def convert_pcl(data):
import py3d
header = '''# .PCD v0.7 - Point Cloud Data file format
VERSION 0.7
FIELDS x y z rgb
SIZE 4 4 4 4
TYPE F F F F
COUNT 1 1 1 1
WIDTH %d
HEIGHT %d
VIEWPOINT 0 0 0 1 0 0 0
POINTS %d
DATA ascii'''
with open(tmp_pcd_name, 'w') as f:
f.write(header % (data.width, data.height, data.width * data.height))
f.write("\n")
for p in pc2.read_points(data, skip_nans=True):
f.write('%f %f %f %e' % (p[0], p[1], p[2], p[3]))
f.write("\n")
cloud_list = []
for p in pc2.read_points(data, skip_nans=False):
cloud_list.append(p[0])
cloud_list.append(p[1])
cloud_list.append(p[2])
cloud_list.append(p[3])
f.write("\n")
pcd = py3d.read_point_cloud(tmp_pcd_name)
return pcd
import sys
import numpy as np
import py3d
sys.path.append("../Open3D/build/lib/")
print("Load a ply point cloud, print it, and render it")
pcd = py3d.read_point_cloud("/home/sanjeev309/Open3D/build/lib/TestData/fragment.ply")
print(pcd)
print(np.asarray(pcd.points))
py3d.draw_geometries([pcd])
print("Downsample the point cloud with a voxel of 0.05")
downpcd = py3d.voxel_down_sample(pcd, voxel_size=0.05)
py3d.draw_geometries([downpcd])
print("Recompute the normal of the downsampled point cloud")
py3d.estimate_normals(downpcd, search_param=py3d.KDTreeSearchParamHybrid(
radius=0.1, max_nn=30))
py3d.draw_geometries([downpcd])
print("")
print("Load a polygon volume and use it to crop the original point cloud")
vol = py3d.read_selection_polygon_volume("/home/sanjeev309/Open3D/build/lib/TestData/Crop/cropped.json")
chair = vol.crop_point_cloud(pcd)
py3d.draw_geometries([chair])
print("")
print("Paint chair")
chair.paint_uniform_color([0, 0, 0])
py3d.draw_geometries([chair])
def example_import_function():
from py3d import read_point_cloud
pcd = read_point_cloud("../../TestData/ICP/cloud_bin_0.pcd")
print(pcd)