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import os
import errno
import shutil
import numpy as np
from scipy.interpolate import griddata, Rbf, LinearNDInterpolator, NearestNDInterpolator
from skimage.measure import regionprops
from DeepDeformationMapRegistration.layers.b_splines import interpolate_spline
from DeepDeformationMapRegistration.utils.thin_plate_splines import ThinPlateSplines
from tensorflow import squeeze
def try_mkdir(dir, verbose=True):
try:
os.makedirs(dir)
except OSError as err:
if err.errno == errno.EEXIST and verbose:
print("Directory " + dir + " already exists")
else:
raise ValueError("Can't create dir " + dir)
else:
print("Created directory " + dir)
def function_decorator(new_name):
""""
Change the __name__ property of a function using new_name.
:param new_name:
:return:
"""
def decorator(func):
func.__name__ = new_name
return func
return decorator
class DatasetCopy:
def __init__(self, dataset_location, copy_location=None, verbose=True):
self.__copy_loc = os.path.join(os.getcwd(), 'temp_dataset') if copy_location is None else copy_location
self.__dst_loc = dataset_location
self.__verbose = verbose
def copy_dataset(self):
shutil.copytree(self.__dst_loc, self.__copy_loc)
if self.__verbose:
print('{} copied to {}'.format(self.__dst_loc, self.__copy_loc))
return self.__copy_loc
def delete_temp(self):
shutil.rmtree(self.__copy_loc)
if self.__verbose:
print('Deleted: ', self.__copy_loc)
class DisplacementMapInterpolator:
def __init__(self,
image_shape=[64, 64, 64],
method='rbf'):
assert method in ['rbf', 'griddata', 'tf', 'tps'], "Method must be 'rbf' or 'griddata'"
self.method = method
self.image_shape = image_shape
self.grid = self.__regular_grid()
def __regular_grid(self):
xx = np.linspace(0, self.image_shape[0], self.image_shape[0], endpoint=False, dtype=np.uint16)
yy = np.linspace(0, self.image_shape[0], self.image_shape[0], endpoint=False, dtype=np.uint16)
zz = np.linspace(0, self.image_shape[0], self.image_shape[0], endpoint=False, dtype=np.uint16)
xx, yy, zz = np.meshgrid(xx, yy, zz)
return np.stack([xx.flatten(), yy.flatten(), zz.flatten()], axis=0).T
def __call__(self, disp_map, interp_points, backwards=False):
disp_map = disp_map.reshape([-1, 3])
grid_pts = self.grid.copy()
if backwards:
grid_pts = np.add(grid_pts, disp_map).astype(np.float32)
disp_map *= -1
if self.method == 'rbf':
interpolator = Rbf(grid_pts[:, 0], grid_pts[:, 1], grid_pts[:, 2], disp_map[:, :],
method='thin_plate', mode='N-D')
disp = interpolator(interp_points)
elif self.method == 'griddata':
linear_interp = LinearNDInterpolator(grid_pts, disp_map)
disp = linear_interp(interp_points).copy()
del linear_interp
if np.any(np.isnan(disp)):
# It might happen (though it shouldn't) that the interpolation point is outside the convex hull of grid points.
# in this situation, linear interpolation fails and will put NaN. Nearest can give a value, so we are going to
# substitute those unexpected NaNs with the nearest value. Unexpected == not in interp_points
nan_disp_idx = set(np.unique(np.argwhere(np.isnan(disp))[:, 0]))
nan_interp_pts_idx = set(np.unique(np.argwhere(np.isnan(interp_points))[:, 0]))
idx = nan_disp_idx - nan_interp_pts_idx if len(nan_disp_idx) > len(nan_interp_pts_idx) else nan_interp_pts_idx - nan_disp_idx
idx = list(idx)
if len(idx):
# We have unexpected NaNs
near_interp = NearestNDInterpolator(grid_pts, disp_map)
near_disp = near_interp(interp_points[idx, ...]).copy()
del near_interp
for n, i in enumerate(idx):
disp[i, ...] = near_disp[n, ...]
elif self.method == 'tf':
# Order: 1 -> linear, 2 -> thin plate, 3 -> cubic
disp = squeeze(interpolate_spline(grid_pts[np.newaxis, ...][::4, :], # Batch axis
disp_map[np.newaxis, ...][::4, :],
interp_points[np.newaxis, ...], order=2), axis=0)
else:
tps_interp = ThinPlateSplines(grid_pts[::8, :], self.grid.copy().astype(np.float32)[::8, :])
disp = tps_interp.interpolate(interp_points).eval()
del tps_interp
return disp
def get_segmentations_centroids(segmentations, ohe=True, expected_lbls=range(0, 28), missing_centroid=[np.nan]*3, brain_study=True):
segmentations = np.squeeze(segmentations)
if ohe:
segmentations = np.sum(segmentations, axis=-1).astype(np.uint8)
missing_lbls = set(expected_lbls) - set(np.unique(segmentations))
if brain_study:
segmentations += np.ones_like(segmentations) # Regionsprops neglect the label 0. But we need it, so offset all labels by 1
else:
missing_lbls = set(expected_lbls) - set(np.unique(segmentations))
seg_props = regionprops(segmentations)
centroids = np.asarray([c.centroid for c in seg_props]).astype(np.float32)
for lbl in missing_lbls:
idx = expected_lbls.index(lbl)
centroids = np.insert(centroids, idx, missing_centroid, axis=0)
return centroids.copy(), missing_lbls
def segmentation_ohe_to_cardinal(segmentation):
cpy = segmentation.copy()
for lbl in range(segmentation.shape[-1]):
cpy[..., lbl] *= (lbl + 1)
# Add the Background
cpy = np.concatenate([np.zeros(segmentation.shape[:-1])[..., np.newaxis], cpy], axis=-1)
return np.argmax(cpy, axis=-1)[..., np.newaxis]
def segmentation_cardinal_to_ohe(segmentation):
# Keep in mind that we don't handle the overlap between the segmentations!
cpy = np.tile(np.zeros_like(segmentation), (1, 1, 1, len(np.unique(segmentation)[1:])))
for ch, lbl in enumerate(np.unique(segmentation)[1:]):
cpy[segmentation == lbl, ch] = 1
return cpy
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