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PoC parallel odm filterpoints 

Former-commit-id: c38628db6f
pull/1161/head
Piero Toffanin 2020-07-07 18:20:57 +00:00
rodzic 73088aba01
commit 055cf26e61
2 zmienionych plików z 126 dodań i 36 usunięć
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159
opendm/point_cloud.py
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@ -1,4 +1,4 @@
import os, sys, shutil, tempfile, json
import os, sys, shutil, tempfile, json, math
from opendm import system
from opendm import log
from opendm import context
@ -7,26 +7,60 @@ from opendm import entwine
from opendm import io
from pipes import quote
def ply_has_normals(input_ply):
def ply_info(input_ply):
if not os.path.exists(input_ply):
return False
# Read PLY header, check if point cloud has normals
has_normals = False
vertex_count = 0
with open(input_ply, 'r') as f:
line = f.readline().strip().lower()
i = 0
while line != "end_header" and i < 100:
line = f.readline().strip().lower()
props = line.split(" ")
if len(props) == 3 and props[0] == "property" and props[2] in ["nx", "normalx", "normal_x"]:
has_normals = True
break
if len(props) == 3:
if props[0] == "property" and props[2] in ["nx", "normalx", "normal_x"]:
has_normals = True
elif props[0] == "element" and props[1] == "vertex":
vertex_count = int(props[2])
i += 1
return has_normals
return {
'has_normals': has_normals,
'vertex_count': vertex_count,
}
def filter(input_point_cloud, output_point_cloud, standard_deviation=2.5, meank=16, sample_radius=0, verbose=False):
def split(input_point_cloud, outdir, filename_template, capacity, dims=None):
log.ODM_INFO("Splitting point cloud filtering in chunks of {} vertices".format(capacity))
if not os.path.exists(input_point_cloud):
log.ODM_ERROR("{} does not exist, cannot split point cloud. The program will now exit.".format(input_point_cloud))
sys.exit(1)
if not os.path.exists(outdir):
system.mkdir_p(outdir)
if len(os.listdir(outdir)) != 0:
log.ODM_ERROR("%s already contains some files. The program will now exit.".format(outdir))
sys.exit(1)
cmd = 'pdal split -i "%s" -o "%s" --capacity %s ' % (input_point_cloud, os.path.join(outdir, filename_template), capacity)
if filename_template.endswith(".ply"):
cmd += ("--writers.ply.sized_types=false "
"--writers.ply.storage_mode='little endian' ")
if dims is not None:
cmd += '--writers.ply.dims="%s"' % dims
system.run(cmd)
return [os.path.join(outdir, f) for f in os.listdir(outdir)]
def filter(input_point_cloud, output_point_cloud, standard_deviation=2.5, meank=16, sample_radius=0, verbose=False, max_concurrency=1):
"""
Filters a point cloud
"""
@ -47,49 +81,87 @@ def filter(input_point_cloud, output_point_cloud, standard_deviation=2.5, meank=
filters.append('sample')
if standard_deviation > 0 and meank > 0:
log.ODM_INFO("Filtering point cloud (statistical, meanK {}, standard deviation {})".format(meank, standard_deviation))
log.ODM_INFO("Filtering {} (statistical, meanK {}, standard deviation {})".format(input_point_cloud, meank, standard_deviation))
filters.append('outlier')
if len(filters) > 0:
filters.append('range')
info = ply_info(input_point_cloud)
dims = "x=float,y=float,z=float,"
if ply_has_normals(input_point_cloud):
if info['has_normals']:
dims += "nx=float,ny=float,nz=float,"
dims += "red=uchar,blue=uchar,green=uchar"
filterArgs = {
'inputFile': input_point_cloud,
'outputFile': output_point_cloud,
'stages': " ".join(filters),
'dims': dims
}
if info['vertex_count'] == 0:
log.ODM_ERROR("Cannot read vertex count for {}".format(input_point_cloud))
sys.exit(1)
cmd = ("pdal translate -i \"{inputFile}\" "
"-o \"{outputFile}\" "
"{stages} "
"--writers.ply.sized_types=false "
"--writers.ply.storage_mode='little endian' "
"--writers.ply.dims=\"{dims}\" "
"").format(**filterArgs)
