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Reference for ultralytics/solutions/vision_eye.py

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


ultralytics.solutions.vision_eye.VisionEye 

VisionEye(**kwargs)

Bases: BaseSolution

A class to manage object detection and vision mapping in images or video streams.

This class extends the BaseSolution class and provides functionality for detecting objects, mapping vision points, and annotating results with bounding boxes and labels.

Attributes:

Name Type Description
vision_point Tuple[int, int]

Coordinates (x, y) where vision will view objects and draw tracks.

Methods:

Name Description
process

Process the input image to detect objects, annotate them, and apply vision mapping.

Examples:

>>> vision_eye = VisionEye()
>>> frame = cv2.imread("frame.jpg")
>>> results = vision_eye.process(frame)
>>> print(f"Total detected instances: {results.total_tracks}")

Parameters:

Name Type Description Default
**kwargs Any

Keyword arguments passed to the parent class and for configuring vision_point.

 {}
Source code in ultralytics/solutions/vision_eye.py 
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def __init__(self, **kwargs):
    """
    Initialize the VisionEye class for detecting objects and applying vision mapping.

    Args:
        **kwargs (Any): Keyword arguments passed to the parent class and for configuring vision_point.
    """
    super().__init__(**kwargs)
    # Set the vision point where the system will view objects and draw tracks
    self.vision_point = kwargs.get("vision_point", (30, 30))

process 

process(im0)

Perform object detection, vision mapping, and annotation on the input image.

Parameters:

Name Type Description Default
im0 ndarray

The input image for detection and annotation.

 required

Returns:

Type Description
SolutionResults

Object containing the annotated image and tracking statistics. - plot_im: Annotated output image with bounding boxes and vision mapping - total_tracks: Number of tracked objects in the frame

Examples:

>>> vision_eye = VisionEye()
>>> frame = cv2.imread("image.jpg")
>>> results = vision_eye.process(frame)
>>> print(f"Detected {results.total_tracks} objects")
Source code in ultralytics/solutions/vision_eye.py 
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def process(self, im0):
    """
    Perform object detection, vision mapping, and annotation on the input image.

    Args:
        im0 (numpy.ndarray): The input image for detection and annotation.

    Returns:
        (SolutionResults): Object containing the annotated image and tracking statistics.
            - plot_im: Annotated output image with bounding boxes and vision mapping
            - total_tracks: Number of tracked objects in the frame

    Examples:
        >>> vision_eye = VisionEye()
        >>> frame = cv2.imread("image.jpg")
        >>> results = vision_eye.process(frame)
        >>> print(f"Detected {results.total_tracks} objects")
    """
    self.extract_tracks(im0)  # Extract tracks (bounding boxes, classes, and masks)
    annotator = SolutionAnnotator(im0, self.line_width)

    for cls, t_id, box in zip(self.clss, self.track_ids, self.boxes):
        # Annotate the image with bounding boxes, labels, and vision mapping
        annotator.box_label(box, label=self.names[cls], color=colors(int(t_id), True))
        annotator.visioneye(box, self.vision_point)

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

    # Return a SolutionResults object with the annotated image and tracking statistics
    return SolutionResults(plot_im=plot_im, total_tracks=len(self.track_ids))



📅 Created 9 days ago ✏️ Updated 9 days ago
RizwanMunawar