def predict_probabilistic(self,
img,
axes,
normalizer=PercentileNormalizer(),
resizer=PadAndCropResizer(),
n_tiles=None):
"""Apply neural network to raw image to predict probability distribution for restored image.
See :func:`predict` for parameter explanations.
Returns
-------
:class:`csbdeep.internals.probability.ProbabilisticPrediction`
Returns the probability distribution of the restored image.
Raises
------
ValueError
If this is not a probabilistic model.
"""
self.config.probabilistic or _raise(
ValueError('This is not a probabilistic model.'))
mean, scale = self._predict_mean_and_scale(img, axes, normalizer,
resizer, n_tiles)
return ProbabilisticPrediction(mean, scale)
def predict_probabilistic(self,
img,
axes,
factor,
normalizer=PercentileNormalizer(),
resizer=PadAndCropResizer(),
batch_size=8):
"""Apply neural network to raw image to predict probability distribution for isotropic restored image.
See :func:`CARE.predict_probabilistic` for documentation.
Parameters
----------
factor : float
Upsampling factor for Z axis. It is important that this is chosen in correspondence
to the subsampling factor used during training data generation.
batch_size : int
Number of image slices that are processed together by the neural network.
Reduce this value if out of memory errors occur.
"""
self.config.probabilistic or _raise(
ValueError('This is not a probabilistic model.'))
mean, scale = self._predict_mean_and_scale(img, axes, factor,
normalizer, resizer,
batch_size)
return ProbabilisticPrediction(mean, scale)