使用Laspy模块的Python中的Houdini自定义LiDAR导入器

Question

我正在尝试用Python为胡迪尼建立一个自定义的LiDAR导入器。到目前为止，Laspy模块(https://pypi.org/project/laspy)通过在Houdini中读取和写入*.*las文件以及过滤分类，做了一件很好的、快速的工作。

但我必须读写*.*las文件，然后再次导入，而不是立即获得胡迪尼内部的点。

现在我在想，我是否可以获取LiDAR点xyz位置，以在胡迪尼内部的点上提供它们。

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文件做了一些类似的事情，它包含xyz位置来构建一个简单的GPS读取器，将位置作为Houdini中的点输出(使用csv模块)。

我附上了一个截图与原始的.las (灰色)和过滤的output.las (红色屋顶)和脚本示例从Laspy手册。

也许代替Laspy有一个更优雅的解决方案？我在胡迪尼中使用的是Python 3，但2.7也可以。

更新，答案从这里工作，几乎完美的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)

THe唯一还不能工作的是正在崩溃的inFile.classifications == x。

Answer 1

来自Odforce的Petr完成了LiDAR导入器。https://forums.odforce.net/topic/46475-custom-lidar-importer-with-python/

加载和读取点的步骤：

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)