from __future__ import print_function
from pclpy.pcdio import loadPCDFile
from pclpy.filters import VoxelGrid
cloud = loadPCDFile('inputCloud.pcd')
print( 'Points before filtering: {} data points {}'.format(\
cloud.width * cloud.height, cloud.field_names ) )
vg = VoxelGrid()
vg.setInputCloud(cloud)
vg.setLeafSize(0.01, 0.01, 0.01)
filtered_cloud = vg.filter()
print( 'Points after filtering: {} data points {}'.format(\
cloud.width * cloud.height, cloud.field_names ) )
from pclpy.visualization import PCLVisualizer
from pclpy.pcdio import loadPCDFile
vis = PCLVisualizer()
cloud1 = loadPCDFile('inputCloud1.pcd')
cloud2 = loadPCDFile('inputCloud2.pcd')
id1 = vis.addPointCloud(cloud1)
id2 = vis.addPointCloud(cloud2)
vis.spin()
vis.removePointCloud(id2)
vis.spin()
from pclpy.pcdio import loadPCDFile
from pclpy.visualization import PCLVisualizer
from pclpy.filters import PassThrough
from pclpy.registration import IterativeClosestPoint
pcd_files = [ 'inputCloud0', 'inputCloud1' ]
clouds = [ loadPCDFile(x+'.pcd') for x in pcd_files ]
_pass = PassThrough()
_pass.setFilterFieldName('z')
_pass.setFilterLimits( 0.0, 5.0 )
# filtrar todas
for i in range(len(clouds)):
_pass.setInputCloud(clouds[i])
clouds[i] = _pass.filter()
icp = IterativeClosestPoint()
vis = PCLVisualizer()
t_cloud = clouds[0]
vis.addPointCloud(t_cloud)
for i in range( 1, len(clouds) ):
icp.setInputSource( clouds[i] )
icp.setInputTarget( t_cloud )
t_cloud = icp.align()
vis.addPointCloud( t_cloud )
vis.spin()
from pclpy.visualization import PCLVisualizer
from pclpy.filters import VoxelGrid
from pclpy.pcdio import loadPCDFile
i_cloud = loadPCDFile('cloud0.pcd')
vg = VoxelGrid()
vg.setInputCloud(i_cloud)
vg.setLeafSize(.01, .01, .01)
r_cloud = vg.filter()
vis = PCLVisualizer()
v1 = vis.createViewPort( 0., 0., .5, 1. )
v2 = vis.createViewPort( .5, 0., 1., 1. )
vis.setBackgroundColor( 255, 0, 0, v1 )
vis.setBackgroundColor( 0, 255, 0, v2 )
vis.addPointCloud( i_cloud, v1 )
vis.addPointCloud( r_cloud, v2 )
vis.spin()