"""This module implements an abstract base class (ABC) 'BaseDataset' for datasets. It also includes common transformation functions (e.g., get_transform, __scale_width), which can be later used in subclasses. """ import random import numpy as np import torch.utils.data as data from PIL import Image,ImageFilter import torchvision.transforms as transforms from abc import ABC, abstractmethod import math class BaseDataset(data.Dataset, ABC): """This class is an abstract base class (ABC) for datasets. To create a subclass, you need to implement the following four functions: -- <__init__>: initialize the class, first call BaseDataset.__init__(self, opt). -- <__len__>: return the size of dataset. -- <__getitem__>: get a data point. -- : (optionally) add dataset-specific options and set default options. """ def __init__(self, opt): """Initialize the class; save the options in the class Parameters: opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions """ self.opt = opt self.root = opt.dataroot @staticmethod def modify_commandline_options(parser, is_train): """Add new dataset-specific options, and rewrite default values for existing options. Parameters: parser -- original option parser is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options. Returns: the modified parser. """ return parser @abstractmethod def __len__(self): """Return the total number of images in the dataset.""" return 0 @abstractmethod def __getitem__(self, index): """Return a data point and its metadata information. Parameters: index - - a random integer for data indexing Returns: a dictionary of data with their names. It ususally contains the data itself and its metadata information. """ pass def get_params(opt, size, test=False): w, h = size new_h = h new_w = w angle = 0 if opt.preprocess == 'resize_and_crop': new_h = new_w = opt.load_size if 'rotate' in opt.preprocess and test is False: angle = random.uniform(0, opt.angle) # print(angle) new_w = int(new_w * math.cos(angle*math.pi/180) \ + new_h*math.sin(angle*math.pi/180)) new_h = int(new_h * math.cos(angle*math.pi/180) \ + new_w*math.sin(angle*math.pi/180)) new_w = min(new_w,new_h) new_h = min(new_w,new_h) # print(new_h,new_w) x = random.randint(0, np.maximum(0, new_w - opt.crop_size)) y = random.randint(0, np.maximum(0, new_h - opt.crop_size)) # print('x,y: ',x,y) flip = random.random() > 0.5 # left-right return {'crop_pos': (x, y), 'flip': flip, 'angle': angle} def get_transform(opt, params=None, grayscale=False, method=Image.BICUBIC, convert=True, normalize=True, test=False): transform_list = [] if grayscale: transform_list.append(transforms.Grayscale(1)) if 'resize' in opt.preprocess: osize = [opt.load_size, opt.load_size] transform_list.append(transforms.Resize(osize, method)) # gaussian blur if 'blur' in opt.preprocess: transform_list.append(transforms.Lambda(lambda img: __blur(img))) if 'rotate' in opt.preprocess and test==False: if params is None: transform_list.append(transforms.RandomRotation(5)) else: degree = params['angle'] transform_list.append(transforms.Lambda(lambda img: __rotate(img, degree))) if 'crop' in opt.preprocess: if params is None: transform_list.append(transforms.RandomCrop(opt.crop_size)) else: transform_list.append(transforms.Lambda(lambda img: __crop(img, params['crop_pos'], opt.crop_size))) if not opt.no_flip: if params is None: transform_list.append(transforms.RandomHorizontalFlip()) elif params['flip']: transform_list.append(transforms.Lambda(lambda img: __flip(img, params['flip']))) if convert: 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 __blur(img): if img.mode == 'RGB': img = img.filter(ImageFilter.GaussianBlur(radius=random.random())) return img def __rotate(img, degree): if img.mode =='RGB': # set img padding == 128 img2 = img.convert('RGBA') rot = img2.rotate(degree,expand=1) fff = Image.new('RGBA', rot.size, (128,) * 4) # 灰色 out = Image.composite(rot, fff, rot) img = out.convert(img.mode) return img else: # set label padding == 0 img2 = img.convert('RGBA') rot = img2.rotate(degree,expand=1) # a white image same size as rotated image fff = Image.new('RGBA', rot.size, (255,) * 4) # create a composite image using the alpha layer of rot as a mask out = Image.composite(rot, fff, rot) img = out.convert(img.mode) return img def __crop(img, pos, size): ow, oh = img.size x1, y1 = pos tw = th = size # print('imagesize:',ow,oh) # only 图像尺寸大于截取尺寸才截取，否则要padding if (ow > tw and oh > th): return img.crop((x1, y1, x1 + tw, y1 + th)) size = [size, size] if img.mode == 'RGB': new_image = Image.new('RGB', size, (128, 128, 128)) new_image.paste(img, (int((1+size[1] - img.size[0]) / 2), int((1+size[0] - img.size[1]) / 2))) return new_image else: new_image = Image.new(img.mode, size, 255) # upper left corner new_image.paste(img, (int((1 + size[1] - img.size[0]) / 2), int((1 + size[0] - img.size[1]) / 2))) return new_image def __flip(img, flip): if flip: return img.transpose(Image.FLIP_LEFT_RIGHT) return img def __print_size_warning(ow, oh, w, h): """Print warning information about image size(only print once)""" if not hasattr(__print_size_warning, 'has_printed'): print("The image size needs to be a multiple of 4. " "The loaded image size was (%d, %d), so it was adjusted to " "(%d, %d). This adjustment will be done to all images " "whose sizes are not multiples of 4" % (ow, oh, w, h)) __print_size_warning.has_printed = True