Source code for aydin.features.base

import os
from abc import ABC, abstractmethod
from os.path import join
import jsonpickle
import numpy

from aydin.util.misc.json import encode_indent
from aydin.util.log.log import lprint, lsection
from aydin.util.offcore.offcore import offcore_array

[docs]class FeatureGeneratorBase(ABC): """ Feature Generator base class """ _max_non_batch_dims = 4 _max_voxels = 512 ** 3 def __init__(self): """ Constructs a feature generator """ self.check_nans = False self.debug_force_memmap = False # Implementations must initialise the dtype so that feature arrays can be created with correct type: self.dtype = None
[docs] def save(self, path: str): """ Saves a 'all-batteries-inlcuded' feature generator at a given path (folder) Parameters ---------- path : str path to save to Returns ------- frozen """ os.makedirs(path, exist_ok=True) frozen = encode_indent(self) lprint(f"Saving feature generator to: {path}") with open(join(path, "feature_generation.json"), "w") as json_file: json_file.write(frozen) return frozen
[docs] @staticmethod def load(path: str): """ Returns a 'all-batteries-inlcuded' feature generator from a given path (folder) Parameters ---------- path : str path to load from Returns ------- thawed """ lprint(f"Loading feature generator from: {path}") with open(join(path, "feature_generation.json"), "r") as json_file: frozen = thawed = jsonpickle.decode(frozen) thawed._load_internals(path) return thawed
@abstractmethod def _load_internals(self, path: str): raise NotImplementedError()
[docs] @abstractmethod def get_receptive_field_radius(self): """ Returns the receptive field radius in pixels """ raise NotImplementedError()
[docs] @abstractmethod def compute( self, image, exclude_center_feature=False, exclude_center_value=False, features=None, feature_last_dim=True, passthrough_channels=None, num_reserved_features=0, excluded_voxels=None, spatial_feature_offset=None, spatial_feature_scale=None, ): """ Computes the features given an image. If the input image is of shape (d,h,w), resulting features are of shape (n,d,h,w) where n is the number of features. Parameters ---------- image : numpy.ndarray image for which features are computed exclude_center_feature : bool exclude_center_value : bool features feature_last_dim : bool passthrough_channels num_reserved_features : int excluded_voxels spatial_feature_offset spatial_feature_scale Returns ------- feature array : numpy.ndarray """ raise NotImplementedError()
[docs] def create_feature_array(self, image, nb_features): """ Creates a feature array of the right size and possibly in a 'lazy' way using memory mapping. Parameters ---------- image : numpy.ndarray image for which features are created nb_features : int Returns ------- feature array : numpy.ndarray """ with lsection(f'Creating feature array for image of shape: {image.shape}'): # That's the shape we need: shape = (nb_features, image.shape[0]) + image.shape[2:] dtype = image.dtype if self.dtype is None else self.dtype dtype = numpy.float32 if dtype == numpy.float16 else dtype array = offcore_array( shape=shape, dtype=dtype, force_memmap=self.debug_force_memmap ) return array