# Do we need to split this?
VERTEX_THRESHOLD = 300000
max_concurrency = min(max_concurrency, math.ceil(info['vertex_count'] / VERTEX_THRESHOLD))
vertices_per_submodel = int(math.ceil(info['vertex_count'] / max(1, max_concurrency)))
should_split = max_concurrency > 1 and info['vertex_count'] > VERTEX_THRESHOLD
if 'sample' in filters:
cmd += "--filters.sample.radius={} ".format(sample_radius)
if 'outlier' in filters:
cmd += ("--filters.outlier.method='statistical' "
"--filters.outlier.mean_k={} "
"--filters.outlier.multiplier={} ").format(meank, standard_deviation)
if 'range' in filters:
# Remove outliers
cmd += "--filters.range.limits='Classification![7:7]' "
if should_split:
partsdir = os.path.join(os.path.dirname(output_point_cloud), "parts")
if os.path.exists(partsdir):
log.ODM_WARNING("Removing existing directory %s" % partsdir)
shutil.rmtree(partsdir)
system.run(cmd)
point_cloud_submodels = split(input_point_cloud, partsdir, "part.ply", capacity=vertices_per_submodel, dims=dims)
# Filter
for pcs in point_cloud_submodels:
# Recurse
filter(pcs, io.related_file_path(pcs, postfix="_filtered"),
standard_deviation=standard_deviation,
meank=meank,
sample_radius=sample_radius,
verbose=verbose,
max_concurrency=1)
# Merge
log.ODM_INFO("Merging %s point cloud chunks to %s" % (len(point_cloud_submodels), output_point_cloud))
filtered_pcs = [io.related_file_path(pcs, postfix="_filtered") for pcs in point_cloud_submodels]
merge_ply(filtered_pcs, output_point_cloud, dims)
# TODO REMOVE parts
else:
# Process point cloud (or a point cloud submodel) in a single step
filterArgs = {
'inputFile': input_point_cloud,
'outputFile': output_point_cloud,
'stages': " ".join(filters),
'dims': dims
}
cmd = ("pdal translate -i \"{inputFile}\" "
"-o \"{outputFile}\" "
"{stages} "
"--writers.ply.sized_types=false "
"--writers.ply.storage_mode='little endian' "
"--writers.ply.dims=\"{dims}\" "
"").format(**filterArgs)
if 'sample' in filters:
cmd += "--filters.sample.radius={} ".format(sample_radius)
if 'outlier' in filters:
cmd += ("--filters.outlier.method='statistical' "
"--filters.outlier.mean_k={} "
"--filters.outlier.multiplier={} ").format(meank, standard_deviation)
if 'range' in filters:
# Remove outliers
cmd += "--filters.range.limits='Classification![7:7]' "
system.run(cmd)
if not os.path.exists(output_point_cloud):
log.ODM_WARNING("{} not found, filtering has failed.".format(output_point_cloud))
def get_extent(input_point_cloud):
fd, json_file = tempfile.mkstemp(suffix='.json')
os.close(fd)
@ -146,7 +218,7 @@ def merge(input_point_cloud_files, output_file, rerun=False):
log.ODM_WARNING("No input point cloud files to process")
return
if rerun and io.file_exists(output_file):
if io.file_exists(output_file):
log.ODM_WARNING("Removing previous point cloud: %s" % output_file)
os.remove(output_file)
@ -158,6 +230,23 @@ def merge(input_point_cloud_files, output_file, rerun=False):
system.run('lasmerge -i {all_inputs} -o "{output}"'.format(**kwargs))
def merge_ply(input_point_cloud_files, output_file, dims=None):
num_files = len(input_point_cloud_files)
if num_files == 0:
log.ODM_WARNING("No input point cloud files to process")
return
cmd = [
'pdal',
'merge',
'--writers.ply.sized_types=false',
'--writers.ply.storage_mode="little endian"',
('--writers.ply.dims="%s"' % dims) if dims is not None else '',
' '.join(map(quote, input_point_cloud_files + [output_file])),
]
system.run(' '.join(cmd))
def post_point_cloud_steps(args, tree):
# XYZ point cloud output
if args.pc_csv:

3
stages/odm_filterpoints.py
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@ -26,7 +26,8 @@ class ODMFilterPoints(types.ODM_Stage):
point_cloud.filter(inputPointCloud, tree.filtered_point_cloud,
standard_deviation=args.pc_filter,
sample_radius=args.pc_sample,
verbose=args.verbose)
verbose=args.verbose,
max_concurrency=args.max_concurrency)
else:
log.ODM_WARNING('Found a valid point cloud file in: %s' %