def create_25dmesh(inPointCloud, outMesh, dsm_radius=0.07, dsm_resolution=0.05, depth=8, samples=1, maxVertexCount=100000, verbose=False, available_cores=None, method='gridded'): # Create DSM from point cloud # Create temporary directory mesh_directory = os.path.dirname(outMesh) tmp_directory = os.path.join(mesh_directory, 'tmp') if os.path.exists(tmp_directory): shutil.rmtree(tmp_directory) os.mkdir(tmp_directory) log.ODM_INFO('Created temporary directory: %s' % tmp_directory) radius_steps = [dsm_radius] log.ODM_INFO('Creating DSM for 2.5D mesh') commands.create_dem(inPointCloud, 'mesh_dsm', output_type='max', radiuses=map(str, radius_steps), gapfill=True, outdir=tmp_directory, resolution=dsm_resolution, verbose=verbose, max_workers=available_cores) if method == 'gridded': mesh = dem_to_mesh_gridded(os.path.join(tmp_directory, 'mesh_dsm.tif'), outMesh, maxVertexCount, verbose) elif method == 'poisson': dsm_points = dem_to_points( os.path.join(tmp_directory, 'mesh_dsm.tif'), os.path.join(tmp_directory, 'dsm_points.ply'), verbose) mesh = screened_poisson_reconstruction(dsm_points, outMesh, depth=depth, samples=samples, maxVertexCount=maxVertexCount, threads=available_cores, verbose=verbose) else: raise 'Not a valid method: ' + method # Cleanup tmp if os.path.exists(tmp_directory): shutil.rmtree(tmp_directory) return mesh
def create_25dmesh(inPointCloud, outMesh, dsm_radius=0.07, dsm_resolution=0.05, depth=8, samples=1, maxVertexCount=100000, verbose=False, available_cores=None, method='gridded'): # Create DSM from point cloud # Create temporary directory mesh_directory = os.path.dirname(outMesh) tmp_directory = os.path.join(mesh_directory, 'tmp') if os.path.exists(tmp_directory): shutil.rmtree(tmp_directory) os.mkdir(tmp_directory) log.ODM_INFO('Created temporary directory: %s' % tmp_directory) radius_steps = [dsm_radius] log.ODM_INFO('Creating DSM for 2.5D mesh') commands.create_dem( inPointCloud, 'mesh_dsm', output_type='max', radiuses=map(str, radius_steps), gapfill=True, outdir=tmp_directory, resolution=dsm_resolution, verbose=verbose, max_workers=available_cores ) if method == 'gridded': mesh = dem_to_mesh_gridded(os.path.join(tmp_directory, 'mesh_dsm.tif'), outMesh, maxVertexCount, verbose) elif method == 'poisson': dsm_points = dem_to_points(os.path.join(tmp_directory, 'mesh_dsm.tif'), os.path.join(tmp_directory, 'dsm_points.ply'), verbose) mesh = screened_poisson_reconstruction(dsm_points, outMesh, depth=depth, samples=samples, maxVertexCount=maxVertexCount, threads=available_cores, verbose=verbose) else: raise 'Not a valid method: ' + method # Cleanup tmp if os.path.exists(tmp_directory): shutil.rmtree(tmp_directory) return mesh
def process(self, args, outputs): tree = outputs['tree'] las_model_found = io.file_exists(tree.odm_georeferencing_model_laz) log.ODM_INFO('Classify: ' + str(args.pc_classify)) log.ODM_INFO('Create DSM: ' + str(args.dsm)) log.ODM_INFO('Create DTM: ' + str(args.dtm)) log.ODM_INFO('DEM input file {0} found: {1}'.format(tree.odm_georeferencing_model_laz, str(las_model_found))) # define paths and create working directories odm_dem_root = tree.path('odm_dem') if not io.dir_exists(odm_dem_root): system.mkdir_p(odm_dem_root) if args.pc_classify and las_model_found: pc_classify_marker = os.path.join(odm_dem_root, 'pc_classify_done.txt') if not