# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod

import cv2
import mmcv
import numpy as np
import pycocotools.mask as maskUtils
import torch
from mmcv.ops.roi_align import roi_align


class BaseInstanceMasks(metaclass=ABCMeta):
    """Base class for instance masks."""

    @abstractmethod
    def rescale(self, scale, interpolation='nearest'):
        """Rescale masks as large as possible while keeping the aspect ratio.
        For details can refer to `mmcv.imrescale`.

        Args:
            scale (tuple[int]): The maximum size (h, w) of rescaled mask.
            interpolation (str): Same as :func:`mmcv.imrescale`.

        Returns:
            BaseInstanceMasks: The rescaled masks.
        """

    @abstractmethod
    def resize(self, out_shape, interpolation='nearest'):
        """Resize masks to the given out_shape.

        Args:
            out_shape: Target (h, w) of resized mask.
            interpolation (str): See :func:`mmcv.imresize`.

        Returns:
            BaseInstanceMasks: The resized masks.
        """

    @abstractmethod
    def flip(self, flip_direction='horizontal'):
        """Flip masks alone the given direction.

        Args:
            flip_direction (str): Either 'horizontal' or 'vertical'.

        Returns:
            BaseInstanceMasks: The flipped masks.
        """

    @abstractmethod
    def pad(self, out_shape, pad_val):
        """Pad masks to the given size of (h, w).

        Args:
            out_shape (tuple[int]): Target (h, w) of padded mask.
            pad_val (int): The padded value.

        Returns:
            BaseInstanceMasks: The padded masks.
        """

    @abstractmethod
    def crop(self, bbox):
        """Crop each mask by the given bbox.

        Args:
            bbox (ndarray): Bbox in format [x1, y1, x2, y2], shape (4, ).

        Return:
            BaseInstanceMasks: The cropped masks.
        """

    @abstractmethod
    def crop_and_resize(self,
                        bboxes,
                        out_shape,
                        inds,
                        device,
                        interpolation='bilinear',
                        binarize=True):
        """Crop and resize masks by the given bboxes.

        This function is mainly used in mask targets computation.
        It firstly align mask to bboxes by assigned_inds, then crop mask by the
        assigned bbox and resize to the size of (mask_h, mask_w)

        Args:
            bboxes (Tensor): Bboxes in format [x1, y1, x2, y2], shape (N, 4)
            out_shape (tuple[int]): Target (h, w) of resized mask
            inds (ndarray): Indexes to assign masks to each bbox,
                shape (N,) and values should be between [0, num_masks - 1].
            device (str): Device of bboxes
            interpolation (str): See `mmcv.imresize`
            binarize (bool): if True fractional values are rounded to 0 or 1
                after the resize operation. if False and unsupported an error
                will be raised. Defaults to True.

        Return:
            BaseInstanceMasks: the cropped and resized masks.
        """

    @abstractmethod
    def expand(self, expanded_h, expanded_w, top, left):
        """see :class:`Expand`."""

    @property
    @abstractmethod
    def areas(self):
        """ndarray: areas of each instance."""

    @abstractmethod
    def to_ndarray(self):
        """Convert masks to the format of ndarray.

        Return:
            ndarray: Converted masks in the format of ndarray.
        """

    @abstractmethod
    def to_tensor(self, dtype, device):
        """Convert masks to the format of Tensor.

        Args:
            dtype (str): Dtype of converted mask.
            device (torch.device): Device of converted masks.

        Returns:
            Tensor: Converted masks in the format of Tensor.
        """

    @abstractmethod
    def translate(self,
                  out_shape,
                  offset,
                  direction='horizontal',
                  fill_val=0,
                  interpolation='bilinear'):
        """Translate the masks.

        Args:
            out_shape (tuple[int]): Shape for output mask, format (h, w).
            offset (int | float): The offset for translate.
            direction (str): The translate direction, either "horizontal"
                or "vertical".
            fill_val (int | float): Border value. Default 0.
            interpolation (str): Same as :func:`mmcv.imtranslate`.

        Returns:
            Translated masks.
        """

    def shear(self,
              out_shape,
              magnitude,
              direction='horizontal',
              border_value=0,
              interpolation='bilinear'):
        """Shear the masks.

        Args:
            out_shape (tuple[int]): Shape for output mask, format (h, w).
            magnitude (int | float): The magnitude used for shear.
            direction (str): The shear direction, either "horizontal"
                or "vertical".
            border_value (int | tuple[int]): Value used in case of a
                constant border. Default 0.
            interpolation (str): Same as in :func:`mmcv.imshear`.

        Returns:
            ndarray: Sheared masks.
        """

    @abstractmethod
    def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0):
        """Rotate the masks.

