# How to build your own cameras ### Define your own config for cameras (unitree_deploy/robot_devices/cameras/config.py) ```python @CameraConfig.register_subclass("opencv") # Define and wrap your own cameras. Here use def __init__(self, config: OpenCVCameraConfig): @dataclass class OpenCVCameraConfig(CameraConfig): """ Example of tested options for Intel Real Sense D405: OpenCVCameraConfig(0, 30, 640, 480) OpenCVCameraConfig(0, 60, 640, 480) OpenCVCameraConfig(0, 90, 640, 480) OpenCVCameraConfig(0, 30, 1280, 720) """ # Define the required camera parameters camera_index: int fps: int | None = None width: int | None = None height: int | None = None color_mode: str = "rgb" channels: int | None = None rotation: int | None = None mock: bool = False def __post_init__(self): if self.color_mode not in ["rgb", "bgr"]: raise ValueError( f"`color_mode` is expected to be 'rgb' or 'bgr', but {self.color_mode} is provided." ) self.channels = 3 if self.rotation not in [-90, None, 90, 180]: raise ValueError(f"`rotation` must be in [-90, None, 90, 180] (got {self.rotation})") # Default parameters go first [parameters that need to be customized], # Non-default parameters go later [fixed parameters] ``` ### Description of methods in your cameras class (unitree_deploy/robot_devices/cameras/utils.py) ```python # Base class for cameras, extensible with required methods class Camera(Protocol): def connect(self): ... def read(self, temporary_color: str | None = None) -> np.ndarray: ... # Single-threaded reading def async_read(self) -> np.ndarray: ... # Multi-threaded def disconnect(self): ... ``` How can external modules implement calls? Use **make_cameras_from_configs [based on configuration files]** to construct the `UnitreeRobot` class. **make_camera [based on camera_type]** is generally used for external module loading. ### Implementation of the `camera` class (unitree_deploy/robot_devices/camera/.../....py) ```python # These need to be completed, focusing on implementing these two parts def read(self, temporary_color: str | None = None) -> np.ndarray: ... # Single-threaded reading def async_read(self) -> np.ndarray: ... # Multi-threaded ``` All cameras use threading to implement `async_read` for internal read and write operations.