def no_resize(src_dir, target_dir, rerun_cell, photo):
# define image paths
path_file = photo.path_file
new_path_file = io.join_paths(target_dir, photo.filename)
# set raw image path in case we want to rerun cell
if io.file_exists(new_path_file) and rerun_cell:
path_file = io.join_paths(src_dir, photo.filename)
if not io.file_exists(new_path_file) or rerun_cell:
img = cv2.imread(path_file)
io.copy(path_file, new_path_file)
photo.path_file = new_path_file
photo.width = img.shape[0]
photo.height = img.shape[1]
photo.update_focal()
# log message
log.ODM_DEBUG('Copied %s | dimensions: %s' %
(photo.filename, img.shape))
else:
# log message
log.ODM_WARNING('Already copied %s | dimensions: %s x %s' %
(photo.filename, photo.width, photo.height))
return photo
def no_resize(src_dir,target_dir,rerun_cell,photo):
# define image paths
path_file = photo.path_file
new_path_file = io.join_paths(target_dir, photo.filename)
# set raw image path in case we want to rerun cell
if io.file_exists(new_path_file) and rerun_cell:
path_file = io.join_paths(src_dir, photo.filename)
if not io.file_exists(new_path_file) or rerun_cell:
img = cv2.imread(path_file)
io.copy(path_file, new_path_file)
photo.path_file = new_path_file
photo.width = img.shape[1]
photo.height = img.shape[0]
photo.update_focal()
# log message
log.ODM_DEBUG('Copied %s | dimensions: %s' %
(photo.filename, img.shape))
else:
# log message
log.ODM_WARNING('Already copied %s | dimensions: %s x %s' %
(photo.filename, photo.width, photo.height))
return photo
def process(self, inputs, outputs):
# Benchmarking
start_time = system.now_raw()
log.ODM_INFO('Running ODM OpenSfM Cell')
# get inputs
tree = inputs.tree
args = inputs.args
reconstruction = inputs.reconstruction
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start OpenSfM')
return ecto.QUIT
# create working directories
system.mkdir_p(tree.opensfm)
system.mkdir_p(tree.pmvs)
# check if we rerun cell or not
rerun_cell = (args.rerun is not None and
args.rerun == 'opensfm') or \
(args.rerun_all) or \
(args.rerun_from is not None and
'opensfm' in args.rerun_from)
if not args.use_pmvs:
output_file = tree.opensfm_model
if args.fast_orthophoto:
output_file = io.join_paths(tree.opensfm, 'reconstruction.ply')
else:
output_file = tree.opensfm_reconstruction
# check if reconstruction was done before
if not io.file_exists(output_file) or rerun_cell:
# create file list
list_path = io.join_paths(tree.opensfm, 'image_list.txt')
has_alt = True
with open(list_path, 'w') as fout:
for photo in photos:
if not photo.altitude:
has_alt = False
fout.write('%s\n' % photo.path_file)
# create config file for OpenSfM
config = [
"use_exif_size: %s" %
('no' if not self.params.use_exif_size else 'yes'),
"feature_process_size: %s" % self.params.feature_process_size,
"feature_min_frames: %s" % self.params.feature_min_frames,
"processes: %s" % self.params.processes,
"matching_gps_neighbors: %s" %
self.params.matching_gps_neighbors,
"depthmap_method: %s" % args.opensfm_depthmap_method,
"depthmap_resolution: %s" % args.opensfm_depthmap_resolution,
"depthmap_min_patch_sd: %s" %
args.opensfm_depthmap_min_patch_sd,
"depthmap_min_consistent_views: %s" %
args.opensfm_depthmap_min_consistent_views,
"optimize_camera_parameters: %s" %
('no' if self.params.fixed_camera_params else 'yes')
]
if has_alt:
log.ODM_DEBUG(
"Altitude data detected, enabling it for GPS alignment")
config.append("use_altitude_tag: True")
config.append("align_method: naive")
if args.use_hybrid_bundle_adjustment:
log.ODM_DEBUG("Enabling hybrid bundle adjustment")
config.append(
"bundle_interval: 100"
) # Bundle after adding 'bundle_interval' cameras
config.append(
"bundle_new_points_ratio: 1.2"
) # Bundle when (new points) / (bundled points) > bundle_new_points_ratio
config.append(
"local_bundle_radius: 1"
) # Max image graph distance for images to be included in local bundle adjustment
if args.matcher_distance > 0:
config.append("matching_gps_distance: %s" %
self.params.matching_gps_distance)
if tree.odm_georeferencing_gcp:
config.append("bundle_use_gcp: yes")
io.copy(tree.odm_georeferencing_gcp, tree.opensfm)
# write config file
log.ODM_DEBUG(config)
config_filename = io.join_paths(tree.opensfm, 'config.yaml')
with open(config_filename, 'w') as fout:
fout.write("\n".join(config))
# run OpenSfM reconstruction
matched_done_file = io.join_paths(tree.opensfm,
'matching_done.txt')
if not io.file_exists(matched_done_file) or rerun_cell:
system.run('PYTHONPATH=%s %s/bin/opensfm extract_metadata %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
system.run('PYTHONPATH=%s %s/bin/opensfm detect_features %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
system.run('PYTHONPATH=%s %s/bin/opensfm match_features %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
with open(matched_done_file, 'w') as fout:
fout.write("Matching done!\n")
else:
log.ODM_WARNING(
'Found a feature matching done progress file in: %s' %
matched_done_file)
if not io.file_exists(tree.opensfm_tracks) or rerun_cell:
system.run('PYTHONPATH=%s %s/bin/opensfm create_tracks %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
else:
log.ODM_WARNING('Found a valid OpenSfM tracks file in: %s' %
tree.opensfm_tracks)
if not io.file_exists(tree.opensfm_reconstruction) or rerun_cell:
system.run('PYTHONPATH=%s %s/bin/opensfm reconstruct %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
else:
log.ODM_WARNING(
'Found a valid OpenSfM reconstruction file in: %s' %
tree.opensfm_reconstruction)
if not io.file_exists(
tree.opensfm_reconstruction_meshed) or rerun_cell:
system.run('PYTHONPATH=%s %s/bin/opensfm mesh %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
else:
log.ODM_WARNING(
'Found a valid OpenSfM meshed reconstruction file in: %s' %
tree.opensfm_reconstruction_meshed)
if not args.use_pmvs:
if not io.file_exists(
tree.opensfm_reconstruction_nvm) or rerun_cell:
system.run(
'PYTHONPATH=%s %s/bin/opensfm export_visualsfm %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
else:
log.ODM_WARNING(
'Found a valid OpenSfM NVM reconstruction file in: %s'
% tree.opensfm_reconstruction_nvm)
system.run('PYTHONPATH=%s %s/bin/opensfm undistort %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
# Skip dense reconstruction if necessary and export
# sparse reconstruction instead
if args.fast_orthophoto:
