如何通过python实现人脸识别验证

Python  /  管理员 发布于 7年前   220

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 base64import cv2import timefrom io import BytesIOfrom tensorflow import kerasfrom PIL import Imagefrom pymongo import MongoClientimport tensorflow as tfimport face_recognitionimport 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_imageslables = []datas = []INPUT_NODE = 128LATER1_NODE = 200OUTPUT_NODE = 0TRAIN_DATA_SIZE = 0TEST_DATA_SIZE = 0def 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_ydef 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()

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