| import os | |
| import cv2 | |
| import face_recognition | |
| import numpy as np | |
| from datetime import datetime | |
| import gradio as gr | |
| import pandas as pd | |
| import plotly.express as px | |
| import json | |
| class FaceRecognitionSystem: | |
| def __init__(self, images_folder='known_faces'): | |
| self.images_folder = images_folder | |
| self.known_face_encodings = [] | |
| self.known_face_names = [] | |
| self.attendance_file = 'attendance.json' | |
| self.load_face_database() | |
| def load_face_database(self): | |
| self.known_face_encodings = [] | |
| self.known_face_names = [] | |
| os.makedirs(self.images_folder, exist_ok=True) | |
| for filename in os.listdir(self.images_folder): | |
| if filename.endswith((".jpg", ".png", ".jpeg")): | |
| image_path = os.path.join(self.images_folder, filename) | |
| try: | |
| image = face_recognition.load_image_file(image_path) | |
| face_locations = face_recognition.face_locations(image) | |
| if face_locations: | |
| face_encoding = face_recognition.face_encodings(image, face_locations)[0] | |
| self.known_face_encodings.append(face_encoding) | |
| self.known_face_names.append(filename.split('.')[0]) | |
| except Exception as e: | |
| print(f"Hata: {filename} dosyası yüklenirken hata oluştu - {str(e)}") | |
| def record_attendance(self, name): | |
| current_time = datetime.now() | |
| attendance_data = self.load_attendance_data() | |
| current_date = current_time.strftime("%Y-%m-%d") | |
| current_time_str = current_time.strftime("%H:%M:%S") | |
| if current_date not in attendance_data: | |
| attendance_data[current_date] = {} | |
| if name not in attendance_data[current_date]: | |
| attendance_data[current_date][name] = [] | |
| attendance_data[current_date][name].append(current_time_str) | |
| self.save_attendance_data(attendance_data) | |
| return True | |
| def load_attendance_data(self): | |
| if os.path.exists(self.attendance_file): | |
| with open(self.attendance_file, 'r') as f: | |
| return json.load(f) | |
| return {} | |
| def save_attendance_data(self, data): | |
| with open(self.attendance_file, 'w') as f: | |
| json.dump(data, f, indent=4) | |
| def process_image(self, image): | |
| if image is None: | |
| return None, [] | |
| rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| face_locations = face_recognition.face_locations(rgb_image) | |
| face_encodings = face_recognition.face_encodings(rgb_image, face_locations) | |
| detected_names = [] | |
| for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings): | |
| matches = face_recognition.compare_faces(self.known_face_encodings, face_encoding, tolerance=0.6) | |
| name = "Bilinmeyen" | |
| if True in matches: | |
| first_match_index = matches.index(True) | |
| name = self.known_face_names[first_match_index] | |
| self.record_attendance(name) | |
| detected_names.append(name) | |
| cv2.rectangle(image, (left, top), (right, bottom), (0, 255, 0), 2) | |
| cv2.rectangle(image, (left, bottom - 35), (right, bottom), (0, 255, 0), cv2.FILLED) | |
| cv2.putText(image, name, (left + 6, bottom - 6), cv2.FONT_HERSHEY_DUPLEX, 0.6, (255, 255, 255), 1) | |
| return image, detected_names | |
| def get_attendance_stats(self): | |
| attendance_data = self.load_attendance_data() | |
| stats = [] | |
| for date, entries in attendance_data.items(): | |
| for name, times in entries.items(): | |
| stats.append({ | |
| 'date': date, | |
| 'name': name, | |
| 'total_entries': len(times), | |
| 'first_entry': min(times), | |
| 'last_entry': max(times) | |
| }) | |
| return pd.DataFrame(stats) | |
| def create_gradio_interface(): | |
| face_system = FaceRecognitionSystem() | |
| def process_uploaded_image(image): | |
| processed_image, detected_names = face_system.process_image(image) | |
| return processed_image, ", ".join(detected_names) if detected_names else "Kimse tespit edilmedi." | |
| def upload_face(image, name): | |
| if image is None or name.strip() == "": | |
| return "Lütfen hem resim hem de isim giriniz." | |
| os.makedirs(face_system.images_folder, exist_ok=True) | |
| file_path = os.path.join(face_system.images_folder, f"{name.strip()}.jpg") | |
| cv2.imwrite(file_path, image) | |
| face_system.load_face_database() | |
| return f"{name} başarıyla kaydedildi!" | |
| def get_attendance_report(): | |
| df = face_system.get_attendance_stats() | |
| if df.empty: | |
| return "Henüz katılım kaydı bulunmamaktadır." | |
| fig = px.bar(df, x='name', y='total_entries', | |
| title='Kişi Bazlı Toplam Katılım', | |
| labels={'name': 'İsim', 'total_entries': 'Toplam Katılım'}) | |
| table_html = df.to_html(classes='table table-striped', index=False) | |
| return f""" | |
| <div style='margin-bottom: 20px;'> | |
| {fig.to_html()} | |
| </div> | |
| <div> | |
| {table_html} | |
| </div> | |
| """ | |
| with gr.Blocks(theme=gr.themes.Soft()) as interface: | |
| gr.Markdown("# 🎥 Yüz Tanıma ve Katılım Takip Sistemi--ERAY COŞKUN") | |
| with gr.Tabs(): | |
| with gr.Tab("Yüz Tanıma"): | |
| gr.Markdown("## 📷 Resim Yükle ve Tanı") | |
| image_input = gr.Image(type="numpy", label="Resim Yükle") | |
| output_image = gr.Image(label="İşlenmiş Resim") | |
| output_text = gr.Textbox(label="Tespit Edilen Kişiler") | |
| process_button = gr.Button("Resmi İşle") | |
| process_button.click( | |
| process_uploaded_image, | |
| inputs=[image_input], | |
| outputs=[output_image, output_text] | |
| ) | |
| with gr.Tab("Yeni Kişi Kaydı"): | |
| gr.Markdown("## 👤 Yeni Kişi Ekle") | |
| with gr.Row(): | |
| new_image_input = gr.Image(type="numpy", label="Kişi Fotoğrafı") | |
| name_input = gr.Textbox(label="Kişi Adı") | |
| upload_button = gr.Button("Kaydet") | |
| upload_result = gr.Textbox(label="Sonuç") | |
| upload_button.click( | |
| upload_face, | |
| inputs=[new_image_input, name_input], | |
| outputs=[upload_result] | |
| ) | |
| with gr.Tab("Katılım Raporu"): | |
| gr.Markdown("## 📊 Katılım İstatistikleri") | |
| refresh_button = gr.Button("Raporu Yenile") | |
| report_html = gr.HTML() | |
| refresh_button.click( | |
| get_attendance_report, | |
| outputs=[report_html] | |
| ) | |
| return interface | |
| if __name__ == "__main__": | |
| interface = create_gradio_interface() | |
| interface.launch() |