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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import warnings
from typing import Optional
import mmengine.fileio as fileio
import numpy as np
import mmcv
from .base import BaseTransform
from .builder import TRANSFORMS
@TRANSFORMS.register_module()
class LoadImageFromFile(BaseTransform):
"""Load an image 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 an uint8 array.
Defaults to False.
color_type (str): The flag argument for :func:`mmcv.imfrombytes`.
Defaults to 'color'.
imdecode_backend (str): The image decoding backend type. The backend
argument for :func:`mmcv.imfrombytes`.
See :func:`mmcv.imfrombytes` for details.
Defaults to 'cv2'.
file_client_args (dict, optional): Arguments to instantiate a
FileClient. See :class:`mmengine.fileio.FileClient` for details.
Defaults to None. It will be deprecated in future. Please use
``backend_args`` instead.
Deprecated in version 2.0.0rc4.
ignore_empty (bool): Whether to allow loading empty image or file path
not existent. Defaults to False.
backend_args (dict, optional): Instantiates the corresponding file
backend. It may contain `backend` key to specify the file
backend. If it contains, the file backend corresponding to this
value will be used and initialized with the remaining values,
otherwise the corresponding file backend will be selected
based on the prefix of the file path. Defaults to None.
New in version 2.0.0rc4.
"""
def __init__(self,
to_float32: bool = False,
color_type: str = 'color',
imdecode_backend: str = 'cv2',
file_client_args: Optional[dict] = None,
ignore_empty: bool = False,
*,
backend_args: Optional[dict] = None) -> None:
self.ignore_empty = ignore_empty
self.to_float32 = to_float32
self.color_type = color_type
self.imdecode_backend = imdecode_backend
self.file_client_args: Optional[dict] = None
self.backend_args: Optional[dict] = None
if file_client_args is not None:
warnings.warn(
'"file_client_args" will be deprecated in future. '
'Please use "backend_args" instead', DeprecationWarning)
if backend_args is not None:
raise ValueError(
'"file_client_args" and "backend_args" cannot be set '
'at the same time.')
self.file_client_args = file_client_args.copy()
if backend_args is not None:
self.backend_args = backend_args.copy()
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.
"""
filename = results['img_path']
try:
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)
except Exception as e:
if self.ignore_empty:
return None
else:
raise e
# in some cases, images are not read successfully, the img would be
# `None`, refer to https://github.com/open-mmlab/mmpretrain/issues/1427
assert img is not None, f'failed to load image: {filename}'
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'ignore_empty={self.ignore_empty}, '
f'to_float32={self.to_float32}, '
f"color_type='{self.color_type}', "
f"imdecode_backend='{self.imdecode_backend}', ")
if self.file_client_args is not None:
repr_str += f'file_client_args={self.file_client_args})'
else:
repr_str += f'backend_args={self.backend_args})'
return repr_str
@TRANSFORMS.register_module()
class LoadAnnotations(BaseTransform):
"""Load and process the ``instances`` and ``seg_map`` annotation provided
by dataset.
The annotation format is as the following:
.. code-block:: python
{
'instances':
[
{
# List of 4 numbers representing the bounding box of the
# instance, in (x1, y1, x2, y2) order.
'bbox': [x1, y1, x2, y2],
# Label of image classification.
'bbox_label': 1,
# Used in key point detection.
# Can only load the format of [x1, y1, v1,…, xn, yn, vn]. v[i]
# means the visibility of this keypoint. n must be equal to the
# number of keypoint categories.
'keypoints': [x1, y1, v1, ..., xn, yn, vn]
}
]
# Filename of semantic or panoptic 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 (x1, y1, x2, y2) order, float type. N is the number of bboxes
# in np.float32
'gt_bboxes': np.ndarray(N, 4)
# In np.int64 type.
'gt_bboxes_labels': np.ndarray(N, )
# In uint8 type.
'gt_seg_map': np.ndarray (H, W)
# with (x, y, v) order, in np.float32 type.
'gt_keypoints': np.ndarray(N, NK, 3)
}
Required Keys:
- instances
- bbox (optional)
- bbox_label
- keypoints (optional)
- seg_map_path (optional)
Added Keys:
- gt_bboxes (np.float32)
- gt_bboxes_labels (np.int64)
- gt_seg_map (np.uint8)
- gt_keypoints (np.float32)
Args:
with_bbox (bool): Whether to parse and load the bbox annotation.
Defaults to True.
with_label (bool): Whether to parse and load the label annotation.
Defaults to True.
with_seg (bool): Whether to parse and load the semantic segmentation
annotation. Defaults to False.
with_keypoints (bool): Whether to parse and load the keypoints
annotation. Defaults to False.
imdecode_backend (str): The image decoding backend type. The backend
argument for :func:`mmcv.imfrombytes`.
