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# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
from ..builder import PIPELINES
@PIPELINES.register_module()
class LoadImageFromFile:
"""Loading image(s) from file.
Required key: "image_file".
Added key: "img".
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): Flags specifying the color type of a loaded image,
candidates are 'color', 'grayscale' and 'unchanged'.
channel_order (str): Order of channel, candidates are 'bgr' and 'rgb'.
file_client_args (dict): Arguments to instantiate a FileClient.
See :class:`mmcv.fileio.FileClient` for details.
Defaults to ``dict(backend='disk')``.
"""
def __init__(self,
to_float32=False,
color_type='color',
channel_order='rgb',
file_client_args=dict(backend='disk')):
self.to_float32 = to_float32
self.color_type = color_type
self.channel_order = channel_order
self.file_client_args = file_client_args.copy()
self.file_client = None
def _read_image(self, path):
img_bytes = self.file_client.get(path)
img = mmcv.imfrombytes(
img_bytes, flag=self.color_type, channel_order=self.channel_order)
if img is None:
raise ValueError(f'Fail to read {path}')
if self.to_float32:
img = img.astype(np.float32)
return img
def __call__(self, results):
"""Loading image(s) from file."""
if self.file_client is None:
self.file_client = mmcv.FileClient(**self.file_client_args)
image_file = results.get('image_file', None)
if isinstance(image_file, (list, tuple)):
# Load images from a list of paths
results['img'] = [self._read_image(path) for path in image_file]
elif image_file is not None:
# Load single image from path
results['img'] = self._read_image(image_file)
else:
if 'img' not in results:
# If `image_file`` is not in results, check the `img` exists
# and format the image. This for compatibility when the image
# is manually set outside the pipeline.
raise KeyError('Either `image_file` or `img` should exist in '
'results.')
assert isinstance(results['img'], np.ndarray)
if self.color_type == 'color' and self.channel_order == 'rgb':
# The original results['img'] is assumed to be image(s) in BGR
# order, so we convert the color according to the arguments.
if results['img'].ndim == 3:
results['img'] = mmcv.bgr2rgb(results['img'])
elif results['img'].ndim == 4:
results['img'] = np.concatenate(
[mmcv.bgr2rgb(img) for img in results['img']], axis=0)
else:
raise ValueError('results["img"] has invalid shape '
f'{results["img"].shape}')
results['image_file'] = None
return results
def __repr__(self):
repr_str = (f'{self.__class__.__name__}('
f'to_float32={self.to_float32}, '
f"color_type='{self.color_type}', "
f'file_client_args={self.file_client_args})')
return repr_str
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