def select_channels(self, dataset: Dataset):
if len(dataset.data_array.shape) == 3:
dataset.data_array = dataset.data_array[np.newaxis, ...]
selected_images_channels = dataset.data_array[...,
self.channels_to_select]
if len(selected_images_channels.shape) == 3:
selected_images_channels = selected_images_channels[...,
np.newaxis]
dataset.data_array = selected_images_channels
return dataset
def normalize_by_image(self, dataset: Dataset):
images = dataset.data_array
images -= np.nanmin(images, axis=(1, 2))[:, np.newaxis, np.newaxis, :]
images = images / np.nanmax(images, axis=(1, 2))[:, np.newaxis,
np.newaxis, :]
dataset.data_array = images
return dataset
def normalize_by_sample(self, dataset: Dataset):
images = dataset.data_array
images -= np.nanmin(images, axis=(1, 2, 3))[..., np.newaxis,
np.newaxis, np.newaxis]
images = images / np.nanmax(
images, axis=(1, 2, 3))[..., np.newaxis, np.newaxis, np.newaxis]
dataset.data_array = images
return dataset
def nan_to_num(self, dataset: Dataset):
samples = dataset.data_array
nans_sample_idx = self._get_nans_samples_idx(samples)
if self.verbose:
print('%i samples with NaNs. NaNs replaced with number %s' %
(len(nans_sample_idx), str(self.number_to_replace_nans)))
samples[np.isnan(samples)] = self.number_to_replace_nans
dataset.data_array = samples
return dataset
def crop_at_center(self, dataset: Dataset):
if self.crop_size is None:
return dataset
samples = dataset.data_array
assert (samples.shape[1] % 2 == self.crop_size % 2)
center = int((samples.shape[1]) / 2)
crop_side = int(self.crop_size / 2)
crop_begin = center - crop_side
if samples.shape[1] % 2 == 0:
crop_end = center + crop_side
elif samples.shape[1] % 2 == 1:
crop_end = center + crop_side + 1
# print(center)
# print(crop_begin, crop_end)
cropped_samples = samples[:, crop_begin:crop_end,
crop_begin:crop_end, :]
dataset.data_array = cropped_samples
return dataset
def check_single_image(self, dataset: Dataset):
if len(dataset.data_array.shape) == 3:
dataset.data_array = dataset.data_array[np.newaxis, ...]
return dataset