io.file_exists(pc_classify_marker) or self.rerun(): log.ODM_INFO("Classifying {} using Simple Morphological Filter".format(tree.odm_georeferencing_model_laz)) commands.classify(tree.odm_georeferencing_model_laz, args.smrf_scalar, args.smrf_slope, args.smrf_threshold, args.smrf_window, verbose=args.verbose ) with open(pc_classify_marker, 'w') as f: f.write('Classify: smrf\n') f.write('Scalar: {}\n'.format(args.smrf_scalar)) f.write('Slope: {}\n'.format(args.smrf_slope)) f.write('Threshold: {}\n'.format(args.smrf_threshold)) f.write('Window: {}\n'.format(args.smrf_window)) progress = 20 self.update_progress(progress) # Do we need to process anything here? if (args.dsm or args.dtm) and las_model_found: dsm_output_filename = os.path.join(odm_dem_root, 'dsm.tif') dtm_output_filename = os.path.join(odm_dem_root, 'dtm.tif') if (args.dtm and not io.file_exists(dtm_output_filename)) or \ (args.dsm and not io.file_exists(dsm_output_filename)) or \ self.rerun(): products = [] if args.dsm: products.append('dsm') if args.dtm: products.append('dtm') resolution = gsd.cap_resolution(args.dem_resolution, tree.opensfm_reconstruction, gsd_error_estimate=-3, ignore_gsd=args.ignore_gsd) radius_steps = [(resolution / 100.0) / 2.0] for _ in range(args.dem_gapfill_steps - 1): radius_steps.append(radius_steps[-1] * 2) # 2 is arbitrary, maybe there's a better value? for product in products: commands.create_dem( tree.odm_georeferencing_model_laz, product, output_type='idw' if product == 'dtm' else 'max', radiuses=map(str, radius_steps), gapfill=args.dem_gapfill_steps > 0, outdir=odm_dem_root, resolution=resolution / 100.0, decimation=args.dem_decimation, verbose=args.verbose, max_workers=args.max_concurrency, keep_unfilled_copy=args.dem_euclidean_map ) dem_geotiff_path = os.path.join(odm_dem_root, "{}.tif".format(product)) bounds_file_path = os.path.join(tree.odm_georeferencing, 'odm_georeferenced_model.bounds.gpkg') if args.crop > 0: # Crop DEM Cropper.crop(bounds_file_path, dem_geotiff_path, utils.get_dem_vars(args)) if args.dem_euclidean_map: unfilled_dem_path = io.related_file_path(dem_geotiff_path, postfix=".unfilled") if args.crop > 0: # Crop unfilled DEM Cropper.crop(bounds_file_path, unfilled_dem_path, utils.get_dem_vars(args)) commands.compute_euclidean_map(unfilled_dem_path, io.related_file_path(dem_geotiff_path, postfix=".euclideand"), overwrite=True) progress += 30 self.update_progress(progress) else: log.ODM_WARNING('Found existing outputs in: %s' % odm_dem_root) else: log.ODM_WARNING('DEM will not be generated')
default=3, type=int, help='Number of steps used to fill areas with gaps. Set to 0 to disable gap filling. ' 'Starting with a radius equal to the output resolution, N different DEMs are generated with ' 'progressively bigger radius using the inverse distance weighted (IDW) algorithm ' 'and merged together. Remaining gaps are then merged using nearest neighbor interpolation. ' 'Default: %(default)s') args = parser.parse_args() if not os.path.exists(args.point_cloud): print("%s does not exist" % args.point_cloud) exit(1) outdir = os.path.dirname(args.point_cloud) radius_steps = [args.resolution / 2.0] for _ in range(args.gapfill_steps - 1): radius_steps.append(radius_steps[-1] * 2) # 2 is arbitrary, maybe there's a better value? commands.create_dem(args.point_cloud, args.type, output_type='idw' if args.type == 'dtm' else 'max', radiuses=list(map(str, radius_steps)), gapfill=args.gapfill_steps > 0, outdir=outdir, resolution=args.resolution, decimation=1, verbose=True, max_workers=multiprocessing.cpu_count(), keep_unfilled_copy=False )
