"""Classes representing batches of normalized or unnormalized data.""" from __future__ import print_function, division, absolute_import import collections import numpy as np from .. import imgaug as ia from . import normalization as nlib from . import utils DEFAULT = "DEFAULT" _AUGMENTABLE_NAMES = [ "images", "heatmaps", "segmentation_maps", "keypoints", "bounding_boxes", "polygons", "line_strings"] _AugmentableColumn = collections.namedtuple( "_AugmentableColumn", ["name", "value", "attr_name"]) def _get_column_names(batch, postfix): return [column.name for column in _get_columns(batch, postfix)] def _get_columns(batch, postfix): result = [] for name in _AUGMENTABLE_NAMES: attr_name = name + postfix value = getattr(batch, name + postfix) # Every data item is either an array or a list. If there are no # items in the array/list, there are also no shapes to change # as shape-changes are imagewise. Hence, we can afford to check # len() here. if value is not None and len(value) > 0: result.append(_AugmentableColumn(name, value, attr_name)) return result # TODO also support (H,W,C) for heatmaps of len(images) == 1 # TODO also support (H,W) for segmaps of len(images) == 1 class UnnormalizedBatch(object): """ Class for batches of unnormalized data before and after augmentation. Parameters ---------- images : None or (N,H,W,C) ndarray or (N,H,W) ndarray or iterable of (H,W,C) ndarray or iterable of (H,W) ndarray The images to augment. heatmaps : None or (N,H,W,C) ndarray or imgaug.augmentables.heatmaps.HeatmapsOnImage or iterable of (H,W,C) ndarray or iterable of imgaug.augmentables.heatmaps.HeatmapsOnImage The heatmaps to augment. If anything else than ``HeatmapsOnImage``, then the number of heatmaps must match the number of images provided via parameter `images`. The number is contained either in ``N`` or the first iterable's size. segmentation_maps : None or (N,H,W) ndarray or imgaug.augmentables.segmaps.SegmentationMapsOnImage or iterable of (H,W) ndarray or iterable of imgaug.augmentables.segmaps.SegmentationMapsOnImage The segmentation maps to augment. If anything else than ``SegmentationMapsOnImage``, then the number of segmaps must match the number of images provided via parameter `images`. The number is contained either in ``N`` or the first iterable's size. keypoints : None or list of (N,K,2) ndarray or tuple of number or imgaug.augmentables.kps.Keypoint or iterable of (K,2) ndarray or iterable of tuple of number or iterable of imgaug.augmentables.kps.Keypoint or iterable of imgaug.augmentables.kps.KeypointOnImage or iterable of iterable of tuple of number or iterable of iterable of imgaug.augmentables.kps.Keypoint The keypoints to augment. If a tuple (or iterable(s) of tuple), then iterpreted as (x,y) coordinates and must hence contain two numbers. A single tuple represents a single coordinate on one image, an iterable of tuples the coordinates on one image and an iterable of iterable of tuples the coordinates on several images. Analogous if ``Keypoint`` objects are used instead of tuples. If an ndarray, then ``N`` denotes the number of images and ``K`` the number of keypoints on each image. If anything else than ``KeypointsOnImage`` is provided, then the number of keypoint groups must match the number of images provided via parameter `images`. The number is contained e.g. in ``N`` or in case of "iterable of iterable of tuples" in the first iterable's size. bounding_boxes : None or (N,B,4) ndarray or tuple of number or imgaug.augmentables.bbs.BoundingBox or imgaug.augmentables.bbs.BoundingBoxesOnImage or iterable of (B,4) ndarray or iterable of tuple of number or iterable of imgaug.augmentables.bbs.BoundingBox or iterable of imgaug.augmentables.bbs.BoundingBoxesOnImage or iterable of iterable of tuple of number or iterable of iterable imgaug.augmentables.bbs.BoundingBox The bounding boxes to augment. This is analogous to the `keypoints` parameter. However, each tuple -- and also the last index in case of arrays -- has size 4, denoting the bounding box coordinates ``x1``, ``y1``, ``x2`` and ``y2``. polygons : None or (N,#polys,#points,2) ndarray or imgaug.augmentables.polys.Polygon or imgaug.augmentables.polys.PolygonsOnImage or iterable of (#polys,#points,2) ndarray or iterable of tuple of number or iterable of imgaug.augmentables.kps.Keypoint or iterable of imgaug.augmentables.polys.Polygon or iterable of imgaug.augmentables.polys.PolygonsOnImage or