def compute_registration_statistic(
cls,
idx_row,
df_experiments,
path_dataset=None,
path_experiment=None,
path_reference=None,
):
""" after successful registration load initial nad estimated landmarks
afterwords compute various statistic for init, and final alignment
:param tuple(int,dict) df_row: row from iterated table
:param DF df_experiments: DataFrame with experiments
:param str|None path_dataset: path to the provided dataset folder
:param str|None path_reference: path to the complete landmark collection folder
:param str|None path_experiment: path to the experiment folder
"""
idx, row = idx_row
row = dict(row) # convert even series to dictionary
# load common landmarks and image size
points_ref, points_move, path_img_ref = cls._load_landmarks(
row, path_dataset)
img_diag = cls._image_diag(row, path_img_ref)
df_experiments.loc[idx, cls.COL_IMAGE_DIAGONAL] = img_diag
# compute landmarks statistic
cls.compute_registration_accuracy(df_experiments,
idx,
points_ref,
points_move,
'init',
img_diag,
wo_affine=False)
# define what is the target and init state according to the experiment results
use_move_warp = isinstance(row.get(cls.COL_POINTS_MOVE_WARP), str)
if use_move_warp:
points_init, points_target = points_move, points_ref
col_source, col_target = cls.COL_POINTS_MOVE, cls.COL_POINTS_REF
col_lnds_warp = cls.COL_POINTS_MOVE_WARP
else:
points_init, points_target = points_ref, points_move
col_lnds_warp = cls.COL_POINTS_REF_WARP
col_source, col_target = cls.COL_POINTS_REF, cls.COL_POINTS_MOVE
# optional filtering
if path_reference:
ratio, points_target, _ = \
filter_paired_landmarks(row, path_dataset, path_reference, col_source, col_target)
df_experiments.loc[idx, COL_PAIRED_LANDMARKS] = np.round(ratio, 2)
# load transformed landmarks
if (cls.COL_POINTS_MOVE_WARP not in row) and (cls.COL_POINTS_REF_WARP
not in row):
logging.error('Statistic: no output landmarks')
return
# check if there are reference landmarks
if points_target is None:
logging.warning(
'Missing landmarks in "%s"',
cls.COL_POINTS_REF if use_move_warp else cls.COL_POINTS_MOVE)
return
# load warped landmarks
path_lnds_warp = update_path(row[col_lnds_warp],
pre_path=path_experiment)
if path_lnds_warp and os.path.isfile(path_lnds_warp):
points_warp = load_landmarks(path_lnds_warp)
points_warp = np.nan_to_num(points_warp)
else:
logging.warning('Invalid path to the landmarks: "%s" <- "%s"',
path_lnds_warp, row[col_lnds_warp])
return
df_experiments.loc[idx, cls.COL_NB_LANDMARKS_INPUT] = min(
len(points_init), len(points_target))
df_experiments.loc[idx, cls.COL_NB_LANDMARKS_WARP] = len(points_warp)
# compute Affine statistic
affine_diff = compute_affine_transf_diff(points_init, points_target,
points_warp)
for name in affine_diff:
df_experiments.loc[idx, name] = affine_diff[name]
# compute landmarks statistic
cls.compute_registration_accuracy(df_experiments,
idx,
points_target,
points_warp,
'elastic',
img_diag,
wo_affine=True)
# compute landmarks statistic
cls.compute_registration_accuracy(df_experiments,
idx,
points_target,
points_warp,
'target',
img_diag,
wo_affine=False)
row_ = dict(df_experiments.loc[idx])
# compute the robustness
if 'TRE Mean' in row_:
df_experiments.loc[idx, cls.COL_ROBUSTNESS] = \
compute_tre_robustness(points_target, points_init, points_warp)
def compute_registration_statistic(cls,
idx_row,
df_experiments,
path_dataset=None,
path_experiment=None):
""" after successful registration load initial nad estimated landmarks
afterwords compute various statistic for init, and final alignment
:param tuple(int,dict) df_row: row from iterated table
:param DF df_experiments: DataFrame with experiments
:param str|None path_dataset: path to the dataset folder
:param str|None path_experiment: path to the experiment folder
"""
idx, row = idx_row
row = dict(row) # convert even series to dictionary
points_ref, points_move, path_img_ref = cls._load_landmarks(
row, path_dataset)
img_diag = cls._image_diag(row, path_img_ref)
df_experiments.loc[idx, cls.COL_IMAGE_DIAGONAL] = img_diag
# compute landmarks statistic
cls.compute_registration_accuracy(df_experiments,
idx,
points_ref,
points_move,
'init',
img_diag,
wo_affine=False)
# load transformed landmarks
if (cls.COL_POINTS_MOVE_WARP not in row) and (cls.COL_POINTS_REF_WARP
not in row):
logging.error('Statistic: no output landmarks')
return
# define what is the target and init state according to the experiment results
is_move_warp = isinstance(row.get(cls.COL_POINTS_MOVE_WARP, None), str)
points_init = points_move if is_move_warp else points_ref
points_target = points_ref if is_move_warp else points_move
col_lnds_warp = cls.COL_POINTS_MOVE_WARP if is_move_warp else cls.COL_POINTS_REF_WARP
# check if there are reference landmarks
if points_target is None:
logging.warning(
'Missing landmarks in "%s"',
cls.COL_POINTS_REF if is_move_warp else cls.COL_POINTS_MOVE)
return
# load warped landmarks
path_lnds_wapr = update_path(row[col_lnds_warp],
pre_path=path_experiment)
if path_lnds_wapr and os.path.isfile(path_lnds_wapr):
points_warp = load_landmarks(path_lnds_wapr)
points_warp = np.nan_to_num(points_warp)
else:
logging.warning('Invalid path to the landmarks: "%s" <- "%s"',
path_lnds_wapr, row[col_lnds_warp])
return
# compute Affine statistic
affine_diff = compute_affine_transf_diff(points_init, points_target,
points_warp)
for name in affine_diff:
df_experiments.loc[idx, name] = affine_diff[name]
# compute landmarks statistic
cls.compute_registration_accuracy(df_experiments,
idx,
points_target,
points_warp,
'elastic',
img_diag,
wo_affine=True)
# compute landmarks statistic
cls.compute_registration_accuracy(df_experiments,
idx,
points_target,
points_warp,
'target',
img_diag,
wo_affine=False)
row_ = dict(df_experiments.loc[idx])
# compute the robustness
if 'TRE Mean' in row_:
df_experiments.loc[idx, cls.COL_ROBUSTNESS] = \
compute_tre_robustness(points_target, points_init, points_warp)