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working on point cloud io

pull/1614/head
HeDo 2023-02-23 18:45:03 +01:00
rodzic 7c855688a1
commit 34311a2380
1 zmienionych plików z 21 dodań i 53 usunięć
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opendm/dem/ground_rectification/io/las_io.py
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@ -12,72 +12,40 @@ def read_cloud(point_cloud_path):
# Open point cloud and read its properties using pdal # Open point cloud and read its properties using pdal
pipeline = pdal.Pipeline('[{"type":"readers.las","filename":"%s"}]' % point_cloud_path) pipeline = pdal.Pipeline('[{"type":"readers.las","filename":"%s"}]' % point_cloud_path)
cnt = pipeline.execute() pipeline.execute()
log.ODM_INFO("pdal arrays: %s" % pipeline.arrays) metadata = pipeline.metadata
arrays = pipeline.arrays
dimensions = pipeline.schema['schema']['dimensions'] # Extract point coordinates, classification, and RGB values
#log.ODM_INFO("Type: %s" % type(pipeline.schema)) x = arrays[0]["X"]
log.ODM_INFO("Dimensions: %s" % dimensions) y = arrays[0]["Y"]
z = arrays[0]["Z"]
# The x column index is the index of the object with the name 'X' classification = arrays[0]["Classification"].astype(np.uint8)
x_index = next((index for (index, d) in enumerate(dimensions) if d['name'] == 'X'), None) red = arrays[0]["Red"]
y_index = next((index for (index, d) in enumerate(dimensions) if d['name'] == 'Y'), None) green = arrays[0]["Green"]
z_index = next((index for (index, d) in enumerate(dimensions) if d['name'] == 'Z'), None) blue = arrays[0]["Blue"]
classification_index = next((index for (index, d) in enumerate(dimensions) if d['name'] == 'Classification'), None)
red_index = next((index for (index, d) in enumerate(dimensions) if d['name'] == 'Red'), None)
green_index = next((index for (index, d) in enumerate(dimensions) if d['name'] == 'Green'), None)
blue_index = next((index for (index, d) in enumerate(dimensions) if d['name'] == 'Blue'), None)
# Log indices
log.ODM_INFO("x_index: %s" % x_index)
log.ODM_INFO("y_index: %s" % y_index)
log.ODM_INFO("z_index: %s" % z_index)
log.ODM_INFO("classification_index: %s" % classification_index)
log.ODM_INFO("red_index: %s" % red_index)
log.ODM_INFO("green_index: %s" % green_index)
log.ODM_INFO("blue_index: %s" % blue_index)
pts = pipeline.arrays[0]
log.ODM_INFO("pts: %s" % pts)
x = (pt[x_index] for pt in pts)
y = (pt[y_index] for pt in pts)
z = (pt[z_index] for pt in pts)
classification = (pt[classification_index] for pt in pts)
red = (pt[red_index] for pt in pts)
green = (pt[green_index] for pt in pts)
blue = (pt[blue_index] for pt in pts)
# Create PointCloud object
cloud = PointCloud.with_dimensions(x, y, z, classification, red, green, blue) cloud = PointCloud.with_dimensions(x, y, z, classification, red, green, blue)
# Return the result # Return the result
return pipeline.metadata, cloud return metadata, cloud
def write_cloud(header, point_cloud, output_point_cloud_path, write_extra_dimensions=False): def write_cloud(metadata, point_cloud, output_point_cloud_path, write_extra_dimensions=False):
# Open output file
output_las_file = laspy.LasData(header)
if write_extra_dimensions: # Create PDAL pipeline to write point cloud
extra_dims = [laspy.ExtraBytesParams(name=name, type=dimension.get_las_type(), description="Dimension added by Ground Extend") for name, dimension in point_cloud.extra_dimensions_metadata.items()] pipeline = pdal.Pipeline('[{"type": "writers.las","filename": "%s","compression": "laszip","extra_dims": %s}]' %
output_las_file.add_extra_dims(extra_dims) (output_point_cloud_path, str(write_extra_dimensions).lower()))
# Assign dimension values
for dimension_name, values in point_cloud.extra_dimensions.items():
setattr(output_las_file, dimension_name, values)
# Adapt points to scale and offset # Adapt points to scale and offset
[x, y] = np.hsplit(point_cloud.xy, 2) [x, y] = np.hsplit(point_cloud.xy, 2)
output_las_file.x = x.ravel() z = point_cloud.z
output_las_file.y = y.ravel()
output_las_file.z = point_cloud.z
# Set color # Set color
[red, green, blue] = np.hsplit(point_cloud.rgb, 3) [red, green, blue] = np.hsplit(point_cloud.rgb, 3)
output_las_file.red = red.ravel()
output_las_file.green = green.ravel()
output_las_file.blue = blue.ravel()
# Set classification # Set classification
output_las_file.classification = point_cloud.classification.astype(np.uint8) classification = point_cloud.classification.astype(np.uint8)
output_las_file.write(output_point_cloud_path) # Write point cloud with PDAL
pipeline.execute(np.column_stack((x, y, z, red, green, blue, classification)))