This page lists all the utility functions used by the image processors, mainly the functional transformations used to process the images.
Most of those are only useful if you are studying the code of the image processors in the library.
Image Transformations transformers.image_transforms.center_crop < source >( 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
np.ndarray) — The image to crop. Tuple[int, int]) — The target size for the cropped image. 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.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.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).
( 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 < source >( 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 < source >( id_map )
Converts unique ID to RGB color.
transformers.image_transforms.normalize < source >( 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
np.ndarray) — The image to normalize. float or Collection[float]) — The mean to use for normalization. float or Collection[float]) — The standard deviation to use for normalization. ChannelDimension, optional) — The channel dimension format of the output image. If unset, will use the inferred format from the input. 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 < source >( 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
np.ndarray) — The image to pad. 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.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.float or Iterable[float], optional) — The value to use for the padding if mode is "constant". 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.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.The padded image.
Pads the image with the specified (height, width) padding and mode.
( color )
Converts RGB color to unique ID.
transformers.image_transforms.rescale < source >( 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
np.ndarray) — The image to rescale. float) — The scale to use for rescaling the image. ChannelDimension, optional) — The channel dimension format of the image. If not provided, it will be the same as the input image. np.dtype, optional, defaults to np.float32) — The dtype of the output image. Defaults to np.float32. Used for backwards compatibility with feature extractors. ChannelDimension, optional) — The channel dimension format of the input image. If not provided, it will be inferred from the input image. The rescaled image.
Rescales image by scale.
( 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
np.ndarray) — The image to resize. Tuple[int, int]) — The size to use for resizing the image. int, optional, defaults to PILImageResampling.BILINEAR) — The filter to user for resampling. 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. ChannelDimension, optional) — The channel dimension format of the output image. If unset, will use the inferred format from the input. 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. ChannelDimension, optional) — The channel dimension format of the input image. If unset, will use the inferred format from the input. The resized image.
Resizes image to (height, width) specified by size using the PIL library.
( 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
PIL.Image.Image or numpy.ndarray or torch.Tensor or tf.Tensor) — The image to convert to the PIL.Image format. 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. str, optional) — The mode to use for the PIL image. If unset, will use the default mode for the input image type. ChannelDimension, optional) — The channel dimension format of the input image. If unset, will use the inferred format from the input. The converted image.
Converts image to a PIL Image. Optionally rescales it and puts the channel dimension back as the last axis if needed.
( **kwargs )
This is an image processor mixin used to provide saving/loading functionality for sequential and image feature extractors.
fetch_images < source >( 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
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. Dict[str, Any]) — Additional parameters from which to initialize the image processor object. The image processor object instantiated from those parameters.
Instantiates a type of ImageProcessingMixin from a Python dictionary of parameters.
from_json_file < source >( json_file: typing.Union[str, os.PathLike] ) → A image processor of type ImageProcessingMixin
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 < 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
str or os.PathLike) — This can be either:
./my_model_directory/../my_model_directory/preprocessor_config.json.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. 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. 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. 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). 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:
image_processor = CLIPImageProcessor.from_pretrained(
"openai/clip-vit-base-patch32"
)
image_processor = CLIPImageProcessor.from_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
str or os.PathLike) — The identifier of the pre-trained checkpoint from which we want the dictionary of parameters. 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. 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.
( 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
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. 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. str, optional) — Message to commit while pushing. Will default to "Upload image processor". 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. 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. 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. bool, optional, defaults to False) — Whether or not to create a PR with the uploaded files or directly commit. bool, optional, defaults to True) — Whether or not to convert the model weights in safetensors format for safer serialization. str, optional) — Branch to push the uploaded files to. str, optional) — The description of the commit that will be created 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")
image processor.push_to_hub("my-finetuned-bert")
image processor.push_to_hub("huggingface/my-finetuned-bert") register_for_auto_class < source >
( auto_class = 'AutoImageProcessor' )
Parameters
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
str or os.PathLike) — Directory where the image processor JSON file will be saved (will be created if it does not exist). 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). 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.
( ) → 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
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
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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