# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
# coding: utf-8
# pylint: disable= arguments-differ
"Image transforms."
from ...block import Block, HybridBlock
from ...nn import Sequential, HybridSequential
from .... import image
from ....base import numeric_types
[docs]class Compose(Sequential):
"""Sequentially composes multiple transforms.
Parameters
----------
transforms : list of transform Blocks.
The list of transforms to be composed.
Inputs:
- **data**: input tensor with shape of the first transform Block requires.
Outputs:
- **out**: output tensor with shape of the last transform Block produces.
Examples
--------
>>> transformer = transforms.Compose([transforms.Resize(300),
... transforms.CenterCrop(256),
... transforms.ToTensor()])
>>> image = mx.nd.random.uniform(0, 255, (224, 224, 3)).astype(dtype=np.uint8)
>>> transformer(image)
"""
def __init__(self, transforms):
super(Compose, self).__init__()
transforms.append(None)
hybrid = []
for i in transforms:
if isinstance(i, HybridBlock):
hybrid.append(i)
continue
elif len(hybrid) == 1:
self.add(hybrid[0])
hybrid = []
elif len(hybrid) > 1:
hblock = HybridSequential()
for j in hybrid:
hblock.add(j)
hblock.hybridize()
self.add(hblock)
hybrid = []
if i is not None:
self.add(i)
[docs]class Cast(HybridBlock):
"""Cast input to a specific data type
Parameters
----------
dtype : str, default 'float32'
The target data type, in string or `numpy.dtype`.
Inputs:
- **data**: input tensor with arbitrary shape.
Outputs:
- **out**: output tensor with the same shape as `data`.
"""
def __init__(self, dtype='float32'):
super(Cast, self).__init__()
self._dtype = dtype
def hybrid_forward(self, F, x):
return F.cast(x, self._dtype)
[docs]class ToTensor(HybridBlock):
"""Converts an image NDArray to a tensor NDArray.
Converts an image NDArray of shape (H x W x C) in the range
[0, 255] to a float32 tensor NDArray of shape (C x H x W) in
the range [0, 1).
Inputs:
- **data**: input tensor with (H x W x C) shape and uint8 type.
Outputs:
- **out**: output tensor with (C x H x W) shape and float32 type.
Examples
--------
>>> transformer = vision.transforms.ToTensor()
>>> image = mx.nd.random.uniform(0, 255, (4, 2, 3)).astype(dtype=np.uint8)
>>> transformer(image)
[[[ 0.85490197 0.72156864]
[ 0.09019608 0.74117649]
[ 0.61960787 0.92941177]
[ 0.96470588 0.1882353 ]]
[[ 0.6156863 0.73725492]
[ 0.46666667 0.98039216]
[ 0.44705883 0.45490196]
[ 0.01960784 0.8509804 ]]
[[ 0.39607844 0.03137255]
[ 0.72156864 0.52941179]
[ 0.16470589 0.7647059 ]
[ 0.05490196 0.70588237]]]
"""
def __init__(self):
super(ToTensor, self).__init__()
def hybrid_forward(self, F, x):
return F.image.to_tensor(x)
[docs]class Normalize(HybridBlock):
"""Normalize an tensor of shape (C x H x W) with mean and
standard deviation.
Given mean `(m1, ..., mn)` and std `(s1, ..., sn)` for `n` channels,
this transform normalizes each channel of the input tensor with::
output[i] = (input[i] - mi) / si
If mean or std is scalar, the same value will be applied to all channels.
Parameters
----------
mean : float or tuple of floats
The mean values.
std : float or tuple of floats
The standard deviation values.
Inputs:
- **data**: input tensor with (C x H x W) shape.
Outputs:
- **out**: output tensor with the shape as `data`.
"""
def __init__(self, mean, std):
super(Normalize, self).__init__()
self._mean = mean
self._std = std
def hybrid_forward(self, F, x):
return F.image.normalize(x, self._mean, self._std)
[docs]class RandomResizedCrop(Block):
"""Crop the input image with random scale and aspect ratio.
