Houdini自定义激光雷达导入器，使用Laspy模块，使用Python

Question

我正在尝试用Python为Houdini构建一个自定义激光雷达导入器。 到目前为止，Laspy模块（https://pypi.org/project/laspy）通过读取和写入Houdini中的*.las文件以及过滤分类，做了一件又好又快的工作

但是我必须读写*.las文件并再次导入，而不是立即在Houdini中获取点

现在我想知道，我是否可以获取激光雷达点xyz位置，以便在胡迪尼内部的点上提供数据

我试图在Laspy手册中找到有用的信息，但找不到任何示例或函数

我用一个*.csv文件做了类似的事情，它有xyz位置来构建一个简单的GPS读取器，将位置输出为Houdini中的点（使用csv模块）

我附加了一个带有原始.las（灰色）和过滤后的output.las（红色屋顶）的屏幕截图，以及Laspy手册中的脚本示例

也许有一个更优雅的解决方案取代Laspy？ 我在Houdini中使用Python3，但2.7也可以使用

更新，这里的答案几乎完美无瑕 https://forums.odforce.net/topic/46475-custom-lidar-importer-with-python/?tab=comments#comment-217104：

from laspy.file import File
import numpy as np

node = hou.pwd()
geo = node.geometry()

file_path = geo.attribValue("file_path")
inFile = File(file_path, mode='r')

# --- load point position
coords = np.vstack((inFile.x, inFile.y, inFile.z)).transpose()
scale = np.array(inFile.header.scale)
offset = np.array(inFile.header.offset) # there is no offset in simple.las example from laspy library
# offset = np.array([1000, 20000, 100000])  # just for testing that offset works

# geo.setPointFloatAttribValues("P", np.concatenate(coords))    # same as Lidar Import SOP - seems that it ignores scale (and offset?)
geo.setPointFloatAttribValues("P", np.concatenate(coords*scale+offset))

# --- load color
color = np.vstack((inFile.red, inFile.green, inFile.blue)).transpose()
geo.addAttrib(hou.attribType.Point, "Cd", (1.0,1.0,1.0), False, False)  # add color atttribute
geo.setPointFloatAttribValues("Cd", np.concatenate(color / 255.0)) # transform from 1-255 to 0.0-1.0 range)

唯一不起作用的是infle.classifications==x 这是胡迪尼的崩溃

Answer 1

Odforce的Petr完成了激光雷达导入程序。 https://forums.odforce.net/topic/46475-custom-lidar-importer-with-python/

要加载和读取点，请执行以下操作：

from laspy.file import File
node = hou.pwd()
geo = node.geometry()
geo.createPoint()   # dummy point for point generate

classification = hou.evalParm("classification")
file_path = hou.evalParm("lidar_file")
inFile = File(file_path, mode='r')
# store how many points lidar attribute has
geo.addAttrib(hou.attribType.Global, "nb_of_points", 0, False, False)
geo.setGlobalAttribValue("nb_of_points", len(inFile.points[inFile.Classification == classification]))

# store file path
geo.addAttrib(hou.attribType.Global, "file_path", "", False, False)
geo.setGlobalAttribValue("file_path", file_path)

# store classification
geo.addAttrib(hou.attribType.Global, "classification", 0, False, False)
geo.setGlobalAttribValue("classification", classification)

# find availible information
pointformat = inFile.point_format
for spec in inFile.point_format:
    print(spec.name)

并加载属性：

from laspy.file import File
import numpy as np

node = hou.pwd()
geo = node.geometry()

file_path = geo.attribValue("file_path")
inFile = File(file_path, mode='r')
classification = geo.attribValue("classification")

selection = inFile.Classification == classification
points = inFile.points[selection]



#  - load point position
coords = np.vstack((inFile.x, inFile.y, inFile.z)).transpose()
scale = np.array(inFile.header.scale)
offset = np.array(inFile.header.offset) # there is no offset in simple.las example from laspy library
# offset = np.array([1000, 20000, 100000])  # just for testing that offset works

# geo.setPointFloatAttribValues("P", np.concatenate(coords))    # same as Lidar Import SOP - seems that it ignores scale (and offset?)
geo.setPointFloatAttribValues("P", np.concatenate(coords[selection]*scale+offset))

#  - load color
missing_color = ["red", "green", "blue"]
for spec in inFile.point_format:
    if spec.name in missing_color:
        missing_color.remove(spec.name)

if not missing_color:       
    color = np.vstack((inFile.red, inFile.green, inFile.blue)).transpose()
    geo.addAttrib(hou.attribType.Point, "Cd", (1.0,1.0,1.0), False, False)  # add color atttribute
    geo.setPointFloatAttribValues("Cd", np.concatenate(color[selection] / 255.0)) # transform from 1-255 to 0.0-1.0 range)

#  - load intensity
geo.addAttrib(hou.attribType.Point, "intensity", 0.0, False, False)  # add intensity atttribute
geo.setPointFloatAttribValues("intensity", inFile.intensity[selection] / 512.0) # transform from 1-255 to 0.0-1.0 range)