How to efficiently convert ROS PointCloud2 to pcl point cloud and visualize it in python

Question

I am trying to do some segmentation in pointcloud from kinect in ROS. At the moment I have this:

import rospy
import pcl
from sensor_msgs.msg import PointCloud2
import sensor_msgs.point_cloud2 as pc2
def on_new_point_cloud(data):
    pc = pc2.read_points(data, skip_nans=True, field_names=("x", "y", "z"))
    pc_list = []
    for p in pc:
        pc_list.append( [p[0],p[1],p[2]] )

    p = pcl.PointCloud()
    p.from_list(pc_list)
    seg = p.make_segmenter()
    seg.set_model_type(pcl.SACMODEL_PLANE)
    seg.set_method_type(pcl.SAC_RANSAC)
    indices, model = seg.segment()

rospy.init_node('listener', anonymous=True)
rospy.Subscriber("/kinect2/hd/points", PointCloud2, on_new_point_cloud)
rospy.spin()

This seems to work, but very slow due to the for loop. My questions:

1) How to efficiently convert from PointCloud2 message to pcl pointcloud

2) How to visualize clouds.

Answer 1

This works for me. I just resize the point cloud since my custom computer (512x x 512px). My code is adapted from @Abdulbaki Aybakan - thanks for that!

My code is:

pc = ros_numpy.numpify(pointcloud)
height = pc.shape[0]
width = pc.shape[1]
np_points = np.zeros((height * width, 3), dtype=np.float32)
np_points[:, 0] = np.resize(pc['x'], height * width)
np_points[:, 1] = np.resize(pc['y'], height * width)
np_points[:, 2] = np.resize(pc['z'], height * width)

ros_numpy, : http://wiki.ros.org/ros_numpy

Answer 2

import rospy
import pcl
from sensor_msgs.msg import PointCloud2
import sensor_msgs.point_cloud2 as pc2
import ros_numpy

def callback(data):
    pc = ros_numpy.numpify(data)
    points=np.zeros((pc.shape[0],3))
    points[:,0]=pc['x']
    points[:,1]=pc['y']
    points[:,2]=pc['z']
    p = pcl.PointCloud(np.array(points, dtype=np.float32))

rospy.init_node('listener', anonymous=True)
rospy.Subscriber("/velodyne_points", PointCloud2, callback)
rospy.spin()

ros_numpy numpy .