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import os.path as osp
import annotator.uniformer.mmcv as mmcv
import numpy as np
from ..builder import PIPELINES
@PIPELINES.register_module()
class LoadImageFromFile(object):
"""Load an image from file.
Required keys are "img_prefix" and "img_info" (a dict that must contain the
key "filename"). Added or updated keys are "filename", "img", "img_shape",
"ori_shape" (same as `img_shape`), "pad_shape" (same as `img_shape`),
"scale_factor" (1.0) and "img_norm_cfg" (means=0 and stds=1).
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.
color_type (str): The flag argument for :func:`mmcv.imfrombytes`.
Defaults to 'color'.
file_client_args (dict): Arguments to instantiate a FileClient.
See :class:`mmcv.fileio.FileClient` for details.
Defaults to ``dict(backend='disk')``.
imdecode_backend (str): Backend for :func:`mmcv.imdecode`. Default:
'cv2'
"""
def __init__(self,
to_float32=False,
color_type='color',
file_client_args=dict(backend='disk'),
imdecode_backend='cv2'):
self.to_float32 = to_float32
self.color_type = color_type
self.file_client_args = file_client_args.copy()
self.file_client = None
self.imdecode_backend = imdecode_backend
def __call__(self, results):
"""Call functions to load image and get image meta information.
Args:
results (dict): Result dict from :obj:`mmseg.CustomDataset`.
Returns:
dict: The dict contains loaded image and meta information.
"""
if self.file_client is None:
self.file_client = mmcv.FileClient(**self.file_client_args)
if results.get('img_prefix') is not None:
filename = osp.join(results['img_prefix'],
results['img_info']['filename'])
else:
filename = results['img_info']['filename']
img_bytes = self.file_client.get(filename)
img = mmcv.imfrombytes(
img_bytes, flag=self.color_type, backend=self.imdecode_backend)
if self.to_float32:
img = img.astype(np.float32)
results['filename'] = filename
results['ori_filename'] = results['img_info']['filename']
results['img'] = img
results['img_shape'] = img.shape
results['ori_shape'] = img.shape
# Set initial values for default meta_keys
results['pad_shape'] = img.shape
results['scale_factor'] = 1.0
num_channels = 1 if len(img.shape) < 3 else img.shape[2]
results['img_norm_cfg'] = dict(
mean=np.zeros(num_channels, dtype=np.float32),
std=np.ones(num_channels, dtype=np.float32),
to_rgb=False)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(to_float32={self.to_float32},'
repr_str += f"color_type='{self.color_type}',"
repr_str += f"imdecode_backend='{self.imdecode_backend}')"
return repr_str
@PIPELINES.register_module()
class LoadAnnotations(object):
"""Load annotations for semantic segmentation.
Args:
reduce_zero_label (bool): Whether reduce all label value by 1.
Usually used for datasets where 0 is background label.
Default: False.
file_client_args (dict): Arguments to instantiate a FileClient.
See :class:`mmcv.fileio.FileClient` for details.
Defaults to ``dict(backend='disk')``.
imdecode_backend (str): Backend for :func:`mmcv.imdecode`. Default:
'pillow'
"""
def __init__(self,
reduce_zero_label=False,
file_client_args=dict(backend='disk'),
imdecode_backend='pillow'):
self.reduce_zero_label = reduce_zero_label
self.file_client_args = file_client_args.copy()
self.file_client = None
self.imdecode_backend = imdecode_backend
def __call__(self, results):
"""Call function to load multiple types annotations.
Args:
results (dict): Result dict from :obj:`mmseg.CustomDataset`.
Returns:
dict: The dict contains loaded semantic segmentation annotations.
"""
if self.file_client is None:
self.file_client = mmcv.FileClient(**self.file_client_args)
if results.get('seg_prefix', None) is not None:
filename = osp.join(results['seg_prefix'],
results['ann_info']['seg_map'])
else:
filename = results['ann_info']['seg_map']
img_bytes = self.file_client.get(filename)
gt_semantic_seg = mmcv.imfrombytes(
img_bytes, flag='unchanged',
backend=self.imdecode_backend).squeeze().astype(np.uint8)
# modify if custom classes
if results.get('label_map', None) is not None:
for old_id, new_id in results['label_map'].items():
gt_semantic_seg[gt_semantic_seg == old_id] = new_id
# 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
results['gt_semantic_seg'] = gt_semantic_seg
results['seg_fields'].append('gt_semantic_seg')
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(reduce_zero_label={self.reduce_zero_label},'
repr_str += f"imdecode_backend='{self.imdecode_backend}')"
return repr_str
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