text: # Copyright (c) OpenMMLab. All rights reserved.
  import mmcv
  import numpy as np
  from mmcv.utils import deprecated_api_warning, is_tuple_of
  from numpy import random
  
  from ..builder import PIPELINES
  
  
  @PIPELINES.register_module()
  class ResizeToMultiple(object):
      """Resize images & seg to multiple of divisor.
  
      Args:
          size_divisor (int): images and gt seg maps need to resize to multiple
              of size_divisor. Default: 32.
          interpolation (str, optional): The interpolation mode of image resize.
              Default: None
      """
  
      def __init__(self, size_divisor=32, interpolation=None):
          self.size_divisor = size_divisor
          self.interpolation = interpolation
  
      def __call__(self, results):
          """Call function to resize images, semantic segmentation map to
          multiple of size divisor.
  
          Args:
              results (dict): Result dict from loading pipeline.
  
          Returns:
              dict: Resized results, 'img_shape', 'pad_shape' keys are updated.
          """
          # Align image to multiple of size divisor.
          img = results['img']
          img = mmcv.imresize_to_multiple(
              img,
              self.size_divisor,
              scale_factor=1,
              interpolation=self.interpolation
              if self.interpolation else 'bilinear')
  
          results['img'] = img
          results['img_shape'] = img.shape
          results['pad_shape'] = img.shape
  
          # Align segmentation map to multiple of size divisor.
          for key in results.get('seg_fields', []):
              gt_seg = results[key]
              gt_seg = mmcv.imresize_to_multiple(
                  gt_seg,
                  self.size_divisor,
                  scale_factor=1,
                  interpolation='nearest')
              results[key] = gt_seg
  
          return results
  
      def __repr__(self):
          repr_str = self.__class__.__name__
          repr_str += (f'(size_divisor={self.size_divisor}, '
                       f'interpolation={self.interpolation})')
          return repr_str
  
  
  @PIPELINES.register_module()
  class Resize(object):
      """Resize images & seg.
  
      This transform resizes the input image to some scale. If the input dict
      contains the key "scale", then the scale in the input dict is used,
      otherwise the specified scale in the init method is used.
  
      ``img_scale`` can be None, a tuple (single-scale) or a list of tuple
      (multi-scale). There are 4 multiscale modes:
  
      - ``ratio_range is not None``:
      1. When img_scale is None, img_scale is the shape of image in results
          (img_scale = results['img'].shape[:2]) and the image is resized based
          on the original size. (mode 1)
      2. When img_scale is a tuple (single-scale), randomly sample a ratio from
          the ratio range and multiply it with the image scale. (mode 2)
  
      - ``ratio_range is None and multiscale_mode == "range"``: randomly sample a
      scale from the a range. (mode 3)
  
      - ``ratio_range is None and multiscale_mode == "value"``: randomly sample a
      scale from multiple scales. (mode 4)
  
      Args:
          img_scale (tuple or list[tuple]): Images scales for resizing.
              Default:None.
          multiscale_mode (str): Either "range" or "value".
              Default: 'range'
          ratio_range (tuple[float]): (min_ratio, max_ratio).
              Default: None
          keep_ratio (bool): Whether to keep the aspect ratio when resizing the
              image. Default: True
      """
  
      def __init__(self,
                   img_scale=None,
                   multiscale_mode='range',
                   ratio_range=None,
                   keep_ratio=True):
          if img_scale is None:
              self.img_scale = None
          else:
              if isinstance(img_scale, list):
                  self.img_scale = img_scale
              else:
                  self.img_scale = [img_scale]
              assert mmcv.is_list_of(self.img_scale, tuple)
  
          if ratio_range is not None:
              # mode 1: given img_scale=None and a range of image ratio
              # mode 2: given a scale and a range of image ratio
              assert self.img_scale is None or len(self.img_scale) == 1
          else:
              # mode 3 and 4: given multiple scales or a range of scales
              assert multiscale_mode in ['value', 'range']
  
          self.multiscale_mode = multiscale_mode
          self.ratio_range = ratio_range
          self.keep_ratio = keep_ratio
  
      @staticmethod
      def random_select(img_scales):
          """Randomly select an img_scale from given candidates.
  
          Args:
              img_scales (list[tuple]): Images scales for selection.
  
