from __future__ import division
import torch
import math
import random
from PIL import Image, ImageOps
try:
import accimage
except ImportError:
accimage = None
import numpy as np
import numbers
import types
import collections
from matplotlib import pyplot as plt
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):
for t in self.transforms:
img = t(img)
return img
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].
"""
def __call__(self, pic):
"""
Args:
pic (PIL.Image or numpy.ndarray): Image to be converted to tensor.
Returns:
Tensor: Converted image.
"""
if isinstance(pic, np.ndarray):
# handle numpy array
img = torch.from_numpy(pic.transpose((2, 0, 1)))
# backward compatibility
return img.float().div(255)
if accimage is not None and isinstance(pic, accimage.Image):
print "I'm in the weird scope"
nppic = np.zeros([pic.channels, pic.height, pic.width], dtype=np.float32)
pic.copyto(nppic)
return torch.from_numpy(nppic)
# handle PIL Image
if pic.mode == 'I':
img = torch.from_numpy(np.array(pic, np.int32, copy=False))
elif pic.mode == 'I;16':
img = torch.from_numpy(np.array(pic, np.int16, copy=False))
else:
img = torch.ByteTensor(torch.ByteStorage.from_buffer(pic.tobytes()))
# PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK
if pic.mode == 'YCbCr':
nchannel = 3
elif pic.mode == 'I;16':
nchannel = 1
else:
nchannel = len(pic.mode)
img = img.view(pic.size[1], pic.size[0], nchannel)
# put it from HWC to CHW format
# yikes, this transpose takes 80% of the loading time/CPU
img = img.transpose(0, 1).transpose(0, 2).contiguous()
if isinstance(img, torch.ByteTensor):
return img.float().div(255)
else:
return img
class ToPILImage(object):
"""Convert a tensor to PIL Image.
Converts a torch.*Tensor of shape C x H x W or a numpy ndarray of shape
H x W x C to a PIL.Image while preserving the value range.
"""
def __call__(self, pic):
"""
Args:
pic (Tensor or numpy.ndarray): Image to be converted to PIL.Image.
Returns:
PIL.Image: Image converted to PIL.Image.
"""
npimg = pic
mode = None
if isinstance(pic, torch.FloatTensor):
pic = pic.mul(255).byte()
if torch.is_tensor(pic):
npimg = np.transpose(pic.numpy(), (1, 2, 0))
assert isinstance(npimg, np.ndarray), 'pic should be Tensor or ndarray'
if npimg.shape[2] == 1:
npimg = npimg[:, :, 0]
if npimg.dtype == np.uint8:
mode = 'L'
if npimg.dtype == np.int16:
mode = 'I;16'
if npimg.dtype == np.int32:
mode = 'I'
elif npimg.dtype == np.float32:
mode = 'F'
else:
if npimg.dtype == np.uint8:
mode = 'RGB'
assert mode is not None, '{} is not supported'.format(npimg.dtype)
return Image.fromarray(npimg, mode=mode)
class Normalize(object):
"""Normalize an tensor image with mean and standard deviation.
Given mean: (R, G, B) and std: (R, G, B),
will normalize each channel of the torch.*Tensor, i.e.
channel = (channel - mean) / std
Args:
mean (sequence): Sequence of means for R, G, B channels respecitvely.
std (sequence): Sequence of standard deviations for R, G, B channels
respecitvely.
"""
def __init__(self, mean, std):
self.mean = mean
self.std = std
def __call__(self, tensor):
"""
Args:
tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
Returns:
Tensor: Normalized image.
"""
# TODO: make efficient
for t, m, s in zip(tensor, self.mean, self.std):
t.sub_(m).div_(s)
return tensor
class Scale(object):
"""Rescale the input PIL.Image to the given size.
Args:
size (sequence or int): Desired output size. If size is a sequence like
(w, h), 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, collections.Iterable) and len(size) == 2)
self.size = size
self.interpolation = interpolation
def __call__(self, img):
"""
Args:
img (PIL.Image): Image to be scaled.
Returns:
PIL.Image: Rescaled image.
"""
if isinstance(self.size, int):
w, h = img.size
if (w <= h and w == self.size) or (h <= w and h == self.size):
return img
if w < h:
ow = self.size
oh = int(self.size * h / w)
return img.resize((ow, oh), self.interpolation)
else:
oh = self.size
ow = int(self.size * w / h)
return img.resize((ow, oh), self.interpolation)
else:
return img.resize(self.size, self.interpolation)
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 (w, h), 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):
"""
Args:
img (PIL.Image): Image to be cropped.
Returns:
PIL.Image: Cropped image.
"""
w, h = img.size
th, tw = self.size
x1 = int(round((w - tw) / 2.))
y1 = int(round((h - th) / 2.))
return img.crop((x1, y1, x1 + tw, y1 + th))
class Pad(object):
"""Pad the given PIL.Image on all sides with the given "pad" value.
Args:
padding (int or sequence): Padding on each border. If a sequence of
length 4, it is used to pad left, top, right and bottom borders respectively.
fill: Pixel fill value. Default is 0.
"""
def __init__(self, padding, fill=0):
assert isinstance(padding, numbers.Number)
assert isinstance(fill, numbers.Number) or isinstance(fill, str) or isinstance(fill, tuple)
self.padding = padding
self.fill = fill
def __call__(self, img):
"""
Args:
img (PIL.Image): Image to be padded.
Returns:
PIL.Image: Padded image.
"""
return ImageOps.expand(img, border=self.padding, fill=self.fill)
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 isinstance(lambd, types.LambdaType)
self.lambd = lambd
def __call__(self, img):
return self.lambd(img)
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 (w, h), a square crop (size, size) is
made.
padding (int or sequence, optional): Optional padding on each border
of the image. Default is 0, i.e no padding. If a sequence of length
4 is provided, it is used to pad left, top, right, bottom borders
respectively.
"""
def __init__(self, size, padding=0):
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
self.size = size
self.padding = padding
def __call__(self, img):
"""
Args:
img (PIL.Image): Image to be cropped.
Returns:
PIL.Image: Cropped image.
"""
if self.padding > 0:
img = ImageOps.expand(img, border=self.padding, fill=0)
w, h = img.size
th, tw = self.size
if w == tw and h == th:
return img
x1 = random.randint(0, w - tw)
y1 = random.randint(0, h - th)
return img.crop((x1, y1, x1 + tw, y1 + th))
class RandomHorizontalFlip(object):
"""Horizontally flip the given PIL.Image randomly with a probability of 0.5."""
def __call__(self, img):
"""
Args:
img (PIL.Image): Image to be flipped.
Returns:
PIL.Image: Randomly flipped image.
"""
if random.random() < 0.5:
return img.transpose(Image.FLIP_LEFT_RIGHT)
return img
class RandomSizedCrop(object):
"""Crop the given PIL.Image to random size and aspect ratio.
A crop of random size of (0.08 to 1.0) of the original size and a random
aspect ratio of 3/4 to 4/3 of the original aspect ratio is made. This crop
is finally resized to given size.
This is popularly used to train the Inception networks.
Args:
size: size of the smaller edge
interpolation: Default: PIL.Image.BILINEAR
"""
def __init__(self, size, interpolation=Image.BILINEAR):
self.size = size
self.interpolation = interpolation
def __call__(self, img):
for attempt in range(10):
area = img.size[0] * img.size[1]
target_area = random.uniform(0.08, 1.0) * area
aspect_ratio = random.uniform(3. / 4, 4. / 3)
w = int(round(math.sqrt(target_area * aspect_ratio)))
h = int(round(math.sqrt(target_area / aspect_ratio)))
if random.random() < 0.5:
w, h = h, w
if w <= img.size[0] and h <= img.size[1]:
x1 = random.randint(0, img.size[0] - w)
y1 = random.randint(0, img.size[1] - h)
img = img.crop((x1, y1, x1 + w, y1 + h))
assert(img.size == (w, h))
return img.resize((self.size, self.size), self.interpolation)
# Fallback
scale = Scale(self.size, interpolation=self.interpolation)
crop = CenterCrop(self.size)
return crop(scale(img))
class ReLabel:
def __init__(self, olabel, nlabel):
self.olabel = olabel
self.nlabel = nlabel
def __call__(self, target):
target[target == self.olabel] = self.nlabel
return target
class ToLabel:
def __call__(self, target):
target = np.asarray(target, dtype=np.uint8)
target.flags.writeable = True
#This code is to verify that labels are in {0,255} and not within
target[target > 20] = 255
target[target <= 20] = 0
return torch.from_numpy(target).float().div(255)
