app.py

from flask import *
from PIL import Image

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

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



#################
from flask_socketio import SocketIO,emit
import base64


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



app = Flask (__name__ )

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


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



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":"mrmr","score":"34"})
    # #######################




# # @app.route('/at')
# # def attend():
# #     # Face recognition variables
    known_faces_names = ["Sarwan Sir", "Vikas","Lalit","Jasmeen","Anita Ma'am"]
    known_face_encodings = []

    # Load known face encodings
    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]

    lalit_image = face_recognition.load_image_file("photos/lalit.jpg")
    lalit_encoding = face_recognition.face_encodings(lalit_image)[0]

    jasmine_image = face_recognition.load_image_file("photos/jasmine.jpg")
    jasmine_encoding = face_recognition.face_encodings(jasmine_image)[0]

    maam_image = face_recognition.load_image_file("photos/maam.png")
    maam_encoding = face_recognition.face_encodings(maam_image)[0]

    known_face_encodings = [sir_encoding, vikas_encoding,lalit_encoding,jasmine_encoding,maam_encoding]
    emit("result",{"name":"level1","score":"34"})
    students = known_faces_names.copy()

    face_locations = []
    face_encodings = []
    face_names = []

    # now = datetime.now()
    # current_date = now.strftime("%Y-%m-%d")
    # csv_file = open(f"{current_date}.csv", "a+", newline="")
    
    # csv_writer = csv.writer(csv_file)
    small_frame = cv2.resize(image, (0, 0), fx=0.25, fy=0.25)
    rgb_small_frame = small_frame[:, :, ::-1]
    emit("result",{"name":"level222","score":"34"})
    face_locations = face_recognition.face_locations(rgb_small_frame)
    face_encodings = face_recognition.face_encodings(small_frame, face_locations)
    face_names = []
    emit("result",{"name":"level 33","score":str(len(face_encodings))})
    for face_encoding in face_encodings:
        emit("result",{"name":"in for ","score":"34"})
        matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
        name = ""
        face_distance = face_recognition.face_distance(known_face_encodings, 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)
    
    emit("result",{"name":str(name),"score":"myScore"})
       
#         # for name in face_names:
#         #     if name in known_faces_names and name in students and name not in existing_names:
#         #         students.remove(name)
#         #         print(students)
#         #         print(f"Attendance recorded for {name}")
#         #         current_time = now.strftime("%H-%M-%S")
#         #         csv_writer.writerow([name, current_time, "Present"])
#         #         existing_names.add(name)  # Add the name to the set of existing names
    
@app.route ("/")
def home():
  return render_template("index.html")


@app.route('/table')
def show_table():
    # Get the current date
    current_date = datetime.now().strftime("%Y-%m-%d")
    # Read the CSV file to get attendance data
    attendance=[]
    try:
        with open(f"{current_date}.csv", newline="") as csv_file:
            csv_reader = csv.reader(csv_file)
            attendance = list(csv_reader)
    except FileNotFoundError:
        pass
    # Render the table.html template and pass the attendance data
    return render_template('attendance.html', attendance=attendance)



if __name__ == '__main__': 
  socket.run(app,host="0.0.0.0", port=7860)
    
###########################################################################
# @app.route('/table')
# def show_table():
#     # Get the current date
#     current_date = datetime.now().strftime("%Y-%m-%d")
#     # Read the CSV file to get attendance data
#     attendance=[]
#     try:
#         with open(f"{current_date}.csv", newline="") as csv_file:
#             csv_reader = csv.reader(csv_file)
#             attendance = list(csv_reader)
#     except FileNotFoundError:
#         pass
#     # Render the table.html template and pass the attendance data
#     return render_template('attendance.html', attendance=attendance)

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

   


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