def process(self, args, outputs): tree = outputs['tree'] reconstruction = outputs['reconstruction'] dem_input = tree.odm_georeferencing_model_laz pc_model_found = io.file_exists(dem_input) ignore_resolution = False pseudo_georeference = False if not reconstruction.is_georeferenced(): # Special case to clear previous run point cloud # (NodeODM will generate a fake georeferenced laz during postprocessing # with non-georeferenced datasets). odm_georeferencing_model_laz should # not be here! Perhaps we should improve this. if io.file_exists( tree.odm_georeferencing_model_laz) and self.rerun(): os.remove(tree.odm_georeferencing_model_laz) log.ODM_WARNING( "Not georeferenced, using ungeoreferenced point cloud...") dem_input = tree.path("odm_filterpoints", "point_cloud.ply") pc_model_found = io.file_exists(dem_input) ignore_resolution = True pseudo_georeference = True resolution = gsd.cap_resolution(args.dem_resolution, tree.opensfm_reconstruction, gsd_error_estimate=-3, ignore_gsd=args.ignore_gsd, ignore_resolution=ignore_resolution, has_gcp=reconstruction.has_gcp()) log.ODM_INFO('Classify: ' + str(args.pc_classify)) log.ODM_INFO('Create DSM: ' + str(args.dsm)) log.ODM_INFO('Create DTM: ' + str(args.dtm)) log.ODM_INFO('DEM input file {0} found: {1}'.format( dem_input, str(pc_model_found))) # define paths and create working directories odm_dem_root = tree.path('odm_dem') if not io.dir_exists(odm_dem_root): system.mkdir_p(odm_dem_root) if args.pc_classify and pc_model_found: pc_classify_marker = os.path.join(odm_dem_root, 'pc_classify_done.txt') if not io.file_exists(pc_classify_marker) or self.rerun(): log.ODM_INFO( "Classifying {} using Simple Morphological Filter".format( dem_input)) commands.classify(dem_input, args.smrf_scalar, args.smrf_slope, args.smrf_threshold, args.smrf_window, verbose=args.verbose) with open(pc_classify_marker, 'w') as f: f.write('Classify: smrf\n') f.write('Scalar: {}\n'.format(args.smrf_scalar)) f.write('Slope: {}\n'.format(args.smrf_slope)) f.write('Threshold: {}\n'.format(args.smrf_threshold)) f.write('Window: {}\n'.format(args.smrf_window)) progress = 20 self.update_progress(progress) if args.pc_rectify: commands.rectify(dem_input, args.debug) # Do we need to process anything here? if (args.dsm or args.dtm) and pc_model_found: dsm_output_filename = os.path.join(odm_dem_root, 'dsm.tif') dtm_output_filename = os.path.join(odm_dem_root, 'dtm.tif') if (args.dtm and not io.file_exists(dtm_output_filename)) or \ (args.dsm and not io.file_exists(dsm_output_filename)) or \ self.rerun(): products = [] if args.dsm or (args.dtm and args.dem_euclidean_map): products.append('dsm') if args.dtm: products.append('dtm') radius_steps = [(resolution / 100.0) / 2.0] for _ in range(args.dem_gapfill_steps - 1): radius_steps.append( radius_steps[-1] * 2) # 2 is arbitrary, maybe there's a better value? for product in products: commands.create_dem( dem_input, product, output_type='idw' if product == 'dtm' else 'max', radiuses=map(str, radius_steps), gapfill=args.dem_gapfill_steps > 0, outdir=odm_dem_root, resolution=resolution / 