PaddleX/paddlex/paddleseg/transforms/transforms.py

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import random

import cv2
import numpy as np
from PIL import Image

from paddlex.paddleseg.cvlibs import manager
from paddlex.paddleseg.transforms import functional


@manager.TRANSFORMS.add_component
class Compose:
    """
    Do transformation on input data with corresponding pre-processing and augmentation operations.
    The shape of input data to all operations is [height, width, channels].

    Args:
        transforms (list): A list contains data pre-processing or augmentation. Empty list means only reading images, no transformation.
        to_rgb (bool, optional): If converting image to RGB color space. Default: True.

    Raises:
        TypeError: When 'transforms' is not a list.
        ValueError: when the length of 'transforms' is less than 1.
    """

    def __init__(self, transforms, to_rgb=True):
        if not isinstance(transforms, list):
            raise TypeError('The transforms must be a list!')
        self.transforms = transforms
        self.to_rgb = to_rgb

    def __call__(self, im, label=None):
        """
        Args:
            im (str|np.ndarray): It is either image path or image object.
            label (str|np.ndarray): It is either label path or label ndarray.

        Returns:
            (tuple). A tuple including image, image info, and label after transformation.
        """
        if isinstance(im, str):
            im = cv2.imread(im).astype('float32')
        if isinstance(label, str):
            label = np.asarray(Image.open(label))
        if im is None:
            raise ValueError('Can\'t read The image file {}!'.format(im))
        if self.to_rgb:
            im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)

        for op in self.transforms:
            outputs = op(im, label)
            im = outputs[0]
            if len(outputs) == 2:
                label = outputs[1]
        im = np.transpose(im, (2, 0, 1))
        return (im, label)


@manager.TRANSFORMS.add_component
class RandomHorizontalFlip:
    """
    Flip an image horizontally with a certain probability.

    Args:
        prob (float, optional): A probability of horizontally flipping. Default: 0.5.
    """

    def __init__(self, prob=0.5):
        self.prob = prob

    def __call__(self, im, label=None):
        if random.random() < self.prob:
            im = functional.horizontal_flip(im)
            if label is not None:
                label = functional.horizontal_flip(label)
        if label is None:
            return (im, )
        else:
            return (im, label)


@manager.TRANSFORMS.add_component
class RandomVerticalFlip:
    """
    Flip an image vertically with a certain probability.

    Args:
        prob (float, optional): A probability of vertical flipping. Default: 0.1.
    """

    def __init__(self, prob=0.1):
        self.prob = prob

    def __call__(self, im, label=None):
        if random.random() < self.prob:
            im = functional.vertical_flip(im)
            if label is not None:
                label = functional.vertical_flip(label)
        if label is None:
            return (im, )
        else:
            return (im, label)


@manager.TRANSFORMS.add_component
class Resize:
    """
    Resize an image.

    Args:
        target_size (list|tuple, optional): The target size of image. Default: (512, 512).
        interp (str, optional): The interpolation mode of resize is consistent with opencv.
            ['NEAREST', 'LINEAR', 'CUBIC', 'AREA', 'LANCZOS4', 'RANDOM']. Note that when it is
            'RANDOM', a random interpolation mode would be specified. Default: "LINEAR".

    Raises:
        TypeError: When 'target_size' type is neither list nor tuple.
        ValueError: When "interp" is out of pre-defined methods ('NEAREST', 'LINEAR', 'CUBIC',
        'AREA', 'LANCZOS4', 'RANDOM').
    """

