app.py

import streamlit as st
from PIL import Image
import face_recognition
import cv2
import numpy as np
import os
import sqlite3
from datetime import datetime

# Establish connection to SQLite database
conn = sqlite3.connect('attendance.db')
c = conn.cursor()

# Create a table for storing attendance if it doesn't exist
c.execute('''CREATE TABLE IF NOT EXISTS attendance 
             (id INTEGER PRIMARY KEY AUTOINCREMENT, name TEXT, time TEXT)''')

st.title("AIMLJan24 - Face Recognition")

# Load images for face recognition
Images = []   
classnames = []  
directory = "photos"
myList = os.listdir(directory)
current_datetime = datetime.now().strftime("%Y-%m-%d %H:%M:%S")

st.write("Photographs found in folder : ")
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)
        st.write(os.path.splitext(cls)[0])
        classnames.append(os.path.splitext(cls)[0])

# Load images for face recognition
encodeListknown = [face_recognition.face_encodings(img)[0] for img in Images]

# camera to take photo of user in question
file_name = st.camera_input("Upload image")

def add_attendance(name):
    username = name
    c.execute("INSERT INTO attendance (name, time) VALUES (?, ?)", (username, current_datetime))
    conn.commit()

if file_name is not None:
    col1, col2 = st.columns(2)

    test_image = Image.open(file_name)
    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)
    
    # List to store recognized names for all faces in the image
    recognized_names = []

    # Iterate over each face location
    for faceLoc in facesCurFrame:
        # Extract the coordinates of the face location
        top, right, bottom, left = faceLoc
        
        # Crop the face region from the image
        face_img = imgS[top:bottom, left:right]

        # Encode the face
        face_encoding = face_recognition.face_encodings(face_img)[0]

        # Compare the face encoding with known face encodings
        matches = face_recognition.compare_faces(encodeListknown, face_encoding)
        
        # Initialize name as Unknown
        name = "Unknown"
        
        # Check if there's a match with known faces
        if True in matches:
            matchIndex = matches.index(True)
            name = classnames[matchIndex].upper()
        
        # Append recognized name to the list
        recognized_names.append(name)
        
        # Draw rectangle around the face
        cv2.rectangle(image, (left*4, top*4), (right*4, bottom*4), (0, 255, 0), 2)
        cv2.putText(image, name, (left*4 + 6, bottom*4 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
        
        # Store attendance in SQLite database
        add_attendance(name)
            
    # Display the image with recognized faces
    st.image(image, use_column_width=True, output_format="PNG")

    # Display recognized names
    st.write("Recognized Names:")
    for i, name in enumerate(recognized_names):
        st.write(f"Face {i+1}: {name}")
    else:
        st.warning("No faces detected in the image. Face recognition failed.")