transformers/main/ko/internal/image_processing_utils.md

이미지 프로세서를 위한 유틸리티 [[utilities-for-image-processors]]

이 페이지는 이미지 프로세서에서 사용되는 유틸리티 함수들을 나열하며, 주로 이미지를 처리하기 위한 함수 기반의 변환 작업들을 다룹니다.

이 함수들 대부분은 라이브러리의 이미지 프로세서 코드를 연구할 때만 유용합니다.

이미지 변환 [[transformers.image_transforms.center_crop]][[transformers.image_transforms.center_crop]]

transformers.image_transforms.center_crop[[transformers.image_transforms.center_crop]]

Source

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).

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.

transformers.image_transforms.center_to_corners_format[[transformers.image_transforms.center_to_corners_format]]

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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[[transformers.image_transforms.corners_to_center_format]]

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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[[transformers.image_transforms.id_to_rgb]]

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Converts unique ID to RGB color.

transformers.image_transforms.normalize[[transformers.image_transforms.normalize]]

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

image = (image - mean) / std

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.

transformers.image_transforms.pad[[transformers.image_transforms.pad]]

Source

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

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.

transformers.image_transforms.rgb_to_id[[transformers.image_transforms.rgb_to_id]]

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Converts RGB color to unique ID.

transformers.image_transforms.rescale[[transformers.image_transforms.rescale]]

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Rescales image by scale.

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.

transformers.image_transforms.resize[[transformers.image_transforms.resize]]

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

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.

transformers.image_transforms.to_pil_image[[transformers.image_transforms.to_pil_image]]

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

Parameters:

image (PIL.Image.Image or numpy.ndarray or torch.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.

ImageProcessingMixin [[transformers.ImageProcessingMixin]][[transformers.ImageProcessingMixin]]

transformers.ImageProcessingMixin[[transformers.ImageProcessingMixin]]

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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/main/src/transformers/image_processing_base.py#L473[{"name": "image_url_or_urls", "val": ": 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_dict[[transformers.ImageProcessingMixin.from_dict]]

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

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](/docs/transformers/main/ko/internal/image_processing_utils#transformers.ImageProcessingMixin)

The image processor object instantiated from those parameters.

from_json_file[[transformers.ImageProcessingMixin.from_json_file]]

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

Parameters:

json_file (str or os.PathLike) : Path to the JSON file containing the parameters.

Returns:

A image processor of type [ImageProcessingMixin](/docs/transformers/main/ko/internal/image_processing_utils#transformers.ImageProcessingMixin)

The image_processor object instantiated from that JSON file.

from_pretrained[[transformers.ImageProcessingMixin.from_pretrained]]

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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}

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 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.

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/<pr_number>".

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.

Returns:

A image processor of type ImageProcessingMixin.

get_image_processor_dict[[transformers.ImageProcessingMixin.get_image_processor_dict]]

Source

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.

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.

push_to_hub[[transformers.ImageProcessingMixin.push_to_hub]]

Source

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")

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.

commit_message (str, optional) : Message to commit while pushing. Will default to "Upload image processor".

commit_description (str, optional) : The description of the commit that will be created

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 (default), will use the token generated when running hf auth login (stored in ~/.huggingface).

revision (str, optional) : Branch to push the uploaded files to.

create_pr (bool, optional, defaults to False) : Whether or not to create a PR with the uploaded files or directly commit.

max_shard_size (int or str, optional, defaults to "50GB") : 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").

tags (list[str], optional) : List of tags to push on the Hub.

register_for_auto_class[[transformers.ImageProcessingMixin.register_for_auto_class]]

Source

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 .

Parameters:

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

save_pretrained[[transformers.ImageProcessingMixin.save_pretrained]]

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

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.

to_dict[[transformers.ImageProcessingMixin.to_dict]]

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Serializes this instance to a Python dictionary.

Returns:

dict[str, Any]

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

to_json_file[[transformers.ImageProcessingMixin.to_json_file]]

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Save this instance to a JSON file.

Parameters:

json_file_path (str or os.PathLike) : Path to the JSON file in which this image_processor instance's parameters will be saved.

to_json_string[[transformers.ImageProcessingMixin.to_json_string]]

Source

Serializes this instance to a JSON string.

Returns:

str

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