          Returns:
              (tuple, int): Returns a tuple ``(img_scale, scale_dix)``,
                  where ``img_scale`` is the selected image scale and
                  ``scale_idx`` is the selected index in the given candidates.
          """
  
          assert mmcv.is_list_of(img_scales, tuple)
          scale_idx = np.random.randint(len(img_scales))
          img_scale = img_scales[scale_idx]
          return img_scale, scale_idx
  
      @staticmethod
      def random_sample(img_scales):
          """Randomly sample an img_scale when ``multiscale_mode=='range'``.
  
          Args:
              img_scales (list[tuple]): Images scale range for sampling.
                  There must be two tuples in img_scales, which specify the lower
                  and upper bound of image scales.
  
          Returns:
              (tuple, None): Returns a tuple ``(img_scale, None)``, where
                  ``img_scale`` is sampled scale and None is just a placeholder
                  to be consistent with :func:`random_select`.
          """
  
          assert mmcv.is_list_of(img_scales, tuple) and len(img_scales) == 2
          img_scale_long = [max(s) for s in img_scales]
          img_scale_short = [min(s) for s in img_scales]
          long_edge = np.random.randint(
              min(img_scale_long),
              max(img_scale_long) + 1)
          short_edge = np.random.randint(
              min(img_scale_short),
              max(img_scale_short) + 1)
          img_scale = (long_edge, short_edge)
          return img_scale, None
  
      @staticmethod
      def random_sample_ratio(img_scale, ratio_range):
          """Randomly sample an img_scale when ``ratio_range`` is specified.
  
          A ratio will be randomly sampled from the range specified by
          ``ratio_range``. Then it would be multiplied with ``img_scale`` to
          generate sampled scale.
  
          Args:
              img_scale (tuple): Images scale base to multiply with ratio.
              ratio_range (tuple[float]): The minimum and maximum ratio to scale
                  the ``img_scale``.
  
          Returns:
              (tuple, None): Returns a tuple ``(scale, None)``, where
                  ``scale`` is sampled ratio multiplied with ``img_scale`` and
                  None is just a placeholder to be consistent with
                  :func:`random_select`.
          """
  
          assert isinstance(img_scale, tuple) and len(img_scale) == 2
          min_ratio, max_ratio = ratio_range
          assert min_ratio <= max_ratio
          ratio = np.random.random_sample() * (max_ratio - min_ratio) + min_ratio
          scale = int(img_scale[0] * ratio), int(img_scale[1] * ratio)
          return scale, None
  
      def _random_scale(self, results):
          """Randomly sample an img_scale according to ``ratio_range`` and
          ``multiscale_mode``.
  
          If ``ratio_range`` is specified, a ratio will be sampled and be
          multiplied with ``img_scale``.
          If multiple scales are specified by ``img_scale``, a scale will be
          sampled according to ``multiscale_mode``.
          Otherwise, single scale will be used.
  
          Args:
              results (dict): Result dict from :obj:`dataset`.
  
          Returns:
              dict: Two new keys 'scale` and 'scale_idx` are added into
                  ``results``, which would be used by subsequent pipelines.
          """
  
          if self.ratio_range is not None:
              if self.img_scale is None:
                  h, w = results['img'].shape[:2]
                  scale, scale_idx = self.random_sample_ratio((w, h),
                                                              self.ratio_range)
              else:
                  scale, scale_idx = self.random_sample_ratio(
                      self.img_scale[0], self.ratio_range)
          elif len(self.img_scale) == 1:
              scale, scale_idx = self.img_scale[0], 0
          elif self.multiscale_mode == 'range':
              scale, scale_idx = self.random_sample(self.img_scale)
          elif self.multiscale_mode == 'value':
              scale, scale_idx = self.random_select(self.img_scale)
          else:
              raise NotImplementedError
  
          results['scale'] = scale
          results['scale_idx'] = scale_idx
  
      def _resize_img(self, results):
          """Resize images with ``results['scale']``."""
          if self.keep_ratio:
              img, scale_factor = mmcv.imrescale(
                  results['img'], results['scale'], return_scale=True)
              # the w_scale and h_scale has minor difference
              # a real fix should be done in the mmcv.imrescale in the future
              new_h, new_w = img.shape[:2]
              h, w = results['img'].shape[:2]
              w_scale = new_w / w
              h_scale = new_h / h
          else:
              img, w_scale, h_scale = mmcv.imresize(
                  results['img'], results['scale'], return_scale=True)
          scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
                                  dtype=np.float32)
          results['img'] = img
          results['img_shape'] = img.shape
          results['pad_shape'] = img.shape  # in case that there is no padding
          results['scale_factor'] = scale_factor
          results['keep_ratio'] = self.keep_ratio
  
