Source code for mindspore.dataset.transforms.vision.c_transforms

# Copyright 2019 Huawei Technologies Co., Ltd
#
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#
# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# ==============================================================================
"""
The module vision.c_transforms is inheritted from _c_dataengine
which is implemented basing on opencv in C++. It's a high performance module to
process image augmentations. Users can apply suitable augmentations on image data
to improve their training models.

.. Note::
Constructor's arguments for every class in this module must be saved into the
class attributes (self.xxx) to support save() and load().

Examples:
>>> import mindspore.dataset as ds
>>> import mindspore.dataset.transforms.c_transforms as c_transforms
>>> import mindspore.dataset.transforms.vision.c_transforms as vision
>>> dataset_dir = "path/to/imagefolder_directory"
>>> # create a dataset that reads all files in dataset_dir with 8 threads
>>> dataset = ds.ImageFolderDatasetV2(dataset_dir, num_parallel_workers=8)
>>> # create a list of transformations to be applied to the image data
>>> transforms_list = [vision.Decode(),
>>>                    vision.Resize((256, 256)),
>>>                    vision.RandomRotation((0, 15)),
>>>                    vision.Normalize((100,  115.0, 121.0), (71.0, 68.0, 70.0)),
>>>                    vision.HWC2CHW()]
>>> onehot_op = c_transforms.OneHot(num_classes)
>>> # apply the transform to the dataset through dataset.map()
>>> dataset = dataset.map(input_columns="image", operations=transforms_list)
>>> dataset = dataset.map(input_columns="label", operations=onehot_op)
"""
import mindspore._c_dataengine as cde

from .utils import Inter, Border
from .validators import check_prob, check_crop, check_resize_interpolation, check_random_resize_crop, \

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}

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}

[docs]class Decode(cde.DecodeOp):
"""
Decode the input image in RGB mode.
"""

def __init__(self, rgb=True):
self.rgb = rgb
super().__init__(self.rgb)

[docs]class CutOut(cde.CutOutOp):
"""
Randomly cut (mask) out a given number of square patches from the input Numpy 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).
"""

@check_cutout
def __init__(self, length, num_patches=1):
self.length = length
self.num_patches = num_patches
fill_value = (0, 0, 0)
super().__init__(length, length, num_patches, False, *fill_value)

[docs]class Normalize(cde.NormalizeOp):
"""
Normalize the input image with respect to mean and standard deviation.

Args:
mean (sequence): List or tuple of mean values for each channel, w.r.t channel order.
std (sequence): List or tuple of standard deviations for each channel, w.r.t. channel order.
"""

@check_normalize_c
def __init__(self, mean, std):
self.mean = mean
self.std = std
super().__init__(*mean, *std)

[docs]class RandomCrop(cde.RandomCropOp):
"""
Crop the input image at a random location.

Args:
size (int or sequence): The output size of the cropped image.
If size is an int, a square crop of size (size, size) is returned.
If size is a sequence of length 2, it should be (height, width).
padding (int or sequence, optional): The number of pixels to pad the image (default=None).
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.
pad_if_needed (bool, optional): Pad the image if either side is smaller than
the given output size (default=False).
fill_value (int or tuple, optional): The pixel intensity of the borders if
the padding_mode is Border.CONSTANT (default=0). If it is a 3-tuple, it is used to
fill R, G, B channels respectively.
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.
"""

@check_random_crop
self.size = size
self.fill_value = fill_value
padding = (0, 0, 0, 0)
if isinstance(fill_value, int):  # temporary fix
fill_value = tuple([fill_value] * 3)

[docs]class RandomHorizontalFlip(cde.RandomHorizontalFlipOp):
"""
Flip the input image horizontally, randomly with a given probability.

Args:
prob (float): Probability of the image being flipped (default=0.5).
"""

@check_prob
def __init__(self, prob=0.5):
self.prob = prob
super().__init__(prob)

[docs]class RandomVerticalFlip(cde.RandomVerticalFlipOp):
"""
Flip the input image vertically, randomly with a given probability.

Args:
prob (float): Probability of the image being flipped (default=0.5).
"""

@check_prob
def __init__(self, prob=0.5):
self.prob = prob
super().__init__(prob)

[docs]class Resize(cde.ResizeOp):
"""
Resize the input image to the given size.

Args:
size (int or sequence): The output size of the resized image.
If size is an int, 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.
"""

@check_resize_interpolation
def __init__(self, size, interpolation=Inter.LINEAR):
self.size = size
self.interpolation = interpolation
interpoltn = DE_C_INTER_MODE[interpolation]
if isinstance(size, int):
super().__init__(size, interpolation=interpoltn)
else:
super().__init__(*size, interpoltn)

[docs]class RandomResizedCrop(cde.RandomCropAndResizeOp):
"""
Crop the input image to a random size and aspect ratio.

