Virtual Cameras

Process camera frames in your own program first, for example with OpenCV or an AI model. Then write the result to a virtual camera, and stream it with pi-webrtc.

A virtual camera is a V4L2 loopback device. Your program writes frames to it, and pi-webrtc reads it like a normal V4L2 camera.

The examples on this page run on a Raspberry Pi. They read the camera with Picamera2.

1. Install the packages複製段落連結

sudo apt install v4l2loopback-dkms python3-opencv python3-picamera2

2. Create a virtual camera複製段落連結

This creates /dev/video42. A high number does not clash with real devices. On a Pi 5, for example, the CSI camera already uses /dev/video2 to /dev/video9.

The first command removes an old virtual camera, if there is one. Then the new settings always apply:

sudo modprobe -r v4l2loopback
sudo modprobe v4l2loopback devices=1 video_nr=42 card_label=ProcessedCam max_buffers=4 exclusive_caps=1

If modprobe -r says that the module is in use, stop the programs that use the virtual camera first.

Check that it exists:

v4l2-ctl --list-devices

You should see ProcessedCam with /dev/video42.

3. Write frames to the virtual camera複製段落連結

The virtual_cam.py example reads the camera, adds a timestamp, and writes YUV 4:2:0 (I420) frames to the virtual camera:

python3 examples/virtual_cam.py --width 1280 --height 720 --camera-id 0 --virtual-device /dev/video42

Leave it running.

To write frames from your own program, copy set_output_format() from the example. It sets the size and the format of the virtual camera.

4. Stream the virtual camera複製段落連結

In a second terminal, start pi-webrtc with the same size and the i420 format:

./pi-webrtc --camera=v4l2:42 --v4l2-format=i420 --width=1280 --height=720 --fps=30 --uid=my-pi --no-audio --use-whep

Object detection with YOLO複製段落連結

The yolo_cam.py example reads the camera, runs YOLO object detection, and draws a box around each object with OpenCV. It writes the result to the virtual camera from step 2, in place of virtual_cam.py.

1. Stop virtual_cam.py if it is running. Only one program can use the camera.

2. Create a Python virtual environment, and install the packages in it. --system-site-packages lets the environment use Picamera2 from apt. Install torch from the CPU index first:

   複製

   python3 -m venv --system-site-packages ~/yolo-venv
   source ~/yolo-venv/bin/activate
   pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
   pip install opencv-python ultralytics

   The default torch for 64-bit Arm also downloads NVIDIA CUDA packages. They are several GB, they fill up /tmp, and a Raspberry Pi cannot use them.

3. Run YOLO in the virtual environment. The first run downloads the model:

   複製

   python examples/yolo_cam.py --width 1280 --height 720 --camera-id 0 --virtual-device /dev/video42

4. In a second terminal, stream the result with the same pi-webrtc command as in step 4.

On a Pi 5, YOLO needs about 250 ms for each frame on the CPU. So the result plays at about 4 fps.

ROS 2 users can use the same idea to stream an image topic. See ROS.

Troubleshooting複製段落連結