iterable of iterable of (#points,2) ndarray or iterable of iterable of tuple of number or iterable of iterable of imgaug.augmentables.kps.Keypoint or iterable of iterable of imgaug.augmentables.polys.Polygon or iterable of iterable of iterable of tuple of number or iterable of iterable of iterable of tuple of imgaug.augmentables.kps.Keypoint The polygons to augment. This is similar to the `keypoints` parameter. However, each polygon may be made up of several ``(x,y)`` coordinates (three or more are required for valid polygons). The following datatypes will be interpreted as a single polygon on a single image: * ``imgaug.augmentables.polys.Polygon`` * ``iterable of tuple of number`` * ``iterable of imgaug.augmentables.kps.Keypoint`` The following datatypes will be interpreted as multiple polygons on a single image: * ``imgaug.augmentables.polys.PolygonsOnImage`` * ``iterable of imgaug.augmentables.polys.Polygon`` * ``iterable of iterable of tuple of number`` * ``iterable of iterable of imgaug.augmentables.kps.Keypoint`` * ``iterable of iterable of imgaug.augmentables.polys.Polygon`` The following datatypes will be interpreted as multiple polygons on multiple images: * ``(N,#polys,#points,2) ndarray`` * ``iterable of (#polys,#points,2) ndarray`` * ``iterable of iterable of (#points,2) ndarray`` * ``iterable of iterable of iterable of tuple of number`` * ``iterable of iterable of iterable of tuple of imgaug.augmentables.kps.Keypoint`` line_strings : None or (N,#lines,#points,2) ndarray or imgaug.augmentables.lines.LineString or imgaug.augmentables.lines.LineStringOnImage or iterable of (#lines,#points,2) ndarray or iterable of tuple of number or iterable of imgaug.augmentables.kps.Keypoint or iterable of imgaug.augmentables.lines.LineString or iterable of imgaug.augmentables.lines.LineStringOnImage or iterable of iterable of (#points,2) ndarray or iterable of iterable of tuple of number or iterable of iterable of imgaug.augmentables.kps.Keypoint or iterable of iterable of imgaug.augmentables.polys.LineString or iterable of iterable of iterable of tuple of number or iterable of iterable of iterable of tuple of imgaug.augmentables.kps.Keypoint The line strings to augment. See `polygons` for more details as polygons follow a similar structure to line strings. data Additional data that is saved in the batch and may be read out after augmentation. This could e.g. contain filepaths to each image in `images`. As this object is usually used for background augmentation with multiple processes, the augmented Batch objects might not be returned in the original order, making this information useful. """ def __init__(self, images=None, heatmaps=None, segmentation_maps=None, keypoints=None, bounding_boxes=None, polygons=None, line_strings=None, data=None): """Construct a new :class:`UnnormalizedBatch` instance.""" self.images_unaug = images self.images_aug = None self.heatmaps_unaug = heatmaps self.heatmaps_aug = None self.segmentation_maps_unaug = segmentation_maps self.segmentation_maps_aug = None self.keypoints_unaug = keypoints self.keypoints_aug = None self.bounding_boxes_unaug = bounding_boxes self.bounding_boxes_aug = None self.polygons_unaug = polygons self.polygons_aug = None self.line_strings_unaug = line_strings self.line_strings_aug = None self.data = data def get_column_names(self): """Get the names of types of augmentables that contain data. This method is intended for situations where one wants to know which data is contained in the batch that has to be augmented, visualized or something similar. Added in 0.4.0. Returns ------- list of str Names of types of augmentables. E.g. ``["images", "polygons"]``. """ return _get_column_names(self, "_unaug") def to_normalized_batch(self): """Convert this unnormalized batch to an instance of Batch. As this method is intended to be called before augmentation, it assumes that none of the ``*_aug`` attributes is yet set. It will produce an AssertionError otherwise. The newly created Batch's ``*_unaug`` attributes will match the ones in this batch, just in normalized form. Returns ------- imgaug.augmentables.batches.Batch The batch, with ``*_unaug`` attributes being normalized. """ contains_no_augmented_data_yet = all([ attr is None for attr_name, attr in self.