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You are reading the documentation for MMClassification 0.x, which will soon be deprecated at the end of 2022. We recommend you upgrade to MMClassification 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check the installation tutorial, migration tutorial and changelog for more details.

Source code for mmcls.datasets.pipelines.loading

# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp

import mmcv
import numpy as np

from ..builder import PIPELINES


[docs]@PIPELINES.register_module() class LoadImageFromFile(object): """Load an image from file. Required keys are "img_prefix" and "img_info" (a dict that must contain the key "filename"). Added or updated keys are "filename", "img", "img_shape", "ori_shape" (same as `img_shape`) and "img_norm_cfg" (means=0 and stds=1). 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'. 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', file_client_args=dict(backend='disk')): self.to_float32 = to_float32 self.color_type = color_type self.file_client_args = file_client_args.copy() self.file_client = None def __call__(self, results): if self.file_client is None: self.file_client = mmcv.FileClient(**self.file_client_args) if results['img_prefix'] is not None: filename = osp.join(results['img_prefix'], results['img_info']['filename']) else: filename = results['img_info']['filename'] img_bytes = self.file_client.get(filename) img = mmcv.imfrombytes(img_bytes, flag=self.color_type) if self.to_float32: img = img.astype(np.float32) results['filename'] = filename results['ori_filename'] = results['img_info']['filename'] results['img'] = img results['img_shape'] = img.shape results['ori_shape'] = img.shape num_channels = 1 if len(img.shape) < 3 else img.shape[2] results['img_norm_cfg'] = dict( mean=np.zeros(num_channels, dtype=np.float32), std=np.ones(num_channels, dtype=np.float32), to_rgb=False) 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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