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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.")
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