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from mmdet.registry import TRANSFORMS
import mmcv
import numpy as np
from mmdet.structures.bbox.box_type import autocast_box_type
from mmcv.image.geometric import _scale_size
from typing import Dict, Iterable, List, Optional, Sequence, Tuple, Union
from mmcv.transforms.base import BaseTransform
def rescale_size(old_size: tuple,
scale: Union[float, int, tuple],
return_scale: bool = False) -> tuple:
"""Calculate the new size to be rescaled to.
Args:
old_size (tuple[int]): The old size (w, h) of image.
scale (float | tuple[int]): The scaling factor or maximum size.
If it is a float number, then the image will be rescaled by this
factor, else if it is a tuple of 2 integers, then the image will
be rescaled as large as possible within the scale.
return_scale (bool): Whether to return the scaling factor besides the
rescaled image size.
Returns:
tuple[int]: The new rescaled image size.
"""
w, h = old_size
if isinstance(scale, (float, int)):
if scale <= 0:
raise ValueError(f'Invalid scale {scale}, must be positive.')
scale_factor = scale
elif isinstance(scale, tuple):
max_long_edge = max(scale)
max_short_edge = min(scale)
scale_factor = min(max_long_edge / max(h, w),
max_short_edge / min(h, w))
else:
raise TypeError(
f'Scale must be a number or tuple of int, but got {type(scale)}')
new_size = _scale_size((w, h), scale_factor)
if return_scale:
return new_size, scale_factor
else:
return new_size
def hsiresize(
img: np.ndarray,
size: Tuple[int, int],
return_scale: bool = False,
) -> Union[Tuple[np.ndarray, float, float], np.ndarray]:
"""Resize image to a given size.
Args:
img (ndarray): The input image.
size (tuple[int]): Target size (w, h).
return_scale (bool): Whether to return `w_scale` and `h_scale`.
interpolation (str): Interpolation method, accepted values are
"nearest", "bilinear", "bicubic", "area", "lanczos" for 'cv2'
backend, "nearest", "bilinear" for 'pillow' backend.
out (ndarray): The output destination.
backend (str | None): The image resize backend type. Options are `cv2`,
`pillow`, `None`. If backend is None, the global imread_backend
specified by ``mmcv.use_backend()`` will be used. Default: None.
Returns:
tuple | ndarray: (`resized_img`, `w_scale`, `h_scale`) or
`resized_img`.
"""
h, w = img.shape[:2]
# assert (size[1]%h == 0) and (size[0]%w == 0), 'size error'
w_scale = int(size[0] / w)
h_scale = int(size[1] / h)
resized_img = np.repeat(np.repeat(img, w_scale, axis=0), h_scale, axis=1)
if not return_scale:
return resized_img
else:
return resized_img, w_scale, h_scale
@TRANSFORMS.register_module()
class HSIResize(BaseTransform):
"""Resize images & bbox & seg & keypoints.
This transform resizes the input image according to ``scale`` or
``scale_factor``. Bboxes, seg map and keypoints are then resized with the
same scale factor.
if ``scale`` and ``scale_factor`` are both set, it will use ``scale`` to
resize.
Required Keys:
- img
- gt_bboxes (optional)
- gt_seg_map (optional)
- gt_keypoints (optional)
Modified Keys:
- img
- gt_bboxes
- gt_seg_map
- gt_keypoints
- img_shape
Added Keys:
- scale
- scale_factor
- keep_ratio
Args:
scale (int or tuple): Images scales for resizing. Defaults to None
scale_factor (float or tuple[float]): Scale factors for resizing.
Defaults to None.
keep_ratio (bool): Whether to keep the aspect ratio when resizing the
image. Defaults to False.
clip_object_border (bool): Whether to clip the objects
outside the border of the image. In some dataset like MOT17, the gt
bboxes are allowed to cross the border of images. Therefore, we
don't need to clip the gt bboxes in these cases. Defaults to True.
backend (str): Image resize backend, choices are 'cv2' and 'pillow'.
These two backends generates slightly different results. Defaults
to 'cv2'.
interpolation (str): Interpolation method, accepted values are
"nearest", "bilinear", "bicubic", "area", "lanczos" for 'cv2'
backend, "nearest", "bilinear" for 'pillow' backend. Defaults
to 'bilinear'.
