kopia lustrzana https://github.com/OpenDroneMap/ODM
120 wiersze
5.1 KiB
Python
120 wiersze
5.1 KiB
Python
import ecto, os, math
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from opendm import log
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from opendm import io
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from opendm import system
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from opendm import context
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from opendm import mesh
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from opendm import gsd
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class ODMeshingCell(ecto.Cell):
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def declare_params(self, params):
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params.declare("max_vertex", 'The maximum vertex count of the output '
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'mesh', 100000)
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params.declare("oct_tree", 'Oct-tree depth used in the mesh reconstruction, '
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'increase to get more vertices, recommended '
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'values are 8-12', 9)
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params.declare("samples", 'Number of points per octree node, recommended '
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'value: 1.0', 1)
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params.declare("point_weight", "specifies the importance that interpolation of the point samples is given in the formulation of the screened Poisson equation.", 4)
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params.declare("max_concurrency", 'max threads', context.num_cores)
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params.declare("verbose", 'print additional messages to console', False)
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def declare_io(self, params, inputs, outputs):
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inputs.declare("tree", "Struct with paths", [])
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inputs.declare("args", "The application arguments.", {})
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inputs.declare("reconstruction", "Clusters output. list of ODMReconstructions", [])
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outputs.declare("reconstruction", "Clusters output. list of ODMReconstructions", [])
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def process(self, inputs, outputs):
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# Benchmarking
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start_time = system.now_raw()
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log.ODM_INFO('Running ODM Meshing Cell')
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# get inputs
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args = inputs.args
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tree = inputs.tree
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reconstruction = inputs.reconstruction
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# define paths and create working directories
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system.mkdir_p(tree.odm_meshing)
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# check if we rerun cell or not
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rerun_cell = (args.rerun is not None and
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args.rerun == 'odm_meshing') or \
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(args.rerun_all) or \
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(args.rerun_from is not None and
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'odm_meshing' in args.rerun_from)
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infile = tree.mve_model
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if args.fast_orthophoto:
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infile = os.path.join(tree.opensfm, 'reconstruction.ply')
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elif args.use_opensfm_dense:
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infile = tree.opensfm_model
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# Create full 3D model unless --skip-3dmodel is set
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if not args.skip_3dmodel:
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if not io.file_exists(tree.odm_mesh) or rerun_cell:
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log.ODM_DEBUG('Writing ODM Mesh file in: %s' % tree.odm_mesh)
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mesh.screened_poisson_reconstruction(infile,
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tree.odm_mesh,
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depth=self.params.oct_tree,
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samples=self.params.samples,
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maxVertexCount=self.params.max_vertex,
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pointWeight=self.params.point_weight,
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threads=self.params.max_concurrency,
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verbose=self.params.verbose)
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else:
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log.ODM_WARNING('Found a valid ODM Mesh file in: %s' %
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tree.odm_mesh)
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# Always generate a 2.5D mesh
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# unless --use-3dmesh is set.
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if not args.use_3dmesh:
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if not io.file_exists(tree.odm_25dmesh) or rerun_cell:
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log.ODM_DEBUG('Writing ODM 2.5D Mesh file in: %s' % tree.odm_25dmesh)
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ortho_resolution = gsd.cap_resolution(args.orthophoto_resolution, tree.opensfm_reconstruction, ignore_gsd=args.ignore_gsd) / 100.0
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dsm_multiplier = max(1.0, gsd.rounded_gsd(tree.opensfm_reconstruction, default_value=4, ndigits=3, ignore_gsd=args.ignore_gsd))
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# A good DSM size depends on the flight altitude.
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# Flights at low altitude need more details (higher resolution)
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# Flights at higher altitude benefit from smoother surfaces (lower resolution)
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dsm_resolution = ortho_resolution * dsm_multiplier
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dsm_radius = dsm_resolution * math.sqrt(2)
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# Sparse point clouds benefits from using
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# a larger radius interolation --> less holes
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if args.fast_orthophoto:
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dsm_radius *= 2
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log.ODM_DEBUG('ODM 2.5D DSM resolution: %s' % dsm_resolution)
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mesh.create_25dmesh(infile, tree.odm_25dmesh,
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dsm_radius=dsm_radius,
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dsm_resolution=dsm_resolution,
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depth=self.params.oct_tree,
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maxVertexCount=self.params.max_vertex,
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samples=self.params.samples,
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verbose=self.params.verbose,
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available_cores=args.max_concurrency,
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method='poisson' if args.fast_orthophoto else 'gridded')
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else:
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log.ODM_WARNING('Found a valid ODM 2.5D Mesh file in: %s' %
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tree.odm_25dmesh)
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outputs.reconstruction = reconstruction
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if args.time:
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system.benchmark(start_time, tree.benchmarking, 'Meshing')
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log.ODM_INFO('Running ODM Meshing Cell - Finished')
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return ecto.OK if args.end_with != 'odm_meshing' else ecto.QUIT
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