def process(self, inputs, outputs):
# 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)]
log.ODM_INFO('Running ODM Load Dataset Cell')
# get inputs
tree = self.inputs.tree
# get images directory
input_dir = tree.input_images
images_dir = tree.dataset_raw
resize_dir = tree.dataset_resize
# Check first if a project already exists. This is a mediocre way to check, by checking the resize dir
if io.dir_exists(resize_dir):
log.ODM_DEBUG("resize dir: %s" % resize_dir)
images_dir = resize_dir
# if first time running, create project directory and copy images over to project/images
else:
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)]
log.ODM_DEBUG('Loading dataset from: %s' % images_dir)
files = get_images(images_dir)
if files:
# create ODMPhoto list
path_files = [io.join_paths(images_dir, f) for f in files]
photos = Pool().map(
partial(make_odm_photo, self.params.force_focal, self.params.force_ccd),
path_files
)
log.ODM_INFO('Found %s usable images' % len(photos))
else:
log.ODM_ERROR('Not enough supported images in %s' % images_dir)
return ecto.QUIT
# append photos to cell output
outputs.photos = photos
log.ODM_INFO('Running ODM Load Dataset Cell - Finished')
return ecto.OK
def process(self, inputs, outputs):
# check if the extension is sopported
def supported_extension(file_name):
(pathfn, ext) = os.path.splitext(file_name)
return ext.lower() in context.supported_extensions
log.ODM_INFO('Running ODM Load Dataset Cell')
# get inputs
tree = self.inputs.tree
# set images directory
images_dir = tree.dataset_resize
if not io.dir_exists(images_dir):
images_dir = tree.dataset_raw
if not io.dir_exists(images_dir):
log.ODM_ERROR("You must put your pictures into an <images> directory")
return ecto.QUIT
log.ODM_DEBUG('Loading dataset from: %s' % images_dir)
# find files in the given directory
files = io.get_files_list(images_dir)
# filter images for its extension type
files = [f for f in files if supported_extension(f)]
if files:
# create ODMPhoto list
photos = []
for f in files:
path_file = io.join_paths(images_dir, f)
photo = types.ODM_Photo(path_file,
self.params.force_focal,
self.params.force_ccd)
photos.append(photo)
log.ODM_INFO('Found %s usable images' % len(photos))
else:
log.ODM_ERROR('Not enough supported images in %s' % images_dir)
return ecto.QUIT
# append photos to cell output
outputs.photos = photos
log.ODM_INFO('Running ODM Load Dataset Cell - Finished')
return ecto.OK
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, args, outputs):
# get inputs
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start OpenMVS')
exit(1)
# check if reconstruction was done before
if not io.file_exists(tree.openmvs_model) or self.rerun():
if io.dir_exists(tree.openmvs):
shutil.rmtree(tree.openmvs)
# export reconstruction from opensfm
octx = OSFMContext(tree.opensfm)
cmd = 'export_openmvs'
if reconstruction.multi_camera:
# Export only the primary band
primary = reconstruction.multi_camera[0]
image_list = os.path.join(
tree.opensfm,
"image_list_%s.txt" % primary['name'].lower())
cmd += ' --image_list "%s"' % image_list
octx.run(cmd)
self.update_progress(10)
depthmaps_dir = os.path.join(tree.openmvs, "depthmaps")
if not io.dir_exists(depthmaps_dir):
os.mkdir(depthmaps_dir)
if outputs["undist_image_max_size"] <= args.depthmap_resolution:
resolution_level = 0
else:
resolution_level = math.floor(
math.log(outputs['undist_image_max_size'] /
float(args.depthmap_resolution)) / math.log(2))
config = [
" --resolution-level %s" % int(resolution_level),
"--min-resolution %s" % int(args.depthmap_resolution),
"--max-resolution %s" % int(outputs['undist_image_max_size']),
"--max-threads %s" % args.max_concurrency,
'-w "%s"' % depthmaps_dir,
"-v 0",
]
log.ODM_INFO(
"Running dense reconstruction. This might take a while.")
system.run(
'%s "%s" %s' %
(context.omvs_densify_path,
os.path.join(tree.openmvs, 'scene.mvs'), ' '.join(config)))
self.update_progress(85)
# Filter points
scene_dense = os.path.join(tree.openmvs, 'scene_dense.mvs')
if os.path.exists(scene_dense):
config = [
"--filter-point-cloud -1",
'-i "%s"' % scene_dense, "-v 0"
]
system.run('%s %s' %
(context.omvs_densify_path, ' '.join(config)))
else:
log.ODM_WARNING(
"Cannot find scene_dense.mvs, dense reconstruction probably failed. Exiting..."
)
exit(1)
self.update_progress(95)
if args.optimize_disk_space:
files = [
scene_dense,
os.path.join(tree.openmvs, 'scene_dense.ply'),
os.path.join(tree.openmvs,
'scene_dense_dense_filtered.mvs')
]
for f in files:
if os.path.exists(f):
os.remove(f)
shutil.rmtree(depthmaps_dir)
else:
log.ODM_WARNING(
'Found a valid OpenMVS reconstruction file in: %s' %
tree.openmvs_model)
def 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) 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: %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 is_feature_matching_done(self):
features_dir = self.path("features")
matches_dir = self.path("matches")
return io.dir_exists(features_dir) and io.dir_exists(matches_dir)
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
if outputs['large']:
if not os.path.exists(tree.submodels_path):
log.ODM_ERROR(
"We reached the merge stage, but %s folder does not exist. Something must have gone wrong at an earlier stage. Check the log and fix possible problem before restarting?"
% tree.submodels_path)
exit(1)
# Merge point clouds
if args.merge in ['all', 'pointcloud']:
if not io.dir_exists(tree.entwine_pointcloud) or self.rerun():
all_point_clouds = get_submodel_paths(
tree.submodels_path, "odm_georeferencing",
"odm_georeferenced_model.laz")
try:
entwine.build(all_point_clouds,
tree.entwine_pointcloud,
max_concurrency=args.max_concurrency,
rerun=self.rerun())
except Exception as e:
log.ODM_WARNING(
"Could not merge EPT point cloud: %s (skipping)" %
str(e))
else:
log.ODM_WARNING("Found merged EPT point cloud in %s" %
tree.entwine_pointcloud)
if not io.file_exists(
tree.odm_georeferencing_model_laz) or self.rerun():
if io.dir_exists(tree.entwine_pointcloud):
try:
system.run('pdal translate "ept://{}" "{}"'.format(
tree.entwine_pointcloud,
tree.odm_georeferencing_model_laz))
except Exception as e:
log.ODM_WARNING(
"Cannot export EPT dataset to LAZ: %s" %
str(e))
else:
log.ODM_WARNING(
"No EPT point cloud found (%s), skipping LAZ conversion)"
% tree.entwine_pointcloud)
else:
log.ODM_WARNING("Found merged point cloud in %s" %
tree.odm_georeferencing_model_laz)
self.update_progress(25)
# Merge crop bounds
merged_bounds_file = os.path.join(
tree.odm_georeferencing, 'odm_georeferenced_model.bounds.gpkg')
if not io.file_exists(merged_bounds_file) or self.rerun():
all_bounds = get_submodel_paths(
tree.submodels_path, 'odm_georeferencing',
'odm_georeferenced_model.bounds.gpkg')
log.ODM_INFO("Merging all crop bounds: %s" % all_bounds)
if len(all_bounds) > 0:
# Calculate a new crop area
# based on the convex hull of all crop areas of all submodels
# (without a buffer, otherwise we are double-cropping)
Cropper.merge_bounds(all_bounds, merged_bounds_file, 0)
else:
log.ODM_WARNING("No bounds found for any submodel.")
# Merge orthophotos
if args.merge in ['all', 'orthophoto']:
if not io.dir_exists(tree.odm_orthophoto):
system.mkdir_p(tree.odm_orthophoto)
if not io.file_exists(tree.odm_orthophoto_tif) or self.rerun():
all_orthos_and_cutlines = get_all_submodel_paths(
tree.submodels_path,
os.path.join("odm_orthophoto", "odm_orthophoto.tif"),
os.path.join("odm_orthophoto", "cutline.gpkg"),
)
if len(all_orthos_and_cutlines) > 1:
log.ODM_INFO(
"Found %s submodels with valid orthophotos and cutlines"
% len(all_orthos_and_cutlines))
# TODO: histogram matching via rasterio
# currently parts have different color tones
merged_geotiff = os.path.join(
tree.odm_orthophoto, "odm_orthophoto.merged.tif")
kwargs = {
'orthophoto_merged':
merged_geotiff,
'input_files':
' '.join(
map(lambda i: quote(i[0]),
all_orthos_and_cutlines)),
'max_memory':
get_max_memory(),
'threads':
args.max_concurrency,
}
# use bounds as cutlines (blending)
if io.file_exists(merged_geotiff):
os.remove(merged_geotiff)
system.run('gdal_merge.py -o {orthophoto_merged} '
#'-createonly '
'-co "BIGTIFF=YES" '
'-co "BLOCKXSIZE=512" '
'-co "BLOCKYSIZE=512" '
'--config GDAL_CACHEMAX {max_memory}% '
'{input_files} '.format(**kwargs))
for ortho_cutline in all_orthos_and_cutlines:
kwargs['input_file'], kwargs[
'cutline'] = ortho_cutline
# Note: cblend has a high performance penalty
system.run(
'gdalwarp -cutline {cutline} '
'-cblend 20 '
'-r bilinear -multi '
'-wo NUM_THREADS={threads} '
'--config GDAL_CACHEMAX {max_memory}% '
'{input_file} {orthophoto_merged}'.format(
**kwargs))
# Apply orthophoto settings (compression, tiling, etc.)
orthophoto_vars = orthophoto.get_orthophoto_vars(args)
if io.file_exists(tree.odm_orthophoto_tif):
os.remove(tree.odm_orthophoto_tif)
kwargs = {
'vars':
' '.join([
'-co %s=%s' % (k, orthophoto_vars[k])
for k in orthophoto_vars
]),
'max_memory':
get_max_memory(),
'merged':
merged_geotiff,
'log':
tree.odm_orthophoto_tif_log,
'orthophoto':
tree.odm_orthophoto_tif,
}
system.run(
'gdal_translate '
'{vars} '
'--config GDAL_CACHEMAX {max_memory}% '
'{merged} {orthophoto} > {log}'.format(**kwargs))
os.remove(merged_geotiff)
# Crop
if args.crop > 0:
Cropper.crop(merged_bounds_file,
tree.odm_orthophoto_tif,
orthophoto_vars)
# Overviews
if args.build_overviews:
orthophoto.build_overviews(tree.odm_orthophoto_tif)
elif len(all_orthos_and_cutlines) == 1:
# Simply copy
log.ODM_WARNING(
"A single orthophoto/cutline pair was found between all submodels."
)
shutil.copyfile(all_orthos_and_cutlines[0][0],
tree.odm_orthophoto_tif)
else:
log.ODM_WARNING(
"No orthophoto/cutline pairs were found in any of the submodels. No orthophoto will be generated."