        Args:
            out_shape (tuple[int]): Shape for output mask, format (h, w).
            angle (int | float): Rotation angle in degrees. Positive values
                mean counter-clockwise rotation.
            center (tuple[float], optional): Center point (w, h) of the
                rotation in source image. If not specified, the center of
                the image will be used.
            scale (int | float): Isotropic scale factor.
            fill_val (int | float): Border value. Default 0 for masks.

        Returns:
            Rotated masks.
        """


class BitmapMasks(BaseInstanceMasks):
    """This class represents masks in the form of bitmaps.

    Args:
        masks (ndarray): ndarray of masks in shape (N, H, W), where N is
            the number of objects.
        height (int): height of masks
        width (int): width of masks

    Example:
        >>> from mmdet.core.mask.structures import *  # NOQA
        >>> num_masks, H, W = 3, 32, 32
        >>> rng = np.random.RandomState(0)
        >>> masks = (rng.rand(num_masks, H, W) > 0.1).astype(np.int)
        >>> self = BitmapMasks(masks, height=H, width=W)

        >>> # demo crop_and_resize
        >>> num_boxes = 5
        >>> bboxes = np.array([[0, 0, 30, 10.0]] * num_boxes)
        >>> out_shape = (14, 14)
        >>> inds = torch.randint(0, len(self), size=(num_boxes,))
        >>> device = 'cpu'
        >>> interpolation = 'bilinear'
        >>> new = self.crop_and_resize(
        ...     bboxes, out_shape, inds, device, interpolation)
        >>> assert len(new) == num_boxes
        >>> assert new.height, new.width == out_shape
    """

    def __init__(self, masks, height, width):
        self.height = height
        self.width = width
        if len(masks) == 0:
            self.masks = np.empty((0, self.height, self.width), dtype=np.uint8)
        else:
            assert isinstance(masks, (list, np.ndarray))
            if isinstance(masks, list):
                assert isinstance(masks[0], np.ndarray)
                assert masks[0].ndim == 2  # (H, W)
            else:
                assert masks.ndim == 3  # (N, H, W)

            self.masks = np.stack(masks).reshape(-1, height, width)
            assert self.masks.shape[1] == self.height
            assert self.masks.shape[2] == self.width

    def __getitem__(self, index):
        """Index the BitmapMask.

        Args:
            index (int | ndarray): Indices in the format of integer or ndarray.

        Returns:
            :obj:`BitmapMasks`: Indexed bitmap masks.
        """
        masks = self.masks[index].reshape(-1, self.height, self.width)
        return BitmapMasks(masks, self.height, self.width)

    def __iter__(self):
        return iter(self.masks)

    def __repr__(self):
        s = self.__class__.__name__ + '('
        s += f'num_masks={len(self.masks)}, '
        s += f'height={self.height}, '
        s += f'width={self.width})'
        return s

    def __len__(self):
        """Number of masks."""
        return len(self.masks)

    def rescale(self, scale, interpolation='nearest'):
        """See :func:`BaseInstanceMasks.rescale`."""
        if len(self.masks) == 0:
            new_w, new_h = mmcv.rescale_size((self.width, self.height), scale)
            rescaled_masks = np.empty((0, new_h, new_w), dtype=np.uint8)
        else:
            rescaled_masks = np.stack([
                mmcv.imrescale(mask, scale, interpolation=interpolation)
                for mask in self.masks
            ])
        height, width = rescaled_masks.shape[1:]
        return BitmapMasks(rescaled_masks, height, width)

    def resize(self, out_shape, interpolation='nearest'):
        """See :func:`BaseInstanceMasks.resize`."""
        if len(self.masks) == 0:
            resized_masks = np.empty((0, *out_shape), dtype=np.uint8)
        else:
            resized_masks = np.stack([
                mmcv.imresize(
                    mask, out_shape[::-1], interpolation=interpolation)
                for mask in self.masks
            ])
        return BitmapMasks(resized_masks, *out_shape)

    def flip(self, flip_direction='horizontal'):
        """See :func:`BaseInstanceMasks.flip`."""
        assert flip_direction in ('horizontal', 'vertical', 'diagonal')

        if len(self.masks) == 0:
            flipped_masks = self.masks
        else:
            flipped_masks = np.stack([
                mmcv.imflip(mask, direction=flip_direction)
                for mask in self.masks
            ])
        return BitmapMasks(flipped_masks, self.height, self.width)

    def pad(self, out_shape, pad_val=0):
        """See :func:`BaseInstanceMasks.pad`."""
        if len(self.masks) == 0:
            padded_masks = np.empty((0, *out_shape), dtype=np.uint8)
        else:
            padded_masks = np.stack([
                mmcv.impad(mask, shape=out_shape, pad_val=pad_val)
                for mask in self.masks
            ])
        return BitmapMasks(padded_masks, *out_shape)

    def crop(self, bbox):
        """See :func:`BaseInstanceMasks.crop`."""
        assert isinstance(bbox, np.ndarray)
        assert bbox.ndim == 1

        # clip the boundary
        bbox = bbox.copy()
        bbox[0::2] = np.clip(bbox[0::2], 0, self.width)
        bbox[1::2] = np.clip(bbox[1::2], 0, self.height)
        x1, y1, x2, y2 = bbox
        w = np.maximum(x2 - x1, 1)
        h = np.maximum(y2 - y1, 1)

        if len(self.masks) == 0:
            cropped_masks = np.empty((0, h, w), dtype=np.uint8)
        else:
            cropped_masks = self.masks[:, y1:y1 + h, x1:x1 + w]
        return BitmapMasks(cropped_masks, h, w)

    def crop_and_resize(self,
                        bboxes,
                        out_shape,
                        inds,
                        device='cpu',
                        interpolation='bilinear',
                        binarize=True):
        """See :func:`BaseInstanceMasks.crop_and_resize`."""
        if len(self.masks) == 0:
            empty_masks = np.empty((0, *out_shape), dtype=np.uint8)
            return BitmapMasks(empty_masks, *out_shape)

        # convert bboxes to tensor
        if isinstance(bboxes, np.ndarray):
            bboxes = torch.from_numpy(bboxes).to(device=device)
        if isinstance(inds, np.ndarray):
            inds = torch.from_numpy(inds).to(device=device)

        num_bbox = bboxes.shape[0]
        fake_inds = torch.arange(
            num_bbox, device=device).to(dtype=bboxes.dtype)[:, None]
        rois = torch.cat([fake_inds, bboxes], dim=1)  # Nx5
        rois = rois.to(device=device)
        if num_bbox > 0:
            gt_masks_th = torch.from_numpy(self.masks).to(device).index_select(
                0, inds).to(dtype=rois.dtype)
            targets = roi_align(gt_masks_th[:, None, :, :], rois, out_shape,
                                1.0, 0, 'avg', True).squeeze(1)
            if binarize:
                resized_masks = (targets >= 0.5).cpu().numpy()
            else:
                resized_masks = targets.cpu().numpy()
        else:
            resized_masks = []
        return BitmapMasks(resized_masks, *out_shape)

    def expand(self, expanded_h, expanded_w, top, left):
        """See :func:`BaseInstanceMasks.expand`."""
        if len(self.masks) == 0:
            expanded_mask = np.empty((0, expanded_h, expanded_w),
                                     dtype=np.uint8)
        else:
            expanded_mask = np.zeros((len(self), expanded_h, expanded_w),
                                     dtype=np.uint8)
            expanded_mask[:, top:top + self.height,
                          left:left + self.width] = self.masks
        return BitmapMasks(expanded_mask, expanded_h, expanded_w)

    def translate(self,
                  out_shape,
                  offset,
                  direction='horizontal',
                  fill_val=0,
                  interpolation='bilinear'):
        """Translate the BitmapMasks.

        Args:
            out_shape (tuple[int]): Shape for output mask, format (h, w).
            offset (int | float): The offset for translate.
            direction (str): The translate direction, either "horizontal"
                or "vertical".
            fill_val (int | float): Border value. Default 0 for masks.
            interpolation (str): Same as :func:`mmcv.imtranslate`.

        Returns:
            BitmapMasks: Translated BitmapMasks.

        Example:
            >>> from mmdet.core.mask.structures import BitmapMasks
            >>> self = BitmapMasks.random(dtype=np.uint8)
            >>> out_shape = (32, 32)
            >>> offset = 4
            >>> direction = 'horizontal'
            >>> fill_val = 0
            >>> interpolation = 'bilinear'
            >>> # Note, There seem to be issues when:
            >>> # * out_shape is different than self's shape
            >>> # * the mask dtype is not supported by cv2.AffineWarp
            >>> new = self.translate(out_shape, offset, direction, fill_val,
            >>>                      interpolation)
            >>> assert len(new) == len(self)
            >>> assert new.height, new.width == out_shape
        """
        if len(self.masks) == 0:
            translated_masks = np.empty((0, *out_shape), dtype=np.uint8)
        else:
            translated_masks = mmcv.imtranslate(
                self.masks.transpose((1, 2, 0)),
                offset,
                direction,
                border_value=fill_val,
                interpolation=interpolation)
            if translated_masks.ndim == 2:
                translated_masks = translated_masks[:, :, None]
            translated_masks = translated_masks.transpose(
                (2, 0, 1)).astype(self.masks.dtype)
        return BitmapMasks(translated_masks, *out_shape)

    def shear(self,
              out_shape,
              magnitude,
              direction='horizontal',
              border_value=0,
              interpolation='bilinear'):
        """Shear the BitmapMasks.

