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from __future__ import division
import random
import sys
from PIL import Image
try:
import accimage
except ImportError:
accimage = None
import collections
import numbers
from torchvision.transforms import functional as F
if sys.version_info < (3, 3):
Sequence = collections.Sequence
Iterable = collections.Iterable
else:
Sequence = collections.abc.Sequence
Iterable = collections.abc.Iterable
_pil_interpolation_to_str = {
Image.NEAREST: "PIL.Image.NEAREST",
Image.BILINEAR: "PIL.Image.BILINEAR",
Image.BICUBIC: "PIL.Image.BICUBIC",
Image.LANCZOS: "PIL.Image.LANCZOS",
Image.HAMMING: "PIL.Image.HAMMING",
Image.BOX: "PIL.Image.BOX",
}
class Compose(object):
"""Composes several transforms together.
Args:
transforms (list of ``Transform`` objects): list of transforms to compose.
Example:
>>> transforms.Compose([
>>> transforms.CenterCrop(10),
>>> transforms.ToTensor(),
>>> ])
"""
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, img, tgt):
for t in self.transforms:
img, tgt = t(img, tgt)
return img, tgt
def __repr__(self):
format_string = self.__class__.__name__ + "("
for t in self.transforms:
format_string += "\n"
format_string += " {0}".format(t)
format_string += "\n)"
return format_string
class Resize(object):
"""Resize the input PIL Image to the given size.
Args:
size (sequence or int): Desired output size. If size is a sequence like
(h, w), output size will be matched to this. If size is an int,
smaller edge of the image will be matched to this number.
i.e, if height > width, then image will be rescaled to
(size * height / width, size)
interpolation (int, optional): Desired interpolation. Default is
``PIL.Image.BILINEAR``
"""
def __init__(self, size, interpolation=Image.BILINEAR):
assert isinstance(size, int) or (isinstance(size, Iterable) and len(size) == 2)
self.size = size
self.interpolation = interpolation
def __call__(self, img, tgt):
"""
Args:
img (PIL Image): Image to be scaled.
Returns:
PIL Image: Rescaled image.
"""
return F.resize(img, self.size, self.interpolation), F.resize(
tgt, self.size, Image.NEAREST
)
def __repr__(self):
interpolate_str = _pil_interpolation_to_str[self.interpolation]
return self.__class__.__name__ + "(size={0}, interpolation={1})".format(
self.size, interpolate_str
)
class CenterCrop(object):
"""Crops the given PIL Image at the center.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made.
"""
def __init__(self, size):
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
self.size = size
def __call__(self, img, tgt):
"""
Args:
img (PIL Image): Image to be cropped.
Returns:
PIL Image: Cropped image.
"""
return F.center_crop(img, self.size), F.center_crop(tgt, self.size)
def __repr__(self):
return self.__class__.__name__ + "(size={0})".format(self.size)
class RandomCrop(object):
"""Crop the given PIL Image at a random location.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made.
padding (int or sequence, optional): Optional padding on each border
of the image. Default is None, i.e no padding. If a sequence of length
4 is provided, it is used to pad left, top, right, bottom borders
respectively. If a sequence of length 2 is provided, it is used to
pad left/right, top/bottom borders, respectively.
pad_if_needed (boolean): It will pad the image if smaller than the
desired size to avoid raising an exception.
fill: Pixel fill value for constant fill. Default is 0. If a tuple of
length 3, it is used to fill R, G, B channels respectively.
This value is only used when the padding_mode is constant
padding_mode: Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant.
- constant: pads with a constant value, this value is specified with fill
- edge: pads with the last value on the edge of the image
- reflect: pads with reflection of image (without repeating the last value on the edge)
padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode
will result in [3, 2, 1, 2, 3, 4, 3, 2]
- symmetric: pads with reflection of image (repeating the last value on the edge)
padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode
will result in [2, 1, 1, 2, 3, 4, 4, 3]
"""
def __init__(
self, size, padding=None, pad_if_needed=False, fill=0, padding_mode="constant"
):
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
self.size = size
self.padding = padding
self.pad_if_needed = pad_if_needed
self.fill = fill
self.padding_mode = padding_mode
@staticmethod
def get_params(img, output_size):
"""Get parameters for ``crop`` for a random crop.