"""
def __init__(self,
scale: Optional[Union[int, Tuple[int, int]]] = None,
scale_factor: Optional[Union[int, Tuple[int,
int]]] = None,
keep_ratio: bool = False,
clip_object_border: bool = True,
backend: str = 'cv2',
interpolation='bilinear') -> None:
assert scale is not None or scale_factor is not None, (
'`scale` and'
'`scale_factor` can not both be `None`')
assert scale is None, ('please input scale_factor instead of scale' )
if scale is None:
self.scale = None
else:
if isinstance(scale, int):
self.scale = (scale, scale)
else:
self.scale = scale
self.backend = backend
self.interpolation = interpolation
self.keep_ratio = keep_ratio
self.clip_object_border = clip_object_border
if scale_factor is None:
self.scale_factor = None
elif isinstance(scale_factor, int):
self.scale_factor = (scale_factor, scale_factor)
elif isinstance(scale_factor, tuple):
assert (len(scale_factor)) == 2
self.scale_factor = scale_factor
else:
raise TypeError(
f'expect scale_factor is float or Tuple(float), but'
f'get {type(scale_factor)}')
# assert (isinstance(scale_factor[0], int) and isinstance(scale_factor[1], int)),\
# 'scale_factor should be an integer'
def _resize_img(self, results: dict) -> None:
"""Resize images with ``results['scale']``."""
if results.get('img', None) is not None:
if self.keep_ratio:
h, w = results['img'].shape[:2]
new_size, scale_factor = rescale_size((w, h), results['scale'], return_scale=True)
img, w_scale, h_scale = hsiresize(results['img'], new_size, return_scale=True,)
else:
img, w_scale, h_scale = hsiresize(results['img'], results['scale'],return_scale=True,)
results['img'] = img
results['img_shape'] = img.shape[:2]
results['scale_factor'] = (w_scale, h_scale)
results['keep_ratio'] = self.keep_ratio
def _resize_masks(self, results: dict) -> None:
"""Resize masks with ``results['scale']``"""
if results.get('gt_masks', None) is not None:
if self.keep_ratio:
results['gt_masks'] = results['gt_masks'].rescale(
results['scale'])
else:
results['gt_masks'] = results['gt_masks'].resize(
results['img_shape'])
def _resize_bboxes(self, results: dict) -> None:
"""Resize bounding boxes with ``results['scale_factor']``."""
if results.get('gt_bboxes', None) is not None:
results['gt_bboxes'].rescale_(results['scale_factor'])
if self.clip_object_border:
results['gt_bboxes'].clip_(results['img_shape'])
def _resize_seg(self, results: dict) -> None:
"""Resize semantic segmentation map with ``results['scale']``."""
if results.get('gt_seg_map', None) is not None:
if self.keep_ratio:
gt_seg = mmcv.imrescale(
results['gt_seg_map'],
results['scale'],
interpolation='nearest',
backend=self.backend)
else:
gt_seg = mmcv.imresize(
results['gt_seg_map'],
results['scale'],
interpolation='nearest',
backend=self.backend)
results['gt_seg_map'] = gt_seg
def _resize_keypoints(self, results: dict) -> None:
"""Resize keypoints with ``results['scale_factor']``."""
if results.get('gt_keypoints', None) is not None:
keypoints = results['gt_keypoints']
keypoints[:, :, :2] = keypoints[:, :, :2] * np.array(
results['scale_factor'])
if self.clip_object_border:
keypoints[:, :, 0] = np.clip(keypoints[:, :, 0], 0,
results['img_shape'][1])
keypoints[:, :, 1] = np.clip(keypoints[:, :, 1], 0,
results['img_shape'][0])
results['gt_keypoints'] = keypoints
def _record_homography_matrix(self, results: dict) -> None:
"""Record the homography matrix for the Resize."""
w_scale, h_scale = results['scale_factor']
homography_matrix = np.array(
[[w_scale, 0, 0], [0, h_scale, 0], [0, 0, 1]], dtype=np.float32)
if results.get('homography_matrix', None) is None:
results['homography_matrix'] = homography_matrix
else:
results['homography_matrix'] = homography_matrix @ results[
'homography_matrix']
@autocast_box_type()
def transform(self, results: dict) -> dict:
"""Transform function to resize images, bounding boxes and semantic
segmentation map.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Resized results, 'img', 'gt_bboxes', 'gt_seg_map',
'scale', 'scale_factor', 'height', 'width', and 'keep_ratio' keys
are updated in result dict.
"""
if self.scale:
results['scale'] = self.scale
else:
img_shape = results['img'].shape[:2]
results['scale'] = _scale_size(img_shape[::-1], self.scale_factor)
self._resize_img(results)
self._resize_bboxes(results)
self._resize_masks(results)
self._resize_seg(results)
self._record_homography_matrix(results)
return results
def __repr__(self) -> str:
repr_str = self.__class__.__name__
repr_str += f'(scale={self.scale}, '
repr_str += f'scale_factor={self.scale_factor}, '
repr_str += f'keep_ratio={self.keep_ratio}, '
repr_str += f'clip_object_border={self.clip_object_border}), '
repr_str += f'backend={self.backend}), '
repr_str += f'interpolation={self.interpolation})'
return repr_str
@TRANSFORMS.register_module()
class ResizePiexlTarget(BaseTransform):
"""Resize images & bbox & seg & keypoints.
