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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 | |
| # 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) | |