# Copyright 2019-2021 Huawei Technologies Co., Ltd
#
# 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.
# ==============================================================================
"""
The module vision.c_transforms is inherited from _c_dataengine
and is implemented based on OpenCV in C++. It's a high performance module to
process images. Users can apply suitable augmentations on image data
to improve their training models.
.. Note::
A constructor's arguments for every class in this module must be saved into the
class attributes (self.xxx) to support save() and load().
Examples:
>>> from mindspore.dataset.vision import Border, Inter
>>> image_folder_dataset_dir = "/path/to/image_folder_dataset_directory"
>>> # create a dataset that reads all files in dataset_dir with 8 threads
>>> image_folder_dataset = ds.ImageFolderDataset(image_folder_dataset_dir,
... num_parallel_workers=8)
>>> # create a list of transformations to be applied to the image data
>>> transforms_list = [c_vision.Decode(),
... c_vision.Resize((256, 256), interpolation=Inter.LINEAR),
... c_vision.RandomCrop(200, padding_mode=Border.EDGE),
... c_vision.RandomRotation((0, 15)),
... c_vision.Normalize((100, 115.0, 121.0), (71.0, 68.0, 70.0)),
... c_vision.HWC2CHW()]
>>> onehot_op = c_transforms.OneHot(num_classes=10)
>>> # apply the transformation to the dataset through data1.map()
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns="image")
>>> image_folder_dataset = image_folder_dataset.map(operations=onehot_op,
... input_columns="label")
"""
import numbers
import numpy as np
from PIL import Image
import mindspore._c_dataengine as cde
from .utils import Inter, Border, ImageBatchFormat, ConvertMode, SliceMode, AutoAugmentPolicy
from .validators import check_prob, check_crop, check_center_crop, check_resize_interpolation, \
check_mix_up_batch_c, check_normalize_c, check_normalizepad_c, check_random_crop, check_random_color_adjust, \
check_random_rotation, check_range, check_resize, check_rescale, check_pad, check_cutout, check_alpha, \
check_uniform_augment_cpp, check_convert_color, check_random_resize_crop, check_random_auto_contrast, \
check_random_adjust_sharpness, check_auto_augment, \
check_bounding_box_augment_cpp, check_random_select_subpolicy_op, check_auto_contrast, check_random_affine, \
check_random_solarize, check_soft_dvpp_decode_random_crop_resize_jpeg, check_positive_degrees, FLOAT_MAX_INTEGER, \
check_cut_mix_batch_c, check_posterize, check_gaussian_blur, check_rotate, check_slice_patches, check_adjust_gamma
from ..transforms.c_transforms import TensorOperation
class ImageTensorOperation(TensorOperation):
"""
Base class of Image Tensor Ops
"""
def __call__(self, *input_tensor_list):
for tensor in input_tensor_list:
if not isinstance(tensor, (np.ndarray, Image.Image)):
raise TypeError(
"Input should be NumPy or PIL image, got {}.".format(type(tensor)))
return super().__call__(*input_tensor_list)
def parse(self):
raise NotImplementedError(
"ImageTensorOperation has to implement parse() method.")
DE_C_AUTO_AUGMENT_POLICY = {AutoAugmentPolicy.IMAGENET: cde.AutoAugmentPolicy.DE_AUTO_AUGMENT_POLICY_IMAGENET,
AutoAugmentPolicy.CIFAR10: cde.AutoAugmentPolicy.DE_AUTO_AUGMENT_POLICY_CIFAR10,
AutoAugmentPolicy.SVHN: cde.AutoAugmentPolicy.DE_AUTO_AUGMENT_POLICY_SVHN}
DE_C_BORDER_TYPE = {Border.CONSTANT: cde.BorderType.DE_BORDER_CONSTANT,
Border.EDGE: cde.BorderType.DE_BORDER_EDGE,
Border.REFLECT: cde.BorderType.DE_BORDER_REFLECT,
Border.SYMMETRIC: cde.BorderType.DE_BORDER_SYMMETRIC}
DE_C_IMAGE_BATCH_FORMAT = {ImageBatchFormat.NHWC: cde.ImageBatchFormat.DE_IMAGE_BATCH_FORMAT_NHWC,
ImageBatchFormat.NCHW: cde.ImageBatchFormat.DE_IMAGE_BATCH_FORMAT_NCHW}
DE_C_INTER_MODE = {Inter.NEAREST: cde.InterpolationMode.DE_INTER_NEAREST_NEIGHBOUR,
Inter.LINEAR: cde.InterpolationMode.DE_INTER_LINEAR,
Inter.CUBIC: cde.InterpolationMode.DE_INTER_CUBIC,
Inter.AREA: cde.InterpolationMode.DE_INTER_AREA,
Inter.PILCUBIC: cde.InterpolationMode.DE_INTER_PILCUBIC}
DE_C_SLICE_MODE = {SliceMode.PAD: cde.SliceMode.DE_SLICE_PAD,
SliceMode.DROP: cde.SliceMode.DE_SLICE_DROP}
DE_C_CONVERT_COLOR_MODE = {ConvertMode.COLOR_BGR2BGRA: cde.ConvertMode.DE_COLOR_BGR2BGRA,
ConvertMode.COLOR_RGB2RGBA: cde.ConvertMode.DE_COLOR_RGB2RGBA,
ConvertMode.COLOR_BGRA2BGR: cde.ConvertMode.DE_COLOR_BGRA2BGR,
ConvertMode.COLOR_RGBA2RGB: cde.ConvertMode.DE_COLOR_RGBA2RGB,
ConvertMode.COLOR_BGR2RGBA: cde.ConvertMode.DE_COLOR_BGR2RGBA,
ConvertMode.COLOR_RGB2BGRA: cde.ConvertMode.DE_COLOR_RGB2BGRA,
ConvertMode.COLOR_RGBA2BGR: cde.ConvertMode.DE_COLOR_RGBA2BGR,
ConvertMode.COLOR_BGRA2RGB: cde.ConvertMode.DE_COLOR_BGRA2RGB,
ConvertMode.COLOR_BGR2RGB: cde.ConvertMode.DE_COLOR_BGR2RGB,
ConvertMode.COLOR_RGB2BGR: cde.ConvertMode.DE_COLOR_RGB2BGR,
ConvertMode.COLOR_BGRA2RGBA: cde.ConvertMode.DE_COLOR_BGRA2RGBA,
ConvertMode.COLOR_RGBA2BGRA: cde.ConvertMode.DE_COLOR_RGBA2BGRA,
ConvertMode.COLOR_BGR2GRAY: cde.ConvertMode.DE_COLOR_BGR2GRAY,
ConvertMode.COLOR_RGB2GRAY: cde.ConvertMode.DE_COLOR_RGB2GRAY,
ConvertMode.COLOR_GRAY2BGR: cde.ConvertMode.DE_COLOR_GRAY2BGR,
ConvertMode.COLOR_GRAY2RGB: cde.ConvertMode.DE_COLOR_GRAY2RGB,
ConvertMode.COLOR_GRAY2BGRA: cde.ConvertMode.DE_COLOR_GRAY2BGRA,
ConvertMode.COLOR_GRAY2RGBA: cde.ConvertMode.DE_COLOR_GRAY2RGBA,
ConvertMode.COLOR_BGRA2GRAY: cde.ConvertMode.DE_COLOR_BGRA2GRAY,
ConvertMode.COLOR_RGBA2GRAY: cde.ConvertMode.DE_COLOR_RGBA2GRAY,
}
def parse_padding(padding):
""" Parses and prepares the padding tuple"""
if isinstance(padding, numbers.Number):
padding = [padding] * 4
if len(padding) == 2:
left = top = padding[0]
right = bottom = padding[1]
padding = (left, top, right, bottom,)
if isinstance(padding, list):
padding = tuple(padding)
return padding
class AdjustGamma(ImageTensorOperation):
r"""
Apply gamma correction on input image. Input image is expected to be in [..., H, W, C] or [H, W] format.
.. math::
I_{\text{out}} = 255 \times \text{gain} \times \left(\frac{I_{\text{in}}}{255}\right)^{\gamma}
See `Gamma Correction`_ for more details.
.. _Gamma Correction: https://en.wikipedia.org/wiki/Gamma_correction
Args:
gamma (float): Non negative real number.
The output image pixel value is exponentially related to the input image pixel value.
gamma larger than 1 make the shadows darker,
while gamma smaller than 1 make dark regions lighter.
gain (float, optional): The constant multiplier (default=1).
Raises:
TypeError: If `gain` is not of type float.
TypeError: If `gamma` is not of type float.
ValueError: If `gamma` is less than 0.
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> transforms_list = [c_vision.Decode(), c_vision.AdjustGamma(gamma=10.0, gain=1.0)]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_adjust_gamma
def __init__(self, gamma, gain=1):
self.gamma = gamma
self.gain = gain
def parse(self):
return cde.AdjustGammaOperation(self.gamma, self.gain)
class AutoAugment(ImageTensorOperation):
"""
Apply AutoAugment data augmentation method based on
`AutoAugment: Learning Augmentation Strategies from Data <https://arxiv.org/pdf/1805.09501.pdf>`_.
This operation works only with 3-channel RGB images.
Args:
policy (AutoAugmentPolicy, optional): AutoAugment policies learned on different datasets
(default=AutoAugmentPolicy.IMAGENET).
It can be any of [AutoAugmentPolicy.IMAGENET, AutoAugmentPolicy.CIFAR10, AutoAugmentPolicy.SVHN].
Randomly apply 2 operations from a candidate set. See auto augmentation details in AutoAugmentPolicy.
- AutoAugmentPolicy.IMAGENET, means to apply AutoAugment learned on ImageNet dataset.
- AutoAugmentPolicy.CIFAR10, means to apply AutoAugment learned on Cifar10 dataset.
- AutoAugmentPolicy.SVHN, means to apply AutoAugment learned on SVHN dataset.
interpolation (Inter, optional): Image interpolation mode for Resize operator (default=Inter.NEAREST).
It can be any of [Inter.NEAREST, Inter.BILINEAR, Inter.BICUBIC, Inter.AREA].
- Inter.NEAREST: means interpolation method is nearest-neighbor interpolation.
- Inter.BILINEAR: means interpolation method is bilinear interpolation.
- Inter.BICUBIC: means the interpolation method is bicubic interpolation.
- Inter.AREA: means the interpolation method is area interpolation.
fill_value (Union[int, tuple], optional): Pixel fill value for the area outside the transformed image.
It can be an int or a 3-tuple. If it is a 3-tuple, it is used to fill R, G, B channels respectively.
