# Copyright 2019 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Keras image preprocessing layers.""" import numpy as np import tensorflow.compat.v2 as tf from keras import backend from keras.engine import base_layer from keras.engine import base_preprocessing_layer from keras.layers.preprocessing import preprocessing_utils as utils from keras.utils import image_utils from keras.utils import tf_utils # isort: off from tensorflow.python.ops import stateless_random_ops from tensorflow.python.util.tf_export import keras_export from tensorflow.tools.docs import doc_controls H_AXIS = -3 W_AXIS = -2 IMAGES = "images" LABELS = "labels" TARGETS = "targets" BOUNDING_BOXES = "bounding_boxes" def check_fill_mode_and_interpolation(fill_mode, interpolation): if fill_mode not in {"reflect", "wrap", "constant", "nearest"}: raise NotImplementedError( "Unknown `fill_mode` {}. Only `reflect`, `wrap`, " "`constant` and `nearest` are supported.".format(fill_mode) ) if interpolation not in {"nearest", "bilinear"}: raise NotImplementedError( "Unknown `interpolation` {}. Only `nearest` and " "`bilinear` are supported.".format(interpolation) ) @keras_export( "keras.layers.Resizing", "keras.layers.experimental.preprocessing.Resizing" ) class Resizing(base_layer.Layer): """A preprocessing layer which resizes images. This layer resizes an image input to a target height and width. The input should be a 4D (batched) or 3D (unbatched) tensor in `"channels_last"` format. Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and of interger or floating point dtype. By default, the layer will output floats. This layer can be called on tf.RaggedTensor batches of input images of distinct sizes, and will resize the outputs to dense tensors of uniform size. For an overview and full list of preprocessing layers, see the preprocessing [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers). Args: height: Integer, the height of the output shape. width: Integer, the width of the output shape. interpolation: String, the interpolation method. Defaults to `"bilinear"`. Supports `"bilinear"`, `"nearest"`, `"bicubic"`, `"area"`, `"lanczos3"`, `"lanczos5"`, `"gaussian"`, `"mitchellcubic"`. crop_to_aspect_ratio: If True, resize the images without aspect ratio distortion. When the original aspect ratio differs from the target aspect ratio, the output image will be cropped so as to return the largest possible window in the image (of size `(height, width)`) that matches the target aspect ratio. By default (`crop_to_aspect_ratio=False`), aspect ratio may not be preserved. """ def __init__( self, height, width, interpolation="bilinear", crop_to_aspect_ratio=False, **kwargs, ): self.height = height self.width = width self.interpolation = interpolation self.crop_to_aspect_ratio = crop_to_aspect_ratio self._interpolation_method = image_utils.get_interpolation( interpolation ) super().__init__(**kwargs) base_preprocessing_layer.keras_kpl_gauge.get_cell("Resizing").set(True) def call(self, inputs): # tf.image.resize will always output float32 and operate more # efficiently on float32 unless interpolation is nearest, in which case # ouput type matches input type. if self.interpolation == "nearest": input_dtype = self.compute_dtype else: input_dtype = tf.float32 inputs = utils.ensure_tensor(inputs, dtype=input_dtype) size = [self.height, self.width] if self.crop_to_aspect_ratio: def resize_to_aspect(x): if tf_utils.is_ragged(inputs): x = x.to_tensor() return image_utils.smart_resize( x, size=size, interpolation=self._interpolation_method ) if tf_utils.is_ragged(inputs): size_as_shape = tf.TensorShape(size) shape = size_as_shape + inputs.shape[-1:] spec = tf.TensorSpec(shape, input_dtype) outputs = tf.map_fn( resize_to_aspect, inputs, fn_output_signature=spec ) else: outputs = resize_to_aspect(inputs) else: outputs = tf.image.resize( inputs, size=size, method=self._interpolation_method ) return tf.cast(outputs, self.compute_dtype) def compute_output_shape(self, input_shape): input_shape = tf.TensorShape(input_shape).as_list() input_shape[H_AXIS] = self.height input_shape[W_AXIS] = self.width return tf.TensorShape(input_shape) def get_config(self): config = { "height": self.height, "width": self.width, "interpolation": self.interpolation, "crop_to_aspect_ratio": self.crop_to_aspect_ratio, } base_config = super().get_config() return dict(list(base_config.items()) + list(config.items())) @keras_export( "keras.layers.CenterCrop", "keras.layers.experimental.preprocessing.CenterCrop", ) class CenterCrop(base_layer.Layer): """A preprocessing layer which crops images. This layers crops the central portion of the images to a target size. If an image is smaller than the target size, it will be resized and cropped so as to return the largest possible window in the image that matches the target aspect ratio. Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and of interger or floating point dtype. By default, the layer will output floats. For an overview and full list of preprocessing layers, see the preprocessing [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers). Input shape: 3D (unbatched) or 4D (batched) tensor with shape: `(..., height, width, channels)`, in `"channels_last"` format. Output shape: 3D (unbatched) or 4D (batched) tensor with shape: `(..., target_height, target_width, channels)`. If the input height/width is even and the target height/width is odd (or inversely), the input image is left-padded by 1 pixel. Args: height: Integer, the height of the output shape. width: Integer, the width of the output shape. """ def __init__(self, height, width, **kwargs): self.height = height self.width = width super().__init__(**kwargs, autocast=False) base_preprocessing_layer.keras_kpl_gauge.get_cell("CenterCrop").set( True ) def call(self, inputs): inputs = utils.ensure_tensor(inputs, self.compute_dtype) input_shape = tf.shape(inputs) h_diff = input_shape[H_AXIS] - self.height w_diff = input_shape[W_AXIS] - self.width def center_crop(): h_start = tf.cast(h_diff / 2, tf.int32) w_start = tf.cast(w_diff / 2, tf.int32) return tf.image.crop_to_bounding_box( inputs, h_start, w_start, self.height, self.width ) def upsize(): outputs = image_utils.smart_resize( inputs, [self.height, self.width] ) # smart_resize will always output float32, so we need to re-cast. return tf.cast(outputs, self.compute_dtype) return tf.cond( tf.reduce_all((h_diff >= 0, w_diff >= 0)), center_crop, upsize ) def compute_output_shape(self, input_shape): input_shape = tf.TensorShape(input_shape).as_list() input_shape[H_AXIS] = self.height input_shape[W_AXIS] = self.width return tf.TensorShape(input_shape) def get_config(self): config = { "height": self.height, "width": self.width, } base_config = super().get_config() return dict(list(base_config.items()) + list(config.items())) @keras_export("keras.__internal__.layers.BaseImageAugmentationLayer") class BaseImageAugmentationLayer(base_layer.BaseRandomLayer): """Abstract base layer for image augmentation. This layer contains base functionalities for preprocessing layers which augment image related data, eg. image and in future, label and bounding boxes. The subclasses could avoid making certain mistakes and reduce code duplications. This layer requires you to implement one method: `augment_image()`, which augments one single image during the training. There are a few additional methods that you can implement for added functionality on the layer: `augment_label()`, which handles label augmentation if the layer supports that. `augment_bounding_boxes()`, which handles the bounding box augmentation, if the layer supports that. `get_random_transformation()`, which should produce a random transformation setting. The tranformation object, which could be any type, will be passed to `augment_image`, `augment_label` and `augment_bounding_boxes`, to coodinate the randomness behavior, eg, in the RandomFlip layer, the image and bounding_boxes should be changed in the same way. The `call()` method support two formats of inputs: 1. Single image tensor with 3D (HWC) or 4D (NHWC) format. 