#
Image Processors的工具

此页面列出了image processors使用的所有实用函数功能，主要是用于处理图像的功能变换。

其中大多数仅在您研究库中image processors的代码时有用。

##
图像转换

####
transformers.image_transforms.center_crop

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( image: ndarray size: tuple data_format: typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None input_data_format: typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None ) →
`np.ndarray`

Parameters

- image (`np.ndarray`) — The image to crop.
- size (`Tuple[int, int]`) — The target size for the cropped image.
- data_format (`str` or `ChannelDimension`, optional) — The channel dimension format for the output 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. If unset, will use the inferred format of the input image.
- input_data_format (`str` or `ChannelDimension`, optional) — The channel dimension format for 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. If unset, will use the inferred format of the input image.

Returns

`np.ndarray`

The cropped image.

Crops the `image` to the specified `size` using a center crop. Note that if the image is too small to be cropped to the size given, it will be padded (so the returned result will always be of size `size`).

####
transformers.image_transforms.center_to_corners_format

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( bboxes_center: TensorType )

Converts bounding boxes from center format to corners format.

center format: contains the coordinate for the center of the box and its width, height dimensions (center_x, center_y, width, height) corners format: contains the coordinates for the top-left and bottom-right corners of the box (top_left_x, top_left_y, bottom_right_x, bottom_right_y)

####
transformers.image_transforms.corners_to_center_format

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( bboxes_corners: TensorType )

Converts bounding boxes from corners format to center format.

corners format: contains the coordinates for the top-left and bottom-right corners of the box (top_left_x, top_left_y, bottom_right_x, bottom_right_y) center format: contains the coordinate for the center of the box and its the width, height dimensions (center_x, center_y, width, height)

####
transformers.image_transforms.id_to_rgb

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( id_map )

Converts unique ID to RGB color.

####
transformers.image_transforms.normalize

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( image: ndarray mean: typing.Union[float, collections.abc.Collection[float]] std: typing.Union[float, collections.abc.Collection[float]] data_format: typing.Optional[transformers.image_utils.ChannelDimension] = None input_data_format: typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None )

Parameters

- image (`np.ndarray`) — The image to normalize.
- mean (`float` or `Collection[float]`) — The mean to use for normalization.
- std (`float` or `Collection[float]`) — The standard deviation to use for normalization.
- data_format (`ChannelDimension`, optional) — The channel dimension format of the output image. If unset, will use the inferred format from the input.
- input_data_format (`ChannelDimension`, optional) — The channel dimension format of the input image. If unset, will use the inferred format from the input.

Normalizes `image` using the mean and standard deviation specified by `mean` and `std`.

image = (image - mean) / std

####
transformers.image_transforms.pad

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( image: ndarray padding: typing.Union[int, tuple[int, int], collections.abc.Iterable[tuple[int, int]]] mode: PaddingMode = <PaddingMode.CONSTANT: 'constant'> constant_values: typing.Union[float, collections.abc.Iterable[float]] = 0.0 data_format: typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None input_data_format: typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None ) →
`np.ndarray`

Parameters

- image (`np.ndarray`) — The image to pad.
- padding (`int` or `Tuple[int, int]` or `Iterable[Tuple[int, int]]`) — Padding to apply to the edges of the height, width axes. Can be one of three formats:
  - `((before_height, after_height), (before_width, after_width))` unique pad widths for each axis.
  - `((before, after),)` yields same before and after pad for height and width.
  - `(pad,)` or int is a shortcut for before = after = pad width for all axes.
- mode (`PaddingMode`) — The padding mode to use. Can be one of:
  - `"constant"`: pads with a constant value.
  - `"reflect"`: pads with the reflection of the vector mirrored on the first and last values of the vector along each axis.
  - `"replicate"`: pads with the replication of the last value on the edge of the array along each axis.
  - `"symmetric"`: pads with the reflection of the vector mirrored along the edge of the array.
- constant_values (`float` or `Iterable[float]`, optional) — The value to use for the padding if `mode` is `"constant"`.
- data_format (`str` or `ChannelDimension`, optional) — The channel dimension format for the output 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. If unset, will use same as the input image.
- input_data_format (`str` or `ChannelDimension`, optional) — The channel dimension format for 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. If unset, will use the inferred format of the input image.

Returns

`np.ndarray`

The padded image.

Pads the `image` with the specified (height, width) `padding` and `mode`.

