2.4 KiB
2.4 KiB
How to build your own cameras
Define your own config for cameras (unitree_deploy/robot_devices/cameras/config.py)
@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)
# 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)
# 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.