def dataset_expand_landmarks(path_annots,
path_dataset,
nb_selected=None,
nb_total=None,
nb_workers=NB_WORKERS):
""" select and expand over whole dataset
:param str path_annots: root path to original dataset
:param str path_dataset: root path to generated dataset
:param float|int|None nb_selected: portion of selected points
:param int|None nb_total: add extra points up to total number
:param int nb_workers: number of jobs running in parallel
:return list(int):
"""
list_sets = list_sub_folders(path_annots)
logging.info('Found sets: %i', len(list_sets))
_wrap_extend = partial(extend_landmarks,
path_dataset=path_dataset,
nb_selected=nb_selected,
nb_total=nb_total)
counts = list(
iterate_mproc_map(_wrap_extend,
sorted(list_sets),
nb_workers=nb_workers,
desc='expand landmarks'))
return counts
def collect_triple_dir(paths_landmarks,
path_dataset,
path_out,
coll_dirs=None,
scales=None,
with_user=False):
""" collect all subdir up to level of scales with user annotations
expected annotation structure is <tissue>/<user>_scale-<number>pc/<csv-file>
expected dataset structure is <tissue>/scale-<number>pc/<image>
:param [str] paths_landmarks: path to landmarks / annotations
:param str path_dataset: path to the dataset with images
:param str path_out: path for exporting statistic
:param [{}] coll_dirs: list of already exiting collections
:param [int] scales: list of allowed scales
:param bool with_user: whether required insert info (as annotation)
:return [{}]: list of already collections
>>> coll_dirs, d = collect_triple_dir([update_path('annotations')],
... update_path('dataset'), 'output')
>>> len(coll_dirs) > 0
True
>>> 'annotations' in coll_dirs[0]['landmarks'].split(os.sep)
True
>>> 'dataset' in coll_dirs[0]['images'].split(os.sep)
True
>>> 'output' in coll_dirs[0]['output'].split(os.sep)
True
>>> d
[]
"""
if coll_dirs is None:
coll_dirs = []
for path_lnds in paths_landmarks:
set_name, scale_name = path_lnds.split(os.sep)[-2:]
scale = parse_path_user_scale(scale_name)[1] \
if with_user else parse_path_scale(scale_name)
# if a scale was not recognised in the last folder name
if np.isnan(scale):
sub_dirs = list_sub_folders(path_lnds)
coll_dirs, sub_dirs = collect_triple_dir(sub_dirs, path_dataset,
path_out, coll_dirs,
scales, with_user)
continue
# skip particular scale if it is not among chosen
if scales is not None and scale not in scales:
continue
coll_dirs.append({
'landmarks':
path_lnds,
'images':
os.path.join(path_dataset, set_name,
TEMPLATE_FOLDER_SCALE % scale),
'output':
os.path.join(path_out, set_name, scale_name)
})
return coll_dirs, []
def load_largest_scale(path_set):
""" in given set find the largest scale and load all landmarks in full size
:param str path_set: path to image/landmark set
:return dict: dictionary of ndarray with loaded landmarks in full scale
"""
scales_folders = [(parse_path_scale(p), os.path.basename(p))
for p in list_sub_folders(path_set)]
if not scales_folders:
return None
scale, folder = sorted(scales_folders, reverse=True)[0]
paths_csv = glob.glob(os.path.join(path_set, folder, '*.csv'))
scaling = 100. / scale
names_lnds = {
os.path.basename(p): load_landmarks_csv(p) * scaling
for p in paths_csv
}
return names_lnds
def dataset_scale_landmarks(path_dataset,
scales=DEFAULT_SCALES,
nb_workers=NB_WORKERS):
""" generate several scales within the same dataset
:param str path_dataset: path to the source/generated dataset
:param [inr] scales: created scales
:param int nb_workers: number of jobs running in parallel
:return:
"""
list_sets = list_sub_folders(path_dataset)
logging.info('Found sets: %i', len(list_sets))
_wrap_scale = partial(scale_set_landmarks, scales=scales)
counts = list(
iterate_mproc_map(_wrap_scale,
sorted(list_sets),
nb_workers=nb_workers,
desc='scaling sets'))
return counts
