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Reference for ultralytics/solutions/trackzone.py

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


ultralytics.solutions.trackzone.TrackZone 

TrackZone(**kwargs)

Bases: BaseSolution

A class to manage region-based object tracking in a video stream.

This class extends the BaseSolution class and provides functionality for tracking objects within a specific region defined by a polygonal area. Objects outside the region are excluded from tracking.

Attributes:

Name Type Description
region ndarray

The polygonal region for tracking, represented as a convex hull of points.
line_width int

Width of the lines used for drawing bounding boxes and region boundaries.
names List[str]

List of class names that the model can detect.
boxes List[ndarray]

Bounding boxes of tracked objects.
track_ids List[int]

Unique identifiers for each tracked object.
clss List[int]

Class indices of tracked objects.

Methods:

Name Description
process

Processes each frame of the video, applying region-based tracking.
extract_tracks

Extracts tracking information from the input frame.
display_output

Displays the processed output.

Examples:

>>> tracker = TrackZone()
>>> frame = cv2.imread("frame.jpg")
>>> results = tracker.process(frame)
>>> cv2.imshow("Tracked Frame", results.plot_im)

Parameters:

Name Type Description Default
**kwargs Any

Additional keyword arguments passed to the parent class.

 {}
Source code in ultralytics/solutions/trackzone.py 
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def __init__(self, **kwargs):
    """
    Initialize the TrackZone class for tracking objects within a defined region in video streams.

    Args:
        **kwargs (Any): Additional keyword arguments passed to the parent class.
    """
    super().__init__(**kwargs)
    default_region = [(150, 150), (1130, 150), (1130, 570), (150, 570)]
    self.region = cv2.convexHull(np.array(self.region or default_region, dtype=np.int32))

process 

process(im0)

Process the input frame to track objects within a defined region.

This method initializes the annotator, creates a mask for the specified region, extracts tracks only from the masked area, and updates tracking information. Objects outside the region are ignored.

Parameters:

Name Type Description Default
im0 ndarray

The input image or frame to be processed.

 required

Returns:

Type Description
SolutionResults

Contains processed image plot_im and total_tracks (int) representing the total number of tracked objects within the defined region.

Examples:

>>> tracker = TrackZone()
>>> frame = cv2.imread("path/to/image.jpg")
>>> results = tracker.process(frame)
Source code in ultralytics/solutions/trackzone.py 
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def process(self, im0):
    """
    Process the input frame to track objects within a defined region.

    This method initializes the annotator, creates a mask for the specified region, extracts tracks
    only from the masked area, and updates tracking information. Objects outside the region are ignored.

    Args:
        im0 (np.ndarray): The input image or frame to be processed.

    Returns:
        (SolutionResults): Contains processed image `plot_im` and `total_tracks` (int) representing the
                           total number of tracked objects within the defined region.

    Examples:
        >>> tracker = TrackZone()
        >>> frame = cv2.imread("path/to/image.jpg")
        >>> results = tracker.process(frame)
    """
    annotator = SolutionAnnotator(im0, line_width=self.line_width)  # Initialize annotator

    # Create a mask for the region and extract tracks from the masked image
    mask = np.zeros_like(im0[:, :, 0])
    mask = cv2.fillPoly(mask, [self.region], 255)
    masked_frame = cv2.bitwise_and(im0, im0, mask=mask)
    self.extract_tracks(masked_frame)

    # Draw the region boundary
    cv2.polylines(im0, [self.region], isClosed=True, color=(255, 255, 255), thickness=self.line_width * 2)

    # Iterate over boxes, track ids, classes indexes list and draw bounding boxes
    for box, track_id, cls in zip(self.boxes, self.track_ids, self.clss):
        annotator.box_label(box, label=f"{self.names[cls]}:{track_id}", color=colors(track_id, True))

    plot_im = annotator.result()
    self.display_output(plot_im)  # display output with base class function

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



📅 Created 3 months ago ✏️ Updated 3 months ago
RizwanMunawar