Ultralytics YOLOv8 object detection model to detect objects in a video stream from a webcam and stream the annotated video to a virtual camera device.

yolov8_virtualcam.py

"""
pip install ultralytics pyvirtualcam

Enable virtual camera on Linux:

modprobe v4l2loopback devices=1 max_buffers=2 exclusive_caps=1 card_label="VirtualCam"

# Remove v4l2
modprobe -r v4l2loopback
"""

from ultralytics import YOLO
from pyvirtualcam import PixelFormat
import cv2
import pyvirtualcam

VIDEO_INPUT_DEVICE = 0
# VIDEO_INPUT_DEVICE = "/home/tun/Videos/inbound499628027817585732.mp4"
VIRTUAL_CAM_DEVICE = "/dev/video2"
MODEL_PATH = "yolov8n.pt"

# Load the YOLOv8n model
model = YOLO(MODEL_PATH)

# Start the webcam
cap = cv2.VideoCapture(VIDEO_INPUT_DEVICE)

width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = cap.get(cv2.CAP_PROP_FPS)

print("[-] width: {} height: {} fps: {}".format(width, height, fps))

with pyvirtualcam.Camera(
    width=width, height=height, fps=fps, device=VIRTUAL_CAM_DEVICE, fmt=PixelFormat.BGR
) as cam:
    print(f"Virtual cam started: {cam.device} ({cam.width}x{cam.height} @ {cam.fps}fps)")

    # Start streaming
    while True:
        # Read the next frame from the webcam
        ret, frame = cap.read()

        # If the frame is empty, break the loop
        if not ret:
            print("No frame")
            break

        # Detect objects in the frame
        results = model(frame)

        # Visualize the results on the frame
        annotated_frame = results[0].plot()

        # Write the frame to the v4l2loopback device
        cam.send(annotated_frame)

        cam.sleep_until_next_frame()

    # Release the webcam and v4l2loopback device
    cap.release()