示例
根据 出版的图书 <Numpy 数据处理详情>-python机器学习和数据科学中的高性能计算方法
代码示例
#!/usr/bin/python3
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import open3d as o3d
def get_from_eye(rotation,trans):
## 单位矩阵-对角线元素为1,非对角线元素全为0的矩的正方矩阵 np.identity
## 二维矩阵-对角线元素为1,非对角线元素全为0的矩的N*M矩阵 np.eye 可以移动对角线
tr_matrix = np.eye(4,dtype=np.float32)
tr_matrix[0:3,0:3]= rotation
tr_matrix[:3,3]= trans
return tr_matrix
def get_from_zero(rotation,trans):
## 生成全为0数组的函数
tr_matrix = np.zeros((4,4),dtype=np.float32)
tr_matrix[0:3,0:3]= rotation
tr_matrix[:3,3]= trans
tr_matrix[3,3]= 1
return tr_matrix
def generate_points_matrix(point):
std_points = np.zeros((point.shape[0],4),dtype=np.float32)
std_points[:,:3]= point[:,:3]
## 生成全为1数组的函数
std_points[:,3]= np.ones(point.shape[0])
return std_points
def generate_points_homogeneous_matrix(point):
## 生成全为1数组的函数-点云齐次矩阵-第四列全为1 homogeneous
std_points = np.ones((point.shape[0],4),dtype=np.float32)
std_points[:,:3]= point[:,:3]
return std_points
def trans_points_matrix(pcd_file,rotation,trans,out_pcd_file):
orig_pcd = o3d.t.io.read_point_cloud(pcd_file)
points_xyz = orig_pcd.point.numpy
intensity = orig_pcd["intensity"].numpy
exp_points = generate_points_matrix(points_xyz)
exp_tr_matrix = get_from_zero(rotation,trans)
new_points = np.dot( np.linalg.inv(exp_tr_matrix),exp_points.T).T
##输出点云
out_pcd = o3d.t.geometry.PointCloud()
out_pcd.point["positions"] = o3d.core.Tensor(new_points[:,:3],dtype=o3d.core.Dtype.Float32)
out_pcd.point["intensity"] = o3d.core.Tensor(intensity,dtype=o3d.core.Dtype.Float32)
o3d.t.io.write_point_cloud(out_pcd_file,out_pcd,write_ascii=False,compressed=False)
if __name__ == '__main__':
rotation_data = np.array([1.1,3,4,5,6,7,8,9,2])
rotation_matrix = rotation_data.reshape(3,-1)
trans_data = np.array([30,20,10])
##生成连续数列或者等差数列的函数
point_wxp = np.array(range(60)).reshape((-1,6))
## .reshape 和 resize 共享内存方式-报错的处理方式
point_wxp = np.arange(60,dtype=np.float32).reshape((-1,6))
#sig_matrix = get_from_eye(rotation_matrix,trans_data)
sig_matrix = get_from_zero(rotation_matrix,trans_data)
print(sig_matrix)
print(np.linalg.inv(sig_matrix))
exp_point = generate_points_matrix(point_wxp)
print(exp_point)