imgaug.augmenters.convolutional

Augmenters that are based on applying convolution kernels to images.

List of augmenters:

For MotionBlur, see blur.py.

class imgaug.augmenters.convolutional.Convolve(matrix=None, seed=None, name=None, random_state='deprecated', deterministic='deprecated')[source]

Bases: imgaug.augmenters.meta.Augmenter

Apply a convolution to input images.

Supported dtypes:

  • uint8: yes; fully tested
  • uint16: yes; tested
  • uint32: no (1)
  • uint64: no (2)
  • int8: yes; tested (3)
  • int16: yes; tested
  • int32: no (2)
  • int64: no (2)
  • float16: yes; tested (4)
  • float32: yes; tested
  • float64: yes; tested
  • float128: no (1)
  • bool: yes; tested (4)
    1. rejected by cv2.filter2D().
    1. causes error: cv2.error: OpenCV(3.4.2) (…)/filter.cpp:4487: error: (-213:The function/feature is not implemented) Unsupported combination of source format (=1), and destination format (=1) in function ‘getLinearFilter’.
    1. mapped internally to int16.
    1. mapped internally to float32.
Parameters:

  • matrix (None or (H, W) ndarray or imgaug.parameters.StochasticParameter or callable, optional) –

    The weight matrix of the convolution kernel to apply.

    • If None, the input images will not be changed.
    • If a 2D numpy array, that array will always be used for all images and channels as the kernel.
    • If a callable, that method will be called for each image via parameter(image, C, random_state). The function must either return a list of C matrices (i.e. one per channel) or a 2D numpy array (will be used for all channels) or a 3D HxWxC numpy array. If a list is returned, each entry may be None, which will result in no changes to the respective channel.

  • seed (None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional) – See __init__().

  • name (None or str, optional) – See __init__().

  • random_state (None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional) – Old name for parameter seed. Its usage will not yet cause a deprecation warning, but it is still recommended to use seed now. Outdated since 0.4.0.

  • deterministic (bool, optional) – Deprecated since 0.4.0. See method to_deterministic() for an alternative and for details about what the “deterministic mode” actually does.

Examples

>>> import imgaug.augmenters as iaa
>>> matrix = np.array([[0, -1, 0],
>>>                    [-1, 4, -1],
>>>                    [0, -1, 0]])
>>> aug = iaa.Convolve(matrix=matrix)

Convolves all input images with the kernel shown in the matrix variable.

>>> def gen_matrix(image, nb_channels, random_state):
>>>     matrix_A = np.array([[0, -1, 0],
>>>                          [-1, 4, -1],
>>>                          [0, -1, 0]])
>>>     matrix_B = np.array([[0, 1, 0],
>>>                          [1, -4, 1],
>>>                          [0, 1, 0]])
>>>     if image.shape[0] % 2 == 0:
>>>         return [matrix_A] * nb_channels
>>>     else:
>>>         return [matrix_B] * nb_channels
>>> aug = iaa.Convolve(matrix=gen_matrix)

Convolves images that have an even height with matrix A and images having an odd height with matrix B.