2. A dict of tensors with stable keys. The supported keys are: `"images"`, `"labels"` and `"bounding_boxes"` at the moment. We might add more keys in future when we support more types of augmentation. The output of the `call()` will be in two formats, which will be the same structure as the inputs. The `call()` will handle the logic detecting the training/inference mode, unpack the inputs, forward to the correct function, and pack the output back to the same structure as the inputs. By default the `call()` method leverages the `tf.vectorized_map()` function. Auto-vectorization can be disabled by setting `self.auto_vectorize = False` in your `__init__()` method. When disabled, `call()` instead relies on `tf.map_fn()`. For example: ```python class SubclassLayer(BaseImageAugmentationLayer): def __init__(self): super().__init__() self.auto_vectorize = False ``` Example: ```python class RandomContrast(BaseImageAugmentationLayer): def __init__(self, factor=(0.5, 1.5), **kwargs): super().__init__(**kwargs) self._factor = factor def augment_image(self, image, transformation): random_factor = tf.random.uniform([], self._factor[0], self._factor[1]) mean = tf.math.reduced_mean(inputs, axis=-1, keep_dim=True) return (inputs - mean) * random_factor + mean ``` Note that since the randomness is also a common functionnality, this layer also includes a tf.keras.backend.RandomGenerator, which can be used to produce the random numbers. The random number generator is stored in the `self._random_generator` attribute. """ def __init__(self, rate=1.0, seed=None, **kwargs): super().__init__(seed=seed, **kwargs) self.rate = rate @property def auto_vectorize(self): """Control whether automatic vectorization occurs. By default the `call()` method leverages the `tf.vectorized_map()` function. Auto-vectorization can be disabled by setting `self.auto_vectorize = False` in your `__init__()` method. When disabled, `call()` instead relies on `tf.map_fn()`. For example: ```python class SubclassLayer(BaseImageAugmentationLayer): def __init__(self): super().__init__() self.auto_vectorize = False ``` """ return getattr(self, "_auto_vectorize", True) @auto_vectorize.setter def auto_vectorize(self, auto_vectorize): self._auto_vectorize = auto_vectorize @property def _map_fn(self): if self.auto_vectorize: return tf.vectorized_map else: return tf.map_fn @doc_controls.for_subclass_implementers def augment_image(self, image, transformation): """Augment a single image during training. Args: image: 3D image input tensor to the layer. Forwarded from `layer.call()`. transformation: The transformation object produced by `get_random_transformation`. Used to coordinate the randomness between image, label and bounding box. Returns: output 3D tensor, which will be forward to `layer.call()`. """ raise NotImplementedError() @doc_controls.for_subclass_implementers def augment_label(self, label, transformation): """Augment a single label during training. Args: label: 1D label to the layer. Forwarded from `layer.call()`. transformation: The transformation object produced by `get_random_transformation`. Used to coordinate the randomness between image, label and bounding box. Returns: output 1D tensor, which will be forward to `layer.call()`. """ raise NotImplementedError() @doc_controls.for_subclass_implementers def augment_target(self, target, transformation): """Augment a single target during training. Args: target: 1D label to the layer. Forwarded from `layer.call()`. transformation: The transformation object produced by `get_random_transformation`. Used to coordinate the randomness between image, label and bounding box. Returns: output 1D tensor, which will be forward to `layer.call()`. """ return self.augment_label(target, transformation) @doc_controls.for_subclass_implementers def augment_bounding_boxes( self, image, bounding_boxes, transformation=None ): """Augment bounding boxes for one image during training. Args: image: 3D image input tensor to the layer. Forwarded from `layer.call()`. bounding_boxes: 2D bounding boxes to the layer. Forwarded from `call()`. transformation: The transformation object produced by `get_random_transformation`. Used to coordinate the randomness between image, label and bounding box. Returns: output 2D tensor, which will be forward to `layer.call()`. """ raise NotImplementedError() @doc_controls.for_subclass_implementers def get_random_transformation( self, image=None, label=None, bounding_box=None ): """Produce random transformation config for one single input. This is used to produce same randomness between image/label/bounding_box. Args: image: 3D image tensor from inputs. label: optional 1D label tensor from inputs. bounding_box: optional 2D bounding boxes tensor from inputs. Returns: Any type of object, which will be forwarded to `augment_image`, `augment_label` and `augment_bounding_box` as the `transformation` parameter. """ return None def call(self, inputs, training=True): inputs = self._ensure_inputs_are_compute_dtype(inputs) if training: inputs, is_dict, use_targets = self._format_inputs(inputs) images = inputs[IMAGES] if images.shape.rank == 3: return self._format_output( self._augment(inputs), is_dict, use_targets ) elif images.shape.rank == 4: return self._format_output( self._batch_augment(inputs), is_dict, use_targets ) else: raise ValueError( "Image augmentation layers are expecting inputs to be " "rank 3 (HWC) or 4D (NHWC) tensors. Got shape: " f"{images.shape}" ) else: return inputs def _augment(self, inputs): image = inputs.get(IMAGES, None) label = inputs.get(LABELS, None) bounding_box = inputs.get(BOUNDING_BOXES, None) transformation = self.get_random_transformation( image=image, label=label, bounding_box=bounding_box ) image = self.augment_image(image, transformation=transformation) result = {IMAGES: image} if label is not None: label = self.augment_target(label, transformation=transformation) result[LABELS] = label if bounding_box is not None: bounding_box = self.augment_bounding_boxes( image, bounding_box, transformation=transformation ) result[BOUNDING_BOXES] = bounding_box return result def _batch_augment(self, inputs): return self._map_fn(self._augment, inputs) def _format_inputs(self, inputs): if tf.is_tensor(inputs): # single image input tensor return {IMAGES: inputs}, False, False elif isinstance(inputs, dict) and TARGETS in inputs: # TODO(scottzhu): Check if it only contains the valid keys inputs[LABELS] = inputs[TARGETS] del inputs[TARGETS] return inputs, True, True elif isinstance(inputs, dict): return inputs, True, False else: raise ValueError( f"Expect the inputs to be image tensor or dict. Got {inputs}" ) def _format_output(self, output, is_dict, use_targets): if not is_dict: return output[IMAGES] elif use_targets: output[TARGETS] = output[LABELS] del output[LABELS] return output else: return output def _ensure_inputs_are_compute_dtype(self, inputs): if isinstance(inputs, dict): inputs[IMAGES] = utils.ensure_tensor( inputs[IMAGES], self.compute_dtype ) else: inputs = utils.ensure_tensor(inputs, self.compute_dtype) return inputs @keras_export( "keras.layers.RandomCrop", "keras.layers.experimental.preprocessing.RandomCrop", v1=[], ) class RandomCrop(BaseImageAugmentationLayer): """A preprocessing layer which randomly crops images during training. During training, this layer will randomly choose a location to crop images down to a target size. The layer will crop all the images in the same batch to the same cropping location. At inference time, and during training if an input image is smaller than the target size, the input will be resized and cropped so as to return the largest possible window in the image that matches the target aspect ratio. If you need to apply random cropping at inference time, set `training` to True when calling the layer. Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and of interger or floating point dtype. By default, the layer will output floats. For an overview and full list of preprocessing layers, see the preprocessing [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers). Input shape: 3D (unbatched) or 4D (batched) tensor with shape: `(..., height, width, channels)`, in `"channels_last"` format. Output shape: 3D (unbatched) or 4D (batched) tensor with shape: `(..., target_height, target_width, channels)`. Args: height: Integer, the height of the output shape. width: Integer, the width of the output shape. seed: Integer. Used to create a random seed. """ def __init__(self, height, width, seed=None, **kwargs): base_preprocessing_layer.keras_kpl_gauge.get_cell("RandomCrop").set( True ) super().