If it is an integer, it is used for all RGB channels. The fill_value values must be in range [0, 255]
(default=0).
Raises:
TypeError: If `policy` is not of type AutoAugmentPolicy.
TypeError: If `interpolation` is not of type Inter.
TypeError: If `fill_value` is not an integer or a tuple of length 3.
RuntimeError: If given tensor shape is not <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.vision import AutoAugmentPolicy, Inter
>>> transforms_list = [c_vision.Decode(), c_vision.AutoAugment(policy=AutoAugmentPolicy.IMAGENET,
... interpolation=Inter.NEAREST,
... fill_value=0)]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_auto_augment
def __init__(self, policy=AutoAugmentPolicy.IMAGENET, interpolation=Inter.NEAREST, fill_value=0):
self.policy = policy
self.interpolation = interpolation
if isinstance(fill_value, int):
fill_value = tuple([fill_value] * 3)
self.fill_value = fill_value
def parse(self):
return cde.AutoAugmentOperation(DE_C_AUTO_AUGMENT_POLICY[self.policy], DE_C_INTER_MODE[self.interpolation],
self.fill_value)
[文档]class AutoContrast(ImageTensorOperation):
"""
Apply automatic contrast on input image. This operator calculates histogram of image, reassign cutoff percent
of the lightest pixels from histogram to 255, and reassign cutoff percent of the darkest pixels from histogram to 0.
Args:
cutoff (float, optional): Percent of lightest and darkest pixels to cut off from
the histogram of input image. The value must be in the range [0.0, 50.0) (default=0.0).
ignore (Union[int, sequence], optional): The background pixel values to ignore,
The ignore values must be in range [0, 255] (default=None).
Raises:
TypeError: If `cutoff` is not of type float.
TypeError: If `ignore` is not of type int or sequence.
ValueError: If `cutoff` is not in range [0, 50.0).
ValueError: If `ignore` is not in range [0, 255].
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> transforms_list = [c_vision.Decode(), c_vision.AutoContrast(cutoff=10.0, ignore=[10, 20])]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_auto_contrast
def __init__(self, cutoff=0.0, ignore=None):
if ignore is None:
ignore = []
if isinstance(ignore, int):
ignore = [ignore]
self.cutoff = cutoff
self.ignore = ignore
def parse(self):
return cde.AutoContrastOperation(self.cutoff, self.ignore)
[文档]class BoundingBoxAugment(ImageTensorOperation):
"""
Apply a given image processing operation on a random selection of bounding box regions of a given image.
Args:
transform (TensorOperation): C++ transformation operation to be applied on random selection
of bounding box regions of a given image.
ratio (float, optional): Ratio of bounding boxes to apply augmentation on.
Range: [0.0, 1.0] (default=0.3).
Raises:
TypeError: If `transform` is not an image processing operation
in :class:`mindspore.dataset.vision.c_transforms`.
TypeError: If `ratio` is not of type float.
ValueError: If `ratio` is not in range [0.0, 1.0].
RuntimeError: If given bounding box is invalid.
Supported Platforms:
``CPU``
Examples:
>>> # set bounding box operation with ratio of 1 to apply rotation on all bounding boxes
>>> bbox_aug_op = c_vision.BoundingBoxAugment(c_vision.RandomRotation(90), 1)
>>> # map to apply ops
>>> image_folder_dataset = image_folder_dataset.map(operations=[bbox_aug_op],
... input_columns=["image", "bbox"],
... output_columns=["image", "bbox"],
... column_order=["image", "bbox"])
"""
@check_bounding_box_augment_cpp
def __init__(self, transform, ratio=0.3):
self.ratio = ratio
self.transform = transform
def parse(self):
if self.transform and getattr(self.transform, 'parse', None):
transform = self.transform.parse()
else:
transform = self.transform
return cde.BoundingBoxAugmentOperation(transform, self.ratio)
[文档]class CenterCrop(ImageTensorOperation):
"""
Crop the input image at the center to the given size. If input image size is smaller than output size,
input image will be padded with 0 before cropping.
Args:
size (Union[int, sequence]): The output size of the cropped image.
If size is an integer, a square crop of size (size, size) is returned.
If size is a sequence of length 2, an image of size (height, width) will be cropped.
The size value(s) must be larger than 0.
Raises:
TypeError: If `size` is not of type int or sequence.
ValueError: If `size` is less than or equal to 0.
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> # crop image to a square
>>> transforms_list1 = [c_vision.Decode(), c_vision.CenterCrop(50)]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list1,
... input_columns=["image"])
>>> # crop image to portrait style
>>> transforms_list2 = [c_vision.Decode(), c_vision.CenterCrop((60, 40))]
>>> image_folder_dataset_1 = image_folder_dataset_1.map(operations=transforms_list2,
... input_columns=["image"])
"""
@check_center_crop
def __init__(self, size):
if isinstance(size, int):
size = (size, size)
self.size = size
def parse(self):
return cde.CenterCropOperation(self.size)
[文档]class ConvertColor(ImageTensorOperation):
"""
Change the color space of the image.
Args:
convert_mode (ConvertMode): The mode of image channel conversion.
- ConvertMode.COLOR_BGR2BGRA, Convert BGR image to BGRA image.
- ConvertMode.COLOR_RGB2RGBA, Convert RGB image to RGBA image.
- ConvertMode.COLOR_BGRA2BGR, Convert BGRA image to BGR image.
- ConvertMode.COLOR_RGBA2RGB, Convert RGBA image to RGB image.
- ConvertMode.COLOR_BGR2RGBA, Convert BGR image to RGBA image.
- ConvertMode.COLOR_RGB2BGRA, Convert RGB image to BGRA image.
- ConvertMode.COLOR_RGBA2BGR, Convert RGBA image to BGR image.
- ConvertMode.COLOR_BGRA2RGB, Convert BGRA image to RGB image.
- ConvertMode.COLOR_BGR2RGB, Convert BGR image to RGB image.
- ConvertMode.COLOR_RGB2BGR, Convert RGB image to BGR image.
- ConvertMode.COLOR_BGRA2RGBA, Convert BGRA image to RGBA image.
- ConvertMode.COLOR_RGBA2BGRA, Convert RGBA image to BGRA image.
- ConvertMode.COLOR_BGR2GRAY, Convert BGR image to GRAY image.
- ConvertMode.COLOR_RGB2GRAY, Convert RGB image to GRAY image.
- ConvertMode.COLOR_GRAY2BGR, Convert GRAY image to BGR image.
- ConvertMode.COLOR_GRAY2RGB, Convert GRAY image to RGB image.
- ConvertMode.COLOR_GRAY2BGRA, Convert GRAY image to BGRA image.
- ConvertMode.COLOR_GRAY2RGBA, Convert GRAY image to RGBA image.
- ConvertMode.COLOR_BGRA2GRAY, Convert BGRA image to GRAY image.
- ConvertMode.COLOR_RGBA2GRAY, Convert RGBA image to GRAY image.
Raises:
TypeError: If `convert_mode` is not of type :class:`mindspore.dataset.vision.c_transforms.ConvertMode`.
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> import mindspore.dataset.vision.utils as mode
>>> # Convert RGB images to GRAY images
>>> convert_op = c_vision.ConvertColor(mode.ConvertMode.COLOR_RGB2GRAY)
>>> image_folder_dataset = image_folder_dataset.map(operations=convert_op,
... input_columns=["image"])
>>> # Convert RGB images to BGR images
>>> convert_op = c_vision.ConvertColor(mode.ConvertMode.COLOR_RGB2BGR)
>>> image_folder_dataset_1 = image_folder_dataset_1.map(operations=convert_op,
... input_columns=["image"])
"""
@check_convert_color
def __init__(self, convert_mode):
self.convert_mode = convert_mode
def parse(self):
return cde.ConvertColorOperation(DE_C_CONVERT_COLOR_MODE[self.convert_mode])
[文档]class Crop(ImageTensorOperation):
"""
Crop the input image at a specific location.
Args:
coordinates(sequence): Coordinates of the upper left corner of the cropping image. Must be a sequence of two
values, in the form of (top, left).
size (Union[int, sequence]): The output size of the cropped image.
If size is an integer, a square crop of size (size, size) is returned.
If size is a sequence of length 2, an image of size (height, width) will be cropped.
The size value(s) must be larger than 0.
Raises:
TypeError: If `coordinates` is not of type sequence.
TypeError: If `size` is not of type int or sequence.
ValueError: If `coordinates` is less than 0.
ValueError: If `size` is less than or equal to 0.
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> decode_op = c_vision.Decode()
>>> crop_op = c_vision.Crop((0, 0), 32)
>>> transforms_list = [decode_op, crop_op]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_crop
def __init__(self, coordinates, size):
if isinstance(size, int):
size = (size, size)
self.coordinates = coordinates
self.size = size
def parse(self):
return cde.CropOperation(self.coordinates, self.size)
[文档]class CutMixBatch(ImageTensorOperation):
"""
Apply CutMix transformation on input batch of images and labels.
Note that you need to make labels into one-hot format and batched before calling this operator.
Args:
image_batch_format (ImageBatchFormat): The method of padding. Can be any of
[ImageBatchFormat.NHWC, ImageBatchFormat.NCHW].
alpha (float, optional): Hyperparameter of beta distribution, must be larger than 0 (default = 1.0).
prob (float, optional): The probability by which CutMix is applied to each image, range: [0, 1] (default = 1.0).
Raises:
TypeError: If `image_batch_format` is not of type :class:`mindspore.dataset.vision.ImageBatchFormat`.
TypeError: If `alpha` is not of type float.
TypeError: If `prob` is not of type float.
ValueError: If `alpha` is less than or equal 0.
ValueError: If `prob` is not in range [0, 1].
RuntimeError: If given tensor shape is not <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.vision import ImageBatchFormat
>>> onehot_op = c_transforms.OneHot(num_classes=10)
>>> image_folder_dataset= image_folder_dataset.map(operations=onehot_op,
... input_columns=["label"])
>>> cutmix_batch_op = c_vision.CutMixBatch(ImageBatchFormat.NHWC, 1.0, 0.5)
>>> image_folder_dataset = image_folder_dataset.batch(5)
>>> image_folder_dataset = image_folder_dataset.map(operations=cutmix_batch_op,
... input_columns=["image", "label"])
"""
@check_cut_mix_batch_c
def __init__(self, image_batch_format, alpha=1.0, prob=1.0):
self.image_batch_format = image_batch_format.value
self.alpha = alpha
self.prob = prob
def parse(self):
return cde.CutMixBatchOperation(DE_C_IMAGE_BATCH_FORMAT[self.image_batch_format], self.alpha, self.prob)
[文档]class CutOut(ImageTensorOperation):
"""
Randomly cut (mask) out a given number of square patches from the input image array.