)
else:
log.ODM_WARNING("Found merged orthophoto in %s" %
tree.odm_orthophoto_tif)
self.update_progress(75)
# Merge DEMs
def merge_dems(dem_filename, human_name):
if not io.dir_exists(tree.path('odm_dem')):
system.mkdir_p(tree.path('odm_dem'))
dem_file = tree.path("odm_dem", dem_filename)
if not io.file_exists(dem_file) or self.rerun():
all_dems = get_submodel_paths(tree.submodels_path,
"odm_dem", dem_filename)
log.ODM_INFO("Merging %ss" % human_name)
# Merge
dem_vars = utils.get_dem_vars(args)
eu_map_source = None # Default
# Use DSM's euclidean map for DTMs
# (requires the DSM to be computed)
if human_name == "DTM":
eu_map_source = "dsm"
euclidean_merge_dems(all_dems,
dem_file,
dem_vars,
euclidean_map_source=eu_map_source)
if io.file_exists(dem_file):
# Crop
if args.crop > 0:
Cropper.crop(merged_bounds_file, dem_file,
dem_vars)
log.ODM_INFO("Created %s" % dem_file)
else:
log.ODM_WARNING("Cannot merge %s, %s was not created" %
(human_name, dem_file))
else:
log.ODM_WARNING("Found merged %s in %s" %
(human_name, dem_filename))
if args.merge in ['all', 'dem'] and args.dsm:
merge_dems("dsm.tif", "DSM")
if args.merge in ['all', 'dem'] and args.dtm:
merge_dems("dtm.tif", "DTM")
# Stop the pipeline short! We're done.
self.next_stage = None
else:
log.ODM_INFO("Normal dataset, nothing to merge.")
self.progress = 0.0
def process(self, inputs, outputs):
# 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)]
log.ODM_INFO('Running ODM Load Dataset Cell')
# get inputs
tree = self.inputs.tree
args = self.inputs.args
# 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 args.use_25dmesh: system.mkdir_p(tree.odm_25dgeoreferencing)
log.ODM_DEBUG('Loading dataset from: %s' % images_dir)
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 files in path_files:
photos += [make_odm_photo(self.params.force_focal, self.params.force_ccd, files)]
dataset_list.write(photos[-1].filename + '\n')
log.ODM_INFO('Found %s usable images' % len(photos))
else:
log.ODM_ERROR('Not enough supported images in %s' % images_dir)
return ecto.QUIT
# append photos to cell output
if not self.params.proj:
if tree.odm_georeferencing_gcp:
outputs.reconstruction = types.ODM_Reconstruction(photos, coords_file=tree.odm_georeferencing_gcp)
else:
verbose = '-verbose' if self.params.verbose else ''
# Generate UTM from images
# odm_georeference definitions
kwargs = {
'bin': context.odm_modules_path,
'imgs': tree.dataset_raw,
'imgs_list': tree.dataset_list,
'coords': tree.odm_georeferencing_coords,
'log': tree.odm_georeferencing_utm_log,
'verbose': verbose
}
# run UTM extraction binary
extract_utm = system.run_and_return('{bin}/odm_extract_utm -imagesPath {imgs}/ '
'-imageListFile {imgs_list} -outputCoordFile {coords} {verbose} '
'-logFile {log}'.format(**kwargs))
if extract_utm != '':
log.ODM_WARNING('Could not generate coordinates file. '
'Ignore if there is a GCP file. Error: %s'
% extract_utm)
outputs.reconstruction = types.ODM_Reconstruction(photos, coords_file=tree.odm_georeferencing_coords)
else:
outputs.reconstruction = types.ODM_Reconstruction(photos, projstring=self.params.proj)
# Save proj to file for future use
with open(io.join_paths(tree.odm_georeferencing, tree.odm_georeferencing_proj), 'w') as f:
f.write(outputs.reconstruction.projection.srs)
log.ODM_INFO('Running ODM Load Dataset Cell - Finished')
return ecto.OK if args.end_with != 'dataset' else ecto.QUIT
def process(self, args, outputs):
# get inputs
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start OpenMVS')
exit(1)
# check if reconstruction was done before
if not io.file_exists(tree.openmvs_model) or self.rerun():
if self.rerun():
if io.dir_exists(tree.openmvs):
shutil.rmtree(tree.openmvs)
# export reconstruction from opensfm
openmvs_scene_file = os.path.join(tree.openmvs, "scene.mvs")
if not io.file_exists(openmvs_scene_file) or self.rerun():
octx = OSFMContext(tree.opensfm)
cmd = 'export_openmvs'
octx.run(cmd)
else:
log.ODM_WARNING("Found existing %s" % openmvs_scene_file)
self.update_progress(10)
depthmaps_dir = os.path.join(tree.openmvs, "depthmaps")
if io.dir_exists(depthmaps_dir) and self.rerun():
shutil.rmtree(depthmaps_dir)
if not io.dir_exists(depthmaps_dir):
os.mkdir(depthmaps_dir)
depthmap_resolution = get_depthmap_resolution(args, photos)
if outputs["undist_image_max_size"] <= depthmap_resolution:
resolution_level = 0
else:
resolution_level = int(
round(math.log(outputs['undist_image_max_size'] / float(depthmap_resolution)) / math.log(2)))
log.ODM_INFO("Running dense reconstruction. This might take a while.")
log.ODM_INFO("Estimating depthmaps")
densify_ini_file = os.path.join(tree.openmvs, 'config.ini')
with open(densify_ini_file, 'w+') as f:
f.write("Optimize = 0\n") # Disable depth-maps re-filtering
config = [
" --resolution-level %s" % int(resolution_level),
"--min-resolution %s" % depthmap_resolution,
"--max-resolution %s" % int(outputs['undist_image_max_size']),
"--max-threads %s" % args.max_concurrency,
"--number-views-fuse 2",
'-w "%s"' % depthmaps_dir,
"-v 0"
]
if args.pc_tile:
config.append("--fusion-mode 1")
system.run('%s "%s" %s' % (context.omvs_densify_path,
openmvs_scene_file,
' '.join(config)))
self.update_progress(85)
files_to_remove = []
scene_dense = os.path.join(tree.openmvs, 'scene_dense.mvs')
if args.pc_tile:
log.ODM_INFO("Computing sub-scenes")
config = [
"--sub-scene-area 660000",
"--max-threads %s" % args.max_concurrency,
'-w "%s"' % depthmaps_dir,
"-v 0",
]
system.run('%s "%s" %s' % (context.omvs_densify_path,
openmvs_scene_file,
' '.join(config)))
scene_files = glob.glob(os.path.join(tree.openmvs, "scene_[0-9][0-9][0-9][0-9].mvs"))
if len(scene_files) == 0:
log.ODM_ERROR(
"No OpenMVS scenes found. This could be a bug, or the reconstruction could not be processed.")
exit(1)
log.ODM_INFO("Fusing depthmaps for %s scenes" % len(scene_files))
scene_ply_files = []
for sf in scene_files:
p, _ = os.path.splitext(sf)
scene_ply = p + "_dense_dense_filtered.ply"
scene_dense_mvs = p + "_dense.mvs"
files_to_remove += [scene_ply, sf, scene_dense_mvs]
scene_ply_files.append(scene_ply)
if not io.file_exists(scene_ply) or self.rerun():
# Fuse
config = [
'--resolution-level %s' % int(resolution_level),
'--min-resolution %s' % depthmap_resolution,
'--max-resolution %s' % int(outputs['undist_image_max_size']),
'--dense-config-file "%s"' % densify_ini_file,
'--number-views-fuse 2',
'--max-threads %s' % args.max_concurrency,
'-w "%s"' % depthmaps_dir,
'-v 0',
]
try:
system.run('%s "%s" %s' % (context.omvs_densify_path, sf, ' '.join(config)))
# Filter
system.run(
'%s "%s" --filter-point-cloud -1 -v 0' % (context.omvs_densify_path, scene_dense_mvs))
except:
log.ODM_WARNING("Sub-scene %s could not be reconstructed, skipping..." % sf)
if not io.file_exists(scene_ply):
scene_ply_files.pop()
log.ODM_WARNING("Could not compute PLY for subscene %s" % sf)
else:
log.ODM_WARNING("Found existing dense scene file %s" % scene_ply)
# Merge
log.ODM_INFO("Merging %s scene files" % len(scene_ply_files))
if len(scene_ply_files) == 0:
log.ODM_ERROR("Could not compute dense point cloud (no PLY files available).")
if len(scene_ply_files) == 1:
# Simply rename
os.rename(scene_ply_files[0], tree.openmvs_model)
log.ODM_INFO("%s --> %s" % (scene_ply_files[0], tree.openmvs_model))
else:
# Merge
fast_merge_ply(scene_ply_files, tree.openmvs_model)
else:
# Filter all at once
if os.path.exists(scene_dense):
config = [
"--filter-point-cloud -1",
'-i "%s"' % scene_dense,
"-v 0"
]
system.run('%s %s' % (context.omvs_densify_path, ' '.join(config)))
else:
log.ODM_WARNING("Cannot find scene_dense.mvs, dense reconstruction probably failed. Exiting...")
exit(1)
# TODO: add support for image masks
self.update_progress(95)
if args.optimize_disk_space:
files = [scene_dense,
os.path.join(tree.openmvs, 'scene_dense.ply'),
os.path.join(tree.openmvs, 'scene_dense_dense_filtered.mvs'),
octx.path("undistorted", "tracks.csv"),
octx.path("undistorted", "reconstruction.json")
] + files_to_remove
for f in files:
if os.path.exists(f):
os.remove(f)
shutil.rmtree(depthmaps_dir)
else:
log.ODM_WARNING('Found a valid OpenMVS reconstruction file in: %s' % tree.openmvs_model)
from opendm import io
import ecto
import os
from scripts.odm_app import ODMApp
if __name__ == '__main__':
args = config.config()
log.ODM_INFO('Initializing OpenDroneMap app - %s' % system.now())
# Add project dir if doesn't exist
args.project_path = io.join_paths(args.project_path, args.name)
if not io.dir_exists(args.project_path):
log.ODM_WARNING('Directory %s does not exist. Creating it now.' %
args.name)
system.mkdir_p(os.path.abspath(args.project_path))
# If user asks to rerun everything, delete all of the existing progress directories.
# TODO: Move this somewhere it's not hard-coded
if args.rerun_all:
log.ODM_DEBUG("Rerun all -- Removing old data")
os.system("rm -rf " + args.project_path + "/images_resize " +
args.project_path + "/odm_georeferencing " +
args.project_path + "/odm_meshing " + args.project_path +
"/odm_orthophoto " + args.project_path + "/odm_texturing " +
args.project_path + "/opensfm " + args.project_path +
"/smvs")
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'),
"undistorted_image_format: png" # mvs-texturing exhibits artifacts with JPG
]
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 = self.get_config_file_path()
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 process(self, args, outputs):
# get inputs
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start OpenMVS')
exit(1)
# check if reconstruction was done before
if not io.file_exists(tree.openmvs_model) or self.rerun():
if io.dir_exists(tree.openmvs):
shutil.rmtree(tree.openmvs)
# export reconstruction from opensfm
octx = OSFMContext(tree.opensfm)
cmd = 'export_openmvs'
octx.run(cmd)
self.update_progress(10)
depthmaps_dir = os.path.join(tree.openmvs, "depthmaps")
if not io.dir_exists(depthmaps_dir):
os.mkdir(depthmaps_dir)
depthmap_resolution = get_depthmap_resolution(args, photos)
if outputs["undist_image_max_size"] <= depthmap_resolution:
resolution_level = 0
else:
resolution_level = int(
round(
math.log(outputs['undist_image_max_size'] /
float(depthmap_resolution)) / math.log(2)))
config = [
" --resolution-level %s" % int(resolution_level),
"--min-resolution %s" % depthmap_resolution,
"--max-resolution %s" % int(outputs['undist_image_max_size']),
"--max-threads %s" % args.max_concurrency,
"--number-views-fuse 3",
'-w "%s"' % depthmaps_dir,
"-v 0",
]
log.ODM_INFO(
"Running dense reconstruction. This might take a while.")