        Args:
            out_shape (tuple[int]): Shape for output mask, format (h, w).
            magnitude (int | float): The magnitude used for shear.
            direction (str): The shear direction, either "horizontal"
                or "vertical".
            border_value (int | tuple[int]): Value used in case of a
                constant border.
            interpolation (str): Same as in :func:`mmcv.imshear`.

        Returns:
            BitmapMasks: The sheared masks.
        """
        if len(self.masks) == 0:
            sheared_masks = np.empty((0, *out_shape), dtype=np.uint8)
        else:
            sheared_masks = mmcv.imshear(
                self.masks.transpose((1, 2, 0)),
                magnitude,
                direction,
                border_value=border_value,
                interpolation=interpolation)
            if sheared_masks.ndim == 2:
                sheared_masks = sheared_masks[:, :, None]
            sheared_masks = sheared_masks.transpose(
                (2, 0, 1)).astype(self.masks.dtype)
        return BitmapMasks(sheared_masks, *out_shape)

    def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0):
        """Rotate the BitmapMasks.

        Args:
            out_shape (tuple[int]): Shape for output mask, format (h, w).
            angle (int | float): Rotation angle in degrees. Positive values
                mean counter-clockwise rotation.
            center (tuple[float], optional): Center point (w, h) of the
                rotation in source image. If not specified, the center of
                the image will be used.
            scale (int | float): Isotropic scale factor.
            fill_val (int | float): Border value. Default 0 for masks.

        Returns:
            BitmapMasks: Rotated BitmapMasks.
        """
        if len(self.masks) == 0:
            rotated_masks = np.empty((0, *out_shape), dtype=self.masks.dtype)
        else:
            rotated_masks = mmcv.imrotate(
                self.masks.transpose((1, 2, 0)),
                angle,
                center=center,
                scale=scale,
                border_value=fill_val)
            if rotated_masks.ndim == 2:
                # case when only one mask, (h, w)
                rotated_masks = rotated_masks[:, :, None]  # (h, w, 1)
            rotated_masks = rotated_masks.transpose(
                (2, 0, 1)).astype(self.masks.dtype)
        return BitmapMasks(rotated_masks, *out_shape)

    @property
    def areas(self):
        """See :py:attr:`BaseInstanceMasks.areas`."""
        return self.masks.sum((1, 2))

    def to_ndarray(self):
        """See :func:`BaseInstanceMasks.to_ndarray`."""
        return self.masks

    def to_tensor(self, dtype, device):
        """See :func:`BaseInstanceMasks.to_tensor`."""
        return torch.tensor(self.masks, dtype=dtype, device=device)

    @classmethod
    def random(cls,
               num_masks=3,
               height=32,
               width=32,
               dtype=np.uint8,
               rng=None):
        """Generate random bitmap masks for demo / testing purposes.

        Example:
            >>> from mmdet.core.mask.structures import BitmapMasks
            >>> self = BitmapMasks.random()
            >>> print('self = {}'.format(self))
            self = BitmapMasks(num_masks=3, height=32, width=32)
        """
        from mmdet.utils.util_random import ensure_rng
        rng = ensure_rng(rng)
        masks = (rng.rand(num_masks, height, width) > 0.1).astype(dtype)
        self = cls(masks, height=height, width=width)
        return self

    def get_bboxes(self):
        num_masks = len(self)
        boxes = np.zeros((num_masks, 4), dtype=np.float32)
        x_any = self.masks.any(axis=1)
        y_any = self.masks.any(axis=2)
        for idx in range(num_masks):
            x = np.where(x_any[idx, :])[0]
            y = np.where(y_any[idx, :])[0]
            if len(x) > 0 and len(y) > 0:
                # use +1 for x_max and y_max so that the right and bottom
                # boundary of instance masks are fully included by the box
                boxes[idx, :] = np.array([x[0], y[0], x[-1] + 1, y[-1] + 1],
                                         dtype=np.float32)
        return boxes


class PolygonMasks(BaseInstanceMasks):
    """This class represents masks in the form of polygons.

    Polygons is a list of three levels. The first level of the list
    corresponds to objects, the second level to the polys that compose the
    object, the third level to the poly coordinates

    Args:
        masks (list[list[ndarray]]): The first level of the list
            corresponds to objects, the second level to the polys that
            compose the object, the third level to the poly coordinates
        height (int): height of masks
        width (int): width of masks

    Example:
        >>> from mmdet.core.mask.structures import *  # NOQA
        >>> masks = [
        >>>     [ np.array([0, 0, 10, 0, 10, 10., 0, 10, 0, 0]) ]
        >>> ]
        >>> height, width = 16, 16
        >>> self = PolygonMasks(masks, height, width)

        >>> # demo translate
        >>> new = self.translate((16, 16), 4., direction='horizontal')
        >>> assert np.all(new.masks[0][0][1::2] == masks[0][0][1::2])
        >>> assert np.all(new.masks[0][0][0::2] == masks[0][0][0::2] + 4)

