# DepthAI Python API DepthAI Python API can be found on Github [luxonis/depthai-python](https://github.com/luxonis/depthai-python). Below is the reference documentation for the Python API. ### depthai Kind: Package #### filters Kind: Package Parameters for filters ##### params Kind: Module Parameters for filters ###### depthai.filters.params.MedianFilter Kind: Class Members: MEDIAN_OFF KERNEL_3x3 KERNEL_5x5 KERNEL_7x7 ###### KERNEL_3x3: typing.ClassVar[MedianFilter] Kind: Class Variable ###### KERNEL_5x5: typing.ClassVar[MedianFilter] Kind: Class Variable ###### KERNEL_7x7: typing.ClassVar[MedianFilter] Kind: Class Variable ###### MEDIAN_OFF: typing.ClassVar[MedianFilter] Kind: Class Variable ###### __members__: typing.ClassVar[dict[str, MedianFilter]] Kind: Class Variable ###### __eq__(self, other: typing.Any) -> bool: bool Kind: Method ###### __getstate__(self) -> int: int Kind: Method ###### __hash__(self) -> int: int Kind: Method ###### __index__(self) -> int: int Kind: Method ###### __init__(self, value: int) Kind: Method ###### __int__(self) -> int: int Kind: Method ###### __ne__(self, other: typing.Any) -> bool: bool Kind: Method ###### __repr__(self) -> str: str Kind: Method ###### __setstate__(self, state: int) Kind: Method ###### __str__() Kind: Method ###### name Kind: Property ###### value Kind: Property ###### depthai.filters.params.SpatialFilter Kind: Class ###### __init__(self) Kind: Method ###### __str__(self) -> str: str Kind: Method ###### alpha Kind: Property The Alpha factor in an exponential moving average with Alpha=1 - no filter. Alpha = 0 - infinite filter. Determines the amount of smoothing. ###### alpha.setter(self, arg0: float) Kind: Method ###### delta Kind: Property Step-size boundary. Establishes the threshold used to preserve "edges". If the disparity value between neighboring pixels exceed the disparity threshold set by this delta parameter, then filtering will be temporarily disabled. Default value 0 means auto: 3 disparity integer levels. In case of subpixel mode it's 3*number of subpixel levels. ###### delta.setter(self, arg0: int) Kind: Method ###### enable Kind: Property Whether to enable or disable the filter. ###### enable.setter(self, arg0: bool) Kind: Method ###### holeFillingRadius Kind: Property An in-place heuristic symmetric hole-filling mode applied horizontally during the filter passes. Intended to rectify minor artefacts with minimal performance impact. Search radius for hole filling. ###### holeFillingRadius.setter(self, arg0: int) Kind: Method ###### numIterations Kind: Property Number of iterations over the image in both horizontal and vertical direction. ###### numIterations.setter(self, arg0: int) Kind: Method ###### depthai.filters.params.SpeckleFilter Kind: Class ###### __init__(self) Kind: Method ###### __str__(self) -> str: str Kind: Method ###### differenceThreshold Kind: Property Maximum difference between neighbor disparity pixels to put them into the same blob. Units in disparity integer levels. ###### differenceThreshold.setter(self, arg0: int) Kind: Method ###### enable Kind: Property Whether to enable or disable the filter. ###### enable.setter(self, arg0: bool) Kind: Method ###### speckleRange Kind: Property Speckle search range. ###### speckleRange.setter(self, arg0: int) Kind: Method ###### depthai.filters.params.TemporalFilter Kind: Class Temporal filtering with optional persistence. ###### depthai.filters.params.TemporalFilter.PersistencyMode Kind: Class Persistency algorithm type. Members: PERSISTENCY_OFF : VALID_8_OUT_OF_8 : VALID_2_IN_LAST_3 : VALID_2_IN_LAST_4 : VALID_2_OUT_OF_8 : VALID_1_IN_LAST_2 : VALID_1_IN_LAST_5 : VALID_1_IN_LAST_8 : PERSISTENCY_INDEFINITELY : ###### PERSISTENCY_INDEFINITELY: typing.ClassVar[TemporalFilter.PersistencyMode] Kind: Class Variable ###### PERSISTENCY_OFF: typing.ClassVar[TemporalFilter.PersistencyMode] Kind: Class Variable ###### VALID_1_IN_LAST_2: typing.ClassVar[TemporalFilter.PersistencyMode] Kind: Class Variable ###### VALID_1_IN_LAST_5: typing.ClassVar[TemporalFilter.PersistencyMode] Kind: Class Variable ###### VALID_1_IN_LAST_8: typing.ClassVar[TemporalFilter.PersistencyMode] Kind: Class Variable ###### VALID_2_IN_LAST_3: typing.ClassVar[TemporalFilter.PersistencyMode] Kind: Class Variable ###### VALID_2_IN_LAST_4: typing.ClassVar[TemporalFilter.PersistencyMode] Kind: Class Variable ###### VALID_2_OUT_OF_8: typing.ClassVar[TemporalFilter.PersistencyMode] Kind: Class Variable ###### VALID_8_OUT_OF_8: typing.ClassVar[TemporalFilter.PersistencyMode] Kind: Class Variable ###### __members__: typing.ClassVar[dict[str, TemporalFilter.PersistencyMode]] Kind: Class Variable ###### __eq__(self, other: typing.Any) -> bool: bool Kind: Method ###### __getstate__(self) -> int: int Kind: Method ###### __hash__(self) -> int: int Kind: Method ###### __index__(self) -> int: int Kind: Method ###### __init__(self, value: int) Kind: Method ###### __int__(self) -> int: int Kind: Method ###### __ne__(self, other: typing.Any) -> bool: bool Kind: Method ###### __repr__(self) -> str: str Kind: Method ###### __setstate__(self, state: int) Kind: Method ###### __str__(self) -> str: str Kind: Method ###### name Kind: Property ###### value Kind: Property ###### __init__(self) Kind: Method ###### __str__(self) -> str: str Kind: Method ###### alpha Kind: Property The Alpha factor in an exponential moving average with Alpha=1 - no filter. Alpha = 0 - infinite filter. Determines the extent of the temporal history that should be averaged. ###### alpha.setter(self, arg0: float) Kind: Method ###### delta Kind: Property Step-size boundary. Establishes the threshold used to preserve surfaces (edges). If the disparity value between neighboring pixels exceed the disparity threshold set by this delta parameter, then filtering will be temporarily disabled. Default value 0 means auto: 3 disparity integer levels. In case of subpixel mode it's 3*number of subpixel levels. ###### delta.setter(self, arg0: int) Kind: Method ###### enable Kind: Property Whether to enable or disable the filter. ###### enable.setter(self, arg0: bool) Kind: Method ###### persistencyMode Kind: Property Persistency mode. If the current disparity/depth value is invalid, it will be replaced by an older value, based on persistency mode. ###### persistencyMode.setter(self, arg0: ...) Kind: Method #### modelzoo Kind: Module Model Zoo ##### getDefaultCachePath() -> os.PathLike: os.PathLike Kind: Function Get the default cache path (where models are cached) ##### getDefaultModelsPath() -> os.PathLike: os.PathLike Kind: Function Get the default models path (where yaml files are stored) ##### getDownloadEndpoint() -> str: str Kind: Function Get the download endpoint (for model querying) ##### getHealthEndpoint() -> str: str Kind: Function Get the health endpoint (for internet check) ##### setDefaultCachePath(path: os.PathLike) Kind: Function Set the default cache path (where models are cached) Parameter ``path``: ##### setDefaultModelsPath(path: os.PathLike) Kind: Function Set the default models path (where yaml files are stored) Parameter ``path``: ##### setDownloadEndpoint(endpoint: str) Kind: Function Set the download endpoint (for model querying) Parameter ``endpoint``: ##### setHealthEndpoint(endpoint: str) Kind: Function Set the health endpoint (for internet check) Parameter ``endpoint``: #### nn_archive Kind: Package ##### v1 Kind: Module ###### depthai.nn_archive.v1.Config Kind: Class The main class of the multi/single-stage model config scheme (multi- stage models consists of interconnected single-stage models). @type config_version: str @ivar config_version: String representing config schema version in format 'x.y' where x is major version and y is minor version @type model: Model @ivar model: A Model object representing the neural network used in the archive. ###### __init__() Kind: Method ###### configVersion Kind: Property String representing config schema version in format 'x.y' where x is major version and y is minor version. ###### configVersion.setter(self, arg0: str | None) Kind: Method ###### model Kind: Property A Model object representing the neural network used in the archive. ###### model.setter(self, arg0: Model) Kind: Method ###### depthai.nn_archive.v1.DataType Kind: Class Data type of the input data (e.g., 'float32'). Represents all existing data types used in i/o streams of the model. Precision of the model weights. Data type of the output data (e.g., 'float32'). Members: BOOLEAN FLOAT16 FLOAT32 FLOAT64 INT4 INT8 INT16 INT32 INT64 UINT4 UINT8 UINT16 UINT32 UINT64 STRING ###### BOOLEAN: typing.ClassVar[DataType] Kind: Class Variable ###### FLOAT16: typing.ClassVar[DataType] Kind: Class Variable ###### FLOAT32: typing.ClassVar[DataType] Kind: Class Variable ###### FLOAT64: typing.ClassVar[DataType] Kind: Class Variable ###### INT16: typing.ClassVar[DataType] Kind: Class Variable ###### INT32: typing.ClassVar[DataType] Kind: Class Variable ###### INT4: typing.ClassVar[DataType] Kind: Class Variable ###### INT64: typing.ClassVar[DataType] Kind: Class Variable ###### INT8: typing.ClassVar[DataType] Kind: Class Variable ###### STRING: typing.ClassVar[DataType] Kind: Class Variable ###### UINT16: typing.ClassVar[DataType] Kind: Class Variable ###### UINT32: typing.ClassVar[DataType] Kind: Class Variable ###### UINT4: typing.ClassVar[DataType] Kind: Class Variable ###### UINT64: typing.ClassVar[DataType] Kind: Class Variable ###### UINT8: typing.ClassVar[DataType] Kind: Class Variable ###### __members__: typing.ClassVar[dict[str, DataType]] Kind: Class Variable ###### __eq__(self, other: typing.Any) -> bool: bool Kind: Method ###### __getstate__(self) -> int: int Kind: Method ###### __hash__(self) -> int: int Kind: Method ###### __index__(self) -> int: int Kind: Method ###### __init__(self, value: int) Kind: Method ###### __int__(self) -> int: int Kind: Method ###### __ne__(self, other: typing.Any) -> bool: bool Kind: Method ###### __repr__(self) -> str: str Kind: Method ###### __setstate__(self, state: int) Kind: Method ###### __str__(self) -> str: str Kind: Method ###### name Kind: Property ###### value Kind: Property ###### depthai.nn_archive.v1.Head Kind: Class Represents head of a model. @type name: str | None @ivar name: Optional name of the head. @type parser: str @ivar parser: Name of the parser responsible for processing the models output. @type outputs: List[str] | None @ivar outputs: Specify which outputs are fed into the parser. If None, all outputs are fed. @type metadata: C{HeadMetadata} | C{HeadObjectDetectionMetadata} | C{HeadClassificationMetadata} | C{HeadObjectDetectionSSDMetadata} | C{HeadSegmentationMetadata} | C{HeadYOLOMetadata} @ivar metadata: Metadata of the parser. ###### __init__(self) Kind: Method ###### metadata Kind: Property Metadata of the parser. ###### metadata.setter(self, arg0: Metadata) Kind: Method ###### name Kind: Property Optional name of the head. ###### name.setter(self, arg0: str | None) Kind: Method ###### outputs Kind: Property Specify which outputs are fed into the parser. If None, all outputs are fed. ###### outputs.setter(self, arg0: list [ str ] | None) Kind: Method ###### parser Kind: Property Name of the parser responsible for processing the models