__dict__.items() if attr_name.endswith("_aug")]) assert contains_no_augmented_data_yet, ( "Expected UnnormalizedBatch to not contain any augmented data " "before normalization, but at least one '*_aug' attribute was " "already set.") images_unaug = nlib.normalize_images(self.images_unaug) shapes = None if images_unaug is not None: shapes = [image.shape for image in images_unaug] return Batch( images=images_unaug, heatmaps=nlib.normalize_heatmaps( self.heatmaps_unaug, shapes), segmentation_maps=nlib.normalize_segmentation_maps( self.segmentation_maps_unaug, shapes), keypoints=nlib.normalize_keypoints( self.keypoints_unaug, shapes), bounding_boxes=nlib.normalize_bounding_boxes( self.bounding_boxes_unaug, shapes), polygons=nlib.normalize_polygons( self.polygons_unaug, shapes), line_strings=nlib.normalize_line_strings( self.line_strings_unaug, shapes), data=self.data ) def fill_from_augmented_normalized_batch_(self, batch_aug_norm): """ Fill this batch with (normalized) augmentation results in-place. This method receives a (normalized) Batch instance, takes all ``*_aug`` attributes out if it and assigns them to this batch *in unnormalized form*. Hence, the datatypes of all ``*_aug`` attributes will match the datatypes of the ``*_unaug`` attributes. Added in 0.4.0. Parameters ---------- batch_aug_norm: imgaug.augmentables.batches.Batch Batch after normalization and augmentation. Returns ------- imgaug.augmentables.batches.UnnormalizedBatch This instance itself. All ``*_unaug`` attributes are unchanged. All ``*_aug`` attributes are taken from `batch_normalized`, converted to unnormalized form. """ self.images_aug = nlib.invert_normalize_images( batch_aug_norm.images_aug, self.images_unaug) self.heatmaps_aug = nlib.invert_normalize_heatmaps( batch_aug_norm.heatmaps_aug, self.heatmaps_unaug) self.segmentation_maps_aug = nlib.invert_normalize_segmentation_maps( batch_aug_norm.segmentation_maps_aug, self.segmentation_maps_unaug) self.keypoints_aug = nlib.invert_normalize_keypoints( batch_aug_norm.keypoints_aug, self.keypoints_unaug) self.bounding_boxes_aug = nlib.invert_normalize_bounding_boxes( batch_aug_norm.bounding_boxes_aug, self.bounding_boxes_unaug) self.polygons_aug = nlib.invert_normalize_polygons( batch_aug_norm.polygons_aug, self.polygons_unaug) self.line_strings_aug = nlib.invert_normalize_line_strings( batch_aug_norm.line_strings_aug, self.line_strings_unaug) return self def fill_from_augmented_normalized_batch(self, batch_aug_norm): """ Fill this batch with (normalized) augmentation results. This method receives a (normalized) Batch instance, takes all ``*_aug`` attributes out if it and assigns them to this batch *in unnormalized form*. Hence, the datatypes of all ``*_aug`` attributes will match the datatypes of the ``*_unaug`` attributes. Parameters ---------- batch_aug_norm: imgaug.augmentables.batches.Batch Batch after normalization and augmentation. Returns ------- imgaug.augmentables.batches.UnnormalizedBatch New UnnormalizedBatch instance. All ``*_unaug`` attributes are taken from the old UnnormalizedBatch (without deepcopying them) and all ``*_aug`` attributes are taken from `batch_normalized`, converted to unnormalized form. """ # we take here the .data from the normalized batch instead of from # self for the rare case where one has decided to somehow change it # during augmentation batch = UnnormalizedBatch( images=self.images_unaug, heatmaps=self.heatmaps_unaug, segmentation_maps=self.segmentation_maps_unaug, keypoints=self.keypoints_unaug, bounding_boxes=self.bounding_boxes_unaug, polygons=self.polygons_unaug, line_strings=self.line_strings_unaug, data=batch_aug_norm.data ) batch.images_aug = nlib.invert_normalize_images( batch_aug_norm.images_aug, self.images_unaug) batch.heatmaps_aug = nlib.invert_normalize_heatmaps( batch_aug_norm.heatmaps_aug, self.heatmaps_unaug) batch.segmentation_maps_aug = nlib.invert_normalize_segmentation_maps( batch_aug_norm.segmentation_maps_aug, self.segmentation_maps_unaug) batch.keypoints_aug = nlib.invert_normalize_keypoints( batch_aug_norm.keypoints_aug, self.keypoints_unaug) batch.bounding_boxes_aug = nlib.invert_normalize_bounding_boxes( batch_aug_norm.bounding_boxes_aug, self.bounding_boxes_unaug) batch.polygons_aug = nlib.invert_normalize_polygons( batch_aug_norm.polygons_aug, self.polygons_unaug) batch.line_strings_aug = nlib.invert_normalize_line_strings( batch_aug_norm.line_strings_aug, self.line_strings_unaug) return batch class Batch(object): """ Class encapsulating a batch before and after augmentation. Parameters ---------- images : None or (N,H,W,C) ndarray