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Add camera device to inference sources (#16866)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Mohammed Yasin 2024-10-13 02:11:30 +08:00 • committed by GitHub
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docs/en/modes/predict.md
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@ -120,6 +120,7 @@ YOLO11 can process different types of input sources for inference, as shown in t
| YouTube ✅ | `'https://youtu.be/LNwODJXcvt4'` | `str` | URL to a YouTube video. | | YouTube ✅ | `'https://youtu.be/LNwODJXcvt4'` | `str` | URL to a YouTube video. |
| stream ✅ | `'rtsp://example.com/media.mp4'` | `str` | URL for streaming protocols such as RTSP, RTMP, TCP, or an IP address. | | stream ✅ | `'rtsp://example.com/media.mp4'` | `str` | URL for streaming protocols such as RTSP, RTMP, TCP, or an IP address. |
| multi-stream ✅ | `'list.streams'` | `str` or `Path` | `*.streams` text file with one stream URL per row, i.e. 8 streams will run at batch-size 8. | | multi-stream ✅ | `'list.streams'` | `str` or `Path` | `*.streams` text file with one stream URL per row, i.e. 8 streams will run at batch-size 8. |
| webcam ✅ | `0` | `int` | Index of the connected camera device to run inference on. |
Below are code examples for using each source type: Below are code examples for using each source type:
@ -376,6 +377,20 @@ Below are code examples for using each source type:
Each row in the file represents a streaming source, allowing you to monitor and perform inference on several video streams at once. Each row in the file represents a streaming source, allowing you to monitor and perform inference on several video streams at once.
=== "Webcam"
You can run inference on a connected camera device by passing the index of that particular camera to `source`.
```python
from ultralytics import YOLO
# Load a pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Run inference on the source
results = model(source=0, stream=True) # generator of Results objects
```
## Inference Arguments ## Inference Arguments
`model.predict()` accepts multiple arguments that can be passed at inference time to override defaults: `model.predict()` accepts multiple arguments that can be passed at inference time to override defaults: