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Unify how optional values are marked in docstings 

* No longer means 'has default value'. Removed where it meant that.
* `Optional[datatype]` is now used instead of `datatype, optional`
* Fixed a handful of incorrect type annotations
This commit is contained in:
LinasKo 2024-08-14 12:46:16 +03:00
parent 5b3ce80a34
commit 2012ec7bcb
18 changed files with 67 additions and 61 deletions
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2
examples/count_people_in_zone/inference_example.py
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@ -75,7 +75,7 @@ def detect(
frame (np.ndarray): The frame to process, expected to be a NumPy array.
model (RoboflowInferenceModel): The Inference model used for processing the
frame.
confidence_threshold (float, optional): The confidence threshold for filtering
confidence_threshold (float): The confidence threshold for filtering
detections. Default is 0.5.
Returns:

2
examples/count_people_in_zone/ultralytics_example.py
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@ -72,7 +72,7 @@ def detect(
Args:
frame (np.ndarray): The frame to process, expected to be a NumPy array.
model (YOLO): The YOLO model used for processing the frame.
confidence_threshold (float, optional): The confidence threshold for filtering
confidence_threshold (float): The confidence threshold for filtering
detections. Default is 0.5.
Returns:

2
examples/time_in_zone/utils/timers.py
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@ -22,7 +22,7 @@ class FPSBasedTimer:
"""Initializes the FPSBasedTimer with the specified frames per second rate.
Args:
fps (int, optional): The frame rate of the video stream. Defaults to 30.
fps (int): The frame rate of the video stream. Defaults to 30.
"""
self.fps = fps
self.frame_id = 0

2
supervision/annotators/core.py
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@ -1975,7 +1975,7 @@ class PercentageBarAnnotator(BaseAnnotator):
border_color: Color = Color.BLACK,
position: Position = Position.TOP_CENTER,
color_lookup: ColorLookup = ColorLookup.CLASS,
border_thickness: int = None,
border_thickness: Optional[int] = None,
):
"""
Args:

20
supervision/dataset/core.py
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@ -181,11 +181,11 @@ class DetectionDataset(BaseDataset):
using the provided split_ratio.
Args:
split_ratio (float, optional): The ratio of the training
split_ratio (float): The ratio of the training
set to the entire dataset.
random_state (int, optional): The seed for the random number generator.
random_state (Optional[int]): The seed for the random number generator.
This is used for reproducibility.
shuffle (bool, optional): Whether to shuffle the data before splitting.
shuffle (bool): Whether to shuffle the data before splitting.
Returns:
Tuple[DetectionDataset, DetectionDataset]: A tuple containing
@ -396,7 +396,7 @@ class DetectionDataset(BaseDataset):
images_directory_path (str): Path to the directory containing the images.
annotations_directory_path (str): Path to the directory
containing the PASCAL VOC XML annotations.
force_masks (bool, optional): If True, forces masks to
force_masks (bool): If True, forces masks to
be loaded for all annotations, regardless of whether they are present.
Returns:
@ -455,10 +455,10 @@ class DetectionDataset(BaseDataset):
containing the YOLO annotation files.
data_yaml_path (str): The path to the data
YAML file containing class information.
force_masks (bool, optional): If True, forces
force_masks (bool): If True, forces
masks to be loaded for all annotations,
regardless of whether they are present.
is_obb (bool, optional): If True, loads the annotations in OBB format.
is_obb (bool): If True, loads the annotations in OBB format.
OBB annotations are defined as `[class_id, x, y, x, y, x, y, x, y]`,
where pairs of [x, y] are box corners.
@ -565,7 +565,7 @@ class DetectionDataset(BaseDataset):
images_directory_path (str): The path to the
directory containing the images.
annotations_path (str): The path to the json annotation files.
force_masks (bool, optional): If True,
force_masks (bool): If True,
forces masks to be loaded for all annotations,
regardless of whether they are present.
@ -784,11 +784,11 @@ class ClassificationDataset(BaseDataset):
using the provided split_ratio.
Args:
split_ratio (float, optional): The ratio of the training
split_ratio (float): The ratio of the training
set to the entire dataset.
random_state (int, optional): The seed for the
random_state (Optional[int]): The seed for the
random number generator. This is used for reproducibility.
shuffle (bool, optional): Whether to shuffle the data before splitting.
shuffle (bool): Whether to shuffle the data before splitting.
Returns:
Tuple[ClassificationDataset, ClassificationDataset]: A tuple containing

