Update app.py

app.py

@@ -28,16 +28,22 @@ yolo_model = project.version(model_version).model
 
 # ========== Fungsi Deteksi Kombinasi ==========
 def detect_combined(image):
-    # Simpan gambar input ke file sementara
+    # Konversi gambar ke format numpy array
+    image_np = np.array(image)
+
+    # Resize gambar ke 640x640
+    image_resized = cv2.resize(image_np, (640, 640))
+
+    # Simpan gambar input yang sudah di-resize ke file sementara
     with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
-        image.save(temp_file, format="JPEG")
+        cv2.imwrite(temp_file.name, image_resized)
         temp_path = temp_file.name
 
     try:
-        # ========== [1] YOLO: Deteksi Produk Nestlé (Per Class) ==========
+        # ========== [1] YOLO: Deteksi Produk Nestlé ==========
         yolo_pred = yolo_model.predict(temp_path, confidence=50, overlap=80).json()
 
-        # Hitung per class Nestlé dan simpan bounding box (format: (x_center, y_center, width, height))
+        # Hitung per kelas dan simpan bounding box (format: (x_center, y_center, width, height))
         nestle_class_count = {}
         nestle_boxes = []
         for pred in yolo_pred['predictions']:
@@ -98,7 +104,7 @@ def detect_combined(image):
             cv2.putText(img, pred['class'], (int(x - w/2), int(y - h/2 - 10)),
                         cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
 
-        # Gambar bounding box untuk kompetitor (Merah) dengan label 'unclassified' jika sesuai
+        # Gambar bounding box untuk kompetitor (Merah)
         for comp in competitor_boxes:
             x1, y1, x2, y2 = comp['box']
             unclassified_classes = ["cans"]
@@ -117,6 +123,7 @@ def detect_combined(image):
     finally:
         os.remove(temp_path)
 
+
 def is_overlap(box1, boxes2, threshold=0.3):
     """
     Fungsi untuk mendeteksi overlap bounding box.