Args:
img (PIL Image): Image to be cropped.
output_size (tuple): Expected output size of the crop.
Returns:
tuple: params (i, j, h, w) to be passed to ``crop`` for random crop.
"""
w, h = img.size
th, tw = output_size
if w == tw and h == th:
return 0, 0, h, w
i = random.randint(0, h - th)
j = random.randint(0, w - tw)
return i, j, th, tw
def __call__(self, img, tgt):
"""
Args:
img (PIL Image): Image to be cropped.
Returns:
PIL Image: Cropped image.
"""
if self.padding is not None:
img = F.pad(img, self.padding, self.fill, self.padding_mode)
tgt = F.pad(tgt, self.padding, self.fill, self.padding_mode)
# pad the width if needed
if self.pad_if_needed and img.size[0] < self.size[1]:
img = F.pad(
img, (self.size[1] - img.size[0], 0), self.fill, self.padding_mode
)
tgt = F.pad(
tgt, (self.size[1] - img.size[0], 0), self.fill, self.padding_mode
)
# pad the height if needed
if self.pad_if_needed and img.size[1] < self.size[0]:
img = F.pad(
img, (0, self.size[0] - img.size[1]), self.fill, self.padding_mode
)
tgt = F.pad(
tgt, (0, self.size[0] - img.size[1]), self.fill, self.padding_mode
)
i, j, h, w = self.get_params(img, self.size)
return F.crop(img, i, j, h, w), F.crop(tgt, i, j, h, w)
def __repr__(self):
return self.__class__.__name__ + "(size={0}, padding={1})".format(
self.size, self.padding
)
class RandomHorizontalFlip(object):
"""Horizontally flip the given PIL Image randomly with a given probability.
Args:
p (float): probability of the image being flipped. Default value is 0.5
"""
def __init__(self, p=0.5):
self.p = p
def __call__(self, img, tgt):
"""
Args:
img (PIL Image): Image to be flipped.
Returns:
PIL Image: Randomly flipped image.
"""
if random.random() < self.p:
return F.hflip(img), F.hflip(tgt)
return img, tgt
def __repr__(self):
return self.__class__.__name__ + "(p={})".format(self.p)
class RandomVerticalFlip(object):
"""Vertically flip the given PIL Image randomly with a given probability.
Args:
p (float): probability of the image being flipped. Default value is 0.5
"""
def __init__(self, p=0.5):
self.p = p
def __call__(self, img, tgt):
"""
Args:
img (PIL Image): Image to be flipped.
Returns:
PIL Image: Randomly flipped image.
"""
if random.random() < self.p:
return F.vflip(img), F.vflip(tgt)
return img, tgt
def __repr__(self):
return self.__class__.__name__ + "(p={})".format(self.p)
class Lambda(object):
"""Apply a user-defined lambda as a transform.
Args:
lambd (function): Lambda/function to be used for transform.
"""
def __init__(self, lambd):
assert callable(lambd), repr(type(lambd).__name__) + " object is not callable"
self.lambd = lambd
def __call__(self, img, tgt):
return self.lambd(img, tgt)
def __repr__(self):
return self.__class__.__name__ + "()"
class ColorJitter(object):
"""Randomly change the brightness, contrast and saturation of an image.
Args:
brightness (float or tuple of float (min, max)): How much to jitter brightness.
brightness_factor is chosen uniformly from [max(0, 1 - brightness), 1 + brightness]
or the given [min, max]. Should be non negative numbers.
contrast (float or tuple of float (min, max)): How much to jitter contrast.
contrast_factor is chosen uniformly from [max(0, 1 - contrast), 1 + contrast]
or the given [min, max]. Should be non negative numbers.
saturation (float or tuple of float (min, max)): How much to jitter saturation.
saturation_factor is chosen uniformly from [max(0, 1 - saturation), 1 + saturation]
or the given [min, max]. Should be non negative numbers.
hue (float or tuple of float (min, max)): How much to jitter hue.
hue_factor is chosen uniformly from [-hue, hue] or the given [min, max].
Should have 0<= hue <= 0.5 or -0.5 <= min <= max <= 0.5.