2
supervision/dataset/formats/pascal_voc.py
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@ -147,7 +147,7 @@ def load_pascal_voc_annotations(
images_directory_path (str): The path to the directory containing the images.
annotations_directory_path (str): The path to the directory containing the
PASCAL VOC annotation files.
force_masks (bool, optional): If True, forces masks to be loaded for all
force_masks (bool): If True, forces masks to be loaded for all
annotations, regardless of whether they are present.
Returns:

4
supervision/dataset/formats/yolo.py
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@ -138,9 +138,9 @@ def load_yolo_annotations(
containing the YOLO annotation files.
data_yaml_path (str): The path to the data
YAML file containing class information.
force_masks (bool, optional): If True, forces masks to be loaded
force_masks (bool): If True, forces masks to be loaded
for all annotations, regardless of whether they are present.
is_obb (bool, optional): If True, loads the annotations in OBB format.
is_obb (bool): If True, loads the annotations in OBB format.
OBB annotations are defined as `[class_id, x, y, x, y, x, y, x, y]`,
where pairs of [x, y] are box corners.

8
supervision/detection/core.py
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@ -1149,10 +1149,10 @@ class Detections:
from a segmentation model, the IoU mask is applied. Otherwise, box IoU is used.
Args:
threshold (float, optional): The intersection-over-union threshold
threshold (float): The intersection-over-union threshold
to use for non-maximum suppression. I'm the lower the value the more
restrictive the NMS becomes. Defaults to 0.5.
class_agnostic (bool, optional): Whether to perform class-agnostic
class_agnostic (bool): Whether to perform class-agnostic
non-maximum suppression. If True, the class_id of each detection
will be ignored. Defaults to False.
@ -1204,9 +1204,9 @@ class Detections:
Perform non-maximum merging on the current set of object detections.
Args:
threshold (float, optional): The intersection-over-union threshold
threshold (float): The intersection-over-union threshold
to use for non-maximum merging. Defaults to 0.5.
class_agnostic (bool, optional): Whether to perform class-agnostic
class_agnostic (bool): Whether to perform class-agnostic
non-maximum merging. If True, the class_id of each detection
will be ignored. Defaults to False.

10
supervision/detection/overlap_filter.py
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@ -55,9 +55,9 @@ def mask_non_max_suppression(
masks (np.ndarray): A 3D array of binary masks corresponding to the predictions.
Shape: `(N, H, W)`, where N is the number of predictions, and H, W are the
dimensions of each mask.
iou_threshold (float, optional): The intersection-over-union threshold
iou_threshold (float): The intersection-over-union threshold
to use for non-maximum suppression.
mask_dimension (int, optional): The dimension to which the masks should be
mask_dimension (int): The dimension to which the masks should be
resized before computing IOU values. Defaults to 640.
Returns:
@ -103,7 +103,7 @@ def box_non_max_suppression(
predictions (np.ndarray): An array of object detection predictions in
the format of `(x_min, y_min, x_max, y_max, score)`
or `(x_min, y_min, x_max, y_max, score, class)`.
iou_threshold (float, optional): The intersection-over-union threshold
iou_threshold (float): The intersection-over-union threshold
to use for non-maximum suppression.
Returns:
@ -158,7 +158,7 @@ def group_overlapping_boxes(
predictions (npt.NDArray[np.float64]): An array of shape `(n, 5)` containing
the bounding boxes coordinates in format `[x1, y1, x2, y2]`
and the confidence scores.
iou_threshold (float, optional): The intersection-over-union threshold
iou_threshold (float): The intersection-over-union threshold
to use for non-maximum suppression. Defaults to 0.5.
Returns:
@ -202,7 +202,7 @@ def box_non_max_merge(
containing the bounding boxes coordinates in format `[x1, y1, x2, y2]`,
the confidence scores and class_ids. Omit class_id column to allow
detections of different classes to be merged.
iou_threshold (float, optional): The intersection-over-union threshold
iou_threshold (float): The intersection-over-union threshold
to use for non-maximum suppression. Defaults to 0.5.
Returns:

2
supervision/detection/tools/polygon_zone.py
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@ -147,7 +147,7 @@ class PolygonZoneAnnotator:
Parameters:
scene (np.ndarray): The image on which the polygon zone will be annotated
label (Optional[str]): An optional label for the count of detected objects
label (Optional[str]): A label for the count of detected objects
within the polygon zone (default: None)
Returns:

2
supervision/detection/utils.py
View File

@ -106,7 +106,7 @@ def mask_iou_batch(
Args:
masks_true (np.ndarray): 3D `np.ndarray` representing ground-truth masks.
masks_detection (np.ndarray): 3D `np.ndarray` representing detection masks.
memory_limit (int, optional): memory limit in MB, default is 1024 * 5 MB (5GB).
memory_limit (int): memory limit in MB, default is 1024 * 5 MB (5GB).
Returns:
np.ndarray: Pairwise IoU of masks from `masks_true` and `masks_detection`.

14
supervision/draw/utils.py
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@ -142,7 +142,7 @@ def draw_polygon(
scene (np.ndarray): The scene to draw the polygon on.
polygon (np.ndarray): The polygon to be drawn, given as a list of vertices.
color (Color): The color of the polygon.
thickness (int, optional): The thickness of the polygon lines, by default 2.
thickness (int): The thickness of the polygon lines, by default 2.
Returns:
np.ndarray: The scene with the polygon drawn on it.
@ -172,14 +172,14 @@ def draw_text(
text (str): The text to be drawn.
text_anchor (Point): The anchor point for the text, represented as a
Point object with x and y attributes.
text_color (Color, optional): The color of the text. Defaults to black.
text_scale (float, optional): The scale of the text. Defaults to 0.5.
text_thickness (int, optional): The thickness of the text. Defaults to 1.
text_padding (int, optional): The amount of padding to add around the text
text_color (Color): The color of the text. Defaults to black.
text_scale (float): The scale of the text. Defaults to 0.5.
text_thickness (int): The thickness of the text. Defaults to 1.
text_padding (int): The amount of padding to add around the text
when drawing a rectangle in the background. Defaults to 10.
text_font (int, optional): The font to use for the text.
text_font (int): The font to use for the text.
Defaults to cv2.FONT_HERSHEY_SIMPLEX.
background_color (Color, optional): The color of the background rectangle,
background_color (Optional[Color]): The color of the background rectangle,
if one is to be drawn. Defaults to None.
Returns:

27
supervision/keypoint/annotators.py
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@ -34,8 +34,8 @@ class VertexAnnotator(BaseKeyPointAnnotator):
) -> None:
"""
Args:
color (Color, optional): The color to use for annotating key points.
radius (int, optional): The radius of the circles used to represent the key
color (Color): The color to use for annotating key points.
radius (int): The radius of the circles used to represent the key
points.
"""
self.color = color
@ -108,8 +108,8 @@ class EdgeAnnotator(BaseKeyPointAnnotator):
) -> None:
"""
Args:
color (Color, optional): The color to use for the edges.
thickness (int, optional): The thickness of the edges.
color (Color): The color to use for the edges.
thickness (int): The thickness of the edges.
edges (Optional[List[Tuple[int, int]]]): The edges to draw.
If set to `None`, will attempt to select automatically.
"""
@ -202,16 +202,16 @@ class VertexLabelAnnotator:
):
"""
Args:
color (Union[Color, List[Color]], optional): The color to use for each
color (Union[Color, List[Color]]): The color to use for each
keypoint label. If a list is provided, the colors will be used in order
for each keypoint.
text_color (Union[Color, List[Color]], optional): The color to use
text_color (Union[Color, List[Color]]): The color to use
for the labels. If a list is provided, the colors will be used in order
for each keypoint.
text_scale (float, optional): The scale of the text.
text_thickness (int, optional): The thickness of the text.
text_padding (int, optional): The padding around the text.
border_radius (int, optional): The radius of the rounded corners of the
text_scale (float): The scale of the text.
text_thickness (int): The thickness of the text.
text_padding (int): The padding around the text.
border_radius (int): The radius of the rounded corners of the
boxes. Set to a high value to produce circles.
"""
self.border_radius: int = border_radius
@ -222,7 +222,10 @@ class VertexLabelAnnotator:
self.text_padding: int = text_padding
def annotate(
self, scene: ImageType, key_points: KeyPoints, labels: List[str] = None
self,
scene: ImageType,
key_points: KeyPoints,
labels: Optional[List[str]] = None,
) -> ImageType:
"""
A class that draws labels of skeleton vertices on images. It uses specified key
@ -234,7 +237,7 @@ class VertexLabelAnnotator:
`PIL.Image.Image`.
key_points (KeyPoints): A collection of key points where each key point
consists of x and y coordinates.
labels (List[str], optional): A list of labels to be displayed on the
labels (Optional[List[str]]): A list of labels to be displayed on the
annotated image. If not provided, keypoint indices will be used.
Returns:

2
supervision/metrics/detection.py
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@ -806,7 +806,7 @@ class MeanAveragePrecision:
prediction_confidence (np.ndarray): Objectness value from 0-1.
prediction_class_ids (np.ndarray): Predicted object classes.
true_class_ids (np.ndarray): True object classes.
eps (float, optional): Small value to prevent division by zero.
eps (float): Small value to prevent division by zero.
Returns:
np.ndarray: Average precision for different IoU levels.