100.0, decimation=args.dem_decimation, verbose=args.verbose, max_workers=args.max_concurrency, keep_unfilled_copy=args.dem_euclidean_map) dem_geotiff_path = os.path.join(odm_dem_root, "{}.tif".format(product)) bounds_file_path = os.path.join( tree.odm_georeferencing, 'odm_georeferenced_model.bounds.gpkg') if args.crop > 0: # Crop DEM Cropper.crop(bounds_file_path, dem_geotiff_path, utils.get_dem_vars(args)) if args.dem_euclidean_map: unfilled_dem_path = io.related_file_path( dem_geotiff_path, postfix=".unfilled") if args.crop > 0: # Crop unfilled DEM Cropper.crop(bounds_file_path, unfilled_dem_path, utils.get_dem_vars(args)) commands.compute_euclidean_map( unfilled_dem_path, io.related_file_path(dem_geotiff_path, postfix=".euclideand"), overwrite=True) if pseudo_georeference: # 0.1 is arbitrary pseudogeo.add_pseudo_georeferencing( dem_geotiff_path, 0.1) progress += 30 self.update_progress(progress) else: log.ODM_WARNING('Found existing outputs in: %s' % odm_dem_root) else: log.ODM_WARNING('DEM will not be generated')
def create_25dmesh(inPointCloud, outMesh, dsm_radius=0.07, dsm_resolution=0.05, depth=8, samples=1, maxVertexCount=100000, verbose=False, available_cores=None, method='gridded', smooth_dsm=True): # Create DSM from point cloud # Create temporary directory mesh_directory = os.path.dirname(outMesh) tmp_directory = os.path.join(mesh_directory, 'tmp') if os.path.exists(tmp_directory): shutil.rmtree(tmp_directory) os.mkdir(tmp_directory) log.ODM_INFO('Created temporary directory: %s' % tmp_directory) radius_steps = [dsm_radius] for _ in range(2): radius_steps.append(radius_steps[-1] * 2) # 2 is arbitrary log.ODM_INFO('Creating DSM for 2.5D mesh') commands.create_dem(inPointCloud, 'mesh_dsm', output_type='max', radiuses=list(map(str, radius_steps)), gapfill=True, outdir=tmp_directory, resolution=dsm_resolution, verbose=verbose, max_workers=available_cores, apply_smoothing=smooth_dsm) if method == 'gridded': mesh = dem_to_mesh_gridded(os.path.join(tmp_directory, 'mesh_dsm.tif'), outMesh, maxVertexCount, verbose, maxConcurrency=max(1, available_cores)) elif method == 'poisson': dsm_points = dem_to_points( os.path.join(tmp_directory, 'mesh_dsm.tif'), os.path.join(tmp_directory, 'dsm_points.ply'), verbose) mesh = screened_poisson_reconstruction( dsm_points, outMesh, depth=depth, samples=samples, maxVertexCount=maxVertexCount, threads=max( 1, available_cores - 1 ), # poissonrecon can get stuck on some machines if --threads == all cores verbose=verbose) else: raise 'Not a valid method: ' + method # Cleanup tmp if os.path.exists(tmp_directory): shutil.rmtree(tmp_directory) return mesh
def process(self, args, outputs): tree = outputs['tree'] reconstruction = outputs['reconstruction'] dem_input = tree.odm_georeferencing_model_laz pc_model_found = io.file_exists(dem_input) ignore_resolution = False pseudo_georeference = False if not reconstruction.is_georeferenced(): log.ODM_WARNING( "Not georeferenced, using ungeoreferenced point cloud...") ignore_resolution = True pseudo_georeference = True # It is probably not reasonable to have accurate DEMs a the same resolution as the source photos, so reduce it # by a factor! gsd_scaling = 2.0 resolution = gsd.cap_resolution(args.dem_resolution, tree.opensfm_reconstruction, gsd_scaling=gsd_scaling, ignore_gsd=args.ignore_gsd, ignore_resolution=ignore_resolution