Args:
size (int or sequence): The size of the output image.
If size is an int, a square crop of size (size, size) is returned.
If size is a sequence of length 2, it should be (height, width).
scale (tuple, optional): Range (min, max) of respective size of the original
size to be cropped (default=(0.08, 1.0)).
ratio (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.
"""

@check_random_resize_crop
def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.),
interpolation=Inter.BILINEAR, max_attempts=10):
self.size = size
self.scale = scale
self.ratio = ratio
self.interpolation = interpolation
self.max_attempts = max_attempts
interpoltn = DE_C_INTER_MODE[interpolation]
super().__init__(*size, *scale, *ratio, interpoltn, max_attempts)

[docs]class CenterCrop(cde.CenterCropOp):
"""
Crops the input image at the center to the given size.

Args:
size (int or sequence): The output size of the cropped image.
If size is an int, a square crop of size (size, size) is returned.
If size is a sequence of length 2, it should be (height, width).
"""

@check_crop
def __init__(self, size):
self.size = size
super().__init__(*size)

"""
Randomly adjust the brightness, contrast, saturation, and hue of the input image.

Args:
brightness (float or 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 (float or 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 (float or 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 (float or 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.
"""

def __init__(self, brightness=(1, 1), contrast=(1, 1), saturation=(1, 1), hue=(0, 0)):
self.brightness = brightness
self.contrast = contrast
self.saturation = saturation
self.hue = hue
super().__init__(*brightness, *contrast, *saturation, *hue)

[docs]class RandomRotation(cde.RandomRotationOp):
"""
Rotate the input image by a random angle.

Args:
degrees (int or float or 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).
If omitted, or if the image has mode "1" or "P", it is set to be 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 (int or tuple, optional): Optional fill color for the area outside the rotated image (default=0).
If it is a 3-tuple, it is used for R, G, B channels respectively.
If it is an int, it is used for all RGB channels.
"""

@check_random_rotation
def __init__(self, degrees, resample=Inter.NEAREST, expand=False, center=None, fill_value=0):
self.degrees = degrees
self.resample = resample
self.expand = expand
self.center = center
self.fill_value = fill_value
if center is None:
center = (-1, -1)
if isinstance(fill_value, int):  # temporary fix
fill_value = tuple([fill_value] * 3)
interpolation = DE_C_INTER_MODE[resample]
super().__init__(*degrees, *center, interpolation, expand, *fill_value)

[docs]class Rescale(cde.RescaleOp):
"""
Tensor operation to rescale the input image.

Args:
rescale (float): Rescale factor.
shift (float): Shift factor.
"""

@check_rescale
def __init__(self, rescale, shift):
self.rescale = rescale
self.shift = shift
super().__init__(rescale, shift)

[docs]class RandomResize(cde.RandomResizeOp):
"""
Tensor operation to resize the input image using a randomly selected interpolation mode.

Args:
size (int or sequence): The output size of the resized image.
If size is an int, 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).
"""

@check_resize
def __init__(self, size):
self.size = size
if isinstance(size, int):
super().__init__(size)
else:
super().__init__(*size)

[docs]class HWC2CHW(cde.ChannelSwapOp):
"""
Transpose the input image; shape (H, W, C) to shape (C, H, W).
"""

[docs]class RandomCropDecodeResize(cde.RandomCropDecodeResizeOp):
"""
Equivalent to RandomResizedCrop, but crops before decodes.

Args:
size (int or sequence, optional): The size of the output image.
If size is an int, a square crop of size (size, size) is returned.
If size is a sequence of length 2, it should be (height, width).
scale (tuple, optional): Range (min, max) of respective size of the
original size to be cropped (default=(0.08, 1.0)).
ratio (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.
"""

@check_random_resize_crop
def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.),
interpolation=Inter.BILINEAR, max_attempts=10):
self.size = size
self.scale = scale
self.ratio = ratio
self.interpolation = interpolation
self.max_attempts = max_attempts
interpoltn = DE_C_INTER_MODE[interpolation]
super().__init__(*size, *scale, *ratio, interpoltn, max_attempts)

"""

Args:
padding (int or 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 (int or tuple, optional): The pixel intensity of the borders if
the padding_mode is Border.CONSTANT (default=0). If it is a 3-tuple, it is used to
fill R, G, B channels respectively.
padding_mode (Border mode): 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.
"""

self.fill_value = fill_value
if isinstance(fill_value, int):  # temporary fix
fill_value = tuple([fill_value] * 3)

[docs]class UniformAugment(cde.UniformAugOp):
"""
Tensor operation to perform randomly selected augmentation

Args:
operations: list of python operations.
NumOps (int): number of OPs to be selected and applied.
"""

@check_uniform_augmentation
def __init__(self, operations, num_ops=2):
self.operations = operations
self.num_ops = num_ops
super().__init__(operations, num_ops)