__init__( **kwargs, autocast=False, seed=seed, force_generator=True ) self.height = height self.width = width self.seed = seed def call(self, inputs, training=True): if training: return super().call(inputs, training) else: inputs = self._ensure_inputs_are_compute_dtype(inputs) inputs, is_dict, targets = self._format_inputs(inputs) output = inputs # self._resize() returns valid results for both batched and # unbatched output["images"] = self._resize(inputs["images"]) return self._format_output(output, is_dict, targets) def get_random_transformation( self, image=None, label=None, bounding_box=None ): input_shape = tf.shape(image) h_diff = input_shape[H_AXIS] - self.height w_diff = input_shape[W_AXIS] - self.width dtype = input_shape.dtype rands = self._random_generator.random_uniform([2], 0, dtype.max, dtype) h_start = rands[0] % (h_diff + 1) w_start = rands[1] % (w_diff + 1) return {"top": h_start, "left": w_start} def augment_image(self, image, transformation): input_shape = tf.shape(image) h_diff = input_shape[H_AXIS] - self.height w_diff = input_shape[W_AXIS] - self.width return tf.cond( tf.reduce_all((h_diff >= 0, w_diff >= 0)), lambda: self._crop(image, transformation), lambda: self._resize(image), ) def _crop(self, image, transformation): top = transformation["top"] left = transformation["left"] return tf.image.crop_to_bounding_box( image, top, left, self.height, self.width ) def _resize(self, image): outputs = image_utils.smart_resize(image, [self.height, self.width]) # smart_resize will always output float32, so we need to re-cast. return tf.cast(outputs, self.compute_dtype) def compute_output_shape(self, input_shape): input_shape = tf.TensorShape(input_shape).as_list() input_shape[H_AXIS] = self.height input_shape[W_AXIS] = self.width return tf.TensorShape(input_shape) def get_config(self): config = { "height": self.height, "width": self.width, "seed": self.seed, } base_config = super().get_config() return dict(list(base_config.items()) + list(config.items())) @keras_export( "keras.layers.Rescaling", "keras.layers.experimental.preprocessing.Rescaling", ) class Rescaling(base_layer.Layer): """A preprocessing layer which rescales input values to a new range. This layer rescales every value of an input (often an image) by multiplying by `scale` and adding `offset`. For instance: 1. To rescale an input in the ``[0, 255]`` range to be in the `[0, 1]` range, you would pass `scale=1./255`. 2. To rescale an input in the ``[0, 255]`` range to be in the `[-1, 1]` range, you would pass `scale=1./127.5, offset=-1`. The rescaling is applied both during training and inference. Inputs can be of integer or floating point dtype, and by default the layer will output floats. For an overview and full list of preprocessing layers, see the preprocessing [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers). Input shape: Arbitrary. Output shape: Same as input. Args: scale: Float, the scale to apply to the inputs. offset: Float, the offset to apply to the inputs. """ def __init__(self, scale, offset=0.0, **kwargs): self.scale = scale self.offset = offset super().__init__(**kwargs) base_preprocessing_layer.keras_kpl_gauge.get_cell("Rescaling").set(True) def call(self, inputs): dtype = self.compute_dtype scale = tf.cast(self.scale, dtype) offset = tf.cast(self.offset, dtype) return tf.cast(inputs, dtype) * scale + offset def compute_output_shape(self, input_shape): return input_shape def get_config(self): config = { "scale": self.scale, "offset": self.offset, } base_config = super().get_config() return dict(list(base_config.items()) + list(config.items())) HORIZONTAL = "horizontal" VERTICAL = "vertical" HORIZONTAL_AND_VERTICAL = "horizontal_and_vertical" @keras_export( "keras.layers.RandomFlip", "keras.layers.experimental.preprocessing.RandomFlip", v1=[], ) class RandomFlip(BaseImageAugmentationLayer): """A preprocessing layer which randomly flips images during training. This layer will flip the images horizontally and or vertically based on the `mode` attribute. During inference time, the output will be identical to input. Call the layer with `training=True` to flip the input. Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and of interger or floating point dtype. By default, the layer will output floats. For an overview and full list of preprocessing layers, see the preprocessing [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers). Input shape: 3D (unbatched) or 4D (batched) tensor with shape: `(..., height, width, channels)`, in `"channels_last"` format. Output shape: 3D (unbatched) or 4D (batched) tensor with shape: `(..., height, width, channels)`, in `"channels_last"` format. Arguments: mode: String indicating which flip mode to use. Can be `"horizontal"`, `"vertical"`, or `"horizontal_and_vertical"`. Defaults to `"horizontal_and_vertical"`. `"horizontal"` is a left-right flip and `"vertical"` is a top-bottom flip. seed: Integer. Used to create a random seed. """ def __init__(self, mode=HORIZONTAL_AND_VERTICAL, seed=None, **kwargs): super().__init__(seed=seed, force_generator=True, **kwargs) base_preprocessing_layer.keras_kpl_gauge.get_cell("RandomFlip").set( True ) self.mode = mode if mode == HORIZONTAL: self.horizontal = True self.vertical = False elif mode == VERTICAL: self.horizontal = False self.vertical = True elif mode == HORIZONTAL_AND_VERTICAL: self.horizontal = True self.vertical = True else: raise ValueError( "RandomFlip layer {name} received an unknown mode " "argument {arg}".format(name=self.name, arg=mode) ) self.auto_vectorize = False def augment_label(self, label, transformation): return label def augment_image(self, image, transformation): flipped_outputs = image if self.horizontal and transformation["flip_horizontal"]: flipped_outputs = tf.image.flip_left_right(flipped_outputs) if self.vertical and transformation["flip_vertical"]: flipped_outputs = tf.image.flip_up_down(flipped_outputs) flipped_outputs.set_shape(image.shape) return flipped_outputs def get_random_transformation( self, image=None, label=None, bounding_box=None ): flip_horizontal = False flip_vertical = False if self.horizontal: flip_horizontal = np.random.choice([True, False]) if self.vertical: flip_vertical = np.random.choice([True, False]) return { "flip_horizontal": flip_horizontal, "flip_vertical": flip_vertical, } def augment_bounding_boxes( self, image, bounding_boxes, transformation=None ): transformation = transformation or self.get_random_transformation() image = tf.expand_dims(image, 0) image_shape = tf.shape(image) h = image_shape[H_AXIS] w = image_shape[W_AXIS] bboxes_out = tf.identity(bounding_boxes) if transformation["flip_horizontal"]: bboxes_out = tf.stack( [ w - bboxes_out[:, 2], bboxes_out[:, 1], w - bboxes_out[:, 0], bboxes_out[:, 3], ], axis=-1, ) if transformation["flip_vertical"]: bboxes_out = tf.stack( [ bboxes_out[:, 0], h - bboxes_out[:, 3], bboxes_out[:, 2], h - bboxes_out[:, 1], ], axis=-1, ) return bboxes_out def compute_output_shape(self, input_shape): return input_shape def get_config(self): config = { "mode": self.mode, } base_config = super().get_config() return dict(list(base_config.items()) + list(config.items())) # TODO(tanzheny): Add examples, here and everywhere. @keras_export( "keras.layers.RandomTranslation", "keras.layers.experimental.preprocessing.RandomTranslation", v1=[], ) class RandomTranslation(BaseImageAugmentationLayer): """A preprocessing layer which randomly translates images during training. This layer will apply random translations to each image during training, filling empty space according to `fill_mode`. Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and of interger or floating point dtype. By default, the layer will output floats. For an overview and full list of preprocessing layers, see the preprocessing [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers). Args: height_factor: a float represented as fraction of value, or a tuple of size 2 representing lower and upper bound for shifting vertically. A negative value means shifting image up, while a positive value means shifting image down. When represented as a single positive float, this value is used for both the upper and lower bound. For instance, `height_factor=(-0.2, 0.3)` results in an output shifted by a random amount in the range `[-20%, +30%]`. `height_factor=0.2` results in an output height shifted by a random amount in the range `[-20%, +20%]`. width_factor: a float represented as fraction of value, or a tuple of size 2 representing lower and upper bound for shifting horizontally. A negative value means shifting image left, while a positive value means shifting image right. When represented as a single positive float, this value is used for both the upper and lower bound. For instance, `width_factor=(-0.2, 0.3)` results in an output shifted left by 20%, and shifted right by 30%. `width_factor=0.2` results in an output height shifted left or right by 20%. fill_mode: Points outside the boundaries of the input are filled according to the given mode (one of `{"constant", "reflect", "wrap", "nearest"}`). - *reflect*: `(d c b a | a b c d | d c b a)` The input is extended by reflecting about the edge of the last pixel. - *constant*: `(k k k k | a b c d | k k k k)` The input is extended by filling all values beyond the edge with the same constant value k = 0. - *wrap*: `(a b c d | a b c d | a b c d)` The input is extended by wrapping around to the opposite edge. - *nearest*: `(a a a a | a b c d | d d d d)` The input is extended by the nearest pixel. interpolation: Interpolation mode. Supported values: `"nearest"`, `"bilinear"`. seed: Integer. Used to create a random seed. fill_value: a float represents the value to be filled outside the boundaries when `fill_mode="constant"`. Input shape: 3D (unbatched) or 4D (batched) tensor with shape: `(..., height, width, channels)`, in `"channels_last"` format. Output shape: 3D (unbatched) or 4D (batched) tensor with shape: `(..., height, width, channels)`, in `"channels_last"` format. """ def __init__( self, height_factor, width_factor, fill_mode="reflect", interpolation="bilinear", seed=None, fill_value=0.0, **kwargs, ): base_preprocessing_layer.keras_kpl_gauge.get_cell( "RandomTranslation" ).set(True) super().__init__(seed=seed, force_generator=True, **kwargs) self.height_factor = height_factor if isinstance(height_factor, (tuple, list)): self.height_lower = height_factor[0] self.height_upper = height_factor[1] else: self.height_lower = -height_factor self.height_upper = height_factor if self.height_upper < self.height_lower: raise ValueError( "`height_factor` cannot have upper bound less than " "lower bound, got {}".format(height_factor) ) if abs(self.height_lower) > 1.0 or abs(self.height_upper) > 1.0: raise ValueError( "`height_factor` must have values between [-1, 1], " "got {}".format(height_factor) ) self.width_factor = width_factor if isinstance(width_factor, (tuple, list)): self.width_lower = width_factor[0] self.width_upper = width_factor[1] else: self.width_lower = -width_factor self.width_upper = width_factor if self.width_upper < self.width_lower: raise ValueError( "`width_factor` cannot have upper bound less than " "lower bound, got {}".format(width_factor) ) if abs(self.width_lower) > 1.0 or abs(self.width_upper) > 1.0: raise ValueError( "`width_factor` must have values between [-1, 1], " "got {}".format(width_factor) ) check_fill_mode_and_interpolation(fill_mode, interpolation) self.fill_mode = fill_mode self.fill_value = fill_value self.interpolation = interpolation self.seed = seed @tf.function def augment_image(self, image, transformation): """Translated inputs with random ops.""" # The transform op only accepts rank 4 inputs, so if we have an # unbatched image, we need to temporarily expand dims to a batch. original_shape = image.shape inputs = tf.expand_dims(image, 0) inputs_shape = tf.shape(inputs) img_hd = tf.cast(inputs_shape[H_AXIS], tf.float32) img_wd = tf.cast(inputs_shape[W_AXIS], tf.float32) height_translation = transformation["height_translation"] width_translation = transformation["width_translation"] height_translation = height_translation * img_hd width_translation = width_translation * img_wd translations = tf.cast( tf.concat([width_translation, height_translation], axis=1), dtype=tf.float32, ) output = transform( inputs, get_translation_matrix(translations), interpolation=self.interpolation, fill_mode=self.fill_mode, fill_value=self.fill_value, ) output = tf.squeeze(output, 0) output.set_shape(original_shape) return output def get_random_transformation( self, image=None, label=None, bounding_box=None ): del image, label, bounding_box batch_size = 1 height_translation = self._random_generator.random_uniform( shape=[batch_size, 1], minval=self.height_lower, maxval=self.height_upper, dtype=tf.float32, ) width_translation = self._random_generator.random_uniform( shape=[batch_size, 1], minval=self.width_lower, maxval=self.width_upper, dtype=tf.float32, ) return { "height_translation": height_translation, "width_translation": width_translation, } def _batch_augment(self, inputs): # Change to vectorized_map for better performance, as well as work # around issue for different tensorspec between inputs and outputs. return tf.vectorized_map(self._augment, inputs) def augment_label(self, label, transformation): return label def compute_output_shape(self, input_shape): return input_shape def get_config(self): config = { "height_factor": self.height_factor, "width_factor": self.width_factor, "fill_mode": self.fill_mode, "fill_value": self.fill_value, "interpolation": self.interpolation, "seed": self.seed, } base_config = super().get_config() return dict(list(base_config.items()) + list(config.items())) def get_translation_matrix(translations, name=None): """Returns projective transform(s) for the given translation(s). Args: translations: A matrix of 2-element lists representing `[dx, dy]` to translate for each image (for a batch of images). name: The name of the op. Returns: A tensor of shape `(num_images, 8)` projective transforms which can be given to `transform`. """ with backend.name_scope(name or "translation_matrix"): num_translations = tf.shape(translations)[0] # The translation matrix looks like: # [[1 0 -dx] # [0 1 -dy] # [0 0 1]] # where the last entry is implicit. # Translation matrices are always float32. return tf.concat( values=[ tf.ones((num_translations, 1), tf.float32), tf.zeros((num_translations, 1), tf.float32), -translations[:, 0, None], tf.zeros((num_translations, 1), tf.float32), tf.ones((num_translations, 1), tf.float32), -translations[:, 1, None], tf.zeros((num_translations, 2), tf.float32), ], axis=1, ) def transform( images, transforms, fill_mode="reflect", fill_value=0.0, interpolation="bilinear", output_shape=None, name=None, ): """Applies the given transform(s) to the image(s). Args: images: A tensor of shape `(num_images, num_rows, num_columns, num_channels)` (NHWC). The rank must be statically known (the shape is not `TensorShape(None)`). transforms: Projective transform matrix/matrices. A vector of length 8 or tensor of size N x 8. If one row of transforms is [a0, a1, a2, b0, b1, b2, c0, c1], then it maps the *output* point `(x, y)` to a transformed *input* point `(x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k)`, where `k = c0 x + c1 y + 1`. The transforms are *inverted* compared to the transform mapping input points to output points. Note that gradients are not backpropagated into transformation parameters. fill_mode: Points outside the boundaries of the input are filled according to the given mode (one of `{"constant", "reflect", "wrap", "nearest"}`). fill_value: a float represents the value to be filled outside the boundaries when `fill_mode="constant"`. interpolation: Interpolation mode. Supported values: `"nearest"`, `"bilinear"`. output_shape: Output dimension after the transform, `[height, width]`. If `None`, output is the same size as input image. name: The name of the op. Fill mode behavior for each valid value is as follows: - reflect (d c b a | a b c d | d c b a) The input is extended by reflecting about the edge of the last pixel. - constant (k k k k | a b c d | k k k k) The input is extended by