system.run(
'PYTHONPATH=%s %s/bin/opensfm export_ply --no-cameras %s'
% (context.pyopencv_path, context.opensfm_path,
tree.opensfm))
else:
system.run(
'PYTHONPATH=%s %s/bin/opensfm compute_depthmaps %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
else:
log.ODM_WARNING(
'Found a valid OpenSfM reconstruction file in: %s' %
tree.opensfm_reconstruction)
# check if reconstruction was exported to bundler before
if not io.file_exists(tree.opensfm_bundle_list) or rerun_cell:
# convert back to bundler's format
system.run(
'PYTHONPATH=%s %s/bin/export_bundler %s' %
(context.pyopencv_path, context.opensfm_path, tree.opensfm))
else:
log.ODM_WARNING('Found a valid Bundler file in: %s' %
tree.opensfm_reconstruction)
if args.use_pmvs:
# check if reconstruction was exported to pmvs before
if not io.file_exists(tree.pmvs_visdat) or rerun_cell:
# run PMVS converter
system.run('PYTHONPATH=%s %s/bin/export_pmvs %s --output %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm, tree.pmvs))
else:
log.ODM_WARNING('Found a valid CMVS file in: %s' %
tree.pmvs_visdat)
if reconstruction.georef:
system.run(
'PYTHONPATH=%s %s/bin/opensfm export_geocoords %s --transformation --proj \'%s\''
% (context.pyopencv_path, context.opensfm_path, tree.opensfm,
reconstruction.georef.projection.srs))
outputs.reconstruction = reconstruction
if args.time:
system.benchmark(start_time, tree.benchmarking, 'OpenSfM')
log.ODM_INFO('Running ODM OpenSfM Cell - Finished')
return ecto.OK if args.end_with != 'opensfm' else ecto.QUIT
def setup(self,
args,
images_path,
reconstruction,
append_config=[],
rerun=False):
"""
Setup a OpenSfM project
"""
if rerun and io.dir_exists(self.opensfm_project_path):
shutil.rmtree(self.opensfm_project_path)
if not io.dir_exists(self.opensfm_project_path):
system.mkdir_p(self.opensfm_project_path)
list_path = os.path.join(self.opensfm_project_path, 'image_list.txt')
if not io.file_exists(list_path) or rerun:
if reconstruction.multi_camera:
photos = get_photos_by_band(reconstruction.multi_camera,
args.primary_band)
if len(photos) < 1:
raise Exception("Not enough images in selected band %s" %
args.primary_band.lower())
log.ODM_INFO("Reconstruction will use %s images from %s band" %
(len(photos), args.primary_band.lower()))
else:
photos = reconstruction.photos
# create file list
has_alt = True
has_gps = False
with open(list_path, 'w') as fout:
for photo in photos:
if not photo.altitude:
has_alt = False
if photo.latitude is not None and photo.longitude is not None:
has_gps = True
fout.write('%s\n' %
os.path.join(images_path, photo.filename))
# check for image_groups.txt (split-merge)
image_groups_file = os.path.join(args.project_path,
"image_groups.txt")
if 'split_image_groups_is_set' in args:
image_groups_file = os.path.abspath(args.split_image_groups)
if io.file_exists(image_groups_file):
dst_groups_file = os.path.join(self.opensfm_project_path,
"image_groups.txt")
io.copy(image_groups_file, dst_groups_file)
log.ODM_INFO("Copied %s to %s" %
(image_groups_file, dst_groups_file))
# check for cameras
if args.cameras:
try:
camera_overrides = camera.get_opensfm_camera_models(
args.cameras)
with open(
os.path.join(self.opensfm_project_path,
"camera_models_overrides.json"),
'w') as f:
f.write(json.dumps(camera_overrides))
log.ODM_INFO(
"Wrote camera_models_overrides.json to OpenSfM directory"
)
except Exception as e:
log.ODM_WARNING(
"Cannot set camera_models_overrides.json: %s" % str(e))
use_bow = args.matcher_type == "bow"
feature_type = "SIFT"
# 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)
log.ODM_INFO("Writing exif overrides")
exif_overrides = {}
for p in photos:
if 'gps_accuracy_is_set' in args:
dop = args.gps_accuracy
elif p.get_gps_dop() is not None:
dop = p.get_gps_dop()
else:
dop = args.gps_accuracy # default value
if p.latitude is not None and p.longitude is not None:
exif_overrides[p.filename] = {
'gps': {
'latitude': p.latitude,
'longitude': p.longitude,
'altitude':
p.altitude if p.altitude is not None else 0,
'dop': dop,
}
}
with open(
os.path.join(self.opensfm_project_path,
"exif_overrides.json"), 'w') as f:
f.write(json.dumps(exif_overrides))
# Check image masks
masks = []
for p in photos:
if p.mask is not None:
masks.append(
(p.filename, os.path.join(images_path, p.mask)))
if masks:
log.ODM_INFO("Found %s image masks" % len(masks))
with open(
os.path.join(self.opensfm_project_path,
"mask_list.txt"), 'w') as f:
for fname, mask in masks:
f.write("{} {}\n".format(fname, mask))
# Compute feature_process_size
feature_process_size = 2048 # default
if 'resize_to_is_set' in args:
# Legacy
log.ODM_WARNING(
"Legacy option --resize-to (this might be removed in a future version). Use --feature-quality instead."
)
feature_process_size = int(args.resize_to)
else:
feature_quality_scale = {
'ultra': 1,
'high': 0.5,
'medium': 0.25,
'low': 0.125,
'lowest': 0.0675,
}
max_dim = find_largest_photo_dim(photos)
if max_dim > 0:
log.ODM_INFO("Maximum photo dimensions: %spx" %
str(max_dim))
feature_process_size = int(
max_dim * feature_quality_scale[args.feature_quality])
else:
log.ODM_WARNING(
"Cannot compute max image dimensions, going with defaults"
)
depthmap_resolution = get_depthmap_resolution(args, photos)
# create config file for OpenSfM
config = [
"use_exif_size: no",
"flann_algorithm: KDTREE", # more stable, faster than KMEANS
"feature_process_size: %s" % feature_process_size,
"feature_min_frames: %s" % args.min_num_features,
"processes: %s" % args.max_concurrency,
"matching_gps_neighbors: %s" % args.matcher_neighbors,
"matching_gps_distance: %s" % args.matcher_distance,
"optimize_camera_parameters: %s" %
('no'
if args.use_fixed_camera_params or args.cameras else 'yes'),
"undistorted_image_format: tif",
"bundle_outlier_filtering_type: AUTO",
"align_orientation_prior: vertical",
"triangulation_type: ROBUST",
"retriangulation_ratio: 2",
]
if args.camera_lens != 'auto':
config.append("camera_projection_type: %s" %
args.camera_lens.upper())
if not has_gps:
log.ODM_INFO("No GPS information, using BOW matching")
use_bow = True
feature_type = args.feature_type.upper()
if use_bow:
config.append("matcher_type: WORDS")
# Cannot use SIFT with BOW
if feature_type == "SIFT":
log.ODM_WARNING(
"Using BOW matching, will use HAHOG feature type, not SIFT"
)
feature_type = "HAHOG"
# GPU acceleration?
if has_gpus() and feature_type == "SIFT":
log.ODM_INFO("Using GPU for extracting SIFT features")
log.ODM_INFO("--min-num-features will be ignored")
feature_type = "SIFT_GPU"
config.append("feature_type: %s" % feature_type)
if has_alt:
log.ODM_INFO(
"Altitude data detected, enabling it for GPS alignment")
config.append("use_altitude_tag: yes")
gcp_path = reconstruction.gcp.gcp_path
if has_alt or gcp_path:
config.append("align_method: auto")
else:
config.append("align_method: orientation_prior")
if args.use_hybrid_bundle_adjustment:
log.ODM_INFO("Enabling hybrid bundle adjustment")
config.append(
"bundle_interval: 100"
) # Bundle after adding 'bundle_interval' cameras
config.append(
"bundle_new_points_ratio: 1.2"
) # Bundle when (new points) / (bundled points) > bundle_new_points_ratio
config.append(
"local_bundle_radius: 1"
) # Max image graph distance for images to be included in local bundle adjustment
else:
config.append("local_bundle_radius: 0")
if gcp_path:
config.append("bundle_use_gcp: yes")
if not args.force_gps:
config.append("bundle_use_gps: no")
io.copy(gcp_path, self.path("gcp_list.txt"))
config = config + append_config
# write config file
log.ODM_INFO(config)
config_filename = self.get_config_file_path()
with open(config_filename, 'w') as fout:
fout.write("\n".join(config))
else:
log.ODM_WARNING("%s already exists, not rerunning OpenSfM setup" %
list_path)
def setup(self, args, images_path, photos, gcp_path=None, append_config = [], rerun=False):
"""
Setup a OpenSfM project
"""
if rerun and io.dir_exists(self.opensfm_project_path):
shutil.rmtree(self.opensfm_project_path)
if not io.dir_exists(self.opensfm_project_path):
system.mkdir_p(self.opensfm_project_path)
list_path = io.join_paths(self.opensfm_project_path, 'image_list.txt')
if not io.file_exists(list_path) or rerun:
# create file list
has_alt = True
with open(list_path, 'w') as fout:
for photo in photos:
if not photo.altitude:
has_alt = False
fout.write('%s\n' % io.join_paths(images_path, photo.filename))
# create config file for OpenSfM
config = [
"use_exif_size: no",
"feature_process_size: %s" % args.resize_to,
"feature_min_frames: %s" % args.min_num_features,
"processes: %s" % args.max_concurrency,
"matching_gps_neighbors: %s" % args.matcher_neighbors,
"matching_gps_distance: %s" % args.matcher_distance,
"depthmap_method: %s" % args.opensfm_depthmap_method,
"depthmap_resolution: %s" % args.depthmap_resolution,
"depthmap_min_patch_sd: %s" % args.opensfm_depthmap_min_patch_sd,
"depthmap_min_consistent_views: %s" % args.opensfm_depthmap_min_consistent_views,
"optimize_camera_parameters: %s" % ('no' if args.use_fixed_camera_params else 'yes'),
]
if has_alt:
log.ODM_DEBUG("Altitude data detected, enabling it for GPS alignment")
config.append("use_altitude_tag: yes")
config.append("align_method: naive")
else:
config.append("align_method: orientation_prior")
config.append("align_orientation_prior: vertical")
if args.use_hybrid_bundle_adjustment:
log.ODM_DEBUG("Enabling hybrid bundle adjustment")
config.append("bundle_interval: 100") # Bundle after adding 'bundle_interval' cameras
config.append("bundle_new_points_ratio: 1.2") # Bundle when (new points) / (bundled points) > bundle_new_points_ratio
config.append("local_bundle_radius: 1") # Max image graph distance for images to be included in local bundle adjustment
if gcp_path:
config.append("bundle_use_gcp: yes")
io.copy(gcp_path, self.path("gcp_list.txt"))
config = config + append_config
# write config file
log.ODM_DEBUG(config)
config_filename = io.join_paths(self.opensfm_project_path, 'config.yaml')
with open(config_filename, 'w') as fout:
fout.write("\n".join(config))
# check for image_groups.txt (split-merge)
image_groups_file = os.path.join(args.project_path, "image_groups.txt")
if io.file_exists(image_groups_file):
log.ODM_DEBUG("Copied image_groups.txt to OpenSfM directory")
io.copy(image_groups_file, os.path.join(self.opensfm_project_path, "image_groups.txt"))
else:
log.ODM_WARNING("%s already exists, not rerunning OpenSfM setup" % list_path)
def setup(self, args, images_path, photos, gcp_path=None, append_config = [], rerun=False):
"""
Setup a OpenSfM project
"""
if rerun and io.dir_exists(self.opensfm_project_path):
shutil.rmtree(self.opensfm_project_path)
if not io.dir_exists(self.opensfm_project_path):
system.mkdir_p(self.opensfm_project_path)
list_path = io.join_paths(self.opensfm_project_path, 'image_list.txt')
if not io.file_exists(list_path) or rerun:
# create file list
has_alt = True
with open(list_path, 'w') as fout:
for photo in photos:
if not photo.altitude:
has_alt = False
fout.write('%s\n' % io.join_paths(images_path, photo.filename))
# check for image_groups.txt (split-merge)
image_groups_file = os.path.join(args.project_path, "image_groups.txt")
if io.file_exists(image_groups_file):
log.ODM_INFO("Copied image_groups.txt to OpenSfM directory")
io.copy(image_groups_file, os.path.join(self.opensfm_project_path, "image_groups.txt"))
# check for cameras
if args.cameras:
try:
camera_overrides = camera.get_opensfm_camera_models(args.cameras)
with open(os.path.join(self.opensfm_project_path, "camera_models_overrides.json"), 'w') as f:
f.write(json.dumps(camera_overrides))
log.ODM_INFO("Wrote camera_models_overrides.json to OpenSfM directory")
except Exception as e:
log.ODM_WARNING("Cannot set camera_models_overrides.json: %s" % str(e))
# create config file for OpenSfM
config = [
"use_exif_size: no",
"feature_process_size: %s" % args.resize_to,
"feature_min_frames: %s" % args.min_num_features,
"processes: %s" % args.max_concurrency,
"matching_gps_neighbors: %s" % args.matcher_neighbors,
"matching_gps_distance: %s" % args.matcher_distance,
"depthmap_method: %s" % args.opensfm_depthmap_method,
"depthmap_resolution: %s" % args.depthmap_resolution,
"depthmap_min_patch_sd: %s" % args.opensfm_depthmap_min_patch_sd,
"depthmap_min_consistent_views: %s" % args.opensfm_depthmap_min_consistent_views,
"optimize_camera_parameters: %s" % ('no' if args.use_fixed_camera_params or args.cameras else 'yes'),
"undistorted_image_format: png", # mvs-texturing exhibits artifacts with JPG
"bundle_outlier_filtering_type: AUTO",
]
# TODO: add BOW matching when dataset is not georeferenced (no gps)
if has_alt:
log.ODM_INFO("Altitude data detected, enabling it for GPS alignment")
config.append("use_altitude_tag: yes")
if has_alt or gcp_path:
config.append("align_method: naive")
else:
config.append("align_method: orientation_prior")
config.append("align_orientation_prior: vertical")
if args.use_hybrid_bundle_adjustment:
log.ODM_INFO("Enabling hybrid bundle adjustment")
config.append("bundle_interval: 100") # Bundle after adding 'bundle_interval' cameras
config.append("bundle_new_points_ratio: 1.2") # Bundle when (new points) / (bundled points) > bundle_new_points_ratio
config.append("local_bundle_radius: 1") # Max image graph distance for images to be included in local bundle adjustment
if gcp_path:
config.append("bundle_use_gcp: yes")
config.append("bundle_use_gps: no")
io.copy(gcp_path, self.path("gcp_list.txt"))
config = config + append_config
# write config file
log.ODM_INFO(config)
config_filename = self.get_config_file_path()
with open(config_filename, 'w') as fout:
fout.write("\n".join(config))
else:
log.ODM_WARNING("%s already exists, not rerunning OpenSfM setup" % list_path)
'num_cores': args.max_concurrency,
'ext': image_ext,
'zoom': args.zoom,
'mm3d': mm3d
}
system.run(
'{mm3d} Malt Ortho .*.{ext} Ground_UTM EZA=1 ZoomI=64 ZoomF={zoom} NbVI=2 HrOr=1 '
'RoundResol=0 ResolOrtho=1 DefCor=0.0005 NbProc={num_cores}'.
