Loading imgutils/preprocess/transformers/blip.py +50 −0 Original line number Diff line number Diff line """ This module provides image transformation utilities specifically designed for BLIP (Bootstrapping Language-Image Pre-training) models. It includes functions to create transformation pipelines for processing images according to BLIP's requirements. The transformations include operations like resizing, RGB conversion, normalization, and tensor conversion, all implemented using Pillow-based operations. """ from PIL import Image from .base import OPENAI_CLIP_STD, OPENAI_CLIP_MEAN, _DEFAULT, _check_transformers, NotProcessorTypeError, \ Loading @@ -18,6 +26,35 @@ def create_blip_transforms( image_std=_DEFAULT, do_convert_rgb: bool = True, ): """ Create a transformation pipeline for BLIP image processing. This function builds a sequence of image transformations commonly used in BLIP models, including RGB conversion, resizing, tensor conversion, rescaling, and normalization. :param do_resize: Whether to resize the image. :type do_resize: bool :param size: Target size for resizing, expects dict with 'height' and 'width' keys. Defaults to {'height': 384, 'width': 384}. :type size: dict :param resample: Resampling filter for resize operation. Defaults to PIL.Image.BICUBIC. :type resample: int :param do_rescale: Whether to rescale pixel values. :type do_rescale: bool :param rescale_factor: Factor to rescale pixel values. Defaults to 1/255. :type rescale_factor: float :param do_normalize: Whether to normalize the image. :type do_normalize: bool :param image_mean: Mean values for normalization. Defaults to OPENAI_CLIP_MEAN. :type image_mean: tuple or list :param image_std: Standard deviation values for normalization. Defaults to OPENAI_CLIP_STD. :type image_std: tuple or list :param do_convert_rgb: Whether to convert image to RGB. :type do_convert_rgb: bool :return: A composed transformation pipeline. :rtype: PillowCompose """ size = size if size is not _DEFAULT else _DEFAULT_SIZE image_mean = image_mean if image_mean is not _DEFAULT else OPENAI_CLIP_MEAN image_std = image_std if image_std is not _DEFAULT else OPENAI_CLIP_STD Loading Loading @@ -48,6 +85,19 @@ def create_blip_transforms( @register_creators_for_transformers() def create_transforms_from_blip_processor(processor): """ Create image transformations from a HuggingFace BLIP processor. This function extracts configuration from a HuggingFace BLIP processor and creates a corresponding transformation pipeline using create_blip_transforms. :param processor: A HuggingFace BLIP image processor instance. :type processor: transformers.BlipImageProcessor :return: A composed transformation pipeline configured according to the processor's settings. :rtype: PillowCompose :raises NotProcessorTypeError: If the provided processor is not a BlipImageProcessor. """ _check_transformers() from transformers import BlipImageProcessor Loading Loading
imgutils/preprocess/transformers/blip.py +50 −0 Original line number Diff line number Diff line """ This module provides image transformation utilities specifically designed for BLIP (Bootstrapping Language-Image Pre-training) models. It includes functions to create transformation pipelines for processing images according to BLIP's requirements. The transformations include operations like resizing, RGB conversion, normalization, and tensor conversion, all implemented using Pillow-based operations. """ from PIL import Image from .base import OPENAI_CLIP_STD, OPENAI_CLIP_MEAN, _DEFAULT, _check_transformers, NotProcessorTypeError, \ Loading @@ -18,6 +26,35 @@ def create_blip_transforms( image_std=_DEFAULT, do_convert_rgb: bool = True, ): """ Create a transformation pipeline for BLIP image processing. This function builds a sequence of image transformations commonly used in BLIP models, including RGB conversion, resizing, tensor conversion, rescaling, and normalization. :param do_resize: Whether to resize the image. :type do_resize: bool :param size: Target size for resizing, expects dict with 'height' and 'width' keys. Defaults to {'height': 384, 'width': 384}. :type size: dict :param resample: Resampling filter for resize operation. Defaults to PIL.Image.BICUBIC. :type resample: int :param do_rescale: Whether to rescale pixel values. :type do_rescale: bool :param rescale_factor: Factor to rescale pixel values. Defaults to 1/255. :type rescale_factor: float :param do_normalize: Whether to normalize the image. :type do_normalize: bool :param image_mean: Mean values for normalization. Defaults to OPENAI_CLIP_MEAN. :type image_mean: tuple or list :param image_std: Standard deviation values for normalization. Defaults to OPENAI_CLIP_STD. :type image_std: tuple or list :param do_convert_rgb: Whether to convert image to RGB. :type do_convert_rgb: bool :return: A composed transformation pipeline. :rtype: PillowCompose """ size = size if size is not _DEFAULT else _DEFAULT_SIZE image_mean = image_mean if image_mean is not _DEFAULT else OPENAI_CLIP_MEAN image_std = image_std if image_std is not _DEFAULT else OPENAI_CLIP_STD Loading Loading @@ -48,6 +85,19 @@ def create_blip_transforms( @register_creators_for_transformers() def create_transforms_from_blip_processor(processor): """ Create image transformations from a HuggingFace BLIP processor. This function extracts configuration from a HuggingFace BLIP processor and creates a corresponding transformation pipeline using create_blip_transforms. :param processor: A HuggingFace BLIP image processor instance. :type processor: transformers.BlipImageProcessor :return: A composed transformