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96 lines
4.7 KiB
96 lines
4.7 KiB
--- |
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comments: true |
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description: Distance Calculation Using Ultralytics YOLOv8 |
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keywords: Ultralytics, YOLOv8, Object Detection, Distance Calculation, Object Tracking, Notebook, IPython Kernel, CLI, Python SDK |
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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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## Visuals |
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| Distance Calculation using Ultralytics YOLOv8 | |
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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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from ultralytics import YOLO |
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from ultralytics.solutions import distance_calculation |
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import cv2 |
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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", |
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cv2.VideoWriter_fourcc(*'mp4v'), |
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fps, |
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(w, h)) |
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# Init distance-calculation obj |
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dist_obj = distance_calculation.DistanceCalculation() |
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dist_obj.set_args(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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### Optional Arguments `set_args` |
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| Name | Type | Default | Description | |
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|------------------|--------|-----------------|--------------------------------------------------------| |
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| `names` | `dict` | `None` | Classes names | |
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| `view_img` | `bool` | `False` | Display frames with counts | |
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| `line_thickness` | `int` | `2` | Increase bounding boxes thickness | |
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| `line_color` | `RGB` | `(255, 255, 0)` | Line Color for centroids mapping on two bounding boxes | |
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| `centroid_color` | `RGB` | `(255, 0, 255)` | Centroid color for each bounding box | |
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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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