Args:
length (int): The side length of each square patch, must be larger than 0.
num_patches (int, optional): Number of patches to be cut out of an image, must be larger than 0. (default=1).
Raises:
TypeError: If `length` is not of type int.
TypeError: If `num_patches` is not of type int.
ValueError: If `length` is less than or equal 0.
ValueError: If `num_patches` is less than or equal 0.
RuntimeError: If given tensor shape is not <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> transforms_list = [c_vision.Decode(), c_vision.CutOut(80, num_patches=10)]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_cutout
def __init__(self, length, num_patches=1):
self.length = length
self.num_patches = num_patches
def parse(self):
return cde.CutOutOperation(self.length, self.num_patches)
[文档]class Decode(ImageTensorOperation):
"""
Decode the input image.
Args:
rgb (bool, optional): Mode of decoding input image (default=True).
If True means format of decoded image is RGB else BGR (deprecated).
Raises:
RuntimeError: If `rgb` is False, since this option is deprecated.
RuntimeError: If given tensor is not a 1D sequence.
Supported Platforms:
``CPU``
Examples:
>>> transforms_list = [c_vision.Decode(), c_vision.RandomHorizontalFlip()]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
def __init__(self, rgb=True):
self.rgb = rgb
def __call__(self, img):
"""
Call method.
Args:
img (NumPy): Image to be decoded.
Returns:
img (NumPy), Decoded image.
"""
if isinstance(img, bytes):
img = np.frombuffer(img, np.uint8)
elif not isinstance(img, np.ndarray) or img.ndim != 1 or img.dtype.type is np.str_:
raise TypeError(
"Input should be an encoded image in 1-D NumPy format, got {}.".format(type(img)))
return super().__call__(img)
def parse(self):
return cde.DecodeOperation(self.rgb)
[文档]class Equalize(ImageTensorOperation):
"""
Apply histogram equalization on input image.
Raises:
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> transforms_list = [c_vision.Decode(), c_vision.Equalize()]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
def parse(self):
return cde.EqualizeOperation()
[文档]class GaussianBlur(ImageTensorOperation):
"""
Blur input image with the specified Gaussian kernel.
Args:
kernel_size (Union[int, Sequence[int]]): Size of the Gaussian kernel to use. The value must be positive and odd.
if only an integer is provided, the kernel size will be (kernel_size, kernel_size). If a sequence of integer
is provided, it must be a sequence of 2 values which represents (width, height).
sigma (Union[float, Sequence[float]], optional): Standard deviation of the Gaussian kernel to use
(default=None). The value must be positive. If only a float is provided, the sigma will be (sigma, sigma).
If a sequence of float is provided, it must be a sequence of 2 values which represents (width, height).
If None is provided, the sigma will be calculated as ((kernel_size - 1) * 0.5 - 1) * 0.3 + 0.8.
Raises:
TypeError: If `kernel_size` is not of type int or Sequence[int].
TypeError: If `sigma` is not of type float or Sequence[float].
ValueError: If `kernel_size` is not positive and odd.
ValueError: If `sigma` is not positive.
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> transforms_list = [c_vision.Decode(), c_vision.GaussianBlur(3, 3)]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_gaussian_blur
def __init__(self, kernel_size, sigma=None):
if isinstance(kernel_size, int):
kernel_size = (kernel_size,)
if sigma is None:
sigma = (0,)
elif isinstance(sigma, (int, float)):
sigma = (float(sigma),)
self.kernel_size = kernel_size
self.sigma = sigma
def parse(self):
return cde.GaussianBlurOperation(self.kernel_size, self.sigma)
[文档]class HorizontalFlip(ImageTensorOperation):
"""
Flip the input image horizontally.
Raises:
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> transforms_list = [c_vision.Decode(), c_vision.HorizontalFlip()]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
def parse(self):
return cde.HorizontalFlipOperation()
[文档]class HWC2CHW(ImageTensorOperation):
"""
Transpose the input image from shape <H, W, C> to shape <C, H, W>. The input image should be 3 channels image.
Note:
This operation supports running on Ascend or GPU platforms by Offload.
Raises:
RuntimeError: If given tensor shape is not <H, W, C>.
Supported Platforms:
``CPU`` ``Ascend`` ``GPU``
Examples:
>>> transforms_list = [c_vision.Decode(),
... c_vision.RandomHorizontalFlip(0.75),
... c_vision.RandomCrop(512),
... c_vision.HWC2CHW()]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
def parse(self):
return cde.HwcToChwOperation()
[文档]class Invert(ImageTensorOperation):
"""
Apply invert on input image in RGB mode. This operator will reassign every pixel to (255 - pixel).
Raises:
RuntimeError: If given tensor shape is not <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> transforms_list = [c_vision.Decode(), c_vision.Invert()]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
def parse(self):
return cde.InvertOperation()
[文档]class MixUpBatch(ImageTensorOperation):
"""
Apply MixUp transformation on input batch of images and labels. Each image is
multiplied by a random weight (lambda) and then added to a randomly selected image from the batch
multiplied by (1 - lambda). The same formula is also applied to the one-hot labels.
The lambda is generated based on the specified alpha value. Two coefficients x1, x2 are randomly generated
in the range [alpha, 1], and lambda = (x1 / (x1 + x2)).
Note that you need to make labels into one-hot format and batched before calling this operator.
Args:
alpha (float, optional): Hyperparameter of beta distribution. The value must be positive (default = 1.0).
Raises:
TypeError: If `alpha` is not of type float.
ValueError: If `alpha` is not positive.
RuntimeError: If given tensor shape is not <N, H, W, C> or <N, C, H, W>.
Supported Platforms:
``CPU``
Examples:
>>> onehot_op = c_transforms.OneHot(num_classes=10)
>>> image_folder_dataset= image_folder_dataset.map(operations=onehot_op,
... input_columns=["label"])
>>> mixup_batch_op = c_vision.MixUpBatch(alpha=0.9)
>>> image_folder_dataset = image_folder_dataset.batch(5)
>>> image_folder_dataset = image_folder_dataset.map(operations=mixup_batch_op,
... input_columns=["image", "label"])
"""
@check_mix_up_batch_c
def __init__(self, alpha=1.0):
self.alpha = alpha
def parse(self):
return cde.MixUpBatchOperation(self.alpha)
[文档]class Normalize(ImageTensorOperation):
"""
Normalize the input image with respect to mean and standard deviation. This operator will normalize
the input image with: output[channel] = (input[channel] - mean[channel]) / std[channel], where channel >= 1.
Note:
This operation supports running on Ascend or GPU platforms by Offload.
Args:
mean (sequence): List or tuple of mean values for each channel, with respect to channel order.
The mean values must be in range [0.0, 255.0].
std (sequence): List or tuple of standard deviations for each channel, with respect to channel order.
The standard deviation values must be in range (0.0, 255.0].
Raises:
TypeError: If `mean` is not of type sequence.
TypeError: If `std` is not of type sequence.
ValueError: If `mean` is not in range [0.0, 255.0].
ValueError: If `mean` is not in range (0.0, 255.0].
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU`` ``Ascend`` ``GPU``
Examples:
>>> decode_op = c_vision.Decode()
>>> normalize_op = c_vision.Normalize(mean=[121.0, 115.0, 100.0], std=[70.0, 68.0, 71.0])
>>> transforms_list = [decode_op, normalize_op]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_normalize_c
def __init__(self, mean, std):
self.mean = mean
self.std = std
def parse(self):
return cde.NormalizeOperation(self.mean, self.std)
[文档]class NormalizePad(ImageTensorOperation):
"""
Normalize the input image with respect to mean and standard deviation then pad an extra channel with value zero.
Args:
mean (sequence): List or tuple of mean values for each channel, with respect to channel order.
The mean values must be in range (0.0, 255.0].
std (sequence): List or tuple of standard deviations for each channel, with respect to channel order.
The standard deviation values must be in range (0.0, 255.0].
dtype (str, optional): Set the dtype of the output image (default is "float32").
Raises:
TypeError: If `mean` is not of type sequence.
TypeError: If `std` is not of type sequence.
TypeError: If `dtype` is not of type str.
ValueError: If `mean` is not in range [0.0, 255.0].
ValueError: If `std` is not in range (0.0, 255.0].
RuntimeError: If given tensor shape is not <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> decode_op = c_vision.Decode()
>>> normalize_pad_op = c_vision.NormalizePad(mean=[121.0, 115.0, 100.0],
... std=[70.0, 68.0, 71.0],
... dtype="float32")
>>> transforms_list = [decode_op, normalize_pad_op]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_normalizepad_c
def __init__(self, mean, std, dtype="float32"):
self.mean = mean
self.std = std
self.dtype = dtype
def parse(self):
return cde.NormalizePadOperation(self.mean, self.std, self.dtype)
[文档]class Pad(ImageTensorOperation):
"""
Pad the image.
Args:
padding (Union[int, Sequence[int]]): The number of pixels to pad each border of the image.
If a single number is provided, it pads all borders with this value.
If a tuple or lists of 2 values are provided, it pads the (left and top)
with the first value and (right and bottom) with the second value.
If 4 values are provided as a list or tuple, it pads the left, top, right and bottom respectively.
The pad values must be non-negative.
fill_value (Union[int, tuple[int]], optional): The pixel intensity of the borders, only valid for
padding_mode Border.CONSTANT. If it is a 3-tuple, it is used to fill R, G, B channels respectively.
If it is an integer, it is used for all RGB channels.
The fill_value values must be in range [0, 255] (default=0).
padding_mode (Border, optional): The method of padding (default=Border.CONSTANT). Can be any of
[Border.CONSTANT, Border.EDGE, Border.REFLECT, Border.SYMMETRIC].
- Border.CONSTANT, means it fills the border with constant values.
- Border.EDGE, means it pads with the last value on the edge.
- Border.REFLECT, means it reflects the values on the edge omitting the last
value of edge.
- Border.SYMMETRIC, means it reflects the values on the edge repeating the last
value of edge.
Raises:
TypeError: If `padding` is not of type int or Sequence[int].
TypeError: If `fill_value` is not of type int or tuple[int].
TypeError: If `padding_mode` is not of type :class:`mindspore.dataset.vision.Border`.
ValueError: If `padding` is negative.
ValueError: If `fill_value` is not in range [0, 255].