      def _resize_seg(self, results):
          """Resize semantic segmentation map with ``results['scale']``."""
          for key in results.get('seg_fields', []):
              if self.keep_ratio:
                  gt_seg = mmcv.imrescale(
                      results[key], results['scale'], interpolation='nearest')
              else:
                  gt_seg = mmcv.imresize(
                      results[key], results['scale'], interpolation='nearest')
              results[key] = gt_seg
  
      def __call__(self, results):
          """Call function to resize images, bounding boxes, masks, semantic
          segmentation map.
  
          Args:
              results (dict): Result dict from loading pipeline.
  
          Returns:
              dict: Resized results, 'img_shape', 'pad_shape', 'scale_factor',
                  'keep_ratio' keys are added into result dict.
          """
  
          if 'scale' not in results:
              self._random_scale(results)
          self._resize_img(results)
          self._resize_seg(results)
          return results
  
      def __repr__(self):
          repr_str = self.__class__.__name__
          repr_str += (f'(img_scale={self.img_scale}, '
                       f'multiscale_mode={self.multiscale_mode}, '
                       f'ratio_range={self.ratio_range}, '
                       f'keep_ratio={self.keep_ratio})')
          return repr_str
  
  
  @PIPELINES.register_module()
  class RandomFlip(object):
      """Flip the image & seg.
  
      If the input dict contains the key "flip", then the flag will be used,
      otherwise it will be randomly decided by a ratio specified in the init
      method.
  
      Args:
          prob (float, optional): The flipping probability. Default: None.
          direction(str, optional): The flipping direction. Options are
              'horizontal' and 'vertical'. Default: 'horizontal'.
      """
  
      @deprecated_api_warning({'flip_ratio': 'prob'}, cls_name='RandomFlip')
      def __init__(self, prob=None, direction='horizontal'):
          self.prob = prob
          self.direction = direction
          if prob is not None:
              assert prob >= 0 and prob <= 1
          assert direction in ['horizontal', 'vertical']
  
      def __call__(self, results):
          """Call function to flip bounding boxes, masks, semantic segmentation
          maps.
  
          Args:
              results (dict): Result dict from loading pipeline.
  
          Returns:
              dict: Flipped results, 'flip', 'flip_direction' keys are added into
                  result dict.
          """
  
          if 'flip' not in results:
              flip = True if np.random.rand() < self.prob else False
              results['flip'] = flip
          if 'flip_direction' not in results:
              results['flip_direction'] = self.direction
          if results['flip']:
              # flip image
              results['img'] = mmcv.imflip(
                  results['img'], direction=results['flip_direction'])
  
              # flip segs
              for key in results.get('seg_fields', []):
                  # use copy() to make numpy stride positive
                  results[key] = mmcv.imflip(
                      results[key], direction=results['flip_direction']).copy()
          return results
  
      def __repr__(self):
          return self.__class__.__name__ + f'(prob={self.prob})'
  
  
  @PIPELINES.register_module()
  class Pad(object):
      """Pad the image & mask.
  
      There are two padding modes: (1) pad to a fixed size and (2) pad to the
      minimum size that is divisible by some number.
      Added keys are "pad_shape", "pad_fixed_size", "pad_size_divisor",
  
      Args:
          size (tuple, optional): Fixed padding size.
          size_divisor (int, optional): The divisor of padded size.
          pad_val (float, optional): Padding value. Default: 0.
          seg_pad_val (float, optional): Padding value of segmentation map.
              Default: 255.
      """
  
      def __init__(self,
                   size=None,
                   size_divisor=None,
                   pad_val=0,
                   seg_pad_val=255):
          self.size = size
          self.size_divisor = size_divisor
          self.pad_val = pad_val
          self.seg_pad_val = seg_pad_val
          # only one of size and size_divisor should be valid
          assert size is not None or size_divisor is not None
          assert size is None or size_divisor is None
  