and args.ignore_gsd, has_gcp=reconstruction.has_gcp()) log.ODM_INFO('Classify: ' + str(args.pc_classify)) log.ODM_INFO('Create DSM: ' + str(args.dsm)) log.ODM_INFO('Create DTM: ' + str(args.dtm)) log.ODM_INFO('DEM input file {0} found: {1}'.format( dem_input, str(pc_model_found))) # define paths and create working directories odm_dem_root = tree.path('odm_dem') if not io.dir_exists(odm_dem_root): system.mkdir_p(odm_dem_root) if args.pc_classify and pc_model_found: pc_classify_marker = os.path.join(odm_dem_root, 'pc_classify_done.txt') if not io.file_exists(pc_classify_marker) or self.rerun(): log.ODM_INFO( "Classifying {} using Simple Morphological Filter".format( dem_input)) commands.classify(dem_input, args.smrf_scalar, args.smrf_slope, args.smrf_threshold, args.smrf_window, verbose=args.verbose) with open(pc_classify_marker, 'w') as f: f.write('Classify: smrf\n') f.write('Scalar: {}\n'.format(args.smrf_scalar)) f.write('Slope: {}\n'.format(args.smrf_slope)) f.write('Threshold: {}\n'.format(args.smrf_threshold)) f.write('Window: {}\n'.format(args.smrf_window)) progress = 20 self.update_progress(progress) if args.pc_rectify: commands.rectify(dem_input, args.debug) # Do we need to process anything here? if (args.dsm or args.dtm) and pc_model_found: dsm_output_filename = os.path.join(odm_dem_root, 'dsm.tif') dtm_output_filename = os.path.join(odm_dem_root, 'dtm.tif') if (args.dtm and not io.file_exists(dtm_output_filename)) or \ (args.dsm and not io.file_exists(dsm_output_filename)) or \ self.rerun(): products = [] if args.dsm or (args.dtm and args.dem_euclidean_map): products.append('dsm') if args.dtm: products.append('dtm') radius_steps = [(resolution / 100.0) / 2.0] for _ in range(args.dem_gapfill_steps - 1): radius_steps.append( radius_steps[-1] * 2) # 2 is arbitrary, maybe there's a better value? for product in products: commands.create_dem( dem_input, product, output_type='idw' if product == 'dtm' else 'max', radiuses=list(map(str, radius_steps)), gapfill=args.dem_gapfill_steps > 0, outdir=odm_dem_root, resolution=resolution / 100.0, decimation=args.dem_decimation, verbose=args.verbose, max_workers=args.max_concurrency, keep_unfilled_copy=args.dem_euclidean_map) dem_geotiff_path = os.path.join(odm_dem_root, "{}.tif".format(product)) bounds_file_path = os.path.join( tree.odm_georeferencing, 'odm_georeferenced_model.bounds.gpkg') if args.crop > 0 or args.boundary: # Crop DEM Cropper.crop( bounds_file_path, dem_geotiff_path, utils.get_dem_vars(args), keep_original=not args.optimize_disk_space) if args.dem_euclidean_map: unfilled_dem_path = io.related_file_path( dem_geotiff_path, postfix=".unfilled") if args.crop > 0 or args.boundary: # Crop unfilled DEM Cropper.crop( bounds_file_path, unfilled_dem_path, utils.get_dem_vars(args), keep_original=not args.optimize_disk_space) commands.compute_euclidean_map( unfilled_dem_path, io.related_file_path(dem_geotiff_path, postfix=".euclideand"), overwrite=True) if pseudo_georeference: pseudogeo.add_pseudo_georeferencing(dem_geotiff_path) if args.tiles: generate_dem_tiles(dem_geotiff_path, tree.path("%s_tiles" % product), args.max_concurrency) if args.cog: convert_to_cogeo(dem_geotiff_path, max_workers=args.max_concurrency) progress += 30 self.update_progress(progress) else: log.ODM_WARNING('Found existing outputs in: %s' % odm_dem_root) else: log.ODM_WARNING('DEM will not be generated')
