Update app.py

app.py

@@ -1,220 +1,199 @@
-from flask import Flask, render_template, request, Response, jsonify, redirect, url_for
-import cv2
-import os
-import numpy as np
-import pickle
-import base64
-from datetime import datetime
-
-app = Flask(__name__)
-
-# Konfigurasi
-FACE_DATA_DIR = 'face_data'
-FACE_CASCADE_PATH = cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'
-
-# Pastikan direktori face_data ada
-if not os.path.exists(FACE_DATA_DIR):
-    os.makedirs(FACE_DATA_DIR)
-
-# Load face cascade
-face_cascade = cv2.CascadeClassifier(FACE_CASCADE_PATH)
-
-# Global variables
-camera = None
-face_recognizer = cv2.face.LBPHFaceRecognizer_create()
-is_trained = False
-
-def load_face_data():
-    """Load data wajah yang sudah disimpan"""
-    global is_trained
-    faces = []
-    labels = []
-    names = []
-    
-    if os.path.exists(os.path.join(FACE_DATA_DIR, 'names.pkl')):
-        with open(os.path.join(FACE_DATA_DIR, 'names.pkl'), 'rb') as f:
-            names = pickle.load(f)
-    
-    for idx, name in enumerate(names):
-        face_dir = os.path.join(FACE_DATA_DIR, name)
-        if os.path.exists(face_dir):
-            for filename in os.listdir(face_dir):
-                if filename.endswith('.jpg'):
-                    img_path = os.path.join(face_dir, filename)
-                    img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
-                    faces.append(img)
-                    labels.append(idx)
-    
-    if faces:
-        face_recognizer.train(faces, np.array(labels))
-        is_trained = True
-        return names
-    
-    return []
-
-def get_camera():
-    """Inisialisasi kamera"""
-    global camera
-    if camera is None:
-        camera = cv2.VideoCapture(0)
-    return camera
-
-@app.route('/')
-def index():
-    """Halaman utama"""
-    names = load_face_data()
-    return render_template('index.html', registered_faces=names)
-
-@app.route('/register')
-def register():
-    """Halaman registrasi wajah baru"""
-    return render_template('register.html')
-
-@app.route('/recognize')
-def recognize():
-    """Halaman pengenalan wajah"""
-    return render_template('recognize.html')
-
-@app.route('/video_feed')
-def video_feed():
-    """Stream video untuk registrasi"""
-    def generate():
-        camera = get_camera()
-        while True:
-            success, frame = camera.read()
-            if not success:
-                break
-            
-            # Convert frame ke format yang bisa dikirim
-            ret, buffer = cv2.imencode('.jpg', frame)
-            frame = buffer.tobytes()
-            yield (b'--frame\r\n'
-                   b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
-    
-    return Response(generate(), mimetype='multipart/x-mixed-replace; boundary=frame')
-
-@app.route('/recognition_feed')
-def recognition_feed():
-    """Stream video untuk pengenalan wajah"""
-    def generate():
-        camera = get_camera()
-        names = load_face_data()
-        
-        while True:
-            success, frame = camera.read()
-            if not success:
-                break
-            
-            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-            faces = face_cascade.detectMultiScale(gray, 1.3, 5)
-            
-            for (x, y, w, h) in faces:
-                cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
-                
-                if is_trained and names:
-                    roi_gray = gray[y:y+h, x:x+w]
-                    roi_gray = cv2.resize(roi_gray, (100, 100))
-                    
-                    id_, confidence = face_recognizer.predict(roi_gray)
-                    
-                    if confidence < 100:  # Threshold untuk confidence
-                        name = names[id_]
-                        confidence_text = f"{name} ({round(100-confidence)}%)"
-                    else:
-                        confidence_text = "Unknown"
-                    
-                    cv2.putText(frame, confidence_text, (x, y-10), 
-                              cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2)
-            
-            ret, buffer = cv2.imencode('.jpg', frame)
-            frame = buffer.tobytes()
-            yield (b'--frame\r\n'
-                   b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
-    
-    return Response(generate(), mimetype='multipart/x-mixed-replace; boundary=frame')
-
-@app.route('/capture_face', methods=['POST'])
-def capture_face():
-    """Capture wajah untuk registrasi"""