Methods

__call__(self, *args, **kwargs) Alias for augment().
augment(self[, return_batch, hooks]) Augment a batch.
augment_batch(self, batch[, hooks]) Deprecated.
augment_batch_(self, batch[, parents, hooks]) Augment a single batch in-place.
augment_batches(self, batches[, hooks, …]) Augment multiple batches.
augment_bounding_boxes(self, …[, parents, …]) Augment a batch of bounding boxes.
augment_heatmaps(self, heatmaps[, parents, …]) Augment a batch of heatmaps.
augment_image(self, image[, hooks]) Augment a single image.
augment_images(self, images[, parents, hooks]) Augment a batch of images.
augment_keypoints(self, keypoints_on_images) Augment a batch of keypoints/landmarks.
augment_line_strings(self, …[, parents, hooks]) Augment a batch of line strings.
augment_polygons(self, polygons_on_images[, …]) Augment a batch of polygons.
augment_segmentation_maps(self, segmaps[, …]) Augment a batch of segmentation maps.
copy(self) Create a shallow copy of this Augmenter instance.
copy_random_state(self, source[, recursive, …]) Copy the RNGs from a source augmenter sequence.
copy_random_state_(self, source[, …]) Copy the RNGs from a source augmenter sequence (in-place).
deepcopy(self) Create a deep copy of this Augmenter instance.
draw_grid(self, images, rows, cols) Augment images and draw the results as a single grid-like image.
find_augmenters(self, func[, parents, flat]) Find augmenters that match a condition.
find_augmenters_by_name(self, name[, regex, …]) Find augmenter(s) by name.
find_augmenters_by_names(self, names[, …]) Find augmenter(s) by names.
get_all_children(self[, flat]) Get all children of this augmenter as a list.
get_children_lists(self) Get a list of lists of children of this augmenter.
get_parameters(self) See get_parameters().
localize_random_state(self[, recursive]) Assign augmenter-specific RNGs to this augmenter and its children.
localize_random_state_(self[, recursive]) Assign augmenter-specific RNGs to this augmenter and its children.
pool(self[, processes, maxtasksperchild, seed]) Create a pool used for multicore augmentation.
remove_augmenters(self, func[, copy, …]) Remove this augmenter or children that match a condition.
remove_augmenters_(self, func[, parents]) Remove in-place children of this augmenter that match a condition.
remove_augmenters_inplace(self, func[, parents]) Deprecated.
reseed(self[, random_state, deterministic_too]) Deprecated.
seed_(self[, entropy, deterministic_too]) Seed this augmenter and all of its children.
show_grid(self, images, rows, cols) Augment images and plot the results as a single grid-like image.
to_deterministic(self[, n]) Convert this augmenter from a stochastic to a deterministic one.
get_parameters(self)[source]

See get_parameters().

class imgaug.augmenters.convolutional.DirectedEdgeDetect(alpha=(0.0, 0.75), direction=(0.0, 1.0), seed=None, name=None, random_state='deprecated', deterministic='deprecated')[source]

Bases: imgaug.augmenters.convolutional.Convolve

Detect edges from specified angles and alpha-blend with the input image.

This augmenter first detects edges along a certain angle. Usually, edges are detected in x- or y-direction, while here the edge detection kernel is rotated to match a specified angle. The result of applying the kernel is a black (non-edges) and white (edges) image. That image is alpha-blended with the input image.

Supported dtypes:

See Convolve.

Parameters:

  • alpha (number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional) – Blending factor of the edge image. At 0.0, only the original image is visible, at 1.0 only the edge image is visible.

    • If a number, exactly that value will always be used.
    • If a tuple (a, b), a random value will be sampled from the interval [a, b] per image.
    • If a list, a random value will be sampled from that list per image.
    • If a StochasticParameter, a value will be sampled from that parameter per image.

  • direction (number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional) – Angle (in degrees) of edges to pronounce, where 0 represents 0 degrees and 1.0 represents 360 degrees (both clockwise, starting at the top). Default value is (0.0, 1.0), i.e. pick a random angle per image.

    • If a number, exactly that value will always be used.
    • If a tuple (a, b), a random value will be sampled from the interval [a, b] will be sampled per image.
    • If a list, then a random value will be sampled from that list per image.
    • If a StochasticParameter, a value will be sampled from the parameter per image.

  • seed (None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional) – See __init__().

  • name (None or str, optional) – See __init__().

  • random_state (None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional) – Old name for parameter seed. Its usage will not yet cause a deprecation warning, but it is still recommended to use seed now. Outdated since 0.4.0.

  • deterministic (bool, optional) – Deprecated since 0.4.0. See method to_deterministic() for an alternative and for details about what the “deterministic mode” actually does.

Examples

>>> import imgaug.augmenters as iaa
>>> aug = iaa.DirectedEdgeDetect(alpha=1.0, direction=0)

Turn input images into edge images in which edges are detected from the top side of the image (i.e. the top sides of horizontal edges are part of the edge image, while vertical edges are ignored).

>>> aug = iaa.DirectedEdgeDetect(alpha=1.0, direction=90/360)

Same as before, but edges are detected from the right. Horizontal edges are now ignored.

>>> aug = iaa.DirectedEdgeDetect(alpha=1.0, direction=(0.0, 1.0))

Same as before, but edges are detected from a random angle sampled uniformly from the interval [0deg, 360deg].

>>> aug = iaa.DirectedEdgeDetect(alpha=(0.0, 0.3), direction=0)

Similar to the previous examples, but here the edge image is alpha-blended with the input image. The result is a mixture between the edge image and the input image. The blending factor is randomly sampled between 0% and 30%.

Methods

__call__(self, *args, **kwargs) Alias for augment().
augment(self[, return_batch, hooks]) Augment a batch.
augment_batch(self, batch[, hooks]) Deprecated.
augment_batch_(self, batch[, parents, hooks]) Augment a single batch in-place.
augment_batches(self, batches[, hooks, …]) Augment multiple batches.
augment_bounding_boxes(self, …[, parents, …]) Augment a batch of bounding boxes.
augment_heatmaps(self, heatmaps[, parents, …]) Augment a batch of heatmaps.
augment_image(self, image[, hooks]) Augment a single image.
augment_images(self, images[, parents, hooks]) Augment a batch of images.
augment_keypoints(self, keypoints_on_images) Augment a batch of keypoints/landmarks.
augment_line_strings(self, …[, parents, hooks]) Augment a batch of line strings.
augment_polygons(self, polygons_on_images[, …]) Augment a batch of polygons.
augment_segmentation_maps(self, segmaps[, …]) Augment a batch of segmentation maps.
copy(self) Create a shallow copy of this Augmenter instance.
copy_random_state(self, source[, recursive, …]) Copy the RNGs from a source augmenter sequence.
copy_random_state_(self, source[, …]) Copy the RNGs from a source augmenter sequence (in-place).
deepcopy(self) Create a deep copy of this Augmenter instance.
draw_grid(self, images, rows, cols) Augment images and draw the results as a single grid-like image.
find_augmenters(self, func[, parents, flat]) Find augmenters that match a condition.
find_augmenters_by_name(self, name[, regex, …]) Find augmenter(s) by name.
find_augmenters_by_names(self, names[, …]) Find augmenter(s) by names.
get_all_children(self[, flat]) Get all children of this augmenter as a list.
get_children_lists(self) Get a list of lists of children of this augmenter.
get_parameters(self) See get_parameters().
localize_random_state(self[, recursive]) Assign augmenter-specific RNGs to this augmenter and its children.
localize_random_state_(self[, recursive]) Assign augmenter-specific RNGs to this augmenter and its children.
pool(self[, processes, maxtasksperchild, seed]) Create a pool used for multicore augmentation.
remove_augmenters(self, func[, copy, …]) Remove this augmenter or children that match a condition.
remove_augmenters_(self, func[, parents]) Remove in-place children of this augmenter that match a condition.
remove_augmenters_inplace(self, func[, parents]) Deprecated.
reseed(self[, random_state, deterministic_too]) Deprecated.
seed_(self[, entropy, deterministic_too]) Seed this augmenter and all of its children.
show_grid(self, images, rows, cols) Augment images and plot the results as a single grid-like image.
to_deterministic(self[, n]) Convert this augmenter from a stochastic to a deterministic one.
class imgaug.augmenters.convolutional.EdgeDetect(alpha=(0.0, 0.75), seed=None, name=None, random_state='deprecated', deterministic='deprecated')[source]

Bases: imgaug.augmenters.convolutional.Convolve

Generate a black & white edge image and alpha-blend it with the input image.

Supported dtypes:

See Convolve.

Parameters:

  • alpha (number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional) – Blending factor of the edge image. At 0.0, only the original image is visible, at 1.0 only the edge image is visible.

    • If a number, exactly that value will always be used.
    • If a tuple (a, b), a random value will be sampled from the interval [a, b] per image.
    • If a list, a random value will be sampled from that list per image.
    • If a StochasticParameter, a value will be sampled from that parameter per image.

  • seed (None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional) – See __init__().

  • name (None or str, optional) – See __init__().

  • random_state (None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional) – Old name for parameter seed. Its usage will not yet cause a deprecation warning, but it is still recommended to use seed now. Outdated since 0.4.0.

  • deterministic (bool, optional) – Deprecated since 0.4.0. See method to_deterministic() for an alternative and for details about what the “deterministic mode” actually does.