This transform resizes the input image according to ``scale`` or
``scale_factor``. Bboxes, seg map and keypoints are then resized with the
same scale factor.
if ``scale`` and ``scale_factor`` are both set, it will use ``scale`` to
resize.
Required Keys:
- img
- gt_bboxes (optional)
- gt_seg_map (optional)
- gt_keypoints (optional)
Modified Keys:
- img
- gt_bboxes
- gt_seg_map
- gt_keypoints
- img_shape
Added Keys:
- scale
- scale_factor
- keep_ratio
Args:
scale (int or tuple): Images scales for resizing. Defaults to None
scale_factor (float or tuple[float]): Scale factors for resizing.
Defaults to None.
keep_ratio (bool): Whether to keep the aspect ratio when resizing the
image. Defaults to False.
clip_object_border (bool): Whether to clip the objects
outside the border of the image. In some dataset like MOT17, the gt
bboxes are allowed to cross the border of images. Therefore, we
don't need to clip the gt bboxes in these cases. Defaults to True.
backend (str): Image resize backend, choices are 'cv2' and 'pillow'.
These two backends generates slightly different results. Defaults
to 'cv2'.
interpolation (str): Interpolation method, accepted values are
"nearest", "bilinear", "bicubic", "area", "lanczos" for 'cv2'
backend, "nearest", "bilinear" for 'pillow' backend. Defaults
to 'bilinear'.
"""
def __init__(self,
scale_factor: float = 1.0,
clip_object_border: bool = True,) -> None:
self.clip_object_border = clip_object_border
self.scale_factor = scale_factor
self.keep_ratio = True
def _resize_img(self, results: dict) -> None:
"""Resize images with ``results['scale']``."""
h, w = results['img'].shape[:2]
new_size = _scale_size((w, h), self.scale_factor)
img, w_scale, h_scale = hsiresize(results['img'], new_size, return_scale=True,)
results['img'] = img
results['img_shape'] = img.shape[:2]
results['scale_factor'] = (w_scale, h_scale)
results['keep_ratio'] = self.keep_ratio
def _resize_bboxes(self, results: dict) -> None:
"""Resize bounding boxes with ``results['scale_factor']``."""
if results.get('gt_bboxes', None) is not None:
results['gt_bboxes'].rescale_(results['scale_factor'])
if self.clip_object_border:
results['gt_bboxes'].clip_(results['img_shape'])
def _resize_masks(self, results: dict) -> None:
"""Resize masks with ``results['scale']``"""
if results.get('gt_masks', None) is not None:
if self.keep_ratio:
results['gt_masks'] = results['gt_masks'].rescale(
results['scale'])
else:
results['gt_masks'] = results['gt_masks'].resize(
results['img_shape'])
def _resize_seg(self, results: dict) -> None:
"""Resize semantic segmentation map with ``results['scale']``."""
if results.get('gt_seg', None) is not None:
h, w = results['gt_seg'].shape[:2]
new_size = _scale_size((w, h), self.scale_factor)
gt_seg = hsiresize(results['gt_seg'], new_size, return_scale=False,)
results['gt_seg'] = gt_seg
def _resize_abu(self, results: dict) -> None:
"""Resize semantic segmentation map with ``results['scale']``."""
if results.get('gt_abu', None) is not None:
h, w = results['gt_abu'].shape[:2]
new_size = _scale_size((w, h), self.scale_factor)
gt_abu = hsiresize(results['gt_abu'], new_size, return_scale=False,)
results['gt_abu'] = gt_abu
def _record_homography_matrix(self, results: dict) -> None:
"""Record the homography matrix for the Resize."""
w_scale, h_scale = results['scale_factor']
homography_matrix = np.array(
[[w_scale, 0, 0], [0, h_scale, 0], [0, 0, 1]], dtype=np.float32)
if results.get('homography_matrix', None) is None:
results['homography_matrix'] = homography_matrix
else:
results['homography_matrix'] = homography_matrix @ results[
'homography_matrix']
def transform(self, results: dict) -> dict:
"""Transform function to resize images, bounding boxes, semantic
segmentation map and keypoints.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Resized results, 'img', 'gt_bboxes', 'gt_seg_map',
'gt_keypoints', 'scale', 'scale_factor', 'img_shape',
and 'keep_ratio' keys are updated in result dict.
"""
# if self.scale:
# results['scale'] = self.scale
# else:
img_shape = results['img'].shape[:2]
results['scale'] = _scale_size(img_shape[::-1],
self.scale_factor) # type: ignore
self._resize_img(results)
self._resize_bboxes(results)
self._resize_masks(results)
self._resize_seg(results)
self._resize_abu(results)
self._record_homography_matrix(results)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'scale_factor={self.scale_factor}, '
return repr_str
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