def clean_data(self):
dataset_dir = self.config['dataset_dir']
work_dir = self.config['work_dir']
# set log file and timer
log_file = os.path.join(work_dir, 'logs/log_clean_data.txt')
self.logger.set_log_file(log_file)
# create a local timer
local_timer = Timer('Data cleaning Module')
local_timer.start()
# clean data
cleaned_data_dir = os.path.join(work_dir, 'cleaned_data')
if os.path.exists(cleaned_data_dir): # remove cleaned_data_dir
shutil.rmtree(cleaned_data_dir)
os.mkdir(cleaned_data_dir)
# check if dataset_dir is a list or tuple
if not (isinstance(dataset_dir, list) or isinstance(dataset_dir, tuple)):
dataset_dir = [dataset_dir, ]
clean_data(dataset_dir, cleaned_data_dir)
# stop local timer
local_timer.mark('Data cleaning done')
logging.info(local_timer.summary())
def run_inspect_sfm_perspective(self):
work_dir = os.path.abspath(self.config['work_dir'])
log_file = os.path.join(work_dir, 'logs/log_inspect_sfm_perspective.txt')
self.logger.set_log_file(log_file)
local_timer = Timer('inspect sfm')
local_timer.start()
# inspect sfm perspective
sfm_dir = os.path.join(work_dir, 'colmap/sfm_perspective')
for subdir in ['tri', 'tri_ba']:
dir = os.path.join(sfm_dir, subdir)
logging.info('\ninspecting {} ...'.format(dir))
inspect_dir = os.path.join(sfm_dir, 'inspect_' + subdir)
if os.path.exists(inspect_dir):
shutil.rmtree(inspect_dir)
db_path = os.path.join(sfm_dir, 'database.db')
sfm_inspector = SparseInspector(dir, db_path, inspect_dir, camera_model='PERSPECTIVE')
sfm_inspector.inspect_all()
# stop local timer
local_timer.mark('inspect sfm perspective done')
logging.info(local_timer.summary())
def run_crop_image_general(
self,
ift,
oft,
execute_parallel,
remove_aux_file,
apply_tone_mapping,
joint_tone_mapping,
):
work_dir = self.config["work_dir"]
# set log file
log_file = os.path.join(work_dir, "logs/log_crop_image.txt")
self.logger.set_log_file(log_file)
# create a local timer
local_timer = Timer("Image cropping module")
local_timer.start()
# crop image and tone map
image_crop_general(
work_dir,
ift,
oft,
execute_parallel,
remove_aux_file,
apply_tone_mapping,
joint_tone_mapping,
)
# stop local timer
local_timer.mark("image cropping done")
logging.info(local_timer.summary())
def clean_data_general(self, ift):
dataset_dir = self.config["dataset_dir"]
work_dir = self.config["work_dir"]
# set log file and timer
log_file = os.path.join(work_dir, "logs/log_clean_data.txt")
self.logger.set_log_file(log_file)
# create a local timer
local_timer = Timer("Data cleaning Module")
local_timer.start()
# clean data
cleaned_data_dir = os.path.join(work_dir, "cleaned_data")
if os.path.exists(cleaned_data_dir): # remove cleaned_data_dir
shutil.rmtree(cleaned_data_dir)
os.mkdir(cleaned_data_dir)
# check if dataset_dir is a list or tuple
if not (isinstance(dataset_dir, list)
or isinstance(dataset_dir, tuple)):
dataset_dir = [
dataset_dir,
]
clean_data_general(dataset_dir, cleaned_data_dir, ift=ift)
# stop local timer
local_timer.mark("Data cleaning done")
logging.info(local_timer.summary())
def run_aggregate_3d(self):
work_dir = self.config['work_dir']
# set log file
log_file = os.path.join(work_dir, 'logs/log_aggregate_3d.txt')
self.logger.set_log_file(log_file)
# create a local timer
local_timer = Timer('3D aggregation module')
local_timer.start()
aggregate_3d.run_fuse(work_dir)
# stop local timer
local_timer.mark('3D aggregation done')
logging.info(local_timer.summary())
def run_crop_image(self):
work_dir = self.config['work_dir']
# set log file
log_file = os.path.join(work_dir, 'logs/log_crop_image.txt')
self.logger.set_log_file(log_file)
# create a local timer
local_timer = Timer('Image cropping module')
local_timer.start()
# crop image and tone map
image_crop(work_dir)
# stop local timer
local_timer.mark('image cropping done')
logging.info(local_timer.summary())
def run_aggregate_2p5d(self):
work_dir = self.config['work_dir']
# set log file
log_file = os.path.join(work_dir, 'logs/log_aggregate_2p5d.txt')
self.logger.set_log_file(log_file)
# create a local timer
local_timer = Timer('2.5D aggregation module')
local_timer.start()
max_processes = -1
if 'aggregate_max_processes' in self.config:
max_processes = self.config['aggregate_max_processes']
aggregate_2p5d.run_fuse(work_dir, max_processes=max_processes)
