ultralytics/docs/en/guides/distance-calculation.md

comments	description	keywords
true	Distance Calculation Using Ultralytics YOLOv8	Ultralytics, YOLOv8, Object Detection, Distance Calculation, Object Tracking, Notebook, IPython Kernel, CLI, Python SDK

Distance Calculation using Ultralytics YOLOv8 🚀

What is Distance Calculation?

Measuring the gap between two objects is known as distance calculation within a specified space. In the case of Ultralytics YOLOv8, the bounding box centroid is employed to calculate the distance for bounding boxes highlighted by the user.

Watch: Distance Calculation using Ultralytics YOLOv8

Visuals

Distance Calculation using Ultralytics YOLOv8

Advantages of Distance Calculation?

• Localization Precision: Enhances accurate spatial positioning in computer vision tasks.
• Size Estimation: Allows estimation of physical sizes for better contextual understanding.
• Scene Understanding: Contributes to a 3D understanding of the environment for improved decision-making.

???+ tip "Distance Calculation"

- Click on any two bounding boxes with Left Mouse click for distance calculation

!!! Example "Distance Calculation using YOLOv8 Example"

=== "Video Stream"

    ```python
    from ultralytics import YOLO
    from ultralytics.solutions import distance_calculation
    import cv2

    model = YOLO("yolov8n.pt")
    names = model.model.names

    cap = cv2.VideoCapture("path/to/video/file.mp4")
    assert cap.isOpened(), "Error reading video file"
    w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))

    # Video writer
    video_writer = cv2.VideoWriter("distance_calculation.avi",
                                   cv2.VideoWriter_fourcc(*'mp4v'),
                                   fps,
                                   (w, h))

    # Init distance-calculation obj
    dist_obj = distance_calculation.DistanceCalculation()
    dist_obj.set_args(names=names, view_img=True)

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

        tracks = model.track(im0, persist=True, show=False)
        im0 = dist_obj.start_process(im0, tracks)
        video_writer.write(im0)

    cap.release()
    video_writer.release()
    cv2.destroyAllWindows()

    ```

???+ tip "Note"

- Mouse Right Click will delete all drawn points
- Mouse Left Click can be used to draw points

Optional Arguments set_args

Name	Type	Default	Description
names	dict	None	Classes names
view_img	bool	False	Display frames with counts
line_thickness	int	2	Increase bounding boxes thickness
line_color	RGB	(255, 255, 0)	Line Color for centroids mapping on two bounding boxes
centroid_color	RGB	(255, 0, 255)	Centroid color for each bounding box

Arguments model.track

Name	Type	Default	Description
source	im0	None	source directory for images or videos
persist	bool	False	persisting tracks between frames
tracker	str	botsort.yaml	Tracking method 'bytetrack' or 'botsort'
conf	float	0.3	Confidence Threshold
iou	float	0.5	IOU Threshold
classes	list	None	filter results by class, i.e. classes=0, or classes=[0,2,3]
verbose	bool	True	Display the object tracking results