Create app.py

app.py

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+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()