# 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, SliceMode
from .validators import check_prob, check_crop, check_center_crop, check_resize_interpolation, check_random_resize_crop, \
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_uniform_augment_cpp, \
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
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_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}
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
[docs]class AutoContrast(ImageTensorOperation):
"""
Apply automatic contrast on input image. This operator calculates histogram of image, reassign cutoff percent
of lightest pixels from histogram to 255, and reassign cutoff percent of 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 (default=None).
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)
[docs]class BoundingBoxAugment(ImageTensorOperation):
"""
Apply a given image transform on a random selection of bounding box regions of a given image.
Args:
transform: C++ transformation operator 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, 1] (default=0.3).
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)
[docs]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, it should be (height, width).
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)
[docs]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, it should be (height, width).
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)
[docs]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 (Image Batch Format): The method of padding. Can be any of
[ImageBatchFormat.NHWC, ImageBatchFormat.NCHW].
alpha (float, optional): hyperparameter of beta distribution (default = 1.0).
prob (float, optional): The probability by which CutMix is applied to each image (default = 1.0).
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)
[docs]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.
num_patches (int, optional): Number of patches to be cut out of an image (default=1).
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)
[docs]class Decode(ImageTensorOperation):
"""
Decode the input image in RGB mode(default) or BGR mode(deprecated).
Args:
rgb (bool, optional): Mode of decoding input image (default=True).
If True means format of decoded image is RGB else BGR(deprecated).
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 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)
[docs]class Equalize(ImageTensorOperation):
"""
Apply histogram equalization on input image.
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()
[docs]class GaussianBlur(ImageTensorOperation):
"""
Blur input image with the specified Gaussian kernel.
Args:
kernel_size (Union[int, sequence]): Size of the Gaussian kernel to use. The value must be positive and odd. If
only an integer is provied, the kernel size will be (size, size). If a sequence of integer is provied, it
must be a sequence of 2 values which represents (width, height).
sigma (Union[float, sequence], optional): Standard deviation of the Gaussian kernel to use (default=None). The
value must be positive. If only an float is provied, the sigma will be (sigma, sigma). If a sequence of
float is provied, it must be a sequence of 2 values which represents the sigma of width and height. If None
is provided, the sigma will be calculated as ((kernel_size - 1) * 0.5 - 1) * 0.3 + 0.8.
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)
[docs]class HorizontalFlip(ImageTensorOperation):
"""
Flip the input image horizontally.
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()
[docs]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.
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()
[docs]class Invert(ImageTensorOperation):
"""
Apply invert on input image in RGB mode. This operator will reassign every pixel to (255 - pixel).
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()
[docs]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 (default = 1.0).
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)
[docs]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.
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].
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)
[docs]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): Set the output data type of normalized image (default is "float32").
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)
[docs]class Pad(ImageTensorOperation):
"""
Pad the image according to padding parameters.
Args:
padding (Union[int, sequence]): The number of pixels to pad the image.
If a single number is provided, it pads all borders with this value.
If a tuple or list 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.
fill_value (Union[int, tuple], 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 mode, 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.
Examples:
>>> from mindspore.dataset.vision import Border
>>> 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])
[docs]class RandomAffine(ImageTensorOperation):
"""
Apply Random affine transformation to the input image.
Args:
degrees (int or float or 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 (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 of 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 (default=None, original scale is used).
shear (int or float or sequence, optional): Range of shear factor (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 of 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 mode, 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 (tuple or 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:
ValueError: If degrees is negative.
ValueError: If translation value is not between -1 and 1.
ValueError: If scale is not positive.
ValueError: If shear is a number but is not positive.
TypeError: If degrees is not a number or a list or a tuple.
If degrees is a list or tuple, its length is not 2.
TypeError: If translate is specified but is not list or a tuple of length 2 or 4.
TypeError: If scale is not a list or tuple of length 2.
TypeError: If shear is not a list or tuple of length 2 or 4.
TypeError: If fill_value is not a single integer or a 3-tuple.
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)
[docs]class RandomColor(ImageTensorOperation):
"""
Adjust the color of the input image by a fixed or random degree.
This operation works only with 3-channel color images.
Args:
degrees (sequence, optional): Range of random color adjustment degrees.
It should be in (min, max) format. If min=max, then it is a
single fixed magnitude operation (default=(0.1, 1.9)).
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)
[docs]class RandomColorAdjust(ImageTensorOperation):
"""
Randomly adjust the brightness, contrast, saturation, and hue of the input image.
Args:
brightness (Union[float, list, tuple], 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, list, tuple], 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, list, tuple], 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, list, tuple], 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.
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
[docs] 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)
[docs]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.
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, it should be (height, width).
padding (Union[int, sequence], optional): The number of pixels to pad the image (default=None).
If padding is not None, pad image firstly with padding values.
If a single number is provided, pad all borders with this value.
If a tuple or list 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], 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 mode, 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.
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)
[docs]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]): The output size of the resized image.
If size is an integer, a square crop of size (size, size) is returned.
If size is a sequence of length 2, it should be (height, width).
scale (list, tuple, optional): Range [min, max) of respective size of the
original size to be cropped (default=(0.08, 1.0)).
ratio (list, tuple, optional): Range [min, max) of aspect ratio to be
cropped (default=(3. / 4., 4. / 3.)).
interpolation (Inter mode, optional): Image interpolation mode for resize operator(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.
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)
[docs]class RandomCropWithBBox(ImageTensorOperation):
"""
Crop the input image at a random location and adjust bounding boxes accordingly.
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, it should be (height, width).
padding (Union[int, sequence], optional): The number of pixels to pad the image (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 list 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], 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 mode, 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.
