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)