face_rec.py

import face_recognition
import cv2
import numpy as np
import csv
import os
import streamlit as st
from datetime import datetime
from ultralytics import YOLO

# Load YOLO model
yolo_model = YOLO("yolov8/yolov8n-face.pt")

# Load and encode images
image_dir = "photos/"
encodings = {}
known_face_names = []

for fname in os.listdir(image_dir):
    file_path = os.path.join(image_dir, fname)
    image = face_recognition.load_image_file(file_path)
    encoding = face_recognition.face_encodings(image)
    if encoding:
        encodings[fname] = encoding[0]
        known_face_names.append(fname.split('.')[0])  # Assuming the names are derived from file names

# Known face encodings and names
known_face_encodings = list(encodings.values())
known_face_names = [name.split('.')[0] for name in encodings.keys()]

# Streamlit app
st.title("Face Recognition Attendance System")
uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])

if uploaded_image is not None:
    # Read and process the uploaded image
    img = face_recognition.load_image_file(uploaded_image)

    # Create or open a CSV file for the current date
    now = datetime.now()
    current_date = now.strftime("%Y-%m-%d")
    attendance_file = current_date + '.csv'

    # Initialize students list
    students = known_face_names.copy()
    attendance_records = []  # Store attendance records

    # Get YOLO results
    yolo_results = yolo_model(img)
    yolo_boxes = yolo_results[0].boxes

    # Convert YOLO bounding boxes to the format (top, right, bottom, left) for face_recognition
    face_locations = []
    for box in yolo_boxes:
        x1, y1, x2, y2 = map(int, box.xyxy[0])  # YOLO format: (x1, y1, x2, y2)
        face_locations.append((y1, x2, y2, x1))  # Convert to face_recognition format

    # Find face encodings for YOLO-detected faces
    face_encodings = face_recognition.face_encodings(img, face_locations)
    
    face_names = []  # List to hold names for all detected faces

    for face_encoding in face_encodings:
        matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
        name = "Unknown"  # Default to "Unknown" for unmatched faces

        if True in matches:  # If there is a match
            best_match_index = np.argmin(face_recognition.face_distance(known_face_encodings, face_encoding))
            name = known_face_names[best_match_index]  # Get the name of the matched face

            # Attendance tracking
            if name in students:
                students.remove(name)
                # Record attendance in the list
                attendance_records.append([name, 'present'])
        face_names.append(name)  # Append the name to the list (either known or "Unknown")

    # Draw rectangles and labels for all detected faces
    for (top, right, bottom, left), name in zip(face_locations, face_names):
        cv2.rectangle(img, (left, top), (right, bottom), (0, 255, 0), 2)
        cv2.putText(img, name, (left, bottom + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 2)

    # Display the image in Streamlit
    st.image(img, caption='Processed Image', channels="RGB")

    # Write to CSV
    with open(attendance_file, 'w', newline='') as f:
        lnwriter = csv.writer(f)
        lnwriter.writerow(["Name", "Status"])  # Write header
        # Write present records
        lnwriter.writerows(attendance_records)
        # Write absent students
        for name in students:
            lnwriter.writerow([name, 'absent'])