import os
import random
import time
import numpy as np
from PIL import Image, ImageOps
import numbers
from torchvision.transforms import Pad,RandomHorizontalFlip
from torchvision.transforms import ToTensor, ToPILImage
def _is_pil_image(img):
return isinstance(img, Image.Image)
def crop(img, i, j, h, w):
"""Crop the given PIL Image.
Args:
img (PIL Image): Image to be cropped.
i: Upper pixel coordinate.
j: Left pixel coordinate.
h: Height of the cropped image.
w: Width of the cropped image.
Returns:
PIL Image: Cropped image.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
return img.crop((j, i, j + w, i + h))
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 0, i.e no padding. If a sequence of length
4 is provided, it is used to pad left, top, right, bottom borders
respectively.
#对pytorch包内RandomCrop做了修改，可以同时处理image和label，保证为同一区域。
"""
def __init__(self, size, padding=0):
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
self.size = size
self.padding = padding
@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
tw, th = 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, label): #crop the same area of ori-image and label
"""
Args:
img (PIL Image): Image to be cropped.
Returns:
PIL Image: Cropped image.
"""
if self.padding > 0:
img = F.pad(img, self.padding)
i, j, h, w = self.get_params(img, self.size)
return crop(img, i, j, h, w), crop(label, i, j, h, w)
def __repr__(self):
return self.__class__.__name__ + '(size={0})'.format(self.size)
class RandomFlip(object):
"""Randomflip the given PIL Image randomly with a given probability. horizontal or vertical
Args:
p (float): probability of the image being flipped. Default value is 0.5
"""
# make sure that crop area of image and label are the same
def __init__(self, p=0.5):
self.p = p
def __call__(self, img, label):
"""
Args:
img (PIL Image): Image to be flipped.
Returns:
PIL Image: Randomly flipped image.
"""
if random.random() < self.p:
img = img.transpose(Image.FLIP_LEFT_RIGHT)
label = label.transpose(Image.FLIP_LEFT_RIGHT) #left or right
if random.random() < self.p:
img = img.transpose(Image.FLIP_TOP_BOTTOM)
label = label.transpose(Image.FLIP_TOP_BOTTOM) # bottom or top
return img, label
def __repr__(self):
return self.__class__.__name__ + '(p={})'.format(self.p)
class CenterCrop(object):
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, label):
"""
Args:
img (PIL Image): Image to be cropped.
Returns:
PIL Image: Cropped image.
"""
w, h = img.size
th, tw = self.size
i = int(round((h - th) / 2.))
j = int(round((w - tw) / 2.))
return crop(img, j,i, tw, th), crop(label, j,i, tw, th)
def __repr__(self):
return self.__class__.__name__ + '(size={0})'.format(self.size)
class RandomRotate(object):
def __init__(self, degree):
self.degree = degree
def __call__(self, img, mask):
rotate_degree = random.random() * 2 * self.degree - self.degree
return img.rotate(rotate_degree, Image.BILINEAR), mask.rotate(rotate_degree, Image.NEAREST)
