from __future__ import division import torch import math import sys import random from PIL import Image try: import accimage except ImportError: accimage = None import numpy as np import numbers import types import collections import warnings from torchvision.transforms import functional as F if sys.version_info < (3, 3): Sequence = collections.Sequence Iterable = collections.Iterable else: Sequence = collections.abc.Sequence Iterable = collections.abc.Iterable __all__ = ["Compose", "ToTensor", "ToPILImage", "Normalize", "Resize", "CenterCrop", "Pad", "Lambda", "RandomApply", "RandomChoice", "RandomOrder", "RandomCrop", "RandomHorizontalFlip", "RandomVerticalFlip", "RandomResizedCrop", "FiveCrop", "TenCrop", "ColorJitter", "RandomRotation", "RandomAffine", "RandomPerspective"] _pil_interpolation_to_str = { Image.NEAREST: 'PIL.Image.NEAREST', Image.BILINEAR: 'PIL.Image.BILINEAR', Image.BICUBIC: 'PIL.Image.BICUBIC', Image.LANCZOS: 'PIL.Image.LANCZOS', Image.HAMMING: 'PIL.Image.HAMMING', Image.BOX: 'PIL.Image.BOX', } class Compose(object): def __init__(self, transforms): self.transforms = transforms def __call__(self, img, mask): for t in self.transforms: img, mask = t(img, mask) return img, mask class ToTensor(object): def __call__(self, img, mask): # return F.to_tensor(img), F.to_tensor(mask) img = np.array(img) img = torch.from_numpy(img).permute(2, 0, 1).float() # TODO add division by 255 to match torch.ToTensor()? mask = torch.from_numpy(np.array(mask)).float() return img, mask class ToPILImage(object): def __init__(self, mode=None): self.mode = mode def __call__(self, img, mask): return F.to_pil_image(img, self.mode), F.to_pil_image(mask, self.mode) class Normalize(object): def __init__(self, mean, std, inplace=False): self.mean = mean self.std = std self.inplace = inplace def __call__(self, img, mask): return F.normalize(img, self.mean, self.std, self.inplace), mask class Resize(object): def __init__(self, size, interpolation=Image.BILINEAR, do_mask=True): assert isinstance(size, int) or (isinstance(size, Iterable) and len(size) == 2) self.size = size self.interpolation = interpolation self.do_mask = do_mask def __call__(self, img, mask): if self.do_mask: return F.resize(img, self.size, Image.BICUBIC), F.resize(mask, self.size, Image.BICUBIC) else: return F.resize(img, self.size, Image.BICUBIC), mask class CenterCrop(object): def __init__(self, size): if isinstance(size, numbers.Number): self.size = (int(size), int(size)) else: self.size = size def __call__(self, img, mask): return F.center_crop(img, self.size), F.center_crop(mask, self.size) class Pad(object): def __init__(self, padding, fill=0, padding_mode='constant'): assert isinstance(padding, (numbers.Number, tuple)) assert isinstance(fill, (numbers.Number, str, tuple)) assert padding_mode in ['constant', 'edge', 'reflect', 'symmetric'] if isinstance(padding, Sequence) and len(padding) not in [2, 4]: raise ValueError("Padding must be an int or a 2, or 4 element tuple, not a " + "{} element tuple".format(len(padding))) self.padding = padding self.fill = fill self.padding_mode = padding_mode def __call__(self, img, mask): return F.pad(img, self.padding, self.fill, self.padding_mode), \ F.pad(mask, self.padding, self.fill, self.padding_mode) class Lambda(object): def __init__(self, lambd): assert callable(lambd), repr(type(lambd).__name__) + " object is not callable" self.lambd = lambd def __call__(self, img, mask): return self.lambd(img), self.lambd(mask) class Lambda_image(object): def __init__(self, lambd): assert callable(lambd), repr(type(lambd).__name__) + " object is not callable" self.lambd = lambd def __call__(self, img, mask): return self.lambd(img), mask class RandomTransforms(object): def __init__(self, transforms): assert isinstance(transforms, (list, tuple)) self.transforms = transforms def __call__(self, *args, **kwargs): raise NotImplementedError() class RandomApply(RandomTransforms): def __init__(self, transforms, p=0.5): super(RandomApply, self).