or list of (H,W,C) ndarray The images to augment. heatmaps : None or list of imgaug.augmentables.heatmaps.HeatmapsOnImage The heatmaps to augment. segmentation_maps : None or list of imgaug.augmentables.segmaps.SegmentationMapsOnImage The segmentation maps to augment. keypoints : None or list of imgaug.augmentables.kps.KeypointOnImage The keypoints to augment. bounding_boxes : None or list of imgaug.augmentables.bbs.BoundingBoxesOnImage The bounding boxes to augment. polygons : None or list of imgaug.augmentables.polys.PolygonsOnImage The polygons to augment. line_strings : None or list of imgaug.augmentables.lines.LineStringsOnImage The line strings to augment. data Additional data that is saved in the batch and may be read out after augmentation. This could e.g. contain filepaths to each image in `images`. As this object is usually used for background augmentation with multiple processes, the augmented Batch objects might not be returned in the original order, making this information useful. """ def __init__(self, images=None, heatmaps=None, segmentation_maps=None, keypoints=None, bounding_boxes=None, polygons=None, line_strings=None, data=None): """Construct a new :class:`Batch` instance.""" self.images_unaug = images self.images_aug = None self.heatmaps_unaug = heatmaps self.heatmaps_aug = None self.segmentation_maps_unaug = segmentation_maps self.segmentation_maps_aug = None self.keypoints_unaug = keypoints self.keypoints_aug = None self.bounding_boxes_unaug = bounding_boxes self.bounding_boxes_aug = None self.polygons_unaug = polygons self.polygons_aug = None self.line_strings_unaug = line_strings self.line_strings_aug = None self.data = data @property @ia.deprecated("Batch.images_unaug") def images(self): """Get unaugmented images.""" return self.images_unaug @property @ia.deprecated("Batch.heatmaps_unaug") def heatmaps(self): """Get unaugmented heatmaps.""" return self.heatmaps_unaug @property @ia.deprecated("Batch.segmentation_maps_unaug") def segmentation_maps(self): """Get unaugmented segmentation maps.""" return self.segmentation_maps_unaug @property @ia.deprecated("Batch.keypoints_unaug") def keypoints(self): """Get unaugmented keypoints.""" return self.keypoints_unaug @property @ia.deprecated("Batch.bounding_boxes_unaug") def bounding_boxes(self): """Get unaugmented bounding boxes.""" return self.bounding_boxes_unaug def get_column_names(self): """Get the names of types of augmentables that contain data. This method is intended for situations where one wants to know which data is contained in the batch that has to be augmented, visualized or something similar. Added in 0.4.0. Returns ------- list of str Names of types of augmentables. E.g. ``["images", "polygons"]``. """ return _get_column_names(self, "_unaug") def to_normalized_batch(self): """Return this batch. This method does nothing and only exists to simplify interfaces that accept both :class:`UnnormalizedBatch` and :class:`Batch`. Added in 0.4.0. Returns ------- imgaug.augmentables.batches.Batch This batch (not copied). """ return self def to_batch_in_augmentation(self): """Convert this batch to a :class:`_BatchInAugmentation` instance. Added in 0.4.0. Returns ------- imgaug.augmentables.batches._BatchInAugmentation The converted batch. """ def _copy(var): # TODO first check here if _aug is set and if it is then use that? if var is not None: return utils.copy_augmentables(var) return var return _BatchInAugmentation( images=_copy(self.images_unaug), heatmaps=_copy(self.heatmaps_unaug), segmentation_maps=_copy(self.segmentation_maps_unaug), keypoints=_copy(self.keypoints_unaug), bounding_boxes=_copy(self.bounding_boxes_unaug), polygons=_copy(self.polygons_unaug), line_strings=_copy(self.line_strings_unaug) ) def fill_from_batch_in_augmentation_(self, batch_in_augmentation): """Set the columns in this batch to the column values of another batch. This method works in-place. Added in 0.4.0. Parameters ---------- batch_in_augmentation : _BatchInAugmentation Batch of which to use the column values. The values are *not* copied. Only their references are used. Returns ------- Batch The updated batch. (Modified in-place.) """ self.images_aug = batch_in_augmentation.images self.heatmaps_aug = batch_in_augmentation.heatmaps self.segmentation_maps_aug = batch_in_augmentation.segmentation_maps self.keypoints_aug = batch_in_augmentation.keypoints self.bounding_boxes_aug = batch_in_augmentation.bounding_boxes self.polygons_aug = batch_in_augmentation.polygons self.line_strings_aug = batch_in_augmentation.line_strings return self def deepcopy(self, images_unaug=DEFAULT, images_aug=DEFAULT, heatmaps_unaug=DEFAULT, heatmaps_aug=DEFAULT, segmentation_maps_unaug=DEFAULT, segmentation_maps_aug=DEFAULT, keypoints_unaug=DEFAULT, keypoints_aug=DEFAULT, bounding_boxes_unaug=DEFAULT, bounding_boxes_aug=DEFAULT, polygons_unaug=DEFAULT, polygons_aug=DEFAULT, line_strings_unaug=DEFAULT, line_strings_aug=DEFAULT): """Copy this batch and all of its column values. Parameters ---------- images_unaug : imgaug.augmentables.batches.DEFAULT or None or (N,H,W,C) ndarray or list of (H,W,C) ndarray Copies the current attribute value without changes if set to ``imgaug.augmentables.batches.DEFAULT``. Otherwise same as in :func:`Batch.__init__`. images_aug : imgaug.augmentables.batches.DEFAULT or None or (N,H,W,C) ndarray or list of (H,W,C) ndarray Copies the current attribute value without changes if set to ``imgaug.augmentables.batches.DEFAULT``. Otherwise same as in :func:`Batch.__init__`. heatmaps_unaug : imgaug.augmentables.batches.DEFAULT or None or list of imgaug.augmentables.heatmaps.HeatmapsOnImage Copies the current attribute value without changes if set to ``imgaug.augmentables.batches.DEFAULT``. Otherwise same as in :func:`Batch.__init__`. heatmaps_aug : imgaug.augmentables.batches.DEFAULT or None or list of imgaug.augmentables.heatmaps.HeatmapsOnImage Copies the current attribute value without changes if set to ``imgaug.augmentables.batches.DEFAULT``. Otherwise same as in :func:`Batch.__init__`. segmentation_maps_unaug : imgaug.augmentables.batches.DEFAULT or None or list of imgaug.augmentables.segmaps.SegmentationMapsOnImage Copies the current attribute value without changes if set to ``imgaug.augmentables.batches.DEFAULT``. Otherwise same as in :func:`Batch.__init__`. segmentation_maps_aug : imgaug.augmentables.batches.DEFAULT or None or list of imgaug.augmentables.segmaps.SegmentationMapsOnImage Copies the current attribute value without changes if set to ``imgaug.augmentables.batches.DEFAULT``. Otherwise same as in :func:`Batch.__init__`. keypoints_unaug : imgaug.augmentables.batches.DEFAULT or None or list of imgaug.augmentables.kps.KeypointOnImage Copies the current attribute value without changes if set to ``imgaug.augmentables.batches.DEFAULT``. Otherwise same as in :func:`Batch.__init__`. keypoints_aug : imgaug.augmentables.batches.DEFAULT or None or list of imgaug.augmentables.kps.KeypointOnImage Copies the current attribute value without changes if set to ``imgaug.augmentables.batches.DEFAULT``. Otherwise same as in :func:`Batch.__init__`. bounding_boxes_unaug : imgaug.augmentables.batches.DEFAULT or None or list of imgaug.augmentables.bbs.BoundingBoxesOnImage Copies the current attribute value without changes if set to ``imgaug.augmentables.batches.DEFAULT``. Otherwise same as in :func:`Batch.__init__`. bounding_boxes_aug : imgaug.augmentables.batches.DEFAULT or None or list of imgaug.augmentables.bbs.BoundingBoxesOnImage Copies the current attribute value without changes if set to ``imgaug.augmentables.batches.DEFAULT``. Otherwise same as in :func:`Batch.__init__`. polygons_unaug : imgaug.augmentables.batches.DEFAULT or None or list of imgaug.augmentables.polys.PolygonsOnImage Copies the current attribute value without changes if set to ``imgaug.augmentables.batches.DEFAULT``. Otherwise same as in :func:`Batch.__init__`. polygons_aug : imgaug.augmentables.batches.DEFAULT or None or list of imgaug.augmentables.polys.PolygonsOnImage Copies the current attribute value without changes if set to ``imgaug.augmentables.batches.DEFAULT``. Otherwise same as in :func:`Batch.__init__`. line_strings_unaug : imgaug.augmentables.batches.DEFAULT or None or list of imgaug.augmentables.lines.LineStringsOnImage Copies the current attribute value without changes if set to ``imgaug.augmentables.batches.DEFAULT``. Otherwise same as in :func:`Batch.__init__`. line_strings_aug : imgaug.augmentables.batches.DEFAULT or None or list of imgaug.augmentables.lines.LineStringsOnImage Copies the current attribute value without changes if set to ``imgaug.augmentables.batches.DEFAULT``. Otherwise same as in :func:`Batch.