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这篇文章主要介绍了如何通过python实现人脸识别验证,文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值,需要的朋友可以参考下
直接上代码，此案例是根据https://github.com/caibojian/face_login修改的,识别率不怎么好，有时挡了半个脸还是成功的
# -*- coding: utf-8 -*-
# __author__="maple"
"""
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┃ ☃ ┃
┃ ┳┛ ┗┳ ┃
┃ ┻ ┃
┗━┓ ┏━┛
┃ ┗━━━┓
┃ 神兽保佑 ┣┓
┃ 永无BUG！ ┏┛
┗┓┓┏━┳┓┏┛
┃┫┫ ┃┫┫
┗┻┛ ┗┻┛
"""
import base64
import cv2
import time
from io import BytesIO
from tensorflow import keras
from PIL import Image
from pymongo import MongoClient
import tensorflow as tf
import face_recognition
import numpy as np
#mongodb连接
conn = MongoClient('mongodb://root:123@localhost:27017/')
db = conn.myface #连接mydb数据库，没有则自动创建
user_face = db.user_face #使用test_set集合，没有则自动创建
face_images = db.face_images
lables = []
datas = []
INPUT_NODE = 128
LATER1_NODE = 200
OUTPUT_NODE = 0
TRAIN_DATA_SIZE = 0
TEST_DATA_SIZE = 0
def generateds():
get_out_put_node()
train_x, train_y, test_x, test_y = np.array(datas),np.array(lables),np.array(datas),np.array(lables)
return train_x, train_y, test_x, test_y
def get_out_put_node():
for item in face_images.find():
lables.append(item['user_id'])
datas.append(item['face_encoding'])
OUTPUT_NODE = len(set(lables))
TRAIN_DATA_SIZE = len(lables)
TEST_DATA_SIZE = len(lables)
return OUTPUT_NODE, TRAIN_DATA_SIZE, TEST_DATA_SIZE
# 验证脸部信息
def predict_image(image):
model = tf.keras.models.load_model('face_model.h6',compile=False)
face_encode = face_recognition.face_encodings(image)
result = []
for j in range(len(face_encode)):
predictions1 = model.predict(np.array(face_encode[j]).reshape(1, 128))
print(predictions1)
if np.max(predictions1[0]) > 0.90:
print(np.argmax(predictions1[0]).dtype)
pred_user = user_face.find_one({'id': int(np.argmax(predictions1[0]))})
print('第%d张脸是%s' % (j+1, pred_user['user_name']))
result.append(pred_user['user_name'])
return result
# 保存脸部信息
def save_face(pic_path,uid):
image = face_recognition.load_image_file(pic_path)
face_encode = face_recognition.face_encodings(image)
print(face_encode[0].shape)
if(len(face_encode) == 1):
face_image = {
'user_id': uid,
'face_encoding':face_encode[0].tolist()
}
face_images.insert_one(face_image)
# 训练脸部信息
def train_face():
train_x, train_y, test_x, test_y = generateds()
dataset = tf.data.Dataset.from_tensor_slices((train_x, train_y))
dataset = dataset.batch(32)
dataset = dataset.repeat()
OUTPUT_NODE, TRAIN_DATA_SIZE, TEST_DATA_SIZE = get_out_put_node()
model = keras.Sequential([
keras.layers.Dense(128, activation=tf.nn.relu),
keras.layers.Dense(128, activation=tf.nn.relu),
keras.layers.Dense(OUTPUT_NODE, activation=tf.nn.softmax)
])
model.compile(optimizer=tf.compat.v1.train.AdamOptimizer(),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
steps_per_epoch = 30
if steps_per_epoch > len(train_x):
steps_per_epoch = len(train_x)
model.fit(dataset, epochs=10, steps_per_epoch=steps_per_epoch)
model.save('face_model.h6')
def register_face(user):
if user_face.find({"user_name": user}).count() > 0:
print("用户已存在")
return
video_capture=cv2.VideoCapture(0)
# 在MongoDB中使用sort()方法对数据进行排序，sort()方法可以通过参数指定排序的字段，并使用 1 和 -1 来指定排序的方式，其中 1 为升序，-1为降序。
finds = user_face.find().sort([("id", -1)]).limit(1)
uid = 0
if finds.count() > 0:
uid = finds[0]['id'] + 1
print(uid)
user_info = {
'id': uid,
'user_name': user,
'create_time': time.time(),
'update_time': time.time()
}
user_face.insert_one(user_info)
while 1:
# 获取一帧视频
ret, frame = video_capture.read()
# 窗口显示
cv2.imshow('Video',frame)
# 调整角度后连续拍5张图片
if cv2.waitKey(1) & 0xFF == ord('q'):
for i in range(1,6):
cv2.imwrite('Myface{}.jpg'.format(i), frame)
with open('Myface{}.jpg'.format(i),"rb")as f:
img=f.read()
img_data = BytesIO(img)
im = Image.open(img_data)
im = im.convert('RGB')
imgArray = np.array(im)
faces = face_recognition.face_locations(imgArray)
save_face('Myface{}.jpg'.format(i),uid)
break
train_face()
video_capture.release()
cv2.destroyAllWindows()
def rec_face():
video_capture = cv2.VideoCapture(0)
while 1:
# 获取一帧视频
ret, frame = video_capture.read()
# 窗口显示
cv2.imshow('Video',frame)
# 验证人脸的5照片
if cv2.waitKey(1) & 0xFF == ord('q'):
for i in range(1,6):
cv2.imwrite('recface{}.jpg'.format(i), frame)
break
res = []
for i in range(1, 6):
with open('recface{}.jpg'.format(i),"rb")as f:
img=f.read()
img_data = BytesIO(img)
im = Image.open(img_data)
im = im.convert('RGB')
imgArray = np.array(im)
predict = predict_image(imgArray)
if predict:
res.extend(predict)
b = set(res) # {2, 3}
if len(b) == 1 and len(res) >= 3:
print(" 验证成功")
else:
print(" 验证失败")
if __name__ == '__main__':
register_face("maple")
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