Makes a crop of the original image with random size (default: 0.08
to 1.0 of the original image size) and random aspect ratio (default:
3/4 to 4/3), then resize it to the specified size.
Parameters
----------
size : int or tuple of (W, H)
Size of the final output.
scale : tuple of two floats
If scale is `(min_area, max_area)`, the cropped image's area will
range from min_area to max_area of the original image's area
ratio : tuple of two floats
Range of aspect ratio of the cropped image before resizing.
interpolation : int
Interpolation method for resizing. By default uses bilinear
interpolation. See OpenCV's resize function for available choices.
Inputs:
- **data**: input tensor with (Hi x Wi x C) shape.
Outputs:
- **out**: output tensor with (H x W x C) shape.
"""
def __init__(self, size, scale=(0.08, 1.0), ratio=(3.0/4.0, 4.0/3.0),
interpolation=2):
super(RandomResizedCrop, self).__init__()
if isinstance(size, numeric_types):
size = (size, size)
self._args = (size, scale[0], ratio, interpolation)
def forward(self, x):
return image.random_size_crop(x, *self._args)[0]
[docs]class CenterCrop(Block):
"""Crops the image `src` to the given `size` by trimming on all four
sides and preserving the center of the image. Upsamples if `src` is
smaller than `size`.
Parameters
----------
size : int or tuple of (W, H)
Size of output image.
interpolation : int
Interpolation method for resizing. By default uses bilinear
interpolation. See OpenCV's resize function for available choices.
Inputs:
- **data**: input tensor with (Hi x Wi x C) shape.
Outputs:
- **out**: output tensor with (H x W x C) shape.
Examples
--------
>>> transformer = vision.transforms.CenterCrop(size=(1000, 500))
>>> image = mx.nd.random.uniform(0, 255, (2321, 3482, 3)).astype(dtype=np.uint8)
>>> transformer(image)
"""
def __init__(self, size, interpolation=2):
super(CenterCrop, self).__init__()
if isinstance(size, numeric_types):
size = (size, size)
self._args = (size, interpolation)
def forward(self, x):
return image.center_crop(x, *self._args)[0]
[docs]class Resize(Block):
"""Resize an image to the given size.
Parameters
----------
size : int or tuple of (W, H)
Size of output image.
interpolation : int
Interpolation method for resizing. By default uses bilinear
interpolation. See OpenCV's resize function for available choices.
Inputs:
- **data**: input tensor with (Hi x Wi x C) shape.
Outputs:
- **out**: output tensor with (H x W x C) shape.
Examples
--------
>>> transformer = vision.transforms.Resize(size=(1000, 500))
>>> image = mx.nd.random.uniform(0, 255, (224, 224, 3)).astype(dtype=np.uint8)
>>> transformer(image)
"""
def __init__(self, size, interpolation=2):
super(Resize, self).__init__()
if isinstance(size, numeric_types):
size = (size, size)
self._args = tuple(size) + (interpolation,)
def forward(self, x):
return image.imresize(x, *self._args)
[docs]class RandomFlipLeftRight(HybridBlock):
"""Randomly flip the input image left to right with a probability
of 0.5.
Inputs:
- **data**: input tensor with (H x W x C) shape.
Outputs:
- **out**: output tensor with same shape as `data`.
"""
def __init__(self):
super(RandomFlipLeftRight, self).__init__()
def hybrid_forward(self, F, x):
return F.image.random_flip_left_right(x)
[docs]class RandomFlipTopBottom(HybridBlock):
"""Randomly flip the input image top to bottom with a probability
of 0.5.
Inputs:
- **data**: input tensor with (H x W x C) shape.
Outputs:
- **out**: output tensor with same shape as `data`.
"""
def __init__(self):
super(RandomFlipTopBottom, self).__init__()
def hybrid_forward(self, F, x):
return F.image.random_flip_top_bottom(x)
[docs]class RandomBrightness(HybridBlock):
"""Randomly jitters image brightness with a factor
chosen from `[max(0, 1 - brightness), 1 + brightness]`.
