class_docstr: This module implements resizing for images and object detection bounding boxes.
class_docstr_tok: ["This","module","implements","resizing","for","images","and","object","detection","bounding","boxes","."]
docstr: Create an instance of ImageResize.
  
  :param height: The height of the resized image.
  :param width: The width of the resized image.
  :param channels_first: Set channels first or last.
  :param label_type: String defining the label type. Currently supported: `classification`, `object_detection`
  :param interpolation: The desired method to resize the image defined by the `cv2::InterpolationFlags` enum.
  :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
         for features.
  :param apply_fit: True if applied during fitting/training.
  :param apply_predict: True if applied during predicting.
  :param verbose: Show progress bars.
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code: def __init__(
      self,
      height: int,
      width: int,
      channels_first: bool = False,
      label_type: str = "classification",
      interpolation: int = cv2.INTER_LINEAR,
      clip_values: Optional["CLIP_VALUES_TYPE"] = None,
      apply_fit: bool = True,
      apply_predict: bool = False,
      verbose: bool = False,
  ):
      """
      Create an instance of ImageResize.
  
      :param height: The height of the resized image.
      :param width: The width of the resized image.
      :param channels_first: Set channels first or last.
      :param label_type: String defining the label type. Currently supported: `classification`, `object_detection`
      :param interpolation: The desired method to resize the image defined by the `cv2::InterpolationFlags` enum.
      :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
             for features.
      :param apply_fit: True if applied during fitting/training.
      :param apply_predict: True if applied during predicting.
      :param verbose: Show progress bars.
      """
      super().__init__(
          is_fitted=True,
          apply_fit=apply_fit,
          apply_predict=apply_predict,
      )
      self.height = height
      self.width = width
      self.channels_first = channels_first
      self.label_type = label_type
      self.interpolation = interpolation
      self.clip_values = clip_values
      self.verbose = verbose
      self._check_params()
  
file_path: adversarial-robustness-toolbox/art/preprocessing/image/image_resize/numpy.py
imports: import logging
  from typing import Dict, List, Optional, TYPE_CHECKING, Tuple, Union
  import numpy
  import cv2
  from tqdm.auto import tqdm
  from art.preprocessing.preprocessing import Preprocessor
label_desc: Use image node_id 1 to create a new ImageResize object from inherited base classes: Preprocessor with example: obj = ImageResize(height, width, channels_first, label_type, interpolation, clip_values, apply_fit, apply_predict, verbose)