139 lines
7.6 KiB
Markdown
139 lines
7.6 KiB
Markdown
---
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comments: true
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description: Learn how to calculate distances between objects using Ultralytics YOLOv8 for accurate spatial positioning and scene understanding.
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keywords: Ultralytics, YOLOv8, distance calculation, computer vision, object tracking, spatial positioning
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---
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# Distance Calculation using Ultralytics YOLOv8
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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 YOLOv8](https://github.com/ultralytics/ultralytics), the 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 YOLOv8
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</p>
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## Visuals
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| Distance Calculation using Ultralytics YOLOv8 |
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| :---------------------------------------------------------------------------------------------------------------------------------------------: |
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|  |
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## Advantages of Distance Calculation?
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- **Localization Precision:** Enhances accurate spatial positioning in computer vision tasks.
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- **Size Estimation:** Allows estimation of physical sizes for better contextual understanding.
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- **Scene Understanding:** Contributes to a 3D understanding of the environment for improved decision-making.
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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 YOLOv8 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 YOLO, solutions
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model = YOLO("yolov8n.pt")
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names = model.model.names
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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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dist_obj = solutions.DistanceCalculation(names=names, view_img=True)
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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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tracks = model.track(im0, persist=True, show=False)
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im0 = dist_obj.start_process(im0, tracks)
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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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???+ tip "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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### Arguments `DistanceCalculation()`
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| `Name` | `Type` | `Default` | Description |
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| ------------------ | ------- | --------------- | --------------------------------------------------------- |
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| `names` | `dict` | `None` | Dictionary mapping class indices to class names. |
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| `pixels_per_meter` | `int` | `10` | Conversion factor from pixels to meters. |
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| `view_img` | `bool` | `False` | Flag to indicate if the video stream should be displayed. |
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| `line_thickness` | `int` | `2` | Thickness of the lines drawn on the image. |
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| `line_color` | `tuple` | `(255, 255, 0)` | Color of the lines drawn on the image (BGR format). |
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| `centroid_color` | `tuple` | `(255, 0, 255)` | Color of the centroids drawn (BGR format). |
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### Arguments `model.track`
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| Name | Type | Default | Description |
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| --------- | ------- | -------------- | ----------------------------------------------------------- |
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| `source` | `im0` | `None` | source directory for images or videos |
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| `persist` | `bool` | `False` | persisting tracks between frames |
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| `tracker` | `str` | `botsort.yaml` | Tracking method 'bytetrack' or 'botsort' |
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| `conf` | `float` | `0.3` | Confidence Threshold |
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| `iou` | `float` | `0.5` | IOU Threshold |
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| `classes` | `list` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] |
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| `verbose` | `bool` | `True` | Display the object tracking results |
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## FAQ
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### How do I calculate distances between objects using Ultralytics YOLOv8?
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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).
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### What are the advantages of using distance calculation with Ultralytics YOLOv8?
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Using distance calculation with Ultralytics YOLOv8 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 YOLOv8?
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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](#distance-calculation-using-ultralytics-yolov8).
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### How do I delete points drawn during distance calculation using Ultralytics YOLOv8?
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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).
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### What are the key arguments for initializing the DistanceCalculation class in Ultralytics YOLOv8?
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The key arguments for initializing the `DistanceCalculation` class in Ultralytics YOLOv8 include:
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- `names`: Dictionary mapping class indices to class names.
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- `pixels_per_meter`: Conversion factor from pixels to meters.
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- `view_img`: Flag to indicate if the video stream should be displayed.
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- `line_thickness`: Thickness of the lines drawn on the image.
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- `line_color`: Color of the lines drawn on the image (BGR format).
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- `centroid_color`: Color of the centroids (BGR format).
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For an exhaustive list and default values, see the [arguments of DistanceCalculation](#arguments-distancecalculation).
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