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> transforms_list = [c_vision.Decode(), c_vision.Pad([100, 100, 100, 100])]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_pad
def __init__(self, padding, fill_value=0, padding_mode=Border.CONSTANT):
padding = parse_padding(padding)
if isinstance(fill_value, int):
fill_value = tuple([fill_value] * 3)
self.padding = padding
self.fill_value = fill_value
self.padding_mode = padding_mode
def parse(self):
return cde.PadOperation(self.padding, self.fill_value, DE_C_BORDER_TYPE[self.padding_mode])
class RandomAdjustSharpness(ImageTensorOperation):
"""
Randomly adjust the sharpness of the input image with a given probability.
Args:
degree (float): Sharpness adjustment degree, which must be non negative.
Degree of 0.0 gives a blurred image, degree of 1.0 gives the original image,
and degree of 2.0 increases the sharpness by a factor of 2.
prob (float, optional): Probability of the image being sharpness adjusted, which
must be in range of [0, 1] (default=0.5).
Raises:
TypeError: If `degree` is not of type float.
TypeError: If `prob` is not of type float.
ValueError: If `degree` is negative.
ValueError: If `prob` is not in range [0, 1].
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> transforms_list = [c_vision.Decode(), c_vision.RandomAdjustSharpness(2.0, 0.5)]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_random_adjust_sharpness
def __init__(self, degree, prob=0.5):
self.prob = prob
self.degree = degree
def parse(self):
return cde.RandomAdjustSharpnessOperation(self.degree, self.prob)
[文档]class RandomAffine(ImageTensorOperation):
"""
Apply Random affine transformation to the input image.
Args:
degrees (Union[int, float, sequence]): Range of the rotation degrees.
If `degrees` is a number, the range will be (-degrees, degrees).
If `degrees` is a sequence, it should be (min, max).
translate (sequence, optional): Sequence (tx_min, tx_max, ty_min, ty_max) of minimum/maximum translation in
x(horizontal) and y(vertical) directions, range [-1.0, 1.0] (default=None).
The horizontal and vertical shift is selected randomly from the range:
(tx_min*width, tx_max*width) and (ty_min*height, ty_max*height), respectively.
If a tuple or list of size 2, then a translate parallel to the X axis in the range of
(translate[0], translate[1]) is applied.
If a tuple or list of size 4, then a translate parallel to the X axis in the range of
(translate[0], translate[1]) and a translate parallel to the Y axis in the range of
(translate[2], translate[3]) are applied.
If None, no translation is applied.
scale (sequence, optional): Scaling factor interval, which must be non negative
(default=None, original scale is used).
shear (Union[int, float, sequence], optional): Range of shear factor, which must be positive (default=None).
If a number, then a shear parallel to the X axis in the range of (-shear, +shear) is applied.
If a tuple or list of size 2, then a shear parallel to the X axis in the range of (shear[0], shear[1])
is applied.
If a tuple or list of size 4, then a shear parallel to X axis in the range of (shear[0], shear[1])
and a shear parallel to Y axis in the range of (shear[2], shear[3]) is applied.
If None, no shear is applied.
resample (Inter, optional): An optional resampling filter (default=Inter.NEAREST).
It can be any of [Inter.BILINEAR, Inter.NEAREST, Inter.BICUBIC].
- Inter.BILINEAR, means resample method is bilinear interpolation.
- Inter.NEAREST, means resample method is nearest-neighbor interpolation.
- Inter.BICUBIC, means resample method is bicubic interpolation.
fill_value (Union[int, tuple[int]], optional): Optional fill_value to fill the area outside the transform
in the output image. There must be three elements in tuple and the value of single element is [0, 255].
(default=0, filling is performed).
Raises:
TypeError: If `degrees` is not of type int, float or sequence.
TypeError: If `translate` is not of type sequence.
TypeError: If `scale` is not of type sequence.
TypeError: If `shear` is not of type int, float or sequence.
TypeError: If `resample` is not of type :class:`mindspore.dataset.vision.Inter`.
TypeError: If `fill_value` is not of type int or tuple[int].
ValueError: If `degrees` is negative.
ValueError: If `translate` is not in range [-1.0, 1.0].
ValueError: If `scale` is negative.
ValueError: If `shear` is not positive.
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.vision import Inter
>>> decode_op = c_vision.Decode()
>>> random_affine_op = c_vision.RandomAffine(degrees=15,
... translate=(-0.1, 0.1, 0, 0),
... scale=(0.9, 1.1),
... resample=Inter.NEAREST)
>>> transforms_list = [decode_op, random_affine_op]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_random_affine
def __init__(self, degrees, translate=None, scale=None, shear=None, resample=Inter.NEAREST, fill_value=0):
# Parameter checking
if shear is not None:
if isinstance(shear, numbers.Number):
shear = (-1 * shear, shear, 0., 0.)
else:
if len(shear) == 2:
shear = [shear[0], shear[1], 0., 0.]
elif len(shear) == 4:
shear = [s for s in shear]
if isinstance(degrees, numbers.Number):
degrees = (-1 * degrees, degrees)
if isinstance(fill_value, numbers.Number):
fill_value = (fill_value, fill_value, fill_value)
# translation
if translate is None:
translate = (0.0, 0.0, 0.0, 0.0)
# scale
if scale is None:
scale = (1.0, 1.0)
# shear
if shear is None:
shear = (0.0, 0.0, 0.0, 0.0)
self.degrees = degrees
self.translate = translate
self.scale_ = scale
self.shear = shear
self.resample = DE_C_INTER_MODE[resample]
self.fill_value = fill_value
def parse(self):
return cde.RandomAffineOperation(self.degrees, self.translate, self.scale_, self.shear, self.resample,
self.fill_value)
class RandomAutoContrast(ImageTensorOperation):
"""
Automatically adjust the contrast of the image with a given probability.
Args:
cutoff (float, optional): Percent of the lightest and darkest pixels to be cut off from
the histogram of the input image. The value must be in range of [0.0, 50.0) (default=0.0).
ignore (Union[int, sequence], optional): The background pixel values to be ignored, each of
which must be in range of [0, 255] (default=None).
prob (float, optional): Probability of the image being automatically contrasted, which
must be in range of [0, 1] (default=0.5).
Raises:
TypeError: If `cutoff` is not of type float.
TypeError: If `ignore` is not of type int or sequence of int.
TypeError: If `prob` is not of type float.
ValueError: If `cutoff` is not in range [0.0, 50.0).
ValueError: If `ignore` is not in range [0, 255].
ValueError: If `prob` is not in range [0, 1].
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> transforms_list = [c_vision.Decode(), c_vision.RandomAutoContrast(cutoff=0.0, ignore=None, prob=0.5)]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_random_auto_contrast
def __init__(self, cutoff=0.0, ignore=None, prob=0.5):
if ignore is None:
ignore = []
if isinstance(ignore, int):
ignore = [ignore]
self.cutoff = cutoff
self.ignore = ignore
self.prob = prob
def parse(self):
return cde.RandomAutoContrastOperation(self.cutoff, self.ignore, self.prob)
[文档]class RandomColor(ImageTensorOperation):
"""
Adjust the color of the input image by a fixed or random degree.
This operation works only with 3-channel RGB images.
Args:
degrees (Sequence[float], optional): Range of random color adjustment degrees, which must be non-negative.
It should be in (min, max) format. If min=max, then it is a
single fixed magnitude operation (default=(0.1, 1.9)).
Raises:
TypeError: If `degrees` is not of type Sequence[float].
ValueError: If `degrees` is negative.
RuntimeError: If given tensor shape is not <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> transforms_list = [c_vision.Decode(), c_vision.RandomColor((0.5, 2.0))]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_positive_degrees
def __init__(self, degrees=(0.1, 1.9)):
self.degrees = degrees
def parse(self):
return cde.RandomColorOperation(*self.degrees)
[文档]class RandomColorAdjust(ImageTensorOperation):
"""
Randomly adjust the brightness, contrast, saturation, and hue of the input image.
Note:
This operation supports running on Ascend or GPU platforms by Offload.
Args:
brightness (Union[float, Sequence[float]], optional): Brightness adjustment factor (default=(1, 1)).
Cannot be negative.
If it is a float, the factor is uniformly chosen from the range [max(0, 1-brightness), 1+brightness].
If it is a sequence, it should be [min, max] for the range.
contrast (Union[float, Sequence[float]], optional): Contrast adjustment factor (default=(1, 1)).
Cannot be negative.
If it is a float, the factor is uniformly chosen from the range [max(0, 1-contrast), 1+contrast].
If it is a sequence, it should be [min, max] for the range.
saturation (Union[float, Sequence[float]], optional): Saturation adjustment factor (default=(1, 1)).
Cannot be negative.
If it is a float, the factor is uniformly chosen from the range [max(0, 1-saturation), 1+saturation].
If it is a sequence, it should be [min, max] for the range.
hue (Union[float, Sequence[float]], optional): Hue adjustment factor (default=(0, 0)).
If it is a float, the range will be [-hue, hue]. Value should be 0 <= hue <= 0.5.
If it is a sequence, it should be [min, max] where -0.5 <= min <= max <= 0.5.
Raises:
TypeError: If `brightness` is not of type float or Sequence[float].
TypeError: If `contrast` is not of type float or Sequence[float].
TypeError: If `saturation` is not of type float or Sequence[float].
TypeError: If `hue` is not of type float or Sequence[float].
ValueError: If `brightness` is negative.
ValueError: If `contrast` is negative.
ValueError: If `saturation` is negative.
ValueError: If `hue` is not in range [-0.5, 0.5].
RuntimeError: If given tensor shape is not <H, W, C>.
Supported Platforms:
``CPU`` ``Ascend`` ``GPU``
Examples:
>>> decode_op = c_vision.Decode()
>>> transform_op = c_vision.RandomColorAdjust(brightness=(0.5, 1),
... contrast=(0.4, 1),
... saturation=(0.3, 1))
>>> transforms_list = [decode_op, transform_op]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_random_color_adjust
def __init__(self, brightness=(1, 1), contrast=(1, 1), saturation=(1, 1), hue=(0, 0)):
brightness = self.__expand_values(brightness)
contrast = self.__expand_values(contrast)
saturation = self.__expand_values(saturation)
hue = self.__expand_values(
hue, center=0, bound=(-0.5, 0.5), non_negative=False)
self.brightness = brightness
self.contrast = contrast
self.saturation = saturation
self.hue = hue
def __expand_values(self, value, center=1, bound=(0, FLOAT_MAX_INTEGER), non_negative=True):
"""Expand input value for vision adjustment factor."""
if isinstance(value, numbers.Number):
value = [center - value, center + value]
if non_negative:
value[0] = max(0, value[0])
check_range(value, bound)
return (value[0], value[1])
def parse(self):
return cde.RandomColorAdjustOperation(self.brightness, self.contrast, self.saturation, self.hue)
[文档]class RandomCrop(ImageTensorOperation):
"""
Crop the input image at a random location. If input image size is smaller than output size,
input image will be padded before cropping.
