def write_cloud(header,
point_cloud,
output_point_cloud_path,
write_extra_dimensions=False):
(h, scale, offset, evlrs, vlrs) = header
# Open output file
output_las_file = File(output_point_cloud_path,
mode='w',
header=h,
evlrs=evlrs,
vlrs=vlrs)
if write_extra_dimensions:
# Create new dimensions
for name, dimension in point_cloud.extra_dimensions_metadata.items():
output_las_file.define_new_dimension(
name=name,
data_type=dimension.get_las_type(),
description="Dimension added by Ground Extend")
# 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
[x_scale, y_scale, z_scale] = scale
[x_offset, y_offset, z_offset] = offset
[x, y] = np.hsplit(point_cloud.xy, 2)
output_las_file.X = (x.ravel() - x_offset) / x_scale
output_las_file.Y = (y.ravel() - y_offset) / y_scale
output_las_file.Z = (point_cloud.z - z_offset) / z_scale
# Set color
[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
output_las_file.Classification = point_cloud.classification.astype(
np.uint8)
# Set header
output_las_file.header.scale = scale
output_las_file.header.offset = offset
# Close files
output_las_file.close()
# Prepare data for writing
pred_data = pred_data.assign(preds_write = 0)
pred_data.loc[pred_data['preds'] == 'barren', 'preds_write'] = 1
pred_data.loc[pred_data['preds'] == 'cropland', 'preds_write'] = 2
pred_data.loc[pred_data['preds'] == 'grassland', 'preds_write'] = 3
pred_data.loc[pred_data['preds'] == 'road', 'preds_write'] = 4
pred_data.loc[pred_data['preds'] == 'shadow', 'preds_write'] = 5
pred_data.loc[pred_data['preds'] == 'shrubland', 'preds_write'] = 6
pred_data.loc[pred_data['preds'] == 'trees', 'preds_write'] = 7
pred_data.loc[pred_data['preds'] == 'water', 'preds_write'] = 8
pred_data.loc[pred_data['preds'] == 'waterveg', 'preds_write'] = 9
pred_data['preds_write'].value_counts()
# Save to las
headfile = File(os.path.join(direct, '0-0-0-0.las'), mode="r")
header = headfile.header
outfile = File("classified_pc.las", mode="w", header=header)
outfile.x = pred_data['x'].to_numpy()
outfile.y = pred_data['y'].to_numpy()
outfile.z = pred_data['z'].to_numpy()
outfile.red = pred_data['red'].to_numpy()
outfile.green = pred_data['green'].to_numpy()
outfile.blue = pred_data['blue'].to_numpy()
outfile.classification = pred_data['preds_write'].to_numpy()
outfile.close()
# Clean
del bound, bound_mask, coords, direct, dsm_sd9, pred_X, pred_data, prednames
del preds
