Update app.py

app.py

@@ -11,30 +11,40 @@ dino_model = pipeline("zero-shot-object-detection", model="IDEA-Research/groundi
 
 # YOLOv8-Erkennung
 def detect_with_yolo(image: Image.Image):
-    results = yolo_model(np.array(image))[0]
-    return Image.fromarray(results.plot())
+    try:
+        results = yolo_model(np.array(image))[0]
+        return Image.fromarray(results.plot())
+    except Exception as e:
+        logging.error(f"YOLOv8 Fehler: {e}")
+        return image
 
 # Grounding DINO-Erkennung
 def detect_with_grounding_dino(image: Image.Image, prompt=["license plate.", "number plate.", "car plate.", "vehicle registration plate."]):
-    results = dino_model(image, candidate_labels=prompt)
-    image_np = np.array(image).copy()
+    try:
+        results = dino_model(image, candidate_labels=prompt)
+        image_np = np.array(image).copy()
 
-    if not results:
-        return Image.fromarray(image_np)
+        if not results:
+            return image  # Keine Erkennung = Originalbild
 
-    results = [result for result in results if result["score"] > 0.4]
+        results = [result for result in results if result["score"] > 0.4]
 
-    for result in results:
-        box = result["box"]
-        score = result["score"]
-        label = "license plate"
+        if not results:
+            return image
 
-        x1, y1, x2, y2 = int(box["xmin"]), int(box["ymin"]), int(box["xmax"]), int(box["ymax"])
-        image_np = cv2.rectangle(image_np, (x1, y1), (x2, y2), (0, 255, 0), 2)
-        image_np = cv2.putText(image_np, f"{label} ({score:.2f})", (x1, y1 - 10),
-                               cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
+        for result in results:
+            box = result["box"]
+            score = result["score"]
+            label = "license plate"
+            x1, y1, x2, y2 = int(box["xmin"]), int(box["ymin"]), int(box["xmax"]), int(box["ymax"])
+            image_np = cv2.rectangle(image_np, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            image_np = cv2.putText(image_np, f"{label} ({score:.2f})", (x1, y1 - 10),
+                                   cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
+        return Image.fromarray(image_np)
 
-    return Image.fromarray(image_np)
+    except Exception as e:
+        logging.error(f"Grounding DINO Fehler: {e}")
+        return image
 
 # Verarbeitung der Bilder
 def process_image(image):