def process_local(self):
octx = OSFMContext(self.path("opensfm"))
log.ODM_INFO("==================================")
log.ODM_INFO("Local Reconstruction %s" % octx.name())
log.ODM_INFO("==================================")
octx.feature_matching(self.params['rerun'])
octx.create_tracks(self.params['rerun'])
octx.reconstruct(self.params['rolling_shutter'], self.params['rerun'])
def process_remote(self, done):
octx = OSFMContext(self.path("opensfm"))
if not octx.is_feature_matching_done() or not octx.is_reconstruction_done() or self.params['rerun']:
self.execute_remote_task(done, seed_files=["opensfm/exif",
"opensfm/camera_models.json",
"opensfm/reference_lla.json"],
seed_touch_files=["opensfm/split_merge_stop_at_reconstruction.txt"],
outputs=["opensfm/matches", "opensfm/features",
"opensfm/reconstruction.json",
"opensfm/tracks.csv",
"cameras.json"])
else:
log.ODM_INFO("Already processed feature matching and reconstruction for %s" % octx.name())
done()
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
outputs['large'] = len(photos) > args.split
if outputs['large']:
# If we have a cluster address, we'll use a distributed workflow
local_workflow = not bool(args.sm_cluster)
octx = OSFMContext(tree.opensfm)
split_done_file = octx.path("split_done.txt")
if not io.file_exists(split_done_file) or self.rerun():
orig_max_concurrency = args.max_concurrency
if not local_workflow:
args.max_concurrency = max(1, args.max_concurrency - 1)
log.ODM_INFO("Setting max-concurrency to %s to better handle remote splits" % args.max_concurrency)
log.ODM_INFO("Large dataset detected (%s photos) and split set at %s. Preparing split merge." % (
len(photos), args.split))
config = [
"submodels_relpath: ../submodels/opensfm",
"submodel_relpath_template: ../submodels/submodel_%04d/opensfm",
"submodel_images_relpath_template: ../submodels/submodel_%04d/images",
"submodel_size: %s" % args.split,
"submodel_overlap: %s" % args.split_overlap,
]
octx.setup(args, tree.dataset_raw, reconstruction=reconstruction, append_config=config,
rerun=self.rerun())
octx.extract_metadata(self.rerun())
self.update_progress(5)
if local_workflow:
octx.feature_matching(self.rerun())
self.update_progress(20)
# Create submodels
if not io.dir_exists(tree.submodels_path) or self.rerun():
if io.dir_exists(tree.submodels_path):
log.ODM_WARNING("Removing existing submodels directory: %s" % tree.submodels_path)
shutil.rmtree(tree.submodels_path)
octx.run("create_submodels")
else:
log.ODM_WARNING("Submodels directory already exist at: %s" % tree.submodels_path)
# Find paths of all submodels
mds = metadataset.MetaDataSet(tree.opensfm)
submodel_paths = [os.path.abspath(p) for p in mds.get_submodel_paths()]
for sp in submodel_paths:
sp_octx = OSFMContext(sp)
# Copy filtered GCP file if needed
# One in OpenSfM's directory, one in the submodel project directory
if reconstruction.gcp and reconstruction.gcp.exists():
submodel_gcp_file = os.path.abspath(sp_octx.path("..", "gcp_list.txt"))
submodel_images_dir = os.path.abspath(sp_octx.path("..", "images"))
if reconstruction.gcp.make_filtered_copy(submodel_gcp_file, submodel_images_dir):
log.ODM_INFO("Copied filtered GCP file to %s" % submodel_gcp_file)
io.copy(submodel_gcp_file, os.path.abspath(sp_octx.path("gcp_list.txt")))
else:
log.ODM_INFO(
"No GCP will be copied for %s, not enough images in the submodel are referenced by the GCP" % sp_octx.name())
# Reconstruct each submodel
log.ODM_INFO(
"Dataset has been split into %s submodels. Reconstructing each submodel..." % len(submodel_paths))
self.update_progress(25)
if local_workflow:
for sp in submodel_paths:
log.ODM_INFO("Reconstructing %s" % sp)
OSFMContext(sp).reconstruct(self.rerun())
else:
lre = LocalRemoteExecutor(args.sm_cluster, self.rerun())
lre.set_projects([os.path.abspath(os.path.join(p, "..")) for p in submodel_paths])
lre.run_reconstruction()
self.update_progress(50)
# TODO: this is currently not working and needs a champion to fix it
# https://community.opendronemap.org/t/filenotfound-error-cameras-json/6047/2
# resplit_done_file = octx.path('resplit_done.txt')
# if not io.file_exists(resplit_done_file) and bool(args.split_multitracks):
# submodels = mds.get_submodel_paths()
# i = 0
# for s in submodels:
# template = octx.path("../aligned_submodels/submodel_%04d")
# with open(s+"/reconstruction.json", "r") as f:
# j = json.load(f)
# for k in range(0, len(j)):
# v = j[k]
# path = template % i
# #Create the submodel path up to opensfm
# os.makedirs(path+"/opensfm")
# os.makedirs(path+"/images")
# #symlinks for common data
# images = os.listdir(octx.path("../images"))
# for image in images:
# os.symlink("../../../images/"+image, path+"/images/"+image)
# os.symlink("../../../opensfm/exif", path+"/opensfm/exif")
# os.symlink("../../../opensfm/features", path+"/opensfm/features")
# os.symlink("../../../opensfm/matches", path+"/opensfm/matches")
# os.symlink("../../../opensfm/reference_lla.json", path+"/opensfm/reference_lla.json")
# os.symlink("../../../opensfm/camera_models.json", path+"/opensfm/camera_models.json")
# shutil.copy(s+"/../cameras.json", path+"/cameras.json")
# shutil.copy(s+"/../images.json", path+"/images.json")
# with open(octx.path("config.yaml")) as f:
# doc = yaml.safe_load(f)
# dmcv = "depthmap_min_consistent_views"
# if dmcv in doc:
# if len(v["shots"]) < doc[dmcv]:
# doc[dmcv] = len(v["shots"])
# print("WARNING: Reduced "+dmcv+" to accommodate short track")
# with open(path+"/opensfm/config.yaml", "w") as f:
# yaml.dump(doc, f)
# #We need the original tracks file for the visualsfm export, since
# #there may still be point matches between the tracks
# shutil.copy(s+"/tracks.csv", path+"/opensfm/tracks.csv")
# #Create our new reconstruction file with only the relevant track
# with open(path+"/opensfm/reconstruction.json", "w") as o:
# json.dump([v], o)
# #Create image lists
# with open(path+"/opensfm/image_list.txt", "w") as o:
# o.writelines(list(map(lambda x: "../images/"+x+'\n', v["shots"].keys())))
# with open(path+"/img_list.txt", "w") as o:
# o.writelines(list(map(lambda x: x+'\n', v["shots"].keys())))
# i+=1
# os.rename(octx.path("../submodels"), octx.path("../unaligned_submodels"))
# os.rename(octx.path("../aligned_submodels"), octx.path("../submodels"))
# octx.touch(resplit_done_file)
mds = metadataset.MetaDataSet(tree.opensfm)
submodel_paths = [os.path.abspath(p) for p in mds.get_submodel_paths()]
# Align
octx.align_reconstructions(self.rerun())
self.update_progress(55)
# Aligned reconstruction is in reconstruction.aligned.json
# We need to rename it to reconstruction.json
remove_paths = []
for sp in submodel_paths:
sp_octx = OSFMContext(sp)
aligned_recon = sp_octx.path('reconstruction.aligned.json')
unaligned_recon = sp_octx.path('reconstruction.unaligned.json')
main_recon = sp_octx.path('reconstruction.json')
if io.file_exists(main_recon) and io.file_exists(unaligned_recon) and not self.rerun():
log.ODM_INFO("Submodel %s has already been aligned." % sp_octx.name())
continue
if not io.file_exists(aligned_recon):
log.ODM_WARNING("Submodel %s does not have an aligned reconstruction (%s). "
"This could mean that the submodel could not be reconstructed "
" (are there enough features to reconstruct it?). Skipping." % (
sp_octx.name(), aligned_recon))
remove_paths.append(sp)
continue
if io.file_exists(main_recon):
shutil.move(main_recon, unaligned_recon)
shutil.move(aligned_recon, main_recon)
log.ODM_INFO("%s is now %s" % (aligned_recon, main_recon))
# Remove invalid submodels
submodel_paths = [p for p in submodel_paths if not p in remove_paths]
# Run ODM toolchain for each submodel
if local_workflow:
for sp in submodel_paths:
sp_octx = OSFMContext(sp)
log.ODM_INFO("========================")
log.ODM_INFO("Processing %s" % sp_octx.name())
log.ODM_INFO("========================")
argv = get_submodel_argv(args, tree.submodels_path, sp_octx.name())
# Re-run the ODM toolchain on the submodel
system.run(" ".join(map(quote, map(str, argv))), env_vars=os.environ.copy())
else:
lre.set_projects([os.path.abspath(os.path.join(p, "..")) for p in submodel_paths])
lre.run_toolchain()
# Restore max_concurrency value
args.max_concurrency = orig_max_concurrency
octx.touch(split_done_file)
else:
log.ODM_WARNING('Found a split done file in: %s' % split_done_file)
else:
log.ODM_INFO("Normal dataset, will process all at once.")
self.progress = 0.0
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
if not photos:
raise system.ExitException(
'Not enough photos in photos array to start OpenSfM')
octx = OSFMContext(tree.opensfm)
octx.setup(args,
tree.dataset_raw,
reconstruction=reconstruction,
rerun=self.rerun())
octx.photos_to_metadata(photos, self.rerun())
self.update_progress(20)
octx.feature_matching(self.rerun())
self.update_progress(30)
octx.reconstruct(self.rerun())
octx.extract_cameras(tree.path("cameras.json"), self.rerun())
self.update_progress(70)
def cleanup_disk_space():
if args.optimize_disk_space:
for folder in ["features", "matches", "reports"]:
folder_path = octx.path(folder)
if os.path.exists(folder_path):
if os.path.islink(folder_path):
os.unlink(folder_path)
else:
shutil.rmtree(folder_path)
# If we find a special flag file for split/merge we stop right here
if os.path.exists(octx.path("split_merge_stop_at_reconstruction.txt")):
log.ODM_INFO("Stopping OpenSfM early because we found: %s" %
octx.path("split_merge_stop_at_reconstruction.txt"))
self.next_stage = None
cleanup_disk_space()
return
# Stats are computed in the local CRS (before geoprojection)
if not args.skip_report:
# TODO: this will fail to compute proper statistics if
# the pipeline is run with --skip-report and is subsequently
# rerun without --skip-report a --rerun-* parameter (due to the reconstruction.json file)
# being replaced below. It's an isolated use case.
octx.export_stats(self.rerun())
self.update_progress(75)
# We now switch to a geographic CRS
if reconstruction.is_georeferenced() and (not io.file_exists(
tree.opensfm_topocentric_reconstruction) or self.rerun()):
octx.run(
'export_geocoords --reconstruction --proj "%s" --offset-x %s --offset-y %s'
% (reconstruction.georef.proj4(),
reconstruction.georef.utm_east_offset,
reconstruction.georef.utm_north_offset))
shutil.move(tree.opensfm_reconstruction,
tree.opensfm_topocentric_reconstruction)
shutil.move(tree.opensfm_geocoords_reconstruction,
tree.opensfm_reconstruction)
else:
log.ODM_WARNING("Will skip exporting %s" %
tree.opensfm_geocoords_reconstruction)
self.update_progress(80)
updated_config_flag_file = octx.path('updated_config.txt')
# Make sure it's capped by the depthmap-resolution arg,
# since the undistorted images are used for MVS
outputs['undist_image_max_size'] = max(
gsd.image_max_size(photos,
args.orthophoto_resolution,
tree.opensfm_reconstruction,
ignore_gsd=args.ignore_gsd,
has_gcp=reconstruction.has_gcp()),
get_depthmap_resolution(args, photos))
if not io.file_exists(updated_config_flag_file) or self.rerun():
octx.update_config({
'undistorted_image_max_size':
outputs['undist_image_max_size']
})
octx.touch(updated_config_flag_file)
# Undistorted images will be used for texturing / MVS
alignment_info = None
primary_band_name = None
largest_photo = None
undistort_pipeline = []
def undistort_callback(shot_id, image):
for func in undistort_pipeline:
image = func(shot_id, image)
return image
def resize_thermal_images(shot_id, image):
photo = reconstruction.get_photo(shot_id)
if photo.is_thermal():
return thermal.resize_to_match(image, largest_photo)
else:
return image
def radiometric_calibrate(shot_id, image):
photo = reconstruction.get_photo(shot_id)
if photo.is_thermal():
return thermal.dn_to_temperature(photo, image,
tree.dataset_raw)
else:
return multispectral.dn_to_reflectance(
photo,
image,
use_sun_sensor=args.radiometric_calibration ==
"camera+sun")
def align_to_primary_band(shot_id, image):
photo = reconstruction.get_photo(shot_id)
# No need to align if requested by user
if args.skip_band_alignment:
return image
# No need to align primary
if photo.band_name == primary_band_name:
return image
ainfo = alignment_info.get(photo.band_name)
if ainfo is not None:
return multispectral.align_image(image, ainfo['warp_matrix'],
ainfo['dimension'])
else:
log.ODM_WARNING(
"Cannot align %s, no alignment matrix could be computed. Band alignment quality might be affected."
% (shot_id))
return image
if reconstruction.multi_camera:
largest_photo = find_largest_photo(photos)
undistort_pipeline.append(resize_thermal_images)
if args.radiometric_calibration != "none":
undistort_pipeline.append(radiometric_calibrate)
image_list_override = None
if reconstruction.multi_camera:
# Undistort only secondary bands
image_list_override = [
os.path.join(tree.dataset_raw, p.filename) for p in photos
] # if p.band_name.lower() != primary_band_name.lower()
# We backup the original reconstruction.json, tracks.csv
# then we augment them by duplicating the primary band
# camera shots with each band, so that exports, undistortion,
# etc. include all bands
# We finally restore the original files later
added_shots_file = octx.path('added_shots_done.txt')
s2p, p2s = None, None
if not io.file_exists(added_shots_file) or self.rerun():
primary_band_name = multispectral.get_primary_band_name(
reconstruction.multi_camera, args.primary_band)
s2p, p2s = multispectral.compute_band_maps(
reconstruction.multi_camera, primary_band_name)
if not args.skip_band_alignment:
alignment_info = multispectral.compute_alignment_matrices(
reconstruction.multi_camera,
primary_band_name,
tree.dataset_raw,
s2p,
p2s,
max_concurrency=args.max_concurrency)
else:
log.ODM_WARNING("Skipping band alignment")
alignment_info = {}
log.ODM_INFO("Adding shots to reconstruction")
octx.backup_reconstruction()
octx.add_shots_to_reconstruction(p2s)
octx.touch(added_shots_file)
undistort_pipeline.append(align_to_primary_band)
octx.convert_and_undistort(self.rerun(), undistort_callback,
image_list_override)
self.update_progress(95)
if reconstruction.multi_camera:
octx.restore_reconstruction_backup()
# Undistort primary band and write undistorted
# reconstruction.json, tracks.csv
octx.convert_and_undistort(self.rerun(),
undistort_callback,
runId='primary')
if not io.file_exists(tree.opensfm_reconstruction_nvm) or self.rerun():
octx.run('export_visualsfm --points')
else:
log.ODM_WARNING(
'Found a valid OpenSfM NVM reconstruction file in: %s' %
tree.opensfm_reconstruction_nvm)
if reconstruction.multi_camera:
log.ODM_INFO("Multiple bands found")
# Write NVM files for the various bands
for band in reconstruction.multi_camera:
nvm_file = octx.path(
"undistorted",
"reconstruction_%s.nvm" % band['name'].lower())
if not io.file_exists(nvm_file) or self.rerun():
img_map = {}
if primary_band_name is None:
primary_band_name = multispectral.get_primary_band_name(
reconstruction.multi_camera, args.primary_band)
if p2s is None:
s2p, p2s = multispectral.compute_band_maps(
reconstruction.multi_camera, primary_band_name)
for fname in p2s:
# Primary band maps to itself
if band['name'] == primary_band_name:
img_map[add_image_format_extension(
fname, 'tif')] = add_image_format_extension(
fname, 'tif')
else:
band_filename = next(
(p.filename for p in p2s[fname]
if p.band_name == band['name']), None)
if band_filename is not None:
img_map[add_image_format_extension(
fname,
'tif')] = add_image_format_extension(
band_filename, 'tif')
else:
log.ODM_WARNING(
"Cannot find %s band equivalent for %s" %
(band, fname))
nvm.replace_nvm_images(tree.opensfm_reconstruction_nvm,
img_map, nvm_file)
else:
log.ODM_WARNING("Found existing NVM file %s" % nvm_file)
# Skip dense reconstruction if necessary and export
# sparse reconstruction instead
if args.fast_orthophoto:
output_file = octx.path('reconstruction.ply')
if not io.file_exists(output_file) or self.rerun():
octx.run('export_ply --no-cameras --point-num-views')
else:
log.ODM_WARNING("Found a valid PLY reconstruction in %s" %
output_file)
cleanup_disk_space()
if args.optimize_disk_space:
os.remove(octx.path("tracks.csv"))
if io.file_exists(octx.recon_backup_file()):
os.remove(octx.recon_backup_file())
if io.dir_exists(octx.path("undistorted", "depthmaps")):
files = glob.glob(
octx.path("undistorted", "depthmaps", "*.npz"))
for f in files:
os.remove(f)
# Keep these if using OpenMVS
if args.fast_orthophoto:
files = [
octx.path("undistorted", "tracks.csv"),
octx.path("undistorted", "reconstruction.json")
]
for f in files:
if os.path.exists(f):
os.remove(f)
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
if not os.path.exists(tree.odm_report): system.mkdir_p(tree.odm_report)
log.ODM_INFO("Exporting shots.geojson")
shots_geojson = os.path.join(tree.odm_report, "shots.geojson")
if not io.file_exists(shots_geojson) or self.rerun():
# Extract geographical camera shots
if reconstruction.is_georeferenced():
shots = get_geojson_shots_from_opensfm(
tree.opensfm_reconstruction,
utm_srs=reconstruction.get_proj_srs(),
utm_offset=reconstruction.georef.utm_offset())
else:
# Pseudo geo
shots = get_geojson_shots_from_opensfm(
tree.opensfm_reconstruction,
pseudo_geotiff=tree.odm_orthophoto_tif)
if shots:
with open(shots_geojson, "w") as fout:
fout.write(json.dumps(shots))
log.ODM_INFO("Wrote %s" % shots_geojson)
else:
log.ODM_WARNING("Cannot extract shots")
else:
log.ODM_WARNING('Found a valid shots file in: %s' % shots_geojson)
if args.skip_report:
# Stop right here
log.ODM_WARNING("Skipping report generation as requested")
return
# Augment OpenSfM stats file with our own stats
odm_stats_json = os.path.join(tree.odm_report, "stats.json")
octx = OSFMContext(tree.opensfm)
osfm_stats_json = octx.path("stats", "stats.json")
odm_stats = None
point_cloud_file = None
views_dimension = None
if not os.path.exists(odm_stats_json) or self.rerun():
if os.path.exists(osfm_stats_json):
with open(osfm_stats_json, 'r') as f:
odm_stats = json.loads(f.read())
# Add point cloud stats
if os.path.exists(tree.odm_georeferencing_model_laz):
point_cloud_file = tree.odm_georeferencing_model_laz
views_dimension = "UserData"
# pc_info_file should have been generated by cropper
pc_info_file = os.path.join(
tree.odm_georeferencing,
"odm_georeferenced_model.info.json")
odm_stats[
'point_cloud_statistics'] = generate_point_cloud_stats(
tree.odm_georeferencing_model_laz, pc_info_file,
self.rerun())
else:
ply_pc = os.path.join(tree.odm_filterpoints,
"point_cloud.ply")
if os.path.exists(ply_pc):
point_cloud_file = ply_pc
views_dimension = "views"
pc_info_file = os.path.join(tree.odm_filterpoints,
"point_cloud.info.json")
odm_stats[
'point_cloud_statistics'] = generate_point_cloud_stats(
ply_pc, pc_info_file, self.rerun())
else:
log.ODM_WARNING("No point cloud found")
odm_stats['point_cloud_statistics'][
'dense'] = not args.fast_orthophoto
# Add runtime stats
total_time = (system.now_raw() -
outputs['start_time']).total_seconds()
odm_stats['odm_processing_statistics'] = {
'total_time':
total_time,
'total_time_human':
hms(total_time),
'average_gsd':
gsd.opensfm_reconstruction_average_gsd(
octx.recon_file(),
use_all_shots=reconstruction.has_gcp()),
}
with open(odm_stats_json, 'w') as f:
f.write(json.dumps(odm_stats))
else:
log.ODM_WARNING(
"Cannot generate report, OpenSfM stats are missing")
else:
log.ODM_WARNING("Reading existing stats %s" % odm_stats_json)
with open(odm_stats_json, 'r') as f:
odm_stats = json.loads(f.read())
# Generate overlap diagram
if odm_stats.get('point_cloud_statistics'
) and point_cloud_file and views_dimension:
bounds = odm_stats['point_cloud_statistics'].get('stats', {}).get(
'bbox', {}).get('native', {}).get('bbox')
if bounds:
image_target_size = 1400 # pixels
osfm_stats_dir = os.path.join(tree.opensfm, "stats")
diagram_tiff = os.path.join(osfm_stats_dir, "overlap.tif")
diagram_png = os.path.join(osfm_stats_dir, "overlap.png")
width = bounds.get('maxx') - bounds.get('minx')
height = bounds.get('maxy') - bounds.get('miny')
max_dim = max(width, height)
resolution = float(max_dim) / float(image_target_size)
radius = resolution * math.sqrt(2)
# Larger radius for sparse point cloud diagram
if not odm_stats['point_cloud_statistics']['dense']:
radius *= 10
system.run("pdal translate -i \"{}\" "
"-o \"{}\" "
"--writer gdal "
"--writers.gdal.resolution={} "
"--writers.gdal.data_type=uint8_t "
"--writers.gdal.dimension={} "
"--writers.gdal.output_type=max "
"--writers.gdal.radius={} ".format(
point_cloud_file, diagram_tiff, resolution,
views_dimension, radius))
report_assets = os.path.abspath(
os.path.join(os.path.dirname(__file__),
"../opendm/report"))
overlap_color_map = os.path.join(report_assets,
"overlap_color_map.txt")
bounds_file_path = os.path.join(
tree.odm_georeferencing,
'odm_georeferenced_model.bounds.gpkg')
if (args.crop > 0
or args.boundary) and os.path.isfile(bounds_file_path):
Cropper.crop(bounds_file_path,
diagram_tiff,
get_orthophoto_vars(args),
keep_original=False)
system.run(
"gdaldem color-relief \"{}\" \"{}\" \"{}\" -of PNG -alpha".
format(diagram_tiff, overlap_color_map, diagram_png))
# Copy assets
for asset in [
"overlap_diagram_legend.png", "dsm_gradient.png"
]:
shutil.copy(os.path.join(report_assets, asset),
os.path.join(osfm_stats_dir, asset))
# Generate previews of ortho/dsm
if os.path.isfile(tree.odm_orthophoto_tif):
osfm_ortho = os.path.join(osfm_stats_dir, "ortho.png")
generate_png(tree.odm_orthophoto_tif, osfm_ortho,
image_target_size)
dems = []
if args.dsm:
dems.append("dsm")
if args.dtm:
dems.append("dtm")
for dem in dems:
dem_file = tree.path("odm_dem", "%s.tif" % dem)
if os.path.isfile(dem_file):
# Resize first (faster)
resized_dem_file = io.related_file_path(
dem_file, postfix=".preview")
system.run(
"gdal_translate -outsize {} 0 \"{}\" \"{}\" --config GDAL_CACHEMAX {}%"
.format(image_target_size, dem_file,
resized_dem_file, get_max_memory()))
log.ODM_INFO("Computing raster stats for %s" %
resized_dem_file)
dem_stats = get_raster_stats(resized_dem_file)
if len(dem_stats) > 0:
odm_stats[dem + '_statistics'] = dem_stats[0]
osfm_dem = os.path.join(osfm_stats_dir, "%s.png" % dem)
colored_dem, hillshade_dem, colored_hillshade_dem = generate_colored_hillshade(
resized_dem_file)
system.run(
"gdal_translate -outsize {} 0 -of png \"{}\" \"{}\" --config GDAL_CACHEMAX {}%"
.format(image_target_size, colored_hillshade_dem,
osfm_dem, get_max_memory()))
for f in [
resized_dem_file, colored_dem, hillshade_dem,
colored_hillshade_dem
]:
if os.path.isfile(f):
os.remove(f)
else:
log.ODM_WARNING(
"Cannot generate overlap diagram, cannot compute point cloud bounds"
)
else:
log.ODM_WARNING(
"Cannot generate overlap diagram, point cloud stats missing")
octx.export_report(os.path.join(tree.odm_report, "report.pdf"),
odm_stats, self.rerun())
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
# Export GCP information if available
gcp_export_file = tree.path("odm_georeferencing", "ground_control_points.gpkg")
gcp_gml_export_file = tree.path("odm_georeferencing", "ground_control_points.gml")
gcp_geojson_export_file = tree.path("odm_georeferencing", "ground_control_points.geojson")
if reconstruction.has_gcp() and (not io.file_exists(gcp_export_file) or self.rerun()):
octx = OSFMContext(tree.opensfm)
gcps = octx.ground_control_points(reconstruction.georef.proj4())
if len(gcps):
gcp_schema = {
'geometry': 'Point',
'properties': OrderedDict([
('id', 'str'),
('observations_count', 'int'),
('observations_list', 'str'),
('error_x', 'float'),
('error_y', 'float'),
('error_z', 'float'),
])
}
# Write GeoPackage
with fiona.open(gcp_export_file, 'w', driver="GPKG",
crs=fiona.crs.from_string(reconstruction.georef.proj4()),
schema=gcp_schema) as f:
for gcp in gcps:
f.write({
'geometry': {
'type': 'Point',
'coordinates': gcp['coordinates'],
},
'properties': OrderedDict([
('id', gcp['id']),
('observations_count', len(gcp['observations'])),
('observations_list', ",".join([obs['shot_id'] for obs in gcp['observations']])),
('error_x', gcp['error'][0]),
('error_y', gcp['error'][1]),
('error_z', gcp['error'][2]),
])
})
# Write GML
try:
system.run('ogr2ogr -of GML "{}" "{}"'.format(gcp_gml_export_file, gcp_export_file))
except Exception as e:
log.ODM_WARNING("Cannot generate ground control points GML file: %s" % str(e))
# Write GeoJSON
geojson = {
'type': 'FeatureCollection',
'features': []
}
from_srs = CRS.from_proj4(reconstruction.georef.proj4())
to_srs = CRS.from_epsg(4326)
transformer = location.transformer(from_srs, to_srs)
for gcp in gcps:
properties = gcp.copy()
del properties['coordinates']
geojson['features'].append({
'type': 'Feature',
'geometry': {
'type': 'Point',
'coordinates': transformer.TransformPoint(*gcp['coordinates']),
},
'properties': properties
})
with open(gcp_geojson_export_file, 'w') as f:
f.write(json.dumps(geojson, indent=4))
else:
log.ODM_WARNING("GCPs could not be loaded for writing to %s" % gcp_export_file)
if not io.file_exists(tree.odm_georeferencing_model_laz) or self.rerun():
cmd = ('pdal translate -i "%s" -o \"%s\"' % (tree.filtered_point_cloud, tree.odm_georeferencing_model_laz))
stages = ["ferry"]
params = [
'--filters.ferry.dimensions="views => UserData"',
'--writers.las.compression="lazip"',
]
if reconstruction.is_georeferenced():
log.ODM_INFO("Georeferencing point cloud")
stages.append("transformation")
params += [
'--filters.transformation.matrix="1 0 0 %s 0 1 0 %s 0 0 1 0 0 0 0 1"' % reconstruction.georef.utm_offset(),
'--writers.las.offset_x=%s' % reconstruction.georef.utm_east_offset,
'--writers.las.offset_y=%s' % reconstruction.georef.utm_north_offset,
'--writers.las.scale_x=0.001',
'--writers.las.scale_y=0.001',
'--writers.las.scale_z=0.001',
'--writers.las.offset_z=0',
'--writers.las.a_srs="%s"' % reconstruction.georef.proj4()
]
if reconstruction.has_gcp() and io.file_exists(gcp_gml_export_file):
log.ODM_INFO("Embedding GCP info in point cloud")
params += [
'--writers.las.vlrs="{\\\"filename\\\": \\\"%s\\\", \\\"user_id\\\": \\\"ODM_GCP\\\", \\\"description\\\": \\\"Ground Control Points (GML)\\\"}"' % gcp_gml_export_file.replace(os.sep, "/")
]
system.run(cmd + ' ' + ' '.join(stages) + ' ' + ' '.join(params))
self.update_progress(50)
if args.crop > 0:
log.ODM_INFO("Calculating cropping area and generating bounds shapefile from point cloud")
cropper = Cropper(tree.odm_georeferencing, 'odm_georeferenced_model')
if args.fast_orthophoto:
decimation_step = 4
else:
decimation_step = 40
# More aggressive decimation for large datasets
if not args.fast_orthophoto:
decimation_step *= int(len(reconstruction.photos) / 1000) + 1
decimation_step = min(decimation_step, 95)
try:
cropper.create_bounds_gpkg(tree.odm_georeferencing_model_laz, args.crop,
decimation_step=decimation_step)
except:
log.ODM_WARNING("Cannot calculate crop bounds! We will skip cropping")
args.crop = 0
if 'boundary' in outputs and args.crop == 0:
log.ODM_INFO("Using boundary JSON as cropping area")
bounds_base, _ = os.path.splitext(tree.odm_georeferencing_model_laz)
bounds_json = bounds_base + ".bounds.geojson"
bounds_gpkg = bounds_base + ".bounds.gpkg"
export_to_bounds_files(outputs['boundary'], reconstruction.get_proj_srs(), bounds_json, bounds_gpkg)
else:
log.ODM_INFO("Converting point cloud (non-georeferenced)")
system.run(cmd + ' ' + ' '.join(stages) + ' ' + ' '.join(params))
point_cloud.post_point_cloud_steps(args, tree, self.rerun())
else:
log.ODM_WARNING('Found a valid georeferenced model in: %s'
% tree.odm_georeferencing_model_laz)
if args.optimize_disk_space and io.file_exists(tree.odm_georeferencing_model_laz) and io.file_exists(tree.filtered_point_cloud):
os.remove(tree.filtered_point_cloud)
def process(self, args, outputs):
# get inputs
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start OpenMVS')
exit(1)
# check if reconstruction was done before
if not io.file_exists(tree.openmvs_model) or self.rerun():
if io.dir_exists(tree.openmvs):
shutil.rmtree(tree.openmvs)
# export reconstruction from opensfm
octx = OSFMContext(tree.opensfm)
cmd = 'export_openmvs'
if reconstruction.multi_camera:
# Export only the primary band
primary = reconstruction.multi_camera[0]
image_list = os.path.join(tree.opensfm, "image_list_%s.txt" % primary['name'].lower())
cmd += ' --image_list "%s"' % image_list
octx.run(cmd)
self.update_progress(10)
depthmaps_dir = os.path.join(tree.openmvs, "depthmaps")
if not io.dir_exists(depthmaps_dir):
os.mkdir(depthmaps_dir)
depthmap_resolution = get_depthmap_resolution(args, photos)
if outputs["undist_image_max_size"] <= depthmap_resolution:
resolution_level = 0
else:
resolution_level = math.floor(math.log(outputs['undist_image_max_size'] / float(depthmap_resolution)) / math.log(2))
config = [
" --resolution-level %s" % int(resolution_level),
"--min-resolution %s" % depthmap_resolution,
"--max-resolution %s" % int(outputs['undist_image_max_size']),
"--max-threads %s" % args.max_concurrency,
'-w "%s"' % depthmaps_dir,
"-v 0",
]
log.ODM_INFO("Running dense reconstruction. This might take a while.")
system.run('%s "%s" %s' % (context.omvs_densify_path,
os.path.join(tree.openmvs, 'scene.mvs'),
' '.join(config)))
self.update_progress(85)
# Filter points
scene_dense = os.path.join(tree.openmvs, 'scene_dense.mvs')
if os.path.exists(scene_dense):
config = [
"--filter-point-cloud -1",
'-i "%s"' % scene_dense,
"-v 0"
]
system.run('%s %s' % (context.omvs_densify_path, ' '.join(config)))
else:
log.ODM_WARNING("Cannot find scene_dense.mvs, dense reconstruction probably failed. Exiting...")
exit(1)
self.update_progress(95)
if args.optimize_disk_space:
files = [scene_dense,
os.path.join(tree.openmvs, 'scene_dense.ply'),
os.path.join(tree.openmvs, 'scene_dense_dense_filtered.mvs'),
octx.path("undistorted", "tracks.csv"),
octx.path("undistorted", "reconstruction.json")
]
for f in files:
if os.path.exists(f):
os.remove(f)
shutil.rmtree(depthmaps_dir)
else:
log.ODM_WARNING('Found a valid OpenMVS reconstruction file in: %s' %
tree.openmvs_model)
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
outputs['large'] = len(photos) > args.split
if outputs['large']:
# If we have a cluster address, we'll use a distributed workflow
local_workflow = not bool(args.sm_cluster)
octx = OSFMContext(tree.opensfm)
split_done_file = octx.path("split_done.txt")
if not io.file_exists(split_done_file) or self.rerun():
orig_max_concurrency = args.max_concurrency
if not local_workflow:
args.max_concurrency = max(1, args.max_concurrency - 1)
log.ODM_INFO(
"Setting max-concurrency to %s to better handle remote splits"
% args.max_concurrency)
log.ODM_INFO(
"Large dataset detected (%s photos) and split set at %s. Preparing split merge."
% (len(photos), args.split))
config = [
"submodels_relpath: ../submodels/opensfm",
"submodel_relpath_template: ../submodels/submodel_%04d/opensfm",
"submodel_images_relpath_template: ../submodels/submodel_%04d/images",
"submodel_size: %s" % args.split,
"submodel_overlap: %s" % args.split_overlap,
]
octx.setup(args,
tree.dataset_raw,
photos,
reconstruction=reconstruction,
append_config=config,
rerun=self.rerun())
octx.extract_metadata(self.rerun())
self.update_progress(5)
if local_workflow:
octx.feature_matching(self.rerun())
self.update_progress(20)
# Create submodels
if not io.dir_exists(tree.submodels_path) or self.rerun():
if io.dir_exists(tree.submodels_path):
log.ODM_WARNING(
"Removing existing submodels directory: %s" %
tree.submodels_path)
shutil.rmtree(tree.submodels_path)
octx.run("create_submodels")
else:
log.ODM_WARNING(
"Submodels directory already exist at: %s" %
tree.submodels_path)
# Find paths of all submodels
mds = metadataset.MetaDataSet(tree.opensfm)
submodel_paths = [
os.path.abspath(p) for p in mds.get_submodel_paths()
]
for sp in submodel_paths:
sp_octx = OSFMContext(sp)
# Copy filtered GCP file if needed
# One in OpenSfM's directory, one in the submodel project directory
if reconstruction.gcp and reconstruction.gcp.exists():
submodel_gcp_file = os.path.abspath(
sp_octx.path("..", "gcp_list.txt"))
submodel_images_dir = os.path.abspath(
sp_octx.path("..", "images"))
if reconstruction.gcp.make_filtered_copy(
submodel_gcp_file, submodel_images_dir):
log.ODM_INFO("Copied filtered GCP file to %s" %
submodel_gcp_file)
io.copy(
submodel_gcp_file,
os.path.abspath(sp_octx.path("gcp_list.txt")))
else:
log.ODM_INFO(
"No GCP will be copied for %s, not enough images in the submodel are referenced by the GCP"
% sp_octx.name())
# Reconstruct each submodel
log.ODM_INFO(
"Dataset has been split into %s submodels. Reconstructing each submodel..."
% len(submodel_paths))
self.update_progress(25)
if local_workflow:
for sp in submodel_paths:
log.ODM_INFO("Reconstructing %s" % sp)
OSFMContext(sp).reconstruct(self.rerun())
else:
lre = LocalRemoteExecutor(args.sm_cluster, self.rerun())
lre.set_projects([
os.path.abspath(os.path.join(p, ".."))
for p in submodel_paths
])
lre.run_reconstruction()
self.update_progress(50)
# Align
octx.align_reconstructions(self.rerun())
self.update_progress(55)
# Aligned reconstruction is in reconstruction.aligned.json
# We need to rename it to reconstruction.json
remove_paths = []
for sp in submodel_paths:
sp_octx = OSFMContext(sp)
aligned_recon = sp_octx.path('reconstruction.aligned.json')
unaligned_recon = sp_octx.path(
'reconstruction.unaligned.json')
main_recon = sp_octx.path('reconstruction.json')
if io.file_exists(main_recon) and io.file_exists(
unaligned_recon) and not self.rerun():
log.ODM_INFO("Submodel %s has already been aligned." %
sp_octx.name())
continue
if not io.file_exists(aligned_recon):
log.ODM_WARNING(
"Submodel %s does not have an aligned reconstruction (%s). "
"This could mean that the submodel could not be reconstructed "
" (are there enough features to reconstruct it?). Skipping."
% (sp_octx.name(), aligned_recon))
remove_paths.append(sp)
continue
if io.file_exists(main_recon):
shutil.move(main_recon, unaligned_recon)
shutil.move(aligned_recon, main_recon)
log.ODM_INFO("%s is now %s" % (aligned_recon, main_recon))
# Remove invalid submodels
submodel_paths = [
p for p in submodel_paths if not p in remove_paths
]
# Run ODM toolchain for each submodel
if local_workflow:
for sp in submodel_paths:
sp_octx = OSFMContext(sp)
log.ODM_INFO("========================")
log.ODM_INFO("Processing %s" % sp_octx.name())
log.ODM_INFO("========================")
argv = get_submodel_argv(args, tree.submodels_path,
sp_octx.name())
# Re-run the ODM toolchain on the submodel
system.run(" ".join(map(quote, argv)),
env_vars=os.environ.copy())
else:
lre.set_projects([
os.path.abspath(os.path.join(p, ".."))
for p in submodel_paths
])
lre.run_toolchain()
# Restore max_concurrency value
args.max_concurrency = orig_max_concurrency
octx.touch(split_done_file)
else:
log.ODM_WARNING('Found a split done file in: %s' %
split_done_file)
else:
log.ODM_INFO("Normal dataset, will process all at once.")
self.progress = 0.0
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start OpenSfM')
exit(1)
octx = OSFMContext(tree.opensfm)
octx.setup(args,
tree.dataset_raw,
photos,
reconstruction=reconstruction,
rerun=self.rerun())
octx.extract_metadata(self.rerun())
self.update_progress(20)
octx.feature_matching(self.rerun())
self.update_progress(30)
octx.reconstruct(self.rerun())
octx.extract_cameras(tree.path("cameras.json"), self.rerun())
self.update_progress(70)
if args.optimize_disk_space:
for folder in ["features", "matches", "exif", "reports"]:
folder_path = octx.path(folder)
if os.path.islink(folder_path):
os.unlink(folder_path)
else:
shutil.rmtree(folder_path)
# If we find a special flag file for split/merge we stop right here
if os.path.exists(octx.path("split_merge_stop_at_reconstruction.txt")):
log.ODM_INFO("Stopping OpenSfM early because we found: %s" %
octx.path("split_merge_stop_at_reconstruction.txt"))
self.next_stage = None
return
if args.fast_orthophoto:
output_file = octx.path('reconstruction.ply')
elif args.use_opensfm_dense:
output_file = tree.opensfm_model
else:
output_file = tree.opensfm_reconstruction
updated_config_flag_file = octx.path('updated_config.txt')
# Make sure it's capped by the depthmap-resolution arg,
# since the undistorted images are used for MVS
outputs['undist_image_max_size'] = max(
gsd.image_max_size(photos,
args.orthophoto_resolution,
tree.opensfm_reconstruction,
ignore_gsd=args.ignore_gsd,
has_gcp=reconstruction.has_gcp()),
args.depthmap_resolution)
if not io.file_exists(updated_config_flag_file) or self.rerun():
octx.update_config({
'undistorted_image_max_size':
outputs['undist_image_max_size']
})
octx.touch(updated_config_flag_file)
# These will be used for texturing / MVS
if args.radiometric_calibration == "none":
octx.convert_and_undistort(self.rerun())
else:
def radiometric_calibrate(shot_id, image):
photo = reconstruction.get_photo(shot_id)
return multispectral.dn_to_reflectance(
photo,
image,
use_sun_sensor=args.radiometric_calibration ==
"camera+sun")
octx.convert_and_undistort(self.rerun(), radiometric_calibrate)
self.update_progress(80)
if reconstruction.multi_camera:
# Dump band image lists
log.ODM_INFO("Multiple bands found")
for band in reconstruction.multi_camera:
log.ODM_INFO("Exporting %s band" % band['name'])
image_list_file = octx.path("image_list_%s.txt" %
band['name'].lower())
if not io.file_exists(image_list_file) or self.rerun():
with open(image_list_file, "w") as f:
f.write("\n".join([p.filename
for p in band['photos']]))
log.ODM_INFO("Wrote %s" % image_list_file)
else:
log.ODM_WARNING(
"Found a valid image list in %s for %s band" %
(image_list_file, band['name']))
nvm_file = octx.path(
"undistorted",
"reconstruction_%s.nvm" % band['name'].lower())
if not io.file_exists(nvm_file) or self.rerun():
octx.run('export_visualsfm --points --image_list "%s"' %
image_list_file)
os.rename(tree.opensfm_reconstruction_nvm, nvm_file)
else:
log.ODM_WARNING(
"Found a valid NVM file in %s for %s band" %
(nvm_file, band['name']))
if not io.file_exists(tree.opensfm_reconstruction_nvm) or self.rerun():
octx.run('export_visualsfm --points')
else:
log.ODM_WARNING(
'Found a valid OpenSfM NVM reconstruction file in: %s' %
tree.opensfm_reconstruction_nvm)
self.update_progress(85)
# Skip dense reconstruction if necessary and export
# sparse reconstruction instead
if args.fast_orthophoto:
if not io.file_exists(output_file) or self.rerun():
octx.run('export_ply --no-cameras')
else:
log.ODM_WARNING("Found a valid PLY reconstruction in %s" %
output_file)
elif args.use_opensfm_dense:
if not io.file_exists(output_file) or self.rerun():
octx.run('compute_depthmaps')
else:
log.ODM_WARNING("Found a valid dense reconstruction in %s" %
output_file)
self.update_progress(90)
if reconstruction.is_georeferenced() and (not io.file_exists(
tree.opensfm_transformation) or self.rerun()):
octx.run('export_geocoords --transformation --proj \'%s\'' %
reconstruction.georef.proj4())
else:
log.ODM_WARNING("Will skip exporting %s" %
tree.opensfm_transformation)
if args.optimize_disk_space:
os.remove(octx.path("tracks.csv"))
os.remove(octx.path("undistorted", "tracks.csv"))
os.remove(octx.path("undistorted", "reconstruction.json"))
if io.dir_exists(octx.path("undistorted", "depthmaps")):
files = glob.glob(
octx.path("undistorted", "depthmaps", "*.npz"))
for f in files:
os.remove(f)
def process(self, args, outputs):
# get inputs
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start MVE')
exit(1)
# check if reconstruction was done before
if not io.file_exists(tree.mve_model) or self.rerun():
# cleanup if a rerun
if io.dir_exists(tree.mve_path) and self.rerun():
shutil.rmtree(tree.mve_path)
# make bundle directory
if not io.file_exists(tree.mve_bundle):
system.mkdir_p(tree.mve_path)
system.mkdir_p(io.join_paths(tree.mve_path, 'bundle'))
octx = OSFMContext(tree.opensfm)
octx.save_absolute_image_list_to(tree.mve_image_list)
io.copy(tree.opensfm_bundle, tree.mve_bundle)
# mve makescene wants the output directory
# to not exists before executing it (otherwise it
# will prompt the user for confirmation)
if io.dir_exists(tree.mve):
shutil.rmtree(tree.mve)
# run mve makescene
if not io.dir_exists(tree.mve_views):
system.run('%s %s %s' %
(context.makescene_path, tree.mve_path, tree.mve),
env_vars={'OMP_NUM_THREADS': args.max_concurrency})
self.update_progress(10)
# Compute mve output scale based on depthmap_resolution
max_width = 0
max_height = 0
for photo in photos:
max_width = max(photo.width, max_width)
max_height = max(photo.height, max_height)
max_pixels = args.depthmap_resolution * args.depthmap_resolution
if max_width * max_height <= max_pixels:
mve_output_scale = 0
else:
ratio = float(max_width * max_height) / float(max_pixels)
mve_output_scale = int(
math.ceil(math.log(ratio) / math.log(4.0)))
dmrecon_config = [
"-s%s" % mve_output_scale,
"--progress=silent",
"--local-neighbors=2",
]
# Run MVE's dmrecon
log.ODM_INFO(
' '
)
log.ODM_INFO(
' ,*/** '
)
log.ODM_INFO(
' ,*@%*/@%* '
)
log.ODM_INFO(
' ,/@%******@&*. '
)
log.ODM_INFO(
' ,*@&*********/@&* '
