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# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from typing import Dict, Optional, Union
import io
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
import imageio
from mmseg.registry import TRANSFORMS
from mmseg.utils import datafrombytes
try:
from osgeo import gdal
except ImportError:
gdal = None
@TRANSFORMS.register_module()
class LoadAnnotationsNpz(MMCV_LoadAnnotations):
"""Load annotations for semantic segmentation provided by dataset.
The annotation format is as the following:
.. code-block:: python
{
# Filename of semantic segmentation 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 semantic segmentation 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 1. 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,
data_key='arr_0',
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
self.data_key = data_key
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='unchanged',
# backend=self.imdecode_backend).squeeze().astype(np.uint8)
gt_semantic_seg = np.load(results['seg_map_path'])[self.data_key].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 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 LoadImageSingleChannel(BaseTransform):
"""Load a Remote Sensing mage from file.
Required Keys:
- img_path
Modified Keys:
- img
- 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 a float64 array.
Defaults to True.
"""
def __init__(self, to_float32: bool = True):
self.to_float32 = to_float32
# self.data_key = data_key
if gdal is None:
raise RuntimeError('gdal is not installed')
def transform(self, results: Dict) -> Dict:
"""Functions to load image.
Args:
results (dict): Result dict from :obj:``mmcv.BaseDataset``.
Returns:
dict: The dict contains loaded image and meta information.
"""
filename = results['img_path']
img = imageio.imread(filename) # h, w, c
img = img[:, :, 0]
# if ds is None:
# raise Exception(f'Unable to open file: {filename}')
# print(img)s
# img = np.einsum('ijk->jki', img)
if self.to_float32:
img = img.astype(np.float32)
results['img'] = img
results['img_shape'] = img.shape[:2]
results['ori_shape'] = img.shape[:2]
return results
def __repr__(self):
repr_str = (f'{self.__class__.__name__}('
f'to_float32={self.to_float32})')
return repr_str
@TRANSFORMS.register_module()
class LoadAnnotationsOil(MMCV_LoadAnnotations):
"""Load annotations for semantic segmentation provided by dataset.
The annotation format is as the following:
.. code-block:: python
{
# Filename of semantic segmentation 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 semantic segmentation 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 1. 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,
data_key='arr_0',
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
self.data_key = data_key
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='unchanged',
# backend=self.imdecode_backend).squeeze().astype(np.uint8)
seg_map = gdal.Open(results['seg_map_path']).ReadAsArray()
gt_semantic_seg = np.zeros_like(seg_map).astype(np.uint8)
gt_semantic_seg[seg_map==3.] = 1
# 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 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 LoadImageFromNpz(BaseTransform):
"""Load a Remote Sensing mage from file.
Required Keys:
- img_path
Modified Keys:
- img
- 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 a float64 array.
Defaults to True.
"""
def __init__(self, data_key='arr_0', to_float32: bool = True):
self.to_float32 = to_float32
self.data_key = data_key
if gdal is None:
raise RuntimeError('gdal is not installed')
def transform(self, results: Dict) -> Dict:
"""Functions to load image.
Args:
results (dict): Result dict from :obj:``mmcv.BaseDataset``.
Returns:
dict: The dict contains loaded image and meta information.
"""
filename = results['img_path']
img = np.load(filename)[self.data_key]
# if ds is None:
# raise Exception(f'Unable to open file: {filename}')
# print(img)s
img = np.einsum('ijk->jki', img)
if self.to_float32:
img = img.astype(np.float32)
results['img'] = img
results['img_shape'] = img.shape[:2]
results['ori_shape'] = img.shape[:2]
return results
def __repr__(self):
repr_str = (f'{self.__class__.__name__}('
f'to_float32={self.to_float32})')
return repr_str
@TRANSFORMS.register_module()
class LoadImageOil(BaseTransform):
"""Load a Remote Sensing mage from file.
Required Keys:
- img_path
Modified Keys:
- img
- 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 a float64 array.
Defaults to True.
"""
def __init__(self, data_key='arr_0', to_float32: bool = True):
self.to_float32 = to_float32
self.data_key = data_key
if gdal is None:
raise RuntimeError('gdal is not installed')
def transform(self, results: Dict) -> Dict:
"""Functions to load image.
Args:
results (dict): Result dict from :obj:``mmcv.BaseDataset``.
Returns:
dict: The dict contains loaded image and meta information.
"""
filename = results['img_path']
img = gdal.Open(filename).ReadAsArray()
img = img[:,:,None]
# if ds is None:
# raise Exception(f'Unable to open file: {filename}')
# print(img)s
# img = np.einsum('ijk->jki', img)
if self.to_float32:
img = img.astype(np.float32)
results['img'] = img
results['img_shape'] = img.shape[:2]
results['ori_shape'] = img.shape[:2]
return results
def __repr__(self):
repr_str = (f'{self.__class__.__name__}('
f'to_float32={self.to_float32})')
return repr_str
@TRANSFORMS.register_module()
class LoadTsImageFromNpz(BaseTransform):
"""Load a Remote Sensing mage from file.
Required Keys:
- img_path
Modified Keys:
- img
- 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 a float64 array.
Defaults to True.
"""
def __init__(self, data_key='arr_0', to_float32: bool = True, ts_size: int=10):
self.to_float32 = to_float32
self.data_key = data_key
if gdal is None:
raise RuntimeError('gdal is not installed')
self.ts_size = ts_size
def transform(self, results: Dict) -> Dict:
"""Functions to load image.
Args:
results (dict): Result dict from :obj:``mmcv.BaseDataset``.
Returns:
dict: The dict contains loaded image and meta information.
"""
filename = results['img_path']
img = np.load(filename)[self.data_key]
ts, c, h, w = img.shape
if ts >= self.ts_size:
selected_indices = np.random.choice(ts, size=self.ts_size, replace=False)
else:
selected_indices = np.random.choice(ts, size=self.ts_size, replace=True)
selected_indices.sort()
img = img[selected_indices, :, :, :]
# print(f'after input shape: {img.shape}')
img = img.transpose(2, 3, 0, 1).reshape(h, w, self.ts_size*c) # h, w, ts, c -> h, w, ts*c
# if ds is None:
# raise Exception(f'Unable to open file: {filename}')
# print(img)s
# img = np.einsum('ijk->jki', img)
if self.to_float32:
img = img.astype(np.float32)
results['img'] = img
results['img_shape'] = img.shape[:2]
results['ori_shape'] = img.shape[:2]
return results
def __repr__(self):
repr_str = (f'{self.__class__.__name__}('
f'to_float32={self.to_float32})')
return repr_str