    # The interpolation mode
    interp_dict = {
        'NEAREST': cv2.INTER_NEAREST,
        'LINEAR': cv2.INTER_LINEAR,
        'CUBIC': cv2.INTER_CUBIC,
        'AREA': cv2.INTER_AREA,
        'LANCZOS4': cv2.INTER_LANCZOS4
    }

    def __init__(self, target_size=(512, 512), interp='LINEAR'):
        self.interp = interp
        if not (interp == "RANDOM" or interp in self.interp_dict):
            raise ValueError("`interp` should be one of {}".format(
                self.interp_dict.keys()))
        if isinstance(target_size, list) or isinstance(target_size, tuple):
            if len(target_size) != 2:
                raise ValueError(
                    '`target_size` should include 2 elements, but it is {}'.
                    format(target_size))
        else:
            raise TypeError(
                "Type of `target_size` is invalid. It should be list or tuple, but it is {}"
                .format(type(target_size)))

        self.target_size = target_size

    def __call__(self, im, label=None):
        """
        Args:
            im (np.ndarray): The Image data.
            label (np.ndarray, optional): The label data. Default: None.

        Returns:
            (tuple). When label is None, it returns (im, ), otherwise it returns (im, label),

        Raises:
            TypeError: When the 'img' type is not numpy.
            ValueError: When the length of "im" shape is not 3.
        """

        if not isinstance(im, np.ndarray):
            raise TypeError("Resize: image type is not numpy.")
        if len(im.shape) != 3:
            raise ValueError('Resize: image is not 3-dimensional.')
        if self.interp == "RANDOM":
            interp = random.choice(list(self.interp_dict.keys()))
        else:
            interp = self.interp
        im = functional.resize(im, self.target_size, self.interp_dict[interp])
        if label is not None:
            label = functional.resize(label, self.target_size,
                                      cv2.INTER_NEAREST)

        if label is None:
            return (im, )
        else:
            return (im, label)


@manager.TRANSFORMS.add_component
class ResizeByLong:
    """
    Resize the long side of an image to given size, and then scale the other side proportionally.

    Args:
        long_size (int): The target size of long side.
    """

    def __init__(self, long_size):
        self.long_size = long_size

    def __call__(self, im, label=None):
        """
        Args:
            im (np.ndarray): The Image data.
            label (np.ndarray, optional): The label data. Default: None.

        Returns:
            (tuple). When label is None, it returns (im, ), otherwise it returns (im, label).
        """

        im = functional.resize_long(im, self.long_size)
        if label is not None:
            label = functional.resize_long(label, self.long_size,
                                           cv2.INTER_NEAREST)

        if label is None:
            return (im, )
        else:
            return (im, label)


@manager.TRANSFORMS.add_component
class LimitLong:
    """
    Limit the long edge of image.

    If the long edge is larger than max_long, resize the long edge
    to max_long, while scale the short edge proportionally.

    If the long edge is smaller than min_long, resize the long edge
    to min_long, while scale the short edge proportionally.

    Args:
        max_long (int, optional): If the long edge of image is larger than max_long,
            it will be resize to max_long. Default: None.
        min_long (int, optional): If the long edge of image is smaller than min_long,
            it will be resize to min_long. Default: None.
    """

    def __init__(self, max_long=None, min_long=None):
        if max_long is not None:
            if not isinstance(max_long, int):
                raise TypeError(
                    "Type of `max_long` is invalid. It should be int, but it is {}"
                    .format(type(max_long)))
        if min_long is not None:
            if not isinstance(min_long, int):
                raise TypeError(
                    "Type of `min_long` is invalid. It should be int, but it is {}"
                    .format(type(min_long)))
        if (max_long is not None) and (min_long is not None):
            if min_long > max_long:
                raise ValueError(
                    '`max_long should not smaller than min_long, but they are {} and {}'
                    .format(max_long, min_long))
        self.max_long = max_long
        self.min_long = min_long

    def __call__(self, im, label=None):
        """
        Args:
            im (np.ndarray): The Image data.
            label (np.ndarray, optional): The label data. Default: None.

        Returns:
            (tuple). When label is None, it returns (im, ), otherwise it returns (im, label).
        """
        h, w = im.shape[0], im.shape[1]
        long_edge = max(h, w)
        target = long_edge
        if (self.max_long is not None) and (long_edge > self.max_long):
            target = self.max_long
        elif (self.min_long is not None) and (long_edge < self.min_long):
            target = self.min_long

        if target != long_edge:
            im = functional.resize_long(im, target)
            if label is not None:
                label = functional.resize_long(label, target,
                                               cv2.INTER_NEAREST)

        if label is None:
            return (im, )
        else:
            return (im, label)


@manager.TRANSFORMS.add_component
class ResizeRangeScaling:
    """
    Resize the long side of an image into a range, and then scale the other side proportionally.

    Args:
        min_value (int, optional): The minimum value of long side after resize. Default: 400.
        max_value (int, optional): The maximum value of long side after resize. Default: 600.
    """

    def __init__(self, min_value=400, max_value=600):
        if min_value > max_value:
            raise ValueError('min_value must be less than max_value, '
                             'but they are {} and {}.'.format(min_value,
                                                              max_value))
        self.min_value = min_value
        self.max_value = max_value

    def __call__(self, im, label=None):
        """
        Args:
            im (np.ndarray): The Image data.
            label (np.ndarray, optional): The label data. Default: None.

        Returns:
            (tuple). When label is None, it returns (im, ), otherwise it returns (im, label).
        """

        if self.min_value == self.max_value:
            random_size = self.max_value
        else:
            random_size = int(
                np.random.uniform(self.min_value, self.max_value) + 0.5)
        im = functional.resize_long(im, random_size, cv2.INTER_LINEAR)
        if label is not None:
            label = functional.resize_long(label, random_size,
                                           cv2.INTER_NEAREST)

        if label is None:
            return (im, )
        else:
            return (im, label)


@manager.TRANSFORMS.add_component
class ResizeStepScaling:
    """
    Scale an image proportionally within a range.

    Args:
        min_scale_factor (float, optional): The minimum scale. Default: 0.75.
        max_scale_factor (float, optional): The maximum scale. Default: 1.25.
        scale_step_size (float, optional): The scale interval. Default: 0.25.

    Raises:
        ValueError: When min_scale_factor is smaller than max_scale_factor.
    """

    def __init__(self,
                 min_scale_factor=0.75,
                 max_scale_factor=1.25,
                 scale_step_size=0.25):
        if min_scale_factor > max_scale_factor:
            raise ValueError(
                'min_scale_factor must be less than max_scale_factor, '
                'but they are {} and {}.'.format(min_scale_factor,
                                                 max_scale_factor))
        self.min_scale_factor = min_scale_factor
        self.max_scale_factor = max_scale_factor
        self.scale_step_size = scale_step_size

    def __call__(self, im, label=None):
        """
        Args:
            im (np.ndarray): The Image data.
            label (np.ndarray, optional): The label data. Default: None.

        Returns:
            (tuple). When label is None, it returns (im, ), otherwise it returns (im, label).
        """

        if self.min_scale_factor == self.max_scale_factor:
            scale_factor = self.min_scale_factor

        elif self.scale_step_size == 0:
            scale_factor = np.random.uniform(self.min_scale_factor,
                                             self.max_scale_factor)

        else:
            num_steps = int((self.max_scale_factor - self.min_scale_factor) /
                            self.scale_step_size + 1)
            scale_factors = np.linspace(self.min_scale_factor,
                                        self.max_scale_factor,
                                        num_steps).tolist()
            np.random.shuffle(scale_factors)
            scale_factor = scale_factors[0]
        w = int(round(scale_factor * im.shape[1]))
        h = int(round(scale_factor * im.shape[0]))

        im = functional.resize(im, (w, h), cv2.INTER_LINEAR)
        if label is not None:
            label = functional.resize(label, (w, h), cv2.INTER_NEAREST)

        if label is None:
            return (im, )
        else:
            return (im, label)


@manager.TRANSFORMS.add_component
class Normalize:
    """
    Normalize an image.

    Args:
        mean (list, optional): The mean value of a data set. Default: [0.5, 0.5, 0.5].
        std (list, optional): The standard deviation of a data set. Default: [0.5, 0.5, 0.5].

