kopia lustrzana https://github.com/OpenDroneMap/ODM
176 wiersze
8.4 KiB
Python
176 wiersze
8.4 KiB
Python
import ecto
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import os
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from opendm import context
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from opendm import types
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from opendm import io
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from opendm import system
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from dataset import ODMLoadDatasetCell
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from run_opensfm import ODMOpenSfMCell
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from mve import ODMMvsCell
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from odm_slam import ODMSlamCell
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from odm_meshing import ODMeshingCell
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from mvstex import ODMMvsTexCell
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from odm_georeferencing import ODMGeoreferencingCell
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from odm_orthophoto import ODMOrthoPhotoCell
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from odm_dem import ODMDEMCell
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class ODMApp(ecto.BlackBox):
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"""ODMApp - a class for ODM Activities
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"""
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def __init__(self, *args, **kwargs):
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ecto.BlackBox.__init__(self, *args, **kwargs)
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self.tree = None
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@staticmethod
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def declare_direct_params(p):
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p.declare("args", "The application arguments.", {})
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@staticmethod
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def declare_cells(p):
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"""
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Implement the virtual function from the base class
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Only cells from which something is forwarded have to be declared
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"""
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cells = {'args': ecto.Constant(value=p.args),
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'dataset': ODMLoadDatasetCell(force_focal=p.args.force_focal,
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force_ccd=p.args.force_ccd,
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verbose=p.args.verbose,
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proj=p.args.proj),
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'opensfm': ODMOpenSfMCell(use_exif_size=False,
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feature_process_size=p.args.resize_to,
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feature_min_frames=p.args.min_num_features,
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processes=p.args.max_concurrency,
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matching_gps_neighbors=p.args.matcher_neighbors,
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matching_gps_distance=p.args.matcher_distance,
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fixed_camera_params=p.args.use_fixed_camera_params,
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hybrid_bundle_adjustment=p.args.use_hybrid_bundle_adjustment),
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'slam': ODMSlamCell(),
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'mve': ODMMveCell(),
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'meshing': ODMeshingCell(max_vertex=p.args.mesh_size,
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oct_tree=p.args.mesh_octree_depth,
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samples=p.args.mesh_samples,
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point_weight=p.args.mesh_point_weight,
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max_concurrency=p.args.max_concurrency,
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verbose=p.args.verbose),
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'texturing': ODMMvsTexCell(data_term=p.args.texturing_data_term,
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outlier_rem_type=p.args.texturing_outlier_removal_type,
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skip_vis_test=p.args.texturing_skip_visibility_test,
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skip_glob_seam_leveling=p.args.texturing_skip_global_seam_leveling,
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skip_loc_seam_leveling=p.args.texturing_skip_local_seam_leveling,
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skip_hole_fill=p.args.texturing_skip_hole_filling,
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keep_unseen_faces=p.args.texturing_keep_unseen_faces,
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tone_mapping=p.args.texturing_tone_mapping),
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'georeferencing': ODMGeoreferencingCell(gcp_file=p.args.gcp,
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use_exif=p.args.use_exif,
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verbose=p.args.verbose),
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'dem': ODMDEMCell(max_concurrency=p.args.max_concurrency,
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verbose=p.args.verbose),
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'orthophoto': ODMOrthoPhotoCell(resolution=p.args.orthophoto_resolution,
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t_srs=p.args.orthophoto_target_srs,
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no_tiled=p.args.orthophoto_no_tiled,
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compress=p.args.orthophoto_compression,
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bigtiff=p.args.orthophoto_bigtiff,
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build_overviews=p.args.build_overviews,
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max_concurrency=p.args.max_concurrency,
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verbose=p.args.verbose)
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}
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return cells
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def configure(self, p, _i, _o):
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tree = types.ODM_Tree(p.args.project_path, p.args.images, p.args.gcp)
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self.tree = ecto.Constant(value=tree)
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# TODO(dakota) put this somewhere better maybe
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if p.args.time and io.file_exists(tree.benchmarking):
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# Delete the previously made file
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os.remove(tree.benchmarking)
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with open(tree.benchmarking, 'a') as b:
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b.write('ODM Benchmarking file created %s\nNumber of Cores: %s\n\n' % (system.now(), context.num_cores))
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def connections(self, p):
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if p.args.video:
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return self.slam_connections(p)
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# define initial task
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# TODO: What is this?
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# initial_task = p.args['start_with']
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# initial_task_id = config.processopts.index(initial_task)
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# define the connections like you would for the plasm
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# load the dataset
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connections = [self.tree[:] >> self.dataset['tree'],
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self.args[:] >> self.dataset['args']]
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# run opensfm with images from load dataset
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connections += [self.tree[:] >> self.opensfm['tree'],
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self.args[:] >> self.opensfm['args'],
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self.dataset['reconstruction'] >> self.opensfm['reconstruction']]
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if p.args.use_opensfm_dense or p.args.fast_orthophoto:
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# create odm mesh from opensfm point cloud
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connections += [self.tree[:] >> self.meshing['tree'],
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self.args[:] >> self.meshing['args'],
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self.opensfm['reconstruction'] >> self.meshing['reconstruction']]
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else:
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# run mve
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connections += [self.tree[:] >> self.mve['tree'],
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self.args[:] >> self.mve['args'],
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self.opensfm['reconstruction'] >> self.mve['reconstruction']]
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# create odm mesh from mve point cloud
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connections += [self.tree[:] >> self.meshing['tree'],
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self.args[:] >> self.meshing['args'],
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self.mve['reconstruction'] >> self.meshing['reconstruction']]
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# create odm texture
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connections += [self.tree[:] >> self.texturing['tree'],
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self.args[:] >> self.texturing['args'],
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self.meshing['reconstruction'] >> self.texturing['reconstruction']]
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# create odm georeference
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connections += [self.tree[:] >> self.georeferencing['tree'],
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self.args[:] >> self.georeferencing['args'],
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self.texturing['reconstruction'] >> self.georeferencing['reconstruction']]
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# create odm dem
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connections += [self.tree[:] >> self.dem['tree'],
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self.args[:] >> self.dem['args'],
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self.georeferencing['reconstruction'] >> self.dem['reconstruction']]
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# create odm orthophoto
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connections += [self.tree[:] >> self.orthophoto['tree'],
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self.args[:] >> self.orthophoto['args'],
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self.georeferencing['reconstruction'] >> self.orthophoto['reconstruction']]
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return connections
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def slam_connections(self, p):
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"""Get connections used when running from video instead of images."""
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connections = []
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# run slam cell
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connections += [self.tree[:] >> self.slam['tree'],
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self.args[:] >> self.slam['args']]
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connections += [self.tree[:] >> self.mve['tree'],
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self.args[:] >> self.mve['args'],
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self.slam['reconstruction'] >> self.mve['reconstruction']]
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# create odm mesh
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connections += [self.tree[:] >> self.meshing['tree'],
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self.args[:] >> self.meshing['args'],
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self.mve['reconstruction'] >> self.meshing['reconstruction']]
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# create odm texture
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connections += [self.tree[:] >> self.texturing['tree'],
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self.args[:] >> self.texturing['args'],
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self.meshing['reconstruction'] >> self.texturing['reconstruction']]
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return connections
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