def __init__(self, root_path, gcp_file=None, geo_file=None):
# root path to the project
self.root_path = io.absolute_path_file(root_path)
self.input_images = os.path.join(self.root_path, 'images')
# modules paths
# here are defined where all modules should be located in
# order to keep track all files al directories during the
# whole reconstruction process.
self.dataset_raw = os.path.join(self.root_path, 'images')
self.opensfm = os.path.join(self.root_path, 'opensfm')
self.openmvs = os.path.join(self.opensfm, 'undistorted', 'openmvs')
self.odm_meshing = os.path.join(self.root_path, 'odm_meshing')
self.odm_texturing = os.path.join(self.root_path, 'odm_texturing')
self.odm_25dtexturing = os.path.join(self.root_path,
'odm_texturing_25d')
self.odm_georeferencing = os.path.join(self.root_path,
'odm_georeferencing')
self.odm_filterpoints = os.path.join(self.root_path,
'odm_filterpoints')
self.odm_orthophoto = os.path.join(self.root_path, 'odm_orthophoto')
self.odm_report = os.path.join(self.root_path, 'odm_report')
# important files paths
# benchmarking
self.benchmarking = os.path.join(self.root_path, 'benchmark.txt')
self.dataset_list = os.path.join(self.root_path, 'img_list.txt')
# opensfm
self.opensfm_image_list = os.path.join(self.opensfm, 'image_list.txt')
self.opensfm_reconstruction = os.path.join(self.opensfm,
'reconstruction.json')
self.opensfm_reconstruction_nvm = os.path.join(
self.opensfm, 'undistorted/reconstruction.nvm')
self.opensfm_geocoords_reconstruction = os.path.join(
self.opensfm, 'reconstruction.geocoords.json')
self.opensfm_topocentric_reconstruction = os.path.join(
self.opensfm, 'reconstruction.topocentric.json')
# OpenMVS
self.openmvs_model = os.path.join(self.openmvs,
'scene_dense_dense_filtered.ply')
# filter points
self.filtered_point_cloud = os.path.join(self.odm_filterpoints,
"point_cloud.ply")
# odm_meshing
self.odm_mesh = os.path.join(self.odm_meshing, 'odm_mesh.ply')
self.odm_meshing_log = os.path.join(self.odm_meshing,
'odm_meshing_log.txt')
self.odm_25dmesh = os.path.join(self.odm_meshing, 'odm_25dmesh.ply')
self.odm_25dmeshing_log = os.path.join(self.odm_meshing,
'odm_25dmeshing_log.txt')
# texturing
self.odm_textured_model_obj = 'odm_textured_model_geo.obj'
# odm_georeferencing
self.odm_georeferencing_coords = os.path.join(self.odm_georeferencing,
'coords.txt')
self.odm_georeferencing_gcp = gcp_file or io.find(
'gcp_list.txt', self.root_path)
self.odm_georeferencing_gcp_utm = os.path.join(self.odm_georeferencing,
'gcp_list_utm.txt')
self.odm_geo_file = geo_file or io.find('geo.txt', self.root_path)
self.odm_georeferencing_proj = 'proj.txt'
self.odm_georeferencing_model_txt_geo = os.path.join(
self.odm_georeferencing, 'odm_georeferencing_model_geo.txt')
self.odm_georeferencing_xyz_file = os.path.join(
self.odm_georeferencing, 'odm_georeferenced_model.csv')
self.odm_georeferencing_model_laz = os.path.join(
self.odm_georeferencing, 'odm_georeferenced_model.laz')
self.odm_georeferencing_model_las = os.path.join(
self.odm_georeferencing, 'odm_georeferenced_model.las')
# odm_orthophoto
self.odm_orthophoto_render = os.path.join(self.odm_orthophoto,
'odm_orthophoto_render.tif')
