How to realize face recognition verification through Python

Time:2020-9-26

This article mainly introduces how to realize face recognition verification through python. The example code is introduced in detail, which has certain reference learning value for everyone’s study or work. Friends in need can refer to it

Code directly, this case is based on https://github.com/caibojian/face_ Login modified, recognition rate is not very good, sometimes blocked half the face is still successful

# -*- coding: utf-8 -*-
# __author__="maple"
"""
       ┏┓   ┏┓
      ┏┛┻━━━┛┻┓
      ┃   ☃   ┃
      ┃ ┳┛ ┗┳ ┃
      ┃   ┻   ┃
      ┗━┓   ┏━┛
        ┃   ┗━━━┓
        ┃┃┣┓
        Never 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 connection
conn = MongoClient('mongodb://root:[email protected]:27017/')
db =  conn.myface  #Connect to mydb database. If not, it will be created automatically
user_ face =  db.user_ Face ා using test_ Set, no set is created automatically
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

#Verify face information
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 ('the% d face is% s'% (j + 1, pred_ user['user_ name']))
      result.append(pred_user['user_name'])
  return result

#Save face information
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)

#Training face information
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 ("user already exists")
    return
  video_capture=cv2.VideoCapture(0)
  #In mongodb, sort() method is used to sort the data. The sort() method can specify the sorting fields through parameters, and use 1 and - 1 to specify the sorting method, where 1 is ascending and - 1 is descending.
  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:
    #Get a video
    ret, frame = video_capture.read()
    #Window display
    cv2.imshow('Video',frame)
    #After adjusting the angle, take 5 pictures in succession
    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:
    #Get a video
    ret, frame = video_capture.read()
    #Window display
    cv2.imshow('Video',frame)
    #Verify 5 photos of face
    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
  else:
    Print ("validation failed")

if __name__ == '__main__':
  register_face("maple")
  rec_face()

The above is the whole content of this article, I hope to help you in your study, and I hope you can support developeppaer more.

Recommended Today

The first Python Programming challenge on the Internet (end)

Date of establishment: March 28, 2020Update Date: April 22, 2020 (end)Personal collection Tool.pywebsite:http://www.pythonchallenge.com/Note: please quote or change this article at will, just mark the source and the author. The author does not guarantee that the content is absolutely correct. Please be responsible for any consequences Title: the first Python Programming challenge on the web Find […]