####
transformers.image_transforms.rgb_to_id

< source >

( color )

Converts RGB color to unique ID.

####
transformers.image_transforms.rescale

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( image: ndarray scale: float data_format: typing.Optional[transformers.image_utils.ChannelDimension] = None dtype: dtype = <class 'numpy.float32'> input_data_format: typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None ) →
`np.ndarray`

Parameters

- image (`np.ndarray`) — The image to rescale.
- scale (`float`) — The scale to use for rescaling the image.
- data_format (`ChannelDimension`, optional) — The channel dimension format of the image. If not provided, it will be the same as the input image.
- dtype (`np.dtype`, optional, defaults to `np.float32`) — The dtype of the output image. Defaults to `np.float32`. Used for backwards compatibility with feature extractors.
- input_data_format (`ChannelDimension`, optional) — The channel dimension format of the input image. If not provided, it will be inferred from the input image.

Returns

`np.ndarray`

The rescaled image.

Rescales `image` by `scale`.

####
transformers.image_transforms.resize

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( image: ndarray size: tuple resample: PILImageResampling = None reducing_gap: typing.Optional[int] = None data_format: typing.Optional[transformers.image_utils.ChannelDimension] = None return_numpy: bool = True input_data_format: typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None ) →
`np.ndarray`

Parameters

- image (`np.ndarray`) — The image to resize.
- size (`Tuple[int, int]`) — The size to use for resizing the image.
- resample (`int`, optional, defaults to `PILImageResampling.BILINEAR`) — The filter to user for resampling.
- reducing_gap (`int`, optional) — Apply optimization by resizing the image in two steps. The bigger `reducing_gap`, the closer the result to the fair resampling. See corresponding Pillow documentation for more details.
- data_format (`ChannelDimension`, optional) — The channel dimension format of the output image. If unset, will use the inferred format from the input.
- return_numpy (`bool`, optional, defaults to `True`) — Whether or not to return the resized image as a numpy array. If False a `PIL.Image.Image` object is returned.
- input_data_format (`ChannelDimension`, optional) — The channel dimension format of the input image. If unset, will use the inferred format from the input.

Returns

`np.ndarray`

The resized image.

Resizes `image` to `(height, width)` specified by `size` using the PIL library.

####
transformers.image_transforms.to_pil_image

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( image: typing.Union[numpy.ndarray, ForwardRef('PIL.Image.Image'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor'), ForwardRef('jnp.ndarray')] do_rescale: typing.Optional[bool] = None image_mode: typing.Optional[str] = None input_data_format: typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None ) →
`PIL.Image.Image`

Parameters

- image (`PIL.Image.Image` or `numpy.ndarray` or `torch.Tensor` or `tf.Tensor`) — The image to convert to the `PIL.Image` format.
- do_rescale (`bool`, optional) — Whether or not to apply the scaling factor (to make pixel values integers between 0 and 255). Will default to `True` if the image type is a floating type and casting to `int` would result in a loss of precision, and `False` otherwise.
- image_mode (`str`, optional) — The mode to use for the PIL image. If unset, will use the default mode for the input image type.
- input_data_format (`ChannelDimension`, optional) — The channel dimension format of the input image. If unset, will use the inferred format from the input.

Returns

`PIL.Image.Image`

The converted image.

Converts `image` to a PIL Image. Optionally rescales it and puts the channel dimension back as the last axis if needed.

##
ImageProcessingMixin

###
class transformers.ImageProcessingMixin

< source >

( **kwargs )

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

####
fetch_images

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( image_url_or_urls: typing.Union[str, list[str]] )

Convert a single or a list of urls into the corresponding `PIL.Image` objects.

If a single url is passed, the return value will be a single object. If a list is passed a list of objects is returned.

####
from_dict

< source >

( image_processor_dict: dict **kwargs ) →
ImageProcessingMixin

Parameters

- image_processor_dict (`Dict[str, Any]`) — Dictionary that will be used to instantiate the image processor object. Such a dictionary can be retrieved from a pretrained checkpoint by leveraging the to_dict() method.
- kwargs (`Dict[str, Any]`) — Additional parameters from which to initialize the image processor object.

Returns

ImageProcessingMixin

The image processor object instantiated from those parameters.

Instantiates a type of ImageProcessingMixin from a Python dictionary of parameters.

####
from_json_file

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( json_file: typing.Union[str, os.PathLike] ) →
A image processor of type ImageProcessingMixin

Parameters

- json_file (`str` or `os.PathLike`) — Path to the JSON file containing the parameters.