self.odm_orthophoto_tif = os.path.join(self.odm_orthophoto,
'odm_orthophoto.tif')
self.odm_orthophoto_corners = os.path.join(
self.odm_orthophoto, 'odm_orthophoto_corners.txt')
self.odm_orthophoto_log = os.path.join(self.odm_orthophoto,
'odm_orthophoto_log.txt')
self.odm_orthophoto_tif_log = os.path.join(self.odm_orthophoto,
'gdal_translate_log.txt')
# tiles
self.orthophoto_tiles = os.path.join(self.root_path,
"orthophoto_tiles")
# Split-merge
self.submodels_path = os.path.join(self.root_path, 'submodels')
# Tiles
self.entwine_pointcloud = self.path("entwine_pointcloud")
self.ogc_tiles = self.path("3d_tiles")
def process(args, tree, reconstruction, current_path):
odm_orthophoto = io.join_paths(current_path, 'orthophoto')
odm_orthophoto_path = odm_orthophoto
odm_orthophoto_render = io.join_paths(odm_orthophoto_path, 'odm_orthophoto_render.tif')
odm_orthophoto_tif = io.join_paths(odm_orthophoto_path, 'odm_orthophoto.tif')
odm_orthophoto_corners = io.join_paths(odm_orthophoto_path, 'odm_orthophoto_corners.tif')
odm_orthophoto_log = io.join_paths(odm_orthophoto_path, 'odm_orthophoto_log.tif')
odm_orthophoto_tif_log = io.join_paths(odm_orthophoto_path, 'gdal_translate_log.txt')
odm_25dgeoreferencing = io.join_paths(current_path, 'odm_georeferencing')
odm_georeferencing = io.join_paths(current_path, 'odm_georeferencing')
odm_georeferencing_coords = io.join_paths(
odm_georeferencing, 'coords.txt')
odm_georeferencing_gcp = io.find('gcp_list.txt', current_path)
odm_georeferencing_gcp_utm = io.join_paths(odm_georeferencing, 'gcp_list_utm.txt')
odm_georeferencing_utm_log = io.join_paths(
odm_georeferencing, 'odm_georeferencing_utm_log.txt')
odm_georeferencing_log = 'odm_georeferencing_log.txt'
odm_georeferencing_transform_file = 'odm_georeferencing_transform.txt'
odm_georeferencing_proj = 'proj.txt'
odm_georeferencing_model_txt_geo = 'odm_georeferencing_model_geo.txt'
odm_georeferencing_model_obj_geo = 'odm_textured_model_geo.obj'
odm_georeferencing_xyz_file = io.join_paths(
odm_georeferencing, 'odm_georeferenced_model.csv')
odm_georeferencing_las_json = io.join_paths(
odm_georeferencing, 'las.json')
odm_georeferencing_model_laz = io.join_paths(
odm_georeferencing, 'odm_georeferenced_model.laz')
odm_georeferencing_model_las = io.join_paths(
odm_georeferencing, 'odm_georeferenced_model.las')
odm_georeferencing_dem = io.join_paths(
odm_georeferencing, 'odm_georeferencing_model_dem.tif')
opensfm_reconstruction = io.join_paths(current_path,'reconstruction.json')
odm_texturing = io.join_paths(current_path,'mvs')
odm_textured_model_obj = io.join_paths(odm_texturing, 'odm_textured_model.obj')
images_dir = io.join_paths(current_path, 'images')
tree = tree
opensfm_bundle = os.path.join(current_path, 'opensfm', 'bundle_r000.out')
opensfm_bundle_list = os.path.join(current_path, 'opensfm', 'list_r000.out')
opensfm_transformation = os.path.join(current_path, 'opensfm', 'geocoords_transformation.txt')
filtered_point_cloud = os.path.join(current_path, 'filterpoints', 'point_cloud.ply')
doPointCloudGeo = True
transformPointCloud = True
verbose =''
class nonloc:
runs = []
def add_run(primary=True, band=None):
subdir = ""
if not primary and band is not None:
subdir = band
# Make sure 2.5D mesh is georeferenced before the 3D mesh
# Because it will be used to calculate a transform
# for the point cloud. If we use the 3D model transform,
# DEMs and orthophoto might not align!
b = True
if b:
nonloc.runs += [{
'georeferencing_dir': os.path.join(odm_georeferencing, subdir),
'texturing_dir': os.path.join(odm_texturing, subdir),
}]
if not args.skip_3dmodel and (primary or args.use_3dmesh):
nonloc.runs += [{
'georeferencing_dir': odm_georeferencing,
'texturing_dir': os.path.join(odm_texturing, subdir),
}]
if reconstruction.multi_camera:
for band in reconstruction.multi_camera:
primary = band == reconstruction.multi_camera[0]
add_run(primary, band['name'].lower())
else:
add_run()
#progress_per_run = 100.0 / len(nonloc.runs)
#progress = 0.0
for r in nonloc.runs:
if not io.dir_exists(r['georeferencing_dir']):
system.mkdir_p(r['georeferencing_dir'])
odm_georeferencing_model_obj_geo = os.path.join(r['texturing_dir'], odm_georeferencing_model_obj_geo)
odm_georeferencing_model_obj = os.path.join(r['texturing_dir'], odm_textured_model_obj)
odm_georeferencing_log = os.path.join(r['georeferencing_dir'], odm_georeferencing_log)
odm_georeferencing_transform_file = os.path.join(r['georeferencing_dir'], odm_georeferencing_transform_file)
odm_georeferencing_model_txt_geo_file = os.path.join(r['georeferencing_dir'], odm_georeferencing_model_txt_geo)
pio = True
if pio:
#if not io.file_exists(odm_georeferencing_model_obj_geo) or \
#not io.file_exists(odm_georeferencing_model_laz)
# odm_georeference definitions
kwargs = {
'bin': context.odm_modules_path,
'input_pc_file': filtered_point_cloud,
'bundle': opensfm_bundle,
'imgs': images_dir,
'imgs_list': opensfm_bundle_list,
'model': odm_georeferencing_model_obj,
'log': odm_georeferencing_log,
'input_trans_file': opensfm_transformation,
'transform_file': odm_georeferencing_transform_file,
'coords': odm_georeferencing_coords,
'output_pc_file': odm_georeferencing_model_laz,
'geo_sys': odm_georeferencing_model_txt_geo_file,
'model_geo': odm_georeferencing_model_obj_geo,
'verbose': verbose
}
if transformPointCloud:
kwargs['pc_params'] = '-inputPointCloudFile {input_pc_file} -outputPointCloudFile {output_pc_file}'.format(**kwargs)
if reconstruction.is_georeferenced():
kwargs['pc_params'] += ' -outputPointCloudSrs %s' % pipes.quote(reconstruction.georef.proj4())
else:
log.ODM_WARNING('NO SRS: The output point cloud will not have a SRS.')
else:
kwargs['pc_params'] = ''
if io.file_exists(opensfm_transformation) and io.file_exists(odm_georeferencing_coords):
log.ODM_INFO('Running georeferencing with OpenSfM transformation matrix')
system.run('{bin}/odm_georef -bundleFile {bundle} -inputTransformFile {input_trans_file} -inputCoordFile {coords} '
'-inputFile {model} -outputFile {model_geo} '
'{pc_params} {verbose} '
'-logFile {log} -outputTransformFile {transform_file} -georefFileOutputPath {geo_sys}'.format(**kwargs))
elif io.file_exists(odm_georeferencing_coords):
print('running georeferencing')
log.ODM_INFO('Running georeferencing with generated coords file.')
system.run('{bin}/odm_georef -bundleFile {bundle} -inputCoordFile {coords} '
'-inputFile {model} -outputFile {model_geo} '
'{pc_params} {verbose} '
'-logFile {log} -outputTransformFile {transform_file} -georefFileOutputPath {geo_sys}'.format(**kwargs))
else:
log.ODM_WARNING('Georeferencing failed. Make sure your '
'photos have geotags in the EXIF or you have '
'provided a GCP file. ')
doPointCloudGeo = False # skip the rest of the georeferencing
if doPointCloudGeo:
reconstruction.georef.extract_offsets(odm_georeferencing_model_txt_geo_file)
point_cloud.post_point_cloud_steps(args, tree)
if args.crop > 0:
log.ODM_INFO("Calculating cropping area and generating bounds shapefile from point cloud")
cropper = Cropper(odm_georeferencing, 'odm_georeferenced_model')
decimation_step = 40 if args.fast_orthophoto or args.use_opensfm_dense else 90
# More aggressive decimation for large datasets
if not args.fast_orthophoto:
decimation_step *= int(len(reconstruction.photos) / 1000) + 1
cropper.create_bounds_gpkg(odm_georeferencing_model_laz, args.crop,
decimation_step=decimation_step)
# Do not execute a second time, since
# We might be doing georeferencing for
# multiple models (3D, 2.5D, ...)
doPointCloudGeo = False
transformPointCloud = False
else:
log.ODM_WARNING('Found a valid georeferenced model in: %s'
% odm_georeferencing_model_laz)
if args.optimize_disk_space and io.file_exists(odm_georeferencing_model_laz) and io.file_exists(filtered_point_cloud):
os.remove(filtered_point_cloud)
def process(args, current_path, max_concurrency, reconstruction):
#args = vars(args)
orthophoto_cutline = True
odm_orthophoto = io.join_paths(current_path, 'orthophoto')
odm_orthophoto_path = odm_orthophoto
odm_orthophoto_render = io.join_paths(odm_orthophoto_path,
'odm_orthophoto_render.tif')
odm_orthophoto_tif = io.join_paths(odm_orthophoto_path,
'odm_orthophoto.tif')
odm_orthophoto_corners = io.join_paths(odm_orthophoto_path,
'odm_orthophoto_corners.tif')
odm_orthophoto_log = io.join_paths(odm_orthophoto_path,
'odm_orthophoto_log.tif')
odm_orthophoto_tif_log = io.join_paths(odm_orthophoto_path,
'gdal_translate_log.txt')
odm_25dgeoreferencing = io.join_paths(current_path, 'odm_georeferencing')
odm_georeferencing = io.join_paths(current_path, 'odm_georeferencing')
odm_georeferencing_coords = io.join_paths(odm_georeferencing, 'coords.txt')
odm_georeferencing_gcp = io.find('gcp_list.txt', current_path)
odm_georeferencing_gcp_utm = io.join_paths(odm_georeferencing,
'gcp_list_utm.txt')
odm_georeferencing_utm_log = io.join_paths(
odm_georeferencing, 'odm_georeferencing_utm_log.txt')
odm_georeferencing_log = 'odm_georeferencing_log.txt'
odm_georeferencing_transform_file = 'odm_georeferencing_transform.txt'
odm_georeferencing_proj = 'proj.txt'
odm_georeferencing_model_txt_geo = 'odm_georeferencing_model_geo.txt'
odm_georeferencing_model_obj_geo = 'odm_textured_model_geo.obj'
odm_georeferencing_xyz_file = io.join_paths(odm_georeferencing,
'odm_georeferenced_model.csv')
odm_georeferencing_las_json = io.join_paths(odm_georeferencing, 'las.json')
odm_georeferencing_model_laz = io.join_paths(
odm_georeferencing, 'odm_georeferenced_model.laz')
odm_georeferencing_model_las = io.join_paths(
odm_georeferencing, 'odm_georeferenced_model.las')
odm_georeferencing_dem = io.join_paths(odm_georeferencing,
'odm_georeferencing_model_dem.tif')
opensfm_reconstruction = io.join_paths(current_path, 'reconstruction.json')
odm_texturing = io.join_paths(current_path, 'mvs')
odm_textured_model_obj = io.join_paths(odm_texturing,
'odm_textured_model.obj')
images_dir = io.join_paths(current_path, 'images')
reconstruction = reconstruction
verbose = ''
#"-verbose"
# define paths and create working directories
system.mkdir_p(odm_orthophoto)
if not io.file_exists(odm_orthophoto_tif):
gsd_error_estimate = 0.1
ignore_resolution = False
if not reconstruction.is_georeferenced():
# Match DEMs
gsd_error_estimate = -3
ignore_resolution = True
orthophoto_resolution = 5
resolution = 1.0 / (
gsd.cap_resolution(orthophoto_resolution,
opensfm_reconstruction,
gsd_error_estimate=gsd_error_estimate,
ignore_gsd=True,
ignore_resolution=ignore_resolution,
has_gcp=reconstruction.has_gcp()) / 100.0)
