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# Copyright (c) Open-CD. All rights reserved.
import warnings
from typing import Dict, Optional, Union
import mmcv
import mmengine.fileio as fileio
import numpy as np
from mmcv.transforms import BaseTransform
from mmcv.transforms import LoadAnnotations as MMCV_LoadAnnotations
from mmcv.transforms import LoadImageFromFile as MMCV_LoadImageFromFile
from opencd.registry import TRANSFORMS
@TRANSFORMS.register_module()
class MultiImgLoadImageFromFile(MMCV_LoadImageFromFile):
"""Load an image pair from files.
Required Keys:
- img_path
Modified Keys:
- img
- img_shape
- ori_shape
"""
def __init__(self, **kwargs) -> None:
super().__init__(**kwargs)
def transform(self, results: dict) -> Optional[dict]:
"""Functions to load image.
Args:
results (dict): Result dict from
:class:`mmengine.dataset.BaseDataset`.
Returns:
dict: The dict contains loaded image and meta information.
"""
filenames = results['img_path']
imgs = []
try:
for filename in filenames:
if self.file_client_args is not None:
file_client = fileio.FileClient.infer_client(
self.file_client_args, filename)
img_bytes = file_client.get(filename)
else:
img_bytes = fileio.get(
filename, backend_args=self.backend_args)
img = mmcv.imfrombytes(
img_bytes, flag=self.color_type, backend=self.imdecode_backend)
if self.to_float32:
img = img.astype(np.float32)
imgs.append(img)
except Exception as e:
if self.ignore_empty:
return None
else:
raise e
results['img'] = imgs
results['img_shape'] = imgs[0].shape[:2]
results['ori_shape'] = imgs[0].shape[:2]
return results
@TRANSFORMS.register_module()
class MultiImgLoadAnnotations(MMCV_LoadAnnotations):
"""Load annotations for change detection provided by dataset.
The annotation format is as the following:
.. code-block:: python
{
# Filename of change detection ground truth file.
'seg_map_path': 'a/b/c'
}
After this module, the annotation has been changed to the format below:
.. code-block:: python
{
# in str
'seg_fields': List
# In uint8 type.
'gt_seg_map': np.ndarray (H, W)
}
Required Keys:
- seg_map_path (str): Path of change detection ground truth file.
Added Keys:
- seg_fields (List)
- gt_seg_map (np.uint8)
Args:
reduce_zero_label (bool, optional): Whether reduce all label value
by 255. Usually used for datasets where 0 is background label.
Defaults to None.
imdecode_backend (str): The image decoding backend type. The backend
argument for :func:``mmcv.imfrombytes``.
See :fun:``mmcv.imfrombytes`` for details.
Defaults to 'pillow'.
backend_args (dict): Arguments to instantiate a file backend.
See https://mmengine.readthedocs.io/en/latest/api/fileio.htm
for details. Defaults to None.
Notes: mmcv>=2.0.0rc4, mmengine>=0.2.0 required.
"""
def __init__(
self,
reduce_zero_label=None,
backend_args=None,
imdecode_backend='pillow',
) -> None:
super().__init__(
with_bbox=False,
with_label=False,
with_seg=True,
with_keypoints=False,
imdecode_backend=imdecode_backend,
backend_args=backend_args)
self.reduce_zero_label = reduce_zero_label
if self.reduce_zero_label is not None:
warnings.warn('`reduce_zero_label` will be deprecated, '
'if you would like to ignore the zero label, please '
'set `reduce_zero_label=True` when dataset '
'initialized')
self.imdecode_backend = imdecode_backend
def _load_seg_map(self, results: dict) -> None:
"""Private function to load semantic segmentation annotations.
Args:
results (dict): Result dict from :obj:``mmcv.BaseDataset``.
Returns:
dict: The dict contains loaded semantic segmentation annotations.
