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()
def save_cloud(inFile, dir_out, filename, classif, conf):
# Problème dans les données, certaines labels sont à 0
label = inFile.classification
ind = np.argwhere(label != 0)
new_header = copy.copy(inFile.header)
#Modification du point format id pour accéder au dimensions RGB
new_header.data_format_id = 3
#Create outfile
outFile = dir_out + filename.replace('.laz', '_classify' + '.laz')
outCloud = File(outFile,
mode="w",
header=new_header,
vlrs=inFile.header.vlrs)
outCloud.define_new_dimension(name='classif',
data_type=9,
description="ntd")
outCloud.define_new_dimension(name='conf', data_type=9, description="ntd")
for dimension in inFile.point_format:
if (dimension == inFile.point_format[13]):
break
dat = inFile.reader.get_dimension(dimension.name)[ind].reshape(-1)
# dat = dat[ind]
outCloud.writer.set_dimension(dimension.name, dat)
multiscale_features_raw = pd.DataFrame()
features_raw = np.hstack([classif.reshape(-1, 1), conf.reshape(-1, 1)])
features_raw = pd.DataFrame(features_raw)
features_raw.columns = np.array(["classif", "conf"])
multiscale_features_raw = pd.concat(
[multiscale_features_raw, features_raw], axis=1)
outCloud.classif = multiscale_features_raw.as_matrix(['classif']).ravel()
outCloud.conf = multiscale_features_raw.as_matrix(['conf']).ravel()
outCloud.close()
def addI_currentfile(inFile):
print 'Generating Importance value for %s' % str(inFile)
outFile = File('localI.las', mode='w', header=inFile.header)
outFile.define_new_dimension(name='gps_time',
data_type=10,
description='gps_time')
for dimension in inFile.point_format:
dat = inFile.reader.get_dimension(dimension.name)
outFile.writer.set_dimension(dimension.name, dat)
outFile.pt_src_id = [random.randint(0, 15) for _ in range(len(outFile))]
outFile.gps_time = []
for i in range(len(outFile.X)):
outFile.importance_value.append(random.randomint(0, 15))
closeLasFile(outFile)
return "localI.las"
point_cloud = point_cloud.loc[:, ~point_cloud.columns.duplicated()]
#point_cloud.to_csv(input_file_path[:-4]+'_ascii.csv',sep=',',index=False,header=True)
########### Output ###########
print("Outputting to las file")
las_path_base = os.path.basename(input_file_path[:-4])
out_filename = '%s_veg_classification_testparams.las' % (las_path_base)
print(out_filename)
out_LAS = File(out_filename, mode="w", header=in_LAS.header)
out_LAS.define_new_dimension(name="delta_z" + "_" + str(k),
data_type=9,
description="Spatial feature")
out_LAS.define_new_dimension(name="std_z" + "_" + str(k),
data_type=9,
description="Spatial feature")
out_LAS.define_new_dimension(name="radius" + "_" + str(k),
data_type=9,
description="Spatial feature")
out_LAS.define_new_dimension(name="density" + "_" + str(k),
data_type=9,
description="Spatial feature")
out_LAS.define_new_dimension(name="norm_z" + "_" + str(k),
data_type=9,
description="Spatial feature")
out_LAS.define_new_dimension(name="linearity" + "_" + str(k),
data_type=9,
