--- comments: true description: Learn how to calculate distances between objects using Ultralytics YOLOv8 for accurate spatial positioning and scene understanding. keywords: 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](https://github.com/ultralytics/ultralytics), the [bounding box](https://www.ultralytics.com/glossary/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 | | :---------------------------------------------------------------------------------------------------------------------------: | | ![Ultralytics YOLOv8 Distance Calculation](https://github.com/ultralytics/docs/releases/download/0/distance-calculation.avif) | ## Advantages of Distance Calculation? - **Localization [Precision](https://www.ultralytics.com/glossary/precision):** Enhances accurate spatial positioning in [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks. - **Size Estimation:** Allows estimation of object size for better contextual understanding. ???+ 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() ``` ???+ note - Mouse Right Click will delete all drawn points - Mouse Left Click can be used to draw points ???+ warning "Distance is Estimate" Distance will be an estimate and may not be fully accurate, as it is calculated using 2-dimensional data, which lacks information about the object's depth. ### Arguments `DistanceCalculation()` | `Name` | `Type` | `Default` | Description | | ---------------- | ------- | --------------- | --------------------------------------------------------- | | `names` | `dict` | `None` | Dictionary of classes names. | | `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` {% include "macros/track-args.md" %} ## FAQ ### How do I calculate distances between objects using Ultralytics YOLOv8? To calculate distances between objects using [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics), 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](#distance-calculation-using-ultralytics-yolov8). ### 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](https://www.ultralytics.com/glossary/opencv), running YOLOv8 [object detection](https://www.ultralytics.com/glossary/object-detection), and using the `DistanceCalculation` class to calculate distances between objects in successive frames. For a detailed implementation, see the [video stream example](#distance-calculation-using-ultralytics-yolov8). ### 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](#distance-calculation-using-ultralytics-yolov8). ### 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. - `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](#arguments-distancecalculation).