    Raises:
        ValueError: When mean/std is not list or any value in std is 0.
    """

    def __init__(self, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)):
        self.mean = mean
        self.std = std
        if not (isinstance(self.mean,
                           (list, tuple)) and isinstance(self.std,
                                                         (list, tuple))):
            raise ValueError(
                "{}: input type is invalid. It should be list or tuple".format(
                    self))
        from functools import reduce
        if reduce(lambda x, y: x * y, self.std) == 0:
            raise ValueError('{}: std is invalid!'.format(self))

    def __call__(self, im, label=None):
        """
        Args:
            im (np.ndarray): The Image data.
            label (np.ndarray, optional): The label data. Default: None.

        Returns:
            (tuple). When label is None, it returns (im, ), otherwise it returns (im, label).
        """

        mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
        std = np.array(self.std)[np.newaxis, np.newaxis, :]
        im = functional.normalize(im, mean, std)

        if label is None:
            return (im, )
        else:
            return (im, label)


@manager.TRANSFORMS.add_component
class Padding:
    """
    Add bottom-right padding to a raw image or annotation image.

    Args:
        target_size (list|tuple): The target size after padding.
        im_padding_value (list, optional): The padding value of raw image.
            Default: [127.5, 127.5, 127.5].
        label_padding_value (int, optional): The padding value of annotation image. Default: 255.

    Raises:
        TypeError: When target_size is neither list nor tuple.
        ValueError: When the length of target_size is not 2.
    """

    def __init__(self,
                 target_size,
                 im_padding_value=(127.5, 127.5, 127.5),
                 label_padding_value=255):
        if isinstance(target_size, list) or isinstance(target_size, tuple):
            if len(target_size) != 2:
                raise ValueError(
                    '`target_size` should include 2 elements, but it is {}'.
                    format(target_size))
        else:
            raise TypeError(
                "Type of target_size is invalid. It should be list or tuple, now is {}"
                .format(type(target_size)))
        self.target_size = target_size
        self.im_padding_value = im_padding_value
        self.label_padding_value = label_padding_value

    def __call__(self, im, label=None):
        """
        Args:
            im (np.ndarray): The Image data.
            label (np.ndarray, optional): The label data. Default: None.

        Returns:
            (tuple). When label is None, it returns (im, ), otherwise it returns (im, label).
        """

        im_height, im_width = im.shape[0], im.shape[1]
        if isinstance(self.target_size, int):
            target_height = self.target_size
            target_width = self.target_size
        else:
            target_height = self.target_size[1]
            target_width = self.target_size[0]
        pad_height = target_height - im_height
        pad_width = target_width - im_width
        if pad_height < 0 or pad_width < 0:
            raise ValueError(
                'The size of image should be less than `target_size`, but the size of image ({}, {}) is larger than `target_size` ({}, {})'
                .format(im_width, im_height, target_width, target_height))
        else:
            im = cv2.copyMakeBorder(
                im,
                0,
                pad_height,
                0,
                pad_width,
                cv2.BORDER_CONSTANT,
                value=self.im_padding_value)
            if label is not None:
                label = cv2.copyMakeBorder(
                    label,
                    0,
                    pad_height,
                    0,
                    pad_width,
                    cv2.BORDER_CONSTANT,
                    value=self.label_padding_value)
        if label is None:
            return (im, )
        else:
            return (im, label)


@manager.TRANSFORMS.add_component
class PaddingByAspectRatio:
    """

    Args:
        aspect_ratio (int|float, optional): The aspect ratio = width / height. Default: 1.
    """

    def __init__(self,
                 aspect_ratio=1,
                 im_padding_value=(127.5, 127.5, 127.5),
                 label_padding_value=255):
        self.aspect_ratio = aspect_ratio
        self.im_padding_value = im_padding_value
        self.label_padding_value = label_padding_value

    def __call__(self, im, label=None):
        """
        Args:
            im (np.ndarray): The Image data.
            label (np.ndarray, optional): The label data. Default: None.