Examples

>>> import imgaug.augmenters as iaa
>>> aug = iaa.EdgeDetect(alpha=(0.0, 1.0))

Detect edges in an image, mark them as black (non-edge) and white (edges) and alpha-blend the result with the original input image using a random blending factor between 0% and 100%.

Methods

__call__(self, *args, **kwargs) Alias for augment().
augment(self[, return_batch, hooks]) Augment a batch.
augment_batch(self, batch[, hooks]) Deprecated.
augment_batch_(self, batch[, parents, hooks]) Augment a single batch in-place.
augment_batches(self, batches[, hooks, …]) Augment multiple batches.
augment_bounding_boxes(self, …[, parents, …]) Augment a batch of bounding boxes.
augment_heatmaps(self, heatmaps[, parents, …]) Augment a batch of heatmaps.
augment_image(self, image[, hooks]) Augment a single image.
augment_images(self, images[, parents, hooks]) Augment a batch of images.
augment_keypoints(self, keypoints_on_images) Augment a batch of keypoints/landmarks.
augment_line_strings(self, …[, parents, hooks]) Augment a batch of line strings.
augment_polygons(self, polygons_on_images[, …]) Augment a batch of polygons.
augment_segmentation_maps(self, segmaps[, …]) Augment a batch of segmentation maps.
copy(self) Create a shallow copy of this Augmenter instance.
copy_random_state(self, source[, recursive, …]) Copy the RNGs from a source augmenter sequence.
copy_random_state_(self, source[, …]) Copy the RNGs from a source augmenter sequence (in-place).
deepcopy(self) Create a deep copy of this Augmenter instance.
draw_grid(self, images, rows, cols) Augment images and draw the results as a single grid-like image.
find_augmenters(self, func[, parents, flat]) Find augmenters that match a condition.
find_augmenters_by_name(self, name[, regex, …]) Find augmenter(s) by name.
find_augmenters_by_names(self, names[, …]) Find augmenter(s) by names.
get_all_children(self[, flat]) Get all children of this augmenter as a list.
get_children_lists(self) Get a list of lists of children of this augmenter.
get_parameters(self) See get_parameters().
localize_random_state(self[, recursive]) Assign augmenter-specific RNGs to this augmenter and its children.
localize_random_state_(self[, recursive]) Assign augmenter-specific RNGs to this augmenter and its children.
pool(self[, processes, maxtasksperchild, seed]) Create a pool used for multicore augmentation.
remove_augmenters(self, func[, copy, …]) Remove this augmenter or children that match a condition.
remove_augmenters_(self, func[, parents]) Remove in-place children of this augmenter that match a condition.
remove_augmenters_inplace(self, func[, parents]) Deprecated.
reseed(self[, random_state, deterministic_too]) Deprecated.
seed_(self[, entropy, deterministic_too]) Seed this augmenter and all of its children.
show_grid(self, images, rows, cols) Augment images and plot the results as a single grid-like image.
to_deterministic(self[, n]) Convert this augmenter from a stochastic to a deterministic one.
class imgaug.augmenters.convolutional.Emboss(alpha=(0.0, 1.0), strength=(0.25, 1.0), seed=None, name=None, random_state='deprecated', deterministic='deprecated')[source]

Bases: imgaug.augmenters.convolutional.Convolve

Emboss images and alpha-blend the result with the original input images.

The embossed version pronounces highlights and shadows, letting the image look as if it was recreated on a metal plate (“embossed”).

Supported dtypes:

See Convolve.

Parameters:

  • alpha (number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional) – Blending factor of the embossed image. At 0.0, only the original image is visible, at 1.0 only its embossed version is visible.

    • If a number, exactly that value will always be used.
    • If a tuple (a, b), a random value will be sampled from the interval [a, b] per image.
    • If a list, a random value will be sampled from that list per image.
    • If a StochasticParameter, a value will be sampled from that parameter per image.

  • strength (number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional) – Parameter that controls the strength of the embossing. Sane values are somewhere in the interval [0.0, 2.0] with 1.0 being the standard embossing effect. Default value is 1.0.

    • If a number, exactly that value will always be used.
    • If a tuple (a, b), a random value will be sampled from the interval [a, b] per image.
    • If a list, then a random value will be sampled from that list per image.
    • If a StochasticParameter, a value will be sampled from the parameter per image.