# odm_orthophoto definitions
kwargs = {
'bin': context.odm_modules_path,
'log': odm_orthophoto_log,
'ortho': odm_orthophoto_render,
'corners': odm_orthophoto_corners,
'res': resolution,
'bands': '',
'verbose': verbose
}
# Check if the georef object is initialized
# (during a --rerun this might not be)
# TODO: this should be moved to a more central location?
if reconstruction.is_georeferenced(
) and not reconstruction.georef.valid_utm_offsets():
georeferencing_dir = odm_georeferencing #if args.use_3dmesh and not args.skip_3dmodel else odm_25dgeoreferencing
odm_georeferencing_model_txt_geo_file = os.path.join(
georeferencing_dir, odm_georeferencing_model_txt_geo)
if io.file_exists(odm_georeferencing_model_txt_geo_file):
reconstruction.georef.extract_offsets(
odm_georeferencing_model_txt_geo_file)
else:
log.ODM_WARNING('Cannot read UTM offset from {}.'.format(
odm_georeferencing_model_txt_geo_file))
models = []
base_dir = odm_texturing
if reconstruction.is_georeferenced():
model_file = odm_georeferencing_model_obj_geo
else:
model_file = odm_textured_model_obj
if reconstruction.multi_camera:
for band in reconstruction.multi_camera:
primary = band == reconstruction.multi_camera[0]
subdir = ""
if not primary:
subdir = band['name'].lower()
models.append(os.path.join(base_dir, subdir, model_file))
kwargs['bands'] = '-bands %s' % (','.join([
quote(b['name'].lower()) for b in reconstruction.multi_camera
]))
else:
models.append(os.path.join(base_dir, model_file))
kwargs['models'] = ','.join(map(quote, models))
# run odm_orthophoto
system.run(
'{bin}/odm_orthophoto -inputFiles {models} '
'-logFile {log} -outputFile {ortho} -resolution {res} {verbose} '
'-outputCornerFile {corners} {bands}'.format(**kwargs))
# Create georeferenced GeoTiff
geotiffcreated = False
if reconstruction.is_georeferenced(
) and reconstruction.georef.valid_utm_offsets():
ulx = uly = lrx = lry = 0.0
with open(odm_orthophoto_corners) as f:
for lineNumber, line in enumerate(f):
if lineNumber == 0:
tokens = line.split(' ')
if len(tokens) == 4:
ulx = float(tokens[0]) + \
float(reconstruction.georef.utm_east_offset)
lry = float(tokens[1]) + \
float(reconstruction.georef.utm_north_offset)
lrx = float(tokens[2]) + \
float(reconstruction.georef.utm_east_offset)
uly = float(tokens[3]) + \
float(reconstruction.georef.utm_north_offset)
log.ODM_INFO('Creating GeoTIFF')
orthophoto_vars = orthophoto.get_orthophoto_vars(args)
kwargs = {
'ulx':
ulx,
'uly':
uly,
'lrx':
lrx,
'lry':
lry,
'vars':
' '.join([
'-co %s=%s' % (k, orthophoto_vars[k])
for k in orthophoto_vars
]),
'proj':
reconstruction.georef.proj4(),
'input':
odm_orthophoto_render,
'output':
odm_orthophoto_tif,
'log':
odm_orthophoto_tif_log,
'max_memory':
get_max_memory(),
}
system.run('gdal_translate -a_ullr {ulx} {uly} {lrx} {lry} '
'{vars} '
'-a_srs \"{proj}\" '
'--config GDAL_CACHEMAX {max_memory}% '
'--config GDAL_TIFF_INTERNAL_MASK YES '
'{input} {output} > {log}'.format(**kwargs))
bounds_file_path = os.path.join(
odm_georeferencing, 'odm_georeferenced_model.bounds.gpkg')