      def _pad_img(self, results):
          """Pad images according to ``self.size``."""
          if self.size is not None:
              padded_img = mmcv.impad(
                  results['img'], shape=self.size, pad_val=self.pad_val)
          elif self.size_divisor is not None:
              padded_img = mmcv.impad_to_multiple(
                  results['img'], self.size_divisor, pad_val=self.pad_val)
          results['img'] = padded_img
          results['pad_shape'] = padded_img.shape
          results['pad_fixed_size'] = self.size
          results['pad_size_divisor'] = self.size_divisor
  
      def _pad_seg(self, results):
          """Pad masks according to ``results['pad_shape']``."""
          for key in results.get('seg_fields', []):
              results[key] = mmcv.impad(
                  results[key],
                  shape=results['pad_shape'][:2],
                  pad_val=self.seg_pad_val)
  
      def __call__(self, results):
          """Call function to pad images, masks, semantic segmentation maps.
  
          Args:
              results (dict): Result dict from loading pipeline.
  
          Returns:
              dict: Updated result dict.
          """
  
          self._pad_img(results)
          self._pad_seg(results)
          return results
  
      def __repr__(self):
          repr_str = self.__class__.__name__
          repr_str += f'(size={self.size}, size_divisor={self.size_divisor}, ' \
                      f'pad_val={self.pad_val})'
          return repr_str
  
  
  @PIPELINES.register_module()
  class Normalize(object):
      """Normalize the image.
  
      Added key is "img_norm_cfg".
  
      Args:
          mean (sequence): Mean values of 3 channels.
          std (sequence): Std values of 3 channels.
          to_rgb (bool): Whether to convert the image from BGR to RGB,
              default is true.
      """
  
      def __init__(self, mean, std, to_rgb=True):
          self.mean = np.array(mean, dtype=np.float32)
          self.std = np.array(std, dtype=np.float32)
          self.to_rgb = to_rgb
  
      def __call__(self, results):
          """Call function to normalize images.
  
          Args:
              results (dict): Result dict from loading pipeline.
  
          Returns:
              dict: Normalized results, 'img_norm_cfg' key is added into
                  result dict.
          """
  
          results['img'] = mmcv.imnormalize(results['img'], self.mean, self.std,
                                            self.to_rgb)
          results['img_norm_cfg'] = dict(
              mean=self.mean, std=self.std, to_rgb=self.to_rgb)
          return results
  
      def __repr__(self):
          repr_str = self.__class__.__name__
          repr_str += f'(mean={self.mean}, std={self.std}, to_rgb=' \
                      f'{self.to_rgb})'
          return repr_str
  
  
  @PIPELINES.register_module()
  class Rerange(object):
      """Rerange the image pixel value.
  
      Args:
          min_value (float or int): Minimum value of the reranged image.
              Default: 0.
          max_value (float or int): Maximum value of the reranged image.
              Default: 255.
      """
  
      def __init__(self, min_value=0, max_value=255):
          assert isinstance(min_value, float) or isinstance(min_value, int)
          assert isinstance(max_value, float) or isinstance(max_value, int)
          assert min_value < max_value
          self.min_value = min_value
          self.max_value = max_value
  
      def __call__(self, results):
          """Call function to rerange images.
  
          Args:
              results (dict): Result dict from loading pipeline.
          Returns:
              dict: Reranged results.
          """
  
          img = results['img']
          img_min_value = np.min(img)
          img_max_value = np.max(img)
  
          assert img_min_value < img_max_value
          # rerange to [0, 1]
          img = (img - img_min_value) / (img_max_value - img_min_value)
          # rerange to [min_value, max_value]
          img = img * (self.max_value - self.min_value) + self.min_value
          results['img'] = img
  
          return results
  
      def __repr__(self):
          repr_str = self.__class__.__name__
          repr_str += f'(min_value={self.min_value}, max_value={self.max_value})'
          return repr_str
  
  
  @PIPELINES.register_module()
  class CLAHE(object):
      """Use CLAHE method to process the image.
  
      See `ZUIDERVELD,K. Contrast Limited Adaptive Histogram Equalization[J].
      Graphics Gems, 1994:474-485.` for more information.
  
