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
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
class TestRemote(unittest.TestCase):
def setUp(self):
self.lre = LocalRemoteExecutor('http://localhost:9001')
projects = []
for i in range(9):
projects.append('/submodels/submodel_00' + str(i).rjust(2, '0'))
self.lre.set_projects(projects)
def test_lre_init(self):
self.assertFalse(self.lre.node_online)
def test_processing_logic(self):
# Fake online status
self.lre.node_online = True
MAX_QUEUE = 2
class nonloc:
local_task_check = False
remote_queue = 1
should_fail = False
task_limit_reached = False
class OdmTaskMock:
def __init__(self, running, queue_num):
self.running = running
self.queue_num = queue_num
self.uuid = 'xxxxx-xxxxx-xxxxx-xxxxx-xxxx' + str(queue_num)
def info(self):
class StatusMock:
status = TaskStatus.RUNNING if self.running else TaskStatus.QUEUED
processing_time = 1
return StatusMock()
def remove(self):
return True
class TaskMock(Task):
def process_local(self):
# First task should be 0000 or 0001
if not nonloc.local_task_check: nonloc.local_task_check = self.project_path.endswith("0000") or self.project_path.endswith("0001")
if nonloc.should_fail:
if self.project_path.endswith("0006"):
raise exceptions.TaskFailedError("FAIL #6")
time.sleep(1)
def process_remote(self, done):
time.sleep(0.05) # file upload
self.remote_task = OdmTaskMock(nonloc.remote_queue <= MAX_QUEUE, nonloc.remote_queue)
self.params['tasks'].append(self.remote_task)
if nonloc.should_fail:
if self.project_path.endswith("0006"):
raise exceptions.TaskFailedError("FAIL #6")
nonloc.remote_queue += 1
# Upload successful
done(error=None, partial=True)
# Async processing
def monitor():
try:
if nonloc.task_limit_reached and random.randint(0, 4) == 0:
nonloc.remote_queue -= 1
raise NodeTaskLimitReachedException("Random fail!")
if not nonloc.task_limit_reached and self.remote_task.queue_num > MAX_QUEUE:
nonloc.remote_queue -= 1
nonloc.task_limit_reached = True
raise NodeTaskLimitReachedException("Delayed task limit reached")
time.sleep(0.5)
nonloc.remote_queue -= 1
done()
except Exception as e:
done(e)
t = threading.Thread(target=monitor)
self.params['threads'].append(t)
t.start()
self.lre.run(TaskMock)
self.assertTrue(nonloc.local_task_check)
nonloc.should_fail = True
nonloc.remote_queue = 1
nonloc.task_limit_reached = False
with self.assertRaises(exceptions.TaskFailedError):
self.lre.run(TaskMock)