Note:
If the input image is more than one, then make sure that the image size is the same.
Args:
size (Union[int, Sequence[int]]): The output size of the cropped image. The size value(s) must be positive.
If size is an integer, a square crop of size (size, size) is returned.
If size is a sequence of length 2, an image of size (height, width) will be cropped.
padding (Union[int, Sequence[int]], optional): The number of pixels to pad each border of the image.
The padding value(s) must be non-nagetive (default=None).
If padding is not None, pad image first with padding values.
If a single number is provided, pad all borders with this value.
If a tuple or lists of 2 values are provided, pad the (left and top)
with the first value and (right and bottom) with the second value.
If 4 values are provided as a list or tuple,
pad the left, top, right and bottom respectively.
pad_if_needed (bool, optional): Pad the image if either side is smaller than
the given output size (default=False).
fill_value (Union[int, tuple[int]], optional): The pixel intensity of the borders, only valid for
padding_mode Border.CONSTANT. If it is a 3-tuple, it is used to fill R, G, B channels respectively.
If it is an integer, it is used for all RGB channels.
The fill_value values must be in range [0, 255] (default=0).
padding_mode (Border, optional): The method of padding (default=Border.CONSTANT). It can be any of
[Border.CONSTANT, Border.EDGE, Border.REFLECT, Border.SYMMETRIC].
- Border.CONSTANT, means it fills the border with constant values.
- Border.EDGE, means it pads with the last value on the edge.
- Border.REFLECT, means it reflects the values on the edge omitting the last
value of edge.
- Border.SYMMETRIC, means it reflects the values on the edge repeating the last
value of edge.
Raises:
TypeError: If `size` is not of type int or Sequence[int].
TypeError: If `padding` is not of type int or Sequence[int].
TypeError: If `pad_if_needed` is not of type boolean.
TypeError: If `fill_value` is not of type int or tuple[int].
TypeError: If `padding_mode` is not of type :class:`mindspore.dataset.vision.Border`.
ValueError: If `size` is not positive.
ValueError: If `padding` is negative.
ValueError: If `fill_value` is not in range [0, 255].
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.vision import Border
>>> decode_op = c_vision.Decode()
>>> random_crop_op = c_vision.RandomCrop(512, [200, 200, 200, 200], padding_mode=Border.EDGE)
>>> transforms_list = [decode_op, random_crop_op]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_random_crop
def __init__(self, size, padding=None, pad_if_needed=False, fill_value=0, padding_mode=Border.CONSTANT):
if isinstance(size, int):
size = (size, size)
if padding is None:
padding = (0, 0, 0, 0)
else:
padding = parse_padding(padding)
if isinstance(fill_value, int):
fill_value = tuple([fill_value] * 3)
self.size = size
self.padding = padding
self.pad_if_needed = pad_if_needed
self.fill_value = fill_value
self.padding_mode = padding_mode.value
def parse(self):
border_type = DE_C_BORDER_TYPE[self.padding_mode]
return cde.RandomCropOperation(self.size, self.padding, self.pad_if_needed, self.fill_value, border_type)
[文档]class RandomCropDecodeResize(ImageTensorOperation):
"""
A combination of `Crop`, `Decode` and `Resize`. It will get better performance for JPEG images. This operator
will crop the input image at a random location, decode the cropped image in RGB mode, and resize the decoded image.
Args:
size (Union[int, Sequence[int]]): The output size of the resized image. The size value(s) must be positive.
If size is an integer, a square crop of size (size, size) is returned.
If size is a sequence of length 2, an image of size (height, width) will be cropped.
scale (Union[list, tuple], optional): Range [min, max) of respective size of the
original size to be cropped, which must be non-negative (default=(0.08, 1.0)).
ratio (Union[list, tuple], optional): Range [min, max) of aspect ratio to be
cropped, which must be non-negative (default=(3. / 4., 4. / 3.)).
interpolation (Inter, optional): Image interpolation mode for resize operator(default=Inter.BILINEAR).
It can be any of [Inter.BILINEAR, Inter.NEAREST, Inter.BICUBIC, Inter.AREA, Inter.PILCUBIC].
- Inter.BILINEAR, means interpolation method is bilinear interpolation.
- Inter.NEAREST, means interpolation method is nearest-neighbor interpolation.
- Inter.BICUBIC, means interpolation method is bicubic interpolation.
- Inter.AREA, means interpolation method is pixel area interpolation.
- Inter.PILCUBIC, means interpolation method is bicubic interpolation like implemented in pillow, input
should be in 3 channels format.
max_attempts (int, optional): The maximum number of attempts to propose a valid crop_area (default=10).
If exceeded, fall back to use center_crop instead. The max_attempts value must be positive.
Raises:
TypeError: If `size` is not of type int or Sequence[int].
TypeError: If `scale` is not of type tuple or list.
TypeError: If `ratio` is not of type tuple or list.
TypeError: If `interpolation` is not of type :class:`mindspore.dataset.vision.Inter`.
TypeError: If `max_attempts` is not of type int.
ValueError: If `size` is not positive.
ValueError: If `scale` is negative.
ValueError: If `ratio` is negative.
ValueError: If `max_attempts` is not positive.
RuntimeError: If given tensor is not a 1D sequence.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.vision import Inter
>>> resize_crop_decode_op = c_vision.RandomCropDecodeResize(size=(50, 75),
... scale=(0.25, 0.5),
... interpolation=Inter.NEAREST,
... max_attempts=5)
>>> transforms_list = [resize_crop_decode_op]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_random_resize_crop
def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.),
interpolation=Inter.BILINEAR, max_attempts=10):
if isinstance(size, int):
size = (size, size)
self.size = size
self.scale = scale
self.ratio = ratio
self.interpolation = interpolation
self.max_attempts = max_attempts
def parse(self):
return cde.RandomCropDecodeResizeOperation(self.size, self.scale, self.ratio,
DE_C_INTER_MODE[self.interpolation],
self.max_attempts)
def __call__(self, img):
if not isinstance(img, np.ndarray):
raise TypeError(
"Input should be an encoded image in 1-D NumPy format, got {}.".format(type(img)))
if img.ndim != 1 or img.dtype.type is not np.uint8:
raise TypeError("Input should be an encoded image with uint8 type in 1-D NumPy format, " +
"got format:{}, dtype:{}.".format(type(img), img.dtype.type))
return super().__call__(img)
[文档]class RandomCropWithBBox(ImageTensorOperation):
"""
Crop the input image at a random location and adjust bounding boxes accordingly.
Args:
size (Union[int, Sequence[int]]): The output size of the cropped image. The size value(s) must be positive.
If size is an integer, a square crop of size (size, size) is returned.
If size is a sequence of length 2, an image of size (height, width) will be cropped.
padding (Union[int, Sequence[int]], optional): The number of pixels to pad the image
The padding value(s) must be non-nagetive (default=None).
If padding is not None, first pad image with padding values.
If a single number is provided, pad all borders with this value.
If a tuple or lists of 2 values are provided, pad the (left and top)
with the first value and (right and bottom) with the second value.
If 4 values are provided as a list or tuple, pad the left, top, right and bottom respectively.
pad_if_needed (bool, optional): Pad the image if either side is smaller than
the given output size (default=False).
fill_value (Union[int, tuple[int]], optional): The pixel intensity of the borders, only valid for
padding_mode Border.CONSTANT. If it is a 3-tuple, it is used to fill R, G, B channels respectively.
If it is an integer, it is used for all RGB channels.
The fill_value values must be in range [0, 255] (default=0).
padding_mode (Border, optional): The method of padding (default=Border.CONSTANT). It can be any of
[Border.CONSTANT, Border.EDGE, Border.REFLECT, Border.SYMMETRIC].
- Border.CONSTANT, means it fills the border with constant values.
- Border.EDGE, means it pads with the last value on the edge.
- Border.REFLECT, means it reflects the values on the edge omitting the last
value of edge.
- Border.SYMMETRIC, means it reflects the values on the edge repeating the last
value of edge.
Raises:
TypeError: If `size` is not of type int or Sequence[int].
TypeError: If `padding` is not of type int or Sequence[int].
TypeError: If `pad_if_needed` is not of type boolean.
TypeError: If `fill_value` is not of type int or tuple[int].
TypeError: If `padding_mode` is not of type :class:`mindspore.dataset.vision.Border`.
ValueError: If `size` is not positive.
ValueError: If `padding` is negative.
ValueError: If `fill_value` is not in range [0, 255].
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> decode_op = c_vision.Decode()
>>> random_crop_with_bbox_op = c_vision.RandomCropWithBBox([512, 512], [200, 200, 200, 200])
>>> transforms_list = [decode_op, random_crop_with_bbox_op]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_random_crop
def __init__(self, size, padding=None, pad_if_needed=False, fill_value=0, padding_mode=Border.CONSTANT):
if isinstance(size, int):
size = (size, size)
if padding is None:
padding = (0, 0, 0, 0)
else:
padding = parse_padding(padding)
if isinstance(fill_value, int):
fill_value = tuple([fill_value] * 3)
self.size = size
self.padding = padding
self.pad_if_needed = pad_if_needed
self.fill_value = fill_value
self.padding_mode = padding_mode.value
def parse(self):
border_type = DE_C_BORDER_TYPE[self.padding_mode]
return cde.RandomCropWithBBoxOperation(self.size, self.padding, self.pad_if_needed, self.fill_value,
border_type)
class RandomEqualize(ImageTensorOperation):
"""
Apply histogram equalization on the input image with a given probability.
Args:
prob (float, optional): Probability of the image being equalized, which
must be in range of [0, 1] (default=0.5).
Raises:
TypeError: If `prob` is not of type float.
ValueError: If `prob` is not in range [0, 1].
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> transforms_list = [c_vision.Decode(), c_vision.RandomEqualize(0.5)]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_prob
def __init__(self, prob=0.5):
self.prob = prob
def parse(self):
return cde.RandomEqualizeOperation(self.prob)
[文档]class RandomHorizontalFlip(ImageTensorOperation):
"""
Randomly flip the input image horizontally with a given probability.
