class_docstr: A simple pipeline to load image.
class_docstr_tok: ["A","simple","pipeline","to","load","image","."]
docstr: Call function to load images into results.
  
  Args:
      results (dict): A result dict contains the file name
          of the image to be read.
  
  Returns:
      dict: ``results`` will be returned containing loaded image.
docstr_tok: ["Call","function","to","load","images","into","results",".","Args",":","results","(","dict",")",":","A","result","dict","contains","the","file","name","of","the","image","to","be","read",".","Returns",":","dict",":","``","results","``","will","be","returned","containing","loaded","image","."]
code: def __call__(self, results):
      """Call function to load images into results.
  
      Args:
          results (dict): A result dict contains the file name
              of the image to be read.
  
      Returns:
          dict: ``results`` will be returned containing loaded image.
      """
  
      if isinstance(results["img"], str):
          results["filename"] = results["img"]
          results["ori_filename"] = results["img"]
      else:
          results["filename"] = None
          results["ori_filename"] = None
      img = mmcv.imread(results["img"])
      results["img"] = img
      results["img_shape"] = img.shape
      results["ori_shape"] = img.shape
      return results
  
file_path: lavis/lavis/common/annotator/uniformer/mmseg/apis/inference.py
imports: import matplotlib.pyplot
  import annotator.uniformer.mmcv
  import torch
  from annotator.uniformer.mmcv.parallel import collate, scatter
  from annotator.uniformer.mmcv.runner import load_checkpoint
  from annotator.uniformer.mmseg.datasets.pipelines import Compose
  from annotator.uniformer.mmseg.models import build_segmentor
label_desc: Use image node_id 1 for calling the LoadImage obj's underlying member method code with example usage: obj.__call__(results) and returns: results