__init__(transforms) self.p = p def __call__(self, img, mask): if self.p < random.random(): return img, mask for t in self.transforms: img, mask = t(img, mask) return img, mask class RandomOrder(RandomTransforms): def __call__(self, img, mask): order = list(range(len(self.transforms))) random.shuffle(order) for i in order: img, mask = self.transforms[i](img, mask) return img, mask class RandomChoice(RandomTransforms): def __call__(self, img, mask): t = random.choice(self.transforms) return t(img, mask) class RandomCrop(object): def __init__(self, size, padding=None, pad_if_needed=False, fill=0, padding_mode='constant'): if isinstance(size, numbers.Number): self.size = (int(size), int(size)) else: self.size = size self.padding = padding self.pad_if_needed = pad_if_needed self.fill = fill self.padding_mode = padding_mode @staticmethod def get_params(img, output_size): w, h = img.size th, tw = 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, mask): if self.padding is not None: img = F.pad(img, self.padding, self.fill, self.padding_mode) # pad the width if needed if self.pad_if_needed and img.size[0] < self.size[1]: img = F.pad(img, (self.size[1] - img.size[0], 0), self.fill, self.padding_mode) # pad the height if needed if self.pad_if_needed and img.size[1] < self.size[0]: img = F.pad(img, (0, self.size[0] - img.size[1]), self.fill, self.padding_mode) i, j, h, w = self.get_params(img, self.size) return F.crop(img, i, j, h, w), F.crop(mask, i, j, h, w) class RandomHorizontalFlip(object): def __init__(self, p=0.5): self.p = p def __call__(self, img, mask): if random.random() < self.p: return F.hflip(img), F.hflip(mask) return img, mask class RandomVerticalFlip(object): def __init__(self, p=0.5): self.p = p def __call__(self, img, mask): if random.random() < self.p: return F.vflip(img), F.vflip(mask) return img, mask class RandomPerspective(object): def __init__(self, distortion_scale=0.5, p=0.5, interpolation=Image.BICUBIC): self.p = p self.interpolation = interpolation self.distortion_scale = distortion_scale def __call__(self, img, mask): if not F._is_pil_image(img): raise TypeError('img should be PIL Image. Got {}'.format(type(img))) if random.random() < self.p: width, height = img.size startpoints, endpoints = self.get_params(width, height, self.distortion_scale) return F.perspective(img, startpoints, endpoints, self.interpolation), \ F.perspective(mask, startpoints, endpoints, Image.NEAREST) return img, mask @staticmethod def get_params(width, height, distortion_scale): half_height = int(height / 2) half_width = int(width / 2) topleft = (random.randint(0, int(distortion_scale * half_width)), random.randint(0, int(distortion_scale * half_height))) topright = (random.randint(width - int(distortion_scale * half_width) - 1, width - 1), random.randint(0, int(distortion_scale * half_height))) botright = (random.randint(width - int(distortion_scale * half_width) - 1, width - 1), random.randint(height - int(distortion_scale * half_height) - 1, height - 1)) botleft = (random.randint(0, int(distortion_scale * half_width)), random.randint(height - int(distortion_scale * half_height) - 1, height - 1)) startpoints = [(0, 0), (width - 1, 0), (width - 1, height - 1), (0, height - 1)] endpoints = [topleft, topright, botright, botleft] return startpoints, endpoints class RandomResizedCrop(object): def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.), interpolation=Image.BILINEAR): if isinstance(size, tuple): self.size = size else: self.size = (size, size) if (scale[0] > scale[1]) or (ratio[0] > ratio[1]): warnings.warn("range should be of kind (min, max)") self.interpolation = interpolation self.scale = scale self.ratio = ratio @staticmethod def get_params(img, scale, ratio): area = img.size[0] * img.size[1] for attempt in range(10): target_area = random.uniform(*scale) * area log_ratio = (math.log(ratio[0]), math.log(ratio[1])) aspect_ratio = math.exp(random.uniform(*log_ratio)) w = int(round(math.sqrt(target_area * aspect_ratio))) h = int(round(math.sqrt(target_area / aspect_ratio))) if w <= img.size[0] and h <= img.size[1]: i = random.randint(0, img.size[1] - h) j = random.randint(0, img.size[0] - w) return i, j, h, w # Fallback to central crop in_ratio = img.size[0] / img.size[1] if (in_ratio < min(ratio)): w = img.size[0] h = w / min(ratio) elif (in_ratio > max(ratio)): h = img.size[1] w = h * max(ratio) else: # whole image w = img.size[0] h = img.size[1] i = (img.size[1] - h) // 2 j = (img.size[0] - w) // 2 return i, j, h, w def __call__(self, img, mask): i, j, h, w = self.get_params(img, self.scale, self.ratio) return F.resized_crop(img, i, j, h, w, self.size, self.interpolation), \ F.resized_crop(mask, i, j, h, w, self.size, Image.NEAREST) class