"""
img_bytes = fileio.get(
results['seg_map_path'], backend_args=self.backend_args)
gt_semantic_seg = mmcv.imfrombytes(
img_bytes, flag='grayscale', # in mmseg: unchanged
backend=self.imdecode_backend).squeeze().astype(np.uint8)
# reduce zero_label
if self.reduce_zero_label is None:
self.reduce_zero_label = results['reduce_zero_label']
assert self.reduce_zero_label == results['reduce_zero_label'], \
'Initialize dataset with `reduce_zero_label` as ' \
f'{results["reduce_zero_label"]} but when load annotation ' \
f'the `reduce_zero_label` is {self.reduce_zero_label}'
if self.reduce_zero_label:
# avoid using underflow conversion
gt_semantic_seg[gt_semantic_seg == 0] = 255
gt_semantic_seg = gt_semantic_seg - 1
gt_semantic_seg[gt_semantic_seg == 254] = 255
# modify to format ann
if results.get('format_seg_map', None) is not None:
if results['format_seg_map'] == 'to_binary':
gt_semantic_seg_copy = gt_semantic_seg.copy()
gt_semantic_seg[gt_semantic_seg_copy < 128] = 0
gt_semantic_seg[gt_semantic_seg_copy >= 128] = 1
else:
raise ValueError('Invalid value {}'.format(results['format_seg_map']))
# modify if custom classes
if results.get('label_map', None) is not None:
# Add deep copy to solve bug of repeatedly
# replace `gt_semantic_seg`, which is reported in
# https://github.com/open-mmlab/mmsegmentation/pull/1445/
gt_semantic_seg_copy = gt_semantic_seg.copy()
for old_id, new_id in results['label_map'].items():
gt_semantic_seg[gt_semantic_seg_copy == old_id] = new_id
results['gt_seg_map'] = gt_semantic_seg
results['seg_fields'].append('gt_seg_map')
def __repr__(self) -> str:
repr_str = self.__class__.__name__
repr_str += f'(reduce_zero_label={self.reduce_zero_label}, '
repr_str += f"imdecode_backend='{self.imdecode_backend}', "
repr_str += f'backend_args={self.backend_args})'
return repr_str
@TRANSFORMS.register_module()
class MultiImgMultiAnnLoadAnnotations(MMCV_LoadAnnotations):
"""Load annotations for semantic change detection provided by dataset.
The annotation format is as the following:
.. code-block:: python
{
# Filename of change detection ground truth file.
'seg_map_path': 'a/b/c'
}
After this module, the annotation has been changed to the format below:
.. code-block:: python
{
# in str
'seg_fields': List
# In uint8 type.
'gt_seg_map': np.ndarray (H, W)
}
Required Keys:
- seg_map_path (str): Path of change detection ground truth file.
Added Keys:
- seg_fields (List)
- gt_seg_map (np.uint8)
Args:
reduce_semantic_zero_label (bool, optional): Whether reduce all label value
by 255. Usually used for datasets where 0 is background label.
Defaults to None.
imdecode_backend (str): The image decoding backend type. The backend
argument for :func:``mmcv.imfrombytes``.
See :fun:``mmcv.imfrombytes`` for details.
Defaults to 'pillow'.
backend_args (dict): Arguments to instantiate a file backend.
See https://mmengine.readthedocs.io/en/latest/api/fileio.htm
for details. Defaults to None.
Notes: mmcv>=2.0.0rc4, mmengine>=0.2.0 required.
"""
def __init__(
self,
reduce_semantic_zero_label=None,
backend_args=None,
imdecode_backend='pillow',
) -> None:
super().__init__(
with_bbox=False,
with_label=False,
with_seg=True,
with_keypoints=False,
imdecode_backend=imdecode_backend,
backend_args=backend_args)
self.reduce_semantic_zero_label = reduce_semantic_zero_label
if self.reduce_semantic_zero_label is not None:
warnings.warn('`reduce_semantic_zero_label` will be deprecated, '
'if you would like to ignore the zero label, please '
'set `reduce_semantic_zero_label=True` when dataset '
'initialized')
self.imdecode_backend = imdecode_backend
def _load_seg_map(self, results: dict) -> None:
"""Private function to load semantic segmentation annotations.
Args:
results (dict): Result dict from :obj:``mmcv.BaseDataset``.
Returns:
dict: The dict contains loaded semantic segmentation annotations.
"""
img_bytes = fileio.get(
results['seg_map_path'], backend_args=self.backend_args)
gt_semantic_seg = mmcv.imfrombytes(
img_bytes, flag='grayscale', # in mmseg: unchanged
backend=self.imdecode_backend).squeeze().astype(np.uint8)
# for semantic anns
img_bytes_from = fileio.get(
results['seg_map_path_from'], backend_args=self.backend_args)
gt_semantic_seg_from = mmcv.imfrombytes(
img_bytes_from, flag='grayscale',
backend=self.imdecode_backend).squeeze().astype(np.uint8)
img_bytes_to = fileio.get(
results['seg_map_path_to'], backend_args=self.backend_args)
gt_semantic_seg_to = mmcv.imfrombytes(
img_bytes_to, flag='grayscale',
backend=self.imdecode_backend).squeeze().astype(np.uint8)
# reduce zero_label
if self.reduce_semantic_zero_label is None:
self.reduce_semantic_zero_label = results['reduce_semantic_zero_label']
assert self.reduce_semantic_zero_label == results['reduce_semantic_zero_label'], \
'Initialize dataset with `reduce_semantic_zero_label` as ' \