      Args:
          clip_limit (float): Threshold for contrast limiting. Default: 40.0.
          tile_grid_size (tuple[int]): Size of grid for histogram equalization.
              Input image will be divided into equally sized rectangular tiles.
              It defines the number of tiles in row and column. Default: (8, 8).
      """
  
      def __init__(self, clip_limit=40.0, tile_grid_size=(8, 8)):
          assert isinstance(clip_limit, (float, int))
          self.clip_limit = clip_limit
          assert is_tuple_of(tile_grid_size, int)
          assert len(tile_grid_size) == 2
          self.tile_grid_size = tile_grid_size
  
      def __call__(self, results):
          """Call function to Use CLAHE method process images.
  
          Args:
              results (dict): Result dict from loading pipeline.
  
          Returns:
              dict: Processed results.
          """
  
          for i in range(results['img'].shape[2]):
              results['img'][:, :, i] = mmcv.clahe(
                  np.array(results['img'][:, :, i], dtype=np.uint8),
                  self.clip_limit, self.tile_grid_size)
  
          return results
  
      def __repr__(self):
          repr_str = self.__class__.__name__
          repr_str += f'(clip_limit={self.clip_limit}, '\
                      f'tile_grid_size={self.tile_grid_size})'
          return repr_str
  
  
  @PIPELINES.register_module()
  class RandomCrop(object):
      """Random crop the image & seg.
  
      Args:
          crop_size (tuple): Expected size after cropping, (h, w).
          cat_max_ratio (float): The maximum ratio that single category could
              occupy.
      """
  
      def __init__(self, crop_size, cat_max_ratio=1., ignore_index=255):
          assert crop_size[0] > 0 and crop_size[1] > 0
          self.crop_size = crop_size
          self.cat_max_ratio = cat_max_ratio
          self.ignore_index = ignore_index
  
      def get_crop_bbox(self, img):
          """Randomly get a crop bounding box."""
          margin_h = max(img.shape[0] - self.crop_size[0], 0)
          margin_w = max(img.shape[1] - self.crop_size[1], 0)
          offset_h = np.random.randint(0, margin_h + 1)
          offset_w = np.random.randint(0, margin_w + 1)
          crop_y1, crop_y2 = offset_h, offset_h + self.crop_size[0]
          crop_x1, crop_x2 = offset_w, offset_w + self.crop_size[1]
  
          return crop_y1, crop_y2, crop_x1, crop_x2
  
      def crop(self, img, crop_bbox):
          """Crop from ``img``"""
          crop_y1, crop_y2, crop_x1, crop_x2 = crop_bbox
          img = img[crop_y1:crop_y2, crop_x1:crop_x2, ...]
          return img
  
      def __call__(self, results):
          """Call function to randomly crop images, semantic segmentation maps.
  
          Args:
              results (dict): Result dict from loading pipeline.
  
          Returns:
              dict: Randomly cropped results, 'img_shape' key in result dict is
                  updated according to crop size.
          """
  
          img = results['img']
          crop_bbox = self.get_crop_bbox(img)
          if self.cat_max_ratio < 1.:
              # Repeat 10 times
              for _ in range(10):
                  seg_temp = self.crop(results['gt_semantic_seg'], crop_bbox)
                  labels, cnt = np.unique(seg_temp, return_counts=True)
                  cnt = cnt[labels != self.ignore_index]
                  if len(cnt) > 1 and np.max(cnt) / np.sum(
                          cnt) < self.cat_max_ratio:
                      break
                  crop_bbox = self.get_crop_bbox(img)
  
          # crop the image
          img = self.crop(img, crop_bbox)
          img_shape = img.shape
          results['img'] = img
          results['img_shape'] = img_shape
  
          # crop semantic seg
          for key in results.get('seg_fields', []):
              results[key] = self.crop(results[key], crop_bbox)
  
          return results
  
      def __repr__(self):
          return self.__class__.__name__ + f'(crop_size={self.crop_size})'
  
  
  @PIPELINES.register_module()
  class RandomRotate(object):
      """Rotate the image & seg.
  
