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sync_transforms.py
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sync_transforms.py
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import random
from PIL import Image, ImageOps, ImageFilter
import numpy as np
# class Compose(object):
# def __init__(self, transforms):
# self.transforms = transforms
# def __call__(self, img, mask):
# assert img.size == mask.size
# for t in self.transforms:
# img, mask = t(img, mask)
# return img, mask
# class RandomScale(object):
# def __init__(self, base_size, crop_size, resize_scale_range):
# self.base_size = base_size
# self.crop_size = crop_size
# self.resize_scale_range = resize_scale_range
# def __call__(self, img, mask):
# w, h = img.size
# # print("img.size:", img.size)
# short_size = random.randint(int(self.base_size * self.resize_scale_range[0]),
# int(self.base_size * self.resize_scale_range[1]))
# # print("short_size:", short_size)
# # if h > w:
# # ow = short_size
# # oh = int(1.0 * h * ow / w)
# # else:
# # oh = short_size
# # ow = int(1.0 * w * oh / h)
# ow, oh = short_size, short_size
# # print("ow, oh = ", ow, oh)
# img, mask = img.resize((ow, oh), Image.BILINEAR), mask.resize((ow, oh), Image.NEAREST)
# if short_size < self.crop_size:
# padh = self.crop_size - oh if oh < self.crop_size else 0
# padw = self.crop_size - ow if ow < self.crop_size else 0
# img = ImageOps.expand(img, border=(0, 0, padw, padh), fill=0)
# mask = ImageOps.expand(mask, border=(0, 0, padw, padh), fill=0)
# w, h = img.size
# img = np.array(img)
# mask = np.array(mask)
# num_crop = 0
# while num_crop < 5:
# x = random.randint(0, w - self.crop_size)
# y = random.randint(0, h - self.crop_size)
# endx = x + self.crop_size
# endy = y + self.crop_size
# patch = img[y:endy, x:endx]
# if (patch == 0).all():
# continue
# else:
# break
# img = img[y:endy, x:endx]
# mask = mask[y:endy, x:endx]
# img, mask = Image.fromarray(img), Image.fromarray(mask)
# return img, mask
# class RandomFlip(object):
# def __init__(self, flip_ratio=0.5):
# self.flip_ratio = flip_ratio
# def __call__(self, img, mask):
# if random.random() < self.flip_ratio:
# img, mask = img.transpose(Image.FLIP_LEFT_RIGHT), mask.transpose(Image.FLIP_LEFT_RIGHT)
# else:
# img, mask = img.transpose(Image.FLIP_TOP_BOTTOM), mask.transpose(Image.FLIP_TOP_BOTTOM)
# return img, mask
# class RandomGaussianBlur(object):
# def __init__(self, prop):
# self.prop = prop
# def __call__(self, img, mask, prop):
# if random.random() < self.prop:
# img = img.filter(ImageFilter.GaussianBlur)(radius=random.random())
# return img, mask
class ComposeWHU(object):
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, img_sar, img_opt, mask):
assert img_sar.size == mask.size and img_opt.size == mask.size
for t in self.transforms: #遍历transforms中的函数，按顺序应用。\
img_sar, img_opt, mask = t(img_sar, img_opt, mask)
return img_sar, img_opt, mask
class RandomFlipWHU(object):
def __init__(self, flip_ratio=0.5):
self.flip_ratio = flip_ratio
def __call__(self, img_sar, img_opt, mask): #实例化后，调用类的时候，会自动调用__call__方法
if random.random() < self.flip_ratio:
img_sar, img_opt, mask = img_sar.transpose(Image.FLIP_LEFT_RIGHT), img_opt.transpose(Image.FLIP_LEFT_RIGHT), mask.transpose(Image.FLIP_LEFT_RIGHT)
else:
img_sar, img_opt, mask = img_sar.transpose(Image.FLIP_TOP_BOTTOM), img_opt.transpose(Image.FLIP_TOP_BOTTOM),mask.transpose(Image.FLIP_TOP_BOTTOM)
return img_sar, img_opt, mask