def process(self, inputs, outputs): # Benchmarking start_time = system.now_raw() log.ODM_INFO('Running ODM DEM Cell') # get inputs args = self.inputs.args tree = self.inputs.tree las_model_found = io.file_exists(tree.odm_georeferencing_model_laz) # check if we rerun cell or not rerun_cell = (args.rerun is not None and args.rerun == 'odm_dem') or \ (args.rerun_all) or \ (args.rerun_from is not None and 'odm_dem' in args.rerun_from) log.ODM_INFO('Classify: ' + str(args.pc_classify)) log.ODM_INFO('Create DSM: ' + str(args.dsm)) log.ODM_INFO('Create DTM: ' + str(args.dtm)) log.ODM_INFO('DEM input file {0} found: {1}'.format( tree.odm_georeferencing_model_laz, str(las_model_found))) # define paths and create working directories odm_dem_root = tree.path('odm_dem') if not io.dir_exists(odm_dem_root): system.mkdir_p(odm_dem_root) if args.pc_classify and las_model_found: pc_classify_marker = os.path.join(odm_dem_root, 'pc_classify_done.txt') if not io.file_exists(pc_classify_marker) or rerun_cell: log.ODM_INFO( "Classifying {} using Simple Morphological Filter".format( tree.odm_georeferencing_model_laz)) commands.classify(tree.odm_georeferencing_model_laz, args.smrf_scalar, args.smrf_slope, args.smrf_threshold, args.smrf_window, verbose=args.verbose) with open(pc_classify_marker, 'w') as f: f.write('Classify: smrf\n') f.write('Scalar: {}\n'.format(args.smrf_scalar)) f.write('Slope: {}\n'.format(args.smrf_slope)) f.write('Threshold: {}\n'.format(args.smrf_threshold)) f.write('Window: {}\n'.format(args.smrf_window)) # Do we need to process anything here? if (args.dsm or args.dtm) and las_model_found: dsm_output_filename = os.path.join(odm_dem_root, 'dsm.tif') dtm_output_filename = os.path.join(odm_dem_root, 'dtm.tif') if (args.dtm and not io.file_exists(dtm_output_filename)) or \ (args.dsm and not io.file_exists(dsm_output_filename)) or \ rerun_cell: products = [] if args.dsm: products.append('dsm') if args.dtm: products.append('dtm') resolution = gsd.cap_resolution(args.dem_resolution, tree.opensfm_reconstruction, gsd_error_estimate=-3, ignore_gsd=args.ignore_gsd) radius_steps = [(resolution / 100.0) / 2.0] for _ in range(args.dem_gapfill_steps - 1): radius_steps.append( radius_steps[-1] * 2) # 2 is arbitrary, maybe there's a better value? for product in products: commands.create_dem( tree.odm_georeferencing_model_laz, product, output_type='idw' if product == 'dtm' else 'max', radiuses=map(str, radius_steps), gapfill=args.dem_gapfill_steps > 0, outdir=odm_dem_root, resolution=resolution / 100.0, decimation=args.dem_decimation, verbose=args.verbose, max_workers=args.max_concurrency) if args.crop > 0: bounds_shapefile_path = os.path.join( tree.odm_georeferencing, 'odm_georeferenced_model.bounds.shp') if os.path.exists(bounds_shapefile_path): Cropper.crop( bounds_shapefile_path, os.path.join(odm_dem_root, "{}.tif".format(product)), { 'TILED': 'YES', 'COMPRESS': 'LZW', 'BLOCKXSIZE': 512, 'BLOCKYSIZE': 512, 'NUM_THREADS': self.params.max_concurrency }) else: log.ODM_WARNING('Found existing outputs in: %s' % odm_dem_root) else: log.ODM_WARNING('DEM will not be generated') if args.time: system.benchmark(start_time, tree.benchmarking, 'Dem') log.ODM_INFO('Running ODM DEM Cell - Finished') return ecto.OK if args.end_with != 'odm_dem' else ecto.QUIT