#
Image Processor

An image processor is in charge of preparing input features for vision models and post processing their outputs. This includes transformations such as resizing, normalization, and conversion to PyTorch, TensorFlow, Flax and Numpy tensors. It may also include model specific post-processing such as converting logits to segmentation masks.

Fast image processors are available for a few models and more will be added in the future. They are based on the torchvision library and provide a significant speed-up, especially when processing on GPU. They have the same API as the base image processors and can be used as drop-in replacements. To use a fast image processor, you need to install the `torchvision` library, and set the `use_fast` argument to `True` when instantiating the image processor:

    from transformers import AutoImageProcessor

    processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-50", use_fast=True)

When using a fast image processor, you can also set the `device` argument to specify the device on which the processing should be done. By default, the processing is done on the same device as the inputs if the inputs are tensors, or on the CPU otherwise.

    from torchvision.io import read_image
    from transformers import DetrImageProcessorFast

    images = read_image("image.jpg")
    processor = DetrImageProcessorFast.from_pretrained("facebook/detr-resnet-50")
    images_processed = processor(images, return_tensors="pt", device="cuda")

Here are some speed comparisons between the base and fast image processors for the `DETR` and `RT-DETR` models, and how they impact overall inference time:

These benchmarks were run on an AWS EC2 g5.2xlarge instance, utilizing an NVIDIA A10G Tensor Core GPU.

##
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[typing.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: typing.Dict[str, int] 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, typing.Iterable[float]] std: typing.Union[float, typing.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.

##
BaseImageProcessorFast

###
class transformers.BaseImageProcessorFast

< source >

( **kwargs )

< > Update on GitHub