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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: ayush chaurasia <ayush.chaurarsia@gmail.com> Co-authored-by: Muhammad Rizwan Munawar <chr043416@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: 曾逸夫（Zeng Yifu） <41098760+Zengyf-CVer@users.noreply.github.com>
90 lines
4.2 KiB
Markdown
90 lines
4.2 KiB
Markdown
---
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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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## 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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