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from flask import Flask,render_template
import face_recognition
import sqlite3
#################
from flask_socketio import SocketIO,emit
import base64
import numpy as np
import cv2
import numpy as np
from keras.models import load_model
##################
app = Flask (__name__ )
#################
app.config['SECRET_KEY'] = 'secret!'
socket = SocketIO(app,async_mode="eventlet")
#######################
######################
# load model and labels
np.set_printoptions(suppress=True)
model = load_model(r"keras_model.h5", compile=False)
class_names = open(r"labels.txt", "r").readlines()
def base64_to_image(base64_string):
# Extract the base64 encoded binary data from the input string
base64_data = base64_string.split(",")[1]
# Decode the base64 data to bytes
image_bytes = base64.b64decode(base64_data)
# Convert the bytes to numpy array
image_array = np.frombuffer(image_bytes, dtype=np.uint8)
# Decode the numpy array as an image using OpenCV
image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
return image
@socket.on("connect")
def test_connect():
print("Connected")
emit("my response", {"data": "Connected"})
@socket.on("image")
def receive_image(image):
# Decode the base64-encoded image data
image = base64_to_image(image)
image = cv2.resize(image, (224, 224), interpolation=cv2.INTER_AREA)
# emit("processed_image", image)
# Make the image a numpy array and reshape it to the models input shape.
image = np.asarray(image, dtype=np.float32).reshape(1, 224, 224, 3)
image = (image / 127.5) - 1
# Predicts the model
prediction = model.predict(image)
index = np.argmax(prediction)
class_name = class_names[index]
confidence_score = prediction[0][index]
emit("result",{"name":str(class_name),"score":str(confidence_score)})
#######################
@app.route ("/")
def home():
return render_template("index.html")
@app.route ("/storedata" , methods =[ 'GET' ] )
def storedata():
# uname =request.form.get("uname")
# matching =request.form.get("matching")
# rtime =request.form.get("rtime")
name =request.args.get("class_name")
score =request.args.get("confidence_score")
# rtime =request.args.get("rtime")
con = sqlite3.connect("facedb") # connect database
con.row_factory = sqlite3.Row # create object of Row
cur = con.cursor() # create cursor object, which will hold records
# being fetched from database.
cur.execute( "insert into userdata (username , matching , recordingtime) values ('%s',%s,'%s')"%(uname,matching,rtime))
#cur.execute("select * from students where email=='%s' and pswd=='%s'"%(useremail,userpswd))
con.commit()
con.close()
return redirect(url_for( 'datafetch'))
@app.route ("/data" )
def datafetch():
# connect to Sqlite database and fetch data
con = sqlite3.connect("facedb") # connect sms database
con.row_factory = sqlite3.Row # create object of Row
cur = con.cursor() # create cursor object, which will hold records
# being fetched from database.
cur.execute( "select id, username, matching, recordingtime from userdata order by id desc limit 10") # execute a SQL query to get the data
rows = cur.fetchall() # all the data pulled from database is stored in rows object
con.close ()
return render_template ('data.html', data = rows)
if __name__ == '__main__':
socket.run(app,host="0.0.0.0", port=7860) |