transformers.image_transforms.center_croptransformers.image_transforms.center_crophttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/image_transforms.py#L455[{"name": "image", "val": ": ndarray"}, {"name": "size", "val": ": tuple"}, {"name": "data_format", "val": ": typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None"}, {"name": "input_data_format", "val": ": typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None"}]- **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.0`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_formattransformers.image_transforms.center_to_corners_formathttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/image_transforms.py#L570[{"name": "bboxes_center", "val": ": 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_formattransformers.image_transforms.corners_to_center_formathttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/image_transforms.py#L630[{"name": "bboxes_corners", "val": ": 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_rgbtransformers.image_transforms.id_to_rgbhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/image_transforms.py#L664[{"name": "id_map", "val": ""}] Converts unique ID to RGB color.

transformers.image_transforms.normalizetransformers.image_transforms.normalizehttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/image_transforms.py#L394[{"name": "image", "val": ": ndarray"}, {"name": "mean", "val": ": typing.Union[float, collections.abc.Collection[float]]"}, {"name": "std", "val": ": typing.Union[float, collections.abc.Collection[float]]"}, {"name": "data_format", "val": ": typing.Optional[transformers.image_utils.ChannelDimension] = None"}, {"name": "input_data_format", "val": ": typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None"}]- **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.0 Normalizes `image` using the mean and standard deviation specified by `mean` and `std`. image = (image - mean) / std

transformers.image_transforms.padtransformers.image_transforms.padhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/image_transforms.py#L694[{"name": "image", "val": ": ndarray"}, {"name": "padding", "val": ": typing.Union[int, tuple[int, int], collections.abc.Iterable[tuple[int, int]]]"}, {"name": "mode", "val": ": PaddingMode = "}, {"name": "constant_values", "val": ": typing.Union[float, collections.abc.Iterable[float]] = 0.0"}, {"name": "data_format", "val": ": typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None"}, {"name": "input_data_format", "val": ": typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None"}]- **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.0`np.ndarray`The padded image. Pads the `image` with the specified (height, width) `padding` and `mode`.

transformers.image_transforms.rgb_to_idtransformers.image_transforms.rgb_to_idhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/image_transforms.py#L653[{"name": "color", "val": ""}] Converts RGB color to unique ID.

transformers.image_transforms.rescaletransformers.image_transforms.rescalehttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/image_transforms.py#L97[{"name": "image", "val": ": ndarray"}, {"name": "scale", "val": ": float"}, {"name": "data_format", "val": ": typing.Optional[transformers.image_utils.ChannelDimension] = None"}, {"name": "dtype", "val": ": dtype = "}, {"name": "input_data_format", "val": ": typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None"}]- **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.0`np.ndarray`The rescaled image. Rescales `image` by `scale`.

transformers.image_transforms.resizetransformers.image_transforms.resizehttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/image_transforms.py#L323[{"name": "image", "val": ": ndarray"}, {"name": "size", "val": ": tuple"}, {"name": "resample", "val": ": typing.Optional[ForwardRef('PILImageResampling')] = None"}, {"name": "reducing_gap", "val": ": typing.Optional[int] = None"}, {"name": "data_format", "val": ": typing.Optional[transformers.image_utils.ChannelDimension] = None"}, {"name": "return_numpy", "val": ": bool = True"}, {"name": "input_data_format", "val": ": typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None"}]- **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.0`np.ndarray`The resized image. Resizes `image` to `(height, width)` specified by `size` using the PIL library.

transformers.image_transforms.to_pil_imagetransformers.image_transforms.to_pil_imagehttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/image_transforms.py#L162[{"name": "image", "val": ": typing.Union[numpy.ndarray, ForwardRef('PIL.Image.Image'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor'), ForwardRef('jnp.ndarray')]"}, {"name": "do_rescale", "val": ": typing.Optional[bool] = None"}, {"name": "image_mode", "val": ": typing.Optional[str] = None"}, {"name": "input_data_format", "val": ": typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None"}]- **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.0`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.

class transformers.ImageProcessingMixintransformers.ImageProcessingMixinhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/image_processing_base.py#L64[{"name": "**kwargs", "val": ""}] This is an image processor mixin used to provide saving/loading functionality for sequential and image feature extractors.

fetch_imagestransformers.ImageProcessingMixin.fetch_imageshttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/image_processing_base.py#L522[{"name": "image_url_or_urls", "val": ": typing.Union[str, list[str], list[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_dicttransformers.ImageProcessingMixin.from_dicthttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/image_processing_base.py#L389[{"name": "image_processor_dict", "val": ": dict"}, {"name": "**kwargs", "val": ""}]- **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()](/docs/transformers/v4.57.0/en/internal/image_processing_utils#transformers.ImageProcessingMixin.to_dict) method. - **kwargs** (`dict[str, Any]`) -- Additional parameters from which to initialize the image processor object.0[ImageProcessingMixin](/docs/transformers/v4.57.0/en/main_classes/image_processor#transformers.ImageProcessingMixin)The image processor object instantiated from those parameters. Instantiates a type of [ImageProcessingMixin](/docs/transformers/v4.57.0/en/main_classes/image_processor#transformers.ImageProcessingMixin) from a Python dictionary of parameters.

