kopia lustrzana https://github.com/OpenDroneMap/ODM
Simplified DTM options, fixed SMRF param ranges
Piero Toffanin
rodzic
5ad5e226d7
commit
cbe3b1544b
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@ -297,13 +297,10 @@ def config():
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'Default: %(default)s'))
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parser.add_argument('--pc-classify',
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metavar='<string>',
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default='none',
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choices=['none', 'smrf', 'pmf'],
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help='Classify the point cloud outputs using either '
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'a Simple Morphological Filter or a Progressive Morphological Filter. '
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'If --dtm is set this parameter defaults to smrf. '
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'You can control the behavior of both smrf and pmf by tweaking the --dem-* parameters. '
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action='store_true',
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default=False,
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help='Classify the point cloud outputs using a Simple Morphological Filter. '
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'You can control the behavior of this option by tweaking the --dem-* parameters. '
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'Default: '
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'%(default)s')
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@ -431,23 +428,6 @@ def config():
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'are discarded. \nDefault: '
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'%(default)s')
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parser.add_argument('--dem-initial-distance',
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metavar='<positive float>',
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type=float,
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default=0.15,
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help='Used to classify ground vs non-ground points. Set this value to account for Z noise in meters. '
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'If you have an uncertainty of around 15 cm, set this value large enough to not exclude these points. '
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'Too small of a value will exclude valid ground points, while too large of a value will misclassify non-ground points for ground ones. '
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'\nDefault: '
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'%(default)s')
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parser.add_argument('--dem-approximate',
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action='store_true',
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default=False,
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help='Use this tag use the approximate progressive '
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'morphological filter, which computes DEMs faster '
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'but is not as accurate.')
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parser.add_argument('--dem-decimation',
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metavar='<positive integer>',
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default=1,
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@ -456,17 +436,6 @@ def config():
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'100 decimates ~99%% of the points. Useful for speeding up '
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'generation.\nDefault=%(default)s')
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parser.add_argument('--dem-terrain-type',
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metavar='<string>',
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choices=['FlatNonForest', 'FlatForest', 'ComplexNonForest', 'ComplexForest'],
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default='ComplexForest',
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help='One of: %(choices)s. Specifies the type of terrain. This mainly helps reduce processing time. '
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'\nFlatNonForest: Relatively flat region with little to no vegetation'
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'\nFlatForest: Relatively flat region that is forested'
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'\nComplexNonForest: Varied terrain with little to no vegetation'
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'\nComplexForest: Varied terrain that is forested'
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'\nDefault=%(default)s')
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parser.add_argument('--orthophoto-resolution',
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metavar='<float > 0.0>',
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default=5,
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@ -548,9 +517,9 @@ def config():
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log.ODM_INFO('Fast orthophoto is turned on, automatically setting --skip-3dmodel')
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args.skip_3dmodel = True
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if args.dtm and args.pc_classify == 'none':
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if args.dtm and not args.pc_classify:
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log.ODM_INFO("DTM is turned on, automatically turning on point cloud classification")
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args.pc_classify = "smrf"
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args.pc_classify = True
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if args.skip_3dmodel and args.use_3dmesh:
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log.ODM_WARNING('--skip-3dmodel is set, but so is --use-3dmesh. --use_3dmesh will be ignored.')
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@ -9,15 +9,11 @@ from functools import partial
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from . import pdal
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def classify(lasFile, smrf=False, slope=1, cellsize=3, maxWindowSize=10, maxDistance=1,
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approximate=False, initialDistance=0.7, verbose=False):
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def classify(lasFile, slope=0.15, cellsize=1, maxWindowSize=18, verbose=False):
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start = datetime.now()
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try:
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if smrf:
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pdal.run_pdaltranslate_smrf(lasFile, lasFile, slope, cellsize, maxWindowSize, verbose)
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else:
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pdal.run_pdalground(lasFile, lasFile, slope, cellsize, maxWindowSize, maxDistance, approximate=approximate, initialDistance=initialDistance, verbose=verbose)
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except:
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raise Exception("Error creating classified file %s" % fout)
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@ -233,33 +233,6 @@ def run_pipeline(json, verbose=False):
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os.remove(jsonfile)
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def run_pdalground(fin, fout, slope, cellsize, maxWindowSize, maxDistance, approximate=False, initialDistance=0.7, verbose=False):
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""" Run PDAL ground """
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cmd = [
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'pdal',
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'ground',
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'-i %s' % fin,
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'-o %s' % fout,
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'--slope %s' % slope,
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'--cell_size %s' % cellsize,
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'--initial_distance %s' % initialDistance
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]
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if maxWindowSize is not None:
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cmd.append('--max_window_size %s' %maxWindowSize)
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if maxDistance is not None:
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cmd.append('--max_distance %s' %maxDistance)
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if approximate:
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cmd.append('--approximate')
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if verbose:
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cmd.append('--developer-debug')
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print ' '.join(cmd)
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print ' '.join(cmd)
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out = system.run(' '.join(cmd))
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if verbose:
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print out
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def run_pdaltranslate_smrf(fin, fout, slope, cellsize, maxWindowSize, verbose=False):
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""" Run PDAL translate """
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cmd = [
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@ -39,45 +39,33 @@ class ODMDEMCell(ecto.Cell):
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(args.rerun_from is not None and
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'odm_dem' in args.rerun_from)
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log.ODM_INFO('Classify: ' + str(args.pc_classify != "none"))
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log.ODM_INFO('Classify: ' + str(args.pc_classify))
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log.ODM_INFO('Create DSM: ' + str(args.dsm))
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log.ODM_INFO('Create DTM: ' + str(args.dtm))
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log.ODM_INFO('DEM input file {0} found: {1}'.format(tree.odm_georeferencing_model_laz, str(las_model_found)))
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# Setup terrain parameters
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terrain_params_map = {
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'flatnonforest': (1, 3),
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'flatforest': (1, 2),
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'complexnonforest': (5, 2),
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'complexforest': (10, 2)
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}
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terrain_params = terrain_params_map[args.dem_terrain_type.lower()]
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slope, cellsize = terrain_params
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slope, cellsize = (0.15, 1)
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# define paths and create working directories
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odm_dem_root = tree.path('odm_dem')
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if not io.dir_exists(odm_dem_root):
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system.mkdir_p(odm_dem_root)
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if args.pc_classify != "none" and las_model_found:
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if args.pc_classify and las_model_found:
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pc_classify_marker = os.path.join(odm_dem_root, 'pc_classify_done.txt')
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if not io.file_exists(pc_classify_marker) or rerun_cell:
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log.ODM_INFO("Classifying {} using {}".format(tree.odm_georeferencing_model_laz, args.pc_classify))
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log.ODM_INFO("Classifying {} using Simple Morphological Filter".format(tree.odm_georeferencing_model_laz))
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commands.classify(tree.odm_georeferencing_model_laz,
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args.pc_classify == "smrf",
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slope,
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cellsize,
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approximate=args.dem_approximate,
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initialDistance=args.dem_initial_distance,
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verbose=args.verbose
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)
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with open(pc_classify_marker, 'w') as f:
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f.write('Classify: {}\n'.format(args.pc_classify))
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f.write('Classify: smrf\n')
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f.write('Slope: {}\n'.format(slope))
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f.write('Cellsize: {}\n'.format(cellsize))
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f.write('Approximate: {}\n'.format(args.dem_approximate))
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f.write('InitialDistance: {}\n'.format(args.dem_initial_distance))
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# Do we need to process anything here?
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if (args.dsm or args.dtm) and las_model_found:
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