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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 of classes 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

FAQ

How do I calculate distances between objects using Ultralytics YOLOv8?

To calculate distances between objects using Ultralytics YOLOv8, you need to identify the bounding box centroids of the detected objects. This process involves initializing the DistanceCalculation class from Ultralytics' solutions module and using the model's tracking outputs to calculate the distances. You can refer to the implementation in the distance calculation example.

What are the advantages of using distance calculation with Ultralytics YOLOv8?

Using distance calculation with Ultralytics YOLOv8 offers several advantages:

Localization Precision: Provides accurate spatial positioning for objects.
Size Estimation: Helps estimate physical sizes, contributing to better contextual understanding.
Scene Understanding: Enhances 3D scene comprehension, aiding improved decision-making in applications like autonomous driving and surveillance.

Can I perform distance calculation in real-time video streams with Ultralytics YOLOv8?

Yes, you can perform distance calculation in real-time video streams with Ultralytics YOLOv8. The process involves capturing video frames using OpenCV, running YOLOv8 object detection, and using the DistanceCalculation class to calculate distances between objects in successive frames. For a detailed implementation, see the video stream example.

How do I delete points drawn during distance calculation using Ultralytics YOLOv8?

To delete points drawn during distance calculation with Ultralytics YOLOv8, you can use a right mouse click. This action will clear all the points you have drawn. For more details, refer to the note section under the distance calculation example.

What are the key arguments for initializing the DistanceCalculation class in Ultralytics YOLOv8?

The key arguments for initializing the DistanceCalculation class in Ultralytics YOLOv8 include:

names: Dictionary mapping class indices to class names.
pixels_per_meter: Conversion factor from pixels to meters.
view_img: Flag to indicate if the video stream should be displayed.
line_thickness: Thickness of the lines drawn on the image.
line_color: Color of the lines drawn on the image (BGR format).
centroid_color: Color of the centroids (BGR format).

For an exhaustive list and default values, see the arguments of DistanceCalculation.

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