# TODO: add support for image masks
system.run(
'%s "%s" %s' %
(context.omvs_densify_path,
os.path.join(tree.openmvs, 'scene.mvs'), ' '.join(config)))
self.update_progress(85)
# Filter points
scene_dense = os.path.join(tree.openmvs, 'scene_dense.mvs')
if os.path.exists(scene_dense):
config = [
"--filter-point-cloud -1",
'-i "%s"' % scene_dense, "-v 0"
]
system.run('%s %s' %
(context.omvs_densify_path, ' '.join(config)))
else:
log.ODM_WARNING(
"Cannot find scene_dense.mvs, dense reconstruction probably failed. Exiting..."
)
exit(1)
self.update_progress(95)
if args.optimize_disk_space:
files = [
scene_dense,
os.path.join(tree.openmvs, 'scene_dense.ply'),
os.path.join(tree.openmvs,
'scene_dense_dense_filtered.mvs'),
octx.path("undistorted", "tracks.csv"),
octx.path("undistorted", "reconstruction.json")
]
for f in files:
if os.path.exists(f):
os.remove(f)
shutil.rmtree(depthmaps_dir)
else:
log.ODM_WARNING(
'Found a valid OpenMVS reconstruction file in: %s' %
tree.openmvs_model)
def process(self, inputs, outputs):
# Benchmarking
start_time = system.now_raw()
log.ODM_INFO('Running ODM DEM Cell')
# get inputs
args = self.inputs.args
tree = self.inputs.tree
las_model_found = io.file_exists(tree.odm_georeferencing_model_laz)
# check if we rerun cell or not
rerun_cell = (args.rerun is not None and
args.rerun == 'odm_dem') or \
(args.rerun_all) or \
(args.rerun_from is not None and
'odm_dem' in args.rerun_from)
log.ODM_INFO('Classify: ' + str(args.pc_classify))
log.ODM_INFO('Create DSM: ' + str(args.dsm))
log.ODM_INFO('Create DTM: ' + str(args.dtm))
log.ODM_INFO('DEM input file {0} found: {1}'.format(
tree.odm_georeferencing_model_laz, str(las_model_found)))
slope, cellsize = (0.15, 1)
# define paths and create working directories
odm_dem_root = tree.path('odm_dem')
if not io.dir_exists(odm_dem_root):
system.mkdir_p(odm_dem_root)
if args.pc_classify and las_model_found:
pc_classify_marker = os.path.join(odm_dem_root,
'pc_classify_done.txt')
if not io.file_exists(pc_classify_marker) or rerun_cell:
log.ODM_INFO(
"Classifying {} using Simple Morphological Filter".format(
tree.odm_georeferencing_model_laz))
commands.classify(tree.odm_georeferencing_model_laz,
slope,
cellsize,
verbose=args.verbose)
with open(pc_classify_marker, 'w') as f:
f.write('Classify: smrf\n')
f.write('Slope: {}\n'.format(slope))
f.write('Cellsize: {}\n'.format(cellsize))
# Do we need to process anything here?
if (args.dsm or args.dtm) and las_model_found:
dsm_output_filename = os.path.join(odm_dem_root, 'dsm.tif')
dtm_output_filename = os.path.join(odm_dem_root, 'dtm.tif')
if (args.dtm and not io.file_exists(dtm_output_filename)) or \
(args.dsm and not io.file_exists(dsm_output_filename)) or \
rerun_cell:
products = []
if args.dsm: products.append('dsm')
if args.dtm: products.append('dtm')
resolution = gsd.cap_resolution(args.dem_resolution,
tree.opensfm_reconstruction,
gsd_error_estimate=-3,
ignore_gsd=args.ignore_gsd)
radius_steps = [(resolution / 100.0) / 2.0]
for _ in range(args.dem_gapfill_steps - 1):
radius_steps.append(
radius_steps[-1] *
2) # 2 is arbitrary, maybe there's a better value?
for product in products:
commands.create_dems(
[tree.odm_georeferencing_model_laz],
product,
radius=map(str, radius_steps),
gapfill=True,
outdir=odm_dem_root,
resolution=resolution / 100.0,
decimation=args.dem_decimation,
verbose=args.verbose,
max_workers=get_max_concurrency_for_dem(
args.max_concurrency,
tree.odm_georeferencing_model_laz))
if args.crop > 0:
bounds_shapefile_path = os.path.join(
tree.odm_georeferencing,
'odm_georeferenced_model.bounds.shp')
if os.path.exists(bounds_shapefile_path):
Cropper.crop(
bounds_shapefile_path,
os.path.join(odm_dem_root,
"{}.tif".format(product)),
{
'TILED': 'YES',
'COMPRESS': 'LZW',
'BLOCKXSIZE': 512,
'BLOCKYSIZE': 512,
'NUM_THREADS': self.params.max_concurrency
})
else:
log.ODM_WARNING('Found existing outputs in: %s' % odm_dem_root)
else:
log.ODM_WARNING('DEM will not be generated')
if args.time:
system.benchmark(start_time, tree.benchmarking, 'Dem')
log.ODM_INFO('Running ODM DEM Cell - Finished')
return ecto.OK if args.end_with != 'odm_dem' else ecto.QUIT
def process(self, inputs, outputs):
# Benchmarking
start_time = system.now_raw()
log.ODM_INFO('Running ODM DEM Cell')
# get inputs
args = self.inputs.args
tree = self.inputs.tree
las_model_found = io.file_exists(tree.odm_georeferencing_model_laz)
# check if we rerun cell or not
rerun_cell = (args.rerun is not None and
args.rerun == 'odm_dem') or \
(args.rerun_all) or \
(args.rerun_from is not None and
'odm_dem' in args.rerun_from)
log.ODM_INFO('Classify: ' + str(args.pc_classify != "none"))
log.ODM_INFO('Create DSM: ' + str(args.dsm))
log.ODM_INFO('Create DTM: ' + str(args.dtm))
log.ODM_INFO('DEM input file {0} found: {1}'.format(
tree.odm_georeferencing_model_laz, str(las_model_found)))
# Setup terrain parameters
terrain_params_map = {
'flatnonforest': (1, 3),
'flatforest': (1, 2),
'complexnonforest': (5, 2),
'complexforest': (10, 2)
}
terrain_params = terrain_params_map[args.dem_terrain_type.lower()]
slope, cellsize = terrain_params
# define paths and create working directories
odm_dem_root = tree.path('odm_dem')
if not io.dir_exists(odm_dem_root):
system.mkdir_p(odm_dem_root)
if args.pc_classify != "none" and las_model_found:
pc_classify_marker = os.path.join(odm_dem_root,
'pc_classify_done.txt')
if not io.file_exists(pc_classify_marker) or rerun_cell:
log.ODM_INFO("Classifying {} using {}".format(
tree.odm_georeferencing_model_laz, args.pc_classify))
commands.classify(tree.odm_georeferencing_model_laz,
args.pc_classify == "smrf",
slope,
cellsize,
approximate=args.dem_approximate,
initialDistance=args.dem_initial_distance,
verbose=args.verbose)
with open(pc_classify_marker, 'w') as f:
f.write('Classify: {}\n'.format(args.pc_classify))
f.write('Slope: {}\n'.format(slope))
f.write('Cellsize: {}\n'.format(cellsize))
f.write('Approximate: {}\n'.format(args.dem_approximate))
f.write('InitialDistance: {}\n'.format(
args.dem_initial_distance))
# Do we need to process anything here?
if (args.dsm or args.dtm) and las_model_found:
dsm_output_filename = os.path.join(odm_dem_root, 'dsm.tif')
dtm_output_filename = os.path.join(odm_dem_root, 'dtm.tif')
if (args.dtm and not io.file_exists(dtm_output_filename)) or \
(args.dsm and not io.file_exists(dsm_output_filename)) or \
rerun_cell:
products = []
if args.dsm: products.append('dsm')
if args.dtm: products.append('dtm')
radius_steps = [args.dem_resolution]
for _ in range(args.dem_gapfill_steps - 1):
radius_steps.append(
radius_steps[-1] *
3) # 3 is arbitrary, maybe there's a better value?
