app.py

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()