# stop local timer
local_timer.mark('2.5D aggregation done')
logging.info(local_timer.summary())
def run_colmap_mvs(self, window_radius=3):
work_dir = self.config['work_dir']
mvs_dir = os.path.join(work_dir, 'colmap/mvs')
# set log file
log_file = os.path.join(work_dir, 'logs/log_mvs.txt')
self.logger.set_log_file(log_file)
# create a local timer
local_timer = Timer('Colmap MVS Module')
local_timer.start()
# first run PMVS without filtering
run_photometric_mvs(mvs_dir, window_radius)
# next do forward-backward checking and filtering
run_consistency_check(mvs_dir, window_radius)
# stop local timer
local_timer.mark('Colmap MVS done')
logging.info(local_timer.summary())
def run_derive_approx(self):
work_dir = self.config['work_dir']
# set log file to 'logs/log_derive_approx.txt'
log_file = os.path.join(work_dir, 'logs/log_derive_approx.txt')
self.logger.set_log_file(log_file)
# create a local timer
local_timer = Timer('Derive Approximation Module')
local_timer.start()
# derive approximations for later uses
appr = CameraApprox(work_dir)
appr.approx_affine_latlonalt()
appr.approx_perspective_enu()
# stop local timer
local_timer.mark('Derive approximation done')
logging.info(local_timer.summary())
def run_colmap_sfm_perspective(self, weight=0.01):
work_dir = os.path.abspath(self.config['work_dir'])
sfm_dir = os.path.join(work_dir, 'colmap/sfm_perspective')
if not os.path.exists(sfm_dir):
os.mkdir(sfm_dir)
log_file = os.path.join(work_dir, 'logs/log_sfm_perspective.txt')
self.logger.set_log_file(log_file)
# create a local timer
local_timer = Timer('Colmap SfM Module, perspective camera')
local_timer.start()
# create a hard link to avoid copying of images
if os.path.exists(os.path.join(sfm_dir, 'images')):
os.unlink(os.path.join(sfm_dir, 'images'))
os.symlink(os.path.relpath(os.path.join(work_dir, 'colmap/subset_for_sfm/images'), sfm_dir),
os.path.join(sfm_dir, 'images'))
init_camera_file = os.path.join(work_dir, 'colmap/subset_for_sfm/perspective_dict.json')
colmap_sfm_perspective.run_sfm(work_dir, sfm_dir, init_camera_file, weight)
# stop local timer
local_timer.mark('Colmap SfM done')
logging.info(local_timer.summary())
def run_reparam_depth(self):
work_dir = self.config['work_dir']
# set log file
log_file = os.path.join(work_dir, 'logs/log_reparam_depth.txt')
self.logger.set_log_file(log_file)
# create a local timer
local_timer = Timer('reparametrize depth')
local_timer.start()
# prepare dense reconstruction
colmap_dir = os.path.join(work_dir, 'colmap')
mvs_dir = os.path.join(colmap_dir, 'mvs')
if not os.path.exists(mvs_dir):
os.mkdir(mvs_dir)
# link to sfm_perspective
if os.path.exists(os.path.join(mvs_dir, 'images')):
os.unlink(os.path.join(mvs_dir, 'images'))
os.symlink(os.path.relpath(os.path.join(colmap_dir, 'sfm_perspective/images'), mvs_dir),
os.path.join(mvs_dir, 'images'))
if os.path.exists(os.path.join(mvs_dir, 'sparse')):
os.unlink(os.path.join(mvs_dir, 'sparse'))
os.symlink(os.path.relpath(os.path.join(colmap_dir, 'sfm_perspective/tri_ba'), mvs_dir),
os.path.join(mvs_dir, 'sparse'))
# compute depth ranges and generate last_rows.txt
reparam_depth(os.path.join(mvs_dir, 'sparse'), mvs_dir, camera_model='perspective')
# prepare stereo directory
stereo_dir = os.path.join(mvs_dir, 'stereo')
for subdir in [stereo_dir,
os.path.join(stereo_dir, 'depth_maps'),
os.path.join(stereo_dir, 'normal_maps'),
os.path.join(stereo_dir, 'consistency_graphs')]:
if not os.path.exists(subdir):
os.mkdir(subdir)
# write patch-match.cfg and fusion.cfg
image_names = sorted(os.listdir(os.path.join(mvs_dir, 'images')))
with open(os.path.join(stereo_dir, 'patch-match.cfg'), 'w') as fp:
for img_name in image_names:
fp.write(img_name + '\n__auto__, 20\n')
# use all images
# fp.write(img_name + '\n__all__\n')
# randomly choose 20 images
# from random import shuffle
# candi_src_images = [x for x in image_names if x != img_name]
# shuffle(candi_src_images)
# max_src_images = 10
# fp.write(img_name + '\n' + ', '.join(candi_src_images[:max_src_images]) + '\n')
with open(os.path.join(stereo_dir, 'fusion.cfg'), 'w') as fp:
for img_name in image_names:
fp.write(img_name + '\n')
# stop local timer
local_timer.mark('reparam depth done')
logging.info(local_timer.summary())
