kopia lustrzana https://github.com/OpenDroneMap/ODM
Refactor PDAL pipeline for filter points, --auto-boundary config addition
Piero Toffanin
rodzic
43870b6411
commit
d6c40929d4
2
VERSION
2
VERSION
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@ -1 +1 @@
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2.6.4
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2.6.5
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@ -7,9 +7,43 @@ from opendm import system
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from pyproj import CRS
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from opendm.location import transformer
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from opendm.utils import double_quote
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from osgeo import ogr
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from opendm.shots import get_origin
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def compute_boundary_from_shots(reconstruction_json, buffer=0, reconstruction_offset=(0, 0)):
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if not os.path.isfile(reconstruction_json):
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raise IOError(reconstruction_json + " does not exist.")
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with open(reconstruction_json) as f:
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data = json.load(f)
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reconstruction = data[0]
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mp = ogr.Geometry(ogr.wkbMultiPoint)
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for shot_image in reconstruction['shots']:
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shot = reconstruction['shots'][shot_image]
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if shot['gps_dop'] < 999999:
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camera = reconstruction['cameras'][shot['camera']]
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p = ogr.Geometry(ogr.wkbPoint)
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origin = get_origin(shot)
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p.AddPoint_2D(origin[0] + reconstruction_offset[0], origin[1] + reconstruction_offset[1])
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mp.AddGeometry(p)
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if mp.GetGeometryCount() < 3:
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return None
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convexhull = mp.ConvexHull()
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boundary = convexhull.Buffer(buffer)
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return load_boundary(boundary.ExportToJson())
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def load_boundary(boundary_json, reproject_to_proj4=None):
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with fiona.open(io.BytesIO(json.dumps(boundary_json).encode('utf-8')), 'r') as src:
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if not isinstance(boundary_json, str):
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boundary_json = json.dumps(boundary_json)
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with fiona.open(io.BytesIO(boundary_json.encode('utf-8')), 'r') as src:
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if len(src) != 1:
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raise IOError("Boundary must have a single polygon (found: %s)" % len(src))
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@ -51,6 +85,9 @@ def boundary_offset(boundary, reconstruction_offset):
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return res
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def as_polygon(boundary):
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if boundary is None:
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return None
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return "POLYGON((" + ", ".join([" ".join(map(str, c)) for c in boundary]) + "))"
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def export_to_bounds_files(boundary, proj4, bounds_json_file, bounds_gpkg_file):
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@ -308,11 +308,19 @@ def config(argv=None, parser=None):
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metavar='<json>',
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action=StoreValue,
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type=path_or_json_string,
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help='GeoJSON polygon defining the boundary of the reconstruction. '
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help='GeoJSON polygon limiting the area of the reconstruction. '
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'Can be specified either as path to a GeoJSON file or as a '
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'JSON string representing the contents of a '
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'GeoJSON file. Default: %(default)s')
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parser.add_argument('--auto-boundary',
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action=StoreTrue,
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nargs=0,
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default=False,
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help='Automatically set a boundary using camera shot locations to limit the area of the reconstruction. '
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'This can help remove far away background artifacts (sky, background landscapes, etc.). See also --boundary. '
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'Default: %(default)s')
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parser.add_argument('--pc-quality',
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metavar='<string>',
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action=StoreValue,
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@ -155,7 +155,7 @@ def run_pipeline(json, verbose=False):
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cmd = [
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'pdal',
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'pipeline',
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'-i %s' % jsonfile
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'-i %s' % double_quote(jsonfile)
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]
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if verbose or sys.platform == 'win32':
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system.run(' '.join(cmd))
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@ -1,4 +1,4 @@
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import os, sys, shutil, tempfile, json, math
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import os, sys, shutil, tempfile, math, json
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from opendm import system
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from opendm import log
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from opendm import context
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@ -8,6 +8,7 @@ from opendm import io
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from opendm.concurrency import parallel_map
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from opendm.utils import double_quote
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from opendm.boundary import as_polygon
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from opendm.dem.pdal import run_pipeline
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def ply_info(input_ply):
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if not os.path.exists(input_ply):
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@ -139,37 +140,40 @@ def filter(input_point_cloud, output_point_cloud, standard_deviation=2.5, meank=
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shutil.rmtree(partsdir)
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else:
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# Process point cloud (or a point cloud submodel) in a single step