__init__`. Returns ------- Batch Deep copy of the batch, optionally with new attributes. """ def _copy_optional(self_attr, arg): return utils.deepcopy_fast(arg if arg is not DEFAULT else self_attr) batch = Batch( images=_copy_optional(self.images_unaug, images_unaug), heatmaps=_copy_optional(self.heatmaps_unaug, heatmaps_unaug), segmentation_maps=_copy_optional(self.segmentation_maps_unaug, segmentation_maps_unaug), keypoints=_copy_optional(self.keypoints_unaug, keypoints_unaug), bounding_boxes=_copy_optional(self.bounding_boxes_unaug, bounding_boxes_unaug), polygons=_copy_optional(self.polygons_unaug, polygons_unaug), line_strings=_copy_optional(self.line_strings_unaug, line_strings_unaug), data=utils.deepcopy_fast(self.data) ) batch.images_aug = _copy_optional(self.images_aug, images_aug) batch.heatmaps_aug = _copy_optional(self.heatmaps_aug, heatmaps_aug) batch.segmentation_maps_aug = _copy_optional(self.segmentation_maps_aug, segmentation_maps_aug) batch.keypoints_aug = _copy_optional(self.keypoints_aug, keypoints_aug) batch.bounding_boxes_aug = _copy_optional(self.bounding_boxes_aug, bounding_boxes_aug) batch.polygons_aug = _copy_optional(self.polygons_aug, polygons_aug) batch.line_strings_aug = _copy_optional(self.line_strings_aug, line_strings_aug) return batch # Added in 0.4.0. class _BatchInAugmentationPropagationContext(object): def __init__(self, batch, augmenter, hooks, parents): self.batch = batch self.augmenter = augmenter self.hooks = hooks self.parents = parents self.noned_info = None def __enter__(self): if self.hooks is not None: self.noned_info = self.batch.apply_propagation_hooks_( self.augmenter, self.hooks, self.parents) return self.batch def __exit__(self, exc_type, exc_val, exc_tb): if self.noned_info is not None: self.batch = \ self.batch.invert_apply_propagation_hooks_(self.noned_info) class _BatchInAugmentation(object): """ Class encapsulating a batch during the augmentation process. Data within the batch is already verified and normalized, similar to :class:`Batch`. Data within the batch may be changed in-place. No initial copy is needed. Added in 0.4.0. Parameters ---------- images : None or (N,H,W,C) ndarray or list of (H,W,C) ndarray The images to augment. heatmaps : None or list of imgaug.augmentables.heatmaps.HeatmapsOnImage The heatmaps to augment. segmentation_maps : None or list of imgaug.augmentables.segmaps.SegmentationMapsOnImage The segmentation maps to augment. keypoints : None or list of imgaug.augmentables.kps.KeypointOnImage The keypoints to augment. bounding_boxes : None or list of imgaug.augmentables.bbs.BoundingBoxesOnImage The bounding boxes to augment. polygons : None or list of imgaug.augmentables.polys.PolygonsOnImage The polygons to augment. line_strings : None or list of imgaug.augmentables.lines.LineStringsOnImage The line strings to augment. """ # Added in 0.4.0. def __init__(self, images=None, heatmaps=None, segmentation_maps=None, keypoints=None, bounding_boxes=None, polygons=None, line_strings=None, data=None): """Create a new :class:`_BatchInAugmentation` instance.""" self.images = images self.heatmaps = heatmaps self.segmentation_maps = segmentation_maps self.keypoints = keypoints self.bounding_boxes = bounding_boxes self.polygons = polygons self.line_strings = line_strings self.data = data @property def empty(self): """Estimate whether this batch is empty, i.e. contains no data. Added in 0.4.0. Returns ------- bool ``True`` if the batch contains no data to augment. ``False`` otherwise. """ return self.nb_rows == 0 @property def nb_rows(self): """Get the number of rows (i.e. examples) in this batch. Note that this method assumes that all columns have the same number of rows. Added in 0.4.0. Returns ------- int Number of rows or ``0`` if there is no data in the batch. """ for augm_name in _AUGMENTABLE_NAMES: value = getattr(self, augm_name) if value is not None: return len(value) return 0 @property def columns(self): """Get the columns of data to augment. Each column represents one datatype and its corresponding data, e.g. images or polygons. Added in 0.4.0. Returns ------- list of _AugmentableColumn The columns to augment within this batch. """ return _get_columns(self, "") def get_column_names(self): """Get the names of types of augmentables that contain data. This method is intended for situations where one wants to know which data is contained in the batch that has to be augmented, visualized or something similar. Added in 0.4.0. Returns ------- list of str Names of types of augmentables. E.g. ``["images", "polygons"]``. """ return _get_column_names(self, "") def