)
log.ODM_INFO(
' ,*@&**************@&* '
)
log.ODM_INFO(
' ,/@&******************@&*. '
)
log.ODM_INFO(
' ,*@&*********************/@&* '
)
log.ODM_INFO(
' ,*@&**************************@&*. '
)
log.ODM_INFO(
' ,/@&******************************&&*, '
)
log.ODM_INFO(
' ,*&&**********************************@&*. '
)
log.ODM_INFO(
' ,*@&**************************************@&*. '
)
log.ODM_INFO(
' ,*@&***************#@@@@@@@@@%****************&&*, '
)
log.ODM_INFO(
' .*&&***************&@@@@@@@@@@@@@@****************@@*. '
)
log.ODM_INFO(
' .*@&***************&@@@@@@@@@@@@@@@@@%****(@@%********@@*. '
)
log.ODM_INFO(
' .*@@***************%@@@@@@@@@@@@@@@@@@@@@#****&@@@@%******&@*, '
)
log.ODM_INFO(
' .*&@****************@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@/*****@@*. '
)
log.ODM_INFO(
' .*@@****************@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@%*************@@*. '
)
log.ODM_INFO(
' .*@@****/***********@@@@@&**(@@@@@@@@@@@@@@@@@@@@@@@#*****************%@*, '
)
log.ODM_INFO(
' */@*******@*******#@@@@%*******/@@@@@@@@@@@@@@@@@@@@********************/@(, '
)
log.ODM_INFO(
' ,*@(********&@@@@@@#**************/@@@@@@@#**(@@&/**********************@&* '
)
log.ODM_INFO(
' *#@/*******************************@@@@@***&@&**********************&@*, '
)
log.ODM_INFO(
' *#@#******************************&@@@***@#*********************&@*, '
)
log.ODM_INFO(
' */@#*****************************@@@************************@@*. '
)
log.ODM_INFO(
' *#@/***************************/@@/*********************%@*, '
)
log.ODM_INFO(
' *#@#**************************#@@%******************%@*, '
)
log.ODM_INFO(
' */@#*************************(@@@@@@@&%/********&@*. '
)
log.ODM_INFO(
' *(@(*********************************/%@@%**%@*, '
)
log.ODM_INFO(
' *(@%************************************%@** '
)
log.ODM_INFO(
' **@%********************************&@*, '
)
log.ODM_INFO(
' *(@(****************************%@/* '
)
log.ODM_INFO(
' ,(@%************************#@/* '
)
log.ODM_INFO(
' ,*@%********************&@/, '
)
log.ODM_INFO(
' */@#****************#@/* '
)
log.ODM_INFO(
' ,/@&************#@/* '
)
log.ODM_INFO(
' ,*@&********%@/, '
)
log.ODM_INFO(
' */@#****(@/* '
)
log.ODM_INFO(
' ,/@@@@(* '
)
log.ODM_INFO(
' .**, '
)
log.ODM_INFO('')
log.ODM_INFO(
"Running dense reconstruction. This might take a while. Please be patient, the process is not dead or hung."
)
log.ODM_INFO(" Process is running")
# TODO: find out why MVE is crashing at random
# MVE *seems* to have a race condition, triggered randomly, regardless of dataset
# https://gist.github.com/pierotofy/6c9ce93194ba510b61e42e3698cfbb89
# Temporary workaround is to retry the reconstruction until we get it right
# (up to a certain number of retries).
retry_count = 1
while retry_count < 10:
try:
system.run(
'%s %s %s' % (context.dmrecon_path,
' '.join(dmrecon_config), tree.mve),
env_vars={'OMP_NUM_THREADS': args.max_concurrency})
break
except Exception as e:
if str(e) == "Child returned 134" or str(
e) == "Child returned 1":
retry_count += 1
log.ODM_WARNING(
"Caught error code, retrying attempt #%s" %
retry_count)
else:
raise e
self.update_progress(90)
scene2pset_config = ["-F%s" % mve_output_scale]
# run scene2pset
system.run('%s %s "%s" "%s"' %
(context.scene2pset_path, ' '.join(scene2pset_config),
tree.mve, tree.mve_model),
env_vars={'OMP_NUM_THREADS': args.max_concurrency})
else:
log.ODM_WARNING('Found a valid MVE reconstruction file in: %s' %
tree.mve_model)
log.ODM_INFO('Found %s usable images' % len(photos))
from opendm import system
system.mkdir_p(os.path.join(submodel_path, 'opensfm'))
# Create reconstruction object
reconstruction = types.ODM_Reconstruction(photos)
opensfm_interface.invent_reference_lla(images_filepath, photo_list,os.path.join(submodel_path, 'opensfm'))
system.mkdir_p(os.path.join(submodel_path,'odm_georeferencing'))
odm_georeferencing = io.join_paths(submodel_path, 'odm_georeferencing')
odm_georeferencing_coords = io.join_paths(odm_georeferencing, 'coords.txt')
reconstruction.georeference_with_gps(photos, odm_georeferencing_coords, True)
odm_geo_proj = io.join_paths(odm_georeferencing, 'proj.txt')
reconstruction.save_proj_srs(odm_geo_proj)
from opendm.osfm import OSFMContext
octx = OSFMContext(os.path.join(submodel_path, 'opensfm'))
print('----------Export geocroods--------')
octx.run('export_geocoords --transformation --proj \'%s\'' % reconstruction.georef.proj4())
print('----------Export Geocoords Ppppp--------')
lib.odm_mesh_function(opensfm_config,submodel_path, max_concurrency, reconstruction)
#lib.odm_mesh_function(submodel_path, max_concurrency)
end = timer()
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start OpenSfM')
exit(1)
octx = OSFMContext(tree.opensfm)
octx.setup(args,
tree.dataset_raw,
photos,
gcp_path=tree.odm_georeferencing_gcp,
rerun=self.rerun())
octx.extract_metadata(self.rerun())
self.update_progress(20)
octx.feature_matching(self.rerun())
self.update_progress(30)
octx.reconstruct(self.rerun())
self.update_progress(70)
# If we find a special flag file for split/merge we stop right here
if os.path.exists(octx.path("split_merge_stop_at_reconstruction.txt")):
log.ODM_INFO("Stopping OpenSfM early because we found: %s" %
octx.path("split_merge_stop_at_reconstruction.txt"))
self.next_stage = None
return
if args.fast_orthophoto:
output_file = octx.path('reconstruction.ply')
elif args.use_opensfm_dense:
output_file = tree.opensfm_model
else:
output_file = tree.opensfm_reconstruction
updated_config_flag_file = octx.path('updated_config.txt')
if not io.file_exists(updated_config_flag_file) or self.rerun():
octx.update_config({
'undistorted_image_max_size':
gsd.image_max_size(photos,
args.orthophoto_resolution,
tree.opensfm_reconstruction,
ignore_gsd=args.ignore_gsd)
})
octx.touch(updated_config_flag_file)
# These will be used for texturing
undistorted_images_path = octx.path("undistorted")
if not io.dir_exists(undistorted_images_path) or self.rerun():
octx.run('undistort')
else:
log.ODM_WARNING("Found an undistorted directory in %s" %
undistorted_images_path)
self.update_progress(80)
if not io.file_exists(tree.opensfm_reconstruction_nvm) or self.rerun():
octx.run('export_visualsfm --undistorted')
else:
log.ODM_WARNING(
'Found a valid OpenSfM NVM reconstruction file in: %s' %
tree.opensfm_reconstruction_nvm)
self.update_progress(85)
# Skip dense reconstruction if necessary and export
# sparse reconstruction instead
if args.fast_orthophoto:
if not io.file_exists(output_file) or self.rerun():
octx.run('export_ply --no-cameras')
else:
log.ODM_WARNING("Found a valid PLY reconstruction in %s" %
output_file)
elif args.use_opensfm_dense:
if not io.file_exists(output_file) or self.rerun():
octx.run('compute_depthmaps')
else:
log.ODM_WARNING("Found a valid dense reconstruction in %s" %
output_file)
# check if reconstruction was exported to bundler before
octx.export_bundler(tree.opensfm_bundle_list, self.rerun())
self.update_progress(90)
if reconstruction.georef and (not io.file_exists(
tree.opensfm_transformation) or self.rerun()):
octx.run('export_geocoords --transformation --proj \'%s\'' %
reconstruction.georef.projection.srs)
else:
log.ODM_WARNING("Will skip exporting %s" %
tree.opensfm_transformation)
def process(self, args, outputs):
# get inputs
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start OpenMVS')
exit(1)
# check if reconstruction was done before
if not io.file_exists(tree.openmvs_model) or self.rerun():
if self.rerun():
if io.dir_exists(tree.openmvs):
shutil.rmtree(tree.openmvs)
# export reconstruction from opensfm
openmvs_scene_file = os.path.join(tree.openmvs, "scene.mvs")
if not io.file_exists(openmvs_scene_file) or self.rerun():
octx = OSFMContext(tree.opensfm)
cmd = 'export_openmvs'
octx.run(cmd)
else:
log.ODM_WARNING("Found existing %s" % openmvs_scene_file)
self.update_progress(10)
depthmaps_dir = os.path.join(tree.openmvs, "depthmaps")
if io.dir_exists(depthmaps_dir) and self.rerun():
shutil.rmtree(depthmaps_dir)
if not io.dir_exists(depthmaps_dir):
os.mkdir(depthmaps_dir)
depthmap_resolution = get_depthmap_resolution(args, photos)
if outputs["undist_image_max_size"] <= depthmap_resolution:
resolution_level = 0
else:
resolution_level = int(
round(
math.log(outputs['undist_image_max_size'] /
float(depthmap_resolution)) / math.log(2)))
log.ODM_INFO(
"Running dense reconstruction. This might take a while.")
