Ruff Docstring formatting (#15793)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

ultralytics/data/augment.py

@@ -1005,7 +1005,6 @@ class RandomPerspective:
             >>> transform = RandomPerspective(degrees=10.0, translate=0.1, scale=0.5, shear=5.0)
             >>> result = transform(labels)  # Apply random perspective to labels
         """
-
         self.degrees = degrees
         self.translate = translate
         self.scale = scale
@@ -1038,7 +1037,6 @@ class RandomPerspective:
             >>> border = (10, 10)
             >>> transformed_img, matrix, scale = affine_transform(img, border)
         """
-
         # Center
         C = np.eye(3, dtype=np.float32)
 

ultralytics/data/converter.py

@@ -115,7 +115,7 @@ def coco91_to_coco80_class():
 
 
 def coco80_to_coco91_class():
-    """
+    r"""
     Converts 80-index (val2014) to 91-index (paper).
     For details see https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/.
 
@@ -243,7 +243,6 @@ def convert_coco(
     Output:
         Generates output files in the specified output directory.
     """
-
     # Create dataset directory
     save_dir = increment_path(save_dir)  # increment if save directory already exists
     for p in save_dir / "labels", save_dir / "images":

ultralytics/data/explorer/explorer.py

@@ -226,6 +226,7 @@ class Explorer:
     def plot_sql_query(self, query: str, labels: bool = True) -> Image.Image:
         """
         Plot the results of a SQL-Like query on the table.
+
         Args:
             query (str): SQL query to run.
             labels (bool): Whether to plot the labels or not.
@@ -457,20 +458,3 @@ class Explorer:
             LOGGER.error("AI generated query is not valid. Please try again with a different prompt")
             LOGGER.error(e)
             return None
-
-    def visualize(self, result):
-        """
-        Visualize the results of a query. TODO.
-
-        Args:
-            result (pyarrow.Table): Table containing the results of a query.
-        """
-        pass
-
-    def generate_report(self, result):
-        """
-        Generate a report of the dataset.
-
-        TODO
-        """
-        pass

ultralytics/data/loaders.py

@@ -240,7 +240,7 @@ class LoadScreenshots:
         return self
 
     def __next__(self):
-        """mss screen capture: get raw pixels from the screen as np array."""
+        """Screen capture with 'mss' to get raw pixels from the screen as np array."""
         im0 = np.asarray(self.sct.grab(self.monitor))[:, :, :3]  # BGRA to BGR
         s = f"screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: "
 

ultralytics/data/split_dota.py

@@ -19,11 +19,19 @@ from shapely.geometry import Polygon
 
 def bbox_iof(polygon1, bbox2, eps=1e-6):
     """
-    Calculate iofs between bbox1 and bbox2.
+    Calculate Intersection over Foreground (IoF) between polygons and bounding boxes.
 
     Args:
-        polygon1 (np.ndarray): Polygon coordinates, (n, 8).
-        bbox2 (np.ndarray): Bounding boxes, (n ,4).
+        polygon1 (np.ndarray): Polygon coordinates, shape (n, 8).
+        bbox2 (np.ndarray): Bounding boxes, shape (n, 4).
+        eps (float, optional): Small value to prevent division by zero. Defaults to 1e-6.
+
+    Returns:
+        (np.ndarray): IoF scores, shape (n, 1) or (n, m) if bbox2 is (m, 4).
+
+    Note:
+        Polygon format: [x1, y1, x2, y2, x3, y3, x4, y4].
+        Bounding box format: [x_min, y_min, x_max, y_max].
     """
     polygon1 = polygon1.reshape(-1, 4, 2)
     lt_point = np.min(polygon1, axis=-2)  # left-top

ultralytics/data/utils.py

@@ -265,7 +265,6 @@ def check_det_dataset(dataset, autodownload=True):
     Returns:
         (dict): Parsed dataset information and paths.
     """
-
     file = check_file(dataset)
 
     # Download (optional)
@@ -363,7 +362,6 @@ def check_cls_dataset(dataset, split=""):
             - 'nc' (int): The number of classes in the dataset.
             - 'names' (dict): A dictionary of class names in the dataset.
     """
-
     # Download (optional if dataset=https://file.zip is passed directly)
     if str(dataset).startswith(("http:/", "https:/")):
         dataset = safe_download(dataset, dir=DATASETS_DIR, unzip=True, delete=False)
@@ -602,7 +600,6 @@ def compress_one_image(f, f_new=None, max_dim=1920, quality=50):
             compress_one_image(f)
         ```
     """
-
     try:  # use PIL
         im = Image.open(f)
         r = max_dim / max(im.height, im.width)  # ratio
@@ -635,7 +632,6 @@ def autosplit(path=DATASETS_DIR / "coco8/images", weights=(0.9, 0.1, 0.0), annot
         autosplit()
         ```
     """
-
     path = Path(path)  # images dir
     files = sorted(x for x in path.rglob("*.*") if x.suffix[1:].lower() in IMG_FORMATS)  # image files only
     n = len(files)  # number of files