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- import torch.utils.data as data
- from PIL import Image
- import torchvision.transforms as transforms
- import numpy as np
- import random
- class BaseDataset(data.Dataset):
- def __init__(self):
- super(BaseDataset, self).__init__()
- def name(self):
- return 'BaseDataset'
- def initialize(self, opt):
- pass
- def get_params(opt, size):
- w, h = size
- new_h = h
- new_w = w
- if opt.resize_or_crop == 'resize_and_crop':
- new_h = new_w = opt.loadSize
- elif opt.resize_or_crop == 'scale_width_and_crop':
- new_w = opt.loadSize
- new_h = opt.loadSize * h // w
- x = random.randint(0, np.maximum(0, new_w - opt.fineSize))
- y = random.randint(0, np.maximum(0, new_h - opt.fineSize))
-
- flip = random.random() > 0.5
- return {'crop_pos': (x, y), 'flip': flip}
- def get_transform(opt, params, method=Image.BICUBIC, normalize=True):
- transform_list = []
- if 'resize' in opt.resize_or_crop:
- osize = [opt.loadSize, opt.loadSize]
- transform_list.append(transforms.Scale(osize, method))
- elif 'scale_width' in opt.resize_or_crop:
- transform_list.append(transforms.Lambda(lambda img: __scale_width(img, opt.loadSize, method)))
-
- if 'crop' in opt.resize_or_crop:
- transform_list.append(transforms.Lambda(lambda img: __crop(img, params['crop_pos'], opt.fineSize)))
- if opt.resize_or_crop == 'none':
- base = float(2 ** opt.n_downsample_global)
- if opt.netG == 'local':
- base *= (2 ** opt.n_local_enhancers)
- transform_list.append(transforms.Lambda(lambda img: __make_power_2(img, base, method)))
- if opt.isTrain and not opt.no_flip:
- transform_list.append(transforms.Lambda(lambda img: __flip(img, params['flip'])))
- transform_list += [transforms.ToTensor()]
- if normalize:
- transform_list += [transforms.Normalize((0.5, 0.5, 0.5),
- (0.5, 0.5, 0.5))]
- return transforms.Compose(transform_list)
- def normalize():
- return transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
- def __make_power_2(img, base, method=Image.BICUBIC):
- ow, oh = img.size
- h = int(round(oh / base) * base)
- w = int(round(ow / base) * base)
- if (h == oh) and (w == ow):
- return img
- return img.resize((w, h), method)
- def __scale_width(img, target_width, method=Image.BICUBIC):
- ow, oh = img.size
- if (ow == target_width):
- return img
- w = target_width
- h = int(target_width * oh / ow)
- return img.resize((w, h), method)
- def __crop(img, pos, size):
- ow, oh = img.size
- x1, y1 = pos
- tw = th = size
- if (ow > tw or oh > th):
- return img.crop((x1, y1, x1 + tw, y1 + th))
- return img
- def __flip(img, flip):
- if flip:
- return img.transpose(Image.FLIP_LEFT_RIGHT)
- return img
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