def __getitem__(self, i):
idx = slice(i*self.batch_size,(i+1)*self.batch_size)
idx = list(self.perm[idx])
arrays = [sample_patches_from_multiple_stacks((self.X[k],self.Y[k]),
patch_size=self.patch_size, n_samples=1,
patch_filter=self.no_background_patches_cached(k)) for k in idx]
X, Y = list(zip(*[(x[0][self.b],y[0]) for x,y in arrays]))
X, Y = self.augmenter(X, Y)
prob = np.stack([edt_prob(lbl[self.b]) for lbl in Y])
if self.shape_completion:
Y_cleared = [clear_border(lbl) for lbl in Y]
dist = np.stack([star_dist(lbl,self.n_rays,mode=self.sd_mode)[self.b+(slice(None),)] for lbl in Y_cleared])
dist_mask = np.stack([edt_prob(lbl[self.b]) for lbl in Y_cleared])
else:
dist = np.stack([star_dist(lbl,self.n_rays,mode=self.sd_mode) for lbl in Y])
dist_mask = prob
X = np.stack(X)
if X.ndim == 3: # input image has no channel axis
X = np.expand_dims(X,-1)
prob = np.expand_dims(prob,-1)
dist_mask = np.expand_dims(dist_mask,-1)
# subsample wth given grid
dist_mask = dist_mask[self.ss_grid]
prob = prob[self.ss_grid]
dist = dist[self.ss_grid]
return [X,dist_mask], [prob,dist]
def __getitem__(self, i):
idx = slice(i * self.batch_size, (i + 1) * self.batch_size)
idx = list(self.perm[idx])
arrays = [
sample_patches_from_multiple_stacks(
(self.Y[k], ) + self.channels_as_tuple(self.X[k]),
patch_size=self.patch_size,
n_samples=1,
patch_filter=self.no_background_patches_cached(k)) for k in idx
]
if self.n_channel is None:
X, Y = list(zip(*[(x[0], y[0]) for y, x in arrays]))
else:
X, Y = list(
zip(*[(np.stack([_x[0] for _x in x], axis=-1), y[0])
for y, *x in arrays]))
X, Y = self.augmenter(X, Y)
if len(Y) == 1:
X = X[0][np.newaxis]
else:
X = np.stack(X, out=self.out_X[:len(Y)])
if X.ndim == 4: # input image has no channel axis
X = np.expand_dims(X, -1)
tmp = [edt_prob(lbl, anisotropy=self.anisotropy) for lbl in Y]
if len(Y) == 1:
prob = tmp[0][np.newaxis]
else:
prob = np.stack(tmp, out=self.out_edt_prob[:len(Y)])
tmp = [star_dist3D(lbl, self.rays, mode=self.sd_mode) for lbl in Y]
if len(Y) == 1:
dist = tmp[0][np.newaxis]
else:
dist = np.stack(tmp, out=self.out_star_dist3D[:len(Y)])
prob = dist_mask = np.expand_dims(prob, -1)
# subsample wth given grid
dist_mask = dist_mask[self.ss_grid]
prob = prob[self.ss_grid]
dist = dist[self.ss_grid]
return [X, dist_mask], [prob, dist]