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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("path/to/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)
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