Note:
This operation supports running on Ascend or GPU platforms by Offload.
Args:
prob (float, optional): Probability of the image being flipped, which must be in range of [0, 1] (default=0.5).
Raises:
TypeError: If `prob` is not of type float.
ValueError: If `prob` is not in range [0, 1].
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU`` ``Ascend`` ``GPU``
Examples:
>>> transforms_list = [c_vision.Decode(), c_vision.RandomHorizontalFlip(0.75)]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_prob
def __init__(self, prob=0.5):
self.prob = prob
def parse(self):
return cde.RandomHorizontalFlipOperation(self.prob)
[文档]class RandomHorizontalFlipWithBBox(ImageTensorOperation):
"""
Flip the input image horizontally randomly with a given probability and adjust bounding boxes accordingly.
Args:
prob (float, optional): Probability of the image being flipped, which must be in range of [0, 1] (default=0.5).
Raises:
TypeError: If `prob` is not of type float.
ValueError: If `prob` is not in range [0, 1].
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> transforms_list = [c_vision.Decode(), c_vision.RandomHorizontalFlipWithBBox(0.70)]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_prob
def __init__(self, prob=0.5):
self.prob = prob
def parse(self):
return cde.RandomHorizontalFlipWithBBoxOperation(self.prob)
class RandomInvert(ImageTensorOperation):
"""
Randomly invert the colors of image with a given probability.
Args:
prob (float, optional): Probability of the image being inverted, which must be in range of [0, 1] (default=0.5).
Raises:
TypeError: If `prob` is not of type float.
ValueError: If `prob` is not in range [0, 1].
RuntimeError: If given tensor shape is not <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> transforms_list = [c_vision.Decode(), c_vision.RandomInvert(0.5)]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_prob
def __init__(self, prob=0.5):
self.prob = prob
def parse(self):
return cde.RandomInvertOperation(self.prob)
class RandomLighting(ImageTensorOperation):
"""
Add AlexNet-style PCA-based noise to an image. The eigenvalue and eigenvectors for Alexnet's PCA noise is
calculated from the imagenet dataset.
Args:
alpha (float, optional): Intensity of the image, which must be non-negative (default=0.05).
Raises:
TypeError: If `alpha` is not of type float.
ValueError: If `alpha` is negative.
RuntimeError: If given tensor shape is not <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> transforms_list = [c_vision.Decode(), c_vision.RandomLighting(0.1)]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_alpha
def __init__(self, alpha=0.05):
self.alpha = alpha
def parse(self):
return cde.RandomLightingOperation(self.alpha)
[文档]class RandomPosterize(ImageTensorOperation):
"""
Reduce the number of bits for each color channel to posterize the input image randomly with a given probability.
Args:
bits (Union[int, Sequence[int]], optional): Range of random posterize to compress image.
Bits values must be in range of [1,8], and include at
least one integer value in the given range. It must be in
(min, max) or integer format. If min=max, then it is a single fixed
magnitude operation (default=(8, 8)).
Raises:
TypeError: If `bits` is not of type int or sequence of int.
ValueError: If `bits` is not in range [1, 8].
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> transforms_list = [c_vision.Decode(), c_vision.RandomPosterize((6, 8))]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_posterize
def __init__(self, bits=(8, 8)):
self.bits = bits
def parse(self):
bits = self.bits
if isinstance(bits, int):
bits = (bits, bits)
return cde.RandomPosterizeOperation(bits)
[文档]class RandomResizedCrop(ImageTensorOperation):
"""
This operator will crop the input image randomly, and resize the cropped image using a selected interpolation mode.
Note:
If the input image is more than one, then make sure that the image size is the same.
Args:
size (Union[int, Sequence[int]]): The output size of the resized image. The size value(s) must be positive.
If size is an integer, a square of size (size, size) will be cropped with this value.
If size is a sequence of length 2, an image of size (height, width) will be cropped.
scale (Union[list, tuple], optional): Range [min, max) of respective size of the original
size to be cropped, which must be non-negative (default=(0.08, 1.0)).
ratio (Union[list, tuple], optional): Range [min, max) of aspect ratio to be
cropped, which must be non-negative (default=(3. / 4., 4. / 3.)).
interpolation (Inter, optional): Method of interpolation (default=Inter.BILINEAR).
It can be any of [Inter.BILINEAR, Inter.NEAREST, Inter.BICUBIC, Inter.AREA, Inter.PILCUBIC].
- Inter.BILINEAR, means interpolation method is bilinear interpolation.
- Inter.NEAREST, means interpolation method is nearest-neighbor interpolation.
- Inter.BICUBIC, means interpolation method is bicubic interpolation.
- Inter.AREA, means interpolation method is pixel area interpolation.
- Inter.PILCUBIC, means interpolation method is bicubic interpolation like implemented in pillow, input
should be in 3 channels format.
max_attempts (int, optional): The maximum number of attempts to propose a valid
crop_area (default=10). If exceeded, fall back to use center_crop instead.
Raises:
TypeError: If `size` is not of type int or Sequence[int].
TypeError: If `scale` is not of type tuple or list.
TypeError: If `ratio` is not of type tuple or list.
TypeError: If `interpolation` is not of type :class:`mindspore.dataset.vision.Inter`.
TypeError: If `max_attempts` is not of type int.
ValueError: If `size` is not positive.
ValueError: If `scale` is negative.
ValueError: If `ratio` is negative.
ValueError: If `max_attempts` is not positive.
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.vision import Inter
>>> decode_op = c_vision.Decode()
>>> resize_crop_op = c_vision.RandomResizedCrop(size=(50, 75), scale=(0.25, 0.5),
... interpolation=Inter.BILINEAR)
>>> transforms_list = [decode_op, resize_crop_op]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_random_resize_crop
def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.),
interpolation=Inter.BILINEAR, max_attempts=10):
if isinstance(size, int):
size = (size, size)
self.size = size
self.scale = scale
self.ratio = ratio
self.interpolation = interpolation
self.max_attempts = max_attempts
def parse(self):
return cde.RandomResizedCropOperation(self.size, self.scale, self.ratio, DE_C_INTER_MODE[self.interpolation],
self.max_attempts)
[文档]class RandomResizedCropWithBBox(ImageTensorOperation):
"""
Crop the input image to a random size and aspect ratio and adjust bounding boxes accordingly.
Args:
size (Union[int, Sequence[int]]): The size of the output image. The size value(s) must be positive.
If size is an integer, a square of size (size, size) will be cropped with this value.
If size is a sequence of length 2, an image of size (height, width) will be cropped.
scale (Union[list, tuple], optional): Range (min, max) of respective size of the original
size to be cropped, which must be non-negative (default=(0.08, 1.0)).
ratio (Union[list, tuple], optional): Range (min, max) of aspect ratio to be
cropped, which must be non-negative (default=(3. / 4., 4. / 3.)).
interpolation (Inter mode, optional): Method of interpolation (default=Inter.BILINEAR).
It can be any of [Inter.BILINEAR, Inter.NEAREST, Inter.BICUBIC] .
- Inter.BILINEAR, means interpolation method is bilinear interpolation.
- Inter.NEAREST, means interpolation method is nearest-neighbor interpolation.
- Inter.BICUBIC, means interpolation method is bicubic interpolation.
max_attempts (int, optional): The maximum number of attempts to propose a valid
crop area (default=10). If exceeded, fall back to use center crop instead.
Raises:
TypeError: If `size` is not of type int or Sequence[int].
TypeError: If `scale` is not of type tuple or list.
TypeError: If `ratio` is not of type tuple or list.
TypeError: If `interpolation` is not of type :class:`mindspore.dataset.vision.Inter`.
TypeError: If `max_attempts` is not of type int.
ValueError: If `size` is not positive.
ValueError: If `scale` is negative.
ValueError: If `ratio` is negative.
ValueError: If `max_attempts` is not positive.
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.vision import Inter
>>> decode_op = c_vision.Decode()
>>> bbox_op = c_vision.RandomResizedCropWithBBox(size=50, interpolation=Inter.NEAREST)
>>> transforms_list = [decode_op, bbox_op]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_random_resize_crop
def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.),
interpolation=Inter.BILINEAR, max_attempts=10):
if isinstance(size, int):
size = (size, size)
self.size = size
self.scale = scale
self.ratio = ratio
self.interpolation = interpolation
self.max_attempts = max_attempts
def parse(self):
return cde.RandomResizedCropWithBBoxOperation(self.size, self.scale, self.ratio,
DE_C_INTER_MODE[self.interpolation], self.max_attempts)
[文档]class RandomResize(ImageTensorOperation):
"""
Resize the input image using a randomly selected interpolation mode.
Args:
size (Union[int, Sequence[int]]): The output size of the resized image. The size value(s) must be positive.
If size is an integer, a square of size (size, size) will be cropped with this value.
If size is a sequence of length 2, an image of size (height, width) will be cropped.
Raises:
TypeError: If `size` is not of type int or Sequence[int].
ValueError: If `size` is not positive.
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> # randomly resize image, keeping aspect ratio
>>> transforms_list1 = [c_vision.Decode(), c_vision.RandomResize(50)]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list1,
... input_columns=["image"])
>>> # randomly resize image to landscape style
>>> transforms_list2 = [c_vision.Decode(), c_vision.RandomResize((40, 60))]
>>> image_folder_dataset_1 = image_folder_dataset_1.map(operations=transforms_list2,
... input_columns=["image"])
"""
@check_resize
def __init__(self, size):
self.size = size
def parse(self):
size = self.size
if isinstance(size, int):
size = (size,)
return cde.RandomResizeOperation(size)
[文档]class RandomResizeWithBBox(ImageTensorOperation):
"""
Tensor operation to resize the input image using a randomly selected interpolation mode and adjust
bounding boxes accordingly.
Args:
size (Union[int, Sequence[int]]): The output size of the resized image. The size value(s) must be positive.
If size is an integer, a square of size (size, size) will be cropped with this value.
If size is a sequence of length 2, an image of size (height, width) will be cropped.
Raises:
TypeError: If `size` is not of type int or Sequence[int].
ValueError: If `size` is not positive.
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> # randomly resize image with bounding boxes, keeping aspect ratio
>>> transforms_list1 = [c_vision.Decode(), c_vision.RandomResizeWithBBox(60)]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list1,
... input_columns=["image"])
>>> # randomly resize image with bounding boxes to portrait style
>>> transforms_list2 = [c_vision.Decode(), c_vision.RandomResizeWithBBox((80, 60))]
>>> image_folder_dataset_1 = image_folder_dataset_1.map(operations=transforms_list2,
... input_columns=["image"])
"""
@check_resize
def __init__(self, size):
self.size = size
def parse(self):
size = self.size
if isinstance(size, int):
size = (size,)
return cde.RandomResizeWithBBoxOperation(size)
[文档]class RandomRotation(ImageTensorOperation):
"""
Rotate the input image randomly within a specified range of degrees.
