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import gradio as gr
import face_recognition
import cv2
import numpy as np
from PIL import Image
import pickle
import firebase_admin
from firebase_admin import credentials
from firebase_admin import db
from firebase_admin import storage
# Initialize Firebase
cred = credentials.Certificate("serviceAccountKey.json") # Update with your credentials path
firebase_app = firebase_admin.initialize_app(cred, {
'databaseURL': 'https://faceantendancerealtime-default-rtdb.firebaseio.com/',
'storageBucket': 'faceantendancerealtime.appspot.com'
})
bucket = storage.bucket()
# Function to download face encodings from Firebase Storage
def download_encodings():
blob = bucket.blob('EncodeFile.p')
blob.download_to_filename('EncodeFile.p')
with open('EncodeFile.p', 'rb') as file:
return pickle.load(file)
encodeListKnownWithIds = download_encodings()
encodeListKnown, studentsIds = encodeListKnownWithIds
def recognize_face(input_image):
# Convert PIL Image to numpy array
img = np.array(input_image)
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
# Detect faces and encode
face_locations = face_recognition.face_locations(img)
face_encodings = face_recognition.face_encodings(img, face_locations)
# Initialize the database reference
ref = db.reference('Students')
# Recognize faces and fetch data from the database
results = []
for face_encoding in face_encodings:
matches = face_recognition.compare_faces(encodeListKnown, face_encoding)
name = "Unknown"
student_info = {}
face_distances = face_recognition.face_distance(encodeListKnown, face_encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
student_id = studentsIds[best_match_index]
student_info = ref.child(student_id).get()
if student_info:
name = student_info['name']
results.append(student_info)
else:
results.append({'name': 'Unknown'})
# Draw rectangles around the faces
for (top, right, bottom, left), name in zip(face_locations, [student_info.get('name', 'Unknown') for student_info in results]):
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 back to PIL Image
pil_img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
return pil_img, results
# Define a function to handle webcam images
def process_webcam_image(image):
# Convert the base64 image to a format that can be processed
# Process the image through the face recognition function
return recognize_face(image)
# Gradio interface
iface = gr.Interface(
fn=recognize_face,
inputs=gr.Image(type="pil"),
outputs=[gr.Image(type="pil"), gr.JSON(label="Student Information")],
title="Face Recognition Attendance System",
description="Upload an image to identify individuals."
)
if __name__ == "__main__":
iface.launch(debug=True,inline=False)
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