Create PointCloud2 with python with rgb

create_cloud_xyzrgb.py

#!/usr/bin/env python
# PointCloud2 color cube
# https://answers.ros.org/question/289576/understanding-the-bytes-in-a-pcl2-message/
import rospy
import struct

from sensor_msgs import point_cloud2
from sensor_msgs.msg import PointCloud2, PointField
from std_msgs.msg import Header


rospy.init_node("create_cloud_xyzrgb")
pub = rospy.Publisher("point_cloud2", PointCloud2, queue_size=2)

points = []
lim = 8
for i in range(lim):
    for j in range(lim):
        for k in range(lim):
            x = float(i) / lim
            y = float(j) / lim
            z = float(k) / lim
            r = int(x * 255.0)
            g = int(y * 255.0)
            b = int(z * 255.0)
            a = 255
            print r, g, b, a
            rgb = struct.unpack('I', struct.pack('BBBB', b, g, r, a))[0]
            print hex(rgb)
            pt = [x, y, z, rgb]
            points.append(pt)

fields = [PointField('x', 0, PointField.FLOAT32, 1),
          PointField('y', 4, PointField.FLOAT32, 1),
          PointField('z', 8, PointField.FLOAT32, 1),
          # PointField('rgb', 12, PointField.UINT32, 1),
          PointField('rgba', 12, PointField.UINT32, 1),
          ]

print points

header = Header()
header.frame_id = "map"
pc2 = point_cloud2.create_cloud(header, fields, points)

while not rospy.is_shutdown():
    pc2.header.stamp = rospy.Time.now()
    pub.publish(pc2)
    rospy.sleep(1.0)

hello, i am new to learn ros, and thanks for your share. i use your code to pub pointscloud2 which generate with a rgb image and a depth image , but it's too slow,even less than 1 fps. i use kernprof to calculate the time consumption, and i found the function point_cloud2.create_cloud and the code for adding points(pack,unpack) consume nearly all time. i have searched for the solution, but have not got the answer yet, Do you have some suggestions for my problem? thanks

the rospy.sleep(1.0) makes the loop at the bottom sleep for 1 second on each iteration. Also if your pointcloud is large, you're going to want to use numpy arrays rather than for loops to speed this up.

how do i do this but for a sphere?