#
Image Processor

画像プロセッサは、ビジョン モデルの入力特徴の準備とその出力の後処理を担当します。これには、サイズ変更、正規化、PyTorch、TensorFlow、Flax、Numpy テンソルへの変換などの変換が含まれます。ロジットをセグメンテーション マスクに変換するなど、モデル固有の後処理も含まれる場合があります。

##
ImageProcessingMixin

###
class transformers.ImageProcessingMixin

< source >

( **kwargs )

This is an image processor mixin used to provide saving/loading functionality for sequential and image feature extractors.

####
from_pretrained

< source >

( pretrained_model_name_or_path: typing.Union[str, os.PathLike] cache_dir: typing.Union[str, os.PathLike, NoneType] = None force_download: bool = False local_files_only: bool = False token: typing.Union[str, bool, NoneType] = None revision: str = 'main' **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — This can be either:
  - a string, the model id of a pretrained image_processor hosted inside a model repo on huggingface.co.
  - a path to a directory containing a image processor file saved using the save_pretrained() method, e.g., `./my_model_directory/`.
  - a path or url to a saved image processor JSON file, e.g., `./my_model_directory/preprocessor_config.json`.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model image processor should be cached if the standard cache should not be used.
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force to (re-)download the image processor files and override the cached versions if they exist.
- resume_download — Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v5 of Transformers.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
- token (`str` or `bool`, optional) — The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.

Instantiate a type of ImageProcessingMixin from an image processor.

Examples:

    # We can't instantiate directly the base class *ImageProcessingMixin* so let's show the examples on a
    # derived class: *CLIPImageProcessor*
    image_processor = CLIPImageProcessor.from_pretrained(
        "openai/clip-vit-base-patch32"
    )  # Download image_processing_config from huggingface.co and cache.
    image_processor = CLIPImageProcessor.from_pretrained(
        "./test/saved_model/"
    )  # E.g. image processor (or model) was saved using *save_pretrained('./test/saved_model/')*
    image_processor = CLIPImageProcessor.from_pretrained("./test/saved_model/preprocessor_config.json")
    image_processor = CLIPImageProcessor.from_pretrained(
        "openai/clip-vit-base-patch32", do_normalize=False, foo=False
    )
    assert image_processor.do_normalize is False
    image_processor, unused_kwargs = CLIPImageProcessor.from_pretrained(
        "openai/clip-vit-base-patch32", do_normalize=False, foo=False, return_unused_kwargs=True
    )
    assert image_processor.do_normalize is False
    assert unused_kwargs == {"foo": False}

####
save_pretrained

< source >

( save_directory: typing.Union[str, os.PathLike] push_to_hub: bool = False **kwargs )

Parameters

- save_directory (`str` or `os.PathLike`) — Directory where the image processor JSON file will be saved (will be created if it does not exist).
- push_to_hub (`bool`, optional, defaults to `False`) — Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the repository you want to push to with `repo_id` (will default to the name of `save_directory` in your namespace).
- kwargs (`Dict[str, Any]`, optional) — Additional key word arguments passed along to the push_to_hub() method.

Save an image processor object to the directory `save_directory`, so that it can be re-loaded using the from_pretrained() class method.

##
BatchFeature

###
class transformers.BatchFeature

< source >

( data: typing.Optional[dict[str, typing.Any]] = None tensor_type: typing.Union[NoneType, str, transformers.utils.generic.TensorType] = None )

Parameters

- data (`dict`, optional) — Dictionary of lists/arrays/tensors returned by the call/pad methods (‘input_values’, ‘attention_mask’, etc.).
- tensor_type (`Union[None, str, TensorType]`, optional) — You can give a tensor_type here to convert the lists of integers in PyTorch/TensorFlow/Numpy Tensors at initialization.

Holds the output of the pad() and feature extractor specific `__call__` methods.

This class is derived from a python dictionary and can be used as a dictionary.

####
convert_to_tensors

< source >

( tensor_type: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None )

Parameters

- tensor_type (`str` or TensorType, optional) — The type of tensors to use. If `str`, should be one of the values of the enum TensorType. If `None`, no modification is done.

Convert the inner content to tensors.

####
to

< source >

( *args **kwargs ) →
BatchFeature

Parameters

- args (`Tuple`) — Will be passed to the `to(...)` function of the tensors.
- kwargs (`Dict`, optional) — Will be passed to the `to(...)` function of the tensors. To enable asynchronous data transfer, set the `non_blocking` flag in `kwargs` (defaults to `False`).

Returns

BatchFeature

The same instance after modification.

Send all values to device by calling `v.to(*args, **kwargs)` (PyTorch only). This should support casting in different `dtypes` and sending the `BatchFeature` to a different `device`.

##
BaseImageProcessor

###
class transformers.BaseImageProcessor

< source >

( **kwargs )

####
center_crop

< source >

( image: ndarray size: dict data_format: typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None input_data_format: typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None **kwargs )

Parameters

- image (`np.ndarray`) — Image to center crop.
- size (`Dict[str, int]`) — Size of the output image.
- data_format (`str` or `ChannelDimension`, optional) — The channel dimension format for the output image. If unset, the channel dimension format of the input image is used. Can be one of:
  - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
  - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- input_data_format (`ChannelDimension` or `str`, optional) — The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of:
  - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
  - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.

Center crop an image to `(size["height"], size["width"])`. If the input size is smaller than `crop_size` along any edge, the image is padded with 0’s and then center cropped.

####
normalize

< source >

( image: ndarray mean: typing.Union[float, collections.abc.Iterable[float]] std: typing.Union[float, collections.abc.Iterable[float]] data_format: typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None input_data_format: typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None **kwargs ) →
`np.ndarray`

Parameters

- image (`np.ndarray`) — Image to normalize.
- mean (`float` or `Iterable[float]`) — Image mean to use for normalization.
- std (`float` or `Iterable[float]`) — Image standard deviation to use for normalization.
- data_format (`str` or `ChannelDimension`, optional) — The channel dimension format for the output image. If unset, the channel dimension format of the input image is used. Can be one of:
  - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
  - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- input_data_format (`ChannelDimension` or `str`, optional) — The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of:
  - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
  - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.

Returns

`np.ndarray`

The normalized image.

Normalize an image. image = (image - image_mean) / image_std.

####
rescale

< source >

( image: ndarray scale: float data_format: typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None input_data_format: typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None **kwargs ) →
`np.ndarray`

Parameters

- image (`np.ndarray`) — Image to rescale.
- scale (`float`) — The scaling factor to rescale pixel values by.
- data_format (`str` or `ChannelDimension`, optional) — The channel dimension format for the output image. If unset, the channel dimension format of the input image is used. Can be one of:
  - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
  - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- input_data_format (`ChannelDimension` or `str`, optional) — The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of:
  - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
  - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.

Returns

`np.ndarray`

The rescaled image.

Rescale an image by a scale factor. image = image * scale.

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