import open3d as op3 import numpy as np import copy def read_and_show(): pcd = op3.io.read_point_cloud("demodata/PointCloudXYZRGB.ply") return pcd def downshape(pcd): downpcd = pcd.voxel_down_sample(voxel_size=0.00001) return downpcd #op3.io.write_point_cloud("pdc1",downpcd) def compute_normal_of_clound(pcd): pcd2 = copy.deepcopy(pcd) pcd2.estimate_normals(search_param=op3.geometry.KDTreeSearchParamHybrid( radius=0.01,max_nn=30)) # #op3.visualization.draw_geometries([pcd2]) return pcd2 #op3.io.write_point_cloud("pdc1",pcd2) if __name__ == "__main__": pcd = read_and_show() downpcd = downshape(pcd) op3.visualization.draw_geometries([pcd]) op3.visualization.draw_geometries([downpcd]) downpcd2 = compute_normal_of_clound(downpcd) #op3.visualization.draw_geometries([downpcd2]) print(downpcd.normals[0]) #print(downpcd2.normals[0]) print(pcd2.normals[0]) 这串代码有什么问题
时间: 2023-06-03 21:06:16 浏览: 113
这段代码使用了 open3d 库,并导入了 numpy 和 copy。read_and_show 函数读取了名为 "PointCloudXYZRGB.ply" 的点云数据,并返回了点云对象。downshape 函数将输入的点云对象进行体素下采样,并返回下采样后的点云对象。
相关问题
if __name__ == "__main__": pcd = read_and_show() downpcd = downshape(pcd) op3.visualization.draw_geometries([pcd]) op3.visualization.draw_geometries([downpcd]) downpcd2 = compute_normal_of_clound(downpcd) #op3.visualization.draw_geometries([downpcd2]) print(downpcd.normals[0]) print(downpcd2.normals[0]) print语句的内容怎么理解
如果__name__ == "__main__":pcd = read_and_show(),则downpcd = downshape(pcd) op3.visualization.draw_geometries([pcd]) op3.visualization.draw_geometries([downpcd]) downpcd2 = compute_normal_of_clound(downpcd)。
完成代码,输入为一个元组类型的数组(x,y)。要求使用tensorflow中进行运算:如果x < y,计算x + x * y;否则执行y * y,返回计算结果。 提示 # tensor计算可以使用Session来执行 tens1 = tf.constant([1,2,3]) with tf.compat.v1.Session() as sess: result = sess.run(tens1).tolist() # .tolist()将tensor计算结果转为列表格式 print(result) # 打印结果为:[1,2,3] 数据范围 m,n为正整数 示例1 输入:(5, 7) 输出:40 示例2 输入:(7, -1) 输出:1
代码如下:
```python
import tensorflow as tf
def compute(x, y):
# 创建常量tensor
tensor_x = tf.constant(x)
tensor_y = tf.constant(y)
# 创建两个占位符,用于存放计算结果
result1 = tf.compat.v1.placeholder(tf.int32)
result2 = tf.compat.v1.placeholder(tf.int32)
# 创建判断条件,如果x<y,则计算x+x*y,否则计算y*y
condition = tf.less(tensor_x, tensor_y)
# 创建两个计算节点
op1 = tf.multiply(tensor_x, tensor_y)
op2 = tf.add(tensor_x, op1)
op3 = tf.multiply(tensor_y, tensor_y)
# 创建Session并执行计算
with tf.compat.v1.Session() as sess:
if sess.run(condition):
# x<y,执行op2
result = sess.run(op2, feed_dict={result1: 0})
else:
# x>=y,执行op3
result = sess.run(op3, feed_dict={result2: 0})
return result
```
测试代码:
```python
print(compute(5, 7)) # 输出:40
print(compute(7, -1)) # 输出:1
```