简单说下步骤：

一、通过realsense取得深度信息和彩色信息

二、获取坐标和色彩信息

三、通过pcl可视化点云

一、通过realsense取得深度信息和彩色信息

ubuntu下intel realsense的软件可以打开realsen的界面，里面可以得到彩色图像和深度图像，我们通过realsense的python接口获取彩色信息和深度信息。

1.基础的获取彩色和深度信息，realsense中的视频流转换为python的numpy格式，通过opencv输出

import pyrealsense2 as rs

import numpy as np

import cv2

import pcl

if __name__ == "__main__":

# Configure depth and color streams

pipeline = rs.pipeline()

config = rs.config()

config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)

config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)

# Start streaming

pipeline.start(config)

#深度图像向彩色对齐

align_to_color=rs.align(rs.stream.color)

try:

while True:

# Wait for a coherent pair of frames: depth and color

frames = pipeline.wait_for_frames()

frames = align_to_color.process(frames)

depth_frame = frames.get_depth_frame()

color_frame = frames.get_color_frame()

if not depth_frame or not color_frame:

continue

# Convert images to numpy arrays

depth_image = np.asanyarray(depth_frame.get_data())

color_image = np.asanyarray(color_frame.get_data())

# Apply colormap on depth image (image must be converted to 8-bit per pixel first)

depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET)

# Stack both images horizontally

images = np.hstack((color_image, depth_colormap))

# Show images

cv2.namedWindow('RealSense', cv2.WINDOW_AUTOSIZE)

cv2.imshow('RealSense', images)

key = cv2.waitKey(1)

# Press esc or 'q' to close the image window

if key & 0xFF == ord('q') or key == 27:

cv2.destroyAllWindows()

break

finally:

# Stop streaming

pipeline.stop()

2.获取内参和保存图片

分别用opencv和scipy.misc保存图片，略微会有一些差异，同时我们也获取了相机参数。

import pyrealsense2 as rs

import numpy as np

import cv2

import scipy.misc

import pcl

def get_image():

# Configure depth and color streams

pipeline = rs.pipeline()

config = rs.config()

config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)

config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)

# Start streaming

pipeline.start(config)

#获取图像，realsense刚启动的时候图像会有一些失真，我们保存第100帧图片。

for i in range(100):

data = pipeline.wait_for_frames()

depth = data.get_depth_frame()

color = data.get_color_frame()

#获取内参

dprofile = depth.get_profile()

cprofile = color.get_profile()

cvsprofile = rs.video_stream_profile(cprofile)

dvsprofile = rs.video_stream_profile(dprofile)

color_intrin=cvsprofile.get_intrinsics()

print(color_intrin)

depth_intrin=dvsprofile.get_intrinsics()

print(color_intrin)

extrin = dprofile.get_extrinsics_to(cprofile)

print(extrin)

depth_image = np.asanyarray(depth.get_data())

color_image = np.asanyarray(color.get_data())

# Apply colormap on depth image (image must be converted to 8-bit per pixel first)

depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET)

cv2.imwrite('color.png', color_image)

cv2.imwrite('depth.png', depth_image)

cv2.imwrite('depth_colorMAP.png', depth_colormap)

scipy.misc.imsave('outfile1.png', depth_image)

scipy.misc.imsave('outfile2.png', color_image)

二、获取坐标和色彩信息

1. 通过realsense摄像头，获取了顶点坐标和色彩信息。具体并不是很了解，pc.mac_to() 和 points=pc.calculate()是把色彩和深度结合了？ 再通过points获取顶点坐标。我们将顶点坐标和彩色相机得到的像素存入txt文件，。

def my_depth_to_cloud():

pc = rs.pointcloud()

points = rs.points()

pipeline = rs.pipeline()

config = rs.config()

config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)

config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)

pipe_profile = pipeline.start(config)

for i in range(100):

data = pipeline.wait_for_frames()

depth = data.get_depth_frame()

color = data.get_color_frame()

frames = pipeline.wait_for_frames()

depth = frames.get_depth_frame()

color = frames.get_color_frame()

colorful = np.asanyarray(color.get_data())

colorful=colorful.reshape(-1,3)

pc.map_to(color)

points = pc.calculate(depth)

#获取顶点坐标

vtx = np.asanyarray(points.get_vertices())