See :func:`mmcv.imfrombytes` for details.
Defaults to 'cv2'.
file_client_args (dict, optional): Arguments to instantiate a
FileClient. See :class:`mmengine.fileio.FileClient` for details.
Defaults to None. It will be deprecated in future. Please use
``backend_args`` instead.
Deprecated in version 2.0.0rc4.
backend_args (dict, optional): Instantiates the corresponding file
backend. It may contain `backend` key to specify the file
backend. If it contains, the file backend corresponding to this
value will be used and initialized with the remaining values,
otherwise the corresponding file backend will be selected
based on the prefix of the file path. Defaults to None.
New in version 2.0.0rc4.
"""
def __init__(
self,
with_bbox: bool = True,
with_label: bool = True,
with_seg: bool = False,
with_keypoints: bool = False,
imdecode_backend: str = 'cv2',
file_client_args: Optional[dict] = None,
*,
backend_args: Optional[dict] = None,
) -> None:
super().__init__()
self.with_bbox = with_bbox
self.with_label = with_label
self.with_seg = with_seg
self.with_keypoints = with_keypoints
self.imdecode_backend = imdecode_backend
self.file_client_args: Optional[dict] = None
self.backend_args: Optional[dict] = None
if file_client_args is not None:
warnings.warn(
'"file_client_args" will be deprecated in future. '
'Please use "backend_args" instead', DeprecationWarning)
if backend_args is not None:
raise ValueError(
'"file_client_args" and "backend_args" cannot be set '
'at the same time.')
self.file_client_args = file_client_args.copy()
if backend_args is not None:
self.backend_args = backend_args.copy()
def _load_bboxes(self, results: dict) -> None:
"""Private function to load bounding box annotations.
Args:
results (dict): Result dict from
:class:`mmengine.dataset.BaseDataset`.
Returns:
dict: The dict contains loaded bounding box annotations.
"""
gt_bboxes = []
for instance in results['instances']:
gt_bboxes.append(instance['bbox'])
results['gt_bboxes'] = np.array(
gt_bboxes, dtype=np.float32).reshape(-1, 4)
def _load_labels(self, results: dict) -> None:
"""Private function to load label annotations.
Args:
results (dict): Result dict from
:class:`mmengine.dataset.BaseDataset`.
Returns:
dict: The dict contains loaded label annotations.
"""
gt_bboxes_labels = []
for instance in results['instances']:
gt_bboxes_labels.append(instance['bbox_label'])
results['gt_bboxes_labels'] = np.array(
gt_bboxes_labels, dtype=np.int64)
def _load_seg_map(self, results: dict) -> None:
"""Private function to load semantic segmentation annotations.
Args:
results (dict): Result dict from
:class:`mmengine.dataset.BaseDataset`.
Returns:
dict: The dict contains loaded semantic segmentation annotations.
"""
if self.file_client_args is not None:
file_client = fileio.FileClient.infer_client(
self.file_client_args, results['seg_map_path'])
img_bytes = file_client.get(results['seg_map_path'])
else:
img_bytes = fileio.get(
results['seg_map_path'], backend_args=self.backend_args)
results['gt_seg_map'] = mmcv.imfrombytes(
img_bytes, flag='unchanged',
backend=self.imdecode_backend).squeeze()
def _load_kps(self, results: dict) -> None:
"""Private function to load keypoints annotations.
Args:
results (dict): Result dict from
:class:`mmengine.dataset.BaseDataset`.
Returns:
dict: The dict contains loaded keypoints annotations.
"""
gt_keypoints = []
for instance in results['instances']:
gt_keypoints.append(instance['keypoints'])
results['gt_keypoints'] = np.array(gt_keypoints, np.float32).reshape(
(len(gt_keypoints), -1, 3))
def transform(self, results: dict) -> dict:
"""Function to load multiple types annotations.
Args:
results (dict): Result dict from
:class:`mmengine.dataset.BaseDataset`.
Returns:
dict: The dict contains loaded bounding box, label and
semantic segmentation and keypoints annotations.
"""
if self.with_bbox:
self._load_bboxes(results)
if self.with_label:
self._load_labels(results)
if self.with_seg:
self._load_seg_map(results)
if self.with_keypoints:
self._load_kps(results)
return results
def __repr__(self) -> str:
repr_str = self.__class__.__name__
repr_str += f'(with_bbox={self.with_bbox}, '
repr_str += f'with_label={self.with_label}, '
repr_str += f'with_seg={self.with_seg}, '
repr_str += f'with_keypoints={self.with_keypoints}, '
repr_str += f"imdecode_backend='{self.imdecode_backend}', "
if self.file_client_args is not None:
repr_str += f'file_client_args={self.file_client_args})'
else:
repr_str += f'backend_args={self.backend_args})'
return repr_str
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