mirror of
https://github.com/THU-MIG/yolov10.git
synced 2025-05-24 06:07:03 +08:00
90 lines
4.1 KiB
Markdown
90 lines
4.1 KiB
Markdown
---
|
|
comments: true
|
|
description: Distance Calculation Using Ultralytics YOLOv8
|
|
keywords: Ultralytics, YOLOv8, Object Detection, Distance Calculation, Object Tracking, Notebook, IPython Kernel, CLI, Python SDK
|
|
---
|
|
|
|
# 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 centroid is employed to calculate the distance for bounding boxes highlighted by the user.
|
|
|
|
## Advantages of Distance Calculation?
|
|
|
|
- **Localization Precision:** Enhances accurate spatial positioning in computer vision tasks.
|
|
- **Size Estimation:** Allows estimation of physical sizes for better contextual understanding.
|
|
- **Scene Understanding:** Contributes to a 3D understanding of the environment for improved decision-making.
|
|
|
|
???+ 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
|
|
from ultralytics import YOLO
|
|
from ultralytics.solutions import distance_calculation
|
|
import cv2
|
|
|
|
model = YOLO("yolov8n.pt")
|
|
names = model.model.names
|
|
|
|
cap = cv2.VideoCapture("path/to/video/file.mp4")
|
|
assert cap.isOpened(), "Error reading video file"
|
|
|
|
# Video writer
|
|
video_writer = cv2.VideoWriter("distance_calculation.avi",
|
|
cv2.VideoWriter_fourcc(*'mp4v'),
|
|
int(cap.get(5)),
|
|
(int(cap.get(3)), int(cap.get(4))))
|
|
|
|
# Init distance-calculation obj
|
|
dist_obj = distance_calculation.DistanceCalculation()
|
|
dist_obj.set_args(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()
|
|
|
|
```
|
|
|
|
???+ tip "Note"
|
|
|
|
- Mouse Right Click will delete all drawn points
|
|
- Mouse Left Click can be used to draw points
|
|
|
|
|
|
### Optional Arguments `set_args`
|
|
|
|
| Name | Type | Default | Description |
|
|
|----------------|--------|-----------------|--------------------------------------------------------|
|
|
| names | `dict` | `None` | Classes names |
|
|
| view_img | `bool` | `False` | Display frames with counts |
|
|
| line_thickness | `int` | `2` | Increase bounding boxes thickness |
|
|
| line_color | `RGB` | `(255, 255, 0)` | Line Color for centroids mapping on two bounding boxes |
|
|
| centroid_color | `RGB` | `(255, 0, 255)` | Centroid color for each bounding box |
|
|
|
|
### Arguments `model.track`
|
|
|
|
| Name | Type | Default | Description |
|
|
|-----------|---------|----------------|-------------------------------------------------------------|
|
|
| `source` | `im0` | `None` | source directory for images or videos |
|
|
| `persist` | `bool` | `False` | persisting tracks between frames |
|
|
| `tracker` | `str` | `botsort.yaml` | Tracking method 'bytetrack' or 'botsort' |
|
|
| `conf` | `float` | `0.3` | Confidence Threshold |
|
|
| `iou` | `float` | `0.5` | IOU Threshold |
|
|
| `classes` | `list` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] |
|
|
| `verbose` | `bool` | `True` | Display the object tracking results |
|