filling all values beyond the edge with the same constant value k = 0. - wrap (a b c d | a b c d | a b c d) The input is extended by wrapping around to the opposite edge. - nearest (a a a a | a b c d | d d d d) The input is extended by the nearest pixel. Input shape: 4D tensor with shape: `(samples, height, width, channels)`, in `"channels_last"` format. Output shape: 4D tensor with shape: `(samples, height, width, channels)`, in `"channels_last"` format. Returns: Image(s) with the same type and shape as `images`, with the given transform(s) applied. Transformed coordinates outside of the input image will be filled with zeros. Raises: TypeError: If `image` is an invalid type. ValueError: If output shape is not 1-D int32 Tensor. """ with backend.name_scope(name or "transform"): if output_shape is None: output_shape = tf.shape(images)[1:3] if not tf.executing_eagerly(): output_shape_value = tf.get_static_value(output_shape) if output_shape_value is not None: output_shape = output_shape_value output_shape = tf.convert_to_tensor( output_shape, tf.int32, name="output_shape" ) if not output_shape.get_shape().is_compatible_with([2]): raise ValueError( "output_shape must be a 1-D Tensor of 2 elements: " "new_height, new_width, instead got " "{}".format(output_shape) ) fill_value = tf.convert_to_tensor( fill_value, tf.float32, name="fill_value" ) return tf.raw_ops.ImageProjectiveTransformV3( images=images, output_shape=output_shape, fill_value=fill_value, transforms=transforms, fill_mode=fill_mode.upper(), interpolation=interpolation.upper(), ) def get_rotation_matrix(angles, image_height, image_width, name=None): """Returns projective transform(s) for the given angle(s). Args: angles: A scalar angle to rotate all images by, or (for batches of images) a vector with an angle to rotate each image in the batch. The rank must be statically known (the shape is not `TensorShape(None)`). image_height: Height of the image(s) to be transformed. image_width: Width of the image(s) to be transformed. name: The name of the op. Returns: A tensor of shape (num_images, 8). Projective transforms which can be given to operation `image_projective_transform_v2`. If one row of transforms is [a0, a1, a2, b0, b1, b2, c0, c1], then it maps the *output* point `(x, y)` to a transformed *input* point `(x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k)`, where `k = c0 x + c1 y + 1`. """ with backend.name_scope(name or "rotation_matrix"): x_offset = ( (image_width - 1) - ( tf.cos(angles) * (image_width - 1) - tf.sin(angles) * (image_height - 1) ) ) / 2.0 y_offset = ( (image_height - 1) - ( tf.sin(angles) * (image_width - 1) + tf.cos(angles) * (image_height - 1) ) ) / 2.0 num_angles = tf.shape(angles)[0] return tf.concat( values=[ tf.cos(angles)[:, None], -tf.sin(angles)[:, None], x_offset[:, None], tf.sin(angles)[:, None], tf.cos(angles)[:, None], y_offset[:, None], tf.zeros((num_angles, 2), tf.float32), ], axis=1, ) @keras_export( "keras.layers.RandomRotation", "keras.layers.experimental.preprocessing.RandomRotation", v1=[], ) class RandomRotation(BaseImageAugmentationLayer): """A preprocessing layer which randomly rotates images during training. This layer will apply random rotations to each image, filling empty space according to `fill_mode`. By default, random rotations are only applied during training. At inference time, the layer does nothing. If you need to apply random rotations at inference time, set `training` to True when calling the layer. Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and of interger or floating point dtype. By default, the layer will output floats. For an overview and full list of preprocessing layers, see the preprocessing [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers). Input shape: 3D (unbatched) or 4D (batched) tensor with shape: `(..., height, width, channels)`, in `"channels_last"` format Output shape: 3D (unbatched) or 4D (batched) tensor with shape: `(..., height, width, channels)`, in `"channels_last"` format Arguments: factor: a float represented as fraction of 2 Pi, or a tuple of size 2 representing lower and upper bound for rotating clockwise and counter-clockwise. A positive values means rotating counter clock-wise, while a negative value means clock-wise. When represented as a single float, this value is used for both the upper and lower bound. For instance, `factor=(-0.2, 0.3)` results in an output rotation by a random amount in the range `[-20% * 2pi, 30% * 2pi]`. `factor=0.2` results in an output rotating by a random amount in the range `[-20% * 2pi, 20% * 2pi]`. fill_mode: Points outside the boundaries of the input are filled according to the given mode (one of `{"constant", "reflect", "wrap", "nearest"}`). - *reflect*: `(d c b a | a b c d | d c b a)` The input is extended by reflecting about the edge of the last pixel. - *constant*: `(k k k k | a b c d | k k k k)` The input is extended by filling all values beyond the edge with the same constant value k = 0. - *wrap*: `(a b c d | a b c d | a b c d)` The input is extended by wrapping around to the opposite edge. - *nearest*: `(a a a a | a b c d | d d d d)` The input is extended by the nearest pixel. interpolation: Interpolation mode. Supported values: `"nearest"`, `"bilinear"`. seed: Integer. Used to create a random seed. fill_value: a float represents the value to be filled outside the boundaries when `fill_mode="constant"`. """ def __init__( self, factor, fill_mode="reflect", interpolation="bilinear", seed=None, fill_value=0.0, **kwargs, ): base_preprocessing_layer.keras_kpl_gauge.get_cell("RandomRotation").set( True ) super().__init__(seed=seed, force_generator=True, **kwargs) self.factor = factor if isinstance(factor, (tuple, list)): self.lower = factor[0] self.upper = factor[1] else: self.lower = -factor self.upper = factor if self.upper < self.lower: raise ValueError( "Factor cannot have negative values, " "got {}".format(factor) ) check_fill_mode_and_interpolation(fill_mode, interpolation) self.fill_mode = fill_mode self.fill_value = fill_value self.interpolation = interpolation self.seed = seed def get_random_transformation( self, image=None, label=None, bounding_box=None ): min_angle = self.lower * 2.0 * np.pi max_angle = self.upper * 2.0 * np.pi angle = self._random_generator.random_uniform( shape=[1], minval=min_angle, maxval=max_angle ) return {"angle": angle} def augment_image(self, image, transformation): image = utils.ensure_tensor(image, self.compute_dtype) original_shape = image.shape image = tf.expand_dims(image, 0) image_shape = tf.shape(image) img_hd = tf.cast(image_shape[H_AXIS], tf.float32) img_wd = tf.cast(image_shape[W_AXIS], tf.float32) angle = transformation["angle"] output = transform( image, get_rotation_matrix(angle, img_hd, img_wd), fill_mode=self.fill_mode, fill_value=self.fill_value, interpolation=self.interpolation, ) output = tf.squeeze(output, 0) output.set_shape(original_shape) return output def augment_bounding_boxes(self, image, bounding_boxes, transformation): image = tf.expand_dims(image, 0) image_shape = tf.shape(image) h = image_shape[H_AXIS] w = image_shape[W_AXIS] bbox_dtype = bounding_boxes.dtype # origin coordinates, all the points on the image are rotated around # this point origin_x, origin_y = int(h / 2), int(w / 2) angle = transformation["angle"] angle = -angle # calculate coordinates of all four corners of the bounding box point = tf.stack( [ tf.stack([bounding_boxes[:, 0], bounding_boxes[:, 1]], axis=1), tf.stack([bounding_boxes[:, 2], bounding_boxes[:, 1]], axis=1), tf.stack([bounding_boxes[:, 2], bounding_boxes[:, 3]], axis=1), tf.stack([bounding_boxes[:, 0], bounding_boxes[:, 3]], axis=1), ], axis=1, ) # point_x : x coordinates of all corners of the bounding box point_x = tf.gather(point, [0], axis=2) # point_y : y cordinates of all corners of the bounding box point_y = tf.gather(point, [1], axis=2) # rotated bbox coordinates # new_x : new position of x coordinates of corners of bounding box new_x = ( origin_x + tf.multiply( tf.cos(angle), tf.cast((point_x - origin_x), dtype=tf.float32) ) - tf.multiply( tf.sin(angle), tf.cast((point_y - origin_y), dtype=tf.float32) ) ) # new_y : new position of y coordinates of corners of bounding box new_y = ( origin_y + tf.multiply( tf.sin(angle), tf.cast((point_x - origin_x), dtype=tf.float32) ) + tf.multiply( tf.cos(angle), tf.cast((point_y - origin_y), dtype=tf.float32) ) ) # rotated bbox coordinates out = tf.concat([new_x, new_y], axis=2) # find readjusted coordinates of bounding box to represent it in corners # format min_cordinates = tf.math.reduce_min(out, axis=1) max_cordinates = tf.math.reduce_max(out, axis=1) bboxes_out = tf.concat([min_cordinates, max_cordinates], axis=1) # cordinates cannot be float values, it is casted to int32 bboxes_out = tf.cast(bboxes_out, bbox_dtype) return bboxes_out def augment_label(self, label, transformation): return label def compute_output_shape(self, input_shape): return input_shape def get_config(self): config = { "factor": self.factor, "fill_mode": self.fill_mode, "fill_value": self.fill_value, "interpolation": self.interpolation, "seed": self.seed, } base_config = super().get_config() return dict(list(base_config.items()) + list(config.items())) @keras_export( "keras.layers.RandomZoom", "keras.layers.experimental.preprocessing.RandomZoom", v1=[], ) class RandomZoom(BaseImageAugmentationLayer): """A preprocessing layer which randomly zooms images during training. This layer will randomly zoom in or out on each axis of an image independently, filling empty space according to `fill_mode`. Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and of interger or floating point dtype. By default, the layer will output floats. For an overview and full list of preprocessing layers, see the preprocessing [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers). Args: height_factor: a float represented as fraction of value, or a tuple of size 2 representing lower and upper bound for zooming vertically. When represented as a single float, this value is used for both the upper and lower bound. A positive value means zooming out, while a negative value means zooming in. For instance, `height_factor=(0.2, 0.3)` result in an output zoomed out by a random amount in the range `[+20%, +30%]`. `height_factor=(-0.3, -0.2)` result in an output zoomed in by a random amount in the range `[+20%, +30%]`. width_factor: a float represented as fraction of value, or a tuple of size 2 representing lower and upper bound for zooming horizontally. When represented as a single float, this value is used for both the upper and lower bound. For instance, `width_factor=(0.2, 0.3)` result in an output zooming out between 20% to 30%. `width_factor=(-0.3, -0.2)` result in an output zooming in between 20% to 30%. Defaults to `None`, i.e., zooming vertical and horizontal directions by preserving the aspect ratio. fill_mode: Points outside the boundaries of the input are filled according to the given mode (one of `{"constant", "reflect", "wrap", "nearest"}`). - *reflect*: `(d c b a | a b c d | d c b a)` The input is extended by reflecting about the edge of the last pixel. - *constant*: `(k k k k | a b c d | k k k k)` The input is extended by filling all values beyond the edge with the same constant value k = 0. - *wrap*: `(a b c d | a b c d | a b c d)` The input is extended by wrapping around to the opposite edge. - *nearest*: `(a a a a | a b c d | d d d d)` The input is extended by the nearest pixel. interpolation: Interpolation mode. Supported values: `"nearest"`, `"bilinear"`. seed: Integer. Used to create a random seed. fill_value: a float represents the value to be filled outside the boundaries when `fill_mode="constant"`. Example: >>> input_img = np.random.random((32, 224, 224, 3)) >>> layer = tf.keras.layers.RandomZoom(.5, .2) >>> out_img = layer(input_img) >>> out_img.shape TensorShape([32, 224, 224, 3]) Input shape: 3D (unbatched) or 4D (batched) tensor with shape: `(..., height, width, channels)`, in `"channels_last"` format. Output shape: 3D (unbatched) or 4D (batched) tensor with shape: `(..., height, width, channels)`, in `"channels_last"` format. """ def __init__( self, height_factor, width_factor=None, fill_mode="reflect", interpolation="bilinear", seed=None, fill_value=0.0, **kwargs, ): base_preprocessing_layer.keras_kpl_gauge.get_cell("RandomZoom").set( True ) super().__init__(seed=seed, force_generator=True, **kwargs) self.height_factor = height_factor if isinstance(height_factor, (tuple, list)): self.height_lower = height_factor[0] self.height_upper = height_factor[1] else: self.height_lower = -height_factor self.height_upper = height_factor if abs(self.height_lower) > 1.0 or abs(self.height_upper) > 1.0: raise ValueError( "`height_factor` must have values between [-1, 1], " "got {}".format(height_factor) ) self.width_factor = width_factor if width_factor is not None: if isinstance(width_factor, (tuple, list)): self.width_lower = width_factor[0] self.width_upper = width_factor[1] else: self.width_lower = -width_factor self.width_upper = width_factor if self.width_lower < -1.0 or self.width_upper < -1.0: raise ValueError( "`width_factor` must have values larger than -1, " "got {}".format(width_factor) ) check_fill_mode_and_interpolation(fill_mode, interpolation) self.fill_mode = fill_mode self.fill_value = fill_value self.interpolation = interpolation self.seed = seed def get_random_transformation( self, image=None, label=None, bounding_box=None ): height_zoom = self._random_generator.random_uniform( shape=[1, 1], minval=1.0 + self.height_lower, maxval=1.0 + self.height_upper, ) if self.width_factor is not None: width_zoom = self._random_generator.random_uniform( shape=[1, 1], minval=1.0 + self.width_lower, maxval=1.0 + self.width_upper, ) else: width_zoom = height_zoom return {"height_zoom": height_zoom, "width_zoom": width_zoom} def augment_image(self, image, transformation): image = utils.ensure_tensor(image, self.compute_dtype) original_shape = image.shape image = tf.expand_dims(image, 0) image_shape = tf.shape(image) img_hd = tf.cast(image_shape[H_AXIS], tf.float32) img_wd = tf.cast(image_shape[W_AXIS], tf.float32) width_zoom = transformation["width_zoom"] height_zoom = transformation["height_zoom"] zooms = tf.cast( tf.concat([width_zoom, height_zoom], axis=1), dtype=tf.float32 ) output = transform( image, get_zoom_matrix(zooms, img_hd, img_wd), fill_mode=self.fill_mode, fill_value=self.fill_value, interpolation=self.interpolation, ) output = tf.squeeze(output, 0) output.set_shape(original_shape) return output def augment_label(self, label, transformation): return label def compute_output_shape(self, input_shape): return input_shape def get_config(self): config = { "height_factor": self.height_factor, "width_factor": self.width_factor, "fill_mode": self.fill_mode, "fill_value": self.fill_value, "interpolation": self.interpolation, "seed": self.seed, } base_config = super().get_config() return dict(list(base_config.items()) + list(config.items())) def get_zoom_matrix(zooms, image_height, image_width, name=None): """Returns projective transform(s) for the given zoom(s). Args: zooms: A matrix of 2-element lists representing `[zx, zy]` to zoom for each image (for a batch of images). image_height: Height of the image(s) to be transformed. image_width: Width of the image(s) to be transformed. name: The name of the op. Returns: A tensor of shape `(num_images, 8)`. Projective transforms which can be given to operation `image_projective_transform_v2`. If one row of transforms is `[a0, a1, a2, b0, b1, b2, c0, c1]`, then it maps the *output* point `(x, y)` to a transformed *input* point `(x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k)`, where `k = c0 x + c1 y + 1`. """ with backend.name_scope(name or "zoom_matrix"): num_zooms = tf.shape(zooms)[0] # The zoom matrix looks like: # [[zx 0 0] # [0 zy 0] # [0 0 1]] # where the last entry is implicit. # Zoom matrices are always float32. x_offset = ((image_width - 1.0) / 2.0) * (1.0 - zooms[:, 0, None]) y_offset = ((image_height - 1.0) / 2.0) * (1.0 - zooms[:, 1, None]) return tf.concat( values=[ zooms[:, 0, None], tf.zeros((num_zooms, 1), tf.float32), x_offset, tf.zeros((num_zooms, 1), tf.float32), zooms[:, 1, None], y_offset, tf.zeros((num_zooms, 2), tf.float32), ], axis=1, ) @keras_export( "keras.layers.RandomContrast", "keras.layers.experimental.preprocessing.RandomContrast", v1=[], ) class RandomContrast(BaseImageAugmentationLayer): """A preprocessing layer which randomly adjusts contrast during training. This layer will randomly adjust the contrast of an image or images by a random factor. Contrast is adjusted independently for each channel of each image during training. For each channel, this layer computes the mean of the image pixels in the channel and then adjusts each component `x` of each pixel to `(x - mean) * contrast_factor + mean`. Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and in integer or floating point dtype. By default, the layer will output floats. The output value will be clipped to the range `[0, 255]`, the valid range of RGB colors. For an overview and full list of preprocessing layers, see the preprocessing [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers). Input shape: 3D (unbatched) or 4D (batched) tensor with shape: `(..., height, width, channels)`, in `"channels_last"` format. Output shape: 3D (unbatched) or 4D (batched) tensor with shape: `(..., height, width, channels)`, in `"channels_last"` format. Arguments: factor: a positive float represented as fraction of value, or a tuple of size 2 representing lower and upper bound. When represented as a single float, lower = upper. The contrast factor will be randomly picked between `[1.0 - lower, 1.0 + upper]`. For any pixel x in the channel, the output will be `(x - mean) * factor + mean` where `mean` is the mean value of the channel. seed: Integer. Used to create a random seed. """ def __init__(self, factor, seed=None, **kwargs): base_preprocessing_layer.keras_kpl_gauge.get_cell("RandomContrast").set( True ) super().__init__(seed=seed, force_generator=True, **kwargs) self.factor = factor if isinstance(factor, (tuple, list)): self.lower = factor[0] self.upper = factor[1] else: self.lower = self.upper = factor if self.lower < 0.0 or self.upper < 0.0 or self.lower > 1.0: raise ValueError( "Factor cannot have negative values or greater than 1.0," " got {}".format(factor) ) self.seed = seed def get_random_transformation( self, image=None, label=None, bounding_box=None ): lower = 1.0 - self.lower upper = 1.0 + self.upper random_seed = self._random_generator.make_seed_for_stateless_op() contrast_factor = stateless_random_ops.stateless_random_uniform( shape=[], minval=lower, maxval=upper, seed=random_seed ) return {"contrast_factor": contrast_factor} def augment_image(self, image, transformation): contrast_factor = transformation["contrast_factor"] output = tf.image.adjust_contrast( image, contrast_factor=contrast_factor ) output = tf.clip_by_value(output, 0, 255) output.set_shape(image.shape) return output def augment_label(self, label, transformation): return label def compute_output_shape(self, input_shape): return input_shape def get_config(self): config = { "factor": self.factor, "seed": self.seed, } base_config = super().get_config() return dict(list(base_config.items()) + list(config.items())) @keras_export("keras.layers.RandomBrightness", v1=[]) class RandomBrightness(BaseImageAugmentationLayer): """A preprocessing layer which randomly adjusts brightness during training. This layer will randomly increase/reduce the brightness for the input RGB images. At inference time, the output will be identical to the input. Call the layer with `training=True` to adjust the brightness of the input. Note that different brightness adjustment factors will be apply to each the images in the batch. For an overview and full list of preprocessing layers, see the preprocessing [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers). Args: factor: Float or a list/tuple of 2 floats between -1.0 and 1.0. The factor is used to determine the lower bound and upper bound of the brightness adjustment. A float value will be chosen randomly between the limits. When -1.0 is chosen, the output image will be black, and when 1.0 is chosen, the image will be fully white. When only one float is provided, eg, 0.2, then -0.2 will be used for lower bound and 0.2 will be used for upper bound. value_range: Optional list/tuple of 2 floats for the lower and upper limit of the values of the input data. Defaults to [0.0, 255.0]. Can be changed to e.g. [0.0, 1.0] if the image input has been scaled before this layer. The brightness adjustment will be scaled to this range, and the output values will be clipped to this range. seed: optional integer, for fixed RNG behavior. Inputs: 3D (HWC) or 4D (NHWC) tensor, with float or int dtype. Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) Output: 3D (HWC) or 4D (NHWC) tensor with brightness adjusted based on the `factor`. By default, the layer will output floats. The output value will be clipped to the range `[0, 255]`, the valid range of RGB colors, and rescaled based on the `value_range` if needed. Sample usage: ```python random_bright = tf.keras.layers.RandomBrightness(factor=0.2) # An image with shape [2, 2, 3] image = [[[1, 2, 3], [4 ,5 ,6]], [[7, 8, 9], [10, 11, 12]]] # Assume we randomly select the factor to be 0.1, then it will apply # 0.1 * 255 to all the channel output = random_bright(image, training=True) # output will be int64 with 25.5 added to each channel and round down. tf.Tensor([[[26.5, 27.5, 28.5] [29.5, 30.5, 31.5]] [[32.5, 33.5, 34.5] [35.5, 36.5, 37.5]]], shape=(2, 2, 3), dtype=int64) ``` """ _FACTOR_VALIDATION_ERROR = ( "The `factor` argument should be a number (or a list of two numbers) " "in the range [-1.0, 1.0]. " ) _VALUE_RANGE_VALIDATION_ERROR = ( "The `value_range` argument should be a list of two numbers. " ) def __init__(self, factor, value_range=(0, 255), seed=None, **kwargs): base_preprocessing_layer.keras_kpl_gauge.get_cell( "RandomBrightness" ).set(True) super().__init__(seed=seed, force_generator=True, **kwargs) self._set_factor(factor) self._set_value_range(value_range) self._seed = seed def augment_image(self, image, transformation): return self._brightness_adjust(image, transformation["rgb_delta"]) def augment_label(self, label, transformation): return label def get_random_transformation( self, image=None, label=None, bounding_box=None ): rgb_delta_shape = (1, 1, 1) random_rgb_delta = self._random_generator.random_uniform( shape=rgb_delta_shape, minval=self._factor[0], maxval=self._factor[1], ) random_rgb_delta = random_rgb_delta * ( self._value_range[1] - self._value_range[0] ) return {"rgb_delta": random_rgb_delta} def _set_value_range(self, value_range): if not isinstance(value_range, (tuple, list)): raise ValueError( self._VALUE_RANGE_VALIDATION_ERROR + f"Got {value_range}" ) if len(value_range) != 2: raise ValueError( self._VALUE_RANGE_VALIDATION_ERROR + f"Got {value_range}" ) self._value_range = sorted(value_range) def _set_factor(self, factor): if isinstance(factor, (tuple, list)): if len(factor) != 2: raise ValueError( self._FACTOR_VALIDATION_ERROR + f"Got {factor}" ) self._check_factor_range(factor[0]) self._check_factor_range(factor[1]) self._factor = sorted(factor) elif isinstance(factor, (int, float)): self._check_factor_range(factor) factor = abs(factor) self._factor = [-factor, factor] else: raise ValueError(self._FACTOR_VALIDATION_ERROR + f"Got {factor}") def _check_factor_range(self, input_number): if input_number > 1.0 or input_number < -1.0: raise ValueError( self._FACTOR_VALIDATION_ERROR + f"Got {input_number}" ) def _brightness_adjust(self, image, rgb_delta): image = utils.ensure_tensor(image, self.compute_dtype) rank = image.shape.rank if rank != 3: raise ValueError( "Expected the input image to be rank 3. Got " f"inputs.shape = {image.shape}" ) rgb_delta = tf.cast(rgb_delta, image.dtype) image += rgb_delta return tf.clip_by_value( image, self._value_range[0], self._value_range[1] ) def get_config(self): config = { "factor": self._factor, "value_range": self._value_range, "seed": self._seed, } base_config = super().get_config() return dict(list(base_config.items()) + list(config.items())) @keras_export( "keras.layers.RandomHeight", "keras.layers.experimental.preprocessing.RandomHeight", v1=[], ) class RandomHeight(BaseImageAugmentationLayer): """A preprocessing layer which randomly varies image height during training. This layer adjusts the height of a batch of images by a random factor. The input should be a 3D (unbatched) or 4D (batched) tensor in the `"channels_last"` image data format. Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and of interger or floating point dtype. By default, the layer will output floats. By default, this layer is inactive during inference. For an overview and full list of preprocessing layers, see the preprocessing [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers). Args: factor: A positive float (fraction of original height), or a tuple of size 2 representing lower and upper bound for resizing vertically. When represented as a single float, this value is used for both the upper and lower bound. For instance, `factor=(0.2, 0.3)` results in an output with height changed by a random amount in the range `[20%, 30%]`. `factor=(-0.2, 0.3)` results in an output with height changed by a random amount in the range `[-20%, +30%]`. `factor=0.2` results in an output with height changed by a random amount in the range `[-20%, +20%]`. interpolation: String, the interpolation method. Defaults to `"bilinear"`. Supports `"bilinear"`, `"nearest"`, `"bicubic"`, `"area"`, `"lanczos3"`, `"lanczos5"`, `"gaussian"`, `"mitchellcubic"`. seed: Integer. Used to create a random seed. Input shape: 3D (unbatched) or 4D (batched) tensor with shape: `(..., height, width, channels)`, in `"channels_last"` format. Output shape: 3D (unbatched) or 4D (batched) tensor with shape: `(..., random_height, width, channels)`. """ def __init__(self, factor, interpolation="bilinear", seed=None, **kwargs): base_preprocessing_layer.keras_kpl_gauge.get_cell("RandomHeight").set( True ) super().__init__(seed=seed, force_generator=True, **kwargs) self.factor = factor if isinstance(factor, (tuple, list)): self.height_lower = factor[0] self.height_upper = factor[1] else: self.height_lower = -factor self.height_upper = factor if self.height_upper < self.height_lower: raise ValueError( "`factor` cannot have upper bound less than " "lower bound, got {}".format(factor) ) if self.height_lower < -1.0 or self.height_upper < -1.0: raise ValueError( "`factor` must have values larger than -1, " "got {}".format(factor) ) self.interpolation = interpolation self._interpolation_method = image_utils.get_interpolation( interpolation ) self.seed = seed def get_random_transformation( self, image=None, label=None, bounding_box=None ): height_factor = self._random_generator.random_uniform( shape=[], minval=(1.0 + self.height_lower), maxval=(1.0 + self.height_upper), ) inputs_shape = tf.shape(image) img_hd = tf.cast(inputs_shape[H_AXIS], tf.float32) adjusted_height = tf.cast(height_factor * img_hd, tf.int32) return {"height": adjusted_height} def _batch_augment(self, inputs): images = self.augment_image( inputs[IMAGES], transformation=self.get_random_transformation(image=inputs[IMAGES]), ) result = {IMAGES: images} # to-do augment bbox to clip bbox to resized height value return result def augment_image(self, image, transformation): # The batch dimension of the input=image is not modified. The output # would be accurate for both unbatched and batched input inputs_shape = tf.shape(image) img_wd = inputs_shape[W_AXIS] adjusted_height = transformation["height"] adjusted_size = tf.stack([adjusted_height, img_wd]) output = tf.image.resize( images=image, size=adjusted_size, method=self._interpolation_method ) # tf.resize will output float32 in many cases regardless of input type. output = tf.cast(output, self.compute_dtype) output_shape = list(image.shape) output_shape[H_AXIS] = None output.set_shape(output_shape) return output def compute_output_shape(self, input_shape): input_shape = tf.TensorShape(input_shape).as_list() input_shape[H_AXIS] = None return tf.TensorShape(input_shape) def get_config(self): config = { "factor": self.factor, "interpolation": self.interpolation, "seed": self.seed, } base_config = super().get_config() return dict(list(base_config.items()) + list(config.items())) @keras_export( "keras.layers.RandomWidth", "keras.layers.experimental.preprocessing.RandomWidth", v1=[], ) class RandomWidth(BaseImageAugmentationLayer): """A preprocessing layer which randomly varies image width during training. This layer will randomly adjusts the width of a batch of images of a batch of images by a random factor. The input should be a 3D (unbatched) or 4D (batched) tensor in the `"channels_last"` image data format. Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and of interger or floating point dtype. By default, the layer will output floats. By default, this layer is inactive during inference. For an overview and full list of preprocessing layers, see the preprocessing [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers). Args: factor: A positive float (fraction of original width), or a tuple of size 2 representing lower and upper bound for resizing vertically. When represented as a single float, this value is used for both the upper and lower bound. For instance, `factor=(0.2, 0.3)` results in an output with width changed by a random amount in the range `[20%, 30%]`. `factor=(-0.2, 0.3)` results in an output with width changed by a random amount in the range `[-20%, +30%]`. `factor=0.2` results in an output with width changed by a random amount in the range `[-20%, +20%]`. interpolation: String, the interpolation method. Defaults to `bilinear`. Supports `"bilinear"`, `"nearest"`, `"bicubic"`, `"area"`, `"lanczos3"`, `"lanczos5"`, `"gaussian"`, `"mitchellcubic"`. seed: Integer. Used to create a random seed. Input shape: 3D (unbatched) or 4D (batched) tensor with shape: `(..., height, width, channels)`, in `"channels_last"` format. Output shape: 3D (unbatched) or 4D (batched) tensor with shape: `(..., height, random_width, channels)`. """ def __init__(self, factor, interpolation="bilinear", seed=None, **kwargs): base_preprocessing_layer.keras_kpl_gauge.get_cell("RandomWidth").set( True ) super().__init__(seed=seed, force_generator=True, **kwargs) self.factor = factor if isinstance(factor, (tuple, list)): self.width_lower = factor[0] self.width_upper = factor[1] else: self.width_lower = -factor self.width_upper = factor if self.width_upper < self.width_lower: raise ValueError( "`factor` cannot have upper bound less than " "lower bound, got {}".format(factor) ) if self.width_lower < -1.0 or self.width_upper < -1.0: raise ValueError( "`factor` must have values larger than -1, " "got {}".format(factor) ) self.interpolation = interpolation self._interpolation_method = image_utils.get_interpolation( interpolation ) self.seed = seed self.auto_vectorize = False def _batch_augment(self, inputs): images = self.augment_image( inputs[IMAGES], transformation=self.get_random_transformation(image=inputs[IMAGES]), ) result = {IMAGES: images} # to-do augment bbox to clip bbox to resized width value return result def augment_image(self, image, transformation): # The batch dimension of the input=image is not modified. The output # would be accurate for both unbatched and batched input inputs = utils.ensure_tensor(image) inputs_shape = tf.shape(inputs) img_hd = inputs_shape[H_AXIS] adjusted_width = transformation["width"] adjusted_size = tf.stack([img_hd, adjusted_width]) output = tf.image.resize( images=inputs, size=adjusted_size, method=self._interpolation_method ) # tf.resize will output float32 in many cases regardless of input type. output = tf.cast(output, self.compute_dtype) output_shape = inputs.shape.as_list() output_shape[W_AXIS] = None output.set_shape(output_shape) return output def get_random_transformation( self, image=None, label=None, bounding_box=None ): inputs_shape = tf.shape(image) img_wd = tf.cast(inputs_shape[W_AXIS], tf.float32) width_factor = self._random_generator.random_uniform( shape=[], minval=(1.0 + self.width_lower), maxval=(1.0 + self.width_upper), ) adjusted_width = tf.cast(width_factor * img_wd, tf.int32) return {"width": adjusted_width} def compute_output_shape(self, input_shape): input_shape = tf.TensorShape(input_shape).as_list() input_shape[W_AXIS] = None return tf.TensorShape(input_shape) def get_config(self): config = { "factor": self.factor, "interpolation": self.interpolation, "seed": self.seed, } base_config = super().get_config() return dict(list(base_config.items()) + list(config.items()))