點雲生成鳥瞰圖（Python）

大緻思路

1. 感興趣區域roi設定
2. 映射點到像素上
3. 移動坐标原點
4. 根據高度值填充像素值

環境配置conda+ros

1. 因為ros中的包是依賴Python2的，但是我們想用Python3b程式設計，是以需要安裝一個conda環境。
2. 在.bashrc中修改預設的conda環境變量。

   # Conda
   # export PATH=/home/s/anaconda3/bin:$PATH  # 注釋掉conda的環境變量
   
   alias condapy3='. /home/s/anaconda3/etc/profile.d/conda.sh && conda activate py3.7'  # 建立Python3.7的環境
              

   打開終端預設還是python2，輸入condapy3才進入python環境。
3. 在py3.7環境中安裝包

   pip install catkin-tools
   pip install rospkg
              

   這樣就可以通過虛拟環境編譯script檔案了

4. 以這個package為例，使用 catkin_make ，然後rosrun script檔案時，要在conda環境下，這樣使用的一些package才是指向該conda環境中的package的對應檔案。

   我們這個conda環境中的opencv版本如下

   

   是以在執行script檔案時列印出來是對應的版本：

   (py3.7) ➜  catkin_ws clear           
   
   (py3.7) ➜  catkin_ws echo $ROS_PACKAGE_PATH
   /home/s/catkin_ws/src:/home/s/ros_study/src:/opt/ros/kinetic/share
   (py3.7) ➜  catkin_ws rosrun aiimooc_syz2 aiimooc_syz2.py
   opencv: 4.2.0
   open3d:0.7.0.0
              

ros實作（Python）

因為cv2和ros自帶的cv有沖突，我們想使用conda中自己安裝的cv2，是以在import之前，先把ros中路徑移除，在import之後在導入。

bird_eyes.py

#!/usr/bin/env python3
# use python3 in current conda env!!!

# import cv2 in conda rather than in ros
import sys
sys.path.remove('/opt/ros/kinetic/lib/python2.7/dist-packages')
import cv2
# 使用完cv2後再導入ros路徑
sys.path.append('/opt/ros/kinetic/lib/python2.7/dist-packages')
from cv_bridge import CvBridge
import numpy as np
import rospy
from sensor_msgs.msg import PointCloud2,Image
import sensor_msgs.point_cloud2 as pc2
import open3d
import os


class pt2brid_eye:
	def __init__(self):
		self.image_pub = rospy.Publisher('/bird_eyes', Image, queue_size=10)
		self.pt_sub=rospy.Subscriber("/rslidar_points", PointCloud2, self.callback)
		self.bridge = CvBridge()

	def callback(self,lidar):
		lidar = pc2.read_points(lidar)
		points = np.array(list(lidar))
		im = self.point_cloud_2_birdseye(points)  # im is a numpy array
    
  

    	
		self.image_pub.publish(self.bridge.cv2_to_imgmsg(im))
		print("convert!")
         


	def point_cloud_2_birdseye(self,points):
		x_points = points[:, 0]
		y_points = points[:, 1]
		z_points = points[:, 2]

		f_filt = np.logical_and((x_points > -50), (x_points < 50))
		s_filt = np.logical_and((y_points > -50), (y_points < 50))
		filter = np.logical_and(f_filt, s_filt)
		indices = np.argwhere(filter)

		x_points = x_points[indices]
		y_points = y_points[indices]
		z_points = z_points[indices]

		x_img = (-y_points*10).astype(np.int32)+500
		y_img = (-x_points *10).astype(np.int32)+500      

		pixel_values = np.clip(z_points,-2,2)
		pixel_values = ((pixel_values +2) / 4.0) * 255
		im=np.zeros([1001,1001],dtype=np.uint8)
		im[y_img, x_img] = pixel_values
		return im


# def cloud_subscribe():
#    rospy.init_node('cloud_subscribe_node')
#    pub = rospy.Publisher('/bird_eyes', Image, queue_size=10)
#    rospy.Subscriber("/rslidar_points", PointCloud2, callback)
    
#    rospy.spin()



if __name__ == '__main__':
	print("opencv: {}".format(cv2.__version__))
	print("open3d:{}".format(open3d.__version__))
	# cloud_subscribe()
	pt2img=pt2brid_eye()
	rospy.init_node('pt_to_brid_eyeImage_node')
	rospy.spin()

           

可視化

完整代碼

https://github.com/suyunzzz/aiimooc_lesson/tree/master/aiimooc_syz2

參考

http://ronny.rest/tutorials/module/pointclouds_01/point_cloud_birdseye/

http://ronny.rest/blog/post_2017_03_26_lidar_birds_eye/

https://blog.csdn.net/qq_33801763/article/details/78923310#t10

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