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Pascal VOC XML

detections_to_voc_xml

Converts Detections object to Pascal VOC XML format.

Parameters:

Name Type Description Default
detections Detections

A Detections object containing bounding boxes, class ids, and other relevant information.

 required
classes List[str]

A list of class names corresponding to the class ids in the Detections object.

 required
filename str

The name of the image file associated with the detections.

 required
width int

The width of the image in pixels.

 required
height int

The height of the image in pixels.

 required
depth int

The number of color channels in the image. Defaults to 3 for RGB images.

 3

Returns:

Name Type Description
str str

An XML string in Pascal VOC format representing the detections.

Examples:

>>> import numpy as np
>>> import supervision as sv

>>> xyxy = np.array([
...     [50, 30, 200, 180],
...     [20, 40, 150, 190]
... ])
>>> class_id = np.array([1, 0])
>>> detections = Detections(xyxy=xyxy, class_id=class_id)

>>> classes = ["dog", "cat"]

>>> voc_xml = detections_to_voc_xml(
...     detections=detections,
...     classes=classes,
...     filename="image1.jpg",
...     width=500,
...     height=400
... )
Source code in supervision/annotation/voc.py 
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def detections_to_voc_xml(
    detections: Detections,
    classes: List[str],
    filename: str,
    width: int,
    height: int,
    depth: int = 3,
) -> str:
    """
    Converts Detections object to Pascal VOC XML format.

    Args:
        detections (Detections): A Detections object containing bounding boxes, class ids, and other relevant information.
        classes (List[str]): A list of class names corresponding to the class ids in the Detections object.
        filename (str): The name of the image file associated with the detections.
        width (int): The width of the image in pixels.
        height (int): The height of the image in pixels.
        depth (int, optional): The number of color channels in the image. Defaults to 3 for RGB images.

    Returns:
        str: An XML string in Pascal VOC format representing the detections.

    Examples:
        ```python
        >>> import numpy as np
        >>> import supervision as sv

        >>> xyxy = np.array([
        ...     [50, 30, 200, 180],
        ...     [20, 40, 150, 190]
        ... ])
        >>> class_id = np.array([1, 0])
        >>> detections = Detections(xyxy=xyxy, class_id=class_id)

        >>> classes = ["dog", "cat"]

        >>> voc_xml = detections_to_voc_xml(
        ...     detections=detections,
        ...     classes=classes,
        ...     filename="image1.jpg",
        ...     width=500,
        ...     height=400
        ... )
        ```
    """

    # Create root element
    annotation = Element("annotation")

    # Add folder element
    folder = SubElement(annotation, "folder")
    folder.text = "VOC"

    # Add filename element
    fname = SubElement(annotation, "filename")
    fname.text = filename

    # Add source element
    source = SubElement(annotation, "source")
    database = SubElement(source, "database")
    database.text = "roboflow.ai"

    # Add size element
    size = SubElement(annotation, "size")
    w = SubElement(size, "width")
    w.text = str(width)
    h = SubElement(size, "height")
    h.text = str(height)
    d = SubElement(size, "depth")
    d.text = str(depth)

    # Add segmented element
    segmented = SubElement(annotation, "segmented")
    segmented.text = "0"

    # Add object elements
    for i in range(detections.xyxy.shape[0]):
        obj = SubElement(annotation, "object")

        class_id = detections.class_id[i] if detections.class_id is not None else None
        name = SubElement(obj, "name")
        name.text = classes[class_id] if class_id is not None else "unknown"

        bndbox = SubElement(obj, "bndbox")
        xmin = SubElement(bndbox, "xmin")
        xmin.text = str(int(detections.xyxy[i, 0]))
        ymin = SubElement(bndbox, "ymin")
        ymin.text = str(int(detections.xyxy[i, 1]))
        xmax = SubElement(bndbox, "xmax")
        xmax.text = str(int(detections.xyxy[i, 2]))
        ymax = SubElement(bndbox, "ymax")
        ymax.text = str(int(detections.xyxy[i, 3]))

    # Generate XML string
    xml_string = parseString(tostring(annotation)).toprettyxml(indent="  ")

    return xml_string