hub-camera

# Camera & Video Capture Nodes > Capture video frames from cameras and depth sensors ## Available Camera Nodes | Node | Install | Description | Platform | |------|---------|-------------|----------| | opencv-video-capture | `pip install opencv-video-capture` | OpenCV camera/video | All | | dora-pyrealsense | `pip install dora-pyrealsense` | Intel RealSense depth | Linux | | dora-pyorbbecksdk | `pip install dora-pyorbbecksdk` | Orbbeck depth camera | All | ## opencv-video-capture OpenCV-based video capture from webcam or video file. ### YAML Configuration ```yaml - id: camera build: pip install opencv-video-capture path: opencv-video-capture inputs: tick: dora/timer/millis/16 # ~60fps outputs: - image env: PATH: "0" # Camera index (0, 1, ...) or video file path IMAGE_WIDTH: 640 # Optional: output width IMAGE_HEIGHT: 480 # Optional: output height ``` ### Outputs **image** - UInt8Array with metadata: ```python metadata = { "width": 640, "height": 480, "encoding": "bgr8", # or "rgb8" "primitive": "image" # for dora-rerun } ``` ### Decoding Output ```python storage = event["value"] metadata = event["metadata"] width = metadata["width"] height = metadata["height"] encoding = metadata["encoding"] channels = 3 frame = storage.to_numpy().astype(np.uint8).reshape((height, width, channels)) # Convert BGR to RGB if needed if encoding == "bgr8": frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) ``` ## dora-pyrealsense Intel RealSense camera with RGB and depth streams. ### YAML Configuration ```yaml - id: realsense build: pip install dora-pyrealsense path: dora-pyrealsense inputs: tick: dora/timer/millis/33 # ~30fps outputs: - image # RGB image - depth # Depth image env: IMAGE_WIDTH: 640 IMAGE_HEIGHT: 480 ``` ### Outputs **image** - RGB UInt8Array ```python metadata = {"width": 640, "height": 480, "encoding": "rgb8"} ``` **depth** - Float32Array depth values (meters) ```python metadata = {"width": 640, "height": 480} ``` ## dora-pyorbbecksdk Orbbeck depth camera support. ### YAML Configuration ```yaml - id: orbbeck build: pip install dora-pyorbbecksdk path: dora-pyorbbecksdk inputs: tick: dora/timer/millis/33 outputs: - image - depth ``` ## Image Data Format All camera nodes output images in Apache Arrow format: ```python import pyarrow as pa import numpy as np # Encoding image image_data = pa.array(frame.ravel()) # UInt8Array # Sending node.send_output("image", image_data, { "width": 640, "height": 480, "encoding": "bgr8", "primitive": "image" # for dora-rerun visualization }) ``` ## Common Patterns ### Camera with Detection Pipeline ```yaml nodes: - id: camera build: pip install opencv-video-capture path: opencv-video-capture inputs: tick: dora/timer/millis/33 outputs: - image env: IMAGE_WIDTH: 640 IMAGE_HEIGHT: 480 - id: yolo build: pip install dora-yolo path: dora-yolo inputs: image: camera/image outputs: - bbox - id: rerun build: pip install dora-rerun path: dora-rerun inputs: image: camera/image detections: yolo/bbox ``` ### Dual Camera Setup ```yaml nodes: - id: camera_left build: pip install opencv-video-capture path: opencv-video-capture inputs: tick: dora/timer/millis/33 outputs: - image env: PATH: "0" - id: camera_right build: pip install opencv-video-capture path: opencv-video-capture inputs: tick: dora/timer/millis/33 outputs: - image env: PATH: "1" ``` ### Depth Camera with 3D Visualization ```yaml nodes: - id: realsense build: pip install dora-pyrealsense path: dora-pyrealsense inputs: tick: dora/timer/millis/33 outputs: - image - depth - id: rerun build: pip install dora-rerun path: dora-rerun inputs: rgb_camera: source: realsense/image metadata: primitive: "image" depth_sensor: source: realsense/depth metadata: primitive: "depth" focal: [600, 600] camera_position: [0, 0, 0] ``` ## Troubleshooting ### Camera not found ```bash # List available cameras v4l2-ctl --list-devices # Linux ``` ### Permission denied ```bash # Add user to video group (Linux) sudo usermod -a -G video $USER ``` ### Frame rate issues - Reduce resolution in env vars - Increase timer interval (e.g., millis/50 for 20fps) ## Related Skills - **hub-detection** - Object detection with YOLO, SAM2 - **hub-visualization** - Rerun visualization - **domain-vision** - Vision pipeline patterns