for product in products:
commands.create_dems([tree.odm_georeferencing_model_laz],
product,
radius=map(str, radius_steps),
gapfill=True,
outdir=odm_dem_root,
resolution=args.dem_resolution,
maxsd=args.dem_maxsd,
maxangle=args.dem_maxangle,
decimation=args.dem_decimation,
verbose=args.verbose)
if args.crop > 0:
bounds_shapefile_path = os.path.join(
tree.odm_georeferencing,
'odm_georeferenced_model.bounds.shp')
if os.path.exists(bounds_shapefile_path):
Cropper.crop(
bounds_shapefile_path,
os.path.join(odm_dem_root,
"{}.tif".format(product)), {
'TILED': 'YES',
'COMPRESS': 'LZW',
'BLOCKXSIZE': 512,
'BLOCKYSIZE': 512,
'NUM_THREADS': 'ALL_CPUS'
})
else:
log.ODM_WARNING('Found existing outputs in: %s' % odm_dem_root)
else:
log.ODM_WARNING('DEM will not be generated')
if args.time:
system.benchmark(start_time, tree.benchmarking, 'Dem')
log.ODM_INFO('Running ODM DEM Cell - Finished')
return ecto.OK if args.end_with != 'odm_dem' else ecto.QUIT
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start OpenSfM')
exit(1)
octx = OSFMContext(tree.opensfm)
octx.setup(args,
tree.dataset_raw,
photos,
reconstruction=reconstruction,
rerun=self.rerun())
octx.extract_metadata(self.rerun())
self.update_progress(20)
octx.feature_matching(self.rerun())
self.update_progress(30)
octx.reconstruct(self.rerun())
octx.extract_cameras(tree.path("cameras.json"), self.rerun())
self.update_progress(70)
# If we find a special flag file for split/merge we stop right here
if os.path.exists(octx.path("split_merge_stop_at_reconstruction.txt")):
log.ODM_INFO("Stopping OpenSfM early because we found: %s" %
octx.path("split_merge_stop_at_reconstruction.txt"))
self.next_stage = None
return
if args.fast_orthophoto:
output_file = octx.path('reconstruction.ply')
elif args.use_opensfm_dense:
output_file = tree.opensfm_model
else:
output_file = tree.opensfm_reconstruction
updated_config_flag_file = octx.path('updated_config.txt')
# Make sure it's capped by the depthmap-resolution arg,
# since the undistorted images are used for MVS
outputs['undist_image_max_size'] = max(
gsd.image_max_size(photos,
args.orthophoto_resolution,
tree.opensfm_reconstruction,
ignore_gsd=args.ignore_gsd,
has_gcp=reconstruction.has_gcp()),
args.depthmap_resolution)
if not io.file_exists(updated_config_flag_file) or self.rerun():
octx.update_config({
'undistorted_image_max_size':
outputs['undist_image_max_size']
})
octx.touch(updated_config_flag_file)
# These will be used for texturing / MVS
undistorted_images_path = octx.path("undistorted", "images")
if not io.dir_exists(undistorted_images_path) or self.rerun():
octx.run('undistort')
else:
log.ODM_WARNING("Found an undistorted directory in %s" %
undistorted_images_path)
self.update_progress(80)
if reconstruction.multi_camera:
# Dump band image lists
log.ODM_INFO("Multiple bands found")
for band in reconstruction.multi_camera:
log.ODM_INFO("Exporting %s band" % band['name'])
image_list_file = octx.path("image_list_%s.txt" %
band['name'].lower())
if not io.file_exists(image_list_file) or self.rerun():
with open(image_list_file, "w") as f:
f.write("\n".join([p.filename
for p in band['photos']]))
log.ODM_INFO("Wrote %s" % image_list_file)
else:
log.ODM_WARNING(
"Found a valid image list in %s for %s band" %
(image_list_file, band['name']))
nvm_file = octx.path(
"undistorted",
"reconstruction_%s.nvm" % band['name'].lower())
if not io.file_exists(nvm_file) or self.rerun():
octx.run('export_visualsfm --points --image_list "%s"' %
image_list_file)
os.rename(tree.opensfm_reconstruction_nvm, nvm_file)
else:
log.ODM_WARNING(
"Found a valid NVM file in %s for %s band" %
(nvm_file, band['name']))
if not io.file_exists(tree.opensfm_reconstruction_nvm) or self.rerun():
octx.run('export_visualsfm --points')
else:
log.ODM_WARNING(
'Found a valid OpenSfM NVM reconstruction file in: %s' %
tree.opensfm_reconstruction_nvm)
self.update_progress(85)
# Skip dense reconstruction if necessary and export
# sparse reconstruction instead
if args.fast_orthophoto:
if not io.file_exists(output_file) or self.rerun():
octx.run('export_ply --no-cameras')
else:
log.ODM_WARNING("Found a valid PLY reconstruction in %s" %
output_file)
elif args.use_opensfm_dense:
if not io.file_exists(output_file) or self.rerun():
octx.run('compute_depthmaps')
else:
log.ODM_WARNING("Found a valid dense reconstruction in %s" %
output_file)
self.update_progress(90)
if reconstruction.is_georeferenced() and (not io.file_exists(
tree.opensfm_transformation) or self.rerun()):
octx.run('export_geocoords --transformation --proj \'%s\'' %
reconstruction.georef.proj4())
else:
log.ODM_WARNING("Will skip exporting %s" %
tree.opensfm_transformation)
def process(self, inputs, outputs):
# Benchmarking
start_time = system.now_raw()
log.ODM_INFO('Running MVS Texturing Cell')
# get inputs
args = inputs.args
tree = inputs.tree
reconstruction = inputs.reconstruction
# define paths and create working directories
system.mkdir_p(tree.odm_texturing)
if not args.use_3dmesh: system.mkdir_p(tree.odm_25dtexturing)
# check if we rerun cell or not
rerun_cell = (args.rerun is not None and
args.rerun == 'mvs_texturing') or \
(args.rerun_all) or \
(args.rerun_from is not None and
'mvs_texturing' in args.rerun_from)
runs = [{
'out_dir': tree.odm_texturing,
'model': tree.odm_mesh,
'nadir': False
}]
if args.skip_3dmodel:
runs = []
if not args.use_3dmesh:
runs += [{
'out_dir': tree.odm_25dtexturing,
'model': tree.odm_25dmesh,
'nadir': True
}]
for r in runs:
odm_textured_model_obj = os.path.join(r['out_dir'], tree.odm_textured_model_obj)
if not io.file_exists(odm_textured_model_obj) or rerun_cell:
log.ODM_DEBUG('Writing MVS Textured file in: %s'
% odm_textured_model_obj)
# Format arguments to fit Mvs-Texturing app
skipGeometricVisibilityTest = ""
skipGlobalSeamLeveling = ""
skipLocalSeamLeveling = ""
skipHoleFilling = ""
keepUnseenFaces = ""
nadir = ""
if (self.params.skip_vis_test):
skipGeometricVisibilityTest = "--skip_geometric_visibility_test"
if (self.params.skip_glob_seam_leveling):
skipGlobalSeamLeveling = "--skip_global_seam_leveling"
if (self.params.skip_loc_seam_leveling):
skipLocalSeamLeveling = "--skip_local_seam_leveling"
if (self.params.skip_hole_fill):
skipHoleFilling = "--skip_hole_filling"
if (self.params.keep_unseen_faces):
keepUnseenFaces = "--keep_unseen_faces"
if (r['nadir']):
nadir = '--nadir_mode'
# mvstex definitions
kwargs = {
'bin': context.mvstex_path,
'out_dir': io.join_paths(r['out_dir'], "odm_textured_model"),
'model': r['model'],
'dataTerm': self.params.data_term,
'outlierRemovalType': self.params.outlier_rem_type,
'skipGeometricVisibilityTest': skipGeometricVisibilityTest,
'skipGlobalSeamLeveling': skipGlobalSeamLeveling,
'skipLocalSeamLeveling': skipLocalSeamLeveling,
'skipHoleFilling': skipHoleFilling,
'keepUnseenFaces': keepUnseenFaces,
'toneMapping': self.params.tone_mapping,
'nadirMode': nadir,
'nadirWeight': 2 ** args.texturing_nadir_weight - 1,
'nvm_file': io.join_paths(tree.opensfm, "reconstruction.nvm")
}
# Make sure tmp directory is empty
mvs_tmp_dir = os.path.join(r['out_dir'], 'tmp')
if io.dir_exists(mvs_tmp_dir):
log.ODM_INFO("Removing old tmp directory {}".format(mvs_tmp_dir))
shutil.rmtree(mvs_tmp_dir)
# run texturing binary
system.run('{bin} {nvm_file} {model} {out_dir} '
'-d {dataTerm} -o {outlierRemovalType} '
'-t {toneMapping} '
'{skipGeometricVisibilityTest} '
'{skipGlobalSeamLeveling} '
'{skipLocalSeamLeveling} '
'{skipHoleFilling} '
'{keepUnseenFaces} '
'{nadirMode} '
'-n {nadirWeight}'.format(**kwargs))
else:
log.ODM_WARNING('Found a valid ODM Texture file in: %s'
% odm_textured_model_obj)
outputs.reconstruction = reconstruction
if args.time:
system.benchmark(start_time, tree.benchmarking, 'Texturing')
log.ODM_INFO('Running ODM Texturing Cell - Finished')
return ecto.OK if args.end_with != 'mvs_texturing' else ecto.QUIT
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start OpenSfM')
exit(1)
octx = OSFMContext(tree.opensfm)
octx.setup(args,
tree.dataset_raw,
photos,
gcp_path=tree.odm_georeferencing_gcp,
rerun=self.rerun())
octx.extract_metadata(self.rerun())
self.update_progress(20)
octx.feature_matching(self.rerun())
self.update_progress(30)
octx.reconstruct(self.rerun())
self.update_progress(70)
# If we find a special flag file for split/merge we stop right here
if os.path.exists(octx.path("split_merge_stop_at_reconstruction.txt")):
log.ODM_INFO("Stopping OpenSfM early because we found: %s" %
octx.path("split_merge_stop_at_reconstruction.txt"))
self.next_stage = None
return
if args.fast_orthophoto:
output_file = octx.path('reconstruction.ply')
elif args.use_opensfm_dense:
output_file = tree.opensfm_model
else:
output_file = tree.opensfm_reconstruction
# Always export VisualSFM's reconstruction and undistort images
# as we'll use these for texturing (after GSD estimation and resizing)
if not args.ignore_gsd:
image_scale = gsd.image_scale_factor(args.orthophoto_resolution,
tree.opensfm_reconstruction)
else:
image_scale = 1.0
if not io.file_exists(tree.opensfm_reconstruction_nvm) or self.rerun():
octx.run(
'export_visualsfm --image_extension png --scale_focal %s' %
image_scale)
else:
log.ODM_WARNING(
'Found a valid OpenSfM NVM reconstruction file in: %s' %
tree.opensfm_reconstruction_nvm)
# These will be used for texturing
undistorted_images_path = octx.path("undistorted")
if not io.dir_exists(undistorted_images_path) or self.rerun():
octx.run('undistort --image_format png --image_scale %s' %
image_scale)
else:
log.ODM_WARNING("Found an undistorted directory in %s" %
undistorted_images_path)
self.update_progress(80)
# Skip dense reconstruction if necessary and export
# sparse reconstruction instead
if args.fast_orthophoto:
if not io.file_exists(output_file) or self.rerun():
octx.run('export_ply --no-cameras')
else:
log.ODM_WARNING("Found a valid PLY reconstruction in %s" %
output_file)
elif args.use_opensfm_dense:
# Undistort images at full scale in JPG
# (TODO: we could compare the size of the PNGs if they are < than depthmap_resolution
# and use those instead of re-exporting full resolution JPGs)
if not io.file_exists(output_file) or self.rerun():
octx.run('undistort')
octx.run('compute_depthmaps')
else:
log.ODM_WARNING("Found a valid dense reconstruction in %s" %
output_file)
# check if reconstruction was exported to bundler before
octx.export_bundler(tree.opensfm_bundle_list, self.rerun())
self.update_progress(90)
if reconstruction.georef and (not io.file_exists(
tree.opensfm_transformation) or self.rerun()):
octx.run('export_geocoords --transformation --proj \'%s\'' %
reconstruction.georef.projection.srs)
else:
log.ODM_WARNING("Will skip exporting %s" %
tree.opensfm_transformation)
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
dem_input = tree.odm_georeferencing_model_laz
pc_model_found = io.file_exists(dem_input)
ignore_resolution = False
pseudo_georeference = False
if not reconstruction.is_georeferenced():
log.ODM_WARNING("Not georeferenced, using ungeoreferenced point cloud...")
ignore_resolution = True
pseudo_georeference = True
resolution = gsd.cap_resolution(args.dem_resolution, tree.opensfm_reconstruction,
gsd_error_estimate=-3,
ignore_gsd=args.ignore_gsd,
ignore_resolution=ignore_resolution,
has_gcp=reconstruction.has_gcp())
log.ODM_INFO('Classify: ' + str(args.pc_classify))
log.ODM_INFO('Create DSM: ' + str(args.dsm))
log.ODM_INFO('Create DTM: ' + str(args.dtm))
log.ODM_INFO('DEM input file {0} found: {1}'.format(dem_input, str(pc_model_found)))
# define paths and create working directories
odm_dem_root = tree.path('odm_dem')
if not io.dir_exists(odm_dem_root):
system.mkdir_p(odm_dem_root)
if args.pc_classify and pc_model_found:
pc_classify_marker = os.path.join(odm_dem_root, 'pc_classify_done.txt')
if not io.file_exists(pc_classify_marker) or self.rerun():
log.ODM_INFO("Classifying {} using Simple Morphological Filter".format(dem_input))
commands.classify(dem_input,
args.smrf_scalar,
args.smrf_slope,
args.smrf_threshold,
args.smrf_window,
verbose=args.verbose
)
with open(pc_classify_marker, 'w') as f:
f.write('Classify: smrf\n')
f.write('Scalar: {}\n'.format(args.smrf_scalar))
f.write('Slope: {}\n'.format(args.smrf_slope))
f.write('Threshold: {}\n'.format(args.smrf_threshold))
f.write('Window: {}\n'.format(args.smrf_window))
progress = 20
self.update_progress(progress)
if args.pc_rectify:
commands.rectify(dem_input, args.debug)
# Do we need to process anything here?
if (args.dsm or args.dtm) and pc_model_found:
dsm_output_filename = os.path.join(odm_dem_root, 'dsm.tif')
dtm_output_filename = os.path.join(odm_dem_root, 'dtm.tif')
if (args.dtm and not io.file_exists(dtm_output_filename)) or \
(args.dsm and not io.file_exists(dsm_output_filename)) or \
self.rerun():
products = []
if args.dsm or (args.dtm and args.dem_euclidean_map): products.append('dsm')
if args.dtm: products.append('dtm')
radius_steps = [(resolution / 100.0) / 2.0]
for _ in range(args.dem_gapfill_steps - 1):
radius_steps.append(radius_steps[-1] * 2) # 2 is arbitrary, maybe there's a better value?
