kopia lustrzana https://github.com/OpenDroneMap/ODM
GCP EPSG code support, fixes
Piero Toffanin
rodzic
a247eb654b
commit
12ea7dbe39
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@ -18,7 +18,7 @@ class GCPFile:
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def read(self):
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if self.exists():
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with open(self.gcp_path, 'r') as f:
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contents = f.read().strip()
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contents = f.read().decode('utf-8-sig').encode('utf-8').strip()
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lines = map(str.strip, contents.split('\n'))
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if lines:
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@ -33,12 +33,9 @@ class GCPFile:
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else:
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log.ODM_WARNING("Malformed GCP line: %s" % line)
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def iter_entries(self, allowed_filenames=None):
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def iter_entries(self):
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for entry in self.entries:
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pe = self.parse_entry(entry)
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if allowed_filenames is None or pe.filename in allowed_filenames:
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yield pe
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yield self.parse_entry(entry)
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def parse_entry(self, entry):
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if entry:
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@ -69,11 +66,12 @@ class GCPFile:
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utm_zone, hemisphere = location.get_utm_zone_and_hemisphere_from(lon, lat)
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return "WGS84 UTM %s%s" % (utm_zone, hemisphere)
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def create_utm_copy(self, gcp_file_output, filenames=None):
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def create_utm_copy(self, gcp_file_output, filenames=None, rejected_entries=None):
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"""
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Creates a new GCP file from an existing GCP file
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by optionally including only filenames and reprojecting each point to
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a UTM CRS
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a UTM CRS. Rejected entries can recorded by passing a list object to
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rejected_entries.
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"""
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if os.path.exists(gcp_file_output):
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os.remove(gcp_file_output)
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@ -82,9 +80,12 @@ class GCPFile:
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target_srs = location.parse_srs_header(output[0])
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transformer = location.transformer(self.srs, target_srs)
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for entry in self.iter_entries(filenames):
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entry.x, entry.y, entry.z = transformer.TransformPoint(entry.x, entry.y, entry.z)
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output.append(str(entry))
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for entry in self.iter_entries():
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if filenames is None or entry.filename in filenames:
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entry.x, entry.y, entry.z = transformer.TransformPoint(entry.x, entry.y, entry.z)
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output.append(str(entry))
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elif isinstance(rejected_entries, list):
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rejected_entries.append(entry)
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with open(gcp_file_output, 'w') as f:
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f.write('\n'.join(output) + '\n')
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@ -58,7 +58,7 @@ class OSFMContext:
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raise Exception("Reconstruction could not be generated")
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def setup(self, args, images_path, photos, gcp_path=None, append_config = [], rerun=False): #georeferenced=True,
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def setup(self, args, images_path, photos, gcp_path=None, append_config = [], rerun=False):
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"""
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Setup a OpenSfM project
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"""
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@ -109,6 +109,8 @@ class OSFMContext:
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"bundle_outlier_filtering_type: AUTO",
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]
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# TODO: add BOW matching when dataset is not georeferenced (no gps)
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if has_alt:
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log.ODM_DEBUG("Altitude data detected, enabling it for GPS alignment")
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config.append("use_altitude_tag: yes")
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@ -117,10 +119,6 @@ class OSFMContext:
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config.append("align_method: orientation_prior")
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config.append("align_orientation_prior: vertical")
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# if not georeferenced:
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# config.append("")
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if args.use_hybrid_bundle_adjustment:
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log.ODM_DEBUG("Enabling hybrid bundle adjustment")
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config.append("bundle_interval: 100") # Bundle after adding 'bundle_interval' cameras
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@ -92,31 +92,53 @@ class ODM_Reconstruction(object):
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def __init__(self, photos):
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self.photos = photos
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self.georef = None
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self.gcp = None
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def is_georeferenced(self):
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return self.georef is not None
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def georeference_with_gcp(self, gcp_file, output_coords_file, reload_coords=False):
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if not io.file_exists(output_coords_file) or reload_coords:
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def georeference_with_gcp(self, gcp_file, output_coords_file, output_gcp_file, rerun=False):
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if not io.file_exists(output_coords_file) or not io.file_exists(output_gcp_file) or rerun:
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gcp = GCPFile(gcp_file)
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if gcp.exists():
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# Create coords file
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# Create coords file, we'll be using this later
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# during georeferencing
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with open(output_coords_file, 'w') as f:
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coords_header = gcp.wgs84_utm_zone()
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f.write(coords_header + "\n")
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log.ODM_DEBUG("Generated coords file from GCP: %s" % coords_header)
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# Convert GCP file to a UTM projection since the rest of the pipeline
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# does not handle other SRS well.
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rejected_entries = []
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utm_gcp = GCPFile(gcp.create_utm_copy(output_gcp_file, filenames=[p.filename for p in self.photos], rejected_entries=rejected_entries))
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if not utm_gcp.exists():
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raise RuntimeError("Could not project GCP file to UTM. Please double check your GCP file for mistakes.")
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for re in rejected_entries:
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log.ODM_WARNING("GCP line ignored (image not found): %s" % str(re))
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if utm_gcp.entries_count() > 0:
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log.ODM_INFO("%s GCP points will be used for georeferencing" % utm_gcp.entries_count())
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else:
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raise RuntimeError("A GCP file was provided, but no valid GCP entries could be used. Note that the GCP file is case sensitive (\".JPG\" is not the same as \".jpg\").")
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self.gcp = utm_gcp
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else:
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log.ODM_WARNING("GCP file does not exist: %s" % gcp_file)
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return
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else:
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log.ODM_INFO("Coordinates file already exist: %s" % output_coords_file)
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log.ODM_INFO("GCP file already exist: %s" % output_gcp_file)
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self.gcp = GCPFile(output_gcp_file)
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self.georef = ODM_GeoRef.FromCoordsFile(output_coords_file)
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return self.georef
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def georeference_with_gps(self, images_path, output_coords_file, reload_coords=False):
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def georeference_with_gps(self, images_path, output_coords_file, rerun=False):
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try:
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if not io.file_exists(output_coords_file) or reload_coords:
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if not io.file_exists(output_coords_file) or rerun:
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location.extract_utm_coords(photos, tree.dataset_raw, output_coords_file)
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else:
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log.ODM_INFO("Coordinates file already exist: %s" % output_coords_file)
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@ -267,6 +289,7 @@ class ODM_Tree(object):
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self.odm_georeferencing_coords = io.join_paths(
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self.odm_georeferencing, 'coords.txt')
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self.odm_georeferencing_gcp = gcp_file or io.find('gcp_list.txt', self.root_path)
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self.odm_georeferencing_gcp_utm = io.join_paths(self.odm_georeferencing, 'gcp_list_utm.txt')
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self.odm_georeferencing_utm_log = io.join_paths(
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self.odm_georeferencing, 'odm_georeferencing_utm_log.txt')
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self.odm_georeferencing_log = 'odm_georeferencing_log.txt'
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@ -101,11 +101,16 @@ class ODMLoadDatasetStage(types.ODM_Stage):
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# Create reconstruction object
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reconstruction = types.ODM_Reconstruction(photos)
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if tree.odm_georeferencing_gcp:
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reconstruction.georeference_with_gcp(tree.odm_georeferencing_gcp, tree.odm_georeferencing_coords, reload_coords=self.rerun())
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reconstruction.georeference_with_gcp(tree.odm_georeferencing_gcp,
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tree.odm_georeferencing_coords,
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tree.odm_georeferencing_gcp_utm,
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rerun=self.rerun())
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else:
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reconstruction.georeference_with_gps(tree.dataset_raw, tree.odm_georeferencing_coords, reload_coords=self.rerun())
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reconstruction.georeference_with_gps(tree.dataset_raw,
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tree.odm_georeferencing_coords,
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rerun=self.rerun())
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reconstruction.save_proj_srs(io.join_paths(tree.odm_georeferencing, tree.odm_georeferencing_proj))
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outputs['reconstruction'] = reconstruction
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@ -26,8 +26,7 @@ class ODMApp:
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"""
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dataset = ODMLoadDatasetStage('dataset', args, progress=5.0,
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verbose=args.verbose,
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proj=args.proj)
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verbose=args.verbose)
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split = ODMSplitStage('split', args, progress=75.0)
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merge = ODMMergeStage('merge', args, progress=100.0)
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opensfm = ODMOpenSfMStage('opensfm', args, progress=25.0)
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@ -59,36 +58,34 @@ class ODMApp:
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verbose=args.verbose)
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orthophoto = ODMOrthoPhotoStage('odm_orthophoto', args, progress=100.0)
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if not args.video:
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# Normal pipeline
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self.first_stage = dataset
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# Normal pipeline
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self.first_stage = dataset
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dataset.connect(split) \
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.connect(merge) \
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.connect(opensfm)
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dataset.connect(split) \
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.connect(merge) \
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.connect(opensfm)
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if args.use_opensfm_dense or args.fast_orthophoto:
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opensfm.connect(filterpoints)
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else:
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opensfm.connect(mve) \
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.connect(filterpoints)
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filterpoints \
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.connect(meshing) \
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.connect(texturing) \
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.connect(georeferencing) \
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.connect(dem) \
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.connect(orthophoto)
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if args.use_opensfm_dense or args.fast_orthophoto:
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opensfm.connect(filterpoints)
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else:
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# SLAM pipeline
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# TODO: this is broken and needs work
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log.ODM_WARNING("SLAM module is currently broken. We could use some help fixing this. If you know Python, get in touch at https://community.opendronemap.org.")
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self.first_stage = slam
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opensfm.connect(mve) \
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.connect(filterpoints)
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filterpoints \
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.connect(meshing) \
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.connect(texturing) \
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.connect(georeferencing) \
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.connect(dem) \
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.connect(orthophoto)
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# # SLAM pipeline
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# # TODO: this is broken and needs work
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# log.ODM_WARNING("SLAM module is currently broken. We could use some help fixing this. If you know Python, get in touch at https://community.opendronemap.org.")
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# self.first_stage = slam
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slam.connect(mve) \
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.connect(meshing) \
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.connect(texturing)
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# slam.connect(mve) \
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# .connect(meshing) \
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# .connect(texturing)
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def execute(self):
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self.first_stage.run()
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@ -16,7 +16,6 @@ class ODMGeoreferencingStage(types.ODM_Stage):
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tree = outputs['tree']
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reconstruction = outputs['reconstruction']
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gcpfile = tree.odm_georeferencing_gcp
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doPointCloudGeo = True
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transformPointCloud = True
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verbose = '-verbose' if self.params.get('verbose') else ''
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@ -65,7 +64,6 @@ class ODMGeoreferencingStage(types.ODM_Stage):
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'output_pc_file': tree.odm_georeferencing_model_laz,
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'geo_sys': odm_georeferencing_model_txt_geo_file,
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'model_geo': odm_georeferencing_model_obj_geo,
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'gcp': gcpfile,
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'verbose': verbose
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}
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@ -21,7 +21,7 @@ class ODMOpenSfMStage(types.ODM_Stage):
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exit(1)
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octx = OSFMContext(tree.opensfm)
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octx.setup(args, tree.dataset_raw, photos, gcp_path=tree.odm_georeferencing_gcp, rerun=self.rerun())
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octx.setup(args, tree.dataset_raw, photos, gcp_path=reconstruction.gcp.gcp_path, rerun=self.rerun())
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octx.extract_metadata(self.rerun())
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self.update_progress(20)
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octx.feature_matching(self.rerun())
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@ -50,7 +50,7 @@ class ODMSplitStage(types.ODM_Stage):
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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=tree.odm_georeferencing_gcp, append_config=config, rerun=self.rerun())
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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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@ -74,18 +74,16 @@ class ODMSplitStage(types.ODM_Stage):
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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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gcp_file = GCPFile(tree.odm_georeferencing_gcp)
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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 gcp_file.exists():
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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 gcp_file.make_filtered_copy(submodel_gcp_file, submodel_images_dir):
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if reconstruction.gcp.make_filtered_copy(submodel_gcp_file, submodel_images_dir):
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log.ODM_DEBUG("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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