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Merge pull request #1610 from pierotofy/305 

Point spacing estimation
pull/1613/head
Piero Toffanin 2023-02-26 13:24:50 -05:00 zatwierdzone przez GitHub
rodzic 22464d85f3 5259fd7007
commit b392c7a09d
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ID klucza GPG: 4AEE18F83AFDEB23
8 zmienionych plików z 50 dodań i 38 usunięć
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2
SuperBuild/cmake/External-FPCFilter.cmake
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@ -8,7 +8,7 @@ ExternalProject_Add(${_proj_name}
#--Download step--------------
DOWNLOAD_DIR ${SB_DOWNLOAD_DIR}
GIT_REPOSITORY https://github.com/OpenDroneMap/FPCFilter
GIT_TAG main
GIT_TAG 305
#--Update/Patch step----------
UPDATE_COMMAND ""
#--Configure step-------------

8
opendm/dem/commands.py
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@ -371,3 +371,11 @@ def window_filter_2d(arr, nodata, window, kernel_size, filter):
win_arr[nodata_locs] = nodata
win_arr = win_arr[window[0] - expanded_window[0] : window[2] - expanded_window[0], window[1] - expanded_window[1] : window[3] - expanded_window[1]]
return win_arr
def get_dem_radius_steps(stats_file, steps, resolution, multiplier = 1.0):
radius_steps = [point_cloud.get_spacing(stats_file, resolution) * multiplier]
for _ in range(steps - 1):
radius_steps.append(radius_steps[-1] * math.sqrt(2))
return radius_steps

9
opendm/mesh.py
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@ -5,10 +5,11 @@ from opendm import system
from opendm import log
from opendm import context
from opendm import concurrency
from opendm import point_cloud
from scipy import signal
import numpy as np
def create_25dmesh(inPointCloud, outMesh, dsm_radius=0.07, dsm_resolution=0.05, depth=8, samples=1, maxVertexCount=100000, available_cores=None, method='gridded', smooth_dsm=True):
def create_25dmesh(inPointCloud, outMesh, radius_steps=["0.05"], dsm_resolution=0.05, depth=8, samples=1, maxVertexCount=100000, available_cores=None, method='gridded', smooth_dsm=True):
# Create DSM from point cloud
# Create temporary directory
@ -19,17 +20,13 @@ def create_25dmesh(inPointCloud, outMesh, dsm_radius=0.07, dsm_resolution=0.05,
os.mkdir(tmp_directory)
log.ODM_INFO('Created temporary directory: %s' % tmp_directory)
radius_steps = [dsm_radius]
for _ in range(2):
radius_steps.append(radius_steps[-1] * math.sqrt(2)) # sqrt(2) is arbitrary
log.ODM_INFO('Creating DSM for 2.5D mesh')
commands.create_dem(
inPointCloud,
'mesh_dsm',
output_type='max',
radiuses=list(map(str, radius_steps)),
radiuses=radius_steps,
gapfill=True,
outdir=tmp_directory,
resolution=dsm_resolution,

31
opendm/point_cloud.py
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@ -71,7 +71,7 @@ def split(input_point_cloud, outdir, filename_template, capacity, dims=None):
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, boundary=None, max_concurrency=1):
def filter(input_point_cloud, output_point_cloud, output_stats, standard_deviation=2.5, sample_radius=0, boundary=None, max_concurrency=1):
"""
Filters a point cloud
"""
@ -89,10 +89,11 @@ def filter(input_point_cloud, output_point_cloud, standard_deviation=2.5, meank=
log.ODM_INFO("Sampling points around a %sm radius" % sample_radius)
args.append('--radius %s' % sample_radius)
if standard_deviation > 0 and meank > 0:
log.ODM_INFO("Filtering {} (statistical, meanK {}, standard deviation {})".format(input_point_cloud, meank, standard_deviation))
args.append('--meank %s' % meank)
args.append('--std %s' % standard_deviation)
meank = 16
log.ODM_INFO("Filtering {} (statistical, meanK {}, standard deviation {})".format(input_point_cloud, meank, standard_deviation))
args.append('--meank %s' % meank)
args.append('--std %s' % standard_deviation)
args.append('--stats "%s"' % output_stats)
if boundary is not None:
log.ODM_INFO("Boundary {}".format(boundary))
@ -107,6 +108,26 @@ def filter(input_point_cloud, output_point_cloud, standard_deviation=2.5, meank=
if not os.path.exists(output_point_cloud):
log.ODM_WARNING("{} not found, filtering has failed.".format(output_point_cloud))
def get_spacing(stats_file, resolution_fallback=5.0):
def fallback():
log.ODM_WARNING("Cannot read %s, falling back to resolution estimate" % stats_file)
return (resolution_fallback / 100.0) / 2.0
if not os.path.isfile(stats_file):
return fallback()
with open(stats_file, 'r') as f:
j = json.loads(f.read())
if "spacing" in j:
d = j["spacing"]
if d > 0:
return round(d, 3)
else:
return fallback()
else:
return fallback()
def export_info_json(pointcloud_path, info_file_path):
system.run('pdal info --dimensions "X,Y,Z" "{0}" > "{1}"'.format(pointcloud_path, info_file_path))