format(**kwargs_malt))
progressbc.send_update(80)
# build ORTHO
porto_src = '/code/micmac/include/XML_MicMac/Param-Tawny.xml'
porto_dst = 'Ortho-MEC-Malt/Param-Tawny.xml'
io.copy(porto_src, porto_dst)
system.run('{mm3d} Porto Ortho-MEC-Malt/Param-Tawny.xml'.format(
**kwargs_malt))
progressbc.send_update(90)
# build PLY
kwargs_nuage = {
'mm3d':
mm3d,
'ply':
io.join_paths(project_dir,
'odm_georeferencing/odm_georeferenced_model.ply'),
'nuage':
get_last_etape('MEC-Malt/NuageImProf_STD-MALT_Etape_*.xml')
}
def setup(self,
args,
images_path,
reconstruction,
append_config=[],
rerun=False):
"""
Setup a OpenSfM project
"""
if rerun and io.dir_exists(self.opensfm_project_path):
shutil.rmtree(self.opensfm_project_path)
if not io.dir_exists(self.opensfm_project_path):
system.mkdir_p(self.opensfm_project_path)
list_path = os.path.join(self.opensfm_project_path, 'image_list.txt')
if not io.file_exists(list_path) or rerun:
if reconstruction.multi_camera:
photos = get_photos_by_band(reconstruction.multi_camera,
args.primary_band)
if len(photos) < 1:
raise Exception("Not enough images in selected band %s" %
args.primary_band.lower())
log.ODM_INFO("Reconstruction will use %s images from %s band" %
(len(photos), args.primary_band.lower()))
else:
photos = reconstruction.photos
# create file list
has_alt = True
has_gps = False
with open(list_path, 'w') as fout:
for photo in photos:
if not photo.altitude:
has_alt = False
if photo.latitude is not None and photo.longitude is not None:
has_gps = True
fout.write('%s\n' %
os.path.join(images_path, photo.filename))
# check for image_groups.txt (split-merge)
image_groups_file = os.path.join(args.project_path,
"image_groups.txt")
if 'split_image_groups_is_set' in args:
image_groups_file = os.path.abspath(args.split_image_groups)
if io.file_exists(image_groups_file):
dst_groups_file = os.path.join(self.opensfm_project_path,
"image_groups.txt")
io.copy(image_groups_file, dst_groups_file)
log.ODM_INFO("Copied %s to %s" %
(image_groups_file, dst_groups_file))
# check for cameras
if args.cameras:
try:
camera_overrides = camera.get_opensfm_camera_models(
args.cameras)
with open(
os.path.join(self.opensfm_project_path,
"camera_models_overrides.json"),
'w') as f:
f.write(json.dumps(camera_overrides))
log.ODM_INFO(
"Wrote camera_models_overrides.json to OpenSfM directory"
)
except Exception as e:
log.ODM_WARNING(
"Cannot set camera_models_overrides.json: %s" % str(e))
# Check image masks
masks = []
for p in photos:
if p.mask is not None:
masks.append(
(p.filename, os.path.join(images_path, p.mask)))
if masks:
log.ODM_INFO("Found %s image masks" % len(masks))
with open(
os.path.join(self.opensfm_project_path,
"mask_list.txt"), 'w') as f:
for fname, mask in masks:
f.write("{} {}\n".format(fname, mask))
# Compute feature_process_size
feature_process_size = 2048 # default
if ('resize_to_is_set' in args) and args.resize_to > 0:
# Legacy
log.ODM_WARNING(
"Legacy option --resize-to (this might be removed in a future version). Use --feature-quality instead."
)
feature_process_size = int(args.resize_to)
else:
feature_quality_scale = {
'ultra': 1,
'high': 0.5,
'medium': 0.25,
'low': 0.125,
'lowest': 0.0675,
}
max_dim = find_largest_photo_dim(photos)
if max_dim > 0:
log.ODM_INFO("Maximum photo dimensions: %spx" %
str(max_dim))
feature_process_size = int(
max_dim * feature_quality_scale[args.feature_quality])
log.ODM_INFO(
"Photo dimensions for feature extraction: %ipx" %
feature_process_size)
else:
log.ODM_WARNING(
"Cannot compute max image dimensions, going with defaults"
)
depthmap_resolution = get_depthmap_resolution(args, photos)
# create config file for OpenSfM
config = [
"use_exif_size: no",
"flann_algorithm: KDTREE", # more stable, faster than KMEANS
"feature_process_size: %s" % feature_process_size,
"feature_min_frames: %s" % args.min_num_features,
"processes: %s" % args.max_concurrency,
"matching_gps_neighbors: %s" % args.matcher_neighbors,
"matching_gps_distance: 0",
"matching_graph_rounds: 50",
"optimize_camera_parameters: %s" %
('no'
if args.use_fixed_camera_params or args.cameras else 'yes'),
"reconstruction_algorithm: %s" % (args.sfm_algorithm),
"undistorted_image_format: tif",
"bundle_outlier_filtering_type: AUTO",
"sift_peak_threshold: 0.066",
"align_orientation_prior: vertical",
"triangulation_type: ROBUST",
"retriangulation_ratio: 2",
]
if args.camera_lens != 'auto':
config.append("camera_projection_type: %s" %
args.camera_lens.upper())
matcher_type = args.matcher_type
feature_type = args.feature_type.upper()
osfm_matchers = {
"bow": "WORDS",
"flann": "FLANN",
"bruteforce": "BRUTEFORCE"
}
if not has_gps and not 'matcher_type_is_set' in args:
log.ODM_INFO(
"No GPS information, using BOW matching by default (you can override this by setting --matcher-type explicitly)"
)
matcher_type = "bow"
if matcher_type == "bow":
# Cannot use anything other than HAHOG with BOW
if feature_type != "HAHOG":
log.ODM_WARNING(
"Using BOW matching, will use HAHOG feature type, not SIFT"
)
feature_type = "HAHOG"
config.append("matcher_type: %s" % osfm_matchers[matcher_type])