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)
[docs]class RandomHorizontalFlip(ImageTensorOperation):
"""
Randomly flip the input image horizontally with a given probability.
Args:
prob (float, optional): Probability of the image being flipped (default=0.5).
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)
[docs]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 (default=0.5).
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)
[docs]class RandomPosterize(ImageTensorOperation):
"""
Reduce the number of bits for each color channel to posterize the input image randomly with a given probability.
Args:
bits (sequence or 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)).
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)
[docs]class RandomResizedCrop(ImageTensorOperation):
"""
Crop the input image to a random size and aspect ratio. This operator will crop the input image randomly, and
resize the cropped image using a selected interpolation mode.
Args:
size (Union[int, sequence]): The output size of the resized image.
If size is an integer, a square crop of size (size, size) is returned.
If size is a sequence of length 2, it should be (height, width).
scale (list, tuple, optional): Range [min, max) of respective size of the original
size to be cropped (default=(0.08, 1.0)).
ratio (list, tuple, optional): Range [min, max) of aspect ratio to be cropped
(default=(3. / 4., 4. / 3.)).
interpolation (Inter mode, optional): Image interpolation mode for resize operator (default=Inter.BILINEAR).
It can be any of [Inter.BILINEAR, Inter.NEAREST, Inter.BICUBIC, 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.
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)
[docs]class RandomResizedCropWithBBox(ImageTensorOperation):
"""
Crop the input image to a random size and aspect ratio and adjust bounding boxes accordingly.
Args:
size (Union[int, sequence]): The size of the output image.
If size is an integer, a square crop of size (size, size) is returned.
If size is a sequence of length 2, it should be (height, width).
scale (list, tuple, optional): Range (min, max) of respective size of the original
size to be cropped (default=(0.08, 1.0)).
ratio (list, tuple, optional): Range (min, max) of aspect ratio to be cropped
(default=(3. / 4., 4. / 3.)).
interpolation (Inter mode, optional): Image interpolation mode (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.
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)
[docs]class RandomResize(ImageTensorOperation):
"""
Resize the input image using a randomly selected interpolation mode.
Args:
size (Union[int, sequence]): 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).
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)
[docs]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]): 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).
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)
[docs]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 mode, 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], 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).
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)
[docs]class RandomSelectSubpolicy(ImageTensorOperation):
"""
Choose a random sub-policy from a policy list to be applied on the input image.
Args:
policy (list(list(tuple(TensorOp, prob (float)))): List of sub-policies to choose from.
A sub-policy is a list of tuples (op, prob), where op is a TensorOp operation and prob is the probability
that this op will be applied, and the prob values must be in range [0, 1]. Once a sub-policy is selected,
each op within the sub-policy with be applied in sequence according to its probability.
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)
[docs]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.
Args:
degrees (Union[list, tuple], optional): Range of random sharpness adjustment degrees. 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 a list or tuple.
ValueError: If degrees is negative.
ValueError: If degrees is in (max, min) format instead of (min, max).
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)
[docs]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).
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)
[docs]class RandomVerticalFlip(ImageTensorOperation):
"""
Randomly flip the input image vertically with a given probability.
Args:
prob (float, optional): Probability of the image being flipped (default=0.5).
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)
[docs]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).
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)
[docs]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.
Args:
rescale (float): Rescale factor.
shift (float): Shift factor.
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)
[docs]class Resize(ImageTensorOperation):
"""
Resize the input image to the given size with a given interpolation mode.
Args:
size (Union[int, sequence]): The output size of the resized image.
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 mode, optional): Image interpolation mode (default=Inter.LINEAR).
It can be any of [Inter.LINEAR, Inter.NEAREST, Inter.BICUBIC, 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.
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])
[docs]class ResizeWithBBox(ImageTensorOperation):
"""
Resize the input image to the given size and adjust bounding boxes accordingly.
Args:
size (Union[int, sequence]): 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 mode, 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.
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.
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()
[docs]class Rotate(ImageTensorOperation):
"""
Rotate the input image by specified degrees.
Args:
degrees (Union[int, float]): Rotation degrees.
resample (Inter mode, 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], 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).
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)
class SlicePatches(ImageTensorOperation):
"""
Slice Tensor to multiple patches in horizontal and vertical directions.
The usage scenerio 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 (default=1).
num_height (int, optional): The number of patches in horizontal direction (default=1).
slice_mode (Inter mode, optional): An 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 (default=0).
Examples:
>>> # default padding mode
>>> slice_patches_op = c_vision.SlicePatches(num_h, num_w)
>>> cols = ['img' + str(x) for x in range(num_h*num_w)]
>>> dataset1 = dataset1.map(operations=decode_op, input_columns=["image"])
>>> dataset1 = dataset1.map(operations=resize_op, input_columns=["image"])
>>> dataset1 = dataset1.map(operations=slice_patches_op, 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)
[docs]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]): The size of the output image.
If size is an integer, a square crop of size (size, size) is returned.
If size is a sequence of length 2, it should be (height, width).
scale (list, tuple, optional): Range [min, max) of respective size of the
original size to be cropped (default=(0.08, 1.0)).
ratio (list, tuple, optional): Range [min, max) of aspect ratio to be
cropped (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.
Examples:
>>> # decode, randomly crop and resize image, keeping aspect ratio
>>> transforms_list1 = [c_vision.Decode(), 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.Decode(), 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)
[docs]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]): 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).
Examples:
>>> # decode and resize image, keeping aspect ratio
>>> transforms_list1 = [c_vision.Decode(), 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.Decode(), 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)
[docs]class VerticalFlip(ImageTensorOperation):
"""
Flip the input image vertically.
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()