from_json_filetransformers.ImageProcessingMixin.from_json_filehttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/image_processing_base.py#L446[{"name": "json_file", "val": ": typing.Union[str, os.PathLike]"}]- **json_file** (`str` or `os.PathLike`) -- Path to the JSON file containing the parameters.0A image processor of type [ImageProcessingMixin](/docs/transformers/v4.57.0/en/main_classes/image_processor#transformers.ImageProcessingMixin)The image_processor object instantiated from that JSON file. Instantiates a image processor of type [ImageProcessingMixin](/docs/transformers/v4.57.0/en/main_classes/image_processor#transformers.ImageProcessingMixin) from the path to a JSON file of parameters.

from_pretrainedtransformers.ImageProcessingMixin.from_pretrainedhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/image_processing_base.py#L91[{"name": "pretrained_model_name_or_path", "val": ": typing.Union[str, os.PathLike]"}, {"name": "cache_dir", "val": ": typing.Union[str, os.PathLike, NoneType] = None"}, {"name": "force_download", "val": ": bool = False"}, {"name": "local_files_only", "val": ": bool = False"}, {"name": "token", "val": ": typing.Union[str, bool, NoneType] = None"}, {"name": "revision", "val": ": str = 'main'"}, {"name": "**kwargs", "val": ""}]- **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()](/docs/transformers/v4.57.0/en/main_classes/image_processor#transformers.ImageProcessingMixin.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 `hf auth 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. To test a pull request you made on the Hub, you can pass `revision="refs/pr/"`. - **return_unused_kwargs** (`bool`, *optional*, defaults to `False`) -- If `False`, then this function returns just the final image processor object. If `True`, then this functions returns a `Tuple(image_processor, unused_kwargs)` where *unused_kwargs* is a dictionary consisting of the key/value pairs whose keys are not image processor attributes: i.e., the part of `kwargs` which has not been used to update `image_processor` and is otherwise ignored. - **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. - **kwargs** (`dict[str, Any]`, *optional*) -- The values in kwargs of any keys which are image processor attributes will be used to override the loaded values. Behavior concerning key/value pairs whose keys are *not* image processor attributes is controlled by the `return_unused_kwargs` keyword parameter.0A image processor of type [ImageProcessingMixin](/docs/transformers/v4.57.0/en/main_classes/image_processor#transformers.ImageProcessingMixin). Instantiate a type of [ImageProcessingMixin](/docs/transformers/v4.57.0/en/main_classes/image_processor#transformers.ImageProcessingMixin) from an image processor. Examples: ```python # 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_dicttransformers.ImageProcessingMixin.get_image_processor_dicthttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/image_processing_base.py#L266[{"name": "pretrained_model_name_or_path", "val": ": typing.Union[str, os.PathLike]"}, {"name": "**kwargs", "val": ""}]- **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.0`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_hubtransformers.ImageProcessingMixin.push_to_hubhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/utils/hub.py#L848[{"name": "repo_id", "val": ": str"}, {"name": "use_temp_dir", "val": ": typing.Optional[bool] = None"}, {"name": "commit_message", "val": ": typing.Optional[str] = None"}, {"name": "private", "val": ": typing.Optional[bool] = None"}, {"name": "token", "val": ": typing.Union[bool, str, NoneType] = None"}, {"name": "max_shard_size", "val": ": typing.Union[str, int, NoneType] = '5GB'"}, {"name": "create_pr", "val": ": bool = False"}, {"name": "safe_serialization", "val": ": bool = True"}, {"name": "revision", "val": ": typing.Optional[str] = None"}, {"name": "commit_description", "val": ": typing.Optional[str] = None"}, {"name": "tags", "val": ": typing.Optional[list[str]] = None"}, {"name": "**deprecated_kwargs", "val": ""}]- **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 `hf auth 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.0 Upload the image processor file to the 🤗 Model Hub. Examples: ```python 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_classtransformers.ImageProcessingMixin.register_for_auto_classhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/image_processing_base.py#L500[{"name": "auto_class", "val": " = 'AutoImageProcessor'"}]- **auto_class** (`str` or `type`, *optional*, defaults to `"AutoImageProcessor "`) -- The auto class to register this new image processor with.0 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 `.

save_pretrainedtransformers.ImageProcessingMixin.save_pretrainedhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/image_processing_base.py#L205[{"name": "save_directory", "val": ": typing.Union[str, os.PathLike]"}, {"name": "push_to_hub", "val": ": bool = False"}, {"name": "**kwargs", "val": ""}]- **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()](/docs/transformers/v4.57.0/en/main_classes/model#transformers.utils.PushToHubMixin.push_to_hub) method.0 Save an image processor object to the directory `save_directory`, so that it can be re-loaded using the [from_pretrained()](/docs/transformers/v4.57.0/en/main_classes/image_processor#transformers.ImageProcessingMixin.from_pretrained) class method.

to_dicttransformers.ImageProcessingMixin.to_dicthttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/image_processing_base.py#L434[]`dict[str, Any]`Dictionary of all the attributes that make up this image processor instance. Serializes this instance to a Python dictionary.

to_json_filetransformers.ImageProcessingMixin.to_json_filehttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/image_processing_base.py#L486[{"name": "json_file_path", "val": ": typing.Union[str, os.PathLike]"}]- **json_file_path** (`str` or `os.PathLike`) -- Path to the JSON file in which this image_processor instance's parameters will be saved.0 Save this instance to a JSON file.

to_json_stringtransformers.ImageProcessingMixin.to_json_stringhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/image_processing_base.py#L465[]`str`String containing all the attributes that make up this feature_extractor instance in JSON format. Serializes this instance to a JSON string.