refactor: ✨ ruff rules enabled and fixed and code refactor made

demo.ipynb

@@ -1357,7 +1357,7 @@
     }
    ],
    "source": [
-    "IMAGE_NAME = list(ds.images.keys())[0]\n",
+    "IMAGE_NAME = next(iter(ds.images.keys()))\n",
     "\n",
     "image = ds.images[IMAGE_NAME]\n",
     "annotations = ds.annotations[IMAGE_NAME]\n",

examples/traffic_analysis/inference_example.py

@@ -1,6 +1,6 @@
 import argparse
 import os
-from typing import Dict, Iterable, List, Set
+from typing import Dict, Iterable, List, Optional, Set
 
 import cv2
 import numpy as np
@@ -77,7 +77,7 @@ def __init__(
         roboflow_api_key: str,
         model_id: str,
         source_video_path: str,
-        target_video_path: str = None,
+        target_video_path: Optional[str] = None,
         confidence_threshold: float = 0.3,
         iou_threshold: float = 0.7,
     ) -> None:

examples/traffic_analysis/ultralytics_example.py

@@ -1,5 +1,5 @@
 import argparse
-from typing import Dict, Iterable, List, Set
+from typing import Dict, Iterable, List, Optional, Set
 
 import cv2
 import numpy as np
@@ -74,7 +74,7 @@ def __init__(
         self,
         source_weights_path: str,
         source_video_path: str,
-        target_video_path: str = None,
+        target_video_path: Optional[str] = None,
         confidence_threshold: float = 0.3,
         iou_threshold: float = 0.7,
     ) -> None:

pyproject.toml

@@ -166,7 +166,7 @@ indent-width = 4
 
 [tool.ruff.lint]
 # Enable pycodestyle (`E`) and Pyflakes (`F`) codes by default.
-select = ["E", "F", "I", "A", "Q", "W"]
+select = ["E", "F", "I", "A", "Q", "W","RUF"]
 ignore = []
 # Allow autofix for all enabled rules (when `--fix`) is provided.
 fixable = [

supervision/detection/core.py

@@ -249,7 +249,7 @@ def from_ultralytics(cls, ultralytics_results) -> Detections:
             results = model(image)[0]
             detections = sv.Detections.from_ultralytics(results)
             ```
-        """  # noqa: E501 // docs
+        """
 
         if hasattr(ultralytics_results, "obb") and ultralytics_results.obb is not None:
             class_id = ultralytics_results.obb.cls.cpu().numpy().astype(int)
@@ -356,7 +356,7 @@ def from_tensorflow(
             result = model(img)
             detections = sv.Detections.from_tensorflow(result)
             ```
-        """  # noqa: E501 // docs
+        """
 
         boxes = tensorflow_results["detection_boxes"][0].numpy()
         boxes[:, [0, 2]] *= resolution_wh[0]
@@ -431,7 +431,7 @@ def from_mmdetection(cls, mmdet_results) -> Detections:
             result = inference_detector(model, image)
             detections = sv.Detections.from_mmdetection(result)
             ```
-        """  # noqa: E501 // docs
+        """
 
         return cls(
             xyxy=mmdet_results.pred_instances.bboxes.cpu().numpy(),
@@ -490,7 +490,7 @@ def from_transformers(
                 id2label=model.config.id2label
             )
             ```
-        """  # noqa: E501 // docs
+        """
 
         if (
             transformers_results.__class__.__name__ == "Tensor"

supervision/detection/line_zone.py

@@ -55,7 +55,7 @@ class LineZone:
         line_zone.in_count, line_zone.out_count
         # 7, 2
         ```
-    """  # noqa: E501 // docs
+    """
 
     def __init__(
         self,

supervision/detection/lmm.py

@@ -113,7 +113,7 @@ def from_florence_2(
             oriented bounding boxes.
     """
     assert len(result) == 1, f"Expected result with a single element. Got: {result}"
-    task = list(result.keys())[0]
+    task = next(iter(result.keys()))
     if task not in SUPPORTED_TASKS_FLORENCE_2:
         raise ValueError(
             f"{task} not supported. Supported tasks are: {SUPPORTED_TASKS_FLORENCE_2}"

supervision/detection/overlap_filter.py

@@ -183,7 +183,7 @@ def group_overlapping_boxes(
         ious = ious.flatten()
 
         above_threshold = ious >= iou_threshold
-        merge_group = [idx] + np.flip(order[above_threshold]).tolist()
+        merge_group = [idx, *np.flip(order[above_threshold]).tolist()]
         merge_groups.append(merge_group)
         order = order[~above_threshold]
     return merge_groups

supervision/detection/tools/csv_sink.py

@@ -48,7 +48,7 @@ class CSVSink:
                 detections = sv.Detections.from_ultralytics(result)
                 sink.append(detections, custom_data={'<CUSTOM_LABEL>':'<CUSTOM_DATA>'})
         ```
-    """  # noqa: E501 // docs
+    """
 
     def __init__(self, file_name: str = "output.csv") -> None:
         """
@@ -104,7 +104,7 @@ def close(self) -> None:
 
