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comments	description	keywords
true	Instance Segmentation with Object Tracking using Ultralytics YOLOv8	Ultralytics, YOLOv8, Instance Segmentation, Object Detection, Object Tracking, Segbbox, Computer Vision, Notebook, IPython Kernel, CLI, Python SDK

Instance Segmentation and Tracking using Ultralytics YOLOv8 🚀

What is Instance Segmentation?

Ultralytics YOLOv8 instance segmentation involves identifying and outlining individual objects in an image, providing a detailed understanding of spatial distribution. Unlike semantic segmentation, it uniquely labels and precisely delineates each object, crucial for tasks like object detection and medical imaging.

There are two types of instance segmentation tracking available in the Ultralytics package:

Instance Segmentation with Class Objects: Each class object is assigned a unique color for clear visual separation.
Instance Segmentation with Object Tracks: Every track is represented by a distinct color, facilitating easy identification and tracking.

Samples

Instance Segmentation	Instance Segmentation + Object Tracking

Ultralytics Instance Segmentation 😍	Ultralytics Instance Segmentation with Object Tracking 🔥

!!! Example "Instance Segmentation and Tracking"

=== "Instance Segmentation"
    ```python
    import cv2
    from ultralytics import YOLO
    from ultralytics.utils.plotting import Annotator, colors

    model = YOLO("yolov8n-seg.pt")  # segmentation model
    names = model.model.names
    cap = cv2.VideoCapture("path/to/video/file.mp4")
    w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))

    out = cv2.VideoWriter('instance-segmentation.avi', cv2.VideoWriter_fourcc(*'MJPG'), fps, (w, h))

    while True:
        ret, im0 = cap.read()
        if not ret:
            print("Video frame is empty or video processing has been successfully completed.")
            break

        results = model.predict(im0)
        annotator = Annotator(im0, line_width=2)

        if results[0].masks is not None:
            clss = results[0].boxes.cls.cpu().tolist()
            masks = results[0].masks.xy
            for mask, cls in zip(masks, clss):
                annotator.seg_bbox(mask=mask,
                                   mask_color=colors(int(cls), True),
                                   det_label=names[int(cls)])

        out.write(im0)
        cv2.imshow("instance-segmentation", im0)

        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    out.release()
    cap.release()
    cv2.destroyAllWindows()

    ```

=== "Instance Segmentation with Object Tracking"
    ```python
    import cv2
    from ultralytics import YOLO
    from ultralytics.utils.plotting import Annotator, colors

    from collections import defaultdict

    track_history = defaultdict(lambda: [])

    model = YOLO("yolov8n-seg.pt")   # segmentation model
    cap = cv2.VideoCapture("path/to/video/file.mp4")
    w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))

    out = cv2.VideoWriter('instance-segmentation-object-tracking.avi', cv2.VideoWriter_fourcc(*'MJPG'), fps, (w, h))

    while True:
        ret, im0 = cap.read()
        if not ret:
            print("Video frame is empty or video processing has been successfully completed.")
            break

        annotator = Annotator(im0, line_width=2)

        results = model.track(im0, persist=True)

        if results[0].boxes.id is not None and results[0].masks is not None:
            masks = results[0].masks.xy
            track_ids = results[0].boxes.id.int().cpu().tolist()

            for mask, track_id in zip(masks, track_ids):
                annotator.seg_bbox(mask=mask,
                                   mask_color=colors(track_id, True),
                                   track_label=str(track_id))

        out.write(im0)
        cv2.imshow("instance-segmentation-object-tracking", im0)

        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    out.release()
    cap.release()
    cv2.destroyAllWindows()
    ```

`seg_bbox` Arguments

Name	Type	Default	Description
`mask`	`array`	`None`	Segmentation mask coordinates
`mask_color`	`tuple`	`(255, 0, 255)`	Mask color for every segmented box
`det_label`	`str`	`None`	Label for segmented object
`track_label`	`str`	`None`	Label for segmented and tracked object

Note

For any inquiries, feel free to post your questions in the Ultralytics Issue Section or the discussion section mentioned below.

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