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Update Raspberry Pi Camera Usage (#13028)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Lakshantha Dissanayake 2024-05-22 13:22:10 -07:00 • committed by GitHub
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docs/en/guides/raspberry-pi.md
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@ -269,41 +269,86 @@ rpicam-hello
Learn more about [`rpicam-hello` usage on official Raspberry Pi documentation](https://www.raspberrypi.com/documentation/computers/camera_software.html#rpicam-hello)
### Initiate TCP Stream with `rpicam-vid`
### Inference with Camera
We need to iniate a TCP stream from the connected camera so that we can use this stream URL as an input when we are inferencing later. Execute the following command to start the TCP stream.
There are 2 methods of using the Raspberry Pi Camera to inference YOLOv8 models.
```bash
rpicam-vid -n -t 0 --inline --listen -o tcp://127.0.0.1:8888
```
!!! Usage
!!! Tip
=== "Method 1"
Learn more about [`rpicam-vid` usage on official Raspberry Pi documentation](https://www.raspberrypi.com/documentation/computers/camera_software.html#rpicam-vid)
We can use `picamera2`which comes pre-installed with Raspberry Pi OS to access the camera and inference YOLOv8 models.
### Perform YOLOv8 Inference
!!! Example
With the TCP stream initiated, you can perform YOLOv8 inference.
=== "Python"
!!! Example
```python
import cv2
from picamera2 import Picamera2
from ultralytics import YOLO
=== "Python"
# Initialize the Picamera2
picam2 = Picamera2()
picam2.preview_configuration.main.size = (1280, 720)
picam2.preview_configuration.main.format = "RGB888"
picam2.preview_configuration.align()
picam2.configure("preview")
picam2.start()
```python
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO("yolov8n.pt")
# Load a YOLOv8n PyTorch model
model = YOLO("yolov8n.pt")
while True:
# Capture frame-by-frame
frame = picam2.capture_array()
# Run inference
results = model("tcp://127.0.0.1:8888")
```
=== "CLI"
# Run YOLOv8 inference on the frame
results = model(frame)
# Visualize the results on the frame
annotated_frame = results[0].plot()
# Display the resulting frame
cv2.imshow("Camera", annotated_frame)
# Break the loop if 'q' is pressed
if cv2.waitKey(1) == ord("q"):
break
# Release resources and close windows
cv2.destroyAllWindows()
```
=== "Method 2"
We need to initiate a TCP stream with `rpicam-vid` from the connected camera so that we can use this stream URL as an input when we are inferencing later. Execute the following command to start the TCP stream.
```bash
yolo predict model=yolov8n.pt source='tcp://127.0.0.1:8888'
rpicam-vid -n -t 0 --inline --listen -o tcp://127.0.0.1:8888
```
Learn more about [`rpicam-vid` usage on official Raspberry Pi documentation](https://www.raspberrypi.com/documentation/computers/camera_software.html#rpicam-vid)
!!! Example
=== "Python"
```python
from ultralytics import YOLO
# Load a YOLOv8n PyTorch model
model = YOLO("yolov8n.pt")
# Run inference
results = model("tcp://127.0.0.1:8888")
```
=== "CLI"
```bash
yolo predict model=yolov8n.pt source="tcp://127.0.0.1:8888"
```
!!! Tip
Check our document on [Inference Sources](https://docs.ultralytics.com/modes/predict/#inference-sources) if you want to change the image/ video input type
@ -314,11 +359,11 @@ There are a couple of best practices to follow in order to enable maximum perfor
1. Use an SSD
When using Raspberry Pi for 24x7 continued usage, it is recommend to use an SSD for the system because an SD card will not be able to withstand continuous writes and might get broken. With the onboard PCIe connector on the Raspberry Pi 5, now you can connect SSDs using an adapter such as the [NVMe Base for Raspberry Pi 5](https://shop.pimoroni.com/products/nvme-base).
When using Raspberry Pi for 24x7 continued usage, it is recommended to use an SSD for the system because an SD card will not be able to withstand continuous writes and might get broken. With the onboard PCIe connector on the Raspberry Pi 5, now you can connect SSDs using an adapter such as the [NVMe Base for Raspberry Pi 5](https://shop.pimoroni.com/products/nvme-base).
2. Flash without GUI
When flashing Raspberry Pi OS, you can choose to not install the Desktop environment (Raspberry Pi OS Lite) and this can save a little bit of RAM on the device, leaving more space for computer vision processing.
When flashing Raspberry Pi OS, you can choose to not install the Desktop environment (Raspberry Pi OS Lite) and this can save a bit of RAM on the device, leaving more space for computer vision processing.
## Next Steps