Initializes the FPSBasedTimer with the specified frames per second rate. Args: fps (float): The frame rate of the video stream. D

Initializes the ClockBasedTimer.

Iterate over the images and annotations in the dataset. Yields: Tuples containing the image path, image data, and its an

Iterate over the images and annotations in the dataset. Yields: Tuples containing the image path, image data, and its an

Iterates over the Detections object and yield a tuple of `(xyxy, mask, confidence, class_id, tracker_id, data)` for each detection.

Iterate over masks as dense ``(H, W)`` boolean arrays.

Iterates over the Keypoint object and yield a tuple of `(xy, keypoint_confidence, class_id, data)` for each object detection.

Returns the number of colors in the palette. Returns: The number of colors.

Returns the number of detections in the Detections object.

Return the number of masks. Returns: Number of masks N. Examples: ```pycon >>> from supervision.

Returns the number of classifications.

Returns the number of objects in the `sv.KeyPoints` object. Returns: The number of objects. Example:

Validate the classification inputs.

Set a value in the data dictionary of the Detections object. Args: key: The key in the data dictionary to set.

Set a value in the data dictionary of the `sv.KeyPoints` object. Args: key: The key in the data dictionary to set.

Format as a pretty string. Example: ```pycon >>> import numpy as np >>> import supervision as sv

Formats the evaluation output metrics to match the structure used by pycocotools Example: ```pycon >>> import

Format as a pretty string. Example: ```pycon >>> import numpy as np >>> import supervision as sv

Format as a pretty string. Example: ```pycon >>> import numpy as np >>> import supervision as sv

Format as a pretty string. Example: ```pycon >>> import numpy as np >>> import supervision as sv

Object-dtype array of n empty lists for coco_raw_segmentation (bbox-only).

Compute and display summary metrics for evaluation results.

Annotates the given scene with given icons. Args: scene: The image where labels will be drawn. `ImageTyp

Draws the line on the frame using the line zone provided. Args: frame: The image on which the line will be drawn.

Annotates the polygon zone within a frame with a count of detected objects. Args: scene: The image on which the polygon

Draws filled semi-transparent covariance ellipses around each keypoint. Args: scene: The image to annotate. ``ImageType`

Draws labels at skeleton vertex positions on the image. Vertices marked not visible via ``key_points.visible`` are skipped.

Calculate the area of each detection in the set of object detections. Selection order: 1. If ``mask`` is set, return the ar

Compute the area (``True`` pixel count) of each mask. Returns: int64 array of shape ``(N,)`` with per-mask pixel counts.

Saves the dataset as a multi-class folder structure. Args: root_directory_path: The path to the directory

Exports the dataset to PASCAL VOC format. This method saves the images and their corresponding annotations in PASCAL VOC format.

Return per-mask inclusive bounding boxes in ``xyxy`` format. Boxes are derived from crop metadata: ``x2 = x1 + crop_w - 1``, ``y2 = y

Calculate confusion matrix from dataset and callback function. Args: dataset: Object detection dataset used for evaluati

Calculate mean average precision from dataset and callback function. Args: dataset: Object detection dataset used for ev

Calculate the area of each bounding box in the set of object detections. Returns: An array of floats containing the area

Compute the aspect ratio (width divided by height) for each bounding box. Returns: Array of shape `(N,)` containing aspe

Calculate optimal line thickness based on image resolution. Adjusts the line thickness for readability depending on the smallest dimension of

Calculate the center point of the vector. Returns: The center point of the vector.