# GPU acceleration?
if has_gpu():
max_photo = find_largest_photo(photos)
w, h = max_photo.width, max_photo.height
if w > h:
h = int((h / w) * feature_process_size)
w = int(feature_process_size)
else:
w = int((w / h) * feature_process_size)
h = int(feature_process_size)
if has_popsift_and_can_handle_texsize(
w, h) and feature_type == "SIFT":
log.ODM_INFO("Using GPU for extracting SIFT features")
feature_type = "SIFT_GPU"
config.append("feature_type: %s" % feature_type)
if has_alt:
log.ODM_INFO(
"Altitude data detected, enabling it for GPS alignment")
config.append("use_altitude_tag: yes")
gcp_path = reconstruction.gcp.gcp_path
if has_alt or gcp_path:
config.append("align_method: auto")
else:
config.append("align_method: orientation_prior")
if args.use_hybrid_bundle_adjustment:
log.ODM_INFO("Enabling hybrid bundle adjustment")
config.append(
"bundle_interval: 100"
) # Bundle after adding 'bundle_interval' cameras
config.append(
"bundle_new_points_ratio: 1.2"
) # Bundle when (new points) / (bundled points) > bundle_new_points_ratio
config.append(
"local_bundle_radius: 1"
) # Max image graph distance for images to be included in local bundle adjustment
else:
config.append("local_bundle_radius: 0")
if gcp_path:
config.append("bundle_use_gcp: yes")
if not args.force_gps:
config.append("bundle_use_gps: no")
io.copy(gcp_path, self.path("gcp_list.txt"))
config = config + append_config
# write config file
log.ODM_INFO(config)
config_filename = self.get_config_file_path()
with open(config_filename, 'w') as fout:
fout.write("\n".join(config))
# We impose our own reference_lla
if reconstruction.is_georeferenced():
self.write_reference_lla(
reconstruction.georef.utm_east_offset,
reconstruction.georef.utm_north_offset,
reconstruction.georef.proj4())
else:
log.ODM_WARNING("%s already exists, not rerunning OpenSfM setup" %
list_path)
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, inputs, outputs):
# Benchmarking
start_time = system.now_raw()
log.ODM_INFO('Running ODM OpenSfM Cell')
# get inputs
tree = inputs.tree
args = inputs.args
reconstruction = inputs.reconstruction
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start OpenSfM')
return ecto.QUIT
# create working directories
system.mkdir_p(tree.opensfm)
# check if we rerun cell or not
rerun_cell = (args.rerun is not None and
args.rerun == 'opensfm') or \
(args.rerun_all) or \
(args.rerun_from is not None and
'opensfm' in args.rerun_from)
if args.fast_orthophoto:
output_file = io.join_paths(tree.opensfm, 'reconstruction.ply')
elif args.use_opensfm_dense:
output_file = tree.opensfm_model
else:
output_file = tree.opensfm_reconstruction
# check if reconstruction was done before
if not io.file_exists(output_file) or rerun_cell:
# create file list
list_path = io.join_paths(tree.opensfm, 'image_list.txt')
has_alt = True
with open(list_path, 'w') as fout:
for photo in photos:
if not photo.altitude:
has_alt = False
fout.write('%s\n' %
io.join_paths(tree.dataset_raw, photo.filename))
# create config file for OpenSfM
config = [
"use_exif_size: %s" %
('no' if not self.params.use_exif_size else 'yes'),
"feature_process_size: %s" % self.params.feature_process_size,
"feature_min_frames: %s" % self.params.feature_min_frames,
"processes: %s" % self.params.processes,
"matching_gps_neighbors: %s" %
self.params.matching_gps_neighbors,
"depthmap_method: %s" % args.opensfm_depthmap_method,
"depthmap_resolution: %s" % args.depthmap_resolution,
"depthmap_min_patch_sd: %s" %
args.opensfm_depthmap_min_patch_sd,
"depthmap_min_consistent_views: %s" %
args.opensfm_depthmap_min_consistent_views,
"optimize_camera_parameters: %s" %
('no' if self.params.fixed_camera_params else 'yes')
]
if has_alt:
log.ODM_DEBUG(
"Altitude data detected, enabling it for GPS alignment")
config.append("use_altitude_tag: yes")
config.append("align_method: naive")
else:
config.append("align_method: orientation_prior")
config.append("align_orientation_prior: vertical")
if args.use_hybrid_bundle_adjustment:
log.ODM_DEBUG("Enabling hybrid bundle adjustment")
config.append(
"bundle_interval: 100"
) # Bundle after adding 'bundle_interval' cameras
config.append(
"bundle_new_points_ratio: 1.2"
) # Bundle when (new points) / (bundled points) > bundle_new_points_ratio
config.append(
"local_bundle_radius: 1"
) # Max image graph distance for images to be included in local bundle adjustment
if args.matcher_distance > 0:
config.append("matching_gps_distance: %s" %
self.params.matching_gps_distance)
if tree.odm_georeferencing_gcp:
config.append("bundle_use_gcp: yes")
io.copy(tree.odm_georeferencing_gcp, tree.opensfm)
# write config file
log.ODM_DEBUG(config)
config_filename = io.join_paths(tree.opensfm, 'config.yaml')
with open(config_filename, 'w') as fout:
fout.write("\n".join(config))
# run OpenSfM reconstruction
matched_done_file = io.join_paths(tree.opensfm,
'matching_done.txt')
if not io.file_exists(matched_done_file) or rerun_cell:
system.run('PYTHONPATH=%s %s/bin/opensfm extract_metadata %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
system.run('PYTHONPATH=%s %s/bin/opensfm detect_features %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
system.run('PYTHONPATH=%s %s/bin/opensfm match_features %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
with open(matched_done_file, 'w') as fout:
fout.write("Matching done!\n")
else:
log.ODM_WARNING(
'Found a feature matching done progress file in: %s' %
matched_done_file)
if not io.file_exists(tree.opensfm_tracks) or rerun_cell:
system.run('PYTHONPATH=%s %s/bin/opensfm create_tracks %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
else:
log.ODM_WARNING('Found a valid OpenSfM tracks file in: %s' %
tree.opensfm_tracks)
if not io.file_exists(tree.opensfm_reconstruction) or rerun_cell:
system.run('PYTHONPATH=%s %s/bin/opensfm reconstruct %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
else:
log.ODM_WARNING(
'Found a valid OpenSfM reconstruction file in: %s' %
tree.opensfm_reconstruction)
# Check that a reconstruction file has been created
if not io.file_exists(tree.opensfm_reconstruction):
log.ODM_ERROR(
"The program could not process this dataset using the current settings. "
"Check that the images have enough overlap, "
"that there are enough recognizable features "
"and that the images are in focus. "
"You could also try to increase the --min-num-features parameter."