      Args:
          prob (float): The rotation probability.
          degree (float, tuple[float]): Range of degrees to select from. If
              degree is a number instead of tuple like (min, max),
              the range of degree will be (``-degree``, ``+degree``)
          pad_val (float, optional): Padding value of image. Default: 0.
          seg_pad_val (float, optional): Padding value of segmentation map.
              Default: 255.
          center (tuple[float], optional): Center point (w, h) of the rotation in
              the source image. If not specified, the center of the image will be
              used. Default: None.
          auto_bound (bool): Whether to adjust the image size to cover the whole
              rotated image. Default: False
      """
  
      def __init__(self,
                   prob,
                   degree,
                   pad_val=0,
                   seg_pad_val=255,
                   center=None,
                   auto_bound=False):
          self.prob = prob
          assert prob >= 0 and prob <= 1
          if isinstance(degree, (float, int)):
              assert degree > 0, f'degree {degree} should be positive'
              self.degree = (-degree, degree)
          else:
              self.degree = degree
          assert len(self.degree) == 2, f'degree {self.degree} should be a ' \
                                        f'tuple of (min, max)'
          self.pal_val = pad_val
          self.seg_pad_val = seg_pad_val
          self.center = center
          self.auto_bound = auto_bound
  
      def __call__(self, results):
          """Call function to rotate image, semantic segmentation maps.
  
          Args:
              results (dict): Result dict from loading pipeline.
  
          Returns:
              dict: Rotated results.
          """
  
          rotate = True if np.random.rand() < self.prob else False
          degree = np.random.uniform(min(*self.degree), max(*self.degree))
          if rotate:
              # rotate image
              results['img'] = mmcv.imrotate(
                  results['img'],
                  angle=degree,
                  border_value=self.pal_val,
                  center=self.center,
                  auto_bound=self.auto_bound)
  
              # rotate segs
              for key in results.get('seg_fields', []):
                  results[key] = mmcv.imrotate(
                      results[key],
                      angle=degree,
                      border_value=self.seg_pad_val,
                      center=self.center,
                      auto_bound=self.auto_bound,
                      interpolation='nearest')
          return results
  
      def __repr__(self):
          repr_str = self.__class__.__name__
          repr_str += f'(prob={self.prob}, ' \
                      f'degree={self.degree}, ' \
                      f'pad_val={self.pal_val}, ' \
                      f'seg_pad_val={self.seg_pad_val}, ' \
                      f'center={self.center}, ' \
                      f'auto_bound={self.auto_bound})'
          return repr_str
  
  
  @PIPELINES.register_module()
  class RGB2Gray(object):
      """Convert RGB image to grayscale image.
  
      This transform calculate the weighted mean of input image channels with
      ``weights`` and then expand the channels to ``out_channels``. When
      ``out_channels`` is None, the number of output channels is the same as
      input channels.
  
      Args:
          out_channels (int): Expected number of output channels after
              transforming. Default: None.
          weights (tuple[float]): The weights to calculate the weighted mean.
              Default: (0.299, 0.587, 0.114).
      """
  
      def __init__(self, out_channels=None, weights=(0.299, 0.587, 0.114)):
          assert out_channels is None or out_channels > 0
          self.out_channels = out_channels
          assert isinstance(weights, tuple)
          for item in weights:
              assert isinstance(item, (float, int))
          self.weights = weights
  
      def __call__(self, results):
          """Call function to convert RGB image to grayscale image.
  
          Args:
              results (dict): Result dict from loading pipeline.
  
          Returns:
              dict: Result dict with grayscale image.
          """
          img = results['img']
          assert len(img.shape) == 3
          assert img.shape[2] == len(self.weights)
          weights = np.array(self.weights).reshape((1, 1, -1))
          img = (img * weights).sum(2, keepdims=True)
          if self.out_channels is None:
              img = img.repeat(weights.shape[2], axis=2)
          else:
              img = img.repeat(self.out_channels, axis=2)
  
          results['img'] = img
          results['img_shape'] = img.shape
  
          return results
  
      def __repr__(self):
          repr_str = self.__class__.__name__
          repr_str += f'(out_channels={self.out_channels}, ' \
                      f'weights={self.weights})'
          return repr_str
  
  
  @PIPELINES.register_module()
  class AdjustGamma(object):
      """Using gamma correction to process the image.
  
      Args:
          gamma (float or int): Gamma value used in gamma correction.
              Default: 1.0.
      """
  
      def __init__(self, gamma=1.0):
          assert isinstance(gamma, float) or isinstance(gamma, int)
          assert gamma > 0
          self.gamma = gamma
          inv_gamma = 1.0 / gamma
          self.table = np.array([(i / 255.0)**inv_gamma * 255
                                 for i in np.arange(256)]).astype('uint8')
  
      def __call__(self, results):
          """Call function to process the image with gamma correction.
  
          Args:
              results (dict): Result dict from loading pipeline.
  