FiveCrop(object): def __init__(self, size): self.size = size if isinstance(size, numbers.Number): self.size = (int(size), int(size)) else: assert len(size) == 2, "Please provide only two dimensions (h, w) for size." self.size = size def __call__(self, img, mask): return F.five_crop(img, self.size), F.five_crop(mask, self.size) class TenCrop(object): def __init__(self, size, vertical_flip=False): self.size = size if isinstance(size, numbers.Number): self.size = (int(size), int(size)) else: assert len(size) == 2, "Please provide only two dimensions (h, w) for size." self.size = size self.vertical_flip = vertical_flip def __call__(self, img, mask): return F.ten_crop(img, self.size, self.vertical_flip), F.ten_crop(mask, self.size, self.vertical_flip) class ColorJitter(object): def __init__(self, brightness=0, contrast=0, saturation=0, hue=0): self.brightness = self._check_input(brightness, 'brightness') self.contrast = self._check_input(contrast, 'contrast') self.saturation = self._check_input(saturation, 'saturation') self.hue = self._check_input(hue, 'hue', center=0, bound=(-0.5, 0.5), clip_first_on_zero=False) def _check_input(self, value, name, center=1, bound=(0, float('inf')), clip_first_on_zero=True): if isinstance(value, numbers.Number): if value < 0: raise ValueError("If {} is a single number, it must be non negative.".format(name)) value = [center - value, center + value] if clip_first_on_zero: value[0] = max(value[0], 0) elif isinstance(value, (tuple, list)) and len(value) == 2: if not bound[0] <= value[0] <= value[1] <= bound[1]: raise ValueError("{} values should be between {}".format(name, bound)) else: raise TypeError("{} should be a single number or a list/tuple with lenght 2.".format(name)) # if value is 0 or (1., 1.) for brightness/contrast/saturation # or (0., 0.) for hue, do nothing if value[0] == value[1] == center: value = None return value @staticmethod def get_params(brightness, contrast, saturation, hue): transforms = [] if brightness is not None: brightness_factor = random.uniform(brightness[0], brightness[1]) transforms.append(Lambda_image(lambda img: F.adjust_brightness(img, brightness_factor))) if contrast is not None: contrast_factor = random.uniform(contrast[0], contrast[1]) transforms.append(Lambda_image(lambda img: F.adjust_contrast(img, contrast_factor))) if saturation is not None: saturation_factor = random.uniform(saturation[0], saturation[1]) transforms.append(Lambda_image(lambda img: F.adjust_saturation(img, saturation_factor))) if hue is not None: hue_factor = random.uniform(hue[0], hue[1]) transforms.append(Lambda_image(lambda img: F.adjust_hue(img, hue_factor))) random.shuffle(transforms) transform = Compose(transforms) return transform def __call__(self, img, mask): transform = self.get_params(self.brightness, self.contrast, self.saturation, self.hue) return transform(img, mask) class RandomRotation(object): def __init__(self, degrees, resample=False, expand=False, center=None): if isinstance(degrees, numbers.Number): if degrees < 0: raise ValueError("If degrees is a single number, it must be positive.") self.degrees = (-degrees, degrees) else: if len(degrees) != 2: raise ValueError("If degrees is a sequence, it must be of len 2.") self.degrees = degrees self.resample = resample self.expand = expand self.center = center @staticmethod def get_params(degrees): angle = random.uniform(degrees[0], degrees[1]) return angle def __call__(self, img, mask): angle = self.get_params(self.degrees) return F.rotate(img, angle, Image.BILINEAR, self.expand, self.center), \ F.rotate(mask, angle, Image.NEAREST, self.expand, self.center) class RandomAffine(object): def __init__(self, degrees, translate=None, scale=None, shear=None, resample=False, fillcolor=0): if isinstance(degrees, numbers.Number): if degrees < 0: raise ValueError("If degrees is a single number, it must be positive.") self.degrees = (-degrees, degrees) else: assert isinstance(degrees, (tuple, list)) and len(degrees) == 2, \ "degrees should be a list or tuple and it must be of length 2." self.degrees = degrees if translate is not None: assert isinstance(translate, (tuple, list)) and len(translate) == 2, \ "translate should be a list or tuple and it must be of length 2." for t in translate: if not (0.0 <= t <= 1.0): raise ValueError("translation values should be