parser = argparse.ArgumentParser()
parser.add_argument("input", help='input file that contains the point cloud', type=str)
parser.add_argument("output", help='path to the output file', type=str)
parser.add_argument("--slice", "-s", help='slice size (default: 15000)', type=int, default=15000)
args = parser.parse_args()
# Load input file
pcd_list = ut.loadPoints(args.input, [2,3,4])
# Compute the number of iterations to do given the slice size
to_process = len(pcd_list)
slice_size = args.slice
iterations = to_process/slice_size
if (iterations == 0 or to_process%slice_size == 0):
iterations += 1
largest_clusters = []
i = 0
for i in range(iterations):
begin = i*slice_size
if (i+1)*slice_size < to_process:
end = (i+1)*slice_size
else:
end = to_process
# Get the largest clusters
largest_clusters += noise_removal(pcd_list[begin:end], ratio=0.1)
largest_clusters = np.array(largest_clusters)
ut.savePoints(largest_clusters, args.output, [0,1,2])
viewer(largest_clusters, c=largest_clusters[:,2], s=2)
'lidar_id': 1,
'lat': 2,
'lon': 3,
'ele': 4,
'val': 5
}
fields_out = {
0: 'id',
1: 'lidar_id',
2: 'lat',
3: 'lon',
4: 'ele',
5: 'val'
}
for c in chunks:
pcd_list = ut.loadPoints(c, fields_in)
# filter on elevation by getting the points between the 35th
# percentile and the 65th percentile for each chunk
elevations = [pcd["ele"] for pcd in pcd_list]
options["minele"] = np.percentile(elevations, 35)
options["maxele"] = np.percentile(elevations, 65)
filtered_list += filterPoints(pcd_list, options)
i += 1.
sys.stderr.write("%.2f\r" % float(100 * i / l))
sys.stderr.write("\nDone\n")
ut.savePoints(filtered_list, args.output, fields_out)
# initialize options for the filter
options = {}
# filter on the intensity of the points
# let's keep points that have an intensity value > 190
options["minval"] = 190
sys.stderr.write("Loading and filtering " + str(int(l)) + " chunks...\n")
filtered_list = []
fields_in = {'id':0, 'lidar_id':1, 'lat':2, 'lon':3, 'ele':4, 'val': 5}
fields_out = {0:'id', 1:'lidar_id', 2:'lat', 3:'lon', 4:'ele', 5:'val'}
for c in chunks:
pcd_list = ut.loadPoints(c, fields_in)
# filter on elevation by getting the points between the 35th
# percentile and the 65th percentile for each chunk
elevations = [pcd["ele"] for pcd in pcd_list]
options["minele"] = np.percentile(elevations, 35)
options["maxele"] = np.percentile(elevations, 65)
filtered_list += filterPoints(pcd_list, options)
i += 1.
sys.stderr.write("%.2f\r" % float(100*i/l))
sys.stderr.write("\nDone\n")
ut.savePoints(filtered_list, args.output, fields_out)