          Returns:
              dict: Processed results.
          """
  
          results['img'] = mmcv.lut_transform(
              np.array(results['img'], dtype=np.uint8), self.table)
  
          return results
  
      def __repr__(self):
          return self.__class__.__name__ + f'(gamma={self.gamma})'
  
  
  @PIPELINES.register_module()
  class SegRescale(object):
      """Rescale semantic segmentation maps.
  
      Args:
          scale_factor (float): The scale factor of the final output.
      """
  
      def __init__(self, scale_factor=1):
          self.scale_factor = scale_factor
  
      def __call__(self, results):
          """Call function to scale the semantic segmentation map.
  
          Args:
              results (dict): Result dict from loading pipeline.
  
          Returns:
              dict: Result dict with semantic segmentation map scaled.
          """
          for key in results.get('seg_fields', []):
              if self.scale_factor != 1:
                  results[key] = mmcv.imrescale(
                      results[key], self.scale_factor, interpolation='nearest')
          return results
  
      def __repr__(self):
          return self.__class__.__name__ + f'(scale_factor={self.scale_factor})'
  
  
  @PIPELINES.register_module()
  class PhotoMetricDistortion(object):
      """Apply photometric distortion to image sequentially, every transformation
      is applied with a probability of 0.5. The position of random contrast is in
      second or second to last.
  
      1. random brightness
      2. random contrast (mode 0)
      3. convert color from BGR to HSV
      4. random saturation
      5. random hue
      6. convert color from HSV to BGR
      7. random contrast (mode 1)
  
      Args:
          brightness_delta (int): delta of brightness.
          contrast_range (tuple): range of contrast.
          saturation_range (tuple): range of saturation.
          hue_delta (int): delta of hue.
      """
  
      def __init__(self,
                   brightness_delta=32,
                   contrast_range=(0.5, 1.5),
                   saturation_range=(0.5, 1.5),
                   hue_delta=18):
          self.brightness_delta = brightness_delta
          self.contrast_lower, self.contrast_upper = contrast_range
          self.saturation_lower, self.saturation_upper = saturation_range
          self.hue_delta = hue_delta
  
      def convert(self, img, alpha=1, beta=0):
          """Multiple with alpha and add beat with clip."""
          img = img.astype(np.float32) * alpha + beta
          img = np.clip(img, 0, 255)
          return img.astype(np.uint8)
  
      def brightness(self, img):
          """Brightness distortion."""
          if random.randint(2):
              return self.convert(
                  img,
                  beta=random.uniform(-self.brightness_delta,
                                      self.brightness_delta))
          return img
  
      def contrast(self, img):
          """Contrast distortion."""
          if random.randint(2):
              return self.convert(
                  img,
                  alpha=random.uniform(self.contrast_lower, self.contrast_upper))
          return img
  
      def saturation(self, img):
          """Saturation distortion."""
          if random.randint(2):
              img = mmcv.bgr2hsv(img)
              img[:, :, 1] = self.convert(
                  img[:, :, 1],
                  alpha=random.uniform(self.saturation_lower,
                                       self.saturation_upper))
              img = mmcv.hsv2bgr(img)
          return img
  
      def hue(self, img):
          """Hue distortion."""
          if random.randint(2):
              img = mmcv.bgr2hsv(img)
              img[:, :,
                  0] = (img[:, :, 0].astype(int) +
                        random.randint(-self.hue_delta, self.hue_delta)) % 180
              img = mmcv.hsv2bgr(img)
          return img
  
      def __call__(self, results):
          """Call function to perform photometric distortion on images.
  
          Args:
              results (dict): Result dict from loading pipeline.
  
          Returns:
              dict: Result dict with images distorted.
          """
  
          img = results['img']
          # random brightness
          img = self.brightness(img)
  
          # mode == 0 --> do random contrast first
          # mode == 1 --> do random contrast last
          mode = random.randint(2)
          if mode == 1:
              img = self.contrast(img)
  
          # random saturation
          img = self.saturation(img)
  
          # random hue
          img = self.hue(img)
  
          # random contrast
          if mode == 0:
              img = self.contrast(img)
  
          results['img'] = img
          return results
  
      def __repr__(self):
          repr_str = self.__class__.__name__
          repr_str += (f'(brightness_delta={self.brightness_delta}, '
                       f'contrast_range=({self.contrast_lower}, '
                       f'{self.contrast_upper}), '
                       f'saturation_range=({self.saturation_lower}, '
                       f'{self.saturation_upper}), '
                       f'hue_delta={self.hue_delta})')
          return repr_str
  
  
  @PIPELINES.register_module()
  class RandomCutOut(object):
      """CutOut operation.
  
