def configure(self, p, _i, _o):
tree = types.ODM_Tree(p.args.project_path)
self.tree = ecto.Constant(value=tree)
# TODO(dakota) put this somewhere better maybe
if p.args.time and io.file_exists(tree.benchmarking):
# Delete the previously made file
os.remove(tree.benchmarking)
with open(tree.benchmarking, 'a') as b:
b.write('ODM Benchmarking file created %s\nNumber of Cores: %s\n\n' % (system.now(), context.num_cores))
def process(self, args, outputs):
outputs['start_time'] = system.now_raw()
tree = types.ODM_Tree(args.project_path, args.gcp, args.geo)
outputs['tree'] = tree
if args.time and io.file_exists(tree.benchmarking):
# Delete the previously made file
os.remove(tree.benchmarking)
with open(tree.benchmarking, 'a') as b:
b.write(
'ODM Benchmarking file created %s\nNumber of Cores: %s\n\n'
% (system.now(), context.num_cores))
# check if the image filename is supported
def valid_image_filename(filename):
(pathfn, ext) = os.path.splitext(filename)
return ext.lower(
) in context.supported_extensions and pathfn[-5:] != "_mask"
# Get supported images from dir
def get_images(in_dir):
log.ODM_DEBUG(in_dir)
entries = os.listdir(in_dir)
valid, rejects = [], []
for f in entries:
if valid_image_filename(f):
valid.append(f)
else:
rejects.append(f)
return valid, rejects
def find_mask(photo_path, masks):
(pathfn, ext) = os.path.splitext(os.path.basename(photo_path))
k = "{}_mask".format(pathfn)
mask = masks.get(k)
if mask:
# Spaces are not supported due to OpenSfM's mask_list.txt format reqs
if not " " in mask:
return mask
else:
log.ODM_WARNING(
"Image mask {} has a space. Spaces are currently not supported for image masks."
.format(mask))
# get images directory
images_dir = tree.dataset_raw
# define paths and create working directories
system.mkdir_p(tree.odm_georeferencing)
log.ODM_INFO('Loading dataset from: %s' % images_dir)
# check if we rerun cell or not
images_database_file = os.path.join(tree.root_path, 'images.json')
if not io.file_exists(images_database_file) or self.rerun():
if not os.path.exists(images_dir):
log.ODM_ERROR(
"There are no images in %s! Make sure that your project path and dataset name is correct. The current is set to: %s"
% (images_dir, args.project_path))
exit(1)
files, rejects = get_images(images_dir)
if files:
# create ODMPhoto list
path_files = [os.path.join(images_dir, f) for f in files]
# Lookup table for masks
masks = {}
for r in rejects:
(p, ext) = os.path.splitext(r)
if p[-5:] == "_mask" and ext.lower(
) in context.supported_extensions:
masks[p] = r
photos = []
with open(tree.dataset_list, 'w') as dataset_list:
log.ODM_INFO("Loading %s images" % len(path_files))
for f in path_files:
p = types.ODM_Photo(f)
p.set_mask(find_mask(f, masks))
photos += [p]
dataset_list.write(photos[-1].filename + '\n')
# Check if a geo file is available
if tree.odm_geo_file is not None and os.path.exists(
tree.odm_geo_file):
log.ODM_INFO("Found image geolocation file")
gf = GeoFile(tree.odm_geo_file)
updated = 0
for p in photos:
entry = gf.get_entry(p.filename)
if entry:
p.update_with_geo_entry(entry)
updated += 1
log.ODM_INFO("Updated %s image positions" % updated)
# Save image database for faster restart
save_images_database(photos, images_database_file)
else:
log.ODM_ERROR('Not enough supported images in %s' % images_dir)
exit(1)
else:
# We have an images database, just load it
photos = load_images_database(images_database_file)
log.ODM_INFO('Found %s usable images' % len(photos))
# Create reconstruction object
reconstruction = types.ODM_Reconstruction(photos)
if tree.odm_georeferencing_gcp and not args.use_exif:
reconstruction.georeference_with_gcp(
tree.odm_georeferencing_gcp,
tree.odm_georeferencing_coords,
tree.odm_georeferencing_gcp_utm,
tree.odm_georeferencing_model_txt_geo,
rerun=self.rerun())
else:
reconstruction.georeference_with_gps(
tree.dataset_raw,
tree.odm_georeferencing_coords,
tree.odm_georeferencing_model_txt_geo,
rerun=self.rerun())
reconstruction.save_proj_srs(
os.path.join(tree.odm_georeferencing,
tree.odm_georeferencing_proj))
outputs['reconstruction'] = reconstruction
def process(self, args, outputs):
outputs['start_time'] = system.now_raw()
tree = types.ODM_Tree(args.project_path, args.gcp, args.geo)
outputs['tree'] = tree
if args.time and io.file_exists(tree.benchmarking):
# Delete the previously made file
os.remove(tree.benchmarking)
with open(tree.benchmarking, 'a') as b:
b.write('ODM Benchmarking file created %s\nNumber of Cores: %s\n\n' % (system.now(), context.num_cores))
# check if the image filename is supported
def valid_image_filename(filename):
(pathfn, ext) = os.path.splitext(filename)
return ext.lower() in context.supported_extensions and pathfn[-5:] != "_mask"
# Get supported images from dir
def get_images(in_dir):
log.ODM_DEBUG(in_dir)
entries = os.listdir(in_dir)
valid, rejects = [], []
for f in entries:
if valid_image_filename(f):
valid.append(f)
else:
rejects.append(f)
return valid, rejects
def find_mask(photo_path, masks):
(pathfn, ext) = os.path.splitext(os.path.basename(photo_path))
k = "{}_mask".format(pathfn)
mask = masks.get(k)
if mask:
# Spaces are not supported due to OpenSfM's mask_list.txt format reqs
if not " " in mask:
return mask
else:
log.ODM_WARNING("Image mask {} has a space. Spaces are currently not supported for image masks.".format(mask))
# get images directory
images_dir = tree.dataset_raw
# define paths and create working directories
system.mkdir_p(tree.odm_georeferencing)
log.ODM_INFO('Loading dataset from: %s' % images_dir)
# check if we rerun cell or not
images_database_file = os.path.join(tree.root_path, 'images.json')
if not io.file_exists(images_database_file) or self.rerun():
if not os.path.exists(images_dir):
raise system.ExitException("There are no images in %s! Make sure that your project path and dataset name is correct. The current is set to: %s" % (images_dir, args.project_path))
files, rejects = get_images(images_dir)
if files:
# create ODMPhoto list
path_files = [os.path.join(images_dir, f) for f in files]
# Lookup table for masks
masks = {}
for r in rejects:
(p, ext) = os.path.splitext(r)
if p[-5:] == "_mask" and ext.lower() in context.supported_extensions:
masks[p] = r
photos = []
with open(tree.dataset_list, 'w') as dataset_list:
log.ODM_INFO("Loading %s images" % len(path_files))
for f in path_files:
try:
p = types.ODM_Photo(f)
p.set_mask(find_mask(f, masks))
photos.append(p)
dataset_list.write(photos[-1].filename + '\n')
except PhotoCorruptedException:
log.ODM_WARNING("%s seems corrupted and will not be used" % os.path.basename(f))
# Check if a geo file is available
if tree.odm_geo_file is not None and os.path.isfile(tree.odm_geo_file):
log.ODM_INFO("Found image geolocation file")
gf = GeoFile(tree.odm_geo_file)
updated = 0
for p in photos:
entry = gf.get_entry(p.filename)
if entry:
p.update_with_geo_entry(entry)
p.compute_opk()
updated += 1
log.ODM_INFO("Updated %s image positions" % updated)
# GPSDOP override if we have GPS accuracy information (such as RTK)
if 'gps_accuracy_is_set' in args:
log.ODM_INFO("Forcing GPS DOP to %s for all images" % args.gps_accuracy)
for p in photos:
p.override_gps_dop(args.gps_accuracy)
# Override projection type
if args.camera_lens != "auto":
log.ODM_INFO("Setting camera lens to %s for all images" % args.camera_lens)
for p in photos:
p.override_camera_projection(args.camera_lens)
# Automatic sky removal
if args.sky_removal:
# For each image that :
# - Doesn't already have a mask, AND
# - Is not nadir (or if orientation info is missing), AND
# - There are no spaces in the image filename (OpenSfM requirement)
# Automatically generate a sky mask
# Generate list of sky images
sky_images = []
for p in photos:
if p.mask is None and (p.pitch is None or (abs(p.pitch) > 20)) and (not " " in p.filename):
sky_images.append({'file': os.path.join(images_dir, p.filename), 'p': p})
if len(sky_images) > 0:
log.ODM_INFO("Automatically generating sky masks for %s images" % len(sky_images))
model = ai.get_model("skyremoval", "https://github.com/OpenDroneMap/SkyRemoval/releases/download/v1.0.5/model.zip", "v1.0.5")
if model is not None:
sf = SkyFilter(model=model)
def parallel_sky_filter(item):
try:
mask_file = sf.run_img(item['file'], images_dir)
# Check and set
if mask_file is not None and os.path.isfile(mask_file):
item['p'].set_mask(os.path.basename(mask_file))
log.ODM_INFO("Wrote %s" % os.path.basename(mask_file))
else:
log.ODM_WARNING("Cannot generate mask for %s" % img)
except Exception as e:
log.ODM_WARNING("Cannot generate mask for %s: %s" % (img, str(e)))
parallel_map(parallel_sky_filter, sky_images, max_workers=args.max_concurrency)
log.ODM_INFO("Sky masks generation completed!")
else:
log.ODM_WARNING("Cannot load AI model (you might need to be connected to the internet?)")
else:
log.ODM_INFO("No sky masks will be generated (masks already provided, or images are nadir)")
# End sky removal
# Save image database for faster restart
save_images_database(photos, images_database_file)
else:
raise system.ExitException('Not enough supported images in %s' % images_dir)
else:
# We have an images database, just load it
photos = load_images_database(images_database_file)
log.ODM_INFO('Found %s usable images' % len(photos))
log.logger.log_json_images(len(photos))
# Create reconstruction object
reconstruction = types.ODM_Reconstruction(photos)
if tree.odm_georeferencing_gcp and not args.use_exif:
reconstruction.georeference_with_gcp(tree.odm_georeferencing_gcp,
tree.odm_georeferencing_coords,
tree.odm_georeferencing_gcp_utm,
tree.odm_georeferencing_model_txt_geo,
rerun=self.rerun())
else:
reconstruction.georeference_with_gps(tree.dataset_raw,
tree.odm_georeferencing_coords,
tree.odm_georeferencing_model_txt_geo,
rerun=self.rerun())
reconstruction.save_proj_srs(os.path.join(tree.odm_georeferencing, tree.odm_georeferencing_proj))
outputs['reconstruction'] = reconstruction
# Try to load boundaries
if args.boundary:
if reconstruction.is_georeferenced():
outputs['boundary'] = boundary.load_boundary(args.boundary, reconstruction.get_proj_srs())
else:
args.boundary = None
log.ODM_WARNING("Reconstruction is not georeferenced, but boundary file provided (will ignore boundary file)")
# If sfm-algorithm is triangulation, check if photos have OPK
if args.sfm_algorithm == 'triangulation':
for p in photos:
if not p.has_opk():
log.ODM_WARNING("No omega/phi/kappa angles found in input photos (%s), switching sfm-algorithm to incremental" % p.filename)
args.sfm_algorithm = 'incremental'
break
def process(self, args, outputs):
# Load tree
tree = types.ODM_Tree(args.project_path, args.images, args.gcp)
outputs['tree'] = tree
if args.time and io.file_exists(tree.benchmarking):
# Delete the previously made file
os.remove(tree.benchmarking)
with open(tree.benchmarking, 'a') as b:
b.write(
'ODM Benchmarking file created %s\nNumber of Cores: %s\n\n'
% (system.now(), context.num_cores))
# check if the extension is supported
def supported_extension(file_name):
(pathfn, ext) = os.path.splitext(file_name)
return ext.lower() in context.supported_extensions
# Get supported images from dir
def get_images(in_dir):
# filter images for its extension type
log.ODM_DEBUG(in_dir)
return [
f for f in io.get_files_list(in_dir) if supported_extension(f)
]
# get images directory
input_dir = tree.input_images
images_dir = tree.dataset_raw
if not io.dir_exists(images_dir):
log.ODM_INFO(
"Project directory %s doesn't exist. Creating it now. " %
images_dir)
system.mkdir_p(images_dir)
copied = [
copyfile(io.join_paths(input_dir, f),
io.join_paths(images_dir, f))
for f in get_images(input_dir)
]
# define paths and create working directories
system.mkdir_p(tree.odm_georeferencing)
if not args.use_3dmesh: system.mkdir_p(tree.odm_25dgeoreferencing)
log.ODM_DEBUG('Loading dataset from: %s' % images_dir)
# check if we rerun cell or not
images_database_file = io.join_paths(tree.root_path, 'images.json')
if not io.file_exists(images_database_file) or self.rerun():
files = get_images(images_dir)
if files:
# create ODMPhoto list
path_files = [io.join_paths(images_dir, f) for f in files]
photos = []
with open(tree.dataset_list, 'w') as dataset_list:
for f in path_files:
photos += [types.ODM_Photo(f)]
dataset_list.write(photos[-1].filename + '\n')
# Save image database for faster restart
save_images_database(photos, images_database_file)
else:
log.ODM_ERROR('Not enough supported images in %s' % images_dir)
exit(1)
else:
# We have an images database, just load it
photos = load_images_database(images_database_file)
log.ODM_INFO('Found %s usable images' % len(photos))
# append photos to cell output
if not self.params.get('proj'):
if tree.odm_georeferencing_gcp:
outputs['reconstruction'] = types.ODM_Reconstruction(
photos, coords_file=tree.odm_georeferencing_gcp)
else:
# Generate UTM from images
try:
if not io.file_exists(
tree.odm_georeferencing_coords) or self.rerun():
location.extract_utm_coords(
photos, tree.dataset_raw,
tree.odm_georeferencing_coords)
else:
log.ODM_INFO("Coordinates file already exist: %s" %
tree.odm_georeferencing_coords)
except:
log.ODM_WARNING('Could not generate coordinates file. '
'Ignore if there is a GCP file')
outputs['reconstruction'] = types.ODM_Reconstruction(
photos, coords_file=tree.odm_georeferencing_coords)
else:
outputs['reconstruction'] = types.ODM_Reconstruction(
photos, projstring=self.params.get('proj'))
# Save proj to file for future use (unless this
# dataset is not georeferenced)
if outputs['reconstruction'].projection:
with open(
io.join_paths(tree.odm_georeferencing,
tree.odm_georeferencing_proj), 'w') as f:
f.write(outputs['reconstruction'].projection.srs)
def process(self, args, outputs):
# Load tree
tree = types.ODM_Tree(args.project_path, args.gcp)
outputs['tree'] = tree
if args.time and io.file_exists(tree.benchmarking):
# Delete the previously made file
os.remove(tree.benchmarking)
with open(tree.benchmarking, 'a') as b:
b.write(
'ODM Benchmarking file created %s\nNumber of Cores: %s\n\n'
% (system.now(), context.num_cores))
# check if the extension is supported
def supported_extension(file_name):
(pathfn, ext) = os.path.splitext(file_name)
return ext.lower() in context.supported_extensions
# Get supported images from dir
def get_images(in_dir):
# filter images for its extension type
log.ODM_DEBUG(in_dir)
return [
f for f in io.get_files_list(in_dir) if supported_extension(f)
]
# get images directory
input_dir = tree.input_images
images_dir = tree.dataset_raw
if not io.dir_exists(images_dir):
log.ODM_INFO(
"Project directory %s doesn't exist. Creating it now. " %
images_dir)
system.mkdir_p(images_dir)
copied = [
copyfile(io.join_paths(input_dir, f),
io.join_paths(images_dir, f))
for f in get_images(input_dir)
]
# define paths and create working directories
system.mkdir_p(tree.odm_georeferencing)
if not args.use_3dmesh: system.mkdir_p(tree.odm_25dgeoreferencing)
log.ODM_INFO('Loading dataset from: %s' % images_dir)
# check if we rerun cell or not
images_database_file = io.join_paths(tree.root_path, 'images.json')
if not io.file_exists(images_database_file) or self.rerun():
files = get_images(images_dir)
if files:
# create ODMPhoto list
path_files = [io.join_paths(images_dir, f) for f in files]
photos = []
with open(tree.dataset_list, 'w') as dataset_list:
log.ODM_INFO("Loading %s images" % len(path_files))
for f in path_files:
photos += [types.ODM_Photo(f)]
dataset_list.write(photos[-1].filename + '\n')
# Save image database for faster restart
save_images_database(photos, images_database_file)
else:
log.ODM_ERROR('Not enough supported images in %s' % images_dir)
exit(1)
else:
# We have an images database, just load it
photos = load_images_database(images_database_file)
log.ODM_INFO('Found %s usable images' % len(photos))
# Create reconstruction object
reconstruction = types.ODM_Reconstruction(photos)
if tree.odm_georeferencing_gcp and not args.use_exif:
reconstruction.georeference_with_gcp(
tree.odm_georeferencing_gcp,
tree.odm_georeferencing_coords,
tree.odm_georeferencing_gcp_utm,
rerun=self.rerun())
else:
reconstruction.georeference_with_gps(
tree.dataset_raw,
tree.odm_georeferencing_coords,
rerun=self.rerun())
reconstruction.save_proj_srs(
io.join_paths(tree.odm_georeferencing,
tree.odm_georeferencing_proj))
outputs['reconstruction'] = reconstruction
def process(self, args, outputs):
outputs['start_time'] = system.now_raw()
tree = types.ODM_Tree(args.project_path, args.gcp, args.geo)
outputs['tree'] = tree
if args.time and io.file_exists(tree.benchmarking):
# Delete the previously made file
os.remove(tree.benchmarking)
with open(tree.benchmarking, 'a') as b:
b.write(
'ODM Benchmarking file created %s\nNumber of Cores: %s\n\n'
% (system.now(), context.num_cores))
# check if the image filename is supported
def valid_image_filename(filename):
(pathfn, ext) = os.path.splitext(filename)
return ext.lower(
) in context.supported_extensions and pathfn[-5:] != "_mask"
# Get supported images from dir
def get_images(in_dir):
log.ODM_DEBUG(in_dir)
entries = os.listdir(in_dir)
valid, rejects = [], []
for f in entries:
if valid_image_filename(f):
valid.append(f)
else:
rejects.append(f)
return valid, rejects
def find_mask(photo_path, masks):
(pathfn, ext) = os.path.splitext(os.path.basename(photo_path))
k = "{}_mask".format(pathfn)
mask = masks.get(k)
if mask:
# Spaces are not supported due to OpenSfM's mask_list.txt format reqs
if not " " in mask:
return mask
else:
log.ODM_WARNING(
"Image mask {} has a space. Spaces are currently not supported for image masks."
