124 lines
6.3 KiB
Markdown
124 lines
6.3 KiB
Markdown
---
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comments: true
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description: Learn how to calculate distances between objects using Ultralytics YOLO11 for accurate spatial positioning and scene understanding.
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keywords: Ultralytics, YOLO11, distance calculation, computer vision, object tracking, spatial positioning
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---
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# Distance Calculation using Ultralytics YOLO11
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## What is Distance Calculation?
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Measuring the gap between two objects is known as distance calculation within a specified space. In the case of [Ultralytics YOLO11](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.
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<p align="center">
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<br>
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<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/LE8am1QoVn4"
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title="YouTube video player" frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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allowfullscreen>
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</iframe>
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<br>
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<strong>Watch:</strong> Distance Calculation using Ultralytics YOLO11
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</p>
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## Visuals
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| Distance Calculation using Ultralytics YOLO11 |
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| :---------------------------------------------------------------------------------------------------------------------------: |
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|  |
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## Advantages of Distance Calculation?
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- **Localization [Precision](https://www.ultralytics.com/glossary/precision):** Enhances accurate spatial positioning in [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks.
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- **Size Estimation:** Allows estimation of object size for better contextual understanding.
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???+ tip "Distance Calculation"
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- Click on any two bounding boxes with Left Mouse click for distance calculation
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!!! example "Distance Calculation using YOLO11 Example"
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=== "Video Stream"
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```python
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import cv2
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from ultralytics import solutions
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cap = cv2.VideoCapture("Path/to/video/file.mp4")
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assert cap.isOpened(), "Error reading video file"
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
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# Video writer
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video_writer = cv2.VideoWriter("distance_calculation.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
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# Init distance-calculation obj
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distance = solutions.DistanceCalculation(model="yolo11n.pt", show=True)
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# Process video
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while cap.isOpened():
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success, im0 = cap.read()
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if not success:
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print("Video frame is empty or video processing has been successfully completed.")
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break
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im0 = distance.calculate(im0)
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video_writer.write(im0)
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cap.release()
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video_writer.release()
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cv2.destroyAllWindows()
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```
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???+ note
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- Mouse Right Click will delete all drawn points
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- Mouse Left Click can be used to draw points
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???+ warning "Distance is Estimate"
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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.
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### Arguments `DistanceCalculation()`
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| `Name` | `Type` | `Default` | Description |
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| ------------ | ------ | --------- | ---------------------------------------------------- |
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| `model` | `str` | `None` | Path to Ultralytics YOLO Model File |
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| `line_width` | `int` | `2` | Line thickness for bounding boxes. |
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| `show` | `bool` | `False` | Flag to control whether to display the video stream. |
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### Arguments `model.track`
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{% include "macros/track-args.md" %}
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## FAQ
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### How do I calculate distances between objects using Ultralytics YOLO11?
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To calculate distances between objects using [Ultralytics YOLO11](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-yolo11).
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### What are the advantages of using distance calculation with Ultralytics YOLO11?
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Using distance calculation with Ultralytics YOLO11 offers several advantages:
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- **Localization Precision:** Provides accurate spatial positioning for objects.
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- **Size Estimation:** Helps estimate physical sizes, contributing to better contextual understanding.
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- **Scene Understanding:** Enhances 3D scene comprehension, aiding improved decision-making in applications like autonomous driving and surveillance.
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### Can I perform distance calculation in real-time video streams with Ultralytics YOLO11?
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Yes, you can perform distance calculation in real-time video streams with Ultralytics YOLO11. The process involves capturing video frames using [OpenCV](https://www.ultralytics.com/glossary/opencv), running YOLO11 [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-yolo11).
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### How do I delete points drawn during distance calculation using Ultralytics YOLO11?
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To delete points drawn during distance calculation with Ultralytics YOLO11, 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-yolo11).
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### What are the key arguments for initializing the DistanceCalculation class in Ultralytics YOLO11?
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The key arguments for initializing the `DistanceCalculation` class in Ultralytics YOLO11 include:
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- `model`: Model file path.
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- `show`: Flag to indicate if the video stream should be displayed.
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- `line_width`: Thickness of bounding box and the lines drawn on the image.
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For an exhaustive list and default values, see the [arguments of DistanceCalculation](#arguments-distancecalculation).
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