for product in products:
commands.create_dem(
dem_input,
product,
output_type='idw' if product == 'dtm' else 'max',
radiuses=list(map(str, radius_steps)),
gapfill=args.dem_gapfill_steps > 0,
outdir=odm_dem_root,
resolution=resolution / 100.0,
decimation=args.dem_decimation,
verbose=args.verbose,
max_workers=args.max_concurrency,
keep_unfilled_copy=args.dem_euclidean_map
)
dem_geotiff_path = os.path.join(odm_dem_root, "{}.tif".format(product))
bounds_file_path = os.path.join(tree.odm_georeferencing, 'odm_georeferenced_model.bounds.gpkg')
if args.crop > 0:
# Crop DEM
Cropper.crop(bounds_file_path, dem_geotiff_path, utils.get_dem_vars(args), keep_original=not args.optimize_disk_space)
if args.dem_euclidean_map:
unfilled_dem_path = io.related_file_path(dem_geotiff_path, postfix=".unfilled")
if args.crop > 0:
# Crop unfilled DEM
Cropper.crop(bounds_file_path, unfilled_dem_path, utils.get_dem_vars(args), keep_original=not args.optimize_disk_space)
commands.compute_euclidean_map(unfilled_dem_path,
io.related_file_path(dem_geotiff_path, postfix=".euclideand"),
overwrite=True)
if pseudo_georeference:
# 0.1 is arbitrary
pseudogeo.add_pseudo_georeferencing(dem_geotiff_path, 0.1)
if pseudo_georeference:
pseudogeo.add_pseudo_georeferencing(dem_geotiff_path)
if args.tiles:
generate_dem_tiles(dem_geotiff_path, tree.path("%s_tiles" % product), args.max_concurrency)
progress += 30
self.update_progress(progress)
else:
log.ODM_WARNING('Found existing outputs in: %s' % odm_dem_root)
else:
log.ODM_WARNING('DEM will not be generated')
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})
# 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
# https://gist.github.com/pierotofy/c49447e86a187e8ede50fb302cf5a47b
# MVE *seems* to have a race condition, triggered randomly, regardless of dataset
# size. Core dump stack trace points to patch_sampler.cc:109.
# Hard to reproduce. Removing -03 optimizations from dmrecon
# seems to reduce the chances of hitting the bug.
# 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
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, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
class nonloc:
runs = []
def add_run(nvm_file, primary=True, band=None):
subdir = ""
if not primary and band is not None:
subdir = band
if not args.skip_3dmodel and (primary or args.use_3dmesh):
nonloc.runs += [{
'out_dir': os.path.join(tree.odm_texturing, subdir),
'model': tree.odm_mesh,
'nadir': False,
'nvm_file': nvm_file
}]
if not args.use_3dmesh:
nonloc.runs += [{
'out_dir': os.path.join(tree.odm_25dtexturing, subdir),
'model': tree.odm_25dmesh,
'nadir': True,
'nvm_file': nvm_file
}]
if reconstruction.multi_camera:
for band in reconstruction.multi_camera:
primary = band == reconstruction.multi_camera[0]
nvm_file = os.path.join(tree.opensfm, "undistorted", "reconstruction_%s.nvm" % band['name'].lower())
add_run(nvm_file, primary, band['name'].lower())
else:
add_run(tree.opensfm_reconstruction_nvm)
progress_per_run = 100.0 / len(nonloc.runs)
progress = 0.0
for r in nonloc.runs:
if not io.dir_exists(r['out_dir']):
system.mkdir_p(r['out_dir'])
odm_textured_model_obj = os.path.join(r['out_dir'], tree.odm_textured_model_obj)
if not io.file_exists(odm_textured_model_obj) or self.rerun():
log.ODM_INFO('Writing MVS Textured file in: %s'
% odm_textured_model_obj)
# Format arguments to fit Mvs-Texturing app
skipGeometricVisibilityTest = ""
skipGlobalSeamLeveling = ""
skipLocalSeamLeveling = ""
skipHoleFilling = ""
keepUnseenFaces = ""
nadir = ""
if (self.params.get('skip_vis_test')):
skipGeometricVisibilityTest = "--skip_geometric_visibility_test"
if (self.params.get('skip_glob_seam_leveling')):
skipGlobalSeamLeveling = "--skip_global_seam_leveling"
if (self.params.get('skip_loc_seam_leveling')):
skipLocalSeamLeveling = "--skip_local_seam_leveling"
if (self.params.get('skip_hole_fill')):
skipHoleFilling = "--skip_hole_filling"
if (self.params.get('keep_unseen_faces')):
keepUnseenFaces = "--keep_unseen_faces"
if (r['nadir']):
nadir = '--nadir_mode'
# mvstex definitions
kwargs = {
'bin': context.mvstex_path,
'out_dir': io.join_paths(r['out_dir'], "odm_textured_model"),
'model': r['model'],
'dataTerm': self.params.get('data_term'),
'outlierRemovalType': self.params.get('outlier_rem_type'),
'skipGeometricVisibilityTest': skipGeometricVisibilityTest,
'skipGlobalSeamLeveling': skipGlobalSeamLeveling,
'skipLocalSeamLeveling': skipLocalSeamLeveling,
'skipHoleFilling': skipHoleFilling,
'keepUnseenFaces': keepUnseenFaces,
'toneMapping': self.params.get('tone_mapping'),
'nadirMode': nadir,
'nadirWeight': 2 ** args.texturing_nadir_weight - 1,
'nvm_file': r['nvm_file']
}
mvs_tmp_dir = os.path.join(r['out_dir'], 'tmp')
# Make sure tmp directory is empty
if io.dir_exists(mvs_tmp_dir):
log.ODM_INFO("Removing old tmp directory {}".format(mvs_tmp_dir))
shutil.rmtree(mvs_tmp_dir)
# run texturing binary
system.run('{bin} {nvm_file} {model} {out_dir} '
'-d {dataTerm} -o {outlierRemovalType} '
'-t {toneMapping} '
'{skipGeometricVisibilityTest} '
'{skipGlobalSeamLeveling} '
'{skipLocalSeamLeveling} '
'{skipHoleFilling} '
'{keepUnseenFaces} '
'{nadirMode} '
'-n {nadirWeight}'.format(**kwargs))
progress += progress_per_run
self.update_progress(progress)
else:
log.ODM_WARNING('Found a valid ODM Texture file in: %s'
% odm_textured_model_obj)
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
num_zero_alt = 0
has_alt = True
has_gps = False
with open(list_path, 'w') as fout:
for photo in photos:
if photo.altitude is None:
has_alt = False
elif photo.altitude == 0:
num_zero_alt += 1
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 0 altitude images percentage when has_alt is True
if has_alt and num_zero_alt / len(photos) > 0.05:
log.ODM_WARNING(
"More than 5% of images have zero altitude, this might be an indicator that the images have no altitude information"
)
has_alt = False
# 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"
)
# create config file for OpenSfM
if args.matcher_neighbors > 0:
matcher_graph_rounds = 0
matcher_neighbors = args.matcher_neighbors
else:
matcher_graph_rounds = 50
matcher_neighbors = 0
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" % matcher_neighbors,
"matching_gps_distance: 0",
"matching_graph_rounds: %s" % matcher_graph_rounds,
"optimize_camera_parameters: %s" %
('no' if args.use_fixed_camera_params 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(args):
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"
self.gpu_sift_feature_extraction = True
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")
else:
config.append("bundle_compensate_gps_bias: yes")
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,
gcp_path=reconstruction.gcp.gcp_path,
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.name,
tree.submodels_path,
sp_octx.name())
# Re-run the ODM toolchain on the submodel
system.run(" ".join(map(quote, argv)),
env_vars=os.environ.copy())
else:
lre.set_projects([
os.path.abspath(os.path.join(p, ".."))
for p in submodel_paths
])
lre.run_toolchain()
# Restore max_concurrency value
args.max_concurrency = orig_max_concurrency
octx.touch(split_done_file)
else:
log.ODM_WARNING('Found a split done file in: %s' %
split_done_file)
else:
log.ODM_INFO("Normal dataset, will process all at once.")