        >>> # demo crop_and_resize
        >>> num_boxes = 3
        >>> bboxes = np.array([[0, 0, 30, 10.0]] * num_boxes)
        >>> out_shape = (16, 16)
        >>> inds = torch.randint(0, len(self), size=(num_boxes,))
        >>> device = 'cpu'
        >>> interpolation = 'bilinear'
        >>> new = self.crop_and_resize(
        ...     bboxes, out_shape, inds, device, interpolation)
        >>> assert len(new) == num_boxes
        >>> assert new.height, new.width == out_shape
    """

    def __init__(self, masks, height, width):
        assert isinstance(masks, list)
        if len(masks) > 0:
            assert isinstance(masks[0], list)
            assert isinstance(masks[0][0], np.ndarray)

        self.height = height
        self.width = width
        self.masks = masks

    def __getitem__(self, index):
        """Index the polygon masks.

        Args:
            index (ndarray | List): The indices.

        Returns:
            :obj:`PolygonMasks`: The indexed polygon masks.
        """
        if isinstance(index, np.ndarray):
            index = index.tolist()
        if isinstance(index, list):
            masks = [self.masks[i] for i in index]
        else:
            try:
                masks = self.masks[index]
            except Exception:
                raise ValueError(
                    f'Unsupported input of type {type(index)} for indexing!')
        if len(masks) and isinstance(masks[0], np.ndarray):
            masks = [masks]  # ensure a list of three levels
        return PolygonMasks(masks, self.height, self.width)

    def __iter__(self):
        return iter(self.masks)

    def __repr__(self):
        s = self.__class__.__name__ + '('
        s += f'num_masks={len(self.masks)}, '
        s += f'height={self.height}, '
        s += f'width={self.width})'
        return s

    def __len__(self):
        """Number of masks."""
        return len(self.masks)

    def rescale(self, scale, interpolation=None):
        """see :func:`BaseInstanceMasks.rescale`"""
        new_w, new_h = mmcv.rescale_size((self.width, self.height), scale)
        if len(self.masks) == 0:
            rescaled_masks = PolygonMasks([], new_h, new_w)
        else:
            rescaled_masks = self.resize((new_h, new_w))
        return rescaled_masks

    def resize(self, out_shape, interpolation=None):
        """see :func:`BaseInstanceMasks.resize`"""
        if len(self.masks) == 0:
            resized_masks = PolygonMasks([], *out_shape)
        else:
            h_scale = out_shape[0] / self.height
            w_scale = out_shape[1] / self.width
            resized_masks = []
            for poly_per_obj in self.masks:
                resized_poly = []
                for p in poly_per_obj:
                    p = p.copy()
                    p[0::2] = p[0::2] * w_scale
                    p[1::2] = p[1::2] * h_scale
                    resized_poly.append(p)
                resized_masks.append(resized_poly)
            resized_masks = PolygonMasks(resized_masks, *out_shape)
        return resized_masks

    def flip(self, flip_direction='horizontal'):
        """see :func:`BaseInstanceMasks.flip`"""
        assert flip_direction in ('horizontal', 'vertical', 'diagonal')
        if len(self.masks) == 0:
            flipped_masks = PolygonMasks([], self.height, self.width)
        else:
            flipped_masks = []
            for poly_per_obj in self.masks:
                flipped_poly_per_obj = []
                for p in poly_per_obj:
                    p = p.copy()
                    if flip_direction == 'horizontal':
                        p[0::2] = self.width - p[0::2]
                    elif flip_direction == 'vertical':
                        p[1::2] = self.height - p[1::2]
                    else:
                        p[0::2] = self.width - p[0::2]
                        p[1::2] = self.height - p[1::2]
                    flipped_poly_per_obj.append(p)
                flipped_masks.append(flipped_poly_per_obj)
            flipped_masks = PolygonMasks(flipped_masks, self.height,
                                         self.width)
        return flipped_masks

    def crop(self, bbox):
        """see :func:`BaseInstanceMasks.crop`"""
        assert isinstance(bbox, np.ndarray)
        assert bbox.ndim == 1