get_rowwise_shapes(self): """Get the shape of each row within this batch. Each row denotes the data of different types (e.g. image array, polygons) corresponding to a single example in the batch. This method assumes that all ``.shape`` attributes contain the same shape and that it is identical to the image's shape. It also assumes that there are no columns containing only ``None`` s. Added in 0.4.0. Returns ------- list of tuple of int The shapes of each row. """ nb_rows = self.nb_rows columns = self.columns shapes = [None] * nb_rows found = np.zeros((nb_rows,), dtype=bool) for column in columns: if column.name == "images" and ia.is_np_array(column.value): shapes = [column.value.shape[1:]] * nb_rows else: for i, item in enumerate(column.value): if item is not None: shapes[i] = item.shape found[i] = True if np.all(found): return shapes return shapes def subselect_rows_by_indices(self, indices): """Reduce this batch to a subset of rows based on their row indices. Added in 0.4.0. Parameters ---------- indices : iterable of int Row indices to select. Returns ------- _BatchInAugmentation Batch containing only a subselection of rows. """ kwargs = {"data": self.data} for augm_name in _AUGMENTABLE_NAMES: rows = getattr(self, augm_name) if rows is not None: if augm_name == "images" and ia.is_np_array(rows): rows = rows[indices] # pylint: disable=unsubscriptable-object else: rows = [rows[index] for index in indices] if len(rows) == 0: rows = None kwargs[augm_name] = rows return _BatchInAugmentation(**kwargs) def invert_subselect_rows_by_indices_(self, indices, batch_subselected): """Reverse the subselection of rows in-place. This is the inverse of :func:`_BatchInAugmentation.subselect_rows_by_indices`. This method has to be executed on the batch *before* subselection. Added in 0.4.0. Parameters ---------- indices : iterable of int Row indices that were selected. (This is the input to batch_subselected : _BatchInAugmentation The batch after :func:`_BatchInAugmentation.subselect_rows_by_indices` was called. Returns ------- _BatchInAugmentation The updated batch. (Modified in-place.) Examples -------- >>> import numpy as np >>> from imgaug.augmentables.batches import _BatchInAugmentation >>> images = np.zeros((2, 10, 20, 3), dtype=np.uint8) >>> batch = _BatchInAugmentation(images=images) >>> batch_sub = batch.subselect_rows_by_indices([0]) >>> batch_sub.images += 1 >>> batch = batch.invert_subselect_rows_by_indices_([0], batch_sub) """ for augm_name in _AUGMENTABLE_NAMES: column = getattr(self, augm_name) if column is not None: column_sub = getattr(batch_subselected, augm_name) if column_sub is None: # list of indices was empty, resulting in the columns # in the subselected batch being empty and replaced # by Nones. We can just re-use the columns before # subselection. pass elif augm_name == "images" and ia.is_np_array(column): # An array does not have to stay an array after # augmentation. The shapes and/or dtypes of rows may # change, turning the array into a list. if ia.is_np_array(column_sub): shapes = {column.shape[1:], column_sub.shape[1:]} dtypes = {column.dtype.name, column_sub.dtype.name} else: shapes = set( [column.shape[1:]] + [image.shape for image in column_sub]) dtypes = set( [column.dtype.name] + [image.dtype.name for image in column_sub]) if len(shapes) == 1 and len(dtypes) == 1: column[indices] = column_sub # pylint: disable=unsupported-assignment-operation else: self.images = list(column) for ith_index, index in enumerate(indices): self.images[index] = column_sub[ith_index] else: for ith_index, index in enumerate(indices): column[index] = column_sub[ith_index] # pylint: disable=unsupported-assignment-operation return self def propagation_hooks_ctx(self, augmenter, hooks, parents): """Start a context in which propagation hooks are applied. Added in 0.4.0. Parameters ---------- augmenter : imgaug.augmenters.meta.Augmenter Augmenter to provide to the propagation hook function. hooks : imgaug.imgaug.HooksImages or imgaug.imgaug.HooksKeypoints The hooks that might contain a propagation hook function. parents : list of imgaug.augmenters.meta.Augmenter The list of parents to provide to the propagation hook function. Returns ------- _BatchInAugmentationPropagationContext The progagation hook context. """ return _BatchInAugmentationPropagationContext( self, augmenter=augmenter, hooks=hooks, parents=parents) def apply_propagation_hooks_(self, augmenter, hooks, parents): """Set