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filterArgs = {
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'inputFile': input_point_cloud,
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'outputFile': output_point_cloud,
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'stages': " ".join(filters),
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'dims': dims
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}
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pipeline = []
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cmd = ("pdal translate -i \"{inputFile}\" "
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"-o \"{outputFile}\" "
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"{stages} "
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"--writers.ply.sized_types=false "
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"--writers.ply.storage_mode=\"little endian\" "
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"--writers.ply.dims=\"{dims}\" "
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"").format(**filterArgs)
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# Input
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pipeline.append(input_point_cloud)
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if 'sample' in filters:
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cmd += "--filters.sample.radius={} ".format(sample_radius)
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if 'outlier' in filters:
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cmd += ("--filters.outlier.method=\"statistical\" "
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"--filters.outlier.mean_k={} "
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"--filters.outlier.multiplier={} ").format(meank, standard_deviation)
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if 'range' in filters:
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# Remove outliers
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cmd += "--filters.range.limits=\"Classification![7:7]\" "
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# Filters
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for f in filters:
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params = {}
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if f == 'sample':
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params = {'radius': sample_radius}
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elif f == 'outlier':
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params = {'method': 'statistical', 'mean_k': meank, 'multiplier': standard_deviation}
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elif f == 'range':
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params = {'limits': 'Classification![7:7]'}
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elif f == 'crop':
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params = {'polygon': as_polygon(boundary)}
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else:
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raise RuntimeError("Invalid filter in PDAL pipeline (this should not have happened, please report it: https://github.com/OpenDroneMap/ODM/issues")
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pipeline.append(dict({
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'type': "filters.%s" % f,
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}, **params))
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if 'crop' in filters:
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cmd += "--filters.crop.polygon=\"%s\"" % as_polygon(boundary)
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# Output
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pipeline.append({
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'type': 'writers.ply',
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'sized_types': False,
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'storage_mode': 'little endian',
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'dims': dims,
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'filename': output_point_cloud
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})
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system.run(cmd)
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run_pipeline(pipeline, verbose=verbose)
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if not os.path.exists(output_point_cloud):
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log.ODM_WARNING("{} not found, filtering has failed.".format(output_point_cloud))
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@ -6,7 +6,8 @@ from opendm import system
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from opendm import context
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from opendm import point_cloud
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from opendm import types
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from opendm.boundary import boundary_offset
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from opendm import gsd
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from opendm.boundary import boundary_offset, compute_boundary_from_shots
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class ODMFilterPoints(types.ODM_Stage):
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def process(self, args, outputs):
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@ -22,6 +23,19 @@ class ODMFilterPoints(types.ODM_Stage):
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else:
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inputPointCloud = tree.openmvs_model
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# Check if we need to compute boundary
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if args.auto_boundary:
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if reconstruction.is_georeferenced():
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if not 'boundary' in outputs:
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avg_gsd = gsd.opensfm_reconstruction_average_gsd(tree.opensfm_reconstruction)
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outputs['boundary'] = compute_boundary_from_shots(tree.opensfm_reconstruction, avg_gsd * 20, reconstruction.get_proj_offset()) # 20 is arbitrary
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if outputs['boundary'] is None:
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log.ODM_WARNING("Cannot compute boundary from camera shots")
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else:
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log.ODM_WARNING("--auto-boundary set but so is --boundary, will use --boundary")
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else:
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log.ODM_WARNING("Not a georeferenced reconstruction, will ignore --auto-boundary")
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point_cloud.filter(inputPointCloud, tree.filtered_point_cloud,
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standard_deviation=args.pc_filter,
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sample_radius=args.pc_sample,
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@ -29,6 +43,13 @@ class ODMFilterPoints(types.ODM_Stage):
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verbose=args.verbose,
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max_concurrency=args.max_concurrency)
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# Quick check
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info = point_cloud.ply_info(tree.filtered_point_cloud)
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if info["vertex_count"] == 0:
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extra_msg = ''
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if 'boundary' in outputs:
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extra_msg = '. Also, since you used a boundary setting, make sure that the boundary polygon you specified covers the reconstruction area correctly.'
