def setup(self, limit=None):
if limit is not None and limit < 1:
raise datasets.DatasetException('Limit must be at least 1.')
# raw experimental images
self.x = self._list_images('exp', limit)
# ground truth
self.y = self._list_images('ground', limit)
self.on_epoch_end()
def load(self, limit=None):
if limit is not None:
if limit < 4:
raise datasets.DatasetException('Limit must be at least 4.')
limit = np.floor(limit / 4)
# noisy dataset
self.X_train = np.array(
self.load_images_from_dir('dipoles_hc_noise', limit)
+ self.load_images_from_dir('dipoles_lc_noise', limit)
+ self.load_images_from_dir('dipoles_vlc_noise', limit)
+ self.load_images_from_dir('constants_noise', limit)
)
# clean dataset
self.Y_train = np.array(
self.load_images_from_dir('dipoles_hc', limit)
+ self.load_images_from_dir('dipoles_lc', limit)
+ self.load_images_from_dir('dipoles_vlc', limit)
+ self.load_images_from_dir('constants', limit)
)