output. ###### parser.setter(self, arg0: str) Kind: Method ###### depthai.nn_archive.v1.Input Kind: Class Represents input stream of a model. @type name: str @ivar name: Name of the input layer. @type dtype: DataType @ivar dtype: Data type of the input data (e.g., 'float32'). @type input_type: InputType @ivar input_type: Type of input data (e.g., 'image'). @type shape: list @ivar shape: Shape of the input data as a list of integers (e.g. [H,W], [H,W,C], [N,H,W,C], ...). @type layout: str @ivar layout: Lettercode interpretation of the input data dimensions (e.g., 'NCHW'). @type preprocessing: PreprocessingBlock @ivar preprocessing: Preprocessing steps applied to the input data. ###### __init__(self) Kind: Method ###### dtype Kind: Property Data type of the input data (e.g., 'float32'). ###### dtype.setter(self, arg0: DataType) Kind: Method ###### inputType Kind: Property Type of input data (e.g., 'image'). ###### inputType.setter(self, arg0: InputType) Kind: Method ###### layout Kind: Property Lettercode interpretation of the input data dimensions (e.g., 'NCHW') ###### layout.setter(self, arg0: str | None) Kind: Method ###### name Kind: Property Name of the input layer. ###### name.setter(self, arg0: str) Kind: Method ###### preprocessing Kind: Property Preprocessing steps applied to the input data. ###### preprocessing.setter(self, arg0: PreprocessingBlock) Kind: Method ###### shape Kind: Property Shape of the input data as a list of integers (e.g. [H,W], [H,W,C], [N,H,W,C], ...). ###### shape.setter(self, arg0: list [ int ]) Kind: Method ###### depthai.nn_archive.v1.InputType Kind: Class Members: IMAGE RAW ###### IMAGE: typing.ClassVar[InputType] Kind: Class Variable ###### RAW: typing.ClassVar[InputType] Kind: Class Variable ###### __members__: typing.ClassVar[dict[str, InputType]] Kind: Class Variable ###### __eq__(self, other: typing.Any) -> bool: bool Kind: Method ###### __getstate__(self) -> int: int Kind: Method ###### __hash__(self) -> int: int Kind: Method ###### __index__(self) -> int: int Kind: Method ###### __init__(self, value: int) Kind: Method ###### __int__(self) -> int: int Kind: Method ###### __ne__(self, other: typing.Any) -> bool: bool Kind: Method ###### __repr__(self) -> str: str Kind: Method ###### __setstate__(self, state: int) Kind: Method ###### __str__(self) -> str: str Kind: Method ###### name Kind: Property ###### value Kind: Property ###### depthai.nn_archive.v1.Metadata Kind: Class Metadata of the parser. Metadata for the object detection head. @type classes: list @ivar classes: Names of object classes detected by the model. @type n_classes: int @ivar n_classes: Number of object classes detected by the model. @type iou_threshold: float @ivar iou_threshold: Non-max supression threshold limiting boxes intersection. @type conf_threshold: float @ivar conf_threshold: Confidence score threshold above which a detected object is considered valid. @type max_det: int @ivar max_det: Maximum detections per image. @type anchors: list @ivar anchors: Predefined bounding boxes of different sizes and aspect ratios. The innermost lists are length 2 tuples of box sizes. The middle lists are anchors for each output. The outmost lists go from smallest to largest output. Metadata for the classification head. @type classes: list @ivar classes: Names of object classes classified by the model. @type n_classes: int @ivar n_classes: Number of object classes classified by the model. @type is_softmax: bool @ivar is_softmax: True, if output is already softmaxed Metadata for the SSD object detection head. @type boxes_outputs: str @ivar boxes_outputs: Output name corresponding to predicted bounding box coordinates. @type scores_outputs: str @ivar scores_outputs: Output name corresponding to predicted bounding box confidence scores. Metadata for the segmentation head. @type classes: list @ivar classes: Names of object classes segmented by the model. @type n_classes: int @ivar n_classes: Number of object classes segmented by the model. @type is_softmax: bool @ivar is_softmax: True, if output is already softmaxed Metadata for the YOLO head. @type yolo_outputs: list @ivar yolo_outputs: A list of output names for each of the different YOLO grid sizes. @type mask_outputs: list | None @ivar mask_outputs: A list of output names for each mask output. @type protos_outputs: str | None @ivar protos_outputs: Output name for the protos. @type keypoints_outputs: list | None @ivar keypoints_outputs: A list of output names for the keypoints. @type angles_outputs: list | None @ivar angles_outputs: A list of output names for the angles. @type subtype: str @ivar subtype: YOLO family decoding subtype (e.g. yolov5, yolov6, yolov7 etc.) @type n_prototypes: int | None @ivar n_prototypes: Number of prototypes per bbox in YOLO instance segmnetation. @type n_keypoints: int | None @ivar n_keypoints: Number of keypoints per bbox in YOLO keypoint detection. @type is_softmax: bool | None @ivar is_softmax: True, if output is already softmaxed in YOLO instance segmentation Metadata for the basic head. It allows you to specify additional fields. @type postprocessor_path: str | None @ivar postprocessor_path: Path to the postprocessor. ###### __init__(self) Kind: Method ###### anchors Kind: Property Predefined bounding boxes of different sizes and aspect ratios. The innermost lists are length 2 tuples of box sizes. The middle lists are anchors for each output. The outmost lists go from smallest to largest output. ###### anchors.setter(self, arg0: list [ list [ list [ float ] ] ] | None) Kind: Method ###### anglesOutputs Kind: Property A list of output names for the angles. ###### anglesOutputs.setter(self, arg0: list [ str ] | None) Kind: Method ###### boxesOutputs Kind: Property Output name corresponding to predicted bounding box coordinates. ###### boxesOutputs.setter(self, arg0: str | None) Kind: Method ###### classes Kind: Property Names of object classes recognized by the model. ###### classes.setter(self, arg0: list [ str ] | None) Kind: Method ###### confThreshold Kind: Property Confidence score threshold above which a detected object is considered valid. ###### confThreshold.setter(self, arg0: float | None) Kind: Method ###### extraParams Kind: Property Additional parameters ###### extraParams.setter(self, arg0: json) Kind: Method ###### iouThreshold Kind: Property Non-max supression threshold limiting boxes intersection. ###### iouThreshold.setter(self, arg0: float | None) Kind: Method ###### isSoftmax Kind: Property True, if output is already softmaxed. True, if output is already softmaxed in YOLO instance segmentation. ###### isSoftmax.setter(self, arg0: bool | None) Kind: Method ###### keypointsOutputs Kind: Property A list of output names for the keypoints. ###### keypointsOutputs.setter(self, arg0: list [ str ] | None) Kind: Method ###### maskOutputs Kind: Property A list of output names for each mask output. ###### maskOutputs.setter(self, arg0: list [ str ] | None) Kind: Method ###### maxDet Kind: Property Maximum detections per image. ###### maxDet.setter(self, arg0: int | None) Kind: Method ###### nClasses Kind: Property Number of object classes recognized by the model. ###### nClasses.setter(self, arg0: int | None) Kind: Method ###### nKeypoints Kind: Property Number of keypoints per bbox in YOLO keypoint detection. ###### nKeypoints.setter(self, arg0: int | None) Kind: Method ###### nPrototypes Kind: Property Number of prototypes per bbox in YOLO instance segmnetation. ###### nPrototypes.setter(self, arg0: int | None) Kind: Method ###### postprocessorPath Kind: Property Path to the postprocessor. ###### postprocessorPath.setter(self, arg0: str | None) Kind: Method ###### protosOutputs Kind: Property Output name for the protos. ###### protosOutputs.setter(self, arg0: str | None) Kind: Method ###### scoresOutputs Kind: Property Output name corresponding to predicted bounding box confidence scores. ###### scoresOutputs.setter(self, arg0: str | None) Kind: Method ###### subtype Kind: Property YOLO family decoding subtype (e.g. yolov5, yolov6, yolov7 etc.). ###### subtype.setter(self, arg0: str | None) Kind: Method ###### yoloOutputs Kind: Property A list of output names for each of the different YOLO grid sizes. ###### yoloOutputs.setter(self, arg0: list [ str ] | None) Kind: Method ###### depthai.nn_archive.v1.MetadataClass Kind: Class Metadata object defining the model metadata. Represents metadata of a model. @type name: str @ivar name: Name of the model. @type path: str @ivar path: Relative path to the model executable. ###### __init__(self) Kind: Method ###### name Kind: Property Name of the model. ###### name.setter(self, arg0: str) Kind: Method ###### path Kind: Property Relative path to the model executable. ###### path.setter(self, arg0: str) Kind: Method ###### precision Kind: Property Precision of the model weights. ###### precision.setter(self, arg0: DataType | None) Kind: Method ###### depthai.nn_archive.v1.Model Kind: Class A Model object representing the neural network used in the archive. Class defining a single-stage model config scheme. @type metadata: Metadata @ivar metadata: Metadata object defining the model metadata. @type inputs: list @ivar inputs: List of Input objects defining the model inputs. @type outputs: list @ivar outputs: List of Output objects defining the model outputs. @type heads: list @ivar heads: List of Head objects defining the model heads. If not defined, we assume a raw output. ###### __init__(self) Kind: Method ###### heads Kind: Property List of Head objects defining the model heads. If not defined, we assume a raw output. ###### heads.setter(self, arg0: list [ Head ] | None) Kind: Method ###### inputs Kind: Property List of Input objects defining the model inputs. ###### inputs.setter(self, arg0: list [ Input ]) Kind: Method ###### metadata Kind: Property Metadata object defining the model metadata. ###### metadata.setter(self, arg0: MetadataClass) Kind: Method ###### outputs Kind: Property List of Output objects defining the model outputs. ###### outputs.setter(self, arg0: list [ Output ]) Kind: Method ###### depthai.nn_archive.v1.Output Kind: Class Represents output stream of a model. @type name: str @ivar name: Name of the output layer. @type dtype: DataType @ivar dtype: Data type of the output data (e.g., 'float32'). ###### __init__(self) Kind: Method ###### dtype Kind: Property Data type of the output data (e.g., 'float32'). ###### dtype.setter(self, arg0: DataType) Kind: Method ###### layout Kind: Property List of letters describing the output layout (e.g. 