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mmcv.transforms.loading 源代码

# Copyright (c) OpenMMLab. All rights reserved.
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


[文档]@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()
[文档] 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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