kopia lustrzana https://github.com/OpenDroneMap/ODM
working on point cloud io
rodzic
7c855688a1
commit
34311a2380
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@ -12,72 +12,40 @@ def read_cloud(point_cloud_path):
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# Open point cloud and read its properties using pdal
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pipeline = pdal.Pipeline('[{"type":"readers.las","filename":"%s"}]' % point_cloud_path)
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cnt = pipeline.execute()
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pipeline.execute()
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log.ODM_INFO("pdal arrays: %s" % pipeline.arrays)
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metadata = pipeline.metadata
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arrays = pipeline.arrays
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dimensions = pipeline.schema['schema']['dimensions']
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#log.ODM_INFO("Type: %s" % type(pipeline.schema))
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log.ODM_INFO("Dimensions: %s" % dimensions)
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# The x column index is the index of the object with the name 'X'
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x_index = next((index for (index, d) in enumerate(dimensions) if d['name'] == 'X'), None)
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y_index = next((index for (index, d) in enumerate(dimensions) if d['name'] == 'Y'), None)
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z_index = next((index for (index, d) in enumerate(dimensions) if d['name'] == 'Z'), None)
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classification_index = next((index for (index, d) in enumerate(dimensions) if d['name'] == 'Classification'), None)
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red_index = next((index for (index, d) in enumerate(dimensions) if d['name'] == 'Red'), None)
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green_index = next((index for (index, d) in enumerate(dimensions) if d['name'] == 'Green'), None)
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blue_index = next((index for (index, d) in enumerate(dimensions) if d['name'] == 'Blue'), None)
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# Log indices
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log.ODM_INFO("x_index: %s" % x_index)
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log.ODM_INFO("y_index: %s" % y_index)
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log.ODM_INFO("z_index: %s" % z_index)
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log.ODM_INFO("classification_index: %s" % classification_index)
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log.ODM_INFO("red_index: %s" % red_index)
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log.ODM_INFO("green_index: %s" % green_index)
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log.ODM_INFO("blue_index: %s" % blue_index)
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pts = pipeline.arrays[0]
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log.ODM_INFO("pts: %s" % pts)
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x = (pt[x_index] for pt in pts)
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y = (pt[y_index] for pt in pts)
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z = (pt[z_index] for pt in pts)
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classification = (pt[classification_index] for pt in pts)
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red = (pt[red_index] for pt in pts)
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green = (pt[green_index] for pt in pts)
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blue = (pt[blue_index] for pt in pts)
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# Extract point coordinates, classification, and RGB values
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x = arrays[0]["X"]
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y = arrays[0]["Y"]
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z = arrays[0]["Z"]
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classification = arrays[0]["Classification"].astype(np.uint8)
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red = arrays[0]["Red"]
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green = arrays[0]["Green"]
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blue = arrays[0]["Blue"]
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# Create PointCloud object
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cloud = PointCloud.with_dimensions(x, y, z, classification, red, green, blue)
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# Return the result
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return pipeline.metadata, cloud
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return metadata, cloud
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def write_cloud(header, point_cloud, output_point_cloud_path, write_extra_dimensions=False):
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# Open output file
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output_las_file = laspy.LasData(header)
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def write_cloud(metadata, point_cloud, output_point_cloud_path, write_extra_dimensions=False):
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if write_extra_dimensions:
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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()]
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output_las_file.add_extra_dims(extra_dims)
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# Assign dimension values
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for dimension_name, values in point_cloud.extra_dimensions.items():
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setattr(output_las_file, dimension_name, values)
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# Create PDAL pipeline to write point cloud
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pipeline = pdal.Pipeline('[{"type": "writers.las","filename": "%s","compression": "laszip","extra_dims": %s}]' %
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(output_point_cloud_path, str(write_extra_dimensions).lower()))
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# Adapt points to scale and offset
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[x, y] = np.hsplit(point_cloud.xy, 2)
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output_las_file.x = x.ravel()
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output_las_file.y = y.ravel()
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output_las_file.z = point_cloud.z
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z = point_cloud.z
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# Set color
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[red, green, blue] = np.hsplit(point_cloud.rgb, 3)
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output_las_file.red = red.ravel()
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output_las_file.green = green.ravel()
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output_las_file.blue = blue.ravel()
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# Set classification
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output_las_file.classification = point_cloud.classification.astype(np.uint8)
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classification = point_cloud.classification.astype(np.uint8)
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output_las_file.write(output_point_cloud_path)
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# Write point cloud with PDAL
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pipeline.execute(np.column_stack((x, y, z, red, green, blue, classification)))
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