'NC'). ###### layout.setter(self, arg0: str | None) Kind: Method ###### name Kind: Property Name of the output layer. ###### name.setter(self, arg0: str) Kind: Method ###### shape Kind: Property Shape of the output as a list of integers (e.g. [1, 1000]). ###### shape.setter(self, arg0: list [ int ] | None) Kind: Method ###### depthai.nn_archive.v1.PreprocessingBlock Kind: Class Preprocessing steps applied to the input data. Represents preprocessing operations applied to the input data. @type mean: list | None @ivar mean: Mean values in channel order. Order depends on the order in which the model was trained on. @type scale: list | None @ivar scale: Standardization values in channel order. Order depends on the order in which the model was trained on. @type reverse_channels: bool | None @ivar reverse_channels: If True input to the model is RGB else BGR. @type interleaved_to_planar: bool | None @ivar interleaved_to_planar: If True input to the model is interleaved (NHWC) else planar (NCHW). @type dai_type: str | None @ivar dai_type: DepthAI input type which is read by DepthAI to automatically setup the pipeline. ###### __init__(self) Kind: Method ###### daiType Kind: Property DepthAI input type which is read by DepthAI to automatically setup the pipeline. ###### daiType.setter(self, arg0: str | None) Kind: Method ###### interleavedToPlanar Kind: Property If True input to the model is interleaved (NHWC) else planar (NCHW). ###### interleavedToPlanar.setter(self, arg0: bool | None) Kind: Method ###### mean Kind: Property Mean values in channel order. Order depends on the order in which the model was trained on. ###### mean.setter(self, arg0: list [ float ] | None) Kind: Method ###### reverseChannels Kind: Property If True input to the model is RGB else BGR. ###### reverseChannels.setter(self, arg0: bool | None) Kind: Method ###### scale Kind: Property Standardization values in channel order. Order depends on the order in which the model was trained on. ###### scale.setter(self, arg0: list [ float ] | None) Kind: Method #### node Kind: Package ##### internal Kind: Module ##### depthai.node.AprilTag(depthai.DeviceNode) Kind: Class AprilTag node. ###### getNumThreads(self) -> int: int Kind: Method Get number of threads to use for AprilTag detection. Returns: Number of threads to use. ###### getWaitForConfigInput(self) -> bool: bool Kind: Method Get whether or not wait until configuration message arrives to inputConfig Input. ###### runOnHost(self) -> bool: bool Kind: Method Check if the node is set to run on host ###### setNumThreads(self, numThreads: int) Kind: Method Set number of threads to use for AprilTag detection. Parameter ``numThreads``: Number of threads to use. ###### setRunOnHost(self, arg0: bool) Kind: Method Specify whether to run on host or device By default, the node will run on device. ###### setWaitForConfigInput(self, wait: bool) Kind: Method Specify whether or not wait until configuration message arrives to inputConfig Input. Parameter ``wait``: True to wait for configuration message, false otherwise. ###### initialConfig Kind: Property Initial config to use when calculating spatial location data. ###### inputConfig Kind: Property Input AprilTagConfig message with ability to modify parameters in runtime. Default queue is non-blocking with size 4. ###### inputImage Kind: Property Input message with depth data used to retrieve spatial information about detected object. Default queue is non-blocking with size 4. ###### out Kind: Property Outputs AprilTags message that carries spatial location results. ###### passthroughInputImage Kind: Property Passthrough message on which the calculation was performed. Suitable for when input queue is set to non-blocking behavior. ##### depthai.node.AutoCalibration(depthai.DeviceNode) Kind: Class ###### initialConfig: depthai.AutoCalibrationConfig Kind: Class Variable ###### build(self, cameraLeft: Camera, cameraRight: Camera) -> AutoCalibration: AutoCalibration Kind: Method ###### output Kind: Property ##### depthai.node.BasaltVIO(depthai.node.ThreadedHostNode) Kind: Class Basalt Visual Inertial Odometry node. Performs VIO on stereo images and IMU data. ###### runSyncOnHost(self, runOnHost: bool) Kind: Method ###### setAccelBias(self, bias: list [ float ]) Kind: Method ###### setAccelNoiseStd(self, noise: list [ float ]) Kind: Method ###### setConfig(self, config: depthai.VioConfig) Kind: Method ###### setConfigPath(self, path: str) Kind: Method ###### setGyroBias(self, bias: list [ float ]) Kind: Method ###### setGyroNoiseStd(self, noise: list [ float ]) Kind: Method ###### setImuExtrinsics(self, imuExtr: depthai.TransformData) Kind: Method ###### setImuUpdateRate(self, rate: int) Kind: Method ###### setLocalTransform(self, transform: depthai.TransformData) Kind: Method ###### imu Kind: Property Input IMU data. ###### left Kind: Property ###### passthrough Kind: Property Output passthrough of left image. ###### right Kind: Property ###### transform Kind: Property Output transform data. ##### depthai.node.BenchmarkIn(depthai.DeviceNode) Kind: Class ###### logReportsAsWarnings(self, logReportsAsWarnings: bool) Kind: Method Log the reports as warnings ###### measureIndividualLatencies(self, attachLatencies: bool) Kind: Method Attach latencies to the report ###### sendReportEveryNMessages(self, num: int) Kind: Method Specify how many messages to measure for each report ###### setRunOnHost(self, runOnHost: bool) Kind: Method Specify whether to run on host or device By default, the node will run on device. ###### input Kind: Property Receive messages as fast as possible ###### passthrough Kind: Property Passthrough for input messages (so the node can be placed between other nodes) ###### report Kind: Property Send a benchmark report when the set number of messages are received ##### depthai.node.BenchmarkOut(depthai.DeviceNode) Kind: Class ###### setFps(self, fps: float) Kind: Method Set FPS at which the node is sending out messages. 0 means as fast as possible ###### setNumMessagesToSend(self, num: int) Kind: Method Sets number of messages to send, by default send messages indefinitely Parameter ``num``: number of messages to send ###### setRunOnHost(self, runOnHost: bool) Kind: Method Specify whether to run on host or device By default, the node will run on device. ###### input Kind: Property Message that will be sent repeatedly ###### out Kind: Property Send messages out as fast as possible ##### depthai.node.Camera(depthai.DeviceNode) Kind: Class ###### build() Kind: Method ###### getBoardSocket(self) -> depthai.CameraBoardSocket: depthai.CameraBoardSocket Kind: Method Retrieves which board socket to use Returns: Board socket to use ###### getImageOrientation(self) -> depthai.CameraImageOrientation: depthai.CameraImageOrientation Kind: Method Get camera image orientation Returns: Image orientation ###### getIspNumFramesPool(self) -> int: int Kind: Method Get number of frames in isp pool Returns: Number of frames ###### getMaxSizePoolIsp(self) -> int: int Kind: Method Get maximum size of isp pool Returns: Maximum size in bytes of isp pool ###### getMaxSizePoolRaw(self) -> int: int Kind: Method Get maximum size of raw pool Returns: Maximum size in bytes of raw pool ###### getOutputsMaxSizePool(self) -> int|None: int|None Kind: Method Get maximum size of outputs pool for all outputs Returns: Maximum size in bytes of image manip pool ###### getOutputsNumFramesPool(self) -> int|None: int|None Kind: Method Get number of frames in outputs pool for all outputs Returns: Number of frames ###### getRawNumFramesPool(self) -> int: int Kind: Method Get number of frames in raw pool Returns: Number of frames ###### getSensorType(self) -> depthai.CameraSensorType: depthai.CameraSensorType Kind: Method Get the sensor type Returns: Sensor type ###### requestFullResolutionOutput(self, type: depthai.ImgFrame.Type | None = None, fps: float | None = None, useHighestResolution: bool = False) -> depthai.Node.Output: depthai.Node.Output Kind: Method Get a high resolution output with full FOV on the sensor. By default the function will not use the resolutions higher than 5000x4000, as those often need a lot of resources, making them hard to use in combination with other nodes. Parameter ``type``: Type of the output (NV12, BGR, ...) - by default it's auto-selected for best performance Parameter ``fps``: FPS of the output - by default it's auto-selected to highest possible that a sensor config support or 30, whichever is lower Parameter ``useHighestResolution``: If true, the function will use the highest resolution available on the sensor, even if it's higher than 5000x4000 ###### requestIspOutput(self, fps: float | None = None) -> depthai.Node.Output: depthai.Node.Output Kind: Method Request output with isp resolution. The fps does not vote. ###### requestOutput() Kind: Method ###### setImageOrientation(self, imageOrientation: depthai.CameraImageOrientation) -> Camera: Camera Kind: Method Set camera image orientation Parameter ``imageOrientation``: Image orientation to set Returns: Shared pointer to the camera node ###### setIspNumFramesPool(self, num: int) -> Camera: Camera Kind: Method Set number of frames in isp pool (will be automatically reduced if the maximum pool memory size is exceeded) Parameter ``num``: Number of frames Returns: Shared pointer to the camera node ###### setMaxSizePoolIsp(self, size: int) -> Camera: Camera Kind: Method Set maximum size of isp pool Parameter ``size``: Maximum size in bytes of isp pool Returns: Shared pointer to the camera node ###### setMaxSizePoolRaw(self, size: int) -> Camera: Camera Kind: Method Set maximum size of raw pool Parameter ``size``: Maximum size in bytes of raw pool Returns: Shared pointer to the camera node ###### setMaxSizePools(self, raw: int, isp: int, imgmanip: int) -> Camera: Camera Kind: Method Set maximum memory size of all pools Parameter ``raw``: Maximum size in bytes of raw pool Parameter ``isp``: Maximum size in bytes of isp pool Parameter ``outputs``: Maximum size in bytes of outputs pools Returns: Shared pointer to the camera node ###### setMockIsp(self, mockIsp: ReplayVideo) -> Camera: Camera Kind: Method Set mock ISP for Camera node. Automatically sets mockIsp size. Parameter ``replay``: ReplayVideo node to use as mock ISP ###### setNumFramesPools(self, raw: int, isp: int, imgmanip: int) -> Camera: Camera Kind: Method Set number of frames in all pools (will be automatically reduced if the maximum pool memory size is exceeded) Parameter ``raw``: Number of frames in raw pool Parameter ``isp``: Number of frames in isp pool Parameter ``outputs``: Number of frames in outputs pools Returns: Shared pointer to the camera node ###### setOutputsMaxSizePool(self, size: int) -> Camera: Camera Kind: Method Set maximum size of pools for all outputs Parameter ``size``: Maximum size in bytes of pools for all outputs Returns: Shared pointer to the camera node ###### setOutputsNumFramesPool(self, num: int) -> Camera: Camera Kind: Method Set number of frames in pools for all outputs Parameter ``num``: Number of frames in pools for all outputs Returns: Shared pointer to the camera node ###### setRawNumFramesPool(self, num: int) -> Camera: Camera Kind: Method Set number of frames in raw pool (will be automatically reduced if the maximum pool memory size is exceeded) Parameter ``num``: Number of frames Returns: Shared pointer to the camera node ###### setSensorType(self, sensorType: depthai.CameraSensorType) -> Camera: Camera Kind: Method Set the sensor type to use Parameter ``sensorType``: Sensor type to use ###### initialControl Kind: Property Initial control options to apply to sensor ###### inputControl Kind: Property Input for CameraControl message, which can modify camera parameters in runtime ###### mockIsp Kind: Property Input for mocking 'isp' functionality on RVC2. Default queue is blocking with size 8 ###### raw Kind: Property Outputs ImgFrame message that carries RAW10-packed (MIPI CSI-2 format) frame data. Captured directly from the camera sensor, and the source for the 'isp' output. ##### depthai.node.ColorCamera(depthai.DeviceNode) Kind: Class ColorCamera node. For use with color sensors. ###### __init__(self) Kind: Method ###### getBoardSocket(self) -> depthai.CameraBoardSocket: depthai.CameraBoardSocket Kind: Method Retrieves which board socket to use Returns: Board socket to use ###### getCamId(self) -> int: int Kind: Method ###### getCamera(self) -> str: str Kind: Method Retrieves which camera to use by name Returns: Name of the camera to use ###### getColorOrder(self) -> depthai.ColorCameraProperties.ColorOrder: depthai.ColorCameraProperties.ColorOrder Kind: Method Get color order of preview output frames. RGB or BGR ###### getFp16(self) -> bool: bool Kind: Method Get fp16 (0..255) data of preview output frames ###### getFps(self) -> float: float Kind: Method Get rate at which camera should produce frames Returns: Rate in frames per second ###### getFrameEventFilter(self) -> list[depthai.FrameEvent]: list[depthai.FrameEvent] Kind: Method ###### getImageOrientation(self) -> depthai.CameraImageOrientation: depthai.CameraImageOrientation Kind: Method Get camera image orientation ###### getInterleaved(self) -> bool: bool Kind: Method Get planar or interleaved data of preview output frames ###### getIspHeight(self) -> int: int Kind: Method Get 'isp' output height ###### getIspNumFramesPool(self) -> int: int Kind: Method Get number of frames in isp pool ###### getIspSize(self) -> tuple[int, int]: tuple[int, int] Kind: Method Get 'isp' output resolution as size, after scaling ###### getIspWidth(self) -> int: int Kind: Method Get 'isp' output width ###### getPreviewHeight(self) -> int: int Kind: Method Get preview height ###### getPreviewKeepAspectRatio(self) -> bool: bool Kind: Method See also: setPreviewKeepAspectRatio Returns: Preview keep aspect ratio option ###### getPreviewNumFramesPool(self) -> int: int Kind: Method Get number of frames in preview pool ###### getPreviewSize(self) -> tuple[int, int]: tuple[int, int] Kind: Method Get preview size as tuple ###### getPreviewWidth(self) -> int: int Kind: Method Get preview width ###### getRawNumFramesPool(self) -> int: int Kind: Method Get number of frames in raw pool ###### getResolution(self) -> depthai.ColorCameraProperties.SensorResolution: depthai.ColorCameraProperties.SensorResolution Kind: Method Get sensor resolution ###### getResolutionHeight(self) -> int: int Kind: Method Get sensor resolution height ###### getResolutionSize(self) -> tuple[int, int]: tuple[int, int] Kind: Method Get sensor resolution as size ###### getResolutionWidth(self) -> int: int Kind: Method Get sensor resolution width ###### getSensorCrop(self) -> tuple[float, float]: tuple[float, float] Kind: Method Returns: Sensor top left crop coordinates ###### getSensorCropX(self) -> float: float Kind: Method Get sensor top left x crop coordinate ###### getSensorCropY(self) -> float: float Kind: Method Get sensor top left y crop coordinate ###### getStillHeight(self) -> int: int Kind: Method Get still height ###### getStillNumFramesPool(self) -> int: int Kind: Method Get number of frames in still pool ###### getStillSize(self) -> tuple[int, int]: tuple[int, int] Kind: Method Get still size as tuple ###### getStillWidth(self) -> int: int Kind: Method Get still width ###### getVideoHeight(self) -> int: int Kind: Method Get video height ###### getVideoNumFramesPool(self) -> int: int Kind: Method Get number of frames in video pool ###### getVideoSize(self) -> tuple[int, int]: tuple[int, int] Kind: Method Get video size as tuple ###### getVideoWidth(self) -> int: int Kind: Method Get video width ###### sensorCenterCrop(self) Kind: Method Specify sensor center crop. Resolution size / video size ###### setBoardSocket(self, boardSocket: depthai.CameraBoardSocket) Kind: Method Specify which board socket to use Parameter ``boardSocket``: Board socket to use ###### setCamId(self, arg0: int) Kind: Method ###### setCamera(self, name: str) Kind: Method Specify which camera to use by name Parameter ``name``: Name of the camera to use ###### setColorOrder(self, colorOrder: depthai.ColorCameraProperties.ColorOrder) Kind: Method Set color order of preview output images. RGB or BGR ###### setFp16(self, fp16: bool) Kind: Method Set fp16 (0..255) data type of preview output frames ###### setFps(self, fps: float) Kind: Method Set rate at which camera should produce frames Parameter ``fps``: Rate in frames per second ###### setFrameEventFilter(self, events: list [ depthai.FrameEvent ]) Kind: Method ###### setImageOrientation(self, imageOrientation: depthai.CameraImageOrientation) Kind: Method Set camera image orientation ###### setInterleaved(self, interleaved: bool) Kind: Method Set planar or interleaved data of preview output frames ###### setIsp3aFps(self, arg0: int) Kind: Method Isp 3A rate (auto focus, auto exposure, auto white balance, camera controls etc.). Default (0) matches the camera FPS, meaning that 3A is running on each frame. Reducing the rate of 3A reduces the CPU usage on CSS, but also increases the convergence rate of 3A. Note that camera controls will be processed at this rate. E.g. if camera is running at 30 fps, and camera control is sent at every frame, but 3A fps is set to 15, the camera control messages will be processed at 15 fps rate, which will lead to queueing. ###### setIspNumFramesPool(self, arg0: int) Kind: Method Set number of frames in isp pool ###### setIspScale() Kind: Method ###### setNumFramesPool(self, raw: int, isp: int, preview: int, video: int, still: int) Kind: Method Set number of frames in all pools ###### setPreviewKeepAspectRatio(self, keep: bool) Kind: Method Specifies whether preview output should preserve aspect ratio, after downscaling from video size or not. Parameter ``keep``: If true, a larger crop region will be considered to still be able to create the final image in the specified aspect ratio. Otherwise video size is resized to fit preview size ###### setPreviewNumFramesPool(self, arg0: int) Kind: Method Set number of frames in preview pool ###### setPreviewSize() Kind: Method ###### setRawNumFramesPool(self, arg0: int) Kind: Method Set number of frames in raw pool ###### setRawOutputPacked(self, packed: bool) Kind: Method Configures whether the camera `raw` frames are saved as MIPI-packed to memory. The packed format is more efficient, consuming less memory on device, and less data to send to host: RAW10: 4 pixels saved on 5 bytes, RAW12: 2 pixels saved on 3 bytes. When packing is disabled (`false`), data is saved lsb-aligned, e.g. a RAW10 pixel will be stored as uint16, on bits 9..0: 0b0000'00pp'pppp'pppp. Default is auto: enabled for standard color/monochrome cameras where ISP can work with both packed/unpacked, but disabled for other cameras like ToF. ###### setResolution(self, resolution: depthai.ColorCameraProperties.SensorResolution) Kind: Method Set sensor resolution ###### setSensorCrop(self, x: float, y: float) Kind: Method Specifies the cropping that happens when converting ISP to video output. By default, video will be center cropped from the ISP output. Note that this doesn't actually do on-sensor cropping (and MIPI-stream only that region), but it does postprocessing on the ISP (on RVC). Parameter ``x``: Top left X coordinate Parameter ``y``: Top left Y coordinate ###### setStillNumFramesPool(self, arg0: int) Kind: Method Set number of frames in preview pool ###### setStillSize() Kind: Method ###### setVideoNumFramesPool(self, arg0: int) Kind: Method Set number of frames in preview pool ###### setVideoSize() Kind: Method ###### frameEvent Kind: Property Outputs metadata-only ImgFrame message as an early indicator of an incoming frame. It's sent on the MIPI SoF (start-of-frame) event, just after the exposure of the current frame has finished and before the exposure for next frame starts. Could be used to synchronize various processes with camera capture. Fields populated: camera id, sequence number, timestamp ###### initialControl Kind: Property Initial control options to apply to sensor ###### inputControl Kind: Property Input for CameraControl message, which can modify camera parameters in runtime ###### isp Kind: Property Outputs ImgFrame message that carries YUV420 planar (I420/IYUV) frame data. Generated by the ISP engine, and the source for the 'video', 'preview' and 'still' outputs ###### preview Kind: Property Outputs ImgFrame message that carries BGR/RGB planar/interleaved encoded frame data. Suitable for use with NeuralNetwork node ###### raw Kind: Property Outputs ImgFrame message that carries RAW10-packed (MIPI CSI-2 format) frame data. Captured directly from the camera sensor, and the source for the 'isp' output. ###### still Kind: Property Outputs ImgFrame message that carries NV12 encoded (YUV420, UV plane interleaved) frame data. The message is sent only when a CameraControl message arrives to inputControl with captureStill command set. ###### video Kind: Property Outputs ImgFrame message that carries NV12 encoded (YUV420, UV plane interleaved) frame data. Suitable for use with VideoEncoder node ##### depthai.node.DetectionNetwork(depthai.DeviceNodeGroup) Kind: Class DetectionNetwork, base for different network specializations ###### depthai.node.DetectionNetwork.Model Kind: Class ###### __init__() Kind: Method ###### __init__(self, input: depthai.Node.Output, nnArchive: depthai.NNArchive, confidenceThreshold: float = 0.5) Kind: Method ###### build() Kind: Method ###### getClasses(self) -> list[str]|None: list[str]|None Kind: Method ###### getConfidenceThreshold(self) -> float: float Kind: Method Retrieves threshold at which to filter the rest of the detections. Returns: Detection confidence ###### getNumInferenceThreads(self) -> int: int Kind: Method How many inference threads will be used to run the network Returns: Number of threads, 0, 1 or 2. Zero means AUTO ###### setBackend(self, setBackend: str) Kind: Method Specifies backend to use Parameter ``backend``: String specifying backend to use ###### setBackendProperties(self, setBackendProperties: dict [ str , str ]) Kind: Method Set backend properties Parameter ``backendProperties``: backend properties map ###### setBlob() Kind: Method ###### setBlobPath(self, path: os.PathLike) Kind: Method Load network blob into assets and use once pipeline is started. Throws: Error if file doesn't exist or isn't a valid network blob. Parameter ``path``: Path to network blob ###### setConfidenceThreshold(self, thresh: float) Kind: Method Specifies confidence threshold at which to filter the rest of the detections. Parameter ``thresh``: Detection confidence must be greater than specified threshold to be added to the list ###### setFromModelZoo(self, description: depthai.NNModelDescription, useCached: bool = False) Kind: Method Download model from zoo and set it for this Node Parameter ``description:``: Model description to download Parameter ``useCached:``: Use cached model if available ###### setModelPath(self, modelPath: os.PathLike) Kind: Method Load network model into assets. Parameter ``modelPath``: Path to the model file. ###### setNNArchive() Kind: Method ###### setNumInferenceThreads(self, numThreads: int) Kind: Method How many threads should the node use to run the network. Parameter ``numThreads``: Number of threads to dedicate to this node ###### setNumNCEPerInferenceThread(self, numNCEPerThread: int) Kind: Method How many Neural Compute Engines should a single thread use for inference Parameter ``numNCEPerThread``: Number of NCE per thread ###### setNumPoolFrames(self, numFrames: int) Kind: Method Specifies how many frames will be available in the pool Parameter ``numFrames``: How many frames will pool have ###### setNumShavesPerInferenceThread(self, numShavesPerInferenceThread: int) Kind: Method How many Shaves should a single thread use for inference Parameter ``numShavesPerThread``: Number of shaves per thread ###### detectionParser Kind: Property ###### input Kind: Property Input message with data to be inferred upon ###### neuralNetwork Kind: Property ###### out Kind: Property Outputs ImgDetections message that carries parsed detection results. Overrides NeuralNetwork 'out' with ImgDetections output message type. ###### outNetwork Kind: Property Outputs unparsed inference results. ###### passthrough Kind: Property Passthrough message on which the inference was performed. Suitable for when input queue is set to non-blocking behavior. ##### depthai.node.DetectionParser(depthai.DeviceNode) Kind: Class DetectionParser node. Parses detection results from Mobilenet-SSD or YOLO neural networks. @note If multiple detection heads are present in the NNArchive, only one type is supported (either YOLO or Mobilenet-SSD) and the last one will be used. ###### build(self, input: depthai.Node.Output, nnArchive: depthai.NNArchive) -> DetectionParser: DetectionParser Kind: Method Build DetectionParser node. Connect output to this node's input. Also call setNNArchive() with provided NNArchive. Parameter ``nnInput:``: Output to link Parameter ``nnArchive:``: Neural network archive ###### getAnchorMasks(self) -> dict[str, list[int]]: dict[str, list[int]] Kind: Method Get anchor masks for anchor-based yolo models ###### getAnchors(self) -> list[float]: list[float] Kind: Method Get anchors for anchor-based yolo models ###### getClasses(self) -> list[str]|None: list[str]|None Kind: Method Get class names to decode. ###### getConfidenceThreshold(self) -> float: float Kind: Method Retrieves threshold at which to filter the rest of the detections. Returns: Detection confidence ###### getCoordinateSize(self) -> int: int Kind: Method Get number of coordinates per bounding box. ###### getDecodeKeypoints(self) -> bool: bool Kind: Method Get whether keypoints decoding is enabled. ###### getDecodeSegmentation(self) -> bool: bool Kind: Method Get whether segmentation mask decoding is enabled. ###### getIouThreshold(self) -> float: float Kind: Method Get IOU threshold for non-maxima suppression ###### getNNFamily(self) -> depthai.DetectionNetworkType: depthai.DetectionNetworkType Kind: Method Gets NN Family to parse ###### getNkeypoints(self) -> int: int Kind: Method Get number of keypoints to decode. ###### getNumClasses(self) -> int: int Kind: Method Get number of classes to decode. ###### getNumFramesPool(self) -> int: int Kind: Method Returns number of frames in pool ###### getStrides(self) -> list[int]: list[int] Kind: Method Get strides for yolo models ###### getSubtype(self) -> str: str Kind: Method Get subtype for the parser. ###### runOnHost(self) -> bool: bool Kind: Method Check if the node is set to run on host ###### setAnchorMasks(self, anchorMasks: dict [ str , list [ int ] ]) Kind: Method Set anchor masks for anchor-based yolo models Parameter ``anchorMasks``: Map of anchor masks ###### setAnchors() Kind: Method ###### setBlob() Kind: Method ###### setBlobPath(self, path: os.PathLike) Kind: Method Load network blob into assets and use once pipeline is started. Throws: Error if file doesn't exist or isn't a valid network blob. Parameter ``path``: Path to network blob ###### setClasses(self, classes: list [ str ]) Kind: Method Set class names. This will clear any previously set number of classes. Parameter ``classes``: Vector of class names ###### setConfidenceThreshold(self, thresh: float) Kind: Method Specifies confidence threshold at which to filter the rest of the detections. Parameter ``thresh``: Detection confidence must be greater than specified threshold to be added to the list ###### setCoordinateSize(self, coordinates: int) Kind: Method Sets the number of coordinates per bounding box. Parameter ``coordinates``: Number of coordinates. Default is 4 ###### setDecodeKeypoints(self, decode: bool) Kind: Method Enable/disable keypoints decoding. If enabled, number of keypoints must also be set. ###### setDecodeSegmentation(self, decode: bool) Kind: Method Enable/disable segmentation mask decoding. ###### setInputImageSize() Kind: Method ###### setIouThreshold(self, thresh: float) Kind: Method Set IOU threshold for non-maxima suppression Parameter ``thresh``: IOU threshold ###### setKeypointEdges(self, edges: list [ typing.Annotated [ list [ int ] , pybind11_stubgen.typing_ext.FixedSize ( 2 ) ] ]) Kind: Method Set edges connections between keypoints. Parameter ``edges``: Vector edges connections represented as pairs of keypoint indices. @note This is only applicable if keypoints decoding is enabled. ###### setNNArchive(self, nnArchive: depthai.NNArchive) Kind: Method Set NNArchive for this Node. If the archive's type is SUPERBLOB, use default number of shaves. Parameter ``nnArchive:``: NNArchive to set ###### setNNFamily(self, type: depthai.DetectionNetworkType) Kind: Method Sets NN Family to parse. Possible values are: DetectionNetworkType::YOLO - 0 DetectionNetworkType::MOBILENET - 1 .. warning:: If NN Family is set manually, user must ensure that it matches the actual model being used. ###### setNumClasses(self, numClasses: int) Kind: Method Set number of classes. This will clear any previously set class names. Parameter ``numClasses``: Number of classes ###### setNumFramesPool(self, numFramesPool: int) Kind: Method Specify number of frames in pool. Parameter ``numFramesPool``: How many frames should the pool have ###### setNumKeypoints(self, numKeypoints: int) Kind: Method Set number of keypoints to decode. Automatically enables keypoints decoding. ###### setRunOnHost(self, runOnHost: bool) Kind: Method Specify whether to run on host or device By default, the node will run on device. ###### setStrides(self, strides: list [ int ]) Kind: Method Set strides for yolo models ###### setSubtype(self, subtype: str) Kind: Method Set subtype for the parser. Parameter ``subtype``: Subtype string, currently supported subtypes are: yolov6r1, yolov6r2 yolov8n, yolov6, yolov8, yolov10, yolov11, yolov3, yolov3-tiny, yolov5, yolov7, yolo-p, yolov5-u ###### input Kind: Property Input NN results with detection data to parse Default queue is blocking with size 5 ###### out Kind: Property Outputs image frame with detected edges ##### depthai.node.DynamicCalibration(depthai.DeviceNode) Kind: Class ###### runOnHost(self) -> bool: bool Kind: Method ###### setRunOnHost(self, runOnHost: bool) Kind: Method Specify whether to run on host or device By default, the node will run on host on RVC2 and on device on RVC4. ###### calibrationOutput Kind: Property Output calibration quality result ###### coverageOutput Kind: Property ###### inputControl Kind: Property Input DynamicCalibrationControl message with ability to modify parameters in runtime. ###### left Kind: Property ###### metricsOutput Kind: Property ###### qualityOutput Kind: Property ###### right Kind: Property ##### depthai.node.EdgeDetector(depthai.DeviceNode) Kind: Class EdgeDetector node. Performs edge detection using 3x3 Sobel filter ###### setMaxOutputFrameSize(self, arg0: int) Kind: Method Specify maximum size of output image. Parameter ``maxFrameSize``: Maximum frame size in bytes ###### setNumFramesPool(self, arg0: int) Kind: Method Specify number of frames in pool. Parameter ``numFramesPool``: How many frames should the pool have ###### initialConfig Kind: Property Initial config to use for edge detection. ###### inputConfig Kind: Property Input EdgeDetectorConfig message with ability to modify parameters in runtime. Default queue is non-blocking with size 4. ###### inputImage Kind: Property Input image on which edge detection is performed. Default queue is non-blocking with size 4. ###### outputImage Kind: Property Outputs image frame with detected edges ##### depthai.node.FeatureTracker(depthai.DeviceNode) Kind: Class FeatureTracker node. Performs feature tracking and reidentification using motion estimation between 2 consecutive frames. ###### setHardwareResources(self, numShaves: int, numMemorySlices: int) Kind: Method Specify allocated hardware resources for feature tracking. 2 shaves/memory slices are required for optical flow, 1 for corner detection only. Parameter ``numShaves``: Number of shaves. Maximum 2. Parameter ``numMemorySlices``: Number of memory slices. Maximum 2. ###### initialConfig Kind: Property Initial config to use for feature tracking. ###### inputConfig Kind: Property Input FeatureTrackerConfig message with ability to modify parameters in runtime. Default queue is non-blocking with size 4. ###### inputImage Kind: Property Input message with frame data on which feature tracking is performed. Default queue is non-blocking with size 4. ###### outputFeatures Kind: Property Outputs TrackedFeatures message that carries tracked features results. ###### passthroughInputImage Kind: Property Passthrough message on which the calculation was performed. Suitable for when input queue is set to non-blocking behavior. ##### depthai.node.Gate(depthai.DeviceNode) Kind: Class Gate Node. This node acts as a valve for data pipelines. It controls the flow of messages from the 'input' to the 'output' based on the state configured via 'inputControl'. It can be configured to stay open indefinitely, stay closed, or open for a specific number of messages. ###### runOnHost(self) -> bool: bool Kind: Method Check if the node is configured to run on the host. Returns: true if running on host, false otherwise. ###### setRunOnHost(self, runOnHost: bool) Kind: Method Specify whether to run on host or device By default, the node will run on device. ###### initialConfig Kind: Property Initial config of the node. ###### initialConfig.setter(self, arg1: depthai.GateControl) Kind: Method ###### input Kind: Property Main data input. * Accepts arbitrary Buffer messages (e.g., ImgFrame, NNData). If the Gate is Open, messages received here are forwarded to 'output'. If the Gate is Closed, messages received here are discarded/dropped. * Default queue size: 1 Blocking: False ###### inputControl Kind: Property Control input. * Accepts 'GateControl' messages to dynamically change the Gate's state. Use this to Open/Close the gate or set it to pass a specific number of frames at runtime. * Default queue size: 4 ###### output Kind: Property Main data output. * Forwards messages that were allowed through the Gate. The data type matches the input message. ##### depthai.node.HostNode(depthai.node.ThreadedHostNode) Kind: Class ###### __init_subclass__ Kind: Class Method ###### __init__(self) Kind: Method ###### createSubnode(self, class_, args, kwargs) Kind: Method ###### onStart(self) Kind: Method ###### onStop(self) Kind: Method ###### processGroup(self, arg0: depthai.MessageGroup) -> depthai.Buffer: depthai.Buffer Kind: Method ###### runSyncingOnDevice(self) Kind: Method ###### runSyncingOnHost(self) Kind: Method ###### sendProcessingToPipeline(self, arg0: bool) Kind: Method Send processing to pipeline. If set to true, it's important to call `pipeline.run()` in the main thread or `pipeline.processTasks()` in the main thread. Otherwise, if set to false, such action is not needed. ###### inputs Kind: Property ###### out Kind: Property ##### depthai.node.IMU(depthai.DeviceNode) Kind: Class IMU node for BNO08X. ###### enableFirmwareUpdate(self, arg0: bool) Kind: Method Whether to perform firmware update or not. Default value: false. ###### enableIMUSensor() Kind: Method ###### getBatchReportThreshold(self) -> int: int Kind: Method Above this packet threshold data will be sent to host, if queue is not blocked ###### getMaxBatchReports(self) -> int: int Kind: Method Maximum number of IMU packets in a batch report ###### setBatchReportThreshold(self, batchReportThreshold: int) Kind: Method Above this packet threshold data will be sent to host, if queue is not blocked ###### setMaxBatchReports(self, maxBatchReports: int) Kind: Method Maximum number of IMU packets in a batch report ###### mockIn Kind: Property Mock IMU data for replaying recorded data ###### out Kind: Property Outputs IMUData message that carries IMU packets. ##### depthai.node.ImageAlign(depthai.DeviceNode) Kind: Class ImageAlign node. Calculates spatial location data on a set of ROIs on depth map. ###### runOnHost(self) -> bool: bool Kind: Method Check if the node is set to run on host ###### setInterpolation(self, interp: depthai.Interpolation) -> ImageAlign: ImageAlign Kind: Method Specify interpolation method to use when resizing ###### setNumFramesPool(self, numFramesPool: int) -> ImageAlign: ImageAlign Kind: Method Specify number of frames in the pool ###### setNumShaves(self, numShaves: int) -> ImageAlign: ImageAlign Kind: Method Specify number of shaves to use for this node ###### setOutKeepAspectRatio(self, keep: bool) -> ImageAlign: ImageAlign Kind: Method Specify whether to keep aspect ratio when resizing ###### setOutputSize(self, alignWidth: int, alignHeight: int) -> ImageAlign: ImageAlign Kind: Method Specify the output size of the aligned image ###### setRunOnHost(self, runOnHost: bool) Kind: Method Specify whether to run on host or device By default, the node will run on device. ###### initialConfig Kind: Property Initial config to use when calculating spatial location data. ###### input Kind: Property Input message. Default queue is non-blocking with size 4. ###### inputAlignTo Kind: Property Input align to message. Default queue is non-blocking with size 1. ###### inputConfig Kind: Property Input message with ability to modify parameters in runtime. Default queue is non-blocking with size 4. ###### outputAligned Kind: Property Outputs ImgFrame message that is aligned to inputAlignTo. ###### passthroughInput Kind: Property Passthrough message on which the calculation was performed. Suitable for when input queue is set to non-blocking behavior. ##### depthai.node.ImageFilters(depthai.DeviceNode) Kind: Class ###### build() Kind: Method ###### runOnHost(self) -> bool: bool Kind: Method Check if the node is set to run on host ###### setRunOnHost(self, runOnHost: bool) Kind: Method Specify whether to run on host or device By default, the node will run on device. ###### initialConfig Kind: Property Initial config for image filters. ###### input Kind: Property Input for image frames to be filtered ###### inputConfig Kind: Property Config to be set for a specific filter ###### output Kind: Property Filtered frame ##### depthai.node.ImageManip(depthai.DeviceNode) Kind: Class ImageManip node. Capability to crop, resize, warp, ... incoming image frames ###### depthai.node.ImageManip.Backend Kind: Class Members: HW CPU ###### CPU: typing.ClassVar[ImageManip.Backend] Kind: Class Variable ###### HW: typing.ClassVar[ImageManip.Backend] Kind: Class Variable ###### __members__: typing.ClassVar[dict[str, ImageManip.Backend]] Kind: Class Variable ###### __eq__(self, other: typing.Any) -> bool: bool Kind: Method ###### __getstate__(self) -> int: int Kind: Method ###### __hash__(self) -> int: int Kind: Method ###### __index__(self) -> int: int Kind: Method ###### __init__(self, value: int) Kind: Method ###### __int__(self) -> int: int Kind: Method ###### __ne__(self, other: typing.Any) -> bool: bool Kind: Method ###### __repr__(self) -> str: str Kind: Method ###### __setstate__(self, state: int) Kind: Method ###### __str__(self) -> str: str Kind: Method ###### name Kind: Property ###### value Kind: Property ###### depthai.node.ImageManip.PerformanceMode Kind: Class Members: BALANCED PERFORMANCE LOW_POWER ###### BALANCED: typing.ClassVar[ImageManip.PerformanceMode] Kind: Class Variable ###### LOW_POWER: typing.ClassVar[ImageManip.PerformanceMode] Kind: Class Variable ###### PERFORMANCE: typing.ClassVar[ImageManip.PerformanceMode] Kind: Class Variable ###### __members__: typing.ClassVar[dict[str, ImageManip.PerformanceMode]] Kind: Class Variable ###### __eq__(self, other: typing.Any) -> bool: bool Kind: Method ###### __getstate__(self) -> int: int Kind: Method ###### __hash__(self) -> int: int Kind: Method ###### __index__(self) -> int: int Kind: Method ###### __init__(self, value: int) Kind: Method ###### __int__(self) -> int: int Kind: Method ###### __ne__(self, other: typing.Any) -> bool: bool Kind: Method ###### __repr__(self) -> str: str Kind: Method ###### __setstate__(self, state: int) Kind: Method ###### __str__(self) -> str: str Kind: Method ###### name Kind: Property ###### value Kind: Property ###### setBackend(self, arg0: ImageManip.Backend) -> ImageManip: ImageManip Kind: Method Set CPU as backend preference Parameter ``backend``: Backend preference ###### setMaxOutputFrameSize(self, arg0: int) Kind: Method Specify maximum size of output image. Parameter ``maxFrameSize``: Maximum frame size in bytes ###### setNumFramesPool(self, arg0: int) Kind: Method Specify number of frames in pool. Parameter ``numFramesPool``: How many frames should the pool have ###### setPerformanceMode(self, arg0: ImageManip.PerformanceMode) -> ImageManip: ImageManip Kind: Method Set performance mode Parameter ``performanceMode``: Performance mode ###### setRunOnHost(self, arg0: bool) -> ImageManip: ImageManip Kind: Method Specify whether to run on host or device Parameter ``runOnHost``: Run node on host ###### initialConfig Kind: Property Initial config to use when manipulating frames ###### inputConfig Kind: Property Input ImageManipConfig message with ability to modify parameters in runtime ###### inputImage Kind: Property Input image to be modified ###### out Kind: Property ##### depthai.node.MessageDemux(depthai.DeviceNode) Kind: Class ###### getProcessor(self) -> depthai.ProcessorType: depthai.ProcessorType Kind: Method Get on which processor the node should run Returns: Processor type - Leon CSS or Leon MSS ###### setProcessor(self, arg0: depthai.ProcessorType) Kind: Method Specify on which processor the node should run. RVC2 only. Parameter ``type``: Processor type - Leon CSS or Leon MSS ###### input Kind: Property Input message of type MessageGroup ###### outputs Kind: Property A map of outputs, where keys are same as in the input MessageGroup ##### depthai.node.MonoCamera(depthai.DeviceNode) Kind: Class MonoCamera node. For use with grayscale sensors. ###### getBoardSocket(self) -> depthai.CameraBoardSocket: depthai.CameraBoardSocket Kind: Method Retrieves which board socket to use Returns: Board socket to use ###### getCamId(self) -> int: int Kind: Method ###### getCamera(self) -> str: str Kind: Method Retrieves which camera to use by name Returns: Name of the camera to use ###### getFps(self) -> float: float Kind: Method Get rate at which camera should produce frames Returns: Rate in frames per second ###### getFrameEventFilter(self) -> list[depthai.FrameEvent]: list[depthai.FrameEvent] Kind: Method ###### getImageOrientation(self) -> depthai.CameraImageOrientation: depthai.CameraImageOrientation Kind: Method Get camera image orientation ###### getNumFramesPool(self) -> int: int Kind: Method Get number of frames in main (ISP output) pool ###### getRawNumFramesPool(self) -> int: int Kind: Method Get number of frames in raw pool ###### getResolution(self) -> depthai.MonoCameraProperties.SensorResolution: depthai.MonoCameraProperties.SensorResolution Kind: Method Get sensor resolution ###### getResolutionHeight(self) -> int: int Kind: Method Get sensor resolution height ###### getResolutionSize(self) -> tuple[int, int]: tuple[int, int] Kind: Method Get sensor resolution as size ###### getResolutionWidth(self) -> int: int Kind: Method Get sensor resolution width ###### setBoardSocket(self, boardSocket: depthai.CameraBoardSocket) Kind: Method Specify which board socket to use Parameter ``boardSocket``: Board socket to use ###### setCamId(self, arg0: int) Kind: Method ###### setCamera(self, name: str) Kind: Method Specify which camera to use by name Parameter ``name``: Name of the camera to use ###### setFps(self, fps: float) Kind: Method Set rate at which camera should produce frames Parameter ``fps``: Rate in frames per second ###### setFrameEventFilter(self, events: list [ depthai.FrameEvent ]) Kind: Method ###### setImageOrientation(self, imageOrientation: depthai.CameraImageOrientation) Kind: Method Set camera image orientation ###### setIsp3aFps(self, arg0: int) Kind: Method Isp 3A rate (auto focus, auto exposure, auto white balance, camera controls etc.). Default (0) matches the camera FPS, meaning that 3A is running on each frame. Reducing the rate of 3A reduces the CPU usage on CSS, but also increases the convergence rate of 3A. Note that camera controls will be processed at this rate. E.g. if camera is running at 30 fps, and camera control is sent at every frame, but 3A fps is set to 15, the camera control messages will be processed at 15 fps rate, which will lead to queueing. ###### setNumFramesPool(self, arg0: int) Kind: Method Set number of frames in main (ISP output) pool ###### setRawNumFramesPool(self, arg0: int) Kind: Method Set number of frames in raw pool ###### setRawOutputPacked(self, packed: bool) Kind: Method Configures whether the camera `raw` frames are saved as MIPI-packed to memory. The packed format is more efficient, consuming less memory on device, and less data to send to host: RAW10: 4 pixels saved on 5 bytes, RAW12: 2 pixels saved on 3 bytes. When packing is disabled (`false`), data is saved lsb-aligned, e.g. a RAW10 pixel will be stored as uint16, on bits 9..0: 0b0000'00pp'pppp'pppp. Default is auto: enabled for standard color/monochrome cameras where ISP can work with both packed/unpacked, but disabled for other cameras like ToF. ###### setResolution(self, resolution: depthai.MonoCameraProperties.SensorResolution) Kind: Method Set sensor resolution ###### frameEvent Kind: Property ###### initialControl Kind: Property Initial control options to apply to sensor ###### inputControl Kind: Property ###### out Kind: Property ###### raw Kind: Property ##### depthai.node.NeuralAssistedStereo(depthai.DeviceNode) Kind: Class NeuralAssistedStereo node. Combines Neural Depth with VPP and traditional Stereo Depth. This composite node internally creates and connects: - Rectification node (full resolution) - NeuralDepth node (low resolution depth estimation) - VPP node (applies virtual projection pattern) - StereoDepth node (final depth computation on VPP-enhanced images) Pipeline structure: Left/Right Cameras → Rectification → [Full res to VPP] ↓ NeuralDepth (low res) → [disparity + confidence to VPP] ↓ VPP (combines neural depth with full res images) ↓ StereoDepth → Final Depth Output ###### build(self, leftInput: depthai.Node.Output, rightInput: depthai.Node.Output, neuralModel: depthai.DeviceModelZoo = ..., rectifyImages: bool = True) -> NeuralAssistedStereo: NeuralAssistedStereo Kind: Method ###### depth Kind: Property ###### disparity Kind: Property ###### inputNeuralConfig Kind: Property ###### inputStereoConfig Kind: Property ###### inputVppConfig Kind: Property ###### left Kind: Property ###### neuralConfidence Kind: Property ###### neuralDepth Kind: Property ###### neuralDisparity Kind: Property ###### rectification Kind: Property ###### rectifiedLeft Kind: Property ###### rectifiedRight Kind: Property ###### right Kind: Property ###### stereoDepth Kind: Property ###### vpp Kind: Property ###### vppLeft Kind: Property ###### vppRight Kind: Property ##### depthai.node.NeuralDepth(depthai.DeviceNode) Kind: Class NeuralDepth node. Compute depth from left-right image pair using neural network. ###### getInputSize(model: depthai.DeviceModelZoo) -> tuple[int, int]: tuple[int, int] Kind: Static Method Get input size for specific model ###### build(self, leftInput: depthai.Node.Output, rightInput: depthai.Node.Output, model: depthai.DeviceModelZoo = ...) -> NeuralDepth: NeuralDepth Kind: Method ###### setRectification(self, enable: bool) -> NeuralDepth: NeuralDepth Kind: Method Enable or disable rectification (useful for prerectified inputs) ###### confidence Kind: Property Output confidence ImgFrame ###### depth Kind: Property Output depth ImgFrame ###### disparity Kind: Property Output disparity ImgFrame ###### edge Kind: Property Output edge ImgFrame ###### initialConfig Kind: Property Initial config to use for NeuralDepth. ###### inputConfig Kind: Property Input config to modify parameters in runtime. ###### left Kind: Property Input for left ImgFrame of left-right pair ###### messageDemux Kind: Property ###### neuralNetwork Kind: Property ###### rectification Kind: Property ###### rectifiedLeft Kind: Property Output for rectified left ImgFrame ###### rectifiedRight Kind: Property Output for rectified right ImgFrame ###### right Kind: Property Input for right ImgFrame of left-right pair ###### sync Kind: Property ##### depthai.node.NeuralNetwork(depthai.DeviceNode) Kind: Class NeuralNetwork node. Runs a neural inference on input data. ###### depthai.node.NeuralNetwork.Model Kind: Class ###### __init__() Kind: Method ###### build() Kind: Method ###### getNNArchive(self) -> depthai.NNArchive|None: depthai.NNArchive|None Kind: Method Get the archive owned by this Node. Returns: constant reference to this Nodes archive ###### getNumInferenceThreads(self) -> int: int Kind: Method How many inference threads will be used to run the network Returns: Number of threads, 0, 1 or 2. Zero means AUTO ###### setBackend(self, setBackend: str) Kind: Method Specifies backend to use Parameter ``backend``: String specifying backend to use ###### setBackendProperties(self, setBackendProperties: dict [ str , str ]) Kind: Method Set backend properties Parameter ``backendProperties``: backend properties map ###### setBlob() Kind: Method ###### setBlobPath(self, path: os.PathLike) Kind: Method Load network blob into assets and use once pipeline is started. Throws: Error if file doesn't exist or isn't a valid network blob. Parameter ``path``: Path to network blob ###### setFromModelZoo(self, description: depthai.NNModelDescription, useCached: bool) Kind: Method Download model from zoo and set it for this Node Parameter ``description:``: Model description to download Parameter ``useCached:``: Use cached model if available ###### setModelFromDeviceZoo(self, model: depthai.DeviceModelZoo) Kind: Method Set model from Device Model Zoo Parameter ``model``: DeviceModelZoo model enum @note Only applicable for RVC4 devices with OS 1.20.5 or higher ###### setModelPath(self, modelPath: os.PathLike) Kind: Method Load network xml and bin files into assets. Parameter ``xmlModelPath``: Path to the neural network model file. ###### setNNArchive() Kind: Method ###### setNumInferenceThreads(self, numThreads: int) Kind: Method How many threads should the node use to run the network. Parameter ``numThreads``: Number of threads to dedicate to this node ###### setNumNCEPerInferenceThread(self, numNCEPerThread: int) Kind: Method How many Neural Compute Engines should a single thread use for inference Parameter ``numNCEPerThread``: Number of NCE per thread ###### setNumPoolFrames(self, numFrames: int) Kind: Method Specifies how many frames will be available in the pool Parameter ``numFrames``: How many frames will pool have ###### setNumShavesPerInferenceThread(self, numShavesPerInferenceThread: int) Kind: Method How many Shaves should a single thread use for inference Parameter ``numShavesPerThread``: Number of shaves per thread ###### input Kind: Property Input message with data to be inferred upon ###### inputs Kind: Property Inputs mapped to network inputs. Useful for inferring from separate data sources Default input is non-blocking with queue size 1 and waits for messages ###### out Kind: Property Outputs NNData message that carries inference results ###### passthrough Kind: Property Passthrough message on which the inference was performed. Suitable for when input queue is set to non-blocking behavior. ###### passthroughs Kind: Property Passthroughs which correspond to specified input ##### depthai.node.ObjectTracker(depthai.DeviceNode) Kind: Class ObjectTracker node. Performs object tracking using Kalman filter and hungarian algorithm. ###### setDetectionLabelsToTrack(self, labels: list [ int ]) Kind: Method Specify detection labels to track. Parameter ``labels``: Detection labels to track. Default every label is tracked from image detection network output. ###### setMaxObjectsToTrack(self, maxObjectsToTrack: int) Kind: Method Specify maximum number of object to track. Parameter ``maxObjectsToTrack``: Maximum number of object to track. Maximum 60 in case of SHORT_TERM_KCF, otherwise 1000. ###### setOcclusionRatioThreshold(self, threshold: float) Kind: Method Set the occlusion ratio threshold. Used to filter out overlapping tracklets. Parameter ``theshold``: Occlusion ratio threshold. Default 0.3. ###### setRunOnHost(self, runOnHost: bool) Kind: Method Specify whether to run on host or device By default, the node will run on device. ###### setSpatialAssociation(self, enabled: bool) Kind: Method Enable or disable spatially-aware association. If disabled, only 2D association is used. Parameter ``enabled``: `true` enables spatially-aware association, `false` uses 2D-only association. Default is false. ###### setSpatialAssociationWeight(self, weight: float) Kind: Method Set spatial association weight in [0,1]. Parameter ``weight``: Spatial association weight in [0,1] used to blend 2D and spatial association scores (0 = 2D-only scoring, 1 = spatial-only scoring). This weight affects candidate scoring only; final acceptance still requires passing the 2D IoU threshold gate. Default is 0.5. ###### setSpatialDepthAwareScale(self, scale: float) Kind: Method Set depth-aware gating scale used for spatial association. Increases gating threshold with increased depth. Parameter ``scale``: Depth-aware gating scale factor. Default is 0.35 ###### setSpatialDistanceThreshold(self, thresholdMeters: float) Kind: Method Set base 3D gating threshold in meters for spatial association. Parameter ``thresholdMeters``: Base spatial gating distance in meters. Default is 1.5m. ###### setTrackerIdAssignmentPolicy(self, type: depthai.TrackerIdAssignmentPolicy) Kind: Method Specify tracker ID assignment policy. Parameter ``type``: Tracker ID assignment policy. ###### setTrackerThreshold(self, threshold: float) Kind: Method Specify tracker threshold. Parameter ``threshold``: Above this threshold the detected objects will be tracked. Default 0, all image detections are tracked. ###### setTrackerType(self, type: depthai.TrackerType) Kind: Method Specify tracker type algorithm. Parameter ``type``: Tracker type. ###### setTrackingPerClass(self, trackingPerClass: bool) Kind: Method Whether tracker should take into consideration class label for tracking. ###### setTrackletBirthThreshold(self, trackletBirthThreshold: int) Kind: Method Set the tracklet birth threshold. Minimum consecutive tracked frames required to consider a tracklet as a new (TRACKED) instance. Parameter ``trackletBirthThreshold``: Tracklet birth threshold. Default 3. ###### setTrackletMaxLifespan(self, trackletMaxLifespan: int) Kind: Method Set the tracklet lifespan in number of frames. Number of frames after which a LOST tracklet is removed. Parameter ``trackletMaxLifespan``: Tracklet lifespan in number of frames. Default 120. ###### inputConfig Kind: Property Input ObjectTrackerConfig message with ability to modify parameters at runtime. Default queue is non-blocking with size 4. ###### inputDetectionFrame Kind: Property Input ImgFrame message on which object detection was performed. Default queue is non-blocking with size 4. ###### inputDetections Kind: Property Input message with image detection from neural network. Default queue is non- blocking with size 4. ###### inputTrackerFrame Kind: Property Input ImgFrame message on which tracking will be performed. RGBp, BGRp, NV12, YUV420p types are supported. Default queue is non-blocking with size 4. ###### out Kind: Property Outputs Tracklets message that carries object tracking results. ###### passthroughDetectionFrame Kind: Property Passthrough ImgFrame message on which object detection was performed. Suitable for when input queue is set to non-blocking behavior. ###### passthroughDetections Kind: Property Passthrough image detections message from neural network output. Suitable for when input queue is set to non-blocking behavior. ###### passthroughTrackerFrame Kind: Property Passthrough ImgFrame message on which tracking was performed. Suitable for when input queue is set to non-blocking behavior. ##### depthai.node.PointCloud(depthai.DeviceNode) Kind: Class PointCloud node. Computes point cloud from depth frames. ###### setNumFramesPool(self, numFramesPool: int) Kind: Method Specify number of frames in pool. Parameter ``numFramesPool``: How many frames should the pool have ###### setRunOnHost(self, runOnHost: bool) Kind: Method Specify whether to run on host or device By default, the node will run on host. ###### setTargetCoordinateSystem() Kind: Method ###### useCPU(self) Kind: Method Use single-threaded CPU for processing ###### useCPUMT(self, numThreads: int = 2) Kind: Method Use multi-threaded CPU for processing ###### useGPU(self, device: int = 0) Kind: Method Use GPU for point cloud computation Parameter ``device``: GPU device index (default 0) ###### initialConfig Kind: Property Initial config to use when computing the point cloud. ###### inputColor Kind: Property ###### inputConfig Kind: Property Input PointCloudConfig message with ability to modify parameters in runtime. Default queue is non-blocking with size 4. ###### inputDepth Kind: Property ###### outputPointCloud Kind: Property Outputs PointCloudData message ###### passthroughDepth Kind: Property Passthrough depth from which the point cloud was calculated. Suitable for when input queue is set to non-blocking behavior. ##### depthai.node.RGBD(depthai.node.ThreadedHostNode) Kind: Class RGBD node. Combines depth and color frames into a single point cloud. ###### build() Kind: Method ###### printDevices(self) Kind: Method Print available GPU devices ###### setDepthUnits(self, units: depthai.LengthUnit) Kind: Method ###### useCPU(self) Kind: Method Use single-threaded CPU for processing ###### useCPUMT(self, numThreads: int = 2) Kind: Method Use multi-threaded CPU for processing Parameter ``numThreads``: Number of threads to use ###### useGPU(self, device: int = 0) Kind: Method Use GPU for processing (needs to be compiled with Kompute support) Parameter ``device``: GPU device index ###### inColor Kind: Property ###### inDepth Kind: Property ###### pcl Kind: Property Output point cloud. ###### rgbd Kind: Property Output RGBD frames. ##### depthai.node.RTABMapSLAM(depthai.node.ThreadedHostNode) Kind: Class RTABMap SLAM node. Performs SLAM on given odometry pose, rectified frame and depth frame. ###### getLocalTransform(self) -> depthai.TransformData: depthai.TransformData Kind: Method ###### saveDatabase(self) Kind: Method ###### setAlphaScaling(self, alpha: float) Kind: Method Set the alpha scaling factor for the camera model. ###### setDatabasePath(self, path: str) Kind: Method Set RTABMap database path. "/tmp/rtabmap.tmp.db" by default. ###### setFreq(self, f: float) Kind: Method Set the frequency at which the node processes data. 1Hz by default. ###### setLoadDatabaseOnStart(self, load: bool) Kind: Method Whether to load the database on start. False by default. ###### setLocalTransform(self, transform: depthai.TransformData) Kind: Method ###### setParams(self, params: dict [ str , str ]) Kind: Method Set RTABMap parameters. For the list of all parameters visit https://github.com/introlab/rtabmap/blob/master/corelib/include/rtabmap/core/Par ameters.h ###### setPublishGrid(self, publish: bool) Kind: Method Whether to publish the ground point cloud. True by default. ###### setPublishGroundCloud(self, publish: bool) Kind: Method Whether to publish the ground point cloud. True by default. ###### setPublishObstacleCloud(self, publish: bool) Kind: Method Whether to publish the obstacle point cloud. True by default. ###### setSaveDatabaseOnClose(self, save: bool) Kind: Method Whether to save the database on close. False by default. ###### setSaveDatabasePeriod(self, period: float) Kind: Method Set the interval at which the database is saved. 30.0s by default. ###### setSaveDatabasePeriodically(self, save: bool) Kind: Method Whether to save the database periodically. False by default. ###### setUseFeatures(self, useFeatures: bool) Kind: Method Whether to use input features for SLAM. False by default. ###### triggerNewMap(self) Kind: Method Trigger a new map. ###### depth Kind: Property ###### features Kind: Property Input tracked features on which SLAM is performed (optional). ###### groundPCL Kind: Property Output ground point cloud. ###### obstaclePCL Kind: Property Output obstacle point cloud. ###### occupancyGridMap Kind: Property Output occupancy grid map. ###### odom Kind: Property Input odometry pose. ###### odomCorrection Kind: Property Output odometry correction (map to odom). ###### passthroughDepth Kind: Property Output passthrough depth image. ###### passthroughFeatures Kind: Property Output passthrough features. ###### passthroughOdom Kind: Property Output passthrough odometry pose. ###### passthroughRect Kind: Property Output passthrough rectified image. ###### rect Kind: Property ###### transform Kind: Property Output transform. ##### depthai.node.RTABMapVIO(depthai.node.ThreadedHostNode) Kind: Class RTABMap Visual Inertial Odometry node. Performs VIO on rectified frame, depth frame and IMU data. ###### reset(self, transform: depthai.TransformData) Kind: Method Reset Odometry. ###### setLocalTransform(self, transform: depthai.TransformData) Kind: Method ###### setParams(self, params: dict [ str , str ]) Kind: Method Set RTABMap parameters. ###### setUseFeatures(self, useFeatures: bool) Kind: Method Whether to use input features or calculate them internally. ###### depth Kind: Property ###### features Kind: Property Input tracked features on which VIO is performed (optional). ###### imu Kind: Property Input IMU data. ###### passthroughDepth Kind: Property Passthrough depth frame. ###### passthroughFeatures Kind: Property Passthrough features. ###### passthroughRect Kind: Property Passthrough rectified frame. ###### rect Kind: Property ###### transform Kind: Property Output transform. ##### depthai.node.RecordMetadataOnly(depthai.node.ThreadedHostNode) Kind: Class RecordMetadataOnly node, used to record a source stream to a file ###### getCompressionLevel(self) -> ...: ... Kind: Method ###### getRecordFile(self) -> os.PathLike: os.PathLike Kind: Method ###### setCompressionLevel(self, compressionLevel: ...) -> RecordMetadataOnly: RecordMetadataOnly Kind: Method ###### setRecordFile(self, recordFile: os.PathLike) -> RecordMetadataOnly: RecordMetadataOnly Kind: Method ###### input Kind: Property Input IMU messages to be recorded (will support other types in the future) Default queue is blocking with size 8 ##### depthai.node.RecordVideo(depthai.node.ThreadedHostNode) Kind: Class RecordVideo node, used to record a video source stream to a file ###### getCompressionLevel(self) -> ...: ... Kind: Method ###### getRecordMetadataFile(self) -> os.PathLike: os.PathLike Kind: Method ###### getRecordVideoFile(self) -> os.PathLike: os.PathLike Kind: Method ###### setCompressionLevel(self, compressionLevel: ...) -> RecordVideo: RecordVideo Kind: Method ###### setFps(self, fps: int) -> RecordVideo: RecordVideo Kind: Method ###### setRecordMetadataFile(self, recordFile: os.PathLike) -> RecordVideo: RecordVideo Kind: Method ###### setRecordVideoFile(self, recordFile: os.PathLike) -> RecordVideo: RecordVideo Kind: Method ###### input Kind: Property Input for ImgFrame or EncodedFrame messages to be recorded Default queue is blocking with size 15 ##### depthai.node.Rectification(depthai.DeviceNode) Kind: Class ###### enableRectification(self, enable: bool) -> Rectification: Rectification Kind: Method Enable or disable rectification (useful for minimal changes during debugging) ###### setOutputSize() Kind: Method ###### setRunOnHost(self, runOnHost: bool) Kind: Method Specify whether to run on host or device By default, the node will run on device. ###### input1 Kind: Property Input images to be rectified ###### input2 Kind: Property ###### output1 Kind: Property Send outputs ###### output2 Kind: Property ###### passthrough1 Kind: Property Passthrough for input messages (so the node can be placed between other nodes) ###### passthrough2 Kind: Property ##### depthai.node.ReplayMetadataOnly(depthai.node.ThreadedHostNode) Kind: Class Replay node, used to replay a file to a source node ###### getFps(self) -> float: float Kind: Method ###### getLoop(self) -> bool: bool Kind: Method ###### getReplayFile(self) -> os.PathLike: os.PathLike Kind: Method ###### setFps(self, fps: float) -> ReplayMetadataOnly: ReplayMetadataOnly Kind: Method ###### setLoop(self, loop: bool) -> ReplayMetadataOnly: ReplayMetadataOnly Kind: Method ###### setReplayFile(self, replayFile: os.PathLike) -> ReplayMetadataOnly: ReplayMetadataOnly Kind: Method ###### out Kind: Property Output for any type of messages to be transferred over XLink stream Default queue is blocking with size 8 ##### depthai.node.ReplayVideo(depthai.node.ThreadedHostNode) Kind: Class Replay node, used to replay a file to a source node ###### getFps(self) -> float: float Kind: Method ###### getLoop(self) -> bool: bool Kind: Method ###### getOutFrameType(self) -> depthai.ImgFrame.Type: depthai.ImgFrame.Type Kind: Method ###### getReplayMetadataFile(self) -> os.PathLike: os.PathLike Kind: Method ###### getReplayVideoFile(self) -> os.PathLike: os.PathLike Kind: Method ###### getSize(self) -> tuple[int, int]: tuple[int, int] Kind: Method ###### setFps(self, fps: float) -> ReplayVideo: ReplayVideo Kind: Method ###### setLoop(self, loop: bool) -> ReplayVideo: ReplayVideo Kind: Method ###### setOutFrameType(self, frameType: depthai.ImgFrame.Type) -> ReplayVideo: ReplayVideo Kind: Method ###### setReplayMetadataFile(self, replayFile: os.PathLike) -> ReplayVideo: ReplayVideo Kind: Method ###### setReplayVideoFile(self, replayVideoFile: os.PathLike) -> ReplayVideo: ReplayVideo Kind: Method ###### setSize() Kind: Method ###### out Kind: Property Output for any type of messages to be transferred over XLink stream Default queue is blocking with size 8 ##### depthai.node.SPIIn(depthai.DeviceNode) Kind: Class SPIIn node. Receives messages over SPI. ###### getBusId(self) -> int: int Kind: Method Get bus id ###### getMaxDataSize(self) -> int: int Kind: Method Get maximum messages size in bytes ###### getNumFrames(self) -> int: int Kind: Method Get number of frames in pool ###### getStreamName(self) -> str: str Kind: Method Get stream name ###### setBusId(self, id: int) Kind: Method Specifies SPI Bus number to use Parameter ``id``: SPI Bus id ###### setMaxDataSize(self, maxDataSize: int) Kind: Method Set maximum message size it can receive Parameter ``maxDataSize``: Maximum size in bytes ###### setNumFrames(self, numFrames: int) Kind: Method Set number of frames in pool for sending messages forward Parameter ``numFrames``: Maximum number of frames in pool ###### setStreamName(self, name: str) Kind: Method Specifies stream name over which the node will receive data Parameter ``name``: Stream name ###### out Kind: Property Outputs message of same type as send from host. ##### depthai.node.SPIOut(depthai.DeviceNode) Kind: Class SPIOut node. Sends messages over SPI. ###### setBusId(self, id: int) Kind: Method Specifies SPI Bus number to use Parameter ``id``: SPI Bus id ###### setStreamName(self, name: str) Kind: Method Specifies stream name over which the node will send data Parameter ``name``: Stream name ###### input Kind: Property Input for any type of messages to be transferred over SPI stream Default queue is blocking with size 8 ##### depthai.node.Script(depthai.DeviceNode) Kind: Class ###### getProcessor(self) -> depthai.ProcessorType: depthai.ProcessorType Kind: Method Get on which processor the script should run Returns: Processor type - Leon CSS or Leon MSS ###### getScriptName(self) -> str: str Kind: Method Get the script name in utf-8. When name set with setScript() or setScriptPath(), returns that name. When script loaded with setScriptPath() with name not provided, returns the utf-8 string of that path. Otherwise, returns "