Args:
degrees (Union[int, float, sequence]): Range of random rotation degrees.
If `degrees` is a number, the range will be converted to (-degrees, degrees).
If `degrees` is a sequence, it should be (min, max).
resample (Inter, optional): An optional resampling filter (default=Inter.NEAREST).
It can be any of [Inter.BILINEAR, Inter.NEAREST, Inter.BICUBIC].
- Inter.BILINEAR, means resample method is bilinear interpolation.
- Inter.NEAREST, means resample method is nearest-neighbor interpolation.
- Inter.BICUBIC, means resample method is bicubic interpolation.
expand (bool, optional): Optional expansion flag (default=False). If set to True, expand the output
image to make it large enough to hold the entire rotated image.
If set to False or omitted, make the output image the same size as the input.
Note that the expand flag assumes rotation around the center and no translation.
center (tuple, optional): Optional center of rotation (a 2-tuple) (default=None).
Origin is the top left corner. None sets to the center of the image.
fill_value (Union[int, tuple[int]], optional): Optional fill color for the area outside the rotated image.
If it is a 3-tuple, it is used to fill R, G, B channels respectively.
If it is an integer, it is used for all RGB channels.
The fill_value values must be in range [0, 255] (default=0).
Raises:
TypeError: If `degrees` is not of type int, float or sequence.
TypeError: If `resample` is not of type :class:`mindspore.dataset.vision.Inter`.
TypeError: If `expand` is not of type boolean.
TypeError: If `center` is not of type tuple.
TypeError: If `fill_value` is not of type int or tuple[int].
ValueError: If `fill_value` is not in range [0, 255].
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.vision import Inter
>>> transforms_list = [c_vision.Decode(),
... c_vision.RandomRotation(degrees=5.0,
... resample=Inter.NEAREST,
... expand=True)]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_random_rotation
def __init__(self, degrees, resample=Inter.NEAREST, expand=False, center=None, fill_value=0):
if isinstance(degrees, (int, float)):
degrees = degrees % 360
degrees = [-degrees, degrees]
elif isinstance(degrees, (list, tuple)):
if degrees[1] - degrees[0] >= 360:
degrees = [-180, 180]
else:
degrees = [degrees[0] % 360, degrees[1] % 360]
if degrees[0] > degrees[1]:
degrees[1] += 360
if center is None:
center = ()
if isinstance(fill_value, int):
fill_value = tuple([fill_value] * 3)
self.degrees = degrees
self.resample = resample
self.expand = expand
self.center = center
self.fill_value = fill_value
def parse(self):
return cde.RandomRotationOperation(self.degrees, DE_C_INTER_MODE[self.resample], self.expand, self.center,
self.fill_value)
[文档]class RandomSelectSubpolicy(ImageTensorOperation):
"""
Choose a random sub-policy from a policy list to be applied on the input image.
Args:
policy (list[list[tuple[TensorOperation, float]]]): List of sub-policies to choose from.
A sub-policy is a list of tuple[operation, prob], where operation is a data processing operation and prob
is the probability that this operation will be applied, and the prob values must be in range [0, 1].
Once a sub-policy is selected, each operation within the sub-policy with be applied in sequence according
to its probability.
Raises:
TypeError: If `policy` contains invalid data processing operations.
Supported Platforms:
``CPU``
Examples:
>>> policy = [[(c_vision.RandomRotation((45, 45)), 0.5),
... (c_vision.RandomVerticalFlip(), 1),
... (c_vision.RandomColorAdjust(), 0.8)],
... [(c_vision.RandomRotation((90, 90)), 1),
... (c_vision.RandomColorAdjust(), 0.2)]]
>>> image_folder_dataset = image_folder_dataset.map(operations=c_vision.RandomSelectSubpolicy(policy),
... input_columns=["image"])
"""
@check_random_select_subpolicy_op
def __init__(self, policy):
self.policy = policy
def parse(self):
policy = []
for list_one in self.policy:
policy_one = []
for list_two in list_one:
if list_two[0] and getattr(list_two[0], 'parse', None):
policy_one.append((list_two[0].parse(), list_two[1]))
else:
policy_one.append((list_two[0], list_two[1]))
policy.append(policy_one)
return cde.RandomSelectSubpolicyOperation(policy)
[文档]class RandomSharpness(ImageTensorOperation):
"""
Adjust the sharpness of the input image by a fixed or random degree. Degree of 0.0 gives a blurred image,
degree of 1.0 gives the original image, and degree of 2.0 gives a sharpened image.
Note:
This operation supports running on Ascend or GPU platforms by Offload.
Args:
degrees (Union[list, tuple], optional): Range of random sharpness adjustment degrees,
which must be non-negative. It should be in (min, max) format. If min=max, then
it is a single fixed magnitude operation (default = (0.1, 1.9)).
Raises:
TypeError : If `degrees` is not of type list or tuple.
ValueError: If `degrees` is negative.
ValueError: If `degrees` is in (max, min) format instead of (min, max).
Supported Platforms:
``CPU`` ``Ascend`` ``GPU``
Examples:
>>> transforms_list = [c_vision.Decode(), c_vision.RandomSharpness(degrees=(0.2, 1.9))]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_positive_degrees
def __init__(self, degrees=(0.1, 1.9)):
self.degrees = degrees
def parse(self):
return cde.RandomSharpnessOperation(self.degrees)
[文档]class RandomSolarize(ImageTensorOperation):
"""
Randomly selects a subrange within the specified threshold range and sets the pixel value within
the subrange to (255 - pixel).
Args:
threshold (tuple, optional): Range of random solarize threshold (default=(0, 255)).
Threshold values should always be in (min, max) format,
where min and max are integers in the range [0, 255], and min <= max.
If min=max, then invert all pixel values above min(max).
Raises:
TypeError : If `threshold` is not of type tuple.
ValueError: If `threshold` is not in range of [0, 255].
Supported Platforms:
``CPU``
Examples:
>>> transforms_list = [c_vision.Decode(), c_vision.RandomSolarize(threshold=(10,100))]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_random_solarize
def __init__(self, threshold=(0, 255)):
self.threshold = threshold
def parse(self):
return cde.RandomSolarizeOperation(self.threshold)
[文档]class RandomVerticalFlip(ImageTensorOperation):
"""
Randomly flip the input image vertically with a given probability.
Note:
This operation supports running on Ascend or GPU platforms by Offload.
Args:
prob (float, optional): Probability of the image being flipped (default=0.5).
Raises:
TypeError: If `prob` is not of type float.
ValueError: If `prob` is not in range [0, 1].
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU`` ``Ascend`` ``GPU``
Examples:
>>> transforms_list = [c_vision.Decode(), c_vision.RandomVerticalFlip(0.25)]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_prob
def __init__(self, prob=0.5):
self.prob = prob
def parse(self):
return cde.RandomVerticalFlipOperation(self.prob)
[文档]class RandomVerticalFlipWithBBox(ImageTensorOperation):
"""
Flip the input image vertically, randomly with a given probability and adjust bounding boxes accordingly.
Args:
prob (float, optional): Probability of the image being flipped (default=0.5).
Raises:
TypeError: If `prob` is not of type float.
ValueError: If `prob` is not in range [0, 1].
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> transforms_list = [c_vision.Decode(), c_vision.RandomVerticalFlipWithBBox(0.20)]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_prob
def __init__(self, prob=0.5):
self.prob = prob
def parse(self):
return cde.RandomVerticalFlipWithBBoxOperation(self.prob)
[文档]class Rescale(ImageTensorOperation):
"""
Rescale the input image with the given rescale and shift. This operator will rescale the input image
with: output = image * rescale + shift.
Note:
This operation supports running on Ascend or GPU platforms by Offload.
Args:
rescale (float): Rescale factor.
shift (float): Shift factor.
Raises:
TypeError: If `rescale` is not of type float.
TypeError: If `shift` is not of type float.
Supported Platforms:
``CPU`` ``Ascend`` ``GPU``
Examples:
>>> transforms_list = [c_vision.Decode(), c_vision.Rescale(1.0 / 255.0, -1.0)]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_rescale
def __init__(self, rescale, shift):
self.rescale = rescale
self.shift = shift
def parse(self):
return cde.RescaleOperation(self.rescale, self.shift)
[文档]class Resize(ImageTensorOperation):
"""
Resize the input image to the given size with a given interpolation mode.
Args:
size (Union[int, Sequence[int]]): The output size of the resized image. The size value(s) must be positive.
If size is an integer, the smaller edge of the image will be resized to this value with
the same image aspect ratio.
If size is a sequence of length 2, it should be (height, width).
interpolation (Inter, optional): Image interpolation mode (default=Inter.LINEAR).
It can be any of [Inter.LINEAR, Inter.NEAREST, Inter.BICUBIC, Inter.AREA, Inter.PILCUBIC].
- Inter.LINEAR, means interpolation method is bilinear interpolation.
- Inter.NEAREST, means interpolation method is nearest-neighbor interpolation.
- Inter.BICUBIC, means interpolation method is bicubic interpolation.
- Inter.AREA, means interpolation method is pixel area interpolation.
- Inter.PILCUBIC, means interpolation method is bicubic interpolation like implemented in pillow, input
should be in 3 channels format.
Raises:
TypeError: If `size` is not of type int or Sequence[int].
TypeError: If `interpolation` is not of type :class:`mindspore.dataset.vision.Inter`.
ValueError: If `size` is not positive.
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.vision import Inter
>>> decode_op = c_vision.Decode()
>>> resize_op = c_vision.Resize([100, 75], Inter.BICUBIC)
>>> transforms_list = [decode_op, resize_op]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_resize_interpolation
def __init__(self, size, interpolation=Inter.LINEAR):
if isinstance(size, int):
size = (size,)
self.size = size
self.interpolation = interpolation
def parse(self):
return cde.ResizeOperation(self.size, DE_C_INTER_MODE[self.interpolation])
[文档]class ResizeWithBBox(ImageTensorOperation):
"""
Resize the input image to the given size and adjust bounding boxes accordingly.
Args:
size (Union[int, Sequence[int]]): The output size of the resized image.
If size is an integer, smaller edge of the image will be resized to this value with
the same image aspect ratio.
