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"""Image processor class for SigLIP.""" |
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from typing import Dict, List, Optional, Union |
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from transformers.image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict |
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from transformers.image_transforms import ( |
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resize, |
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to_channel_dimension_format, |
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) |
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from transformers.image_utils import ( |
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IMAGENET_STANDARD_MEAN, |
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IMAGENET_STANDARD_STD, |
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ChannelDimension, |
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ImageInput, |
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PILImageResampling, |
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infer_channel_dimension_format, |
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is_scaled_image, |
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make_list_of_images, |
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to_numpy_array, |
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valid_images, |
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) |
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from transformers.utils import TensorType, is_vision_available, logging |
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logger = logging.get_logger(__name__) |
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if is_vision_available(): |
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import PIL |
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class SiglipImageProcessor(BaseImageProcessor): |
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r""" |
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Constructs a SigLIP image processor. |
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Args: |
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do_resize (`bool`, *optional*, defaults to `True`): |
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Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by |
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`do_resize` in the `preprocess` method. |
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size (`Dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`): |
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Size of the image after resizing. Can be overridden by `size` in the `preprocess` method. |
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resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`): |
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Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method. |
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do_rescale (`bool`, *optional*, defaults to `True`): |
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Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by `do_rescale` in |
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the `preprocess` method. |
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rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): |
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Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess` |
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method. |
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do_normalize (`bool`, *optional*, defaults to `True`): |
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Whether to normalize the image by the specified mean and standard deviation. Can be overridden by |
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`do_normalize` in the `preprocess` method. |
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image_mean (`float` or `List[float]`, *optional*, defaults to `[0.5, 0.5, 0.5]`): |
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Mean to use if normalizing the image. This is a float or list of floats the length of the number of |
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channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. |
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image_std (`float` or `List[float]`, *optional*, defaults to `[0.5, 0.5, 0.5]`): |
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Standard deviation to use if normalizing the image. This is a float or list of floats the length of the |
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number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. |
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Can be overridden by the `image_std` parameter in the `preprocess` method. |
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""" |
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model_input_names = ["pixel_values"] |
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def __init__( |
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self, |
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do_resize: bool = True, |
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size: Dict[str, int] = None, |
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resample: PILImageResampling = PILImageResampling.BICUBIC, |
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do_rescale: bool = True, |
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rescale_factor: Union[int, float] = 1 / 255, |
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do_normalize: bool = True, |
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image_mean: Optional[Union[float, List[float]]] = None, |
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image_std: Optional[Union[float, List[float]]] = None, |
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**kwargs, |
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) -> None: |
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super().__init__(**kwargs) |
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size = size if size is not None else {"height": 224, "width": 224} |
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image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN |
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image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD |
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self.do_resize = do_resize |
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self.size = size |
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self.resample = resample |
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self.do_rescale = do_rescale |
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self.rescale_factor = rescale_factor |
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self.do_normalize = do_normalize |
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self.image_mean = image_mean |
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self.image_std = image_std |
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def preprocess( |
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self, |
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images: ImageInput, |
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do_resize: bool = None, |
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size: Dict[str, int] = None, |
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resample: PILImageResampling = None, |
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do_rescale: bool = None, |
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rescale_factor: float = None, |
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do_normalize: bool = None, |
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image_mean: Optional[Union[float, List[float]]] = None, |
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image_std: Optional[Union[float, List[float]]] = None, |
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return_tensors: Optional[Union[str, TensorType]] = None, |
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data_format: Optional[ChannelDimension] = ChannelDimension.FIRST, |
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input_data_format: Optional[Union[str, ChannelDimension]] = None, |
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**kwargs, |
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) -> PIL.Image.Image: |
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""" |
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Preprocess an image or batch of images. |
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Args: |
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images (`ImageInput`): |
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Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If |
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passing in images with pixel values between 0 and 1, set `do_rescale=False`. |
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do_resize (`bool`, *optional*, defaults to `self.do_resize`): |
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Whether to resize the image. |
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size (`Dict[str, int]`, *optional*, defaults to `self.size`): |
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Size of the image after resizing. |
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resample (`int`, *optional*, defaults to `self.resample`): |
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Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only |
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has an effect if `do_resize` is set to `True`. |
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do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): |
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Whether to rescale the image. |
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rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): |
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Rescale factor to rescale the image by if `do_rescale` is set to `True`. |
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do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): |
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Whether to normalize the image. |
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image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): |
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Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`. |
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image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): |
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Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to |
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`True`. |
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return_tensors (`str` or `TensorType`, *optional*): |
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The type of tensors to return. Can be one of: |
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- Unset: Return a list of `np.ndarray`. |
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- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. |
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- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. |
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- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. |
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- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. |
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data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): |
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The channel dimension format for the output image. Can be one of: |
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- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. |
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- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. |
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- Unset: Use the channel dimension format of the input image. |
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input_data_format (`ChannelDimension` or `str`, *optional*): |
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The channel dimension format for the input image. If unset, the channel dimension format is inferred |
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from the input image. Can be one of: |
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- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. |
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- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. |
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- `"none"` or `ChannelDimension.NONE`: image in (height, width) format. |
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""" |
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do_resize = do_resize if do_resize is not None else self.do_resize |
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size = size if size is not None else self.size |
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size = get_size_dict(size, param_name="size", default_to_square=False) |
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resample = resample if resample is not None else self.resample |
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do_rescale = do_rescale if do_rescale is not None else self.do_rescale |
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rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor |
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do_normalize = do_normalize if do_normalize is not None else self.do_normalize |
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image_mean = image_mean if image_mean is not None else self.image_mean |
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image_std = image_std if image_std is not None else self.image_std |
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images = make_list_of_images(images) |
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if not valid_images(images): |
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raise ValueError( |
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"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " |
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"torch.Tensor, tf.Tensor or jax.ndarray." |
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) |
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if do_resize and size is None: |
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raise ValueError("Size must be specified if do_resize is True.") |
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if do_rescale and rescale_factor is None: |
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raise ValueError("Rescale factor must be specified if do_rescale is True.") |
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images = [to_numpy_array(image) for image in images] |
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if is_scaled_image(images[0]) and do_rescale: |
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logger.warning_once( |
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"It looks like you are trying to rescale already rescaled images. If the input" |
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" images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again." |
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) |
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if input_data_format is None: |
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input_data_format = infer_channel_dimension_format(images[0]) |
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if do_resize: |
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height, width = size["height"], size["width"] |
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images = [ |
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resize(image=image, size=(height, width), resample=resample, input_data_format=input_data_format) |
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for image in images |
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] |
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if do_rescale: |
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images = [ |
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self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format) |
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for image in images |
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] |
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if do_normalize: |
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images = [ |
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self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format) |
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for image in images |
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] |
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images = [ |
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to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images |
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] |
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data = {"pixel_values": images} |
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return BatchFeature(data=data, tensor_type=return_tensors) |
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