        Returns:
            (tuple). When label is None, it returns (im, ), otherwise it returns (im, label).
        """

        img_height = im.shape[0]
        img_width = im.shape[1]
        ratio = img_width / img_height
        if ratio == self.aspect_ratio:
            if label is None:
                return (im, )
            else:
                return (im, label)
        elif ratio > self.aspect_ratio:
            img_height = int(img_width / self.aspect_ratio)
        else:
            img_width = int(img_height * self.aspect_ratio)
        padding = Padding(
            (img_width, img_height),
            im_padding_value=self.im_padding_value,
            label_padding_value=self.label_padding_value)
        return padding(im, label)


@manager.TRANSFORMS.add_component
class RandomPaddingCrop:
    """
    Crop a sub-image from a raw image and annotation image randomly. If the target cropping size
    is larger than original image, then the bottom-right padding will be added.

    Args:
        crop_size (tuple, optional): The target cropping size. Default: (512, 512).
        im_padding_value (list, optional): The padding value of raw image.
            Default: [127.5, 127.5, 127.5].
        label_padding_value (int, optional): The padding value of annotation image. Default: 255.

    Raises:
        TypeError: When crop_size is neither list nor tuple.
        ValueError: When the length of crop_size is not 2.
    """

    def __init__(self,
                 crop_size=(512, 512),
                 im_padding_value=(127.5, 127.5, 127.5),
                 label_padding_value=255):
        if isinstance(crop_size, list) or isinstance(crop_size, tuple):
            if len(crop_size) != 2:
                raise ValueError(
                    'Type of `crop_size` is list or tuple. It should include 2 elements, but it is {}'
                    .format(crop_size))
        else:
            raise TypeError(
                "The type of `crop_size` is invalid. It should be list or tuple, but it is {}"
                .format(type(crop_size)))
        self.crop_size = crop_size
        self.im_padding_value = im_padding_value
        self.label_padding_value = label_padding_value

    def __call__(self, im, label=None):
        """
        Args:
            im (np.ndarray): The Image data.
            label (np.ndarray, optional): The label data. Default: None.

        Returns:
            (tuple). When label is None, it returns (im, ), otherwise it returns (im, label).
        """

        if isinstance(self.crop_size, int):
            crop_width = self.crop_size
            crop_height = self.crop_size
        else:
            crop_width = self.crop_size[0]
            crop_height = self.crop_size[1]

        img_height = im.shape[0]
        img_width = im.shape[1]

        if img_height == crop_height and img_width == crop_width:
            if label is None:
                return (im, )
            else:
                return (im, label)
        else:
            pad_height = max(crop_height - img_height, 0)
            pad_width = max(crop_width - img_width, 0)
            if (pad_height > 0 or pad_width > 0):
                im = cv2.copyMakeBorder(
                    im,
                    0,
                    pad_height,
                    0,
                    pad_width,
                    cv2.BORDER_CONSTANT,
                    value=self.im_padding_value)
                if label is not None:
                    label = cv2.copyMakeBorder(
                        label,
                        0,
                        pad_height,
                        0,
                        pad_width,
                        cv2.BORDER_CONSTANT,
                        value=self.label_padding_value)
                img_height = im.shape[0]
                img_width = im.shape[1]

            if crop_height > 0 and crop_width > 0:
                h_off = np.random.randint(img_height - crop_height + 1)
                w_off = np.random.randint(img_width - crop_width + 1)

                im = im[h_off:(crop_height + h_off), w_off:(w_off + crop_width
                                                            ), :]
                if label is not None:
                    label = label[h_off:(crop_height + h_off), w_off:(
                        w_off + crop_width)]
        if label is None:
            return (im, )
        else:
            return (im, label)


@manager.TRANSFORMS.add_component
class RandomBlur:
    """
    Blurring an image by a Gaussian function with a certain probability.

    Args:
        prob (float, optional): A probability of blurring an image. Default: 0.1.
    """

    def __init__(self, prob=0.1):
        self.prob = prob

    def __call__(self, im, label=None):
        """
        Args:
            im (np.ndarray): The Image data.
            label (np.ndarray, optional): The label data. Default: None.

