def __init__(self, data_path):
"""
Create dataset instance. Empty directories (for EXIF, matches, etc) will be created if they don't exist
already.
:param data_path: Path to directory containing dataset
"""
self.data_path = data_path
# Load configuration.
config_file = os.path.join(self.data_path, 'config.yaml')
self.config = config.load_config(config_file)
# Load list of images.
image_list_file = os.path.join(self.data_path, 'image_list.txt')
if os.path.isfile(image_list_file):
with open(image_list_file) as fin:
lines = fin.read().splitlines()
self.set_image_list(lines)
else:
self.set_image_path(os.path.join(self.data_path, 'images'))
# Create output folders.
for p in [self.__exif_path(),
self.__feature_path(),
self.__matches_path()]:
io.mkdir_p(p)
def load_opensfm_config(filepath):
"""
filepath: image file path
"""
# calls opensfm config.py
return config.load_config(filepath)
def __init__(self, data_path):
'''
Create meta dataset instance for large scale reconstruction.
:param data_path: Path to directory containing meta dataset
'''
self.data_path = data_path
config_file = os.path.join(self.data_path, 'config.yaml')
self.config = config.load_config(config_file)
self._image_list_file_name = 'image_list_with_gps.tsv'
self._clusters_file_name = 'clusters.npz'
self._clusters_with_neighbors_file_name = 'clusters_with_neighbors.npz'
io.mkdir_p(self._submodels_path())
def __init__(self, data_path):
'''
Create meta dataset instance for large scale reconstruction.
:param data_path: Path to directory containing meta dataset
'''
self.data_path = data_path
config_file = os.path.join(self.data_path, 'config.yaml')
self.config = config.load_config(config_file)
self._image_list_file_name = 'image_list_with_gps.tsv'
self._clusters_file_name = 'clusters.npz'
self._clusters_with_neighbors_file_name = 'clusters_with_neighbors.npz'
self._clusters_with_neighbors_geojson_file_name = 'clusters_with_neighbors.geojson'
io.mkdir_p(self._submodels_path())
def __init__(self, data_path):
"""
Create meta dataset instance for large scale reconstruction.
:param data_path: Path to directory containing meta dataset
"""
self.data_path = os.path.abspath(data_path)
config_file = os.path.join(self.data_path, "config.yaml")
self.config = config.load_config(config_file)
self._image_list_file_name = "image_list_with_gps.tsv"
self._clusters_file_name = "clusters.npz"
self._clusters_with_neighbors_file_name = "clusters_with_neighbors.npz"
self._clusters_with_neighbors_geojson_file_name = (
"clusters_with_neighbors.geojson")
self._clusters_geojson_file_name = "clusters.geojson"
io.mkdir_p(self._submodels_path())
def _load_config(self):
config_file = os.path.join(self.data_path, 'config.yaml')
self.config = config.load_config(config_file)
pdr_shots_paths = []
for pdr_shots_type in ('./pdr_shots.txt', './osfm/pdr_shots.txt'):
pdr_shots_paths.extend(glob.glob(pdr_shots_type))
if not pdr_shots_paths or not os.path.exists(pdr_shots_paths[0]):
logger.error("pdr shots not found!")
exit(0)
config_file_paths = []
for config_file_type in ('./config.yaml', './osfm/config.yaml'):
config_file_paths.extend(glob.glob(config_file_type))
if not config_file_paths or not os.path.exists(config_file_paths[0]):
logger.error("config file not found!")
exit(0)
else:
data_config = config.load_config(config_file_paths[0])
recon_file_paths = []
for recon_file_type in ('./reconstruction.json',
'./osfm/reconstruction.json'):
recon_file_paths.extend(glob.glob(recon_file_type))
if not recon_file_paths or not os.path.exists(recon_file_paths[0]):
logger.error("reconstructions not found!")
exit(0)
pdr_gps_picker(plan_paths[0], pdr_shots_paths[0], recon_file_paths[0],
data_config['reconstruction_scale_factor'],
pdr_extrapolation_frames)
def _load_config(self):
config_file = os.path.join(self.data_path, 'config.yaml')
self.config = config.load_config(config_file)
def load_config(self):
self.config = config.load_config(self._config_file())
def load_config(self):
config_file = os.path.join(self.data_path, "config.yaml")
self.config = config.load_config(config_file)
