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---
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.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/LE8am1QoVn4"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Distance Calculation using Ultralytics YOLOv8
</p>
## Visuals
| Distance Calculation using Ultralytics YOLOv8 |
|:-----------------------------------------------------------------------------------------------------------------------------------------------:|
| ![Ultralytics YOLOv8 Distance Calculation](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/6b6b735d-3c49-4b84-a022-2bf6e3c72f8b) |
## 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 |