f'{results["reduce_semantic_zero_label"]} but when load annotation ' \
f'the `reduce_semantic_zero_label` is {self.reduce_semantic_zero_label}'
if self.reduce_semantic_zero_label:
# avoid using underflow conversion
gt_semantic_seg_from[gt_semantic_seg_from == 0] = 255
gt_semantic_seg_from = gt_semantic_seg_from - 1
gt_semantic_seg_from[gt_semantic_seg_from == 254] = 255
gt_semantic_seg_to[gt_semantic_seg_to == 0] = 255
gt_semantic_seg_to = gt_semantic_seg_to - 1
gt_semantic_seg_to[gt_semantic_seg_to == 254] = 255
# modify to format ann
if results.get('format_seg_map', None) is not None:
if results['format_seg_map'] == 'to_binary':
gt_semantic_seg_copy = gt_semantic_seg.copy()
gt_semantic_seg[gt_semantic_seg_copy < 128] = 0
gt_semantic_seg[gt_semantic_seg_copy >= 128] = 1
else:
raise ValueError('Invalid value {}'.format(results['format_seg_map']))
# modify if custom classes
if results.get('label_map', None) is not None:
# Add deep copy to solve bug of repeatedly
# replace `gt_semantic_seg`, which is reported in
# https://github.com/open-mmlab/mmsegmentation/pull/1445/
gt_semantic_seg_copy = gt_semantic_seg.copy()
for old_id, new_id in results['label_map'].items():
gt_semantic_seg[gt_semantic_seg_copy == old_id] = new_id
if results.get('semantic_label_map', None) is not None:
''' Just for semantic anns here '''
# Add deep copy to solve bug of repeatedly
# replace `gt_semantic_seg`, which is reported in
# https://github.com/open-mmlab/mmsegmentation/pull/1445/
gt_semantic_seg_from_copy = gt_semantic_seg_from.copy()
for old_id, new_id in results['label_map'].items():
gt_semantic_seg_from[gt_semantic_seg_from_copy == old_id] = new_id
gt_semantic_seg_to_copy = gt_semantic_seg_to.copy()
for old_id, new_id in results['label_map'].items():
gt_semantic_seg_to[gt_semantic_seg_to_copy == old_id] = new_id
results['gt_seg_map'] = gt_semantic_seg
results['gt_seg_map_from'] = gt_semantic_seg_from
results['gt_seg_map_to'] = gt_semantic_seg_to
results['seg_fields'].extend(['gt_seg_map',
'gt_seg_map_from', 'gt_seg_map_to'])
def __repr__(self) -> str:
repr_str = self.__class__.__name__
repr_str += f'(reduce_semantic_zero_label={self.reduce_semantic_zero_label}, '
repr_str += f"imdecode_backend='{self.imdecode_backend}', "
repr_str += f'backend_args={self.backend_args})'
return repr_str
@TRANSFORMS.register_module()
class MultiImgLoadLoadImageFromNDArray(MultiImgLoadImageFromFile):
"""Load an image pair from ``results['img']``.
Similar with :obj:`LoadImageFromFile`, but the image has been loaded as
:obj:`np.ndarray` in ``results['img']``. Can be used when loading image
from webcam.
Required Keys:
- img
Modified Keys:
- img
- img_path
- img_shape
- ori_shape
Args:
to_float32 (bool): Whether to convert the loaded image to a float32
numpy array. If set to False, the loaded image is an uint8 array.
Defaults to False.
"""
def transform(self, results: dict) -> dict:
"""Transform function to add image meta information.
Args:
results (dict): Result dict with Webcam read image in
``results['img']``.
Returns:
dict: The dict contains loaded image and meta information.
"""
imgs = []
if self.to_float32:
for img in results['img']:
img = img.astype(np.float32)
imgs.append(img)
results['img_path'] = None
results['img'] = imgs
results['img_shape'] = imgs[0].shape[:2]
results['ori_shape'] = imgs[0].shape[:2]
return results
@TRANSFORMS.register_module()
class MultiImgLoadInferencerLoader(BaseTransform):
"""Load an image pair from ``results['img']``.
Similar with :obj:`LoadImageFromFile`, but the image has been loaded as
:obj:`np.ndarray` in ``results['img']``. Can be used when loading image
from webcam.
Required Keys:
- img
Modified Keys:
- img
- img_path
- img_shape
- ori_shape
Args:
to_float32 (bool): Whether to convert the loaded image to a float32
numpy array. If set to False, the loaded image is an uint8 array.
Defaults to False.
"""
def __init__(self, **kwargs) -> None:
super().__init__()
self.from_file = TRANSFORMS.build(
dict(type='MultiImgLoadImageFromFile', **kwargs))
self.from_ndarray = TRANSFORMS.build(
dict(type='MultiImgLoadLoadImageFromNDArray', **kwargs))
def transform(self, single_input: Union[str, np.ndarray, dict]) -> dict:
"""Transform function to add image meta information.
Args:
results (dict): Result dict with Webcam read image in
``results['img']``.
Returns:
dict: The dict contains loaded image and meta information.
"""
assert len(single_input) == 2, \
'In `MultiImgLoadInferencerLoader`,' \
'`single_input` contains bi-temporal images'
if isinstance(single_input[0], str):
inputs = dict(img_path=single_input)
elif isinstance(single_input[0], Union[np.ndarray, list]):
inputs = dict(img=single_input)
elif isinstance(single_input[0], dict):
inputs = single_input
else:
raise NotImplementedError
if 'img' in inputs:
return self.from_ndarray(inputs)
return self.from_file(inputs)