Parameters
----------
brightness: float
How much to jitter brightness. brightness factor is randomly
chosen from `[max(0, 1 - brightness), 1 + brightness]`.
Inputs:
- **data**: input tensor with (H x W x C) shape.
Outputs:
- **out**: output tensor with same shape as `data`.
"""
def __init__(self, brightness):
super(RandomBrightness, self).__init__()
self._args = (max(0, 1-brightness), 1+brightness)
def hybrid_forward(self, F, x):
return F.image.random_brightness(x, *self._args)
[docs]class RandomContrast(HybridBlock):
"""Randomly jitters image contrast with a factor
chosen from `[max(0, 1 - contrast), 1 + contrast]`.
Parameters
----------
contrast: float
How much to jitter contrast. contrast factor is randomly
chosen from `[max(0, 1 - contrast), 1 + contrast]`.
Inputs:
- **data**: input tensor with (H x W x C) shape.
Outputs:
- **out**: output tensor with same shape as `data`.
"""
def __init__(self, contrast):
super(RandomContrast, self).__init__()
self._args = (max(0, 1-contrast), 1+contrast)
def hybrid_forward(self, F, x):
return F.image.random_contrast(x, *self._args)
[docs]class RandomSaturation(HybridBlock):
"""Randomly jitters image saturation with a factor
chosen from `[max(0, 1 - saturation), 1 + saturation]`.
Parameters
----------
saturation: float
How much to jitter saturation. saturation factor is randomly
chosen from `[max(0, 1 - saturation), 1 + saturation]`.
Inputs:
- **data**: input tensor with (H x W x C) shape.
Outputs:
- **out**: output tensor with same shape as `data`.
"""
def __init__(self, saturation):
super(RandomSaturation, self).__init__()
self._args = (max(0, 1-saturation), 1+saturation)
def hybrid_forward(self, F, x):
return F.image.random_saturation(x, *self._args)
[docs]class RandomHue(HybridBlock):
"""Randomly jitters image hue with a factor
chosen from `[max(0, 1 - hue), 1 + hue]`.
Parameters
----------
hue: float
How much to jitter hue. hue factor is randomly
chosen from `[max(0, 1 - hue), 1 + hue]`.
Inputs:
- **data**: input tensor with (H x W x C) shape.
Outputs:
- **out**: output tensor with same shape as `data`.
"""
def __init__(self, hue):
super(RandomHue, self).__init__()
self._args = (max(0, 1-hue), 1+hue)
def hybrid_forward(self, F, x):
return F.image.random_hue(x, *self._args)
[docs]class RandomColorJitter(HybridBlock):
"""Randomly jitters the brightness, contrast, saturation, and hue
of an image.
Parameters
----------
brightness : float
How much to jitter brightness. brightness factor is randomly
chosen from `[max(0, 1 - brightness), 1 + brightness]`.
contrast : float
How much to jitter contrast. contrast factor is randomly
chosen from `[max(0, 1 - contrast), 1 + contrast]`.
saturation : float
How much to jitter saturation. saturation factor is randomly
chosen from `[max(0, 1 - saturation), 1 + saturation]`.
hue : float
How much to jitter hue. hue factor is randomly
chosen from `[max(0, 1 - hue), 1 + hue]`.
Inputs:
- **data**: input tensor with (H x W x C) shape.
Outputs:
- **out**: output tensor with same shape as `data`.
"""
def __init__(self, brightness=0, contrast=0, saturation=0, hue=0):
super(RandomColorJitter, self).__init__()
self._args = (brightness, contrast, saturation, hue)
def hybrid_forward(self, F, x):
return F.image.random_color_jitter(x, *self._args)
[docs]class RandomLighting(HybridBlock):
"""Add AlexNet-style PCA-based noise to an image.
Parameters
----------
alpha : float
Intensity of the image.
Inputs:
- **data**: input tensor with (H x W x C) shape.
Outputs:
- **out**: output tensor with same shape as `data`.
"""
def __init__(self, alpha):
super(RandomLighting, self).__init__()
self._alpha = alpha
def hybrid_forward(self, F, x):
return F.image.random_lighting(x, self._alpha)