Returns

A image processor of type ImageProcessingMixin

The image_processor object instantiated from that JSON file.

Instantiates a image processor of type ImageProcessingMixin from the path to a JSON file of parameters.

####
from_pretrained

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( 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[bool, str, 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}

####
get_image_processor_dict

< source >

( pretrained_model_name_or_path: typing.Union[str, os.PathLike] **kwargs ) →
`Tuple[Dict, Dict]`

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.
- subfolder (`str`, optional, defaults to `""`) — In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can specify the folder name here.
- image_processor_filename (`str`, optional, defaults to `"config.json"`) — The name of the file in the model directory to use for the image processor config.

Returns

`Tuple[Dict, Dict]`

The dictionary(ies) that will be used to instantiate the image processor object.

From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a image processor of type `~image_processor_utils.ImageProcessingMixin` using `from_dict`.

####
push_to_hub

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( repo_id: str use_temp_dir: typing.Optional[bool] = None commit_message: typing.Optional[str] = None private: typing.Optional[bool] = None token: typing.Union[bool, str, NoneType] = None max_shard_size: typing.Union[int, str, NoneType] = '5GB' create_pr: bool = False safe_serialization: bool = True revision: typing.Optional[str] = None commit_description: typing.Optional[str] = None tags: typing.Optional[list[str]] = None **deprecated_kwargs )

Parameters

- repo_id (`str`) — The name of the repository you want to push your image processor to. It should contain your organization name when pushing to a given organization.
- use_temp_dir (`bool`, optional) — Whether or not to use a temporary directory to store the files saved before they are pushed to the Hub. Will default to `True` if there is no directory named like `repo_id`, `False` otherwise.
- commit_message (`str`, optional) — Message to commit while pushing. Will default to `"Upload image processor"`.
- private (`bool`, optional) — Whether to make the repo private. If `None` (default), the repo will be public unless the organization’s default is private. This value is ignored if the repo already exists.
- token (`bool` or `str`, optional) — The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`). Will default to `True` if `repo_url` is not specified.
- max_shard_size (`int` or `str`, optional, defaults to `"5GB"`) — Only applicable for models. The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size lower than this size. If expressed as a string, needs to be digits followed by a unit (like `"5MB"`). We default it to `"5GB"` so that users can easily load models on free-tier Google Colab instances without any CPU OOM issues.
- create_pr (`bool`, optional, defaults to `False`) — Whether or not to create a PR with the uploaded files or directly commit.
- safe_serialization (`bool`, optional, defaults to `True`) — Whether or not to convert the model weights in safetensors format for safer serialization.
- revision (`str`, optional) — Branch to push the uploaded files to.
- commit_description (`str`, optional) — The description of the commit that will be created
- tags (`List[str]`, optional) — List of tags to push on the Hub.

Upload the image processor file to the 🤗 Model Hub.

Examples:

    from transformers import AutoImageProcessor

    image processor = AutoImageProcessor.from_pretrained("google-bert/bert-base-cased")

    # Push the image processor to your namespace with the name "my-finetuned-bert".
    image processor.push_to_hub("my-finetuned-bert")

    # Push the image processor to an organization with the name "my-finetuned-bert".
    image processor.push_to_hub("huggingface/my-finetuned-bert")

####
register_for_auto_class

< source >

( auto_class = 'AutoImageProcessor' )

Parameters

- auto_class (`str` or `type`, optional, defaults to `"AutoImageProcessor "`) — The auto class to register this new image processor with.

Register this class with a given auto class. This should only be used for custom image processors as the ones in the library are already mapped with `AutoImageProcessor` .

This API is experimental and may have some slight breaking changes in the next releases.

####
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.

####
to_dict

< source >

( ) →
`Dict[str, Any]`

Returns

`Dict[str, Any]`

Dictionary of all the attributes that make up this image processor instance.

Serializes this instance to a Python dictionary.

####
to_json_file

< source >

( json_file_path: typing.Union[str, os.PathLike] )

Parameters

- json_file_path (`str` or `os.PathLike`) — Path to the JSON file in which this image_processor instance’s parameters will be saved.

Save this instance to a JSON file.

####
to_json_string

< source >

( ) →
`str`

Returns

`str`

String containing all the attributes that make up this feature_extractor instance in JSON format.

Serializes this instance to a JSON string.

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