"The program will now exit.")
sys.exit(1)
# 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 rerun_cell:
system.run(
'PYTHONPATH=%s %s/bin/opensfm export_visualsfm --image_extension png --scale_focal %s %s'
% (context.pyopencv_path, context.opensfm_path,
image_scale, tree.opensfm))
else:
log.ODM_WARNING(
'Found a valid OpenSfM NVM reconstruction file in: %s' %
tree.opensfm_reconstruction_nvm)
# These will be used for texturing
system.run(
'PYTHONPATH=%s %s/bin/opensfm undistort --image_format png --image_scale %s %s'
% (context.pyopencv_path, context.opensfm_path, image_scale,
tree.opensfm))
# Skip dense reconstruction if necessary and export
# sparse reconstruction instead
if args.fast_orthophoto:
system.run(
'PYTHONPATH=%s %s/bin/opensfm export_ply --no-cameras %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
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)
system.run('PYTHONPATH=%s %s/bin/opensfm undistort %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
system.run(
'PYTHONPATH=%s %s/bin/opensfm compute_depthmaps %s' %
(context.pyopencv_path, context.opensfm_path,
tree.opensfm))
else:
log.ODM_WARNING(
'Found a valid OpenSfM reconstruction file in: %s' %
tree.opensfm_reconstruction)
# check if reconstruction was exported to bundler before
if not io.file_exists(tree.opensfm_bundle_list) or rerun_cell:
# convert back to bundler's format
system.run(
'PYTHONPATH=%s %s/bin/export_bundler %s' %
(context.pyopencv_path, context.opensfm_path, tree.opensfm))
else:
log.ODM_WARNING('Found a valid Bundler file in: %s' %
tree.opensfm_reconstruction)
if reconstruction.georef:
system.run(
'PYTHONPATH=%s %s/bin/opensfm export_geocoords %s --transformation --proj \'%s\''
% (context.pyopencv_path, context.opensfm_path, tree.opensfm,
reconstruction.georef.projection.srs))
outputs.reconstruction = reconstruction
if args.time:
system.benchmark(start_time, tree.benchmarking, 'OpenSfM')
log.ODM_INFO('Running ODM OpenSfM Cell - Finished')
return ecto.OK if args.end_with != 'opensfm' else ecto.QUIT
if args.gcp:
system.run(
'{mm3d} Campari .*.{ext} Ground_Init_RTL Ground_RTL '
'GCP=[ground.xml,0.005,images.xml,0.01] NbIterEnd=6 AllFree=1 DetGCP=1'
.format(**kwargs_campari))
else:
system.run(
'{mm3d} Campari .*.{ext} Ground_Init_RTL Ground_RTL '
'EmGPS=[RAWGNSS_N,5] AllFree=0'.format(**kwargs_campari))
progressbc.send_update(60)
# change projection and system coords to UTM from relative for GPS only
kwargs_chg = {'ext': image_ext, 'mm3d': mm3d}
if args.gcp:
io.copy('Ori-Ground_RTL', 'Ori-Ground_UTM')
system.run(
'{mm3d} GCPCtrl .*.{ext} Ground_UTM ground.xml images.xml'.
format(**kwargs_chg))
else:
system.run(
'{mm3d} ChgSysCo .*.{ext} Ground_RTL [email protected] Ground_UTM'
.format(**kwargs_chg))
progressbc.send_update(65)
# camera cloud
if args.camera_cloud:
kwargs_camera_cloud = {
'ext': image_ext,
'mm3d': mm3d,
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)
def process(self, inputs, outputs):
# Benchmarking
start_time = system.now_raw()
log.ODM_INFO('Running MVE Cell')
# get inputs
tree = inputs.tree
args = inputs.args
reconstruction = inputs.reconstruction
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start MVE')
return ecto.QUIT
# check if we rerun cell or not
rerun_cell = (args.rerun is not None and
args.rerun == 'mve') or \
(args.rerun_all) or \
(args.rerun_from is not None and
'mve' in args.rerun_from)
# check if reconstruction was done before
if not io.file_exists(tree.mve_model) or rerun_cell:
# cleanup if a rerun
if io.dir_exists(tree.mve_path) and rerun_cell:
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'))
io.copy(tree.opensfm_image_list, 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})
# 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",
"--force",
]
# 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")
system.run('%s %s %s' % (context.dmrecon_path, ' '.join(dmrecon_config), tree.mve), env_vars={'OMP_NUM_THREADS': args.max_concurrency})
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)
outputs.reconstruction = reconstruction
if args.time:
system.benchmark(start_time, tree.benchmarking, 'MVE')
log.ODM_INFO('Running ODM MVE Cell - Finished')
return ecto.OK if args.end_with != 'mve' else ecto.QUIT
def process(self, inputs, outputs):
# Benchmarking
start_time = system.now_raw()
log.ODM_INFO('Running MVE Cell')
# get inputs
tree = inputs.tree
args = inputs.args
reconstruction = inputs.reconstruction
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start MVE')
return ecto.QUIT
# check if we rerun cell or not
rerun_cell = (args.rerun is not None and
args.rerun == 'mve') or \
(args.rerun_all) or \
(args.rerun_from is not None and
'mve' in args.rerun_from)
# check if reconstruction was done before
if not io.file_exists(tree.mve_model) or rerun_cell:
# cleanup if a rerun
if io.dir_exists(tree.mve_path) and rerun_cell:
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'))
io.copy(tree.opensfm_image_list, 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})
# 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",
"--force",
]
# 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")
system.run(
'%s %s %s' %
(context.dmrecon_path, ' '.join(dmrecon_config), tree.mve),
env_vars={'OMP_NUM_THREADS': args.max_concurrency})
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)
outputs.reconstruction = reconstruction
if args.time:
system.benchmark(start_time, tree.benchmarking, 'MVE')
log.ODM_INFO('Running ODM MVE Cell - Finished')
return ecto.OK if args.end_with != 'mve' else ecto.QUIT
def process(self, inputs, outputs):
# Benchmarking
start_time = system.now_raw()
log.ODM_INFO('Running SMVS Cell')
# get inputs
tree = inputs.tree
args = inputs.args
reconstruction = inputs.reconstruction
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start SMVS')
return ecto.QUIT
# check if we rerun cell or not
rerun_cell = (args.rerun is not None and
args.rerun == 'smvs') or \
(args.rerun_all) or \
(args.rerun_from is not None and
'smvs' in args.rerun_from)
# check if reconstruction was done before
if not io.file_exists(tree.smvs_model) or rerun_cell:
# cleanup if a rerun
if io.dir_exists(tree.mve_path) and rerun_cell:
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'))
io.copy(tree.opensfm_image_list, 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.smvs):
shutil.rmtree(tree.smvs)
# run mve makescene
if not io.dir_exists(tree.mve_views):
system.run('%s %s %s' % (context.makescene_path, tree.mve_path, tree.smvs))
# config
config = [
"-t%s" % self.params.threads,
"-a%s" % self.params.alpha,
