---
comments: true
description: Distance Calculation Using Ultralytics YOLOv8
keywords: 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](https://github.com/ultralytics/ultralytics), the bounding box centroid is employed to calculate the distance for bounding boxes highlighted by the user.

## 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                         |
