Nodes
Nodes are the building blocks when populating the Pipeline. Each node provides a specific functionality on the DepthAI, a set of configurable properties and inputs/outputs. After you create a node on a pipeline, you can also configure it as desired and link it to other nodes.On the table of contents (left side of the page) all nodes are listed under theNode entry. You can click on them to find out more.Inputs and outputs
Each node can have zero, one or multiple inputs and outputs. For example, SystemLogger node has no inputs and 1 output and EdgeDetector has 2 inputs and 1 output (as shown below). Script node can have any number of inputs/ouputs.Each node's output has its pool; block in RAM where it stores messages. Each node's input has its queue for message pointers (doesn't store the message itself, just a pointer to it).
Node input
Node input queue is a queue for Messages. It can be linked with other node's output (that's how you link up nodes). Node inputs are configurable - withinput.setBlocking(bool) and input.setQueueSize(num), eg. edgeDetector.inputImage.setQueueSize(10). If the input queue fills up, behavior of the input depends on blocking attribute.Let's say we have linked ColorCamera preview output with NeuralNetwork input input.
If input is set to blocking mode, and input queue fills up, no new messages from ColorCamera will be able to enter the input queue. This means ColorCamera will block and wait with sending its messages until it can push the message to the queue of NeuralNetwork input. If ColorCamera preview is connected to multiple inputs, the same behavior implies, with the messages being pushed sequentially to each input.If blocking is disabled, new messages will push out old messages. This eliminates the risk of pipeline freezing, but can result in dropped messages (eg. ImgFrame).Node output
Node outputs Messages. Some nodes have a configurable output message pool. Output message pool is a reserved memory region (to reduce memory fragmentation) that holds output messages. After the node creates an output message (for example ImgFrame), it will send it to other nodes as specified when linking the inputs/outputs of the node. Currently, some nodes (VideoEncoder, NeuralNetwork, ImageManip, XLinkIn) can have the pool size configured. The size of the pool specifies how many messages can be created and sent out while other messages are already somewhere in the pipeline.Pipeline freezing
When all the messages from pool are sent out and none yet returned, that's when the node will block (freeze) and wait until a message is released (not used by any node in the pipeline).
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depthai.Node
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Connection
Connection between an Input and Output
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Id
Node identificator. Unique for every node on a single Pipeline
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getInputs(self) -> list[Node.Input]: list[Node.Input]Retrieves all nodes inputs
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getName(self) -> str: strRetrieves nodes name
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getOutputs(self) -> list[Node.Output]: list[Node.Output]Retrieves all nodes outputs
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id
Id of node
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depthai.Node.Connection
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depthai.Node.DatatypeHierarchy
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depthai.Node.Input
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Members: SReceiver MReceiver
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getBlocking(self) -> bool: boolGet input queue behavior Returns: True blocking, false overwriting
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getQueueSize(self) -> int: intGet input queue size. Returns: Maximum input queue size
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getReusePreviousMessage(self) -> bool: boolEquivalent to getWaitForMessage but with inverted logic.
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getWaitForMessage(self) -> bool: boolGet behavior whether to wait for this input when a Node processes certain data or not Returns: Whether to wait for message to arrive to this input or not
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setBlocking(self, blocking: bool)Overrides default input queue behavior. Parameter ``blocking``: True blocking, false overwriting
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setQueueSize(self, size: int)Overrides default input queue size. If queue size fills up, behavior depends on `blocking` attribute Parameter ``size``: Maximum input queue size
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setReusePreviousMessage(self, reusePreviousMessage: bool)Equivalent to setWaitForMessage but with inverted logic.
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setWaitForMessage(self, waitForMessage: bool)Overrides default wait for message behavior. Applicable for nodes with multiple inputs. Specifies behavior whether to wait for this input when a Node processes certain data or not. Parameter ``waitForMessage``: Whether to wait for message to arrive to this input or not
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depthai.Node.Input.Type
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depthai.Node.InputMap
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__bool__(self) -> bool: boolCheck whether the map is nonempty
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depthai.Node.Output
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Members: MSender SSender
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canConnect(self, input: Node.Input) -> bool: boolCheck if connection is possible Parameter ``in``: Input to connect to Returns: True if connection is possible, false otherwise
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getConnections(self) -> list[Node.Connection]: list[Node.Connection]Retrieve all connections from this output Returns: Vector of connections
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isSamePipeline(self, input: Node.Input) -> bool: boolCheck if this output and given input are on the same pipeline. See also: canConnect for checking if connection is possible Returns: True if output and input are on the same pipeline
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link(self, input: Node.Input)Link current output to input. Throws an error if this output cannot be linked to given input, or if they are already linked Parameter ``in``: Input to link to
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unlink(self, input: Node.Input)Unlink a previously linked connection Throws an error if not linked. Parameter ``in``: Input from which to unlink from
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depthai.Node.Output.Type
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depthai.Node.OutputMap
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__bool__(self) -> bool: boolCheck whether the map is nonempty
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