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from PIL import Image
from flask import *
import face_recognition
import cv2
import numpy as np
import csv
from datetime import datetime
from matplotlib import pyplot as plt # this lets you draw inline pictures in the notebooks
import pylab # this allows you to control figure size
pylab.rcParams['figure.figsize'] = (10.0, 8.0) # this controls figure size in the notebook
import io
import streamlit as st
bytes_data=None
app = Flask(__name__)
@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]
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)
def run_face_recognition():
video_capture = cv2.VideoCapture(0)
s = True
existing_names = set(row[0] for row in csv.reader(csv_file)) # Collect existing names from the CSV file
while s:
_, frame = video_capture.read()
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
rgb_small_frame = small_frame[:, :, ::-1]
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_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)
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
s = False # Set s to False to exit the loop after recording attendance
break # Break the loop once attendance has been recorded for a name
cv2.imshow("Attendance System", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
cv2.destroyAllWindows()
csv_file.close()
# Call the function to run face recognition
run_face_recognition()
return redirect(url_for('show_table'))
@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 index():
return render_template('index.html')
if __name__ == '__main__':
# Start Flask application
app.run(debug=True)
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