"--max-pixels=%s" % int(self.params.max_pixels),
"-o%s" % self.params.output_scale,
"--debug-lvl=%s" % ('1' if self.params.verbose else '0'),
"%s" % '-S' if self.params.shading else '',
"%s" % '-g' if self.params.gamma_srgb and self.params.shading else '',
"--force" if rerun_cell else ''
]
# run smvs
system.run('%s %s %s' % (context.smvs_path, ' '.join(config), tree.smvs))
# find and rename the output file for simplicity
smvs_files = glob.glob(os.path.join(tree.smvs, 'smvs-*'))
smvs_files.sort(key=os.path.getmtime) # sort by last modified date
if len(smvs_files) > 0:
old_file = smvs_files[-1]
if not (io.rename_file(old_file, tree.smvs_model)):
log.ODM_WARNING("File %s does not exist, cannot be renamed. " % old_file)
# Filter
point_cloud.filter(tree.smvs_model, standard_deviation=args.pc_filter, verbose=args.verbose)
else:
log.ODM_WARNING("Cannot find a valid point cloud (smvs-XX.ply) in %s. Check the console output for errors." % tree.smvs)
else:
log.ODM_WARNING('Found a valid SMVS reconstruction file in: %s' %
tree.smvs_model)
outputs.reconstruction = reconstruction
if args.time:
system.benchmark(start_time, tree.benchmarking, 'SMVS')
log.ODM_INFO('Running ODM SMVS Cell - Finished')
return ecto.OK if args.end_with != 'smvs' else ecto.QUIT
def setup(self,
args,
images_path,
photos,
reconstruction,
append_config=[],
rerun=False):
"""
Setup a OpenSfM project
"""
if rerun and io.dir_exists(self.opensfm_project_path):
shutil.rmtree(self.opensfm_project_path)
if not io.dir_exists(self.opensfm_project_path):
system.mkdir_p(self.opensfm_project_path)
list_path = io.join_paths(self.opensfm_project_path, 'image_list.txt')
if not io.file_exists(list_path) or rerun:
# create file list
has_alt = True
has_gps = False
with open(list_path, 'w') as fout:
for photo in photos:
if not photo.altitude:
has_alt = False
if photo.latitude is not None and photo.longitude is not None:
has_gps = True
fout.write('%s\n' %
io.join_paths(images_path, photo.filename))
# check for image_groups.txt (split-merge)
image_groups_file = os.path.join(args.project_path,
"image_groups.txt")
if io.file_exists(image_groups_file):
log.ODM_INFO("Copied image_groups.txt to OpenSfM directory")
io.copy(
image_groups_file,
os.path.join(self.opensfm_project_path,
"image_groups.txt"))
# check for cameras
if args.cameras:
try:
camera_overrides = camera.get_opensfm_camera_models(
args.cameras)
with open(
os.path.join(self.opensfm_project_path,
"camera_models_overrides.json"),
'w') as f:
f.write(json.dumps(camera_overrides))
log.ODM_INFO(
"Wrote camera_models_overrides.json to OpenSfM directory"
)
except Exception as e:
log.ODM_WARNING(
"Cannot set camera_models_overrides.json: %s" % str(e))
use_bow = False
feature_type = "SIFT"
matcher_neighbors = args.matcher_neighbors
if matcher_neighbors != 0 and reconstruction.multi_camera is not None:
matcher_neighbors *= len(reconstruction.multi_camera)
log.ODM_INFO(
"Increasing matcher neighbors to %s to accomodate multi-camera setup"
% matcher_neighbors)
log.ODM_INFO("Multi-camera setup, using BOW matching")
use_bow = True
# GPSDOP override if we have GPS accuracy information (such as RTK)
override_gps_dop = 'gps_accuracy_is_set' in args
for p in photos:
if p.get_gps_dop() is not None:
override_gps_dop = True
break
if override_gps_dop:
if 'gps_accuracy_is_set' in args:
log.ODM_INFO("Forcing GPS DOP to %s for all images" %
args.gps_accuracy)
else:
log.ODM_INFO(
"Looks like we have RTK accuracy info for some photos. Good! We'll use it."
)
exif_overrides = {}
for p in photos:
dop = args.gps_accuracy if 'gps_accuracy_is_set' in args else p.get_gps_dop(
)
if dop is not None and p.latitude is not None and p.longitude is not None:
exif_overrides[p.filename] = {
'gps': {
'latitude':
p.latitude,
'longitude':
p.longitude,
'altitude':
p.altitude if p.altitude is not None else 0,
'dop':
dop,
}
}
with open(
os.path.join(self.opensfm_project_path,
"exif_overrides.json"), 'w') as f:
f.write(json.dumps(exif_overrides))
# create config file for OpenSfM
config = [
"use_exif_size: no",
"flann_algorithm: KDTREE", # more stable, faster than KMEANS
"feature_process_size: %s" % args.resize_to,
"feature_min_frames: %s" % args.min_num_features,
"processes: %s" % args.max_concurrency,
"matching_gps_neighbors: %s" % matcher_neighbors,
"matching_gps_distance: %s" % args.matcher_distance,
"depthmap_method: %s" % args.opensfm_depthmap_method,
"depthmap_resolution: %s" % args.depthmap_resolution,
"depthmap_min_patch_sd: %s" %
args.opensfm_depthmap_min_patch_sd,
"depthmap_min_consistent_views: %s" %
args.opensfm_depthmap_min_consistent_views,
"optimize_camera_parameters: %s" %
('no'
if args.use_fixed_camera_params or args.cameras else 'yes'),
"undistorted_image_format: tif",
"bundle_outlier_filtering_type: AUTO",
"align_orientation_prior: vertical",
"triangulation_type: ROBUST",
"bundle_common_position_constraints: %s" %
('no' if reconstruction.multi_camera is None else 'yes'),
]
if args.camera_lens != 'auto':
config.append("camera_projection_type: %s" %
args.camera_lens.upper())
if not has_gps:
log.ODM_INFO("No GPS information, using BOW matching")
use_bow = True
feature_type = args.feature_type.upper()
if use_bow:
config.append("matcher_type: WORDS")
# Cannot use SIFT with BOW
if feature_type == "SIFT":
log.ODM_WARNING(
"Using BOW matching, will use HAHOG feature type, not SIFT"
)
feature_type = "HAHOG"
config.append("feature_type: %s" % feature_type)
if has_alt:
log.ODM_INFO(
"Altitude data detected, enabling it for GPS alignment")
config.append("use_altitude_tag: yes")
gcp_path = reconstruction.gcp.gcp_path
if has_alt or gcp_path:
config.append("align_method: auto")
else:
config.append("align_method: orientation_prior")
if args.use_hybrid_bundle_adjustment:
log.ODM_INFO("Enabling hybrid bundle adjustment")
config.append(
"bundle_interval: 100"
) # Bundle after adding 'bundle_interval' cameras
config.append(
"bundle_new_points_ratio: 1.2"
) # Bundle when (new points) / (bundled points) > bundle_new_points_ratio
config.append(
"local_bundle_radius: 1"
) # Max image graph distance for images to be included in local bundle adjustment
else:
config.append("local_bundle_radius: 0")
if gcp_path:
config.append("bundle_use_gcp: yes")
if not args.force_gps:
config.append("bundle_use_gps: no")
io.copy(gcp_path, self.path("gcp_list.txt"))
config = config + append_config
# write config file
log.ODM_INFO(config)
config_filename = self.get_config_file_path()
with open(config_filename, 'w') as fout:
fout.write("\n".join(config))
else:
log.ODM_WARNING("%s already exists, not rerunning OpenSfM setup" %
list_path)