     @staticmethod
     def parse_detection_data(
-        detections: Detections, custom_data: Dict[str, Any] = None
+        detections: Detections, custom_data: Optional[Dict[str, Any]] = None
     ) -> List[Dict[str, Any]]:
         parsed_rows = []
         for i in range(len(detections.xyxy)):
@@ -137,7 +137,7 @@ def parse_detection_data(
         return parsed_rows
 
     def append(
-        self, detections: Detections, custom_data: Dict[str, Any] = None
+        self, detections: Detections, custom_data: Optional[Dict[str, Any]] = None
     ) -> None:
         """
         Append detection data to the CSV file.

supervision/detection/tools/json_sink.py

@@ -38,7 +38,7 @@ class JSONSink:
                 detections = sv.Detections.from_ultralytics(result)
                 sink.append(detections, custom_data={'<CUSTOM_LABEL>':'<CUSTOM_DATA>'})
         ```
-    """  # noqa: E501 // docs
+    """
 
     def __init__(self, file_name: str = "output.json") -> None:
         """
@@ -92,7 +92,7 @@ def write_and_close(self) -> None:
 
     @staticmethod
     def parse_detection_data(
-        detections: Detections, custom_data: Dict[str, Any] = None
+        detections: Detections, custom_data: Optional[Dict[str, Any]] = None
     ) -> List[Dict[str, Any]]:
         parsed_rows = []
         for i in range(len(detections.xyxy)):
@@ -126,7 +126,7 @@ def parse_detection_data(
         return parsed_rows
 
     def append(
-        self, detections: Detections, custom_data: Dict[str, Any] = None
+        self, detections: Detections, custom_data: Optional[Dict[str, Any]] = None
     ) -> None:
         """
         Append detection data to the JSON file.

supervision/detection/tools/smoother.py

@@ -53,7 +53,7 @@ class DetectionsSmoother:
                 annotated_frame = box_annotator.annotate(frame.copy(), detections)
                 sink.write_frame(annotated_frame)
         ```
-    """  # noqa: E501 // docs
+    """
 
     def __init__(self, length: int = 5) -> None:
         """

supervision/keypoint/core.py

@@ -429,7 +429,7 @@ def from_yolo_nas(cls, yolo_nas_results) -> KeyPoints:
             results = model.predict(image, conf=0.1)
             key_points = sv.KeyPoints.from_yolo_nas(results)
             ```
-        """  # noqa: E501 // docs
+        """
         if len(yolo_nas_results.prediction.poses) == 0:
             return cls.empty()
 

supervision/keypoint/skeletons.py

@@ -1,11 +1,11 @@
 from enum import Enum
-from typing import Dict, List, Tuple
+from typing import ClassVar, Dict, List, Tuple
 
 Edges = List[Tuple[int, int]]
 
 
 class Skeleton(Enum):
-    COCO = [
+    COCO: ClassVar[List[Tuple[int, int]]] = [
         (1, 2),
         (1, 3),
         (2, 3),
@@ -25,7 +25,7 @@ class Skeleton(Enum):
         (17, 15),
     ]
 
-    GHUM = [
+    GHUM: ClassVar[List[Tuple[int, int]]] = [
         (1, 2),
         (1, 5),
         (2, 3),
@@ -63,7 +63,7 @@ class Skeleton(Enum):
         (31, 33),
     ]
 
-    FACEMESH_TESSELATION_NO_IRIS = [
+    FACEMESH_TESSELATION_NO_IRIS: ClassVar[List[Tuple[int, int]]] = [
         (128, 35),
         (35, 140),
         (140, 128),
@@ -2622,7 +2622,7 @@ class Skeleton(Enum):
         (256, 340),
     ]
 
-    FACEMESH_TESSELATION = [
+    FACEMESH_TESSELATION: ClassVar[List[Tuple[int, int]]] = [
         (474, 474),
         (475, 476),
         (476, 477),
@@ -2633,7 +2633,8 @@ class Skeleton(Enum):
         (471, 472),
         (472, 473),
         (473, 470),
-    ] + FACEMESH_TESSELATION_NO_IRIS
+        *FACEMESH_TESSELATION_NO_IRIS,
+    ]
 
 
 SKELETONS_BY_EDGE_COUNT: Dict[int, Edges] = {}

supervision/metrics/detection.py

@@ -440,8 +440,8 @@ def plot(
         class_names = classes if classes is not None else self.classes
         use_labels_for_ticks = class_names is not None and (0 < len(class_names) < 99)
         if use_labels_for_ticks:
-            x_tick_labels = class_names + ["FN"]
-            y_tick_labels = class_names + ["FP"]
+            x_tick_labels = [*class_names, "FN"]
+            y_tick_labels = [*class_names, "FP"]
             num_ticks = len(x_tick_labels)
         else:
             x_tick_labels = None

test/dataset/formats/test_coco.py

@@ -1,5 +1,5 @@
 from contextlib import ExitStack as DoesNotRaise
-from typing import Dict, List, Tuple, Union
+from typing import Dict, List, Optional, Tuple, Union
 
 import numpy as np
 import pytest
@@ -21,7 +21,7 @@ def mock_coco_annotation(
     category_id: int = 0,
     bbox: Tuple[float, float, float, float] = (0.0, 0.0, 0.0, 0.0),
     area: float = 0.0,
-    segmentation: Union[List[list], Dict] = None,
+    segmentation: Optional[Union[List[list], Dict]] = None,
     iscrowd: bool = False,
 ) -> dict:
     if not segmentation: