PointCloudConfig

PointCloudConfig is a configuration class used to adjust settings for point cloud generation within the DepthAI ecosystem. It allows users to configure properties such as sparsity and transformation matrices, which are crucial for tailoring the point cloud data to specific application requirements.

Configuration Options

• Sparsity: Determines whether the generated point cloud should be sparse. Sparse point clouds may omit points based on certain criteria, such as depth value thresholds, to reduce data volume and processing requirements.

• Transformation Matrix: Applies a transformation matrix to the point cloud data, enabling rotations, translations, and scaling to align the point cloud with a world or application-specific coordinate system.

Usage

Configuring PointCloudConfig allows for precise control over the generation of point cloud data. Here’s an example of how to configure and apply PointCloudConfig in a DepthAI application:

Python

C++

import depthai as dai

# Create pipeline
pipeline = dai.Pipeline()

# Create PointCloud node
pointCloud = pipeline.create(dai.node.PointCloud)

pointCloud.initialConfig.setSparse(True) # Enable sparse point cloud generation

# Define a transformation matrix
transformationMatrix = [
    [1.0, 0.0, 0.0, 0.0],
    [0.0, 1.0, 0.0, 0.0],
    [0.0, 0.0, 1.0, 0.0],
    [0.0, 0.0, 0.0, 1.0]
]
pointCloud.initialConfig.setTransformationMatrix(transformationMatrix) # Apply transformation matrix

# Further pipeline setup and execution...

#include "depthai/depthai.hpp"

int main() {
    // Create pipeline
    dai::Pipeline pipeline;

    // Create PointCloud node
    auto pointCloud = pipeline.create<dai::node::PointCloud>();

    pointCloud->initialConfig.setSparse(true); // Enable sparse point cloud generation

    // Define a transformation matrix
    std::vector<std::vector<float>> transformationMatrix = {
        {1.0, 0.0, 0.0, 0.0},
        {0.0, 1.0, 0.0, 0.0},
        {0.0, 0.0, 1.0, 0.0},
        {0.0, 0.0, 0.0, 1.0}
    };
    pointCloud->initialConfig.setTransformationMatrix(transformationMatrix); // Apply transformation matrix

    // Further pipeline setup and execution...

    return 0;
}

This example demonstrates initializing PointCloudConfig, setting it to generate sparse point clouds, and applying a transformation matrix. This configuration is then applied to a PointCloud node within the DepthAI pipeline.

Examples of Functionality

• 3D Object Localization: Adjusting the transformation matrix to align point clouds with a known coordinate system for precise object placement.

• Scene Optimization: Utilizing sparse point clouds for efficient processing in large-scale or complex scenes.

• Data Alignment: Applying transformation matrices for seamless integration of point cloud data with other sensor data or pre-existing 3D models.

Reference

Python

C++

class depthai.PointCloudConfig

get(self: depthai.PointCloudConfig) → depthai.RawPointCloudConfig

getData(self: object) → numpy.ndarray[numpy.uint8]

getRaw(self: depthai.ADatatype) → depthai.RawBuffer

getSequenceNum(self: depthai.Buffer) → int

getSparse(self: depthai.PointCloudConfig) → bool

getTimestamp(self: depthai.Buffer) → datetime.timedelta

getTimestampDevice(self: depthai.Buffer) → datetime.timedelta

getTransformationMatrix(self: depthai.PointCloudConfig) → Annotated[list[Annotated[list[float], FixedSize(4)]], FixedSize(4)]

set(self: depthai.PointCloudConfig, config: depthai.RawPointCloudConfig) → depthai.PointCloudConfig

setData(*args, **kwargs)

Overloaded function.

1. setData(self: depthai.Buffer, arg0: list[int]) -> None

2. setData(self: depthai.Buffer, arg0: numpy.ndarray[numpy.uint8]) -> None

setSequenceNum(self: depthai.Buffer, arg0: int) → depthai.Buffer

setSparse(self: depthai.PointCloudConfig, arg0: bool) → depthai.PointCloudConfig

setTimestamp(self: depthai.Buffer, arg0: datetime.timedelta) → depthai.Buffer

setTimestampDevice(self: depthai.Buffer, arg0: datetime.timedelta) → depthai.Buffer

setTransformationMatrix(*args, **kwargs)

Overloaded function.

1. setTransformationMatrix(self: depthai.PointCloudConfig, arg0: Annotated[list[Annotated[list[float], FixedSize(3)]], FixedSize(3)]) -> depthai.PointCloudConfig

2. setTransformationMatrix(self: depthai.PointCloudConfig, arg0: Annotated[list[Annotated[list[float], FixedSize(4)]], FixedSize(4)]) -> depthai.PointCloudConfig

class dai::PointCloudConfig : public dai::Buffer

PointCloudConfig message. Carries ROI (region of interest) and threshold for depth calculation

Public Functions

PointCloudConfig()

Construct PointCloudConfig message.

PointCloudConfig(std::shared_ptr<RawPointCloudConfig> ptr)

~PointCloudConfig() = default

PointCloudConfig &set(dai::RawPointCloudConfig config)

Set explicit configuration.

Parameters

• config: Explicit configuration

dai::RawPointCloudConfig get() const

Retrieve configuration data for SpatialLocationCalculator.

Return

config for SpatialLocationCalculator

bool getSparse() const

Retrieve sparse point cloud calculation status.

Return

true if sparse point cloud calculation is enabled, false otherwise

std::array<std::array<float, 4>, 4> getTransformationMatrix() const

Retrieve transformation matrix for point cloud calculation.

Return

4x4 transformation matrix

PointCloudConfig &setSparse(bool enable)

Enable or disable sparse point cloud calculation.

Parameters

• enable:

PointCloudConfig &setTransformationMatrix(const std::array<std::array<float, 4>, 4> &transformationMatrix)

Set 4x4 transformation matrix for point cloud calculation. Default is an identity matrix.

Parameters

• transformationMatrix:

PointCloudConfig &setTransformationMatrix(const std::array<std::array<float, 3>, 3> &transformationMatrix)

Set 3x3 transformation matrix for point cloud calculation. Default is an identity matrix.

Parameters

• transformationMatrix:

Private Functions

std::shared_ptr<RawBuffer> serialize() const override

Private Members

RawPointCloudConfig &cfg

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