.format(mask))
# get images directory
images_dir = tree.dataset_raw
# define paths and create working directories
system.mkdir_p(tree.odm_georeferencing)
log.ODM_INFO('Loading dataset from: %s' % images_dir)
# check if we rerun cell or not
images_database_file = os.path.join(tree.root_path, 'images.json')
if not io.file_exists(images_database_file) or self.rerun():
if not os.path.exists(images_dir):
raise system.ExitException(
"There are no images in %s! Make sure that your project path and dataset name is correct. The current is set to: %s"
% (images_dir, args.project_path))
files, rejects = get_images(images_dir)
if files:
# create ODMPhoto list
path_files = [os.path.join(images_dir, f) for f in files]
# Lookup table for masks
masks = {}
for r in rejects:
(p, ext) = os.path.splitext(r)
if p[-5:] == "_mask" and ext.lower(
) in context.supported_extensions:
masks[p] = r
photos = []
with open(tree.dataset_list, 'w') as dataset_list:
log.ODM_INFO("Loading %s images" % len(path_files))
for f in path_files:
try:
p = types.ODM_Photo(f)
p.set_mask(find_mask(f, masks))
photos += [p]
dataset_list.write(photos[-1].filename + '\n')
except PhotoCorruptedException:
log.ODM_WARNING(
"%s seems corrupted and will not be used" %
os.path.basename(f))
# Check if a geo file is available
if tree.odm_geo_file is not None and os.path.isfile(
tree.odm_geo_file):
log.ODM_INFO("Found image geolocation file")
gf = GeoFile(tree.odm_geo_file)
updated = 0
for p in photos:
entry = gf.get_entry(p.filename)
if entry:
p.update_with_geo_entry(entry)
p.compute_opk()
updated += 1
log.ODM_INFO("Updated %s image positions" % updated)
# GPSDOP override if we have GPS accuracy information (such as RTK)
if 'gps_accuracy_is_set' in args:
log.ODM_INFO("Forcing GPS DOP to %s for all images" %
args.gps_accuracy)
for p in photos:
p.override_gps_dop(args.gps_accuracy)
# Override projection type
if args.camera_lens != "auto":
log.ODM_INFO("Setting camera lens to %s for all images" %
args.camera_lens)
for p in photos:
p.override_camera_projection(args.camera_lens)
# Save image database for faster restart
save_images_database(photos, images_database_file)
else:
raise system.ExitException(
'Not enough supported images in %s' % images_dir)
else:
# We have an images database, just load it
photos = load_images_database(images_database_file)
log.ODM_INFO('Found %s usable images' % len(photos))
log.logger.log_json_images(len(photos))
# Create reconstruction object
reconstruction = types.ODM_Reconstruction(photos)
if tree.odm_georeferencing_gcp and not args.use_exif:
reconstruction.georeference_with_gcp(
tree.odm_georeferencing_gcp,
tree.odm_georeferencing_coords,
tree.odm_georeferencing_gcp_utm,
tree.odm_georeferencing_model_txt_geo,
rerun=self.rerun())
else:
reconstruction.georeference_with_gps(
tree.dataset_raw,
tree.odm_georeferencing_coords,
tree.odm_georeferencing_model_txt_geo,
rerun=self.rerun())
reconstruction.save_proj_srs(
os.path.join(tree.odm_georeferencing,
tree.odm_georeferencing_proj))
outputs['reconstruction'] = reconstruction
# Try to load boundaries
if args.boundary:
if reconstruction.is_georeferenced():
outputs['boundary'] = boundary.load_boundary(
args.boundary, reconstruction.get_proj_srs())
else:
args.boundary = None
log.ODM_WARNING(
"Reconstruction is not georeferenced, but boundary file provided (will ignore boundary file)"
)
# If sfm-algorithm is triangulation, check if photos have OPK
if args.sfm_algorithm == 'triangulation':
for p in photos:
if not p.has_opk():
log.ODM_WARNING(
"No omega/phi/kappa angles found in input photos (%s), switching sfm-algorithm to incremental"
% p.filename)
args.sfm_algorithm = 'incremental'
break