"""
def __init__(self, brightness=0, contrast=0, saturation=0, hue=0):
self.brightness = self._check_input(brightness, "brightness")
self.contrast = self._check_input(contrast, "contrast")
self.saturation = self._check_input(saturation, "saturation")
self.hue = self._check_input(
hue, "hue", center=0, bound=(-0.5, 0.5), clip_first_on_zero=False
)
def _check_input(
self, value, name, center=1, bound=(0, float("inf")), clip_first_on_zero=True
):
if isinstance(value, numbers.Number):
if value < 0:
raise ValueError(
"If {} is a single number, it must be non negative.".format(name)
)
value = [center - value, center + value]
if clip_first_on_zero:
value[0] = max(value[0], 0)
elif isinstance(value, (tuple, list)) and len(value) == 2:
if not bound[0] <= value[0] <= value[1] <= bound[1]:
raise ValueError("{} values should be between {}".format(name, bound))
else:
raise TypeError(
"{} should be a single number or a list/tuple with lenght 2.".format(
name
)
)
# if value is 0 or (1., 1.) for brightness/contrast/saturation
# or (0., 0.) for hue, do nothing
if value[0] == value[1] == center:
value = None
return value
@staticmethod
def get_params(brightness, contrast, saturation, hue):
"""Get a randomized transform to be applied on image.
Arguments are same as that of __init__.
Returns:
Transform which randomly adjusts brightness, contrast and
saturation in a random order.
"""
transforms = []
if brightness is not None:
brightness_factor = random.uniform(brightness[0], brightness[1])
transforms.append(
Lambda(
lambda img, tgt: (F.adjust_brightness(img, brightness_factor), tgt)
)
)
if contrast is not None:
contrast_factor = random.uniform(contrast[0], contrast[1])
transforms.append(
Lambda(lambda img, tgt: (F.adjust_contrast(img, contrast_factor), tgt))
)
if saturation is not None:
saturation_factor = random.uniform(saturation[0], saturation[1])
transforms.append(
Lambda(
lambda img, tgt: (F.adjust_saturation(img, saturation_factor), tgt)
)
)
if hue is not None:
hue_factor = random.uniform(hue[0], hue[1])
transforms.append(
Lambda(lambda img, tgt: (F.adjust_hue(img, hue_factor), tgt))
)
random.shuffle(transforms)
transform = Compose(transforms)
return transform
def __call__(self, img, tgt):
"""
Args:
img (PIL Image): Input image.
Returns:
PIL Image: Color jittered image.
"""
transform = self.get_params(
self.brightness, self.contrast, self.saturation, self.hue
)
return transform(img, tgt)
def __repr__(self):
format_string = self.__class__.__name__ + "("
format_string += "brightness={0}".format(self.brightness)
format_string += ", contrast={0}".format(self.contrast)
format_string += ", saturation={0}".format(self.saturation)
format_string += ", hue={0})".format(self.hue)
return format_string
class Normalize(object):
"""Normalize a tensor image with mean and standard deviation.
Given mean: ``(M1,...,Mn)`` and std: ``(S1,..,Sn)`` for ``n`` channels, this transform
will normalize each channel of the input ``torch.*Tensor`` i.e.
``input[channel] = (input[channel] - mean[channel]) / std[channel]``
.. note::
This transform acts out of place, i.e., it does not mutates the input tensor.
Args:
mean (sequence): Sequence of means for each channel.
std (sequence): Sequence of standard deviations for each channel.
"""
def __init__(self, mean, std, inplace=False):
self.mean = mean
self.std = std
self.inplace = inplace
def __call__(self, img, tgt):
"""
Args:
tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
Returns:
Tensor: Normalized Tensor image.
"""
# return F.normalize(img, self.mean, self.std, self.inplace), tgt
return F.normalize(img, self.mean, self.std), tgt
def __repr__(self):
return self.__class__.__name__ + "(mean={0}, std={1})".format(
self.mean, self.std
)
class ToTensor(object):
"""Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
Converts a PIL Image or numpy.ndarray (H x W x C) in the range
[0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0]
if the PIL Image belongs to one of the modes (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1)
or if the numpy.ndarray has dtype = np.uint8
In the other cases, tensors are returned without scaling.
"""
def __call__(self, img, tgt):
"""
Args:
pic (PIL Image or numpy.ndarray): Image to be converted to tensor.
Returns:
Tensor: Converted image.
"""
return F.to_tensor(img), tgt
def __repr__(self):
return self.__class__.__name__ + "()"
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