columns in this batch to ``None`` based on a propagation hook. This method works in-place. Added in 0.4.0. Parameters ---------- augmenter : imgaug.augmenters.meta.Augmenter Augmenter to provide to the propagation hook function. hooks : imgaug.imgaug.HooksImages or imgaug.imgaug.HooksKeypoints The hooks that might contain a propagation hook function. parents : list of imgaug.augmenters.meta.Augmenter The list of parents to provide to the propagation hook function. Returns ------- list of tuple of str Information about which columns were set to ``None``. Each tuple contains ``(column attribute name, column value before setting it to None)``. This information is required when calling :func:`_BatchInAugmentation.invert_apply_propagation_hooks_`. """ if hooks is None: return None noned_info = [] for column in self.columns: is_prop = hooks.is_propagating( column.value, augmenter=augmenter, parents=parents, default=True) if not is_prop: setattr(self, column.attr_name, None) noned_info.append((column.attr_name, column.value)) return noned_info def invert_apply_propagation_hooks_(self, noned_info): """Set columns from ``None`` back to their original values. This is the inverse of :func:`_BatchInAugmentation.apply_propagation_hooks_`. This method works in-place. Added in 0.4.0. Parameters ---------- noned_info : list of tuple of str Information about which columns were set to ``None`` and their original values. This is the output of :func:`_BatchInAugmentation.apply_propagation_hooks_`. Returns ------- _BatchInAugmentation The updated batch. (Modified in-place.) """ for attr_name, value in noned_info: setattr(self, attr_name, value) return self def to_batch_in_augmentation(self): """Convert this batch to a :class:`_BatchInAugmentation` instance. This method simply returns the batch itself. It exists for consistency with the other batch classes. Added in 0.4.0. Returns ------- imgaug.augmentables.batches._BatchInAugmentation The batch itself. (Not copied.) """ return self def fill_from_batch_in_augmentation_(self, batch_in_augmentation): """Set the columns in this batch to the column values of another batch. This method works in-place. Added in 0.4.0. Parameters ---------- batch_in_augmentation : _BatchInAugmentation Batch of which to use the column values. The values are *not* copied. Only their references are used. Returns ------- _BatchInAugmentation The updated batch. (Modified in-place.) """ if batch_in_augmentation is self: return self self.images = batch_in_augmentation.images self.heatmaps = batch_in_augmentation.heatmaps self.segmentation_maps = batch_in_augmentation.segmentation_maps self.keypoints = batch_in_augmentation.keypoints self.bounding_boxes = batch_in_augmentation.bounding_boxes self.polygons = batch_in_augmentation.polygons self.line_strings = batch_in_augmentation.line_strings return self def to_batch(self, batch_before_aug): """Convert this batch into a :class:`Batch` instance. Added in 0.4.0. Parameters ---------- batch_before_aug : imgaug.augmentables.batches.Batch The batch before augmentation. It is required to set the input data of the :class:`Batch` instance, e.g. ``images_unaug`` or ``data``. Returns ------- imgaug.augmentables.batches.Batch Batch, with original unaugmented inputs from `batch_before_aug` and augmented outputs from this :class:`_BatchInAugmentation` instance. """ batch = Batch( images=batch_before_aug.images_unaug, heatmaps=batch_before_aug.heatmaps_unaug, segmentation_maps=batch_before_aug.segmentation_maps_unaug, keypoints=batch_before_aug.keypoints_unaug, bounding_boxes=batch_before_aug.bounding_boxes_unaug, polygons=batch_before_aug.polygons_unaug, line_strings=batch_before_aug.line_strings_unaug, data=batch_before_aug.data ) batch.images_aug = self.images batch.heatmaps_aug = self.heatmaps batch.segmentation_maps_aug = self.segmentation_maps batch.keypoints_aug = self.keypoints batch.bounding_boxes_aug = self.bounding_boxes batch.polygons_aug = self.polygons batch.line_strings_aug = self.line_strings return batch def deepcopy(self): """Copy this batch and all of its column values. Added in 0.4.0. Returns ------- _BatchInAugmentation Deep copy of this batch. """ batch = _BatchInAugmentation(data=utils.deepcopy_fast(self.data)) for augm_name in _AUGMENTABLE_NAMES: value = getattr(self, augm_name) if value is not None: setattr(batch, augm_name, utils.copy_augmentables(value)) return batch