mmsegmentation/mmseg/datasets/transforms/transforms.py

# Copyright (c) OpenMMLab. All rights reserved.
import copy
from typing import Sequence, Tuple, Union

import mmcv
import numpy as np
from mmcv.transforms.base import BaseTransform
from mmcv.transforms.utils import cache_randomness
from mmcv.utils import is_tuple_of
from numpy import random

from mmseg.registry import TRANSFORMS


@TRANSFORMS.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


@TRANSFORMS.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


@TRANSFORMS.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


@TRANSFORMS.register_module()
class RandomCrop(BaseTransform):
    """Random crop the image & seg.

    Required Keys:

    - img
    - gt_semantic_seg

    Modified Keys:

    - img
    - img_shape
    - gt_semantic_seg


    Args:
        crop_size (Union[int, Tuple[int, int]]):  Expected size after cropping
            with the format of (h, w). If set to an integer, then cropping
            width and height are equal to this integer.
        cat_max_ratio (float): The maximum ratio that single category could
            occupy.
        ignore_index (int): The label index to be ignored. Default: 255
    """

    def __init__(self,
                 crop_size: Union[int, Tuple[int, int]],
                 cat_max_ratio: float = 1.,
                 ignore_index: int = 255):
        super().__init__()
        assert isinstance(crop_size, int) or (
            isinstance(crop_size, tuple) and len(crop_size) == 2
        ), 'The expected crop_size is an integer, or a tuple containing two '
        'intergers'

        if isinstance(crop_size, int):
            crop_size = (crop_size, crop_size)
        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

    @cache_randomness
    def crop_bbox(self, results: dict) -> tuple:
        """get a crop bounding box.

        Args:
            results (dict): Result dict from loading pipeline.

        Returns:
            tuple: Coordinates of the cropped image.
        """

        def generate_crop_bbox(img: np.ndarray) -> tuple:
            """Randomly get a crop bounding box.

            Args:
                img (np.ndarray): Original input image.

            Returns:
                tuple: Coordinates of the cropped image.
            """

            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

        img = results['img']
        crop_bbox = generate_crop_bbox(img)
        if self.cat_max_ratio < 1.:
            # Repeat 10 times
            for _ in range(10):
                seg_temp = self.crop(results['gt_seg_map'], 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 = generate_crop_bbox(img)

        return crop_bbox

    def crop(self, img: np.ndarray, crop_bbox: tuple) -> np.ndarray:
        """Crop from ``img``

        Args:
            img (np.ndarray): Original input image.
            crop_bbox (tuple): Coordinates of the cropped image.

        Returns:
            np.ndarray: The cropped image.
        """

        crop_y1, crop_y2, crop_x1, crop_x2 = crop_bbox
        img = img[crop_y1:crop_y2, crop_x1:crop_x2, ...]
        return img

    def transform(self, results: dict) -> dict:
        """Transform 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.crop_bbox(results)

        # crop the image
        img = self.crop(img, crop_bbox)

        # crop semantic seg
        for key in results.get('seg_fields', []):
            results[key] = self.crop(results[key], crop_bbox)
        img_shape = img.shape
        results['img'] = img
        results['img_shape'] = img_shape
        return results

    def __repr__(self):
        return self.__class__.__name__ + f'(crop_size={self.crop_size})'


@TRANSFORMS.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


@TRANSFORMS.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


@TRANSFORMS.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})'


@TRANSFORMS.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})'


@TRANSFORMS.register_module()
class PhotoMetricDistortion(BaseTransform):
    """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)

    Required Keys:

    - img

    Modified Keys:

    - img

    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: int = 32,
                 contrast_range: Sequence[float] = (0.5, 1.5),
                 saturation_range: Sequence[float] = (0.5, 1.5),
                 hue_delta: int = 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: np.ndarray,
                alpha: int = 1,
                beta: int = 0) -> np.ndarray:
        """Multiple with alpha and add beat with clip.

        Args:
            img (np.ndarray): The input image.
            alpha (int): Image weights, change the contrast/saturation
                of the image. Default: 1
            beta (int): Image bias, change the brightness of the
                image. Default: 0

        Returns:
            np.ndarray: The transformed image.
        """

        img = img.astype(np.float32) * alpha + beta
        img = np.clip(img, 0, 255)
        return img.astype(np.uint8)

    def brightness(self, img: np.ndarray) -> np.ndarray:
        """Brightness distortion.

        Args:
            img (np.ndarray): The input image.
        Returns:
            np.ndarray: Image after brightness change.
        """

        if random.randint(2):
            return self.convert(
                img,
                beta=random.uniform(-self.brightness_delta,
                                    self.brightness_delta))
        return img

    def contrast(self, img: np.ndarray) -> np.ndarray:
        """Contrast distortion.

        Args:
            img (np.ndarray): The input image.
        Returns:
            np.ndarray: Image after contrast change.
        """

        if random.randint(2):
            return self.convert(
                img,
                alpha=random.uniform(self.contrast_lower, self.contrast_upper))
        return img

    def saturation(self, img: np.ndarray) -> np.ndarray:
        """Saturation distortion.