        # clip the boundary
        bbox = bbox.copy()
        bbox[0::2] = np.clip(bbox[0::2], 0, self.width)
        bbox[1::2] = np.clip(bbox[1::2], 0, self.height)
        x1, y1, x2, y2 = bbox
        w = np.maximum(x2 - x1, 1)
        h = np.maximum(y2 - y1, 1)

        if len(self.masks) == 0:
            cropped_masks = PolygonMasks([], h, w)
        else:
            cropped_masks = []
            for poly_per_obj in self.masks:
                cropped_poly_per_obj = []
                for p in poly_per_obj:
                    # pycocotools will clip the boundary
                    p = p.copy()
                    p[0::2] = p[0::2] - bbox[0]
                    p[1::2] = p[1::2] - bbox[1]
                    cropped_poly_per_obj.append(p)
                cropped_masks.append(cropped_poly_per_obj)
            cropped_masks = PolygonMasks(cropped_masks, h, w)
        return cropped_masks

    def pad(self, out_shape, pad_val=0):
        """padding has no effect on polygons`"""
        return PolygonMasks(self.masks, *out_shape)

    def expand(self, *args, **kwargs):
        """TODO: Add expand for polygon"""
        raise NotImplementedError

    def crop_and_resize(self,
                        bboxes,
                        out_shape,
                        inds,
                        device='cpu',
                        interpolation='bilinear',
                        binarize=True):
        """see :func:`BaseInstanceMasks.crop_and_resize`"""
        out_h, out_w = out_shape
        if len(self.masks) == 0:
            return PolygonMasks([], out_h, out_w)

        if not binarize:
            raise ValueError('Polygons are always binary, '
                             'setting binarize=False is unsupported')

        resized_masks = []
        for i in range(len(bboxes)):
            mask = self.masks[inds[i]]
            bbox = bboxes[i, :]
            x1, y1, x2, y2 = bbox
            w = np.maximum(x2 - x1, 1)
            h = np.maximum(y2 - y1, 1)
            h_scale = out_h / max(h, 0.1)  # avoid too large scale
            w_scale = out_w / max(w, 0.1)

            resized_mask = []
            for p in mask:
                p = p.copy()
                # crop
                # pycocotools will clip the boundary
                p[0::2] = p[0::2] - bbox[0]
                p[1::2] = p[1::2] - bbox[1]

                # resize
                p[0::2] = p[0::2] * w_scale
                p[1::2] = p[1::2] * h_scale
                resized_mask.append(p)
            resized_masks.append(resized_mask)
        return PolygonMasks(resized_masks, *out_shape)

    def translate(self,
                  out_shape,
                  offset,
                  direction='horizontal',
                  fill_val=None,
                  interpolation=None):
        """Translate the PolygonMasks.

        Example:
            >>> self = PolygonMasks.random(dtype=np.int)
            >>> out_shape = (self.height, self.width)
            >>> new = self.translate(out_shape, 4., direction='horizontal')
            >>> assert np.all(new.masks[0][0][1::2] == self.masks[0][0][1::2])
            >>> assert np.all(new.masks[0][0][0::2] == self.masks[0][0][0::2] + 4)  # noqa: E501
        """
        assert fill_val is None or fill_val == 0, 'Here fill_val is not '\
            f'used, and defaultly should be None or 0. got {fill_val}.'
        if len(self.masks) == 0:
            translated_masks = PolygonMasks([], *out_shape)
        else:
            translated_masks = []
            for poly_per_obj in self.masks:
                translated_poly_per_obj = []
                for p in poly_per_obj:
                    p = p.copy()
                    if direction == 'horizontal':
                        p[0::2] = np.clip(p[0::2] + offset, 0, out_shape[1])
                    elif direction == 'vertical':
                        p[1::2] = np.clip(p[1::2] + offset, 0, out_shape[0])
                    translated_poly_per_obj.append(p)
                translated_masks.append(translated_poly_per_obj)
            translated_masks = PolygonMasks(translated_masks, *out_shape)
        return translated_masks

    def shear(self,
              out_shape,
              magnitude,
              direction='horizontal',
              border_value=0,
              interpolation='bilinear'):
        """See :func:`BaseInstanceMasks.shear`."""
        if len(self.masks) == 0:
            sheared_masks = PolygonMasks([], *out_shape)
        else:
            sheared_masks = []
            if direction == 'horizontal':
                shear_matrix = np.stack([[1, magnitude],
                                         [0, 1]]).astype(np.float32)
            elif direction == 'vertical':
                shear_matrix = np.stack([[1, 0], [magnitude,
                                                  1]]).astype(np.float32)
            for poly_per_obj in self.masks:
                sheared_poly = []
                for p in poly_per_obj:
                    p = np.stack([p[0::2], p[1::2]], axis=0)  # [2, n]
                    new_coords = np.matmul(shear_matrix, p)  # [2, n]
                    new_coords[0, :] = np.clip(new_coords[0, :], 0,
                                               out_shape[1])
                    new_coords[1, :] = np.clip(new_coords[1, :], 0,
                                               out_shape[0])
                    sheared_poly.append(
                        new_coords.transpose((1, 0)).reshape(-1))
                sheared_masks.append(sheared_poly)
            sheared_masks = PolygonMasks(sheared_masks, *out_shape)
        return sheared_masks