  • seed (None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional) – See __init__().

  • name (None or str, optional) – See __init__().

  • random_state (None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional) – Old name for parameter seed. Its usage will not yet cause a deprecation warning, but it is still recommended to use seed now. Outdated since 0.4.0.

  • deterministic (bool, optional) – Deprecated since 0.4.0. See method to_deterministic() for an alternative and for details about what the “deterministic mode” actually does.

Examples

>>> import imgaug.augmenters as iaa
>>> aug = iaa.Emboss(alpha=(0.0, 1.0), strength=(0.5, 1.5))

Emboss an image with a strength sampled uniformly from the interval [0.5, 1.5] and alpha-blend the result with the original input image using a random blending factor between 0% and 100%.

Methods

__call__(self, *args, **kwargs) Alias for augment().
augment(self[, return_batch, hooks]) Augment a batch.
augment_batch(self, batch[, hooks]) Deprecated.
augment_batch_(self, batch[, parents, hooks]) Augment a single batch in-place.
augment_batches(self, batches[, hooks, …]) Augment multiple batches.
augment_bounding_boxes(self, …[, parents, …]) Augment a batch of bounding boxes.
augment_heatmaps(self, heatmaps[, parents, …]) Augment a batch of heatmaps.
augment_image(self, image[, hooks]) Augment a single image.
augment_images(self, images[, parents, hooks]) Augment a batch of images.
augment_keypoints(self, keypoints_on_images) Augment a batch of keypoints/landmarks.
augment_line_strings(self, …[, parents, hooks]) Augment a batch of line strings.
augment_polygons(self, polygons_on_images[, …]) Augment a batch of polygons.
augment_segmentation_maps(self, segmaps[, …]) Augment a batch of segmentation maps.
copy(self) Create a shallow copy of this Augmenter instance.
copy_random_state(self, source[, recursive, …]) Copy the RNGs from a source augmenter sequence.
copy_random_state_(self, source[, …]) Copy the RNGs from a source augmenter sequence (in-place).
deepcopy(self) Create a deep copy of this Augmenter instance.
draw_grid(self, images, rows, cols) Augment images and draw the results as a single grid-like image.
find_augmenters(self, func[, parents, flat]) Find augmenters that match a condition.
find_augmenters_by_name(self, name[, regex, …]) Find augmenter(s) by name.
find_augmenters_by_names(self, names[, …]) Find augmenter(s) by names.
get_all_children(self[, flat]) Get all children of this augmenter as a list.
get_children_lists(self) Get a list of lists of children of this augmenter.
get_parameters(self) See get_parameters().
localize_random_state(self[, recursive]) Assign augmenter-specific RNGs to this augmenter and its children.
localize_random_state_(self[, recursive]) Assign augmenter-specific RNGs to this augmenter and its children.
pool(self[, processes, maxtasksperchild, seed]) Create a pool used for multicore augmentation.
remove_augmenters(self, func[, copy, …]) Remove this augmenter or children that match a condition.
remove_augmenters_(self, func[, parents]) Remove in-place children of this augmenter that match a condition.
remove_augmenters_inplace(self, func[, parents]) Deprecated.
reseed(self[, random_state, deterministic_too]) Deprecated.
seed_(self[, entropy, deterministic_too]) Seed this augmenter and all of its children.
show_grid(self, images, rows, cols) Augment images and plot the results as a single grid-like image.
to_deterministic(self[, n]) Convert this augmenter from a stochastic to a deterministic one.
class imgaug.augmenters.convolutional.Sharpen(alpha=(0.0, 0.2), lightness=(0.8, 1.2), seed=None, name=None, random_state='deprecated', deterministic='deprecated')[source]

Bases: imgaug.augmenters.convolutional.Convolve

Sharpen images and alpha-blend the result with the original input images.

Supported dtypes:

See Convolve.

Parameters:

  • alpha (number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional) – Blending factor of the sharpened image. At 0.0, only the original image is visible, at 1.0 only its sharpened version is visible.

    • If a number, exactly that value will always be used.
    • If a tuple (a, b), a random value will be sampled from the interval [a, b] per image.
    • If a list, a random value will be sampled from that list per image.
    • If a StochasticParameter, a value will be sampled from that parameter per image.

