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import streamlit as st
import numpy as np
import cv2
from PIL import Image
import requests
import face_recognition
from keras.models import load_model
import os
# Set page title and description
st.set_page_config(
page_title="Face Recognition Attendance System With Emotion Detection",
page_icon="📷",
layout="centered",
initial_sidebar_state="collapsed"
)
st.title("Attendance System Using Face Recognition and Emotion Detection 📷")
st.markdown("This app recognizes faces in an image, detects emotions, and updates attendance records with the current timestamp.")
# Load emotion detection model
@st.cache_resource
def load_emotion_model():
model = load_model('CNN_Model_acc_75.h5')
return model
emotion_model = load_emotion_model()
# Emotion labels
emotion_labels = ['angry', 'fear', 'happy', 'neutral', 'sad', 'surprise']
# Load known faces and classnames
Images = []
classnames = []
directory = "photos"
myList = os.listdir(directory)
for cls in myList:
if os.path.splitext(cls)[1] in [".jpg", ".jpeg"]:
img_path = os.path.join(directory, cls)
curImg = cv2.imread(img_path)
Images.append(curImg)
classnames.append(os.path.splitext(cls)[0])
# Function to update attendance data
def update_data(name, emotion):
url = "https://huggingface.glitch.me"
url1 = "/update"
data = {'name': name, 'emotion': emotion}
try:
response = requests.post(url + url1, data=data)
if response.status_code == 200:
st.success("Attendance updated successfully!")
else:
st.warning("Failed to update attendance!")
except Exception as e:
st.error(f"Error updating attendance: {e}")
# Function to display image with overlay
def display_image_with_overlay(image, name, emotion):
cv2.putText(image, f"{name} is feeling {emotion}", (20, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
st.image(image, use_column_width=True, output_format="PNG")
# Load images for face recognition
encodeListknown = [face_recognition.face_encodings(img)[0] for img in Images]
# Upload image using the file uploader
img_file_buffer = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
if img_file_buffer is not None:
test_image = Image.open(img_file_buffer)
image = np.asarray(test_image)
imgS = cv2.resize(image, (0, 0), None, 0.25, 0.25)
imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)
facesCurFrame = face_recognition.face_locations(imgS)
encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)
name = "Unknown" # Default name for unknown faces
match_found = False # Flag to track if a match is found
# Emotion detection part
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
emotion = "Neutral" # Default emotion
if len(encodesCurFrame) > 0:
for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
# Emotion detection
y1, x2, y2, x1 = faceLoc
roi = imgS[y1:y2, x1:x2]
roi = cv2.resize(roi, (48, 48)) # Resize to fit model
roi = cv2.cvtColor(roi, cv2.COLOR_BGR2RGB)
roi = np.expand_dims(roi, axis=0) / 255.0 # Preprocess the image
emotion_predictions = emotion_model.predict(roi)
emotion = emotion_labels[np.argmax(emotion_predictions)]
# Face recognition logic
matches = face_recognition.compare_faces(encodeListknown, encodeFace)
faceDis = face_recognition.face_distance(encodeListknown, encodeFace)
matchIndex = np.argmin(faceDis)
if matches[matchIndex]:
name = classnames[matchIndex].upper()
update_data(name, emotion)
match_found = True
y1, x2, y2, x1 = faceLoc
y1, x2, y2, x1 = y1 * 4, x2 * 4, y2 * 4, x1 * 4
cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.rectangle(image, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED)
cv2.putText(image, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
display_image_with_overlay(image, name, emotion)
if match_found:
st.success(f"Face recognized: {name} and Emotion: {emotion}")
else:
st.warning("Face not detected, or no match found in the database.")
else:
st.warning("No faces detected in the image. Face recognition failed.")
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