log.ODM_INFO("Estimating depthmaps")
densify_ini_file = os.path.join(tree.openmvs, 'config.ini')
with open(densify_ini_file, 'w+') as f:
f.write("Optimize = 0\n") # Disable depth-maps re-filtering
config = [
" --resolution-level %s" % int(resolution_level),
"--min-resolution %s" % depthmap_resolution,
"--max-resolution %s" % int(outputs['undist_image_max_size']),
"--max-threads %s" % args.max_concurrency,
"--number-views-fuse 2",
'-w "%s"' % depthmaps_dir, "-v 0"
]
if args.pc_tile:
config.append("--fusion-mode 1")
system.run('%s "%s" %s' % (context.omvs_densify_path,
openmvs_scene_file, ' '.join(config)))
self.update_progress(85)
files_to_remove = []
scene_dense = os.path.join(tree.openmvs, 'scene_dense.mvs')
if args.pc_tile:
log.ODM_INFO("Computing sub-scenes")
config = [
"--sub-scene-area 660000",
"--max-threads %s" % args.max_concurrency,
'-w "%s"' % depthmaps_dir,
"-v 0",
]
system.run('%s "%s" %s' %
(context.omvs_densify_path, openmvs_scene_file,
' '.join(config)))
scene_files = glob.glob(
os.path.join(tree.openmvs,
"scene_[0-9][0-9][0-9][0-9].mvs"))
if len(scene_files) == 0:
log.ODM_ERROR(
"No OpenMVS scenes found. This could be a bug, or the reconstruction could not be processed."
)
exit(1)
log.ODM_INFO("Fusing depthmaps for %s scenes" %
len(scene_files))
scene_ply_files = []
for sf in scene_files:
p, _ = os.path.splitext(sf)
scene_ply = p + "_dense_dense_filtered.ply"
scene_dense_mvs = p + "_dense.mvs"
files_to_remove += [scene_ply, sf, scene_dense_mvs]
scene_ply_files.append(scene_ply)
if not io.file_exists(scene_ply) or self.rerun():
# Fuse
config = [
'--resolution-level %s' % int(resolution_level),
'--min-resolution %s' % depthmap_resolution,
'--max-resolution %s' %
int(outputs['undist_image_max_size']),
'--dense-config-file "%s"' % densify_ini_file,
'--number-views-fuse 2',
'--max-threads %s' % args.max_concurrency,
'-w "%s"' % depthmaps_dir,
'-v 0',
]
try:
system.run('%s "%s" %s' %
(context.omvs_densify_path, sf,
' '.join(config)))
# Filter
system.run(
'%s "%s" --filter-point-cloud -1 -v 0' %
(context.omvs_densify_path, scene_dense_mvs))
except:
log.ODM_WARNING(
"Sub-scene %s could not be reconstructed, skipping..."
% sf)
if not io.file_exists(scene_ply):
scene_ply_files.pop()
log.ODM_WARNING(
"Could not compute PLY for subscene %s" % sf)
else:
log.ODM_WARNING("Found existing dense scene file %s" %
scene_ply)
# Merge
log.ODM_INFO("Merging %s scene files" % len(scene_ply_files))
if len(scene_ply_files) == 0:
log.ODM_ERROR(
"Could not compute dense point cloud (no PLY files available)."
)
if len(scene_ply_files) == 1:
# Simply rename
os.rename(scene_ply_files[0], tree.openmvs_model)
log.ODM_INFO("%s --> %s" %
(scene_ply_files[0], tree.openmvs_model))
else:
# Merge
fast_merge_ply(scene_ply_files, tree.openmvs_model)
else:
# Filter all at once
if os.path.exists(scene_dense):
config = [
"--filter-point-cloud -1",
'-i "%s"' % scene_dense, "-v 0"
]
system.run('%s %s' %
(context.omvs_densify_path, ' '.join(config)))
else:
log.ODM_WARNING(
"Cannot find scene_dense.mvs, dense reconstruction probably failed. Exiting..."
)
exit(1)
# TODO: add support for image masks
self.update_progress(95)
if args.optimize_disk_space:
files = [
scene_dense,
os.path.join(tree.openmvs, 'scene_dense.ply'),
os.path.join(tree.openmvs,
'scene_dense_dense_filtered.mvs'),
octx.path("undistorted", "tracks.csv"),
octx.path("undistorted", "reconstruction.json")
] + files_to_remove
for f in files:
if os.path.exists(f):
os.remove(f)
shutil.rmtree(depthmaps_dir)
else:
log.ODM_WARNING(
'Found a valid OpenMVS reconstruction file in: %s' %
tree.openmvs_model)
def process(self, args, outputs):
# get inputs
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
octx = OSFMContext(tree.opensfm)
if not photos:
raise system.ExitException('Not enough photos in photos array to start OpenMVS')
# check if reconstruction was done before
if not io.file_exists(tree.openmvs_model) or self.rerun():
if self.rerun():
if io.dir_exists(tree.openmvs):
shutil.rmtree(tree.openmvs)
# export reconstruction from opensfm
openmvs_scene_file = os.path.join(tree.openmvs, "scene.mvs")
if not io.file_exists(openmvs_scene_file) or self.rerun():
cmd = 'export_openmvs'
octx.run(cmd)
else:
log.ODM_WARNING("Found existing %s" % openmvs_scene_file)
self.update_progress(10)
depthmaps_dir = os.path.join(tree.openmvs, "depthmaps")
if io.dir_exists(depthmaps_dir) and self.rerun():
shutil.rmtree(depthmaps_dir)
if not io.dir_exists(depthmaps_dir):
os.mkdir(depthmaps_dir)
depthmap_resolution = get_depthmap_resolution(args, photos)
log.ODM_INFO("Depthmap resolution set to: %spx" % depthmap_resolution)
if outputs["undist_image_max_size"] <= depthmap_resolution:
resolution_level = 0
else:
resolution_level = int(round(math.log(outputs['undist_image_max_size'] / float(depthmap_resolution)) / math.log(2)))
log.ODM_INFO("Running dense reconstruction. This might take a while.")
log.ODM_INFO("Estimating depthmaps")
number_views_fuse = 2
densify_ini_file = os.path.join(tree.openmvs, 'Densify.ini')
subres_levels = 2 # The number of lower resolutions to process before estimating output resolution depthmap.