  • lightness (number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional) – Lightness/brightness of the sharped image. Sane values are somewhere in the interval [0.5, 2.0]. The value 0.0 results in an edge map. Values higher than 1.0 create bright images. Default value is 1.0.

    • If a number, exactly that value will always be used.
    • If a tuple (a, b), a random value will be sampled from the interval [a, b] per image.
    • If a list, a random value will be sampled from that list per image.
    • If a StochasticParameter, a value will be sampled from that parameter per image.

  • seed (None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional) – See __init__().

  • name (None or str, optional) – See __init__().

  • random_state (None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional) – Old name for parameter seed. Its usage will not yet cause a deprecation warning, but it is still recommended to use seed now. Outdated since 0.4.0.

  • deterministic (bool, optional) – Deprecated since 0.4.0. See method to_deterministic() for an alternative and for details about what the “deterministic mode” actually does.

Examples

>>> import imgaug.augmenters as iaa
>>> aug = iaa.Sharpen(alpha=(0.0, 1.0))

Sharpens input images and blends the sharpened image with the input image using a random blending factor between 0% and 100% (uniformly sampled).

>>> aug = iaa.Sharpen(alpha=(0.0, 1.0), lightness=(0.75, 2.0))

Sharpens input images with a variable lightness sampled uniformly from the interval [0.75, 2.0] and with a fully random blending factor (as in the above example).

Methods

__call__(self, *args, **kwargs) Alias for augment().
augment(self[, return_batch, hooks]) Augment a batch.
augment_batch(self, batch[, hooks]) Deprecated.
augment_batch_(self, batch[, parents, hooks]) Augment a single batch in-place.
augment_batches(self, batches[, hooks, …]) Augment multiple batches.
augment_bounding_boxes(self, …[, parents, …]) Augment a batch of bounding boxes.
augment_heatmaps(self, heatmaps[, parents, …]) Augment a batch of heatmaps.
augment_image(self, image[, hooks]) Augment a single image.
augment_images(self, images[, parents, hooks]) Augment a batch of images.
augment_keypoints(self, keypoints_on_images) Augment a batch of keypoints/landmarks.
augment_line_strings(self, …[, parents, hooks]) Augment a batch of line strings.
augment_polygons(self, polygons_on_images[, …]) Augment a batch of polygons.
augment_segmentation_maps(self, segmaps[, …]) Augment a batch of segmentation maps.
copy(self) Create a shallow copy of this Augmenter instance.
copy_random_state(self, source[, recursive, …]) Copy the RNGs from a source augmenter sequence.
copy_random_state_(self, source[, …]) Copy the RNGs from a source augmenter sequence (in-place).
deepcopy(self) Create a deep copy of this Augmenter instance.
draw_grid(self, images, rows, cols) Augment images and draw the results as a single grid-like image.
find_augmenters(self, func[, parents, flat]) Find augmenters that match a condition.
find_augmenters_by_name(self, name[, regex, …]) Find augmenter(s) by name.
find_augmenters_by_names(self, names[, …]) Find augmenter(s) by names.
get_all_children(self[, flat]) Get all children of this augmenter as a list.
get_children_lists(self) Get a list of lists of children of this augmenter.
get_parameters(self) See get_parameters().
localize_random_state(self[, recursive]) Assign augmenter-specific RNGs to this augmenter and its children.
localize_random_state_(self[, recursive]) Assign augmenter-specific RNGs to this augmenter and its children.
pool(self[, processes, maxtasksperchild, seed]) Create a pool used for multicore augmentation.
remove_augmenters(self, func[, copy, …]) Remove this augmenter or children that match a condition.
remove_augmenters_(self, func[, parents]) Remove in-place children of this augmenter that match a condition.
remove_augmenters_inplace(self, func[, parents]) Deprecated.
reseed(self[, random_state, deterministic_too]) Deprecated.
seed_(self[, entropy, deterministic_too]) Seed this augmenter and all of its children.
show_grid(self, images, rows, cols) Augment images and plot the results as a single grid-like image.
to_deterministic(self[, n]) Convert this augmenter from a stochastic to a deterministic one.