face_recognition 简单识别人脸案例

案例1

import face_recognition

lxt_image = face_recognition.load_image_file("./1.jpg")
zxc_image = face_recognition.load_image_file("./3.jpg")

# 从已知图像中获取脸部编码特征
lxt_face_encoding = face_recognition.face_encodings(lxt_image)[0]
zxc_face_encoding = face_recognition.face_encodings(zxc_image)[0]

known_encodings = [
    lxt_face_encoding,
    zxc_face_encoding
]
label=['小可爱','美少女']

# 装载图片并获取其编码特征
image_to_test = face_recognition.load_image_file("./2.jpg")
image_to_test_encoding = face_recognition.face_encodings(image_to_test)[0]

# See how far apart the test image is from the known faces
face_distances = face_recognition.face_distance(known_encodings, image_to_test_encoding)

for i, face_distance in enumerate(face_distances):
    print("测试图片与已知图片间的距离是 {:.2} 已知图片#{}".format(face_distance, label[i]))
    print("普通界限0.6, 测试图片是否匹配已知图片? {}".format(face_distance < 0.6))
    print("严格界限0.5, 测试图片是否匹配已知图片? {}".format(face_distance < 0.5))
    print()

案例：封装成类

前提：创建一个文件夹用于存放脸库，并以图片名作为标签

import glob  # 用于遍历文件夹
import face_recognition

class face_recognizer:
    # faces_lib_path 为脸库文件夹路径  extension 为脸库图片的后缀
    def __init__(self, faces_lib_path='./FaceLibs/', extension='*.jpg'):
        # 遍历人脸库的图片
        self.face_file_names = glob.glob(os.path.join(faces_lib_path, extension))  # 获取图片名的列表
        self.face_targets = [name.split('\\')[-1].split('.')[0] for name in self.face_file_names]  # 以图片名作为标签

        self.tips = ''  # 显示预测结果
        
        self.known_encodings = []  # 存放编码集
        
        for i, image_name in enumerate(self.face_file_names):
            image_arr = face_recognition.load_image_file(image_name)  # 加载图片为numpy数组
            try:  
                encodings = face_recognition.face_encodings(image_arr)  # 获得图片中每张脸的编码
                assert len(encodings) == 1  # 保证一张图只存在一张脸
                image_name_encoding = encodings[0]  # 获取脸的编码
                self.known_encodings.append(image_name_encoding)
            except:  # 如果编码为空或者过多（多张人脸）则剔除
                self.face_file_names.pop(i)  
                self.face_targets.pop(i)
        
    
    def predict(self, file_path):
        image_arr = face_recognition.load_image_file(file_path)  # 加载图片
        image_encodings = face_recognition.face_encodings(image_arr)  # 获得图片中每张脸的编码
        if len(image_encodings) == 1:  # 保证测试图片只有一张脸
            image_encoding = image_encodings[0]  # 获取脸的编码 
            self.face_distances = face_recognition.face_distance(self.known_encodings, image_encoding) # 计算距离
            # 如果最小的距离小于0.5，则不识别，否则将标签传给tips
            self.tips = self.face_targets[self.face_distances.argmin()] if min(self.face_distances) < 0.5 else '不认识'  
        else:
            self.tips = '图片中的没有脸或者脸数超过1'

测试

r = face_recognizer()
r.predict(file_path='./3.jpg')
print(r.tips)