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

comments	description	keywords
true	Learn how to calculate distances between objects using Ultralytics YOLOv8 for accurate spatial positioning and scene understanding.	Ultralytics, YOLOv8, distance calculation, computer vision, object tracking, spatial positioning

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
    import cv2

    from ultralytics import YOLO, solutions

    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 = solutions.DistanceCalculation(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

Arguments DistanceCalculation()

Name	Type	Default	Description
names	dict	None	Dictionary mapping class indices to class names.
pixels_per_meter	int	10	Conversion factor from pixels to meters.
view_img	bool	False	Flag to indicate if the video stream should be displayed.
line_thickness	int	2	Thickness of the lines drawn on the image.
line_color	tuple	(255, 255, 0)	Color of the lines drawn on the image (BGR format).
centroid_color	tuple	(255, 0, 255)	Color of the centroids drawn (BGR format).

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