kopia lustrzana https://github.com/OpenDroneMap/ODM
337 wiersze
16 KiB
Python
337 wiersze
16 KiB
Python
import os
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import shutil
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from opendm import log
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from opendm.osfm import OSFMContext, get_submodel_argv, get_submodel_paths, get_all_submodel_paths
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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 opendm import orthophoto
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from opendm.gcp import GCPFile
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from opendm.dem import pdal, utils
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from opendm.dem.merge import euclidean_merge_dems
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from opensfm.large import metadataset
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from opendm.cropper import Cropper
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from opendm.concurrency import get_max_memory
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from opendm.remote import LocalRemoteExecutor
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from opendm import entwine
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from pipes import quote
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class ODMSplitStage(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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photos = reconstruction.photos
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outputs['large'] = len(photos) > args.split
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if outputs['large']:
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# If we have a cluster address, we'll use a distributed workflow
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local_workflow = not bool(args.sm_cluster)
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octx = OSFMContext(tree.opensfm)
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split_done_file = octx.path("split_done.txt")
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if not io.file_exists(split_done_file) or self.rerun():
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orig_max_concurrency = args.max_concurrency
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if not local_workflow:
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args.max_concurrency = max(1, args.max_concurrency - 1)
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log.ODM_INFO("Setting max-concurrency to %s to better handle remote splits" % args.max_concurrency)
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log.ODM_INFO("Large dataset detected (%s photos) and split set at %s. Preparing split merge." % (len(photos), args.split))
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config = [
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"submodels_relpath: ../submodels/opensfm",
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"submodel_relpath_template: ../submodels/submodel_%04d/opensfm",
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"submodel_images_relpath_template: ../submodels/submodel_%04d/images",
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"submodel_size: %s" % args.split,
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"submodel_overlap: %s" % args.split_overlap,
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]
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octx.setup(args, tree.dataset_raw, photos, gcp_path=reconstruction.gcp.gcp_path, append_config=config, rerun=self.rerun())
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octx.extract_metadata(self.rerun())
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self.update_progress(5)
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if local_workflow:
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octx.feature_matching(self.rerun())
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self.update_progress(20)
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# Create submodels
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if not io.dir_exists(tree.submodels_path) or self.rerun():
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if io.dir_exists(tree.submodels_path):
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log.ODM_WARNING("Removing existing submodels directory: %s" % tree.submodels_path)
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shutil.rmtree(tree.submodels_path)
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octx.run("create_submodels")
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else:
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log.ODM_WARNING("Submodels directory already exist at: %s" % tree.submodels_path)
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# Find paths of all submodels
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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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for sp in submodel_paths:
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sp_octx = OSFMContext(sp)
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# Copy filtered GCP file if needed
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# One in OpenSfM's directory, one in the submodel project directory
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if reconstruction.gcp and reconstruction.gcp.exists():
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submodel_gcp_file = os.path.abspath(sp_octx.path("..", "gcp_list.txt"))
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submodel_images_dir = os.path.abspath(sp_octx.path("..", "images"))
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if reconstruction.gcp.make_filtered_copy(submodel_gcp_file, submodel_images_dir):
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log.ODM_INFO("Copied filtered GCP file to %s" % submodel_gcp_file)
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io.copy(submodel_gcp_file, os.path.abspath(sp_octx.path("gcp_list.txt")))
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else:
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log.ODM_INFO("No GCP will be copied for %s, not enough images in the submodel are referenced by the GCP" % sp_octx.name())
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# Reconstruct each submodel
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log.ODM_INFO("Dataset has been split into %s submodels. Reconstructing each submodel..." % len(submodel_paths))
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self.update_progress(25)
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if local_workflow:
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for sp in submodel_paths:
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log.ODM_INFO("Reconstructing %s" % sp)
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OSFMContext(sp).reconstruct(self.rerun())
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else:
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lre = LocalRemoteExecutor(args.sm_cluster, self.rerun())
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lre.set_projects([os.path.abspath(os.path.join(p, "..")) for p in submodel_paths])
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lre.run_reconstruction()
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self.update_progress(50)
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# Align
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octx.align_reconstructions(self.rerun())
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self.update_progress(55)
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# Aligned reconstruction is in reconstruction.aligned.json
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# We need to rename it to reconstruction.json
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remove_paths = []
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for sp in submodel_paths:
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sp_octx = OSFMContext(sp)
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aligned_recon = sp_octx.path('reconstruction.aligned.json')
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unaligned_recon = sp_octx.path('reconstruction.unaligned.json')
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main_recon = sp_octx.path('reconstruction.json')
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if io.file_exists(main_recon) and io.file_exists(unaligned_recon) and not self.rerun():
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log.ODM_INFO("Submodel %s has already been aligned." % sp_octx.name())
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continue
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if not io.file_exists(aligned_recon):
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log.ODM_WARNING("Submodel %s does not have an aligned reconstruction (%s). "
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"This could mean that the submodel could not be reconstructed "
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" (are there enough features to reconstruct it?). Skipping." % (sp_octx.name(), aligned_recon))
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remove_paths.append(sp)
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continue
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if io.file_exists(main_recon):
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shutil.move(main_recon, unaligned_recon)
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shutil.move(aligned_recon, main_recon)
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log.ODM_INFO("%s is now %s" % (aligned_recon, main_recon))
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# Remove invalid submodels
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submodel_paths = [p for p in submodel_paths if not p in remove_paths]
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# Run ODM toolchain for each submodel
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if local_workflow:
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for sp in submodel_paths:
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sp_octx = OSFMContext(sp)
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log.ODM_INFO("========================")
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log.ODM_INFO("Processing %s" % sp_octx.name())
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log.ODM_INFO("========================")
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argv = get_submodel_argv(args.name, tree.submodels_path, sp_octx.name())
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# Re-run the ODM toolchain on the submodel
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system.run(" ".join(map(quote, argv)), env_vars=os.environ.copy())
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else:
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lre.set_projects([os.path.abspath(os.path.join(p, "..")) for p in submodel_paths])
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lre.run_toolchain()
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# Restore max_concurrency value
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args.max_concurrency = orig_max_concurrency
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octx.touch(split_done_file)
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else:
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log.ODM_WARNING('Found a split done file in: %s' % split_done_file)
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else:
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log.ODM_INFO("Normal dataset, will process all at once.")
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self.progress = 0.0
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class ODMMergeStage(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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if outputs['large']:
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if not os.path.exists(tree.submodels_path):
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log.ODM_ERROR("We reached the merge stage, but %s folder does not exist. Something must have gone wrong at an earlier stage. Check the log and fix possible problem before restarting?" % tree.submodels_path)
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exit(1)
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# Merge point clouds
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if args.merge in ['all', 'pointcloud']:
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if not io.file_exists(tree.odm_georeferencing_model_laz) or self.rerun():
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all_point_clouds = get_submodel_paths(tree.submodels_path, "odm_georeferencing", "odm_georeferenced_model.laz")
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try:
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# pdal.merge_point_clouds(all_point_clouds, tree.odm_georeferencing_model_laz, args.verbose)
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entwine.build(all_point_clouds, tree.entwine_pointcloud, max_concurrency=args.max_concurrency, rerun=self.rerun())
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except Exception as e:
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log.ODM_WARNING("Could not merge point cloud: %s (skipping)" % str(e))
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if io.dir_exists(tree.entwine_pointcloud):
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try:
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system.run('pdal translate "ept://{}" "{}"'.format(tree.entwine_pointcloud, tree.odm_georeferencing_model_laz))
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except Exception as e:
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log.ODM_WARNING("Cannot export EPT dataset to LAZ: %s" % str(e))
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else:
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log.ODM_WARNING("Found merged point cloud in %s" % tree.odm_georeferencing_model_laz)
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self.update_progress(25)
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# Merge crop bounds
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merged_bounds_file = os.path.join(tree.odm_georeferencing, 'odm_georeferenced_model.bounds.gpkg')
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if not io.file_exists(merged_bounds_file) or self.rerun():
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all_bounds = get_submodel_paths(tree.submodels_path, 'odm_georeferencing', 'odm_georeferenced_model.bounds.gpkg')
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log.ODM_INFO("Merging all crop bounds: %s" % all_bounds)
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if len(all_bounds) > 0:
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# Calculate a new crop area
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# based on the convex hull of all crop areas of all submodels
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# (without a buffer, otherwise we are double-cropping)
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Cropper.merge_bounds(all_bounds, merged_bounds_file, 0)
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else:
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log.ODM_WARNING("No bounds found for any submodel.")