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raise system.ExitException("Uh oh! We ended up with an empty point cloud. This means that the reconstruction did not succeed. Have you followed best practices for data acquisition? See https://docs.opendronemap.org/flying/%s" % extra_msg)
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else:
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log.ODM_WARNING('Found a valid point cloud file in: %s' %
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tree.filtered_point_cloud)
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@ -105,71 +105,6 @@ class ODMSplitStage(types.ODM_Stage):
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self.update_progress(50)
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# TODO: this is currently not working and needs a champion to fix it
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# https://community.opendronemap.org/t/filenotfound-error-cameras-json/6047/2
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# resplit_done_file = octx.path('resplit_done.txt')
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# if not io.file_exists(resplit_done_file) and bool(args.split_multitracks):
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# submodels = mds.get_submodel_paths()
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# i = 0
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# for s in submodels:
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# template = octx.path("../aligned_submodels/submodel_%04d")
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# with open(s+"/reconstruction.json", "r") as f:
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# j = json.load(f)
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# for k in range(0, len(j)):
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# v = j[k]
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# path = template % i
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# #Create the submodel path up to opensfm
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# os.makedirs(path+"/opensfm")
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# os.makedirs(path+"/images")
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# #symlinks for common data
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# images = os.listdir(octx.path("../images"))
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# for image in images:
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# os.symlink("../../../images/"+image, path+"/images/"+image)
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# os.symlink("../../../opensfm/exif", path+"/opensfm/exif")
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# os.symlink("../../../opensfm/features", path+"/opensfm/features")
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# os.symlink("../../../opensfm/matches", path+"/opensfm/matches")
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# os.symlink("../../../opensfm/reference_lla.json", path+"/opensfm/reference_lla.json")
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# os.symlink("../../../opensfm/camera_models.json", path+"/opensfm/camera_models.json")
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# shutil.copy(s+"/../cameras.json", path+"/cameras.json")
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# shutil.copy(s+"/../images.json", path+"/images.json")
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# with open(octx.path("config.yaml")) as f:
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# doc = yaml.safe_load(f)
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# dmcv = "depthmap_min_consistent_views"
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# if dmcv in doc:
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# if len(v["shots"]) < doc[dmcv]:
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# doc[dmcv] = len(v["shots"])
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# print("WARNING: Reduced "+dmcv+" to accommodate short track")
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# with open(path+"/opensfm/config.yaml", "w") as f:
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# yaml.dump(doc, f)
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# #We need the original tracks file for the visualsfm export, since
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# #there may still be point matches between the tracks
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# shutil.copy(s+"/tracks.csv", path+"/opensfm/tracks.csv")
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# #Create our new reconstruction file with only the relevant track
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# with open(path+"/opensfm/reconstruction.json", "w") as o:
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# json.dump([v], o)
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# #Create image lists
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# with open(path+"/opensfm/image_list.txt", "w") as o:
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# o.writelines(list(map(lambda x: "../images/"+x+'\n', v["shots"].keys())))
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# with open(path+"/img_list.txt", "w") as o:
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# o.writelines(list(map(lambda x: x+'\n', v["shots"].keys())))
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# i+=1
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# os.rename(octx.path("../submodels"), octx.path("../unaligned_submodels"))
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# os.rename(octx.path("../aligned_submodels"), octx.path("../submodels"))
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# octx.touch(resplit_done_file)
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mds = metadataset.MetaDataSet(tree.opensfm)
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submodel_paths = [os.path.abspath(p) for p in mds.get_submodel_paths()]
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