kopia lustrzana https://github.com/OpenDroneMap/ODM
Dakota Benjamin
rodzic
4b5738924d
commit
dccf5d6f59
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@ -14,16 +14,16 @@ In a word, OpenDroneMap is a toolchain for processing raw civilian UAS imagery t
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2. Digital Surface Models
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3. Textured Digital Surface Models
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4. Orthorectified Imagery
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5. Classified Point Clouds
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5. Classified Point Clouds (coming soon)
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6. Digital Elevation Models
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7. etc.
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So far, it does Point Clouds, Digital Surface Models, Textured Digital Surface Models, and Orthorectified Imagery. Open Drone Map now includes state-of-the-art 3D reconstruction work by Michael Waechter, Nils Moehrle, and Michael Goesele. See their publication at http://www.gcc.tu-darmstadt.de/media/gcc/papers/Waechter-2014-LTB.pdf.
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Open Drone Map now includes state-of-the-art 3D reconstruction work by Michael Waechter, Nils Moehrle, and Michael Goesele. See their publication at http://www.gcc.tu-darmstadt.de/media/gcc/papers/Waechter-2014-LTB.pdf.
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## QUICKSTART
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OpenDroneMap can run natively on Ubuntu 14.04 or later, see [Build and Run Using Docker](#build-and-run-using-docker) for running on Windows / MacOS. A Vagrant VM is also available: https://github.com/OpenDroneMap/odm_vagrant.
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OpenDroneMap can run natively on Ubuntu 14.04 or later, see [Build and Run Using Docker](#build-and-run-using-docker) for running on Windows / MacOS. A [Vagrant VM](https://github.com/OpenDroneMap/odm_vagrant) is also available.
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*Support for Ubuntu 12.04 is currently BROKEN with the addition of OpenSfM and Ceres-Solver. It is likely to remain broken unless a champion is found to fix it.*
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@ -68,7 +68,7 @@ Note that using `run.sh` sets these temporarily in the shell.
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First you need a set of images, taken from a drone or otherwise. Example data can be obtained from https://github.com/OpenDroneMap/odm_data
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Next, you need to copy over the settings file `default.settings.yaml` and edit it. The only setting you must edit is the `project-path` key. Set this to an empty directory within projects will be saved. There are many options for tuning your project. See the [wiki](https://github.com/OpenDroneMap/OpenDroneMap/wiki/Run-Time-Parameters) or run `python run.py -h`
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Next, you need to edit the `settings.yaml` file. The only setting you must edit is the `project-path` key. Set this to an empty directory within projects will be saved. There are many options for tuning your project. See the [wiki](https://github.com/OpenDroneMap/OpenDroneMap/wiki/Run-Time-Parameters) or run `python run.py -h`
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Then run:
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@ -42,6 +42,10 @@ project_path: '/' #DO NOT CHANGE THIS OR DOCKER WILL NOT WORK. It should be '/'
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#texturing_keep_unseen_faces: False
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#texturing_tone_mapping: 'none'
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#gcp: !!null # YAML tag for None
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#dem: False
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#dem_sample_radius: 1.0
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#dem_resolution: 2
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#dem_radius: 0.5
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#use_exif: False # Set to True if you have a GCP file (it auto-detects) and want to use EXIF
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#orthophoto_resolution: 20.0 # Pixels/meter
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#orthophoto_target_srs: !!null # Currently does nothing
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@ -320,6 +320,28 @@ def config():
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help='Use this tag to build a DEM using a progressive '
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'morphological filter in PDAL.')
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parser.add_argument('--dem-sample-radius',
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metavar='<float>',
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default=1.0,
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type=float,
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help='Minimum distance between samples for DEM '
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'generation.\nDefault=%(default)s')
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parser.add_argument('--dem-resolution',
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metavar='<float>',
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type=float,
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default=2,
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help='Length of raster cell edges in X/Y units.'