        Returns:
            (tuple). When label is None, it returns (im, ), otherwise it returns (im, label).
        """

        if self.prob <= 0:
            n = 0
        elif self.prob >= 1:
            n = 1
        else:
            n = int(1.0 / self.prob)
        if n > 0:
            if np.random.randint(0, n) == 0:
                radius = np.random.randint(3, 10)
                if radius % 2 != 1:
                    radius = radius + 1
                if radius > 9:
                    radius = 9
                im = cv2.GaussianBlur(im, (radius, radius), 0, 0)

        if label is None:
            return (im, )
        else:
            return (im, label)


@manager.TRANSFORMS.add_component
class RandomRotation:
    """
    Rotate an image randomly with padding.

    Args:
        max_rotation (float, optional): The maximum rotation degree. Default: 15.
        im_padding_value (list, optional): The padding value of raw image.
            Default: [127.5, 127.5, 127.5].
        label_padding_value (int, optional): The padding value of annotation image. Default: 255.
    """

    def __init__(self,
                 max_rotation=15,
                 im_padding_value=(127.5, 127.5, 127.5),
                 label_padding_value=255):
        self.max_rotation = max_rotation
        self.im_padding_value = im_padding_value
        self.label_padding_value = label_padding_value

    def __call__(self, im, label=None):
        """
        Args:
            im (np.ndarray): The Image data.
            label (np.ndarray, optional): The label data. Default: None.

        Returns:
            (tuple). When label is None, it returns (im, ), otherwise it returns (im, label).
        """

        if self.max_rotation > 0:
            (h, w) = im.shape[:2]
            do_rotation = np.random.uniform(-self.max_rotation,
                                            self.max_rotation)
            pc = (w // 2, h // 2)
            r = cv2.getRotationMatrix2D(pc, do_rotation, 1.0)
            cos = np.abs(r[0, 0])
            sin = np.abs(r[0, 1])

            nw = int((h * sin) + (w * cos))
            nh = int((h * cos) + (w * sin))

            (cx, cy) = pc
            r[0, 2] += (nw / 2) - cx
            r[1, 2] += (nh / 2) - cy
            dsize = (nw, nh)
            im = cv2.warpAffine(
                im,
                r,
                dsize=dsize,
                flags=cv2.INTER_LINEAR,
                borderMode=cv2.BORDER_CONSTANT,
                borderValue=self.im_padding_value)
            if label is not None:
                label = cv2.warpAffine(
                    label,
                    r,
                    dsize=dsize,
                    flags=cv2.INTER_NEAREST,
                    borderMode=cv2.BORDER_CONSTANT,
                    borderValue=self.label_padding_value)

        if label is None:
            return (im, )
        else:
            return (im, label)


@manager.TRANSFORMS.add_component
class RandomScaleAspect:
    """
    Crop a sub-image from an original image with a range of area ratio and aspect and
    then scale the sub-image back to the size of the original image.

    Args:
        min_scale (float, optional): The minimum area ratio of cropped image to the original image. Default: 0.5.
        aspect_ratio (float, optional): The minimum aspect ratio. Default: 0.33.
    """

    def __init__(self, min_scale=0.5, aspect_ratio=0.33):
        self.min_scale = min_scale
        self.aspect_ratio = aspect_ratio

    def __call__(self, im, label=None):
        """
        Args:
            im (np.ndarray): The Image data.
            label (np.ndarray, optional): The label data. Default: None.