If size is a sequence of length 2, it should be (height, width).
interpolation (Inter, optional): Image interpolation mode (default=Inter.LINEAR).
It can be any of [Inter.LINEAR, Inter.NEAREST, Inter.BICUBIC].
- Inter.LINEAR, means interpolation method is bilinear interpolation.
- Inter.NEAREST, means interpolation method is nearest-neighbor interpolation.
- Inter.BICUBIC, means interpolation method is bicubic interpolation.
Raises:
TypeError: If `size` is not of type int or Sequence[int].
TypeError: If `interpolation` is not of type :class:`mindspore.dataset.vision.Inter`.
ValueError: If `size` is not positive.
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.vision import Inter
>>> decode_op = c_vision.Decode()
>>> bbox_op = c_vision.ResizeWithBBox(50, Inter.NEAREST)
>>> transforms_list = [decode_op, bbox_op]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_resize_interpolation
def __init__(self, size, interpolation=Inter.LINEAR):
self.size = size
self.interpolation = interpolation
def parse(self):
size = self.size
if isinstance(size, int):
size = (size,)
return cde.ResizeWithBBoxOperation(size, DE_C_INTER_MODE[self.interpolation])
class RgbToBgr(ImageTensorOperation):
"""
Convert RGB image to BGR.
Raises:
RuntimeError: If given tensor shape is not <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.vision import Inter
>>>
>>> decode_op = c_vision.Decode()
>>> rgb2bgr_op = c_vision.RgbToBgr()
>>> transforms_list = [decode_op, rgb2bgr_op]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
def parse(self):
return cde.RgbToBgrOperation()
[文档]class Rotate(ImageTensorOperation):
"""
Rotate the input image by specified degrees.
Args:
degrees (Union[int, float]): Rotation degrees.
resample (Inter, optional): An optional resampling filter (default=Inter.NEAREST).
It can be any of [Inter.BILINEAR, Inter.NEAREST, Inter.BICUBIC].
- Inter.BILINEAR, means resample method is bilinear interpolation.
- Inter.NEAREST, means resample method is nearest-neighbor interpolation.
- Inter.BICUBIC, means resample method is bicubic interpolation.
expand (bool, optional): Optional expansion flag (default=False). If set to True, expand the output
image to make it large enough to hold the entire rotated image.
If set to False or omitted, make the output image the same size as the input.
Note that the expand flag assumes rotation around the center and no translation.
center (tuple, optional): Optional center of rotation (a 2-tuple) (default=None).
Origin is the top left corner. None sets to the center of the image.
fill_value (Union[int, tuple[int]], optional): Optional fill color for the area outside the rotated image.
If it is a 3-tuple, it is used to fill R, G, B channels respectively.
If it is an integer, it is used for all RGB channels.
The fill_value values must be in range [0, 255] (default=0).
Raises:
TypeError: If `degrees` is not of type int or float.
TypeError: If `resample` is not of type :class:`mindspore.dataset.vision.Inter`.
TypeError: If `expand` is not of type bool.
TypeError: If `center` is not of type tuple.
TypeError: If `fill_value` is not of type int or tuple[int].
ValueError: If `fill_value` is not in range [0, 255].
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.vision import Inter
>>> transforms_list = [c_vision.Decode(),
... c_vision.Rotate(degrees=30.0,
... resample=Inter.NEAREST,
... expand=True)]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
@check_rotate
def __init__(self, degrees, resample=Inter.NEAREST, expand=False, center=None, fill_value=0):
if isinstance(degrees, (int, float)):
degrees = degrees % 360
if center is None:
center = ()
if isinstance(fill_value, int):
fill_value = tuple([fill_value] * 3)
self.degrees = degrees
self.resample = resample
self.expand = expand
self.center = center
self.fill_value = fill_value
def parse(self):
return cde.RotateOperation(self.degrees, DE_C_INTER_MODE[self.resample], self.expand, self.center,
self.fill_value)
[文档]class SlicePatches(ImageTensorOperation):
"""
Slice Tensor to multiple patches in horizontal and vertical directions.
The usage scenario is suitable to large height and width Tensor. The Tensor
will keep the same if set both num_height and num_width to 1. And the
number of output tensors is equal to num_height*num_width.
Args:
num_height (int, optional): The number of patches in vertical direction, which must be positive (default=1).
num_width (int, optional): The number of patches in horizontal direction, which must be positive (default=1).
slice_mode (Inter, optional): A mode represents pad or drop (default=SliceMode.PAD).
It can be any of [SliceMode.PAD, SliceMode.DROP].
fill_value (int, optional): The border width in number of pixels in
right and bottom direction if slice_mode is set to be SliceMode.PAD.
The fill_value must be in range [0, 255] (default=0).
Raises:
TypeError: If `num_height` is not of type int.
TypeError: If `num_width` is not of type int.
TypeError: If `slice_mode` is not of type :class:`mindspore.dataset.vision.Inter`.
TypeError: If `fill_value` is not of type int.
ValueError: If `num_height` is not positive.
ValueError: If `num_width` is not positive.
ValueError: If `fill_value` is not in range [0, 255].
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> # default padding mode
>>> decode_op = c_vision.Decode()
>>> num_h, num_w = (1, 4)
>>> slice_patches_op = c_vision.SlicePatches(num_h, num_w)
>>> transforms_list = [decode_op, slice_patches_op]
>>> cols = ['img' + str(x) for x in range(num_h*num_w)]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"],
... output_columns=cols, column_order=cols)
"""
@check_slice_patches
def __init__(self, num_height=1, num_width=1, slice_mode=SliceMode.PAD, fill_value=0):
self.num_height = num_height
self.num_width = num_width
self.slice_mode = slice_mode
self.fill_value = fill_value
def parse(self):
return cde.SlicePatchesOperation(self.num_height, self.num_width,
DE_C_SLICE_MODE[self.slice_mode], self.fill_value)
[文档]class SoftDvppDecodeRandomCropResizeJpeg(ImageTensorOperation):
"""
A combination of `Crop`, `Decode` and `Resize` using the simulation algorithm of Ascend series chip DVPP module.
The usage scenario is consistent with SoftDvppDecodeResizeJpeg.
The input image size should be in range [32*32, 8192*8192].
The zoom-out and zoom-in multiples of the image length and width should in the range [1/32, 16].
Only images with an even resolution can be output. The output of odd resolution is not supported.
Args:
size (Union[int, Sequence[int]]): The size of the output image. The size value(s) must be positive.
If size is an integer, a square crop of size (size, size) is returned.
If size is a sequence of length 2, an image of size (height, width) will be cropped.
scale (Union[list, tuple], optional): Range [min, max) of respective size of the
original size to be cropped, which must be non-negative (default=(0.08, 1.0)).
ratio (Union[list, tuple], optional): Range [min, max) of aspect ratio to be
cropped, which must be non-negative (default=(3. / 4., 4. / 3.)).
max_attempts (int, optional): The maximum number of attempts to propose a valid crop_area (default=10).
If exceeded, fall back to use center_crop instead. The max_attempts value must be positive.
Raises:
TypeError: If `size` is not of type int or Sequence[int].
TypeError: If `scale` is not of type tuple or list.
TypeError: If `ratio` is not of type tuple or list.
TypeError: If `max_attempts` is not of type int.
ValueError: If `size` is not positive.
ValueError: If `scale` is negative.
ValueError: If `ratio` is negative.
ValueError: If `max_attempts` is not positive.
RuntimeError: If given tensor is not a 1D sequence.
Supported Platforms:
``CPU``
Examples:
>>> # decode, randomly crop and resize image, keeping aspect ratio
>>> transforms_list1 = [c_vision.SoftDvppDecodeRandomCropResizeJpeg(90)]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list1,
... input_columns=["image"])
>>> # decode, randomly crop and resize to landscape style
>>> transforms_list2 = [c_vision.SoftDvppDecodeRandomCropResizeJpeg((80, 100))]
>>> image_folder_dataset_1 = image_folder_dataset_1.map(operations=transforms_list2,
... input_columns=["image"])
"""
@check_soft_dvpp_decode_random_crop_resize_jpeg
def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.), max_attempts=10):
if isinstance(size, int):
size = (size, size)
self.size = size
self.scale = scale
self.ratio = ratio
self.max_attempts = max_attempts
def parse(self):
return cde.SoftDvppDecodeRandomCropResizeJpegOperation(self.size, self.scale, self.ratio, self.max_attempts)
[文档]class SoftDvppDecodeResizeJpeg(ImageTensorOperation):
"""
Decode and resize JPEG image using the simulation algorithm of Ascend series chip DVPP module.
It is recommended to use this algorithm in the following scenarios:
When training, the DVPP of the Ascend chip is not used,
and the DVPP of the Ascend chip is used during inference,
and the accuracy of inference is lower than the accuracy of training;
and the input image size should be in range [32*32, 8192*8192].
The zoom-out and zoom-in multiples of the image length and width should in the range [1/32, 16].
Only images with an even resolution can be output. The output of odd resolution is not supported.
Args:
size (Union[int, Sequence[int]]): The output size of the resized image. The size value(s) must be positive.
If size is an integer, smaller edge of the image will be resized to this value with
the same image aspect ratio.
If size is a sequence of length 2, an image of size (height, width) will be cropped.
Raises:
TypeError: If `size` is not of type int or Sequence[int].
ValueError: If `size` is not positive.
RuntimeError: If given tensor is not a 1D sequence.
Supported Platforms:
``CPU``
Examples:
>>> # decode and resize image, keeping aspect ratio
>>> transforms_list1 = [c_vision.SoftDvppDecodeResizeJpeg(70)]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list1,
... input_columns=["image"])
>>> # decode and resize to portrait style
>>> transforms_list2 = [c_vision.SoftDvppDecodeResizeJpeg((80, 60))]
>>> image_folder_dataset_1 = image_folder_dataset_1.map(operations=transforms_list2,
... input_columns=["image"])
"""
@check_resize
def __init__(self, size):
if isinstance(size, int):
size = (size,)
self.size = size
def parse(self):
return cde.SoftDvppDecodeResizeJpegOperation(self.size)
[文档]class VerticalFlip(ImageTensorOperation):
"""
Flip the input image vertically.
Raises:
RuntimeError: If given tensor shape is not <H, W> or <H, W, C>.
Supported Platforms:
``CPU``
Examples:
>>> transforms_list = [c_vision.Decode(), c_vision.VerticalFlip()]
>>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
... input_columns=["image"])
"""
def parse(self):
return cde.VerticalFlipOperation()