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import cv2
import numpy as np
import os
import pandas as pd
import streamlit as st
import matplotlib.pyplot as plt
atnd = []
# Train the face recognition model using the collected dataset
def train_model():
data_path = 'data'
face_classifier = cv2.CascadeClassifier("./Student_Attentiveness/haarcascade_frontalface_default.xml")
training_data = []
labels = []
for root, dirs, files in os.walk(data_path):
for file in files:
if file.endswith('jpg'):
path = os.path.join(root, file)
label = int(path.split('.')[1])
image = cv2.imread(path)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
face = face_classifier.detectMultiScale(gray, 1.3, 5)
if face is not ():
for (x, y, w, h) in face:
cropped_face = gray[y:y + h, x:x + w]
training_data.append(cropped_face)
labels.append(label)
labels = np.array(labels)
model = cv2.face.LBPHFaceRecognizer_create()
model.train(training_data, labels)
return model
# Implement the student attention monitoring system
def monitor_attention():
face_classifier = cv2.CascadeClassifier("./Student_Attentiveness/haarcascade_frontalface_default.xml")
eye_classifier = cv2.CascadeClassifier("./Student_Attentiveness/haarcascade_eye.xml")
model = train_model()
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_classifier.detectMultiScale(gray, 1.3, 5)
if faces is not ():
for (x, y, w, h) in faces:
cropped_face = gray[y:y + h, x:x + w]
label, confidence = model.predict(cropped_face)
if confidence < 100:
eyes = eye_classifier.detectMultiScale(cropped_face)
if eyes is not ():
for (ex, ey, ew, eh) in eyes:
atnd.append(1)
else:
atnd.append(0)
# cv2.imshow('Student Attention', frame)
df = pd.DataFrame(atnd, columns=['Attention'])
df.to_csv('./attention.csv')
if cv2.waitKey(1) == 13:
break
cap.release()
cv2.destroyAllWindows()
# Streamlit app
st.title('Student Attention Monitoring System')
# Start monitoring attention
monitor_attention()
# Display the graph of attentiveness output live
st.line_chart(pd.DataFrame(atnd, columns=['Attention']))
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