        Returns:
            (tuple). When label is None, it returns (im, ), otherwise it returns (im, label).
        """

        if self.min_scale != 0 and self.aspect_ratio != 0:
            img_height = im.shape[0]
            img_width = im.shape[1]
            for i in range(0, 10):
                area = img_height * img_width
                target_area = area * np.random.uniform(self.min_scale, 1.0)
                aspectRatio = np.random.uniform(self.aspect_ratio,
                                                1.0 / self.aspect_ratio)

                dw = int(np.sqrt(target_area * 1.0 * aspectRatio))
                dh = int(np.sqrt(target_area * 1.0 / aspectRatio))
                if (np.random.randint(10) < 5):
                    tmp = dw
                    dw = dh
                    dh = tmp

                if (dh < img_height and dw < img_width):
                    h1 = np.random.randint(0, img_height - dh)
                    w1 = np.random.randint(0, img_width - dw)

                    im = im[h1:(h1 + dh), w1:(w1 + dw), :]
                    im = cv2.resize(
                        im, (img_width, img_height),
                        interpolation=cv2.INTER_LINEAR)
                    if label is not None:
                        label = label[h1:(h1 + dh), w1:(w1 + dw)]
                        label = cv2.resize(
                            label, (img_width, img_height),
                            interpolation=cv2.INTER_NEAREST)
                    break
        if label is None:
            return (im, )
        else:
            return (im, label)


@manager.TRANSFORMS.add_component
class RandomDistort:
    """
    Distort an image with random configurations.

    Args:
        brightness_range (float, optional): A range of brightness. Default: 0.5.
        brightness_prob (float, optional): A probability of adjusting brightness. Default: 0.5.
        contrast_range (float, optional): A range of contrast. Default: 0.5.
        contrast_prob (float, optional): A probability of adjusting contrast. Default: 0.5.
        saturation_range (float, optional): A range of saturation. Default: 0.5.
        saturation_prob (float, optional): A probability of adjusting saturation. Default: 0.5.
        hue_range (int, optional): A range of hue. Default: 18.
        hue_prob (float, optional): A probability of adjusting hue. Default: 0.5.
    """

    def __init__(self,
                 brightness_range=0.5,
                 brightness_prob=0.5,
                 contrast_range=0.5,
                 contrast_prob=0.5,
                 saturation_range=0.5,
                 saturation_prob=0.5,
                 hue_range=18,
                 hue_prob=0.5):
        self.brightness_range = brightness_range
        self.brightness_prob = brightness_prob
        self.contrast_range = contrast_range
        self.contrast_prob = contrast_prob
        self.saturation_range = saturation_range
        self.saturation_prob = saturation_prob
        self.hue_range = hue_range
        self.hue_prob = hue_prob

    def __call__(self, im, label=None):
        """
        Args:
            im (np.ndarray): The Image data.
            label (np.ndarray, optional): The label data. Default: None.

        Returns:
            (tuple). When label is None, it returns (im, ), otherwise it returns (im, label).
        """

        brightness_lower = 1 - self.brightness_range
        brightness_upper = 1 + self.brightness_range
        contrast_lower = 1 - self.contrast_range
        contrast_upper = 1 + self.contrast_range
        saturation_lower = 1 - self.saturation_range
        saturation_upper = 1 + self.saturation_range
        hue_lower = -self.hue_range
        hue_upper = self.hue_range
        ops = [
            functional.brightness, functional.contrast, functional.saturation,
            functional.hue
        ]
        random.shuffle(ops)
        params_dict = {
            'brightness': {
                'brightness_lower': brightness_lower,
                'brightness_upper': brightness_upper
            },
            'contrast': {
                'contrast_lower': contrast_lower,
                'contrast_upper': contrast_upper
            },
            'saturation': {
                'saturation_lower': saturation_lower,
                'saturation_upper': saturation_upper
            },
            'hue': {
                'hue_lower': hue_lower,
                'hue_upper': hue_upper
            }
        }
        prob_dict = {
            'brightness': self.brightness_prob,
            'contrast': self.contrast_prob,
            'saturation': self.saturation_prob,
            'hue': self.hue_prob
        }
        im = im.astype('uint8')
        im = Image.fromarray(im)
        for id in range(len(ops)):
            params = params_dict[ops[id].__name__]
            prob = prob_dict[ops[id].__name__]
            params['im'] = im
            if np.random.uniform(0, 1) < prob:
                im = ops[id](**params)
        im = np.asarray(im).astype('float32')
        if label is None:
            return (im, )
        else:
            return (im, label)