my

app.py

@@ -75,7 +75,7 @@ def upload_file():
         })
 
 def process_image():
-    model = YOLOv10(f'/runs/detect/train3/weights/best (1).pt')
+    model = YOLOv10(f'./runs/detect/train3/weights/best (1).pt')
     dataset = sv.DetectionDataset.from_yolo(
         images_directory_path=f"/data/MyNewVersion5.0Dataset/valid/images",
         annotations_directory_path=f"/data/MyNewVersion5.0Dataset/valid/labels",
@@ -83,20 +83,20 @@ def process_image():
     )
     bounding_box_annotator = sv.BoundingBoxAnnotator()
     label_annotator = sv.LabelAnnotator()
-    image_dir = "/data1"
+    image_dir = "./data1"
     files = os.listdir('/data1')
     files.sort()
     files = files[0:100]
     print(files)
     counter = 0
     for ii in files:
-        random_image_data = cv2.imread('/data1/' + ii)
-        random_image_data1 = cv2.imread('/data1/' + ii)
-        results = model(source='/data1/' + ii, conf=0.07)[0]
+        random_image_data = cv2.imread('./data1/' + ii)
+        random_image_data1 = cv2.imread('./data1/' + ii)
+        results = model(source='./data1/' + ii, conf=0.07)[0]
         detections = sv.Detections.from_ultralytics(results)
         annotated_image = bounding_box_annotator.annotate(scene=random_image_data, detections=detections)
         annotated_image = label_annotator.annotate(scene=annotated_image, detections=detections)
-        save_path = "/Folder1/" + "detection" + ii
+        save_path = "./Folder1/" + "detection" + ii
         cv2.imwrite(save_path, annotated_image)
         print(f"Annotated image saved at {save_path}")
         bounding_boxes = results.boxes.xyxy.cpu().numpy()