kopia lustrzana https://github.com/OpenDroneMap/ODM
142 wiersze
6.2 KiB
Python
Executable File
142 wiersze
6.2 KiB
Python
Executable File
import os, json
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from shutil import copyfile
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from opendm import io
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from opendm import log
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from opendm import system
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from opendm import context
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from opendm import types
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from opendm import gsd
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from opendm.dem import commands, utils
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from opendm.cropper import Cropper
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from opendm import pseudogeo
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class ODMDEMStage(types.ODM_Stage):
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def process(self, args, outputs):
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tree = outputs['tree']
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reconstruction = outputs['reconstruction']
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dem_input = tree.odm_georeferencing_model_laz
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pc_model_found = io.file_exists(dem_input)
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ignore_resolution = False
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pseudo_georeference = False
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if not reconstruction.is_georeferenced():
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# Special case to clear previous run point cloud
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# (NodeODM will generate a fake georeferenced laz during postprocessing
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# with non-georeferenced datasets). odm_georeferencing_model_laz should
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# not be here! Perhaps we should improve this.
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if io.file_exists(tree.odm_georeferencing_model_laz) and self.rerun():
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os.remove(tree.odm_georeferencing_model_laz)
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log.ODM_WARNING("Not georeferenced, using ungeoreferenced point cloud...")
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dem_input = tree.path("odm_filterpoints", "point_cloud.ply")
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pc_model_found = io.file_exists(dem_input)
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ignore_resolution = True
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pseudo_georeference = True
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resolution = gsd.cap_resolution(args.dem_resolution, tree.opensfm_reconstruction,
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gsd_error_estimate=-3,
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ignore_gsd=args.ignore_gsd,
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ignore_resolution=ignore_resolution,
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has_gcp=reconstruction.has_gcp())
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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(dem_input, str(pc_model_found)))
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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 and pc_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 self.rerun():
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log.ODM_INFO("Classifying {} using Simple Morphological Filter".format(dem_input))
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commands.classify(dem_input,
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args.smrf_scalar,
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args.smrf_slope,
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args.smrf_threshold,
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args.smrf_window,
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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: smrf\n')
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f.write('Scalar: {}\n'.format(args.smrf_scalar))
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f.write('Slope: {}\n'.format(args.smrf_slope))
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f.write('Threshold: {}\n'.format(args.smrf_threshold))
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f.write('Window: {}\n'.format(args.smrf_window))
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progress = 20
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self.update_progress(progress)
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if args.rectify:
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commands.rectify(dem_input, args.debug)
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# Do we need to process anything here?
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if (args.dsm or args.dtm) and pc_model_found:
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dsm_output_filename = os.path.join(odm_dem_root, 'dsm.tif')
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dtm_output_filename = os.path.join(odm_dem_root, 'dtm.tif')
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if (args.dtm and not io.file_exists(dtm_output_filename)) or \
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(args.dsm and not io.file_exists(dsm_output_filename)) or \
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self.rerun():
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products = []
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if args.dsm or (args.dtm and args.dem_euclidean_map): products.append('dsm')
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if args.dtm: products.append('dtm')
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radius_steps = [(resolution / 100.0) / 2.0]
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for _ in range(args.dem_gapfill_steps - 1):
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radius_steps.append(radius_steps[-1] * 2) # 2 is arbitrary, maybe there's a better value?
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for product in products:
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commands.create_dem(
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dem_input,
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product,
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output_type='idw' if product == 'dtm' else 'max',
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radiuses=map(str, radius_steps),
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gapfill=args.dem_gapfill_steps > 0,
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outdir=odm_dem_root,
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resolution=resolution / 100.0,
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decimation=args.dem_decimation,
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verbose=args.verbose,
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max_workers=args.max_concurrency,
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keep_unfilled_copy=args.dem_euclidean_map
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)
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dem_geotiff_path = os.path.join(odm_dem_root, "{}.tif".format(product))
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bounds_file_path = os.path.join(tree.odm_georeferencing, 'odm_georeferenced_model.bounds.gpkg')
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if args.crop > 0:
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# Crop DEM
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Cropper.crop(bounds_file_path, dem_geotiff_path, utils.get_dem_vars(args))
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if args.dem_euclidean_map:
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unfilled_dem_path = io.related_file_path(dem_geotiff_path, postfix=".unfilled")
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if args.crop > 0:
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# Crop unfilled DEM
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Cropper.crop(bounds_file_path, unfilled_dem_path, utils.get_dem_vars(args))
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commands.compute_euclidean_map(unfilled_dem_path,
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io.related_file_path(dem_geotiff_path, postfix=".euclideand"),
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overwrite=True)
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if pseudo_georeference:
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# 0.1 is arbitrary
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pseudogeo.add_pseudo_georeferencing(dem_geotiff_path, 0.1)
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progress += 30
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self.update_progress(progress)
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else:
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log.ODM_WARNING('Found existing outputs in: %s' % odm_dem_root)
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else:
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log.ODM_WARNING('DEM will not be generated')
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