self.progress = 0.0
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start OpenSfM')
exit(1)
octx = OSFMContext(tree.opensfm)
octx.setup(args,
tree.dataset_raw,
photos,
gcp_path=reconstruction.gcp.gcp_path,
rerun=self.rerun())
octx.extract_metadata(self.rerun())
self.update_progress(20)
octx.feature_matching(self.rerun())
self.update_progress(30)
octx.reconstruct(self.rerun())
self.update_progress(70)
# If we find a special flag file for split/merge we stop right here
if os.path.exists(octx.path("split_merge_stop_at_reconstruction.txt")):
log.ODM_INFO("Stopping OpenSfM early because we found: %s" %
octx.path("split_merge_stop_at_reconstruction.txt"))
self.next_stage = None
return
if args.fast_orthophoto:
output_file = octx.path('reconstruction.ply')
elif args.use_opensfm_dense:
output_file = tree.opensfm_model
else:
output_file = tree.opensfm_reconstruction
updated_config_flag_file = octx.path('updated_config.txt')
if not io.file_exists(updated_config_flag_file) or self.rerun():
octx.update_config({
'undistorted_image_max_size':
gsd.image_max_size(photos,
args.orthophoto_resolution,
tree.opensfm_reconstruction,
ignore_gsd=args.ignore_gsd)
})
octx.touch(updated_config_flag_file)
# These will be used for texturing
undistorted_images_path = octx.path("undistorted")
if not io.dir_exists(undistorted_images_path) or self.rerun():
octx.run('undistort')
else:
log.ODM_WARNING("Found an undistorted directory in %s" %
undistorted_images_path)
self.update_progress(80)
if not io.file_exists(tree.opensfm_reconstruction_nvm) or self.rerun():
octx.run('export_visualsfm --undistorted')
else:
log.ODM_WARNING(
'Found a valid OpenSfM NVM reconstruction file in: %s' %
tree.opensfm_reconstruction_nvm)
self.update_progress(85)
# Skip dense reconstruction if necessary and export
# sparse reconstruction instead
if args.fast_orthophoto:
if not io.file_exists(output_file) or self.rerun():
octx.run('export_ply --no-cameras')
else:
log.ODM_WARNING("Found a valid PLY reconstruction in %s" %
output_file)
elif args.use_opensfm_dense:
if not io.file_exists(output_file) or self.rerun():
octx.run('compute_depthmaps')
else:
log.ODM_WARNING("Found a valid dense reconstruction in %s" %
output_file)
# check if reconstruction was exported to bundler before
octx.export_bundler(tree.opensfm_bundle_list, self.rerun())
self.update_progress(90)
if reconstruction.is_georeferenced() and (not io.file_exists(
tree.opensfm_transformation) or self.rerun()):
octx.run('export_geocoords --transformation --proj \'%s\'' %
reconstruction.georef.proj4())
else:
log.ODM_WARNING("Will skip exporting %s" %
tree.opensfm_transformation)
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
photos = reconstruction.photos
if not photos:
log.ODM_ERROR('Not enough photos in photos array to start OpenSfM')
exit(1)
octx = OSFMContext(tree.opensfm)
octx.setup(args,
tree.dataset_raw,
reconstruction=reconstruction,
rerun=self.rerun())
octx.extract_metadata(self.rerun())
self.update_progress(20)
octx.feature_matching(self.rerun())
self.update_progress(30)
octx.reconstruct(self.rerun())
octx.extract_cameras(tree.path("cameras.json"), self.rerun())
self.update_progress(70)
if args.optimize_disk_space:
for folder in ["features", "matches", "exif", "reports"]:
folder_path = octx.path(folder)
if os.path.islink(folder_path):
os.unlink(folder_path)
else:
shutil.rmtree(folder_path)
# If we find a special flag file for split/merge we stop right here
if os.path.exists(octx.path("split_merge_stop_at_reconstruction.txt")):
log.ODM_INFO("Stopping OpenSfM early because we found: %s" %
octx.path("split_merge_stop_at_reconstruction.txt"))
self.next_stage = None
return
updated_config_flag_file = octx.path('updated_config.txt')
# Make sure it's capped by the depthmap-resolution arg,
# since the undistorted images are used for MVS
outputs['undist_image_max_size'] = max(
gsd.image_max_size(photos,
args.orthophoto_resolution,
tree.opensfm_reconstruction,
ignore_gsd=args.ignore_gsd,
has_gcp=reconstruction.has_gcp()),
get_depthmap_resolution(args, photos))
if not io.file_exists(updated_config_flag_file) or self.rerun():
octx.update_config({
'undistorted_image_max_size':
outputs['undist_image_max_size']
})
octx.touch(updated_config_flag_file)
# Undistorted images will be used for texturing / MVS
alignment_info = None
primary_band_name = None
undistort_pipeline = []
def undistort_callback(shot_id, image):
for func in undistort_pipeline:
image = func(shot_id, image)
return image
def radiometric_calibrate(shot_id, image):
photo = reconstruction.get_photo(shot_id)
return multispectral.dn_to_reflectance(
photo,
image,
use_sun_sensor=args.radiometric_calibration == "camera+sun")
def align_to_primary_band(shot_id, image):
photo = reconstruction.get_photo(shot_id)
# No need to align if requested by user
if args.skip_band_alignment:
return image
# No need to align primary
if photo.band_name == primary_band_name:
return image
ainfo = alignment_info.get(photo.band_name)
if ainfo is not None:
return multispectral.align_image(image, ainfo['warp_matrix'],
ainfo['dimension'])
else:
log.ODM_WARNING(
"Cannot align %s, no alignment matrix could be computed. Band alignment quality might be affected."
% (shot_id))
return image
if args.radiometric_calibration != "none":
undistort_pipeline.append(radiometric_calibrate)
image_list_override = None
if reconstruction.multi_camera:
# Undistort only secondary bands
image_list_override = [
os.path.join(tree.dataset_raw, p.filename) for p in photos
] # if p.band_name.lower() != primary_band_name.lower()
# We backup the original reconstruction.json, tracks.csv
# then we augment them by duplicating the primary band
# camera shots with each band, so that exports, undistortion,
# etc. include all bands
# We finally restore the original files later
added_shots_file = octx.path('added_shots_done.txt')
if not io.file_exists(added_shots_file) or self.rerun():
primary_band_name = multispectral.get_primary_band_name(
reconstruction.multi_camera, args.primary_band)
s2p, p2s = multispectral.compute_band_maps(
reconstruction.multi_camera, primary_band_name)
if not args.skip_band_alignment:
alignment_info = multispectral.compute_alignment_matrices(
reconstruction.multi_camera,
primary_band_name,
tree.dataset_raw,
s2p,
p2s,
max_concurrency=args.max_concurrency)
else:
log.ODM_WARNING("Skipping band alignment")
alignment_info = {}
log.ODM_INFO("Adding shots to reconstruction")
octx.backup_reconstruction()
octx.add_shots_to_reconstruction(p2s)
octx.touch(added_shots_file)
undistort_pipeline.append(align_to_primary_band)
octx.convert_and_undistort(self.rerun(), undistort_callback,
image_list_override)
self.update_progress(80)
if reconstruction.multi_camera:
octx.restore_reconstruction_backup()
# Undistort primary band and write undistorted
# reconstruction.json, tracks.csv
octx.convert_and_undistort(self.rerun(),
undistort_callback,
runId='primary')
if not io.file_exists(tree.opensfm_reconstruction_nvm) or self.rerun():
octx.run('export_visualsfm --points')
else:
log.ODM_WARNING(
'Found a valid OpenSfM NVM reconstruction file in: %s' %
tree.opensfm_reconstruction_nvm)
if reconstruction.multi_camera:
log.ODM_INFO("Multiple bands found")
# Write NVM files for the various bands
for band in reconstruction.multi_camera:
nvm_file = octx.path(
"undistorted",
"reconstruction_%s.nvm" % band['name'].lower())
if not io.file_exists(nvm_file) or self.rerun():
img_map = {}
if primary_band_name is None:
primary_band_name = multispectral.get_primary_band_name(
reconstruction.multi_camera, args.primary_band)
if p2s is None:
s2p, p2s = multispectral.compute_band_maps(
reconstruction.multi_camera, primary_band_name)
for fname in p2s:
# Primary band maps to itself
if band['name'] == primary_band_name:
img_map[fname + '.tif'] = fname + '.tif'
else:
band_filename = next(
(p.filename for p in p2s[fname]
if p.band_name == band['name']), None)
if band_filename is not None:
img_map[fname +
'.tif'] = band_filename + '.tif'
else:
log.ODM_WARNING(
"Cannot find %s band equivalent for %s" %
(band, fname))
nvm.replace_nvm_images(tree.opensfm_reconstruction_nvm,
img_map, nvm_file)
else:
log.ODM_WARNING("Found existing NVM file %s" % nvm_file)
self.update_progress(85)
# Skip dense reconstruction if necessary and export
# sparse reconstruction instead
if args.fast_orthophoto:
output_file = octx.path('reconstruction.ply')
if not io.file_exists(output_file) or self.rerun():
octx.run('export_ply --no-cameras')
else:
log.ODM_WARNING("Found a valid PLY reconstruction in %s" %
output_file)
elif args.use_opensfm_dense:
output_file = tree.opensfm_model
if not io.file_exists(output_file) or self.rerun():
octx.run('compute_depthmaps')
else:
log.ODM_WARNING("Found a valid dense reconstruction in %s" %
output_file)
self.update_progress(90)
if reconstruction.is_georeferenced() and (not io.file_exists(
tree.opensfm_transformation) or self.rerun()):
octx.run('export_geocoords --transformation --proj \'%s\'' %
reconstruction.georef.proj4())
else:
log.ODM_WARNING("Will skip exporting %s" %
tree.opensfm_transformation)
if args.optimize_disk_space:
os.remove(octx.path("tracks.csv"))
if io.file_exists(octx.recon_backup_file()):
os.remove(octx.recon_backup_file())
if io.dir_exists(octx.path("undistorted", "depthmaps")):
files = glob.glob(
octx.path("undistorted", "depthmaps", "*.npz"))
for f in files:
os.remove(f)
# Keep these if using OpenMVS
if args.fast_orthophoto or args.use_opensfm_dense:
files = [
octx.path("undistorted", "tracks.csv"),
octx.path("undistorted", "reconstruction.json")
]
for f in files:
if os.path.exists(f):
os.remove(f)
def process(self, args, outputs):
# 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",
# "--filter-width=3",
]
# 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})
# run cleanmesh (filter points by MVE confidence threshold)
if args.mve_confidence > 0:
mve_filtered_model = io.related_file_path(tree.mve_model, postfix=".filtered")
system.run('%s -t%s --no-clean --component-size=0 "%s" "%s"' % (context.meshclean_path, min(1.0, args.mve_confidence), tree.mve_model, mve_filtered_model), env_vars={'OMP_NUM_THREADS': args.max_concurrency})
if io.file_exists(mve_filtered_model):
os.remove(tree.mve_model)
os.rename(mve_filtered_model, tree.mve_model)
else:
log.ODM_WARNING("Couldn't filter MVE model (%s does not exist)." % mve_filtered_model)
else:
log.ODM_WARNING('Found a valid MVE reconstruction file in: %s' %
tree.mve_model)
def process(self, inputs, outputs):
# 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)
]
log.ODM_INFO('Running ODM Load Dataset Cell')
# get inputs
tree = self.inputs.tree
# get images directory
input_dir = tree.input_images
images_dir = tree.dataset_raw
resize_dir = tree.dataset_resize
# Check first if a project already exists. This is a mediocre way to check, by checking the resize dir
if io.dir_exists(resize_dir):
log.ODM_DEBUG("resize dir: %s" % resize_dir)
images_dir = resize_dir
# if first time running, create project directory and copy images over to project/images
else:
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)
]
log.ODM_DEBUG('Loading dataset from: %s' % images_dir)
files = get_images(images_dir)
if files:
# create ODMPhoto list
path_files = [io.join_paths(images_dir, f) for f in files]
photos = Pool().map(
partial(make_odm_photo, self.params.force_focal,
self.params.force_ccd), path_files)
log.ODM_INFO('Found %s usable images' % len(photos))
else:
log.ODM_ERROR('Not enough supported images in %s' % images_dir)
return ecto.QUIT
# append photos to cell output
outputs.photos = photos
log.ODM_INFO('Running ODM Load Dataset Cell - Finished')
return ecto.OK
def process(args, tree, reconstruction, current_path):
odm_orthophoto = io.join_paths(current_path, 'orthophoto')
odm_orthophoto_path = odm_orthophoto
odm_orthophoto_render = io.join_paths(odm_orthophoto_path, 'odm_orthophoto_render.tif')
odm_orthophoto_tif = io.join_paths(odm_orthophoto_path, 'odm_orthophoto.tif')
odm_orthophoto_corners = io.join_paths(odm_orthophoto_path, 'odm_orthophoto_corners.tif')
odm_orthophoto_log = io.join_paths(odm_orthophoto_path, 'odm_orthophoto_log.tif')
odm_orthophoto_tif_log = io.join_paths(odm_orthophoto_path, 'gdal_translate_log.txt')
odm_25dgeoreferencing = io.join_paths(current_path, 'odm_georeferencing')
odm_georeferencing = io.join_paths(current_path, 'odm_georeferencing')
odm_georeferencing_coords = io.join_paths(
odm_georeferencing, 'coords.txt')
odm_georeferencing_gcp = io.find('gcp_list.txt', current_path)
odm_georeferencing_gcp_utm = io.join_paths(odm_georeferencing, 'gcp_list_utm.txt')
odm_georeferencing_utm_log = io.join_paths(
odm_georeferencing, 'odm_georeferencing_utm_log.txt')
odm_georeferencing_log = 'odm_georeferencing_log.txt'
odm_georeferencing_transform_file = 'odm_georeferencing_transform.txt'
odm_georeferencing_proj = 'proj.txt'
odm_georeferencing_model_txt_geo = 'odm_georeferencing_model_geo.txt'
odm_georeferencing_model_obj_geo = 'odm_textured_model_geo.obj'
odm_georeferencing_xyz_file = io.join_paths(
odm_georeferencing, 'odm_georeferenced_model.csv')
odm_georeferencing_las_json = io.join_paths(
odm_georeferencing, 'las.json')
odm_georeferencing_model_laz = io.join_paths(
odm_georeferencing, 'odm_georeferenced_model.laz')
odm_georeferencing_model_las = io.join_paths(
odm_georeferencing, 'odm_georeferenced_model.las')
odm_georeferencing_dem = io.join_paths(
odm_georeferencing, 'odm_georeferencing_model_dem.tif')
opensfm_reconstruction = io.join_paths(current_path,'reconstruction.json')
odm_texturing = io.join_paths(current_path,'mvs')
odm_textured_model_obj = io.join_paths(odm_texturing, 'odm_textured_model.obj')
images_dir = io.join_paths(current_path, 'images')
tree = tree
opensfm_bundle = os.path.join(current_path, 'opensfm', 'bundle_r000.out')
opensfm_bundle_list = os.path.join(current_path, 'opensfm', 'list_r000.out')
opensfm_transformation = os.path.join(current_path, 'opensfm', 'geocoords_transformation.txt')
filtered_point_cloud = os.path.join(current_path, 'filterpoints', 'point_cloud.ply')
doPointCloudGeo = True
transformPointCloud = True
verbose =''
class nonloc:
runs = []
def add_run(primary=True, band=None):
subdir = ""
if not primary and band is not None:
subdir = band
# Make sure 2.5D mesh is georeferenced before the 3D mesh
# Because it will be used to calculate a transform
# for the point cloud. If we use the 3D model transform,
# DEMs and orthophoto might not align!
b = True
if b:
nonloc.runs += [{
'georeferencing_dir': os.path.join(odm_georeferencing, subdir),
'texturing_dir': os.path.join(odm_texturing, subdir),
}]
if not args.skip_3dmodel and (primary or args.use_3dmesh):
nonloc.runs += [{
'georeferencing_dir': odm_georeferencing,
'texturing_dir': os.path.join(odm_texturing, subdir),
}]
if reconstruction.multi_camera:
for band in reconstruction.multi_camera:
primary = band == reconstruction.multi_camera[0]
add_run(primary, band['name'].lower())
else:
add_run()
#progress_per_run = 100.0 / len(nonloc.runs)
#progress = 0.0
for r in nonloc.runs:
if not io.dir_exists(r['georeferencing_dir']):
system.mkdir_p(r['georeferencing_dir'])
odm_georeferencing_model_obj_geo = os.path.join(r['texturing_dir'], odm_georeferencing_model_obj_geo)
odm_georeferencing_model_obj = os.path.join(r['texturing_dir'], odm_textured_model_obj)
odm_georeferencing_log = os.path.join(r['georeferencing_dir'], odm_georeferencing_log)
odm_georeferencing_transform_file = os.path.join(r['georeferencing_dir'], odm_georeferencing_transform_file)
odm_georeferencing_model_txt_geo_file = os.path.join(r['georeferencing_dir'], odm_georeferencing_model_txt_geo)
pio = True
if pio:
#if not io.file_exists(odm_georeferencing_model_obj_geo) or \
#not io.file_exists(odm_georeferencing_model_laz)
# odm_georeference definitions
kwargs = {
'bin': context.odm_modules_path,
'input_pc_file': filtered_point_cloud,
'bundle': opensfm_bundle,
'imgs': images_dir,
'imgs_list': opensfm_bundle_list,
'model': odm_georeferencing_model_obj,
'log': odm_georeferencing_log,
'input_trans_file': opensfm_transformation,
'transform_file': odm_georeferencing_transform_file,
'coords': odm_georeferencing_coords,
'output_pc_file': odm_georeferencing_model_laz,
'geo_sys': odm_georeferencing_model_txt_geo_file,
'model_geo': odm_georeferencing_model_obj_geo,
'verbose': verbose
}
if transformPointCloud:
kwargs['pc_params'] = '-inputPointCloudFile {input_pc_file} -outputPointCloudFile {output_pc_file}'.format(**kwargs)
if reconstruction.is_georeferenced():
kwargs['pc_params'] += ' -outputPointCloudSrs %s' % pipes.quote(reconstruction.georef.proj4())
else:
log.ODM_WARNING('NO SRS: The output point cloud will not have a SRS.')
else:
kwargs['pc_params'] = ''
if io.file_exists(opensfm_transformation) and io.file_exists(odm_georeferencing_coords):
log.ODM_INFO('Running georeferencing with OpenSfM transformation matrix')
system.run('{bin}/odm_georef -bundleFile {bundle} -inputTransformFile {input_trans_file} -inputCoordFile {coords} '
'-inputFile {model} -outputFile {model_geo} '
'{pc_params} {verbose} '
'-logFile {log} -outputTransformFile {transform_file} -georefFileOutputPath {geo_sys}'.format(**kwargs))
elif io.file_exists(odm_georeferencing_coords):
print('running georeferencing')
log.ODM_INFO('Running georeferencing with generated coords file.')
system.run('{bin}/odm_georef -bundleFile {bundle} -inputCoordFile {coords} '
'-inputFile {model} -outputFile {model_geo} '
'{pc_params} {verbose} '
'-logFile {log} -outputTransformFile {transform_file} -georefFileOutputPath {geo_sys}'.format(**kwargs))
else:
log.ODM_WARNING('Georeferencing failed. Make sure your '
'photos have geotags in the EXIF or you have '
'provided a GCP file. ')
doPointCloudGeo = False # skip the rest of the georeferencing
if doPointCloudGeo:
reconstruction.georef.extract_offsets(odm_georeferencing_model_txt_geo_file)
point_cloud.post_point_cloud_steps(args, tree)
if args.crop > 0:
log.ODM_INFO("Calculating cropping area and generating bounds shapefile from point cloud")
cropper = Cropper(odm_georeferencing, 'odm_georeferenced_model')
decimation_step = 40 if args.fast_orthophoto or args.use_opensfm_dense else 90
# More aggressive decimation for large datasets
if not args.fast_orthophoto:
decimation_step *= int(len(reconstruction.photos) / 1000) + 1
cropper.create_bounds_gpkg(odm_georeferencing_model_laz, args.crop,
decimation_step=decimation_step)
# Do not execute a second time, since
# We might be doing georeferencing for
# multiple models (3D, 2.5D, ...)
doPointCloudGeo = False
transformPointCloud = False
else:
log.ODM_WARNING('Found a valid georeferenced model in: %s'
% odm_georeferencing_model_laz)
if args.optimize_disk_space and io.file_exists(odm_georeferencing_model_laz) and io.file_exists(filtered_point_cloud):
os.remove(filtered_point_cloud)
def extract_metadata(self, rerun=False):
metadata_dir = self.path("exif")
if not io.dir_exists(metadata_dir) or rerun:
self.run('extract_metadata')
def process(self, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
class nonloc:
runs = []
def add_run(nvm_file, primary=True, band=None):
subdir = ""
if not primary and band is not None:
subdir = band
if not args.skip_3dmodel and (primary or args.use_3dmesh):
nonloc.runs += [{
'out_dir': os.path.join(tree.odm_texturing, subdir),
'model': tree.odm_mesh,
'nadir': False,
'primary': primary,
'nvm_file': nvm_file,
'labeling_file': os.path.join(tree.odm_texturing, "odm_textured_model_geo_labeling.vec") if subdir else None
}]
if not args.use_3dmesh:
nonloc.runs += [{
'out_dir': os.path.join(tree.odm_25dtexturing, subdir),
'model': tree.odm_25dmesh,
'nadir': True,
'primary': primary,
'nvm_file': nvm_file,
'labeling_file': os.path.join(tree.odm_25dtexturing, "odm_textured_model_geo_labeling.vec") if subdir else None
}]
if reconstruction.multi_camera:
for band in reconstruction.multi_camera:
primary = band['name'] == get_primary_band_name(reconstruction.multi_camera, args.primary_band)
nvm_file = os.path.join(tree.opensfm, "undistorted", "reconstruction_%s.nvm" % band['name'].lower())
add_run(nvm_file, primary, band['name'].lower())
# Sort to make sure primary band is processed first
nonloc.runs.sort(key=lambda r: r['primary'], reverse=True)
else:
add_run(tree.opensfm_reconstruction_nvm)
progress_per_run = 100.0 / len(nonloc.runs)
progress = 0.0
for r in nonloc.runs:
if not io.dir_exists(r['out_dir']):
system.mkdir_p(r['out_dir'])
odm_textured_model_obj = os.path.join(r['out_dir'], tree.odm_textured_model_obj)
if not io.file_exists(odm_textured_model_obj) or self.rerun():
log.ODM_INFO('Writing MVS Textured file in: %s'
% odm_textured_model_obj)
# Format arguments to fit Mvs-Texturing app
skipGlobalSeamLeveling = ""
skipLocalSeamLeveling = ""
keepUnseenFaces = ""
nadir = ""
if (self.params.get('skip_glob_seam_leveling')):
skipGlobalSeamLeveling = "--skip_global_seam_leveling"
if (self.params.get('skip_loc_seam_leveling')):
skipLocalSeamLeveling = "--skip_local_seam_leveling"
if (self.params.get('keep_unseen_faces')):
keepUnseenFaces = "--keep_unseen_faces"
if (r['nadir']):
nadir = '--nadir_mode'
# mvstex definitions
kwargs = {
'bin': context.mvstex_path,
'out_dir': os.path.join(r['out_dir'], "odm_textured_model_geo"),
'model': r['model'],
'dataTerm': self.params.get('data_term'),
'outlierRemovalType': self.params.get('outlier_rem_type'),
'skipGlobalSeamLeveling': skipGlobalSeamLeveling,
'skipLocalSeamLeveling': skipLocalSeamLeveling,
'keepUnseenFaces': keepUnseenFaces,
'toneMapping': self.params.get('tone_mapping'),
'nadirMode': nadir,
'nvm_file': r['nvm_file'],
'intermediate': '--no_intermediate_results' if (r['labeling_file'] or not reconstruction.multi_camera) else '',
'labelingFile': '-L "%s"' % r['labeling_file'] if r['labeling_file'] else ''
}
mvs_tmp_dir = os.path.join(r['out_dir'], 'tmp')
# Make sure tmp directory is empty
if io.dir_exists(mvs_tmp_dir):
log.ODM_INFO("Removing old tmp directory {}".format(mvs_tmp_dir))
shutil.rmtree(mvs_tmp_dir)
# run texturing binary
system.run('"{bin}" "{nvm_file}" "{model}" "{out_dir}" '
'-d {dataTerm} -o {outlierRemovalType} '
'-t {toneMapping} '
'{intermediate} '
'{skipGlobalSeamLeveling} '
'{skipLocalSeamLeveling} '
'{keepUnseenFaces} '
'{nadirMode} '
'{labelingFile} '.format(**kwargs))
# Backward compatibility: copy odm_textured_model_geo.mtl to odm_textured_model.mtl
# for certain older WebODM clients which expect a odm_textured_model.mtl
# to be present for visualization
# We should remove this at some point in the future
geo_mtl = os.path.join(r['out_dir'], 'odm_textured_model_geo.mtl')
if io.file_exists(geo_mtl):
nongeo_mtl = os.path.join(r['out_dir'], 'odm_textured_model.mtl')
shutil.copy(geo_mtl, nongeo_mtl)
progress += progress_per_run
self.update_progress(progress)
else:
log.ODM_WARNING('Found a valid ODM Texture file in: %s'
% odm_textured_model_obj)
if args.optimize_disk_space:
for r in nonloc.runs:
if io.file_exists(r['model']):
os.remove(r['model'])
undistorted_images_path = os.path.join(tree.opensfm, "undistorted", "images")
if io.dir_exists(undistorted_images_path):
shutil.rmtree(undistorted_images_path)
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
# 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" % 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: " +
os.path.join("..", "submodels", "opensfm"),
"submodel_relpath_template: " + os.path.join(
"..", "submodels", "submodel_%04d", "opensfm"),
"submodel_images_relpath_template: " +
os.path.join("..", "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(double_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 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, args, outputs):
tree = outputs['tree']
reconstruction = outputs['reconstruction']
if outputs['large']:
if not os.path.exists(tree.submodels_path):
log.ODM_ERROR(
"We reached the merge stage, but %s folder does not exist. Something must have gone wrong at an earlier stage. Check the log and fix possible problem before restarting?"