# Cutline computation, before cropping
# We want to use the full orthophoto, not the cropped one.
pio = True
if pio:
cutline_file = os.path.join(odm_orthophoto, "cutline.gpkg")
compute_cutline(odm_orthophoto_tif,
bounds_file_path,
cutline_file,
max_concurrency,
tmpdir=os.path.join(odm_orthophoto,
"grass_cutline_tmpdir"),
scale=0.25)
orthophoto.compute_mask_raster(odm_orthophoto_tif,
cutline_file,
os.path.join(
odm_orthophoto,
"odm_orthophoto_cut.tif"),
blend_distance=20,
only_max_coords_feature=True)
orthophoto.post_orthophoto_steps(args, bounds_file_path,
odm_orthophoto_tif)
# Generate feathered orthophoto also
if pio:
orthophoto.feather_raster(odm_orthophoto_tif,
os.path.join(
odm_orthophoto,
"odm_orthophoto_feathered.tif"),
blend_distance=20)
geotiffcreated = True
if not geotiffcreated:
if io.file_exists(odm_orthophoto_render):
pseudogeo.add_pseudo_georeferencing(odm_orthophoto_render)
log.ODM_INFO("Renaming %s --> %s" %
(odm_orthophoto_render, odm_orthophoto_tif))
os.rename(odm_orthophoto_render, odm_orthophoto_tif)
else:
log.ODM_WARNING(
"Could not generate an orthophoto (it did not render)")
else:
log.ODM_WARNING('Found a valid orthophoto in: %s' % odm_orthophoto_tif)
#generate png
orthophoto.generate_png(odm_orthophoto_tif)
def __init__(self, root_path, gcp_file = None, geo_file = None):
# root path to the project
self.root_path = io.absolute_path_file(root_path)
self.input_images = io.join_paths(self.root_path, 'images')
# modules paths
# here are defined where all modules should be located in
# order to keep track all files al directories during the
# whole reconstruction process.
self.dataset_raw = io.join_paths(self.root_path, 'images')
self.opensfm = io.join_paths(self.root_path, 'opensfm')
self.mve = io.join_paths(self.root_path, 'mve')
self.odm_meshing = io.join_paths(self.root_path, 'odm_meshing')
self.odm_texturing = io.join_paths(self.root_path, 'odm_texturing')
self.odm_25dtexturing = io.join_paths(self.root_path, 'odm_texturing_25d')
self.odm_georeferencing = io.join_paths(self.root_path, 'odm_georeferencing')
self.odm_25dgeoreferencing = io.join_paths(self.root_path, 'odm_georeferencing_25d')
self.odm_filterpoints = io.join_paths(self.root_path, 'odm_filterpoints')
self.odm_orthophoto = io.join_paths(self.root_path, 'odm_orthophoto')
self.odm_report = io.join_paths(self.root_path, 'odm_report')
# important files paths
# benchmarking
self.benchmarking = io.join_paths(self.root_path, 'benchmark.txt')
self.dataset_list = io.join_paths(self.root_path, 'img_list.txt')
# opensfm
self.opensfm_tracks = io.join_paths(self.opensfm, 'tracks.csv')
self.opensfm_bundle = io.join_paths(self.opensfm, 'bundle_r000.out')
self.opensfm_bundle_list = io.join_paths(self.opensfm, 'list_r000.out')
self.opensfm_image_list = io.join_paths(self.opensfm, 'image_list.txt')
self.opensfm_reconstruction = io.join_paths(self.opensfm, 'reconstruction.json')
self.opensfm_reconstruction_nvm = io.join_paths(self.opensfm, 'undistorted/reconstruction.nvm')