      Randomly drop some regions of image used in
      `Cutout <https://arxiv.org/abs/1708.04552>`_.
      Args:
          prob (float): cutout probability.
          n_holes (int | tuple[int, int]): Number of regions to be dropped.
              If it is given as a list, number of holes will be randomly
              selected from the closed interval [`n_holes[0]`, `n_holes[1]`].
          cutout_shape (tuple[int, int] | list[tuple[int, int]]): The candidate
              shape of dropped regions. It can be `tuple[int, int]` to use a
              fixed cutout shape, or `list[tuple[int, int]]` to randomly choose
              shape from the list.
          cutout_ratio (tuple[float, float] | list[tuple[float, float]]): The
              candidate ratio of dropped regions. It can be `tuple[float, float]`
              to use a fixed ratio or `list[tuple[float, float]]` to randomly
              choose ratio from the list. Please note that `cutout_shape`
              and `cutout_ratio` cannot be both given at the same time.
          fill_in (tuple[float, float, float] | tuple[int, int, int]): The value
              of pixel to fill in the dropped regions. Default: (0, 0, 0).
          seg_fill_in (int): The labels of pixel to fill in the dropped regions.
              If seg_fill_in is None, skip. Default: None.
      """
  
      def __init__(self,
                   prob,
                   n_holes,
                   cutout_shape=None,
                   cutout_ratio=None,
                   fill_in=(0, 0, 0),
                   seg_fill_in=None):
  
          assert 0 <= prob and prob <= 1
          assert (cutout_shape is None) ^ (cutout_ratio is None), \
              'Either cutout_shape or cutout_ratio should be specified.'
          assert (isinstance(cutout_shape, (list, tuple))
                  or isinstance(cutout_ratio, (list, tuple)))
          if isinstance(n_holes, tuple):
              assert len(n_holes) == 2 and 0 <= n_holes[0] < n_holes[1]
          else:
              n_holes = (n_holes, n_holes)
          if seg_fill_in is not None:
              assert (isinstance(seg_fill_in, int) and 0 <= seg_fill_in
                      and seg_fill_in <= 255)
          self.prob = prob
          self.n_holes = n_holes
          self.fill_in = fill_in
          self.seg_fill_in = seg_fill_in
          self.with_ratio = cutout_ratio is not None
          self.candidates = cutout_ratio if self.with_ratio else cutout_shape
          if not isinstance(self.candidates, list):
              self.candidates = [self.candidates]
  
      def __call__(self, results):
          """Call function to drop some regions of image."""
          cutout = True if np.random.rand() < self.prob else False
          if cutout:
              h, w, c = results['img'].shape
              n_holes = np.random.randint(self.n_holes[0], self.n_holes[1] + 1)
              for _ in range(n_holes):
                  x1 = np.random.randint(0, w)
                  y1 = np.random.randint(0, h)
                  index = np.random.randint(0, len(self.candidates))
                  if not self.with_ratio:
                      cutout_w, cutout_h = self.candidates[index]
                  else:
                      cutout_w = int(self.candidates[index][0] * w)
                      cutout_h = int(self.candidates[index][1] * h)
  
                  x2 = np.clip(x1 + cutout_w, 0, w)
                  y2 = np.clip(y1 + cutout_h, 0, h)
                  results['img'][y1:y2, x1:x2, :] = self.fill_in
  
                  if self.seg_fill_in is not None:
                      for key in results.get('seg_fields', []):
                          results[key][y1:y2, x1:x2] = self.seg_fill_in
  
          return results
  
      def __repr__(self):
          repr_str = self.__class__.__name__
          repr_str += f'(prob={self.prob}, '
          repr_str += f'n_holes={self.n_holes}, '
          repr_str += (f'cutout_ratio={self.candidates}, ' if self.with_ratio
                       else f'cutout_shape={self.candidates}, ')
          repr_str += f'fill_in={self.fill_in}, '
          repr_str += f'seg_fill_in={self.seg_fill_in})'
          return repr_str
  