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Source code for mmpose.datasets.transforms.loading

# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional

import numpy as np
from mmcv.transforms import LoadImageFromFile

from mmpose.registry import TRANSFORMS


[docs]@TRANSFORMS.register_module() class LoadImage(LoadImageFromFile): """Load an image from file or from the np.ndarray in ``results['img']``. Required Keys: - img_path - img (optional) Modified Keys: - img - img_shape - ori_shape - img_path (optional) 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'. backend_args (dict, optional): Arguments to instantiate the preifx of uri corresponding backend. Defaults to None. ignore_empty (bool): Whether to allow loading empty image or file path not existent. Defaults to False. """
[docs] def transform(self, results: dict) -> Optional[dict]: """The transform function of :class:`LoadImage`. Args: results (dict): The result dict Returns: dict: The result dict. """ try: if 'img' not in results: # Load image from file by :meth:`LoadImageFromFile.transform` results = super().transform(results) else: img = results['img'] assert isinstance(img, np.ndarray) if self.to_float32: img = img.astype(np.float32) if 'img_path' not in results: results['img_path'] = None results['img_shape'] = img.shape[:2] results['ori_shape'] = img.shape[:2] except Exception as e: e = type(e)( f'`{str(e)}` occurs when loading `{results["img_path"]}`.' 'Please check whether the file exists.') raise e return results
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