self.opensfm_model = io.join_paths(self.opensfm, 'undistorted/depthmaps/merged.ply')
self.opensfm_transformation = io.join_paths(self.opensfm, 'geocoords_transformation.txt')
# mve
self.mve_model = io.join_paths(self.mve, 'mve_dense_point_cloud.ply')
self.mve_views = io.join_paths(self.mve, 'views')
# filter points
self.filtered_point_cloud = io.join_paths(self.odm_filterpoints, "point_cloud.ply")
# odm_meshing
self.odm_mesh = io.join_paths(self.odm_meshing, 'odm_mesh.ply')
self.odm_meshing_log = io.join_paths(self.odm_meshing, 'odm_meshing_log.txt')
self.odm_25dmesh = io.join_paths(self.odm_meshing, 'odm_25dmesh.ply')
self.odm_25dmeshing_log = io.join_paths(self.odm_meshing, 'odm_25dmeshing_log.txt')
# texturing
self.odm_texturing_undistorted_image_path = io.join_paths(
self.odm_texturing, 'undistorted')
self.odm_textured_model_obj = 'odm_textured_model.obj'
self.odm_textured_model_mtl = 'odm_textured_model.mtl'
# Log is only used by old odm_texturing
self.odm_texuring_log = 'odm_texturing_log.txt'
# odm_georeferencing
self.odm_georeferencing_coords = io.join_paths(
self.odm_georeferencing, 'coords.txt')
self.odm_georeferencing_gcp = gcp_file or io.find('gcp_list.txt', self.root_path)
self.odm_georeferencing_gcp_utm = io.join_paths(self.odm_georeferencing, 'gcp_list_utm.txt')
self.odm_geo_file = geo_file or io.find('geo.txt', self.root_path)
self.odm_georeferencing_utm_log = io.join_paths(
self.odm_georeferencing, 'odm_georeferencing_utm_log.txt')
self.odm_georeferencing_log = 'odm_georeferencing_log.txt'
self.odm_georeferencing_transform_file = 'odm_georeferencing_transform.txt'
self.odm_georeferencing_proj = 'proj.txt'
self.odm_georeferencing_model_txt_geo = 'odm_georeferencing_model_geo.txt'
self.odm_georeferencing_model_obj_geo = 'odm_textured_model_geo.obj'
self.odm_georeferencing_xyz_file = io.join_paths(
self.odm_georeferencing, 'odm_georeferenced_model.csv')
self.odm_georeferencing_las_json = io.join_paths(
self.odm_georeferencing, 'las.json')
self.odm_georeferencing_model_laz = io.join_paths(
self.odm_georeferencing, 'odm_georeferenced_model.laz')
self.odm_georeferencing_model_las = io.join_paths(
self.odm_georeferencing, 'odm_georeferenced_model.las')
self.odm_georeferencing_dem = io.join_paths(
self.odm_georeferencing, 'odm_georeferencing_model_dem.tif')
# odm_orthophoto
self.odm_orthophoto_render = io.join_paths(self.odm_orthophoto, 'odm_orthophoto_render.tif')
self.odm_orthophoto_tif = io.join_paths(self.odm_orthophoto, 'odm_orthophoto.tif')
self.odm_orthophoto_corners = io.join_paths(self.odm_orthophoto, 'odm_orthophoto_corners.txt')
self.odm_orthophoto_log = io.join_paths(self.odm_orthophoto, 'odm_orthophoto_log.txt')
self.odm_orthophoto_tif_log = io.join_paths(self.odm_orthophoto, 'gdal_translate_log.txt')
# tiles
self.orthophoto_tiles = io.join_paths(self.root_path, "orthophoto_tiles")
# Split-merge
self.submodels_path = io.join_paths(self.root_path, 'submodels')
# Tiles
self.entwine_pointcloud = self.path("entwine_pointcloud")