between 0 and 1") self.translate = translate if scale is not None: assert isinstance(scale, (tuple, list)) and len(scale) == 2, \ "scale should be a list or tuple and it must be of length 2." for s in scale: if s <= 0: raise ValueError("scale values should be positive") self.scale = scale if shear is not None: if isinstance(shear, numbers.Number): if shear < 0: raise ValueError("If shear is a single number, it must be positive.") self.shear = (-shear, shear) else: assert isinstance(shear, (tuple, list)) and len(shear) == 2, \ "shear should be a list or tuple and it must be of length 2." self.shear = shear else: self.shear = shear self.resample = resample self.fillcolor = fillcolor @staticmethod def get_params(degrees, translate, scale_ranges, shears, img_size): angle = random.uniform(degrees[0], degrees[1]) if translate is not None: max_dx = translate[0] * img_size[0] max_dy = translate[1] * img_size[1] translations = (np.round(random.uniform(-max_dx, max_dx)), np.round(random.uniform(-max_dy, max_dy))) else: translations = (0, 0) if scale_ranges is not None: scale = random.uniform(scale_ranges[0], scale_ranges[1]) else: scale = 1.0 if shears is not None: shear = random.uniform(shears[0], shears[1]) else: shear = 0.0 return angle, translations, scale, shear def __call__(self, img, mask): ret = self.get_params(self.degrees, self.translate, self.scale, self.shear, img.size) return F.affine(img, *ret, interpolation=Image.BILINEAR, fill=self.fillcolor), \ F.affine(mask, *ret, interpolation=Image.NEAREST, fill=self.fillcolor) def get_cub_transform(): transform_train = Compose([ ToPILImage(), Resize((256, 256)), RandomHorizontalFlip(), RandomAffine(22, scale=(0.75, 1.25)), ToTensor(), Normalize(mean=[255*0.485, 255*0.456, 255*0.406], std=[255*0.229, 255*0.224, 255*0.225]) ]) transform_test = Compose([ ToPILImage(), Resize((256, 256)), ToTensor(), Normalize(mean=[255*0.485, 255*0.456, 255*0.406], std=[255*0.229, 255*0.224, 255*0.225]) ]) return transform_train, transform_test def get_glas_transform(): transform_train = Compose([ ToPILImage(), # Resize((256, 256)), ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1), RandomHorizontalFlip(), RandomAffine(5, scale=(0.75, 1.25)), ToTensor(), # Normalize(mean=[255*0.485, 255*0.456, 255*0.406], std=[255*0.229, 255*0.224, 255*0.225]) ]) transform_test = Compose([ ToPILImage(), # Resize((256, 256)), ToTensor(), # Normalize(mean=[255*0.485, 255*0.456, 255*0.406], std=[255*0.229, 255*0.224, 255*0.225]) ]) return transform_train, transform_test # def get_glas_transform(): # transform_train = Compose([ # ToPILImage(), # Resize((256, 256)), # ColorJitter(brightness=0.2, # contrast=0.2, # saturation=0.2, # hue=0.1), # RandomHorizontalFlip(), # RandomAffine(5, scale=(0.75, 1.25)), # ToTensor(), # Normalize(mean=[255*0.485, 255*0.456, 255*0.406], std=[255*0.229, 255*0.224, 255*0.225]) # ]) # transform_test = Compose([ # ToPILImage(), # Resize((256, 256)), # ToTensor(), # Normalize(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375]) # ]) # return transform_train, transform_test def get_monu_transform(args): Idim = int(args['Idim']) transform_train = Compose([ ToPILImage(), # Resize((Idim, Idim)), ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1), RandomHorizontalFlip(), RandomAffine(int(args['rotate']), scale=(float(args['scale1']), float(args['scale2']))), ToTensor(), # Normalize(mean=[142.07, 98.48, 132.96], std=[65.78, 57.05, 57.78]) ]) transform_test = Compose([ ToPILImage(), # Resize((Idim, Idim)), ToTensor(), # Normalize(mean=[142.07, 98.48, 132.96], std=[65.78, 57.05, 57.78]) ]) return transform_train, transform_test def get_polyp_transform(): transform_train = Compose([ # Resize((352, 352)), ToPILImage(), ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1), RandomVerticalFlip(), RandomHorizontalFlip(), RandomAffine(90, scale=(0.75, 1.25)), ToTensor(), # Normalize([105.61, 63.69, 45.67], # [83.08, 55.86, 42.59]) ]) transform_test = Compose([ # Resize((352, 352)), ToPILImage(), ToTensor(), # Normalize([105.61, 63.69, 45.67], # [83.08, 55.86, 42.59]) ]) return transform_train, transform_test def get_polyp_support_train_transform(): transform_train = Compose([ ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1), RandomVerticalFlip(), RandomHorizontalFlip(), RandomAffine(90, scale=(0.75, 1.25)), ]) return transform_train