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# Copyright (c) OpenMMLab. All rights reserved.
import copy
import warnings
from typing import 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 mmocr.registry import TRANSFORMS
@TRANSFORMS.register_module()
class LoadImageFromFile(MMCV_LoadImageFromFile):
"""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): 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 1.0.0rc6.
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 1.0.0rc6.
ignore_empty (bool): Whether to allow loading empty image or file path
not existent. Defaults to False.
min_size (int): The minimum size of the image to be loaded. If the
image is smaller than the minimum size, it will be regarded as a
broken image. Defaults to 0.
"""
def __init__(
self,
to_float32: bool = False,
color_type: str = 'color',
imdecode_backend: str = 'cv2',
file_client_args: Optional[dict] = None,
min_size: int = 0,
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.min_size = min_size
self.file_client_args = file_client_args
self.backend_args = backend_args
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 :obj:``mmcv.BaseDataset``.
Returns:
dict: The dict contains loaded image and meta information.
"""
filename = results['img_path']
try:
if getattr(self, 'file_client_args', None) 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:
warnings.warn(f'Failed to load {filename} due to {e}')
return None
else:
raise e
if img is None or min(img.shape[:2]) < self.min_size:
if self.ignore_empty:
warnings.warn(f'Ignore broken image: {filename}')
return None
raise IOError(f'{filename} is broken')
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'min_size={self.min_size}, '
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 LoadImageFromNDArray(LoadImageFromFile):
"""Load an image 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.
"""
img = results['img']
if self.to_float32:
img = img.astype(np.float32)
if self.color_type == 'grayscale':
img = mmcv.image.rgb2gray(img)
results['img'] = img
if results.get('img_path', None) is None:
results['img_path'] = None
results['img_shape'] = img.shape[:2]
results['ori_shape'] = img.shape[:2]
return results
@TRANSFORMS.register_module()
class InferencerLoader(BaseTransform):
"""Load the image in Inferencer's pipeline.
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='LoadImageFromFile', **kwargs))
self.from_ndarray = TRANSFORMS.build(
dict(type='LoadImageFromNDArray', **kwargs))
def transform(self, single_input: Union[str, np.ndarray, dict]) -> dict:
"""Transform function to add image meta information.
Args:
single_input (str or dict or np.ndarray): The raw input from
inferencer.
Returns:
dict: The dict contains loaded image and meta information.
"""
if isinstance(single_input, str):
inputs = dict(img_path=single_input)
elif isinstance(single_input, np.ndarray):
inputs = dict(img=single_input)
elif isinstance(single_input, dict):
inputs = single_input
else:
raise NotImplementedError
if 'img' in inputs:
return self.from_ndarray(inputs)
return self.from_file(inputs)
@TRANSFORMS.register_module()
class LoadOCRAnnotations(MMCV_LoadAnnotations):
"""Load and process the ``instances`` 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.
# used in text detection or text spotting tasks.
'bbox': [x1, y1, x2, y2],
# Label of instance, usually it's 0.
# used in text detection or text spotting tasks.
'bbox_label': 0,
# List of n numbers representing the polygon of the
# instance, in (xn, yn) order.
# used in text detection/ textspotter.
"polygon": [x1, y1, x2, y2, ... xn, yn],
# The flag indicating whether the instance should be ignored.
# used in text detection or text spotting tasks.
"ignore": False,
# The groundtruth of text.
# used in text recognition or text spotting tasks.
"text": 'tmp',
}
]
}
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 (x1, y1,..., xk, yk) order, float type.
# in list[np.float32]
'gt_polygons': list[np.ndarray(2k, )]
# In np.bool_ type.
'gt_ignored': np.ndarray(N, )
# In list[str]
'gt_texts': list[str]
}
Required Keys:
- instances
- bbox (optional)
- bbox_label (optional)
- polygon (optional)
- ignore (optional)
- text (optional)
Added Keys:
- gt_bboxes (np.float32)
- gt_bboxes_labels (np.int64)
- gt_polygons (list[np.float32])
- gt_ignored (np.bool_)
- gt_texts (list[str])
Args:
with_bbox (bool): Whether to parse and load the bbox annotation.
Defaults to False.
with_label (bool): Whether to parse and load the label annotation.
Defaults to False.
with_polygon (bool): Whether to parse and load the polygon annotation.
Defaults to False.
with_text (bool): Whether to parse and load the text annotation.
Defaults to False.
"""
def __init__(self,
with_bbox: bool = False,
with_label: bool = False,
with_polygon: bool = False,
with_text: bool = False,
**kwargs) -> None:
super().__init__(with_bbox=with_bbox, with_label=with_label, **kwargs)
self.with_polygon = with_polygon
self.with_text = with_text
self.with_ignore = with_bbox or with_polygon
def _load_ignore_flags(self, results: dict) -> None:
"""Private function to load ignore annotations.
Args:
results (dict): Result dict from :obj:``OCRDataset``.
Returns:
dict: The dict contains loaded ignore annotations.
"""
gt_ignored = []
for instance in results['instances']:
gt_ignored.append(instance['ignore'])
results['gt_ignored'] = np.array(gt_ignored, dtype=np.bool_)
def _load_polygons(self, results: dict) -> None:
"""Private function to load polygon annotations.