10
supervision/tracker/byte_tracker/core.py
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@ -197,19 +197,19 @@ class ByteTrack:
</video>
Parameters:
track_activation_threshold (float, optional): Detection confidence threshold
track_activation_threshold (float): Detection confidence threshold
for track activation. Increasing track_activation_threshold improves accuracy
and stability but might miss true detections. Decreasing it increases
completeness but risks introducing noise and instability.
lost_track_buffer (int, optional): Number of frames to buffer when a track is lost.
lost_track_buffer (int): Number of frames to buffer when a track is lost.
Increasing lost_track_buffer enhances occlusion handling, significantly
reducing the likelihood of track fragmentation or disappearance caused
by brief detection gaps.
minimum_matching_threshold (float, optional): Threshold for matching tracks with detections.
minimum_matching_threshold (float): Threshold for matching tracks with detections.
Increasing minimum_matching_threshold improves accuracy but risks fragmentation.
Decreasing it improves completeness but risks false positives and drift.
frame_rate (int, optional): The frame rate of the video.
minimum_consecutive_frames (int, optional): Number of consecutive frames that an object must
frame_rate (int): The frame rate of the video.
minimum_consecutive_frames (int): Number of consecutive frames that an object must
be tracked before it is considered a 'valid' track.
Increasing minimum_consecutive_frames prevents the creation of accidental tracks from
false detection or double detection, but risks missing shorter tracks.

8
supervision/utils/image.py
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@ -158,7 +158,7 @@ def resize_image(
accepting either `numpy.ndarray` or `PIL.Image.Image`.
resolution_wh (Tuple[int, int]): The target resolution as
`(width, height)`.
keep_aspect_ratio (bool, optional): Flag to maintain the image's original
keep_aspect_ratio (bool): Flag to maintain the image's original
aspect ratio. Defaults to `False`.
Returns:
@ -360,9 +360,9 @@ class ImageSink:
Args:
target_dir_path (str): The target directory where images will be saved.
overwrite (bool, optional): Whether to overwrite the existing directory.
overwrite (bool): Whether to overwrite the existing directory.
Defaults to False.
image_name_pattern (str, optional): The image file name pattern.
image_name_pattern (str): The image file name pattern.
Defaults to "image_{:05d}.png".
Examples:
@ -399,7 +399,7 @@ class ImageSink:
Args:
image (np.ndarray): The image to be saved. The image must be in BGR color
format.
image_name (str, optional): The name to use for the saved image.
image_name (Optional[str]): The name to use for the saved image.
If not provided, a name will be
generated using the `image_name_pattern`.
"""

9
supervision/utils/internal.py
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@ -56,9 +56,9 @@ def deprecated_parameter(
Parameters:
old_parameter (str): The name of the deprecated parameter.
new_parameter (str): The name of the parameter that should be used instead.
map_function (Callable, optional): A function used to map the value of the old
map_function (Callable): A function used to map the value of the old
parameter to the new parameter. Defaults to the identity function.
warning_message (str, optional): The warning message to be displayed when the
warning_message (str): The warning message to be displayed when the
deprecated parameter is used. Defaults to a generic warning message with
placeholders for the old parameter, new parameter, and function name.
**message_kwargs: Additional keyword arguments that can be used to customize
@ -121,7 +121,9 @@ def deprecated(reason: str):
return decorator
T = TypeVar('T')
T = TypeVar("T")
class classproperty(Generic[T]):
"""
@ -134,6 +136,7 @@ class classproperty(Generic[T]):
def my_method(cls):
...
"""
def __init__(self, fget: Callable[..., T]):
"""
Args:

2
supervision/utils/video.py
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@ -19,7 +19,7 @@ class VideoInfo:
width (int): width of the video in pixels
height (int): height of the video in pixels
fps (int): frames per second of the video
total_frames (int, optional): total number of frames in the video,
total_frames (Optional[int]): total number of frames in the video,
default is None
Examples: