PointCloud
The PointCloud node enables on-device point cloud generation from depth map.How to place it
Python
C++
Python
Python
1pipeline = dai.Pipeline()
2pointCloud = pipeline.create(dai.node.PointCloud)Inputs and Outputs
Example visualization with Open3D
Python
C++
Python
Python
1import open3d as o3d
2import numpy as np
3import depthai as dai
4
5pcd = o3d.geometry.PointCloud()
6vis = o3d.visualization.VisualizerWithKeyCallback()
7vis.create_window()
8
9with dai.Device(pipeline) as device:
10 coordinateFrame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=1000, origin=[0,0,0])
11 vis.add_geometry(coordinateFrame)
12
13 while device.isPipelineRunning():
14 inMessage = q.get()
15 inColor = inMessage["rgb"]
16 inPointCloud = inMessage["pcl"]
17 cvColorFrame = inColor.getCvFrame()
18
19 if inPointCloud:
20 points = inPointCloud.getPoints().astype(np.float64)
21 pcd.points = o3d.utility.Vector3dVector(points)
22 colors = (cvRGBFrame.reshape(-1, 3) / 255.0).astype(np.float64)
23 pcd.colors = o3d.utility.Vector3dVector(colors)
24 vis.update_geometry(pcd)
25
26 vis.poll_events()
27 vis.update_renderer()
28
29 vis.destroy_window()Examples using PointCloud
Reference
Python
C++
Python
class
depthai.node.PointCloud(depthai.Node)
method
setNumFramesPool(self, arg0: int)Specify number of frames in pool. Parameter ``numFramesPool``: How many frames should the pool have
property
initialConfig
Initial config to use when computing the point cloud.
property
inputConfig
Input PointCloudConfig message with ability to modify parameters in runtime. Default queue is non-blocking with size 4.
property
inputDepth
Input message with depth data used to create the point cloud. Default queue is non-blocking with size 4.
property
outputPointCloud
Outputs PointCloudData message
property
passthroughDepth
Passthrough depth from which the point cloud was calculated. Suitable for when input queue is set to non-blocking behavior.
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