如何通过python实现人脸识别验证

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，

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"
"""
 ┏┓ ┏┓
 ┏┛┻━━━┛┻┓
 ┃ ☃ ┃
 ┃ ┳┛ ┗┳ ┃
 ┃ ┻ ┃
 ┗━┓ ┏━┛
 ┃ ┗━━━┓
 ┃ 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.h5',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.h5')



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")
 rec_face()

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。

python 人脸 识别