsample=0, model="cnn")

face_locations = face_recognition.face_locations(small_frame, model="cnn")

import face_recognition

image = face_recognition.load_image_file(“your_file.jpg”)

#查找圖片中人臉的所有面部特征（眉毛，眼睛，鼻子，上下嘴唇，面部輪廓）

#face_landmarks_list是個(gè)二維數(shù)組

face_landmarks_list = face_recognition.face_landmarks(image)

import face_recognition

known_image = face_recognition.load_image_file(“biden.jpg”)

unknown_imag = face_recognition.load_image_file(“unknown.jpg”)

#獲取每個(gè)圖像文件中每個(gè)面部的面部編碼

#由于每個(gè)圖像中可能有多個(gè)人臉，所以返回一個(gè)編碼列表。

#但是事先知道每個(gè)圖像只有一個(gè)人臉，每個(gè)圖像中的第一個(gè)編碼，取索引0。

Biden_encoding =face_recognition.face_encodings(known_image)[0]

Unknown_encoding=face_recognition.face_encodings(unknown_image)[0]

#如果圖像中有多個(gè)人臉 獲取圖像中多個(gè)人臉編碼

face_locations = face_recognition.face_locations(unknow_image)

face_encodings = face_recognition.face_encodings(unknown_image, face_locations)

#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 

#[true, false,false]

Results=face_recognition.compare_faces([biden_encoding],unknown_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 設(shè)定比對(duì)結(jié)果的閥值

#[true, false,false]

 match = face_recognition.compare_faces(known_faces, face_encoding, tolerance=0.50)

#結(jié)果是小于1的值 例如0.5 0.7等

face_distances = face_recognition.face_distance(known_encodings, image_to_test_encoding)

設(shè)定閥值 05或者0.6等

face_distances < 閥值