    def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0):
        """See :func:`BaseInstanceMasks.rotate`."""
        if len(self.masks) == 0:
            rotated_masks = PolygonMasks([], *out_shape)
        else:
            rotated_masks = []
            rotate_matrix = cv2.getRotationMatrix2D(center, -angle, scale)
            for poly_per_obj in self.masks:
                rotated_poly = []
                for p in poly_per_obj:
                    p = p.copy()
                    coords = np.stack([p[0::2], p[1::2]], axis=1)  # [n, 2]
                    # pad 1 to convert from format [x, y] to homogeneous
                    # coordinates format [x, y, 1]
                    coords = np.concatenate(
                        (coords, np.ones((coords.shape[0], 1), coords.dtype)),
                        axis=1)  # [n, 3]
                    rotated_coords = np.matmul(
                        rotate_matrix[None, :, :],
                        coords[:, :, None])[..., 0]  # [n, 2, 1] -> [n, 2]
                    rotated_coords[:, 0] = np.clip(rotated_coords[:, 0], 0,
                                                   out_shape[1])
                    rotated_coords[:, 1] = np.clip(rotated_coords[:, 1], 0,
                                                   out_shape[0])
                    rotated_poly.append(rotated_coords.reshape(-1))
                rotated_masks.append(rotated_poly)
            rotated_masks = PolygonMasks(rotated_masks, *out_shape)
        return rotated_masks

    def to_bitmap(self):
        """convert polygon masks to bitmap masks."""
        bitmap_masks = self.to_ndarray()
        return BitmapMasks(bitmap_masks, self.height, self.width)

    @property
    def areas(self):
        """Compute areas of masks.

        This func is modified from `detectron2
        <https://github.com/facebookresearch/detectron2/blob/ffff8acc35ea88ad1cb1806ab0f00b4c1c5dbfd9/detectron2/structures/masks.py#L387>`_.
        The function only works with Polygons using the shoelace formula.

        Return:
            ndarray: areas of each instance
        """  # noqa: W501
        area = []
        for polygons_per_obj in self.masks:
            area_per_obj = 0
            for p in polygons_per_obj:
                area_per_obj += self._polygon_area(p[0::2], p[1::2])
            area.append(area_per_obj)
        return np.asarray(area)

    def _polygon_area(self, x, y):
        """Compute the area of a component of a polygon.

        Using the shoelace formula:
        https://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates

        Args:
            x (ndarray): x coordinates of the component
            y (ndarray): y coordinates of the component

        Return:
            float: the are of the component
        """  # noqa: 501
        return 0.5 * np.abs(
            np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1)))

    def to_ndarray(self):
        """Convert masks to the format of ndarray."""
        if len(self.masks) == 0:
            return np.empty((0, self.height, self.width), dtype=np.uint8)
        bitmap_masks = []
        for poly_per_obj in self.masks:
            bitmap_masks.append(
                polygon_to_bitmap(poly_per_obj, self.height, self.width))
        return np.stack(bitmap_masks)

    def to_tensor(self, dtype, device):
        """See :func:`BaseInstanceMasks.to_tensor`."""
        if len(self.masks) == 0:
            return torch.empty((0, self.height, self.width),
                               dtype=dtype,
                               device=device)
        ndarray_masks = self.to_ndarray()
        return torch.tensor(ndarray_masks, dtype=dtype, device=device)

    @classmethod
    def random(cls,
               num_masks=3,
               height=32,
               width=32,
               n_verts=5,
               dtype=np.float32,
               rng=None):
        """Generate random polygon masks for demo / testing purposes.

        Adapted from [1]_

        References:
            .. [1] https://gitlab.kitware.com/computer-vision/kwimage/-/blob/928cae35ca8/kwimage/structs/polygon.py#L379  # noqa: E501

        Example:
            >>> from mmdet.core.mask.structures import PolygonMasks
            >>> self = PolygonMasks.random()
            >>> print('self = {}'.format(self))
        """
        from mmdet.utils.util_random import ensure_rng
        rng = ensure_rng(rng)

        def _gen_polygon(n, irregularity, spikeyness):
            """Creates the polygon by sampling points on a circle around the
            centre.  Random noise is added by varying the angular spacing
            between sequential points, and by varying the radial distance of
            each point from the centre.