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'\nDefault: %(default)s')
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parser.add_argument('--dem-radius',
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metavar='<float>',
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type=float,
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default=0.5,
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help='Radius about cell center bounding points to '
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'use to calculate a cell value.\nDefault: '
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'%(default)s')
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parser.add_argument('--orthophoto-resolution',
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metavar='<float > 0.0>',
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default=20.0,
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@ -194,7 +194,7 @@ class ODM_GeoRef(object):
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system.run('{bin}/pdal pipeline -i {json} --readers.ply.filename={f_in} '
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'--writers.las.filename={f_out}'.format(**kwargs))
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def convert_to_dem(self, _file, _file_out, pdalJSON):
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def convert_to_dem(self, _file, _file_out, pdalJSON, sample_radius, gdal_res, gdal_radius):
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# Check if exists f_in
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if not io.file_exists(_file):
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log.ODM_ERROR('LAS file does not exist')
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@ -203,29 +203,32 @@ class ODM_GeoRef(object):
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kwargs = {
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'bin': context.pdal_path,
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'f_in': _file,
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'f_out': _file_out, # todo: add options
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'sample_radius': sample_radius,
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'gdal_res': gdal_res,
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'gdal_radius': gdal_radius,
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'f_out': _file_out,
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'json': pdalJSON
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}
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pipelineJSON = '{' \
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pipelineJSON = '{{' \
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' "pipeline":[' \
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' "input.las",' \
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' {' \
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' {{' \
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' "type":"filters.sample",' \
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' "radius":"0.5"' \
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' },' \
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' {' \
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' "radius":"{sample_radius}"' \
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' }},' \
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' {{' \
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' "type":"filters.pmf",' \
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' "extract":"true"' \
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' },' \
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' {' \
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' "resolution": 2,' \
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' "radius": 5,' \
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' }},' \
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' {{' \
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' "resolution": {gdal_res},' \
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' "radius": {gdal_radius},' \
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' "output_type":"idw",' \
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' "filename":"outputfile.tif"' \
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' }' \
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' }}' \
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' ]' \
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'}'
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'}}'.format(**kwargs)
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with open(pdalJSON, 'w') as f:
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f.write(pipelineJSON)
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@ -72,6 +72,9 @@ class ODMApp(ecto.BlackBox):
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gcp_file=p.args.gcp,
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use_exif=p.args.use_exif,
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dem=p.args.dem,
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sample_radius=p.args.dem_sample_radius,
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gdal_res=p.args.dem_resolution,
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gdal_radius=p.args.dem_radius,
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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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@ -18,6 +18,9 @@ class ODMGeoreferencingCell(ecto.Cell):
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params.declare("img_size", 'image size used in calibration', 2400)
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params.declare("use_exif", 'use exif', False)
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params.declare("dem", 'Generate a dem', False)
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params.declare("sample_radius", "Minimum distance between samples for DEM gen", 3)
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params.declare("gdal_res", "Length of raster cell edges in X/Y units ", 2)
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params.declare("gdal_radius", "Radius about cell center bounding points to use to calculate a cell value", 0.5)
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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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@ -153,14 +156,19 @@ class ODMGeoreferencingCell(ecto.Cell):
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# If --dem, create a DEM
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if args.dem:
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demcreated = geo_ref.convert_to_dem(tree.odm_georeferencing_model_las, tree.odm_georeferencing_dem, tree.odm_georeferencing_dem_json)
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demcreated = geo_ref.convert_to_dem(tree.odm_georeferencing_model_las,
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tree.odm_georeferencing_dem,
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tree.odm_georeferencing_dem_json,
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self.params.sample_radius,
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self.params.gdal_res,
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self.params.gdal_radius)
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if not demcreated:
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log.ODM_WARNING('Something went wrong. Check the logs in odm_georeferencing.')
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else:
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log.ODM_INFO('DEM created at {0}'.format(tree.odm_georeferencing_dem))
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# XYZ point cloud output
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log.ODM_INFO("Creating geo-referenced CSV file (XYZ format, can be used with GRASS to create DEM)")
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log.ODM_INFO("Creating geo-referenced CSV file (XYZ format)")
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with open(tree.odm_georeferencing_xyz_file, "wb") as csvfile:
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csvfile_writer = csv.writer(csvfile, delimiter=",")
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reachedpoints = False
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@ -43,6 +43,10 @@ project_path: '' # Example: '/home/user/ODMProjects
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#texturing_tone_mapping: 'none'
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#gcp: !!null # YAML tag for None
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#use_exif: False # Set to True if you have a GCP file (it auto-detects) and want to use EXIF
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#dem: False
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#dem_sample_radius: 1.0
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#dem_resolution: 2
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#dem_radius: 0.5
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#orthophoto_resolution: 20.0 # Pixels/meter
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#orthophoto_target_srs: !!null # Currently does nothing
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#orthophoto_no_tiled: False
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