dataloaders/PolypTransforms.py

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