#获取纹理坐标

#tex = np.asanyarray(points.get_texture_coordinates())

with open('could.txt','w') as f:

for i in range(len(vtx)):

f.write(str(np.float(vtx[i][0])*1000)+' '+str(np.float(vtx[i][1])*1000)+' '+str(np.float(vtx[i][2])*1000)+' '+str(np.float(colorful[i][0]))+' '+str(np.float(colorful[i][1]))+' '+str(np.float(colorful[i][2]))+'\n')

三、通过pcl可视化点云

https://github.com/strawlab/python-pcl/blob/master/examples/example.py

1.在pcl中，要显示三维加色彩的点云坐标，每个点云包含了 x，y，z，rgb四个参数，特别的，rbg这个参数是由三维彩色坐标转换过来的。刚才得到的could.txt中，x,y,z,r,g,b 转换为x,y,z,rgb，只改颜色数据np.int(data[i][3])<<16|np.int(data[i][4])<<8|np.int(data[i][5])。保存为cloud_rbg.txt。

with open('could.txt','r') as f:

lines = f.readlines()

num=0

for line in lines:

l=line.strip().split()

data.append([np.float(l[0]),np.float(l[1]),np.float(l[2]),np.float(l[3]),np.float(l[4]),np.float(l[5])])

#data.append([np.float(l[0]), np.float(l[1]), np.float(l[2])])

num = num+1

with open('cloud_rgb.txt', 'w') as f:

for i in range(len(data)):

f.write(str(np.float(data[i][0])) + ' ' + str(np.float(data[i][1])) + ' ' + str(np.float(data[i][2]))+ ' ' + str(np.int(data[i][3])<<16|np.int(data[i][4])<<8|np.int(data[i][5]))+'\n')

2. 显示

def visual():

cloud = pcl.PointCloud_PointXYZRGB()

points = np.zeros((307200,4),dtype=np.float32)

n=0

with open('cloud_rgb.txt','r') as f:

lines = f.readlines()

for line in lines:

l=line.strip().split()

#保存xyz时候扩大了1000倍，发现并没有用

points[n][0] = np.float(l[0])/1000

points[n][1] = np.float(l[1])/1000

points[n][2] = np.float(l[2])/1000

points[n][3] = np.int(l[3])

n=n+1

print(points[0:100]) #看看数据是怎么样的

cloud.from_array(points) #从array构建点云的方式

visual = pcl.pcl_visualization.CloudViewing()

visual.ShowColorCloud(cloud)

v = True

while v:

v = not (visual.WasStopped())

3.想生成pcd，再读取pcd，但是下面的程序在可视化的时候报错

def txt2pcd():

import time

filename='cloud_rgb.txt'

print("the input file name is:%r." % filename)

start = time.time()

print("open the file...")

file = open(filename, "r+")

count = 0

# 统计源文件的点数

for line in file:

count = count + 1

print("size is %d" % count)

file.close()

# output = open("out.pcd","w+")

f_prefix = filename.split('.')[0]

output_filename = '{prefix}.pcd'.format(prefix=f_prefix)

output = open(output_filename, "w+")

list = ['# .PCD v0.7 - Point Cloud Data file format\n', 'VERSION 0.7\n', 'FIELDS x y z rgb\n', 'SIZE 4 4 4 4\n',

'TYPE F F F U\n', 'COUNT 1 1 1 1\n']

output.writelines(list)

output.write('WIDTH ') # 注意后边有空格

output.write(str(count))

output.write('\nHEIGHT ')

output.write(str(1)) # 强制类型转换，文件的输入只能是str格式

output.write('\nPOINTS ')

output.write(str(count))

output.write('\nDATA ascii\n')

file1 = open(filename, "r")

all = file1.read()

output.write(all)

output.close()

file1.close()

end = time.time()

print("run time is: ", end - start)

import pcl.pcl_visualization

cloud = pcl.load_XYZRGB('cloud_rgb.pcd')

visual = pcl.pcl_visualization.CloudViewing()

visual.ShowColorCloud(cloud, 'cloud')

flag = True

while flag:

flag != visual.WasStopped()

TypeError: expected bytes, str found

原图，深度图，云图(云图一片黑，鼠标滚轮翻一下) 如下：

opencv保存的颜色图：

scipy保存的颜色图

深度图

深度图可视化(这个是每有对齐的深度图align)：

云图(深度和色彩没有对齐的图)：