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| import gradio as gr | |
| import face_recognition | |
| import cv2 | |
| import numpy as np | |
| import os | |
| from PIL import Image | |
| import io | |
| import firebase_admin | |
| from firebase_admin import credentials | |
| from firebase_admin import db | |
| from firebase_admin import storage | |
| from datetime import datetime | |
| # Initialize Firebase | |
| cred = credentials.Certificate("serviceAccountKey.json") # Update the path to your Firebase credentials | |
| firebase_admin.initialize_app(cred, { | |
| 'databaseURL': 'https://faceantendancerealtime-default-rtdb.firebaseio.com/', | |
| 'storageBucket': 'faceantendancerealtime.appspot.com' | |
| }) | |
| bucket = storage.bucket() | |
| # Load the known face encodings and their IDs from Firebase | |
| def load_known_encodings(): | |
| # Code to download the 'EncodeFile.p' from Firebase Storage | |
| # Assume 'EncodeFile.p' is already uploaded to Firebase Storage | |
| blob = bucket.blob('EncodeFile.p') | |
| blob.download_to_filename('/tmp/EncodeFile.p') | |
| with open('/tmp/EncodeFile.p', 'rb') as file: | |
| encodeListKnownWithIds = pickle.load(file) | |
| return encodeListKnownWithIds | |
| encodeListKnownWithIds = load_known_encodings() | |
| encodeListKnown, studentsIds = encodeListKnownWithIds | |
| def recognize_face(input_image): | |
| # Convert the PIL Image to a numpy array | |
| img = np.array(input_image) | |
| # Convert the image to RGB | |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) | |
| # Resize image for faster processing | |
| img_small = cv2.resize(img, (0, 0), None, 0.25, 0.25) | |
| # Find faces in the image | |
| face_locations = face_recognition.face_locations(img_small) | |
| face_encodings = face_recognition.face_encodings(img_small, face_locations) | |
| # Convert the coordinates to full scale since the image was scaled to 1/4 size | |
| face_locations = [(top*4, right*4, bottom*4, left*4) for top, right, bottom, left in face_locations] | |
| # Recognize faces | |
| for face_encoding, (top, right, bottom, left) in zip(face_encodings, face_locations): | |
| matches = face_recognition.compare_faces(encodeListKnown, face_encoding) | |
| name = "Unknown" | |
| # Use the known face with the smallest distance to the new face | |
| face_distances = face_recognition.face_distance(encodeListKnown, face_encoding) | |
| best_match_index = np.argmin(face_distances) | |
| if matches[best_match_index]: | |
| name = studentsIds[best_match_index] | |
| # Draw rectangles and names on the original image | |
| cv2.rectangle(img, (left, top), (right, bottom), (0, 0, 255), 2) | |
| cv2.putText(img, name, (left + 6, bottom - 6), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1) | |
| # Convert the image back to PIL format | |
| return Image.fromarray(img) | |
| # Define the Gradio interface | |
| iface = gr.Interface( | |
| fn=recognize_face, | |
| inputs=gr.inputs.Image(type="pil"), | |
| outputs=gr.outputs.Image(type="pil"), | |
| title="Face Recognition Attendance System", | |
| description="Upload an image to identify registered students." | |
| ) | |
| # Run the Gradio app | |
| if __name__ == "__main__": | |
| iface.launch(inline=False) | |