-    name = request.json.get('name', '').strip()
-    
-    if not name:
-        return jsonify({'error': 'Nama tidak boleh kosong'})
-    
-    camera = get_camera()
-    success, frame = camera.read()
-    
-    if not success:
-        return jsonify({'error': 'Gagal mengambil gambar dari kamera'})
-    
-    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
-    
-    if len(faces) == 0:
-        return jsonify({'error': 'Tidak ada wajah yang terdeteksi'})
-    
-    if len(faces) > 1:
-        return jsonify({'error': 'Terdeteksi lebih dari satu wajah'})
-    
-    # Ambil wajah pertama
-    (x, y, w, h) = faces[0]
-    face_roi = gray[y:y+h, x:x+w]
-    face_roi = cv2.resize(face_roi, (100, 100))
-    
-    # Buat direktori untuk nama ini
-    person_dir = os.path.join(FACE_DATA_DIR, name)
-    if not os.path.exists(person_dir):
-        os.makedirs(person_dir)
-    
-    # Simpan gambar wajah
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    filename = f"{timestamp}.jpg"
-    cv2.imwrite(os.path.join(person_dir, filename), face_roi)
-    
-    # Update daftar nama
-    names_file = os.path.join(FACE_DATA_DIR, 'names.pkl')
-    if os.path.exists(names_file):
-        with open(names_file, 'rb') as f:
-            names = pickle.load(f)
-    else:
-        names = []
-    
-    if name not in names:
-        names.append(name)
-        with open(names_file, 'wb') as f:
-            pickle.dump(names, f)
-    
-    # Retrain model
-    load_face_data()
-    
-    return jsonify({'success': f'Wajah {name} berhasil didaftarkan'})
-
-@app.route('/delete_face/<name>')
-def delete_face(name):
-    """Hapus data wajah"""
-    person_dir = os.path.join(FACE_DATA_DIR, name)
-    
-    if os.path.exists(person_dir):
-        # Hapus semua file di direktori
-        for filename in os.listdir(person_dir):
-            os.remove(os.path.join(person_dir, filename))
-        os.rmdir(person_dir)
-        
-        # Update daftar nama
-        names_file = os.path.join(FACE_DATA_DIR, 'names.pkl')
-        if os.path.exists(names_file):
-            with open(names_file, 'rb') as f:
-                names = pickle.load(f)
-            
-            if name in names:
-                names.remove(name)
-                with open(names_file, 'wb') as f:
-                    pickle.dump(names, f)
-        
-        # Retrain model
-        load_face_data()
-    
-    return redirect(url_for('index'))
-
-if __name__ == '__main__':
-    app.run(debug=True, host='0.0.0.0', port=5000)
+from flask import Flask, render_template, request, Response, jsonify, redirect, url_for
+import cv2
+import os
+import numpy as np
+import pickle
+from datetime import datetime
+
+# ==== Bagian Flask (kode kamu tetap) ====
+flask_app = Flask(__name__)
+
+FACE_DATA_DIR = 'face_data'
+FACE_CASCADE_PATH = cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'
+
+if not os.path.exists(FACE_DATA_DIR):
+    os.makedirs(FACE_DATA_DIR)
+
+face_cascade = cv2.CascadeClassifier(FACE_CASCADE_PATH)
+camera = None
+face_recognizer = cv2.face.LBPHFaceRecognizer_create()
+is_trained = False
+
+def load_face_data():
+    global is_trained
+    faces = []
+    labels = []
+    names = []
+    
+    if os.path.exists(os.path.join(FACE_DATA_DIR, 'names.pkl')):
+        with open(os.path.join(FACE_DATA_DIR, 'names.pkl'), 'rb') as f:
+            names = pickle.load(f)
+    
+    for idx, name in enumerate(names):
+        face_dir = os.path.join(FACE_DATA_DIR, name)
+        if os.path.exists(face_dir):
+            for filename in os.listdir(face_dir):
+                if filename.endswith('.jpg'):
+                    img_path = os.path.join(face_dir, filename)
+                    img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
+                    faces.append(img)
+                    labels.append(idx)
+    
+    if faces:
+        face_recognizer.train(faces, np.array(labels))
+        is_trained = True
+        return names
+    return []
+
+def get_camera():
+    global camera
+    if camera is None:
+        camera = cv2.VideoCapture(0)
+    return camera
+
+@flask_app.route('/')
+def index():
+    names = load_face_data()
+    return render_template('index.html', registered_faces=names)
+
+@flask_app.route('/register')
+def register():
+    return render_template('register.html')
+
+@flask_app.route('/recognize')
+def recognize():
+    return render_template('recognize.html')
+
+@flask_app.route('/video_feed')
+def video_feed():
+    def generate():
+        camera = get_camera()
+        while True:
+            success, frame = camera.read()
+            if not success:
+                break
+            ret, buffer = cv2.imencode('.jpg', frame)
+            frame = buffer.tobytes()
+            yield (b'--frame\r\n'
+                   b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
+    return Response(generate(), mimetype='multipart/x-mixed-replace; boundary=frame')
+
+@flask_app.route('/recognition_feed')
+def recognition_feed():
+    def generate():
+        camera = get_camera()
+        names = load_face_data()
+        
+        while True:
+            success, frame = camera.read()
+            if not success:
+                break
+            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+            faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+            
+            for (x, y, w, h) in faces:
+                cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
+                
+                if is_trained and names:
+                    roi_gray = gray[y:y+h, x:x+w]
+                    roi_gray = cv2.resize(roi_gray, (100, 100))
+                    id_, confidence = face_recognizer.predict(roi_gray)
+                    
+                    if confidence < 100:
+                        name = names[id_]
+                        confidence_text = f"{name} ({round(100-confidence)}%)"
+                    else:
+                        confidence_text = "Unknown"
+                    
+                    cv2.putText(frame, confidence_text, (x, y-10), 
+                              cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2)
+            
+            ret, buffer = cv2.imencode('.jpg', frame)
+            frame = buffer.tobytes()
+            yield (b'--frame\r\n'
+                   b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
+    return Response(generate(), mimetype='multipart/x-mixed-replace; boundary=frame')
+
+@flask_app.route('/capture_face', methods=['POST'])
+def capture_face():
+    name = request.json.get('name', '').strip()
+    if not name:
+        return jsonify({'error': 'Nama tidak boleh kosong'})
+    
+    camera = get_camera()
+    success, frame = camera.read()
+    if not success:
+        return jsonify({'error': 'Gagal mengambil gambar dari kamera'})
+    
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+    
+    if len(faces) == 0:
+        return jsonify({'error': 'Tidak ada wajah yang terdeteksi'})
+    if len(faces) > 1:
+        return jsonify({'error': 'Terdeteksi lebih dari satu wajah'})
+    
+    (x, y, w, h) = faces[0]
+    face_roi = gray[y:y+h, x:x+w]
+    face_roi = cv2.resize(face_roi, (100, 100))
+    
+    person_dir = os.path.join(FACE_DATA_DIR, name)
+    if not os.path.exists(person_dir):
+        os.makedirs(person_dir)
+    
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    filename = f"{timestamp}.jpg"
+    cv2.imwrite(os.path.join(person_dir, filename), face_roi)
+    
+    names_file = os.path.join(FACE_DATA_DIR, 'names.pkl')
+    if os.path.exists(names_file):
+        with open(names_file, 'rb') as f:
+            names = pickle.load(f)
+    else:
+        names = []
+    
+    if name not in names:
+        names.append(name)
+        with open(names_file, 'wb') as f:
+            pickle.dump(names, f)
+    
+    load_face_data()
+    return jsonify({'success': f'Wajah {name} berhasil didaftarkan'})
+
+@flask_app.route('/delete_face/<name>')
+def delete_face(name):
+    person_dir = os.path.join(FACE_DATA_DIR, name)
+    if os.path.exists(person_dir):
+        for filename in os.listdir(person_dir):
+            os.remove(os.path.join(person_dir, filename))
+        os.rmdir(person_dir)
+        
+        names_file = os.path.join(FACE_DATA_DIR, 'names.pkl')
+        if os.path.exists(names_file):
+            with open(names_file, 'rb') as f:
+                names = pickle.load(f)
+            if name in names:
+                names.remove(name)
+                with open(names_file, 'wb') as f:
+                    pickle.dump(names, f)
+        load_face_data()
+    return redirect(url_for('index'))
+
+# ==== Bungkus Flask ke FastAPI untuk Hugging Face ====
+from fastapi import FastAPI
+from fastapi.middleware.wsgi import WSGIMiddleware
+
+app = FastAPI()
+
+# Endpoint bawaan FastAPI
+@app.get("/hello")
+def greet_json():
+    return {"Hello": "World!"}
+
+# Mount Flask app ke FastAPI
+app.mount("/", WSGIMiddleware(flask_app))
+
+# Jalankan dengan uvicorn kalau lokal
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)