python实现人脸识别 人脸匹配

import numpy as np
import cv2
import time
import face_recognition
 
# Threshold = 0.65 # 人脸置信度阈值
 
#windows用户：
#Just install dlib and face_recognition (not always on the newest version):
#pip install dlib and then pip install face_recognition.
 

功能：计算两张图片的相似度，范围：[0,1]
输入：
    1）人脸A的特征向量
    2）人脸B的特征向量
输出：
    1）sim：AB的相似度

def simcos(A,B):
    A=np.array(A)
    B=np.array(B)
    dist = np.linalg.norm(A - B) # 二范数
    sim = 1.0 / (1.0 + dist) #
    return sim
 
 

功能：
输入：
    1）x:人脸库向量（n维）
    2）y：被测人脸的特征向量(1维)
输出：
    1)match：与人脸库匹配列表，如[False,True,True,False]
             表示被测人脸y与人脸库x的第2,3张图片匹配，与1,4不匹配
    2)max(ressim):最大相似度

def compare_faces(x,y,Threshold):
    ressim = []
    match = [False]*len(x)
    for fet in x:
        sim = simcos(fet,y)
        ressim.append(sim)
    if max(ressim) > Threshold:  #置信度阈值
        match[ressim.index(max(ressim))] = True
    return match,max(ressim)
 
 

注册身份
输入：
    1）libpath：人脸库地址
输出：
    1）known_face_encodings：人脸库特征向量
    2）known_face_names：人脸库名字标签

def registeredIdentity(libpath):
    known_face_encodings, known_face_names = [], []
    with open(libpath + liblist.txt, r) as f:
        lines = f.readlines()
    for line in lines:
        img_lable_name = line.split()
        image = face_recognition.load_image_file(libpath + str(img_lable_name[0]))
        face_locations = face_recognition.face_locations(image)
        # face_locations = face_recognition.face_locations(image, model=cnn)
 
        face_encoding = face_recognition.face_encodings(image, face_locations)[0]
        # face_encoding = face_recognition.face_encodings(image, face_locations)
        known_face_encodings.append(face_encoding)
        known_face_names.append(str(img_lable_name[1]))
    return known_face_encodings, known_face_names
 
 

输入：
    1）testimg：测试图片
    2）known_face_encodings：人脸库特征向量
    3）known_face_names：人脸库名字标签
输出：
    1）retname：预测的名字
    2）retscore：相似度得分
    3）face_locations：人脸位置坐标

def identityRecognition(testimg,known_face_encodings,known_face_names,Threshold):
    face_locations = face_recognition.face_locations(testimg)
    # face_locations = face_recognition.face_locations(testimg, model="cnn")
    face_encodings = face_recognition.face_encodings(testimg, face_locations)
    retname, retscore = "Noface", 0
    for face_encoding in face_encodings:
        matches, score = compare_faces(known_face_encodings, face_encoding,Threshold)
        retname, retscore = "Unknow", 0
        if True in matches:
            first_match_index = matches.index(True)
            name = known_face_names[first_match_index]
            if score > retscore:
                retname = name
                retscore = score
    return retname, retscore,face_locations
 
 

输入：
    1）img:摄像头得到的未裁剪图片
    2）face_locations:人脸位置坐标
    3) name:预测的名字
输出：
    img:加框加年龄备注之后的画面

def age_show(img , face_locations,name):
    for (y0, x1, y1, x0) in face_locations:
        cv2.rectangle(img, (x0, y0), (x1, y1), ( 0, 0,255), 2)
        info = str(name)
        t_size = cv2.getTextSize(str(info), cv2.FONT_HERSHEY_PLAIN, 1, 2)[0]
        x2,y2 = x0 + t_size[0] + 3, y0 + t_size[1] + 4
        cv2.rectangle(img, (x0,y0), (x2,y2), (0, 0, 255), -1)  # -1填充作为文字框底色
        cv2.putText(img, info, (x0, y0 +t_size[1]+4), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 0), 1)
    return img
 
 
#4个接口
#人脸检测：face_recognition.face_locations(img, number_of_times_to_upsample=1, model="hog")
#检测面部特征点： face_landmarks(face_image, face_locations=None, model="large")
#给脸部编码：face_encodings(face_image, known_face_locations=None, num_jitters=1)
#从编码中找出人的名字：compare_faces(known_face_encodings, face_encoding_to_check, tolerance=0.6)