app.py

import face_recognition
import cv2
import numpy as np
import csv
from datetime import datetime

###########################
from flask import Flask,render_template
from flask_socketio import SocketIO,emit
import base64


app = Flask(__name__)
app.config['SECRET_KEY'] = 'secret!'
socket = SocketIO(app,async_mode="eventlet")


###################################



video_capture=cv2.VideoCapture(1)
if not video_capture.isOpened():
    print("Failed to open the video capture.")
    exit()




sir_image=face_recognition.load_image_file("photos/sir.jpeg")
sir_encoding=face_recognition.face_encodings(sir_image)[0]

vikas_image=face_recognition.load_image_file("photos/vikas.jpg")
vikas_encoding=face_recognition.face_encodings(vikas_image)[0]

known_face_encoding=[sir_encoding,vikas_encoding]

known_faces_names=["Sarwan Sir","Vikas"]

students=known_faces_names.copy()

face_locations=[]
face_encodings=[]
face_names=[]
s=True



now=datetime.now()
current_date=now.strftime("%Y-%m-%d")


f=open(current_date+'.csv','w+',newline='')
lnwriter=csv.writer(f)

############################
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):
    return render_template("index.html" , myimage = image , cname = class_name )
    while True:
        # _,frame=camera.read()
        frame=base64_to_image(image)
     
        small_frame=cv2.resize(frame,(0,0),fx=0.25,fy=0.25)
        rgb_small_frame=small_frame[:,:,::-1]
        if s:
            face_locations=face_recognition.face_locations(rgb_small_frame)
            face_encodings = face_recognition.face_encodings(small_frame, face_locations)
            face_names=[]
            for face_encoding in face_encodings:
                matches=face_recognition.compare_faces(known_face_encoding,face_encoding)
                name=""
                face_distance=face_recognition.face_distance(known_face_encoding,face_encoding)
                best_match_index=np.argmin(face_distance)
                if matches[best_match_index]:
                    name=known_faces_names[best_match_index]
    
                face_names.append(name)
                if name in known_faces_names:
                    if name in students:
                        students.remove(name)
                        print(students)
                        current_time=now.strftime("%H-%M-%S")
                        lnwriter.writerow([name,current_time,"Present"])
        cv2.imshow("attendence system",frame)
        if cv2.waitKey(1) & 0xFF==ord('q'):
            break
    
    f.close()


#######################################
@app.route("/")
def home():
    return render_template("index.html")

if __name__ == '__main__':
    # app.run(debug=True)
    socket.run(app,host="0.0.0.0", port=7860)
#######################################