1
opendm/types.py
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@ -290,6 +290,7 @@ class ODM_Tree(object):
# filter points
self.filtered_point_cloud = os.path.join(self.odm_filterpoints, "point_cloud.ply")
self.filtered_point_cloud_stats = os.path.join(self.odm_filterpoints, "point_cloud_stats.json")
# odm_meshing
self.odm_mesh = os.path.join(self.odm_meshing, 'odm_mesh.ply')

10
stages/odm_dem.py
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@ -29,12 +29,8 @@ class ODMDEMStage(types.ODM_Stage):
ignore_resolution = True
pseudo_georeference = True
# It is probably not reasonable to have accurate DEMs a the same resolution as the source photos, so reduce it
# by a factor!
gsd_scaling = 2.0
resolution = gsd.cap_resolution(args.dem_resolution, tree.opensfm_reconstruction,
gsd_scaling=gsd_scaling,
gsd_scaling=1.0,
ignore_gsd=args.ignore_gsd,
ignore_resolution=ignore_resolution and args.ignore_gsd,
has_gcp=reconstruction.has_gcp())
@ -88,9 +84,7 @@ class ODMDEMStage(types.ODM_Stage):
if args.dsm or (args.dtm and args.dem_euclidean_map): products.append('dsm')
if args.dtm: products.append('dtm')
radius_steps = [(resolution / 100.0) / 2.0]
for _ in range(args.dem_gapfill_steps - 1):
radius_steps.append(radius_steps[-1] * math.sqrt(2)) # sqrt(2) is arbitrary, maybe there's a better value?
radius_steps = commands.get_dem_radius_steps(tree.filtered_point_cloud_stats, args.dem_gapfill_steps, resolution)
for product in products:
commands.create_dem(

2
stages/odm_filterpoints.py
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@ -49,7 +49,7 @@ class ODMFilterPoints(types.ODM_Stage):
else:
log.ODM_WARNING("Not a georeferenced reconstruction, will ignore --auto-boundary")
point_cloud.filter(inputPointCloud, tree.filtered_point_cloud,
point_cloud.filter(inputPointCloud, tree.filtered_point_cloud, tree.filtered_point_cloud_stats,
standard_deviation=args.pc_filter,
sample_radius=args.pc_sample,
boundary=boundary_offset(outputs.get('boundary'), reconstruction.get_proj_offset()),

25
stages/odm_meshing.py
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@ -7,6 +7,7 @@ from opendm import context
from opendm import mesh
from opendm import gsd
from opendm import types
from opendm.dem import commands
class ODMeshingStage(types.ODM_Stage):
def process(self, args, outputs):
@ -40,28 +41,18 @@ class ODMeshingStage(types.ODM_Stage):
if not io.file_exists(tree.odm_25dmesh) or self.rerun():
log.ODM_INFO('Writing ODM 2.5D Mesh file in: %s' % tree.odm_25dmesh)
ortho_resolution = gsd.cap_resolution(args.orthophoto_resolution, tree.opensfm_reconstruction,
ignore_gsd=args.ignore_gsd,
ignore_resolution=(not reconstruction.is_georeferenced()) and args.ignore_gsd,
has_gcp=reconstruction.has_gcp()) / 100.0
dsm_multiplier = max(1.0, gsd.rounded_gsd(tree.opensfm_reconstruction, default_value=4, ndigits=3, ignore_gsd=args.ignore_gsd))
# A good DSM size depends on the flight altitude.
# Flights at low altitude need more details (higher resolution)
# Flights at higher altitude benefit from smoother surfaces (lower resolution)
dsm_resolution = ortho_resolution * dsm_multiplier
dsm_radius = dsm_resolution * math.sqrt(2)
if args.fast_orthophoto:
dsm_radius *= 2
dsm_resolution *= 8
multiplier = math.pi / 2.0
radius_steps = commands.get_dem_radius_steps(tree.filtered_point_cloud_stats, 3, args.orthophoto_resolution, multiplier=multiplier)
dsm_resolution = radius_steps[0] / multiplier
log.ODM_INFO('ODM 2.5D DSM resolution: %s' % dsm_resolution)
if args.fast_orthophoto:
dsm_resolution *= 8.0
mesh.create_25dmesh(tree.filtered_point_cloud, tree.odm_25dmesh,
dsm_radius=dsm_radius,
radius_steps,
dsm_resolution=dsm_resolution,
depth=self.params.get('oct_tree'),
maxVertexCount=self.params.get('max_vertex'),