        Args:
            img (np.ndarray): The input image.
        Returns:
            np.ndarray: Image after saturation change.
        """

        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: np.ndarray) -> np.ndarray:
        """Hue distortion.

        Args:
            img (np.ndarray): The input image.
        Returns:
            np.ndarray: Image after hue change.
        """

        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 transform(self, results: dict) -> dict:
        """Transform 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


@TRANSFORMS.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


@TRANSFORMS.register_module()
class RandomMosaic(object):
    """Mosaic augmentation. Given 4 images, mosaic transform combines them into
    one output image. The output image is composed of the parts from each sub-
    image.

    .. code:: text

                        mosaic transform
                           center_x
                +------------------------------+
                |       pad        |  pad      |
                |      +-----------+           |
                |      |           |           |
                |      |  image1   |--------+  |
                |      |           |        |  |
                |      |           | image2 |  |
     center_y   |----+-------------+-----------|
                |    |   cropped   |           |
                |pad |   image3    |  image4   |
                |    |             |           |
                +----|-------------+-----------+
                     |             |
                     +-------------+

     The mosaic transform steps are as follows:
         1. Choose the mosaic center as the intersections of 4 images
         2. Get the left top image according to the index, and randomly
            sample another 3 images from the custom dataset.
         3. Sub image will be cropped if image is larger than mosaic patch

    Args:
        prob (float): mosaic probability.
        img_scale (Sequence[int]): Image size after mosaic pipeline of
            a single image. The size of the output image is four times
            that of a single image. The output image comprises 4 single images.
            Default: (640, 640).
        center_ratio_range (Sequence[float]): Center ratio range of mosaic
            output. Default: (0.5, 1.5).
        pad_val (int): Pad value. Default: 0.
        seg_pad_val (int): Pad value of segmentation map. Default: 255.
    """

    def __init__(self,
                 prob,
                 img_scale=(640, 640),
                 center_ratio_range=(0.5, 1.5),
                 pad_val=0,
                 seg_pad_val=255):
        assert 0 <= prob and prob <= 1
        assert isinstance(img_scale, tuple)
        self.prob = prob
        self.img_scale = img_scale
        self.center_ratio_range = center_ratio_range
        self.pad_val = pad_val
        self.seg_pad_val = seg_pad_val

    def __call__(self, results):
        """Call function to make a mosaic of image.

        Args:
            results (dict): Result dict.

        Returns:
            dict: Result dict with mosaic transformed.
        """
        mosaic = True if np.random.rand() < self.prob else False
        if mosaic:
            results = self._mosaic_transform_img(results)
            results = self._mosaic_transform_seg(results)
        return results

    def get_indexes(self, dataset):
        """Call function to collect indexes.

        Args:
            dataset (:obj:`MultiImageMixDataset`): The dataset.

        Returns:
            list: indexes.
        """

        indexes = [random.randint(0, len(dataset)) for _ in range(3)]
        return indexes

    def _mosaic_transform_img(self, results):
        """Mosaic transform function.

        Args:
            results (dict): Result dict.

        Returns:
            dict: Updated result dict.
        """

        assert 'mix_results' in results
        if len(results['img'].shape) == 3:
            mosaic_img = np.full(
                (int(self.img_scale[0] * 2), int(self.img_scale[1] * 2), 3),
                self.pad_val,
                dtype=results['img'].dtype)
        else:
            mosaic_img = np.full(
                (int(self.img_scale[0] * 2), int(self.img_scale[1] * 2)),
                self.pad_val,
                dtype=results['img'].dtype)

        # mosaic center x, y
        self.center_x = int(
            random.uniform(*self.center_ratio_range) * self.img_scale[1])
        self.center_y = int(
            random.uniform(*self.center_ratio_range) * self.img_scale[0])
        center_position = (self.center_x, self.center_y)

        loc_strs = ('top_left', 'top_right', 'bottom_left', 'bottom_right')
        for i, loc in enumerate(loc_strs):
            if loc == 'top_left':
                result_patch = copy.deepcopy(results)
            else:
                result_patch = copy.deepcopy(results['mix_results'][i - 1])

            img_i = result_patch['img']
            h_i, w_i = img_i.shape[:2]
            # keep_ratio resize
            scale_ratio_i = min(self.img_scale[0] / h_i,
                                self.img_scale[1] / w_i)
            img_i = mmcv.imresize(
                img_i, (int(w_i * scale_ratio_i), int(h_i * scale_ratio_i)))

            # compute the combine parameters
            paste_coord, crop_coord = self._mosaic_combine(
                loc, center_position, img_i.shape[:2][::-1])
            x1_p, y1_p, x2_p, y2_p = paste_coord
            x1_c, y1_c, x2_c, y2_c = crop_coord

            # crop and paste image
            mosaic_img[y1_p:y2_p, x1_p:x2_p] = img_i[y1_c:y2_c, x1_c:x2_c]

        results['img'] = mosaic_img
        results['img_shape'] = mosaic_img.shape
        results['ori_shape'] = mosaic_img.shape

        return results

    def _mosaic_transform_seg(self, results):
        """Mosaic transform function for label annotations.