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, inputs, outputs):
# Benchmarking
start_time = system.now_raw()
log.ODM_INFO('Running ODM OpenSfM Cell')
# get inputs
tree = inputs.tree
args = inputs.args
reconstruction = inputs.reconstruction
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start OpenSfM')
return ecto.QUIT
# create working directories
system.mkdir_p(tree.opensfm)
# check if we rerun cell or not
rerun_cell = (args.rerun is not None and
args.rerun == 'opensfm') or \
(args.rerun_all) or \
(args.rerun_from is not None and
'opensfm' in args.rerun_from)
if args.fast_orthophoto:
output_file = io.join_paths(tree.opensfm, 'reconstruction.ply')
elif args.use_opensfm_dense:
output_file = tree.opensfm_model
else:
output_file = tree.opensfm_reconstruction
# check if reconstruction was done before
if not io.file_exists(output_file) or rerun_cell:
# create file list
list_path = io.join_paths(tree.opensfm, 'image_list.txt')
has_alt = True
with open(list_path, 'w') as fout:
for photo in photos:
if not photo.altitude:
has_alt = False
fout.write('%s\n' % io.join_paths(tree.dataset_raw, photo.filename))
# create config file for OpenSfM
config = [
"use_exif_size: %s" % ('no' if not self.params.use_exif_size else 'yes'),
"feature_process_size: %s" % self.params.feature_process_size,
"feature_min_frames: %s" % self.params.feature_min_frames,
"processes: %s" % self.params.processes,
"matching_gps_neighbors: %s" % self.params.matching_gps_neighbors,
"depthmap_method: %s" % args.opensfm_depthmap_method,
"depthmap_resolution: %s" % args.depthmap_resolution,
"depthmap_min_patch_sd: %s" % args.opensfm_depthmap_min_patch_sd,
"depthmap_min_consistent_views: %s" % args.opensfm_depthmap_min_consistent_views,
"optimize_camera_parameters: %s" % ('no' if self.params.fixed_camera_params else 'yes')
]
if has_alt:
log.ODM_DEBUG("Altitude data detected, enabling it for GPS alignment")
config.append("use_altitude_tag: yes")
config.append("align_method: naive")
else:
config.append("align_method: orientation_prior")
config.append("align_orientation_prior: vertical")
if args.use_hybrid_bundle_adjustment:
log.ODM_DEBUG("Enabling hybrid bundle adjustment")
config.append("bundle_interval: 100") # Bundle after adding 'bundle_interval' cameras
config.append("bundle_new_points_ratio: 1.2") # Bundle when (new points) / (bundled points) > bundle_new_points_ratio
config.append("local_bundle_radius: 1") # Max image graph distance for images to be included in local bundle adjustment
if args.matcher_distance > 0:
config.append("matching_gps_distance: %s" % self.params.matching_gps_distance)
if tree.odm_georeferencing_gcp:
config.append("bundle_use_gcp: yes")
io.copy(tree.odm_georeferencing_gcp, tree.opensfm)
# write config file
log.ODM_DEBUG(config)
config_filename = io.join_paths(tree.opensfm, 'config.yaml')
with open(config_filename, 'w') as fout:
fout.write("\n".join(config))
# run OpenSfM reconstruction
matched_done_file = io.join_paths(tree.opensfm, 'matching_done.txt')
if not io.file_exists(matched_done_file) or rerun_cell:
system.run('PYTHONPATH=%s %s/bin/opensfm extract_metadata %s' %
(context.pyopencv_path, context.opensfm_path, tree.opensfm))
system.run('PYTHONPATH=%s %s/bin/opensfm detect_features %s' %
(context.pyopencv_path, context.opensfm_path, tree.opensfm))
system.run('PYTHONPATH=%s %s/bin/opensfm match_features %s' %
(context.pyopencv_path, context.opensfm_path, tree.opensfm))
with open(matched_done_file, 'w') as fout:
fout.write("Matching done!\n")
else:
log.ODM_WARNING('Found a feature matching done progress file in: %s' %
matched_done_file)
if not io.file_exists(tree.opensfm_tracks) or rerun_cell:
system.run('PYTHONPATH=%s %s/bin/opensfm create_tracks %s' %
(context.pyopencv_path, context.opensfm_path, tree.opensfm))
else:
log.ODM_WARNING('Found a valid OpenSfM tracks file in: %s' %
tree.opensfm_tracks)
if not io.file_exists(tree.opensfm_reconstruction) or rerun_cell:
system.run('PYTHONPATH=%s %s/bin/opensfm reconstruct %s' %
(context.pyopencv_path, context.opensfm_path, tree.opensfm))
else:
log.ODM_WARNING('Found a valid OpenSfM reconstruction file in: %s' %
tree.opensfm_reconstruction)
# Check that a reconstruction file has been created
if not io.file_exists(tree.opensfm_reconstruction):
log.ODM_ERROR("The program could not process this dataset using the current settings. "
"Check that the images have enough overlap, "
"that there are enough recognizable features "
"and that the images are in focus. "
"You could also try to increase the --min-num-features parameter."
"The program will now exit.")
sys.exit(1)
# 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 rerun_cell:
system.run('PYTHONPATH=%s %s/bin/opensfm export_visualsfm --image_extension png --scale_focal %s %s' %
(context.pyopencv_path, context.opensfm_path, image_scale, tree.opensfm))
else:
log.ODM_WARNING('Found a valid OpenSfM NVM reconstruction file in: %s' %
tree.opensfm_reconstruction_nvm)
# These will be used for texturing
system.run('PYTHONPATH=%s %s/bin/opensfm undistort --image_format png --image_scale %s %s' %
(context.pyopencv_path, context.opensfm_path, image_scale, tree.opensfm))
# Skip dense reconstruction if necessary and export
# sparse reconstruction instead
if args.fast_orthophoto:
system.run('PYTHONPATH=%s %s/bin/opensfm export_ply --no-cameras %s' %
(context.pyopencv_path, context.opensfm_path, tree.opensfm))
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)
system.run('PYTHONPATH=%s %s/bin/opensfm undistort %s' %
(context.pyopencv_path, context.opensfm_path, tree.opensfm))
system.run('PYTHONPATH=%s %s/bin/opensfm compute_depthmaps %s' %
(context.pyopencv_path, context.opensfm_path, tree.opensfm))
else:
log.ODM_WARNING('Found a valid OpenSfM reconstruction file in: %s' %
tree.opensfm_reconstruction)
# check if reconstruction was exported to bundler before
if not io.file_exists(tree.opensfm_bundle_list) or rerun_cell:
# convert back to bundler's format
system.run('PYTHONPATH=%s %s/bin/export_bundler %s' %
(context.pyopencv_path, context.opensfm_path, tree.opensfm))
else:
log.ODM_WARNING('Found a valid Bundler file in: %s' %
tree.opensfm_reconstruction)
if reconstruction.georef:
system.run('PYTHONPATH=%s %s/bin/opensfm export_geocoords %s --transformation --proj \'%s\'' %
(context.pyopencv_path, context.opensfm_path, tree.opensfm, reconstruction.georef.projection.srs))
outputs.reconstruction = reconstruction
if args.time:
system.benchmark(start_time, tree.benchmarking, 'OpenSfM')
log.ODM_INFO('Running ODM OpenSfM Cell - Finished')
return ecto.OK if args.end_with != 'opensfm' else ecto.QUIT
