kopia lustrzana https://github.com/OpenDroneMap/ODM
Merge pull request #549 from dakotabenjamin/pdal-dem
Add DEM generation using PDAL
Former-commit-id: eaf9ab2e26
pull/1161/head
Dakota Benjamin
GitHub
commit
f07ddc182a
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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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@ -74,7 +74,7 @@ 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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@ -314,6 +314,34 @@ def config():
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help=('Use this tag if you have a gcp_list.txt but '
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'want to use the exif geotags instead'))
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parser.add_argument('--dem',
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action='store_true',
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default=False,
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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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127
opendm/types.py
127
opendm/types.py
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@ -143,13 +143,13 @@ class ODM_GeoRef(object):
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def coord_to_fractions(self, coord, refs):
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deg_dec = abs(float(coord))
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deg = int(deg_dec)
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minute_dec = (deg_dec-deg)*60
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minute_dec = (deg_dec - deg) * 60
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minute = int(minute_dec)
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sec_dec = (minute_dec-minute)*60
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sec_dec = round(sec_dec,3)
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sec_dec = (minute_dec - minute) * 60
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sec_dec = round(sec_dec, 3)
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sec_denominator = 1000
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sec_numerator = int(sec_dec*sec_denominator)
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sec_numerator = int(sec_dec * sec_denominator)
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if float(coord) >= 0:
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latRef = refs[0]
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else:
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@ -158,7 +158,7 @@ class ODM_GeoRef(object):
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output = str(deg) + '/1 ' + str(minute) + '/1 ' + str(sec_numerator) + '/' + str(sec_denominator)
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return output, latRef
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def convert_to_las(self, _file, pdalXML):
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def convert_to_las(self, _file, _file_out, json_file):
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if not self.epsg:
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log.ODM_ERROR('Empty EPSG: Could not convert to LAS')
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@ -166,50 +166,82 @@ class ODM_GeoRef(object):
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kwargs = {'bin': context.pdal_path,
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'f_in': _file,
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'f_out': _file + '.las',
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'f_out': _file_out,
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'east': self.utm_east_offset,
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'north': self.utm_north_offset,
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'epsg': self.epsg,
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'xml': pdalXML}
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'json': json_file}
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# call txt2las
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# system.run('{bin}/txt2las -i {f_in} -o {f_out} -skip 30 -parse xyzRGBssss ' \
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# '-set_scale 0.01 0.01 0.01 -set_offset {east} {north} 0 ' \
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# '-translate_xyz 0 -epsg {epsg}'.format(**kwargs))
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#
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# create pipeline file transform.xml to enable transformation
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pipelineXml = '<?xml version=\"1.0\" encoding=\"utf-8\"?>'
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pipelineXml += '<Pipeline version=\"1.0\">'
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pipelineXml += ' <Writer type=\"writers.las\">'
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pipelineXml += ' <Option name=\"filename\">'
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pipelineXml += ' transformed.las'
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pipelineXml += ' </Option>'
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pipelineXml += ' <Option name=\"a_srs\">'
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pipelineXml += ' EPSG:{epsg}'.format(**kwargs)
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pipelineXml += ' </Option>'
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pipelineXml += ' <Filter type=\"filters.transformation\">'
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pipelineXml += ' <Option name=\"matrix\">'
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pipelineXml += ' 1 0 0 {east}'.format(**kwargs)
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pipelineXml += ' 0 1 0 {north}'.format(**kwargs)
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pipelineXml += ' 0 0 1 0'
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pipelineXml += ' 0 0 0 1'
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pipelineXml += ' </Option>'
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pipelineXml += ' <Reader type=\"readers.ply\">'
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pipelineXml += ' <Option name=\"filename\">'
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pipelineXml += ' untransformed.ply'
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pipelineXml += ' </Option>'
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pipelineXml += ' </Reader>'
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pipelineXml += ' </Filter>'
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pipelineXml += ' </Writer>'
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pipelineXml += '</Pipeline>'
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pipeline = '{{' \
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' "pipeline":[' \
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' "untransformed.ply",' \
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' {{' \
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' "type":"filters.transformation",' \
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' "matrix":"1 0 0 {east} 0 1 0 {north} 0 0 1 0 0 0 0 1"' \
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' }},' \
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' {{' \
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' "a_srs":"EPSG:{epsg}",' \
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' "forward":"scale",' \
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' "filename":"transformed.las"' \
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' }}' \
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' ]' \
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'}}'.format(**kwargs)
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with open(pdalXML, 'w') as f:
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f.write(pipelineXml)
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with open(json_file, 'w') as f:
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f.write(pipeline)
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# call pdal
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system.run('{bin}/pdal pipeline -i {xml} --readers.ply.filename={f_in} '
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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, 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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return False
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kwargs = {
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'bin': context.pdal_path,
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'f_in': _file,
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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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' "pipeline":[' \
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' "input.las",' \
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' {{' \
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' "type":"filters.sample",' \
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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": {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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'}}'.format(**kwargs)
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with open(pdalJSON, 'w') as f:
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f.write(pipelineJSON)
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system.run('{bin}/pdal pipeline {json} --readers.las.filename={f_in} '
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'--writers.gdal.filename={f_out}'.format(**kwargs))
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if io.file_exists(kwargs['f_out']):
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return True
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else:
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return False
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def utm_to_latlon(self, _file, _photo, idx):
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gcp = self.gcps[idx]
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@ -271,7 +303,7 @@ class ODM_GeoRef(object):
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metadata[key] = pyexiv2.ExifTag(key, value)
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# GPS altitude
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altitude = abs(int(float(latlon[2])*100))
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altitude = abs(int(float(latlon[2]) * 100))
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key = 'Exif.GPSInfo.GPSAltitude'
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value = Fraction(altitude, 1)
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metadata[key] = pyexiv2.ExifTag(key, value)
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@ -301,7 +333,7 @@ class ODM_GeoRef(object):
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log.ODM_DEBUG('Line: %s' % line)
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ref = line.split(' ')
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# match_wgs_utm = re.search('WGS84 UTM (\d{1,2})(N|S)', line, re.I)
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if ref[0] == 'WGS84' and ref[1] == 'UTM': # match_wgs_utm:
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if ref[0] == 'WGS84' and ref[1] == 'UTM': # match_wgs_utm:
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self.datum = ref[0]
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self.utm_pole = ref[2][len(ref) - 1]
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self.utm_zone = int(ref[2][:len(ref) - 1])
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@ -331,6 +363,7 @@ class ODM_GeoRef(object):
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x, y = xyz[:2]
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z = 0
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self.gcps.append(ODM_GCPoint(float(x), float(y), float(z)))
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# Write to json file
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class ODM_Tree(object):
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@ -390,7 +423,7 @@ class ODM_Tree(object):
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self.odm_texturing, 'odm_textured_model.obj')
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self.odm_textured_model_mtl = io.join_paths(
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self.odm_texturing, 'odm_textured_model.mtl')
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# Log is only used by old odm_texturing
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# Log is only used by old odm_texturing
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self.odm_texuring_log = io.join_paths(
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self.odm_texturing, 'odm_texturing_log.txt')
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@ -415,8 +448,14 @@ class ODM_Tree(object):
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self.odm_texturing, 'odm_textured_model_geo.mtl') # these files will be kept in odm_texturing/
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self.odm_georeferencing_xyz_file = io.join_paths(
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self.odm_georeferencing, 'odm_georeferenced_model.csv')
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self.odm_georeferencing_pdal = io.join_paths(
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self.odm_georeferencing, 'pipeline.xml')
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self.odm_georeferencing_las_json = io.join_paths(
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self.odm_georeferencing, 'las.json')
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self.odm_georeferencing_model_las = io.join_paths(
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self.odm_georeferencing, 'odm_georeferenced_model.las')
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self.odm_georeferencing_dem = io.join_paths(
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self.odm_georeferencing, 'odm_georeferencing_model_dem.tif')
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self.odm_georeferencing_dem_json = io.join_paths(
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self.odm_georeferencing, 'dem.json')
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# odm_orthophoto
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self.odm_orthophoto_file = io.join_paths(self.odm_orthophoto, 'odm_orthophoto.png')
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@ -71,6 +71,10 @@ class ODMApp(ecto.BlackBox):
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'georeferencing': ODMGeoreferencingCell(img_size=p.args.resize_to,
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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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@ -17,6 +17,10 @@ class ODMGeoreferencingCell(ecto.Cell):
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'northing height pixelrow pixelcol imagename', 'gcp_list.txt')
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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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@ -147,10 +151,24 @@ class ODMGeoreferencingCell(ecto.Cell):
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# convert ply model to LAS reference system
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geo_ref.convert_to_las(tree.odm_georeferencing_model_ply_geo,
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tree.odm_georeferencing_pdal)
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tree.odm_georeferencing_model_las,
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tree.odm_georeferencing_las_json)
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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,
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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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