Args:
results (dict): Result dict from :obj:``OCRDataset``.
Returns:
dict: The dict contains loaded polygon annotations.
"""
gt_polygons = []
for instance in results['instances']:
gt_polygons.append(np.array(instance['polygon'], dtype=np.float32))
results['gt_polygons'] = gt_polygons
def _load_texts(self, results: dict) -> None:
"""Private function to load text annotations.
Args:
results (dict): Result dict from :obj:``OCRDataset``.
Returns:
dict: The dict contains loaded text annotations.
"""
gt_texts = []
for instance in results['instances']:
gt_texts.append(instance['text'])
results['gt_texts'] = gt_texts
def transform(self, results: dict) -> dict:
"""Function to load multiple types annotations.
Args:
results (dict): Result dict from :obj:``OCRDataset``.
Returns:
dict: The dict contains loaded bounding box, label polygon and
text annotations.
"""
results = super().transform(results)
if self.with_polygon:
self._load_polygons(results)
if self.with_text:
self._load_texts(results)
if self.with_ignore:
self._load_ignore_flags(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_polygon={self.with_polygon}, '
repr_str += f'with_text={self.with_text}, '
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
@TRANSFORMS.register_module()
class LoadKIEAnnotations(MMCV_LoadAnnotations):
"""Load and process the ``instances`` annotation provided by dataset.
The annotation format is as the following:
.. code-block:: python
{
# A nested list of 4 numbers representing the bounding box of the
# instance, in (x1, y1, x2, y2) order.
'bbox': np.array([[x1, y1, x2, y2], [x1, y1, x2, y2], ...],
dtype=np.int32),
# Labels of boxes. Shape is (N,).
'bbox_labels': np.array([0, 2, ...], dtype=np.int32),
# Labels of edges. Shape (N, N).
'edge_labels': np.array([0, 2, ...], dtype=np.int32),
# List of texts.
"texts": ['text1', 'text2', ...],
}
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 np.int32 type.
'gt_edges_labels': np.ndarray(N, N),
# In list[str]
'gt_texts': list[str],
# tuple(int)
'ori_shape': (H, W)
}
Required Keys:
- bboxes
- bbox_labels
- edge_labels
- texts
Added Keys:
- gt_bboxes (np.float32)
- gt_bboxes_labels (np.int64)
- gt_edges_labels (np.int64)
- gt_texts (list[str])
- ori_shape (tuple[int])
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_text (bool): Whether to parse and load the text annotation.
Defaults to True.
directed (bool): Whether build edges as a directed graph.
Defaults to False.
key_node_idx (int, optional): Key node label, used to mask out edges
that are not connected from key nodes to value nodes. It has to be
specified together with ``value_node_idx``. Defaults to None.
value_node_idx (int, optional): Value node label, used to mask out
edges that are not connected from key nodes to value nodes. It has
to be specified together with ``key_node_idx``. Defaults to None.
"""
def __init__(self,
with_bbox: bool = True,
with_label: bool = True,
with_text: bool = True,
directed: bool = False,
key_node_idx: Optional[int] = None,
value_node_idx: Optional[int] = None,
**kwargs) -> None:
super().__init__(with_bbox=with_bbox, with_label=with_label, **kwargs)
self.with_text = with_text
self.directed = directed
if key_node_idx is not None or value_node_idx is not None:
assert key_node_idx is not None and value_node_idx is not None
self.key_node_idx = key_node_idx
self.value_node_idx = value_node_idx
def _load_texts(self, results: dict) -> None:
"""Private function to load text annotations.
Args:
results (dict): Result dict from :obj:``OCRDataset``.
"""
gt_texts = []
for instance in results['instances']:
gt_texts.append(instance['text'])
results['gt_texts'] = gt_texts
def _load_labels(self, results: dict) -> None:
"""Private function to load label annotations.
Args:
results (dict): Result dict from :obj:``WildReceiptDataset``.
"""
bbox_labels = []
edge_labels = []
for instance in results['instances']:
bbox_labels.append(instance['bbox_label'])
edge_labels.append(instance['edge_label'])
bbox_labels = np.array(bbox_labels, np.int32)
edge_labels = np.array(edge_labels)
edge_labels = (edge_labels[:, None] == edge_labels[None, :]).astype(
np.int32)
if self.directed:
edge_labels = (edge_labels & bbox_labels == 1).astype(np.int32)
if hasattr(self, 'key_node_idx'):
key_nodes_mask = bbox_labels == self.key_node_idx
value_nodes_mask = bbox_labels == self.value_node_idx
key2value_mask = key_nodes_mask[:,
None] * value_nodes_mask[None, :]
edge_labels[~key2value_mask] = -1
np.fill_diagonal(edge_labels, -1)
results['gt_edges_labels'] = edge_labels.astype(np.int64)
results['gt_bboxes_labels'] = bbox_labels.astype(np.int64)
def transform(self, results: dict) -> dict:
"""Function to load multiple types annotations.
Args:
results (dict): Result dict from :obj:``OCRDataset``.
Returns:
dict: The dict contains loaded bounding box, label polygon and
text annotations.
"""
if 'ori_shape' not in results:
results['ori_shape'] = copy.deepcopy(results['img_shape'])
results = super().transform(results)
if self.with_text:
self._load_texts(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_text={self.with_text})'
return repr_str
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