config = [
" --resolution-level %s" % int(resolution_level),
'--dense-config-file "%s"' % densify_ini_file,
"--min-resolution %s" % depthmap_resolution,
"--max-resolution %s" % int(outputs['undist_image_max_size']),
"--max-threads %s" % args.max_concurrency,
"--number-views-fuse %s" % number_views_fuse,
"--sub-resolution-levels %s" % subres_levels,
'-w "%s"' % depthmaps_dir,
"-v 0"
]
gpu_config = []
if not has_gpu(args):
gpu_config.append("--cuda-device -2")
if args.pc_tile:
config.append("--fusion-mode 1")
extra_config = []
if not args.pc_geometric:
extra_config.append("--geometric-iters 0")
masks_dir = os.path.join(tree.opensfm, "undistorted", "masks")
masks = os.path.exists(masks_dir) and len(os.listdir(masks_dir)) > 0
if masks:
extra_config.append("--ignore-mask-label 0")
sharp = args.pc_geometric
with open(densify_ini_file, 'w+') as f:
f.write("Optimize = %s\n" % (7 if sharp else 3))
def run_densify():
system.run('"%s" "%s" %s' % (context.omvs_densify_path,
openmvs_scene_file,
' '.join(config + gpu_config + extra_config)))
try:
run_densify()
except system.SubprocessException as e:
# If the GPU was enabled and the program failed,
# try to run it again without GPU
if e.errorCode == 1 and len(gpu_config) == 0:
log.ODM_WARNING("OpenMVS failed with GPU, is your graphics card driver up to date? Falling back to CPU.")
gpu_config.append("--cuda-device -2")
run_densify()
else:
raise e
self.update_progress(85)
files_to_remove = []
scene_dense = os.path.join(tree.openmvs, 'scene_dense.mvs')
if args.pc_tile:
log.ODM_INFO("Computing sub-scenes")
subscene_densify_ini_file = os.path.join(tree.openmvs, 'subscene-config.ini')
with open(subscene_densify_ini_file, 'w+') as f:
f.write("Optimize = 0\n")
config = [
"--sub-scene-area 660000",
"--max-threads %s" % args.max_concurrency,
'-w "%s"' % depthmaps_dir,
"-v 0",
]
system.run('"%s" "%s" %s' % (context.omvs_densify_path,
openmvs_scene_file,
' '.join(config + gpu_config)))
scene_files = glob.glob(os.path.join(tree.openmvs, "scene_[0-9][0-9][0-9][0-9].mvs"))
if len(scene_files) == 0:
raise system.ExitException("No OpenMVS scenes found. This could be a bug, or the reconstruction could not be processed.")
log.ODM_INFO("Fusing depthmaps for %s scenes" % len(scene_files))
scene_ply_files = []
for sf in scene_files:
p, _ = os.path.splitext(sf)
scene_ply_unfiltered = p + "_dense.ply"
scene_ply = p + "_dense_dense_filtered.ply"
scene_dense_mvs = p + "_dense.mvs"
files_to_remove += [scene_ply, sf, scene_dense_mvs, scene_ply_unfiltered]
scene_ply_files.append(scene_ply)
if not io.file_exists(scene_ply) or self.rerun():
# Fuse
config = [
'--resolution-level %s' % int(resolution_level),
'--min-resolution %s' % depthmap_resolution,
'--max-resolution %s' % int(outputs['undist_image_max_size']),
'--dense-config-file "%s"' % subscene_densify_ini_file,
'--number-views-fuse %s' % number_views_fuse,
'--max-threads %s' % args.max_concurrency,
'-w "%s"' % depthmaps_dir,
'-v 0',
]
try:
system.run('"%s" "%s" %s' % (context.omvs_densify_path, sf, ' '.join(config + gpu_config + extra_config)))
# Filter
if args.pc_filter > 0:
system.run('"%s" "%s" --filter-point-cloud -1 -v 0 %s' % (context.omvs_densify_path, scene_dense_mvs, ' '.join(gpu_config)))
else:
# Just rename
log.ODM_INFO("Skipped filtering, %s --> %s" % (scene_ply_unfiltered, scene_ply))
os.rename(scene_ply_unfiltered, scene_ply)
except:
log.ODM_WARNING("Sub-scene %s could not be reconstructed, skipping..." % sf)
if not io.file_exists(scene_ply):
scene_ply_files.pop()
log.ODM_WARNING("Could not compute PLY for subscene %s" % sf)
else:
log.ODM_WARNING("Found existing dense scene file %s" % scene_ply)
# Merge
log.ODM_INFO("Merging %s scene files" % len(scene_ply_files))
if len(scene_ply_files) == 0:
log.ODM_ERROR("Could not compute dense point cloud (no PLY files available).")
if len(scene_ply_files) == 1:
# Simply rename
os.replace(scene_ply_files[0], tree.openmvs_model)
log.ODM_INFO("%s --> %s"% (scene_ply_files[0], tree.openmvs_model))
else:
# Merge
fast_merge_ply(scene_ply_files, tree.openmvs_model)
else:
# Filter all at once
if args.pc_filter > 0:
if os.path.exists(scene_dense):
config = [
"--filter-point-cloud -1",
'-i "%s"' % scene_dense,
"-v 0"
]
system.run('"%s" %s' % (context.omvs_densify_path, ' '.join(config + gpu_config + extra_config)))
else:
raise system.ExitException("Cannot find scene_dense.mvs, dense reconstruction probably failed. Exiting...")
else:
# Just rename
scene_dense_ply = os.path.join(tree.openmvs, 'scene_dense.ply')
log.ODM_INFO("Skipped filtering, %s --> %s" % (scene_dense_ply, tree.openmvs_model))
os.rename(scene_dense_ply, tree.openmvs_model)
self.update_progress(95)
if args.optimize_disk_space:
files = [scene_dense,
os.path.join(tree.openmvs, 'scene_dense.ply'),
os.path.join(tree.openmvs, 'scene_dense_dense_filtered.mvs'),
octx.path("undistorted", "tracks.csv"),
octx.path("undistorted", "reconstruction.json")
] + files_to_remove
for f in files:
if os.path.exists(f):
os.remove(f)
shutil.rmtree(depthmaps_dir)
else:
log.ODM_WARNING('Found a valid OpenMVS reconstruction file in: %s' %
tree.openmvs_model)
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start OpenSfM')
exit(1)
octx = OSFMContext(tree.opensfm)
octx.setup(args,
tree.dataset_raw,
photos,
gcp_path=tree.odm_georeferencing_gcp,
rerun=self.rerun())
octx.extract_metadata(self.rerun())
self.update_progress(20)
octx.feature_matching(self.rerun())
self.update_progress(30)
octx.reconstruct(self.rerun())
self.update_progress(70)
# If we find a special flag file for split/merge we stop right here
if os.path.exists(octx.path("split_merge_stop_at_reconstruction.txt")):
log.ODM_INFO("Stopping OpenSfM early because we found: %s" %
octx.path("split_merge_stop_at_reconstruction.txt"))
self.next_stage = None
return
if args.fast_orthophoto:
output_file = octx.path('reconstruction.ply')
elif args.use_opensfm_dense:
output_file = tree.opensfm_model
else:
output_file = tree.opensfm_reconstruction
# Always export VisualSFM's reconstruction and undistort images
# as we'll use these for texturing (after GSD estimation and resizing)
if not args.ignore_gsd:
image_scale = gsd.image_scale_factor(args.orthophoto_resolution,
tree.opensfm_reconstruction)
else:
image_scale = 1.0
if not io.file_exists(tree.opensfm_reconstruction_nvm) or self.rerun():
octx.run(
'export_visualsfm --image_extension png --scale_focal %s' %
image_scale)
else:
log.ODM_WARNING(
'Found a valid OpenSfM NVM reconstruction file in: %s' %
tree.opensfm_reconstruction_nvm)
# These will be used for texturing
undistorted_images_path = octx.path("undistorted")
if not io.dir_exists(undistorted_images_path) or self.rerun():
octx.run('undistort --image_format png --image_scale %s' %
image_scale)
else:
log.ODM_WARNING("Found an undistorted directory in %s" %
undistorted_images_path)
self.update_progress(80)
# Skip dense reconstruction if necessary and export
# sparse reconstruction instead
if args.fast_orthophoto:
if not io.file_exists(output_file) or self.rerun():
octx.run('export_ply --no-cameras')
else:
log.ODM_WARNING("Found a valid PLY reconstruction in %s" %
output_file)
elif args.use_opensfm_dense:
# Undistort images at full scale in JPG
# (TODO: we could compare the size of the PNGs if they are < than depthmap_resolution
# and use those instead of re-exporting full resolution JPGs)
if not io.file_exists(output_file) or self.rerun():
octx.run('undistort')
octx.run('compute_depthmaps')
else:
log.ODM_WARNING("Found a valid dense reconstruction in %s" %
output_file)
# check if reconstruction was exported to bundler before
octx.export_bundler(tree.opensfm_bundle_list, self.rerun())
self.update_progress(90)
if reconstruction.georef and (not io.file_exists(
tree.opensfm_transformation) or self.rerun()):
octx.run('export_geocoords --transformation --proj \'%s\'' %
reconstruction.georef.projection.srs)
else:
log.ODM_WARNING("Will skip exporting %s" %
tree.opensfm_transformation)