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# Merge orthophotos
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if args.merge in ['all', 'orthophoto']:
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if not io.dir_exists(tree.odm_orthophoto):
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system.mkdir_p(tree.odm_orthophoto)
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if not io.file_exists(tree.odm_orthophoto_tif) or self.rerun():
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all_orthos_and_cutlines = get_all_submodel_paths(tree.submodels_path,
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os.path.join("odm_orthophoto", "odm_orthophoto.tif"),
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os.path.join("odm_orthophoto", "cutline.gpkg"),
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)
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if len(all_orthos_and_cutlines) > 1:
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log.ODM_INFO("Found %s submodels with valid orthophotos and cutlines" % len(all_orthos_and_cutlines))
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# TODO: histogram matching via rasterio
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# currently parts have different color tones
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merged_geotiff = os.path.join(tree.odm_orthophoto, "odm_orthophoto.merged.tif")
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kwargs = {
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'orthophoto_merged': merged_geotiff,
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'input_files': ' '.join(map(lambda i: quote(i[0]), all_orthos_and_cutlines)),
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'max_memory': get_max_memory(),
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'threads': args.max_concurrency,
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}
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# use bounds as cutlines (blending)
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if io.file_exists(merged_geotiff):
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os.remove(merged_geotiff)
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system.run('gdal_merge.py -o {orthophoto_merged} '
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#'-createonly '
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'-co "BIGTIFF=YES" '
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'-co "BLOCKXSIZE=512" '
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'-co "BLOCKYSIZE=512" '
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'--config GDAL_CACHEMAX {max_memory}% '
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'{input_files} '.format(**kwargs)
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)
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for ortho_cutline in all_orthos_and_cutlines:
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kwargs['input_file'], kwargs['cutline'] = ortho_cutline
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# Note: cblend has a high performance penalty
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system.run('gdalwarp -cutline {cutline} '
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'-cblend 20 '
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'-r bilinear -multi '
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'-wo NUM_THREADS={threads} '
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'--config GDAL_CACHEMAX {max_memory}% '
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'{input_file} {orthophoto_merged}'.format(**kwargs)
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)
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# Apply orthophoto settings (compression, tiling, etc.)
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orthophoto_vars = orthophoto.get_orthophoto_vars(args)
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if io.file_exists(tree.odm_orthophoto_tif):
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os.remove(tree.odm_orthophoto_tif)
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kwargs = {
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'vars': ' '.join(['-co %s=%s' % (k, orthophoto_vars[k]) for k in orthophoto_vars]),
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'max_memory': get_max_memory(),
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'merged': merged_geotiff,
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'log': tree.odm_orthophoto_tif_log,
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'orthophoto': tree.odm_orthophoto_tif,
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}
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system.run('gdal_translate '
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'{vars} '
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'--config GDAL_CACHEMAX {max_memory}% '
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'{merged} {orthophoto} > {log}'.format(**kwargs))
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os.remove(merged_geotiff)
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# Crop
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if args.crop > 0:
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Cropper.crop(merged_bounds_file, tree.odm_orthophoto_tif, orthophoto_vars)
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# Overviews
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if args.build_overviews:
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orthophoto.build_overviews(tree.odm_orthophoto_tif)
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elif len(all_orthos_and_cutlines) == 1:
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# Simply copy
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log.ODM_WARNING("A single orthophoto/cutline pair was found between all submodels.")
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shutil.copyfile(all_orthos_and_cutlines[0][0], tree.odm_orthophoto_tif)
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else:
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log.ODM_WARNING("No orthophoto/cutline pairs were found in any of the submodels. No orthophoto will be generated.")
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else:
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log.ODM_WARNING("Found merged orthophoto in %s" % tree.odm_orthophoto_tif)
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self.update_progress(75)
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# Merge DEMs
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def merge_dems(dem_filename, human_name):
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if not io.dir_exists(tree.path('odm_dem')):
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system.mkdir_p(tree.path('odm_dem'))
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dem_file = tree.path("odm_dem", dem_filename)
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if not io.file_exists(dem_file) or self.rerun():
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all_dems = get_submodel_paths(tree.submodels_path, "odm_dem", dem_filename)
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log.ODM_INFO("Merging %ss" % human_name)
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# Merge
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dem_vars = utils.get_dem_vars(args)
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euclidean_merge_dems(all_dems, dem_file, dem_vars)
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if io.file_exists(dem_file):
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# Crop
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if args.crop > 0:
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Cropper.crop(merged_bounds_file, dem_file, dem_vars)
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log.ODM_INFO("Created %s" % dem_file)
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else:
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log.ODM_WARNING("Cannot merge %s, %s was not created" % (human_name, dem_file))
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else:
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log.ODM_WARNING("Found merged %s in %s" % (human_name, dem_filename))
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if args.merge in ['all', 'dem'] and args.dsm:
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merge_dems("dsm.tif", "DSM")
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if args.merge in ['all', 'dem'] and args.dtm:
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merge_dems("dtm.tif", "DTM")
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# Stop the pipeline short! We're done.
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self.next_stage = None
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else:
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log.ODM_INFO("Normal dataset, nothing to merge.")
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self.progress = 0.0
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