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Reference for ultralytics/solutions/instance_segmentation.py

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This file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/solutions/instance_segmentation.py. If you spot a problem please help fix it by contributing a Pull Request 🛠️. Thank you 🙏!


ultralytics.solutions.instance_segmentation.InstanceSegmentation 

InstanceSegmentation(**kwargs)

Bases: BaseSolution

A class to manage instance segmentation in images or video streams.

This class extends the BaseSolution class and provides functionality for performing instance segmentation, including drawing segmented masks with bounding boxes and labels.

Attributes:

Name Type Description
model str

The segmentation model to use for inference.
line_width int

Width of the bounding box and text lines.
names Dict[int, str]

Dictionary mapping class indices to class names.
clss List[int]

List of detected class indices.
track_ids List[int]

List of track IDs for detected instances.
masks List[ndarray]

List of segmentation masks for detected instances.

Methods:

Name Description
process

Process the input image to perform instance segmentation and annotate results.
extract_tracks

Extract tracks including bounding boxes, classes, and masks from model predictions.

Examples:

>>> segmenter = InstanceSegmentation()
>>> frame = cv2.imread("frame.jpg")
>>> results = segmenter.segment(frame)
>>> print(f"Total segmented instances: {results['total_tracks']}")

Parameters:

Name Type Description Default
**kwargs Any

Keyword arguments passed to the BaseSolution parent class. model (str): Model name or path, defaults to "yolo11n-seg.pt".

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Source code in ultralytics/solutions/instance_segmentation.py 
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def __init__(self, **kwargs):
    """
    Initialize the InstanceSegmentation class for detecting and annotating segmented instances.

    Args:
        **kwargs (Any): Keyword arguments passed to the BaseSolution parent class.
            model (str): Model name or path, defaults to "yolo11n-seg.pt".
    """
    kwargs["model"] = kwargs.get("model", "yolo11n-seg.pt")
    super().__init__(**kwargs)

process 

process(im0)

Perform instance segmentation on the input image and annotate the results.

Parameters:

Name Type Description Default
im0 ndarray

The input image for segmentation.

 required

Returns:

Type Description
SolutionResults

Object containing the annotated image and total number of tracked instances.

Examples:

>>> segmenter = InstanceSegmentation()
>>> frame = cv2.imread("image.jpg")
>>> summary = segmenter.segment(frame)
>>> print(summary)
Source code in ultralytics/solutions/instance_segmentation.py 
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def process(self, im0):
    """
    Perform instance segmentation on the input image and annotate the results.

    Args:
        im0 (numpy.ndarray): The input image for segmentation.

    Returns:
        (SolutionResults): Object containing the annotated image and total number of tracked instances.

    Examples:
        >>> segmenter = InstanceSegmentation()
        >>> frame = cv2.imread("image.jpg")
        >>> summary = segmenter.segment(frame)
        >>> print(summary)
    """
    self.extract_tracks(im0)  # Extract tracks (bounding boxes, classes, and masks)
    annotator = SolutionAnnotator(im0, self.line_width)

    # Iterate over detected classes, track IDs, and segmentation masks
    if self.masks is None:
        self.LOGGER.warning("⚠️ No masks detected! Ensure you're using a supported Ultralytics segmentation model.")
    else:
        for cls, t_id, mask in zip(self.clss, self.track_ids, self.masks):
            # Annotate the image with segmentation mask, mask color, and label
            annotator.segmentation_mask(mask=mask, mask_color=colors(t_id, True), label=self.names[cls])

    plot_im = annotator.result()
    self.display_output(plot_im)  # Display the annotated output using the base class function

    # Return SolutionResults
    return SolutionResults(plot_im=plot_im, total_tracks=len(self.track_ids))



📅 Created 9 days ago ✏️ Updated 9 days ago
RizwanMunawar