        Args:
            results (dict): Result dict.

        Returns:
            dict: Updated result dict.
        """

        assert 'mix_results' in results
        for key in results.get('seg_fields', []):
            mosaic_seg = np.full(
                (int(self.img_scale[0] * 2), int(self.img_scale[1] * 2)),
                self.seg_pad_val,
                dtype=results[key].dtype)

            # mosaic center x, y
            center_position = (self.center_x, self.center_y)

            loc_strs = ('top_left', 'top_right', 'bottom_left', 'bottom_right')
            for i, loc in enumerate(loc_strs):
                if loc == 'top_left':
                    result_patch = copy.deepcopy(results)
                else:
                    result_patch = copy.deepcopy(results['mix_results'][i - 1])

                gt_seg_i = result_patch[key]
                h_i, w_i = gt_seg_i.shape[:2]
                # keep_ratio resize
                scale_ratio_i = min(self.img_scale[0] / h_i,
                                    self.img_scale[1] / w_i)
                gt_seg_i = mmcv.imresize(
                    gt_seg_i,
                    (int(w_i * scale_ratio_i), int(h_i * scale_ratio_i)),
                    interpolation='nearest')

                # compute the combine parameters
                paste_coord, crop_coord = self._mosaic_combine(
                    loc, center_position, gt_seg_i.shape[:2][::-1])
                x1_p, y1_p, x2_p, y2_p = paste_coord
                x1_c, y1_c, x2_c, y2_c = crop_coord

                # crop and paste image
                mosaic_seg[y1_p:y2_p, x1_p:x2_p] = gt_seg_i[y1_c:y2_c,
                                                            x1_c:x2_c]

            results[key] = mosaic_seg

        return results

    def _mosaic_combine(self, loc, center_position_xy, img_shape_wh):
        """Calculate global coordinate of mosaic image and local coordinate of
        cropped sub-image.

        Args:
            loc (str): Index for the sub-image, loc in ('top_left',
              'top_right', 'bottom_left', 'bottom_right').
            center_position_xy (Sequence[float]): Mixing center for 4 images,
                (x, y).
            img_shape_wh (Sequence[int]): Width and height of sub-image

        Returns:
            tuple[tuple[float]]: Corresponding coordinate of pasting and
                cropping
                - paste_coord (tuple): paste corner coordinate in mosaic image.
                - crop_coord (tuple): crop corner coordinate in mosaic image.
        """

        assert loc in ('top_left', 'top_right', 'bottom_left', 'bottom_right')
        if loc == 'top_left':
            # index0 to top left part of image
            x1, y1, x2, y2 = max(center_position_xy[0] - img_shape_wh[0], 0), \
                             max(center_position_xy[1] - img_shape_wh[1], 0), \
                             center_position_xy[0], \
                             center_position_xy[1]
            crop_coord = img_shape_wh[0] - (x2 - x1), img_shape_wh[1] - (
                y2 - y1), img_shape_wh[0], img_shape_wh[1]

        elif loc == 'top_right':
            # index1 to top right part of image
            x1, y1, x2, y2 = center_position_xy[0], \
                             max(center_position_xy[1] - img_shape_wh[1], 0), \
                             min(center_position_xy[0] + img_shape_wh[0],
                                 self.img_scale[1] * 2), \
                             center_position_xy[1]
            crop_coord = 0, img_shape_wh[1] - (y2 - y1), min(
                img_shape_wh[0], x2 - x1), img_shape_wh[1]

        elif loc == 'bottom_left':
            # index2 to bottom left part of image
            x1, y1, x2, y2 = max(center_position_xy[0] - img_shape_wh[0], 0), \
                             center_position_xy[1], \
                             center_position_xy[0], \
                             min(self.img_scale[0] * 2, center_position_xy[1] +
                                 img_shape_wh[1])
            crop_coord = img_shape_wh[0] - (x2 - x1), 0, img_shape_wh[0], min(
                y2 - y1, img_shape_wh[1])

        else:
            # index3 to bottom right part of image
            x1, y1, x2, y2 = center_position_xy[0], \
                             center_position_xy[1], \
                             min(center_position_xy[0] + img_shape_wh[0],
                                 self.img_scale[1] * 2), \
                             min(self.img_scale[0] * 2, center_position_xy[1] +
                                 img_shape_wh[1])
            crop_coord = 0, 0, min(img_shape_wh[0],
                                   x2 - x1), min(y2 - y1, img_shape_wh[1])

        paste_coord = x1, y1, x2, y2
        return paste_coord, crop_coord

    def __repr__(self):
        repr_str = self.__class__.__name__
        repr_str += f'(prob={self.prob}, '
        repr_str += f'img_scale={self.img_scale}, '
        repr_str += f'center_ratio_range={self.center_ratio_range}, '
        repr_str += f'pad_val={self.pad_val}, '
        repr_str += f'seg_pad_val={self.pad_val})'
        return repr_str