            Based on original code by Mike Ounsworth

            Args:
                n (int): number of vertices
                irregularity (float): [0,1] indicating how much variance there
                    is in the angular spacing of vertices. [0,1] will map to
                    [0, 2pi/numberOfVerts]
                spikeyness (float): [0,1] indicating how much variance there is
                    in each vertex from the circle of radius aveRadius. [0,1]
                    will map to [0, aveRadius]

            Returns:
                a list of vertices, in CCW order.
            """
            from scipy.stats import truncnorm

            # Generate around the unit circle
            cx, cy = (0.0, 0.0)
            radius = 1

            tau = np.pi * 2

            irregularity = np.clip(irregularity, 0, 1) * 2 * np.pi / n
            spikeyness = np.clip(spikeyness, 1e-9, 1)

            # generate n angle steps
            lower = (tau / n) - irregularity
            upper = (tau / n) + irregularity
            angle_steps = rng.uniform(lower, upper, n)

            # normalize the steps so that point 0 and point n+1 are the same
            k = angle_steps.sum() / (2 * np.pi)
            angles = (angle_steps / k).cumsum() + rng.uniform(0, tau)

            # Convert high and low values to be wrt the standard normal range
            # https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.truncnorm.html
            low = 0
            high = 2 * radius
            mean = radius
            std = spikeyness
            a = (low - mean) / std
            b = (high - mean) / std
            tnorm = truncnorm(a=a, b=b, loc=mean, scale=std)

            # now generate the points
            radii = tnorm.rvs(n, random_state=rng)
            x_pts = cx + radii * np.cos(angles)
            y_pts = cy + radii * np.sin(angles)

            points = np.hstack([x_pts[:, None], y_pts[:, None]])

            # Scale to 0-1 space
            points = points - points.min(axis=0)
            points = points / points.max(axis=0)

            # Randomly place within 0-1 space
            points = points * (rng.rand() * .8 + .2)
            min_pt = points.min(axis=0)
            max_pt = points.max(axis=0)

            high = (1 - max_pt)
            low = (0 - min_pt)
            offset = (rng.rand(2) * (high - low)) + low
            points = points + offset
            return points

        def _order_vertices(verts):
            """
            References:
                https://stackoverflow.com/questions/1709283/how-can-i-sort-a-coordinate-list-for-a-rectangle-counterclockwise
            """
            mlat = verts.T[0].sum() / len(verts)
            mlng = verts.T[1].sum() / len(verts)

            tau = np.pi * 2
            angle = (np.arctan2(mlat - verts.T[0], verts.T[1] - mlng) +
                     tau) % tau
            sortx = angle.argsort()
            verts = verts.take(sortx, axis=0)
            return verts

        # Generate a random exterior for each requested mask
        masks = []
        for _ in range(num_masks):
            exterior = _order_vertices(_gen_polygon(n_verts, 0.9, 0.9))
            exterior = (exterior * [(width, height)]).astype(dtype)
            masks.append([exterior.ravel()])

        self = cls(masks, height, width)
        return self

    def get_bboxes(self):
        num_masks = len(self)
        boxes = np.zeros((num_masks, 4), dtype=np.float32)
        for idx, poly_per_obj in enumerate(self.masks):
            # simply use a number that is big enough for comparison with
            # coordinates
            xy_min = np.array([self.width * 2, self.height * 2],
                              dtype=np.float32)
            xy_max = np.zeros(2, dtype=np.float32)
            for p in poly_per_obj:
                xy = np.array(p).reshape(-1, 2).astype(np.float32)
                xy_min = np.minimum(xy_min, np.min(xy, axis=0))
                xy_max = np.maximum(xy_max, np.max(xy, axis=0))
            boxes[idx, :2] = xy_min
            boxes[idx, 2:] = xy_max

        return boxes


def polygon_to_bitmap(polygons, height, width):
    """Convert masks from the form of polygons to bitmaps.

    Args:
        polygons (list[ndarray]): masks in polygon representation
        height (int): mask height
        width (int): mask width

    Return:
        ndarray: the converted masks in bitmap representation
    """
    rles = maskUtils.frPyObjects(polygons, height, width)
    rle = maskUtils.merge(rles)
    bitmap_mask = maskUtils.decode(rle).astype(np.bool)
    return bitmap_mask


def bitmap_to_polygon(bitmap):
    """Convert masks from the form of bitmaps to polygons.

    Args:
        bitmap (ndarray): masks in bitmap representation.

    Return:
        list[ndarray]: the converted mask in polygon representation.
        bool: whether the mask has holes.
    """
    bitmap = np.ascontiguousarray(bitmap).astype(np.uint8)
    # cv2.RETR_CCOMP: retrieves all of the contours and organizes them
    #   into a two-level hierarchy. At the top level, there are external
    #   boundaries of the components. At the second level, there are
    #   boundaries of the holes. If there is another contour inside a hole
    #   of a connected component, it is still put at the top level.
    # cv2.CHAIN_APPROX_NONE: stores absolutely all the contour points.
    outs = cv2.findContours(bitmap, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
    contours = outs[-2]
    hierarchy = outs[-1]
    if hierarchy is None:
        return [], False
    # hierarchy[i]: 4 elements, for the indexes of next, previous,
    # parent, or nested contours. If there is no corresponding contour,
    # it will be -1.
    with_hole = (hierarchy.reshape(-1, 4)[:, 3] >= 0).any()
    contours = [c.reshape(-1, 2) for c in contours]
    return contours, with_hole