Update app.py

app.py

@@ -57,15 +57,15 @@ def predict_and_show_bounding_boxes(image_path, model_choice, conf_threshold=0.5
             results = yolo_model(img, conf=conf_threshold)[0]
             boxes = results.boxes
             if len(boxes) == 0:
-                return cv2.cvtColor(img, cv2.COLOR_BGR2RGB), "No defects detected."
+                return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
             for box in boxes:
                 xyxy = box.xyxy[0].tolist()
                 x_min, y_min, x_max, y_max = map(int, xyxy[:4])
                 conf = box.conf[0].item()
                 cls = int(box.cls[0])
-                cv2.rectangle(img, (x_min, y_min), (x_max, y_max), (0, 255, 0), 2)
+                cv2.rectangle(img, (x_min, y_min), (x_max, y_max), (0, 255, 0), 1)  # Thinner box (thickness 1)
                 label = f"{results.names[cls]}: {conf:.2f}"
-                cv2.putText(img, label, (x_min, y_min - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+                cv2.putText(img, label, (x_min, y_min - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)  # Smaller font (scale 0.5, thickness 1)
             return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
         except Exception as e:
             return None, f"Error during YOLO prediction: {e}"
@@ -82,13 +82,13 @@ def predict_and_show_bounding_boxes(image_path, model_choice, conf_threshold=0.5
                 overlap_width_ratio=0.1,
             )
             if len(result.object_prediction_list) == 0:
-                return cv2.cvtColor(img, cv2.COLOR_BGR2RGB), "No defects detected."
+                return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
             for pred in result.object_prediction_list:
                 box = pred.bbox.to_xyxy()
                 x_min, y_min, x_max, y_max = map(int, box)
                 label = f"{pred.category.name}: {pred.score.value:.2f}"
-                cv2.rectangle(img, (x_min, y_min), (x_max, y_max), (255, 0, 0), 2)
-                cv2.putText(img, label, (x_min, y_min - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 0), 2)
+                cv2.rectangle(img, (x_min, y_min), (x_max, y_max), (255, 0, 0), 1)  # Thinner box (thickness 1)
+                cv2.putText(img, label, (x_min, y_min - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)  # Smaller font (scale 0.5, thickness 1)
             return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
         except Exception as e:
             return None, f"Error during SAHI prediction: {e}"