ultralytics/docs/en/guides/instance-segmentation-and-tracking.md

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true	Master instance segmentation and tracking with Ultralytics YOLOv8. Learn techniques for precise object identification and tracking.	instance segmentation, tracking, YOLOv8, Ultralytics, object detection, machine learning, computer vision, python

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.

Watch: Instance Segmentation with Object Tracking using Ultralytics YOLOv8

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):
                color = colors(int(cls), True)
                txt_color = annotator.get_txt_color(color)
                annotator.seg_bbox(mask=mask, mask_color=color, label=names[int(cls)], txt_color=txt_color)

        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
    from collections import defaultdict

    import cv2

    from ultralytics import YOLO
    from ultralytics.utils.plotting import Annotator, colors

    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):
                color = colors(int(track_id), True)
                txt_color = annotator.get_txt_color(color)
                annotator.seg_bbox(mask=mask, mask_color=color, label=str(track_id), txt_color=txt_color)

        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	RGB	(255, 0, 255)	Mask color for every segmented box
label	str	None	Label for segmented object
txt_color	RGB	None	Label color 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.

FAQ

How do I perform instance segmentation using Ultralytics YOLOv8?

To perform instance segmentation using Ultralytics YOLOv8, initialize the YOLO model with a segmentation version of YOLOv8 and process video frames through it. Here's a simplified code example:

!!! example

=== "Python"

    ```python
    import cv2

    from ultralytics import YOLO
    from ultralytics.utils.plotting import Annotator, colors

    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.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))

    while True:
        ret, im0 = cap.read()
        if not ret:
            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=model.model.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()
    ```

Learn more about instance segmentation in the Ultralytics YOLOv8 guide.

What is the difference between instance segmentation and object tracking in Ultralytics YOLOv8?

Instance segmentation identifies and outlines individual objects within an image, giving each object a unique label and mask. Object tracking extends this by assigning consistent labels to objects across video frames, facilitating continuous tracking of the same objects over time. Learn more about the distinctions in the Ultralytics YOLOv8 documentation.

Why should I use Ultralytics YOLOv8 for instance segmentation and tracking over other models like Mask R-CNN or Faster R-CNN?

Ultralytics YOLOv8 offers real-time performance, superior accuracy, and ease of use compared to other models like Mask R-CNN or Faster R-CNN. YOLOv8 provides a seamless integration with Ultralytics HUB, allowing users to manage models, datasets, and training pipelines efficiently. Discover more about the benefits of YOLOv8 in the Ultralytics blog.

How can I implement object tracking using Ultralytics YOLOv8?

To implement object tracking, use the model.track method and ensure that each object's ID is consistently assigned across frames. Below is a simple example:

!!! example

=== "Python"

    ```python
    from collections import defaultdict

    import cv2

    from ultralytics import YOLO
    from ultralytics.utils.plotting import Annotator, colors

    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:
            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()
    ```

Find more in the Instance Segmentation and Tracking section.

Are there any datasets provided by Ultralytics suitable for training YOLOv8 models for instance segmentation and tracking?

Yes, Ultralytics offers several datasets suitable for training YOLOv8 models, including segmentation and tracking datasets. Dataset examples, structures, and instructions for use can be found in the Ultralytics Datasets documentation.