Update app.py

app.py

@@ -8,23 +8,101 @@ import pandas as pd
 import plotly.express as px
 import json
 
-# ... (FaceRecognitionSystem sınıfı ve diğer fonksiyonlar aynı kalacak)
+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_webcam(frame):
-        processed_frame, detected_names = face_system.process_frame(frame)
-        return processed_frame
+    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, cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
-
+        cv2.imwrite(file_path, image)
         face_system.load_face_database()
         return f"{name} başarıyla kaydedildi!"
 
@@ -32,13 +110,10 @@ def create_gradio_interface():
         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()}
@@ -49,30 +124,31 @@ def create_gradio_interface():
         """
 
     with gr.Blocks(theme=gr.themes.Soft()) as interface:
-        gr.Markdown("# 🎥 Yüz Tanıma ve Katılım Takip Sistemi--ERAY COSKUN")
+        gr.Markdown("# 🎥 Yüz Tanıma ve Katılım Takip Sistemi--ERAY COŞKUN")
 
         with gr.Tabs():
-            with gr.Tab("Canlı Tanıma"):
-                gr.Markdown("## 📹 Kamera Görüntüsü")
-                webcam = gr.Image(shape=(480, 640))  # Değiştirildi
-                output_image = gr.Image()
-                capture_button = gr.Button("Görüntü Yakala")  # Eklendi
-
-                def capture_and_process(image):
-                    return process_webcam(image)
-
-                capture_button.click(capture_and_process, inputs=[webcam], outputs=[output_image])  # Eklendi
+            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():
-                    image_input = gr.Image(type="numpy", label="Kişi Fotoğrafı")
+                    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=[image_input, name_input],
+                    inputs=[new_image_input, name_input],
                     outputs=[upload_result]
                 )