% tree.submodels_path)
exit(1)
# Merge point clouds
if args.merge in ['all', 'pointcloud']:
if not io.file_exists(
tree.odm_georeferencing_model_laz) or self.rerun():
all_point_clouds = get_submodel_paths(
tree.submodels_path, "odm_georeferencing",
"odm_georeferenced_model.laz")
try:
point_cloud.merge(all_point_clouds,
tree.odm_georeferencing_model_laz,
rerun=self.rerun())
point_cloud.post_point_cloud_steps(
args, tree, self.rerun())
except Exception as e:
log.ODM_WARNING(
"Could not merge point cloud: %s (skipping)" %
str(e))
else:
log.ODM_WARNING("Found merged point cloud in %s" %
tree.odm_georeferencing_model_laz)
self.update_progress(25)
# Merge crop bounds
merged_bounds_file = os.path.join(
tree.odm_georeferencing, 'odm_georeferenced_model.bounds.gpkg')
if not io.file_exists(merged_bounds_file) or self.rerun():
all_bounds = get_submodel_paths(
tree.submodels_path, 'odm_georeferencing',
'odm_georeferenced_model.bounds.gpkg')
log.ODM_INFO("Merging all crop bounds: %s" % all_bounds)
if len(all_bounds) > 0:
# Calculate a new crop area
# based on the convex hull of all crop areas of all submodels
# (without a buffer, otherwise we are double-cropping)
Cropper.merge_bounds(all_bounds, merged_bounds_file, 0)
else:
log.ODM_WARNING("No bounds found for any submodel.")
# Merge orthophotos
if args.merge in ['all', 'orthophoto']:
if not io.dir_exists(tree.odm_orthophoto):
system.mkdir_p(tree.odm_orthophoto)
if not io.file_exists(tree.odm_orthophoto_tif) or self.rerun():
all_orthos_and_ortho_cuts = get_all_submodel_paths(
tree.submodels_path,
os.path.join("odm_orthophoto",
"odm_orthophoto_feathered.tif"),
os.path.join("odm_orthophoto",
"odm_orthophoto_cut.tif"),
)
if len(all_orthos_and_ortho_cuts) > 1:
log.ODM_INFO(
"Found %s submodels with valid orthophotos and cutlines"
% len(all_orthos_and_ortho_cuts))
# TODO: histogram matching via rasterio
# currently parts have different color tones
if io.file_exists(tree.odm_orthophoto_tif):
os.remove(tree.odm_orthophoto_tif)
orthophoto_vars = orthophoto.get_orthophoto_vars(args)
orthophoto.merge(all_orthos_and_ortho_cuts,
tree.odm_orthophoto_tif,
orthophoto_vars)
orthophoto.post_orthophoto_steps(
args, merged_bounds_file, tree.odm_orthophoto_tif,
tree.orthophoto_tiles)
elif len(all_orthos_and_ortho_cuts) == 1:
# Simply copy
log.ODM_WARNING(
"A single orthophoto/cutline pair was found between all submodels."
)
shutil.copyfile(all_orthos_and_ortho_cuts[0][0],
tree.odm_orthophoto_tif)
else:
log.ODM_WARNING(
"No orthophoto/cutline pairs were found in any of the submodels. No orthophoto will be generated."
)
else:
log.ODM_WARNING("Found merged orthophoto in %s" %
tree.odm_orthophoto_tif)
self.update_progress(75)
# Merge DEMs
def merge_dems(dem_filename, human_name):
if not io.dir_exists(tree.path('odm_dem')):
system.mkdir_p(tree.path('odm_dem'))
dem_file = tree.path("odm_dem", dem_filename)
if not io.file_exists(dem_file) or self.rerun():
all_dems = get_submodel_paths(tree.submodels_path,
"odm_dem", dem_filename)
log.ODM_INFO("Merging %ss" % human_name)
# Merge
dem_vars = utils.get_dem_vars(args)
eu_map_source = None # Default
# Use DSM's euclidean map for DTMs
# (requires the DSM to be computed)
if human_name == "DTM":
eu_map_source = "dsm"
euclidean_merge_dems(all_dems,
dem_file,
dem_vars,
euclidean_map_source=eu_map_source)
if io.file_exists(dem_file):
# Crop
if args.crop > 0:
Cropper.crop(
merged_bounds_file,
dem_file,
dem_vars,
keep_original=not args.optimize_disk_space)
log.ODM_INFO("Created %s" % dem_file)
if args.tiles:
generate_dem_tiles(
dem_file,
tree.path("%s_tiles" % human_name.lower()),
args.max_concurrency)
if args.cog:
convert_to_cogeo(dem_file,
max_workers=args.max_concurrency)
else:
log.ODM_WARNING("Cannot merge %s, %s was not created" %
(human_name, dem_file))
else:
log.ODM_WARNING("Found merged %s in %s" %
(human_name, dem_filename))
if args.merge in ['all', 'dem'] and args.dsm:
merge_dems("dsm.tif", "DSM")
if args.merge in ['all', 'dem'] and args.dtm:
merge_dems("dtm.tif", "DTM")
self.update_progress(95)
# Merge reports
if not io.dir_exists(tree.odm_report):
system.mkdir_p(tree.odm_report)
geojson_shots = tree.path(tree.odm_report, "shots.geojson")
if not io.file_exists(geojson_shots) or self.rerun():
geojson_shots_files = get_submodel_paths(
tree.submodels_path, "odm_report", "shots.geojson")
log.ODM_INFO("Merging %s shots.geojson files" %
len(geojson_shots_files))
merge_geojson_shots(geojson_shots_files, geojson_shots)
else:
log.ODM_WARNING("Found merged shots.geojson in %s" %
tree.odm_report)
# Stop the pipeline short! We're done.
self.next_stage = None
else:
log.ODM_INFO("Normal dataset, nothing to merge.")
self.progress = 0.0
from opendm import io
import ecto
import os
from scripts.odm_app import ODMApp
if __name__ == '__main__':
args = config.config()
log.ODM_INFO('Initializing OpenDroneMap app - %s' % system.now())
# Add project dir if doesn't exist
args.project_path = io.join_paths(args.project_path, args.name)
if not io.dir_exists(args.project_path):
log.ODM_WARNING('Directory %s does not exist. Creating it now.' % args.name)
system.mkdir_p(os.path.abspath(args.project_path))
# If user asks to rerun everything, delete all of the existing progress directories.
# TODO: Move this somewhere it's not hard-coded
if args.rerun_all:
os.system("rm -rf "
+ args.project_path + "images_resize/ "
+ args.project_path + "odm_georeferencing/ "
+ args.project_path + "odm_meshing/ "
+ args.project_path + "odm_orthophoto/ "
+ args.project_path + "odm_texturing/ "
+ args.project_path + "opensfm/ "
+ args.project_path + "pmvs/")
def compute_cutline(orthophoto_file,
crop_area_file,
destination,
max_concurrency=1,
tmpdir=None,
scale=1):
if io.file_exists(orthophoto_file) and io.file_exists(crop_area_file):
from opendm.grass_engine import grass
log.ODM_INFO("Computing cutline")
if tmpdir and not io.dir_exists(tmpdir):
system.mkdir_p(tmpdir)
scale = max(0.0001, min(1, scale))
scaled_orthophoto = None
if scale < 1:
log.ODM_INFO("Scaling orthophoto to %s%% to compute cutline" %
(scale * 100))
scaled_orthophoto = os.path.join(
tmpdir,
os.path.basename(
io.related_file_path(orthophoto_file, postfix=".scaled")))
# Scale orthophoto before computing cutline
system.run("gdal_translate -outsize {}% 0 "
"-co NUM_THREADS={} "
"--config GDAL_CACHEMAX {}% "
"{} {}".format(scale * 100, max_concurrency,
concurrency.get_max_memory(),
orthophoto_file, scaled_orthophoto))
orthophoto_file = scaled_orthophoto
try:
ortho_width, ortho_height = get_image_size.get_image_size(
orthophoto_file, fallback_on_error=False)
log.ODM_INFO("Orthophoto dimensions are %sx%s" %
(ortho_width, ortho_height))
number_lines = int(
max(8, math.ceil(min(ortho_width, ortho_height) / 256.0)))
except:
log.ODM_INFO(
"Cannot compute orthophoto dimensions, setting arbitrary number of lines."
)
number_lines = 32
log.ODM_INFO("Number of lines: %s" % number_lines)
gctx = grass.create_context({'auto_cleanup': False, 'tmpdir': tmpdir})
gctx.add_param('orthophoto_file', orthophoto_file)
gctx.add_param('crop_area_file', crop_area_file)
gctx.add_param('number_lines', number_lines)
gctx.add_param('max_concurrency', max_concurrency)
gctx.add_param('memory', int(concurrency.get_max_memory_mb(300)))
gctx.set_location(orthophoto_file)
cutline_file = gctx.execute(
os.path.join("opendm", "grass", "compute_cutline.grass"))
if cutline_file != 'error':
if io.file_exists(cutline_file):
shutil.move(cutline_file, destination)
log.ODM_INFO("Generated cutline file: %s --> %s" %
(cutline_file, destination))
gctx.cleanup()
return destination
else:
log.ODM_WARNING(
"Unexpected script result: %s. No cutline file has been generated."
% cutline_file)
else:
log.ODM_WARNING(
"Could not generate orthophoto cutline. An error occured when running GRASS. No orthophoto will be generated."
)
else:
log.ODM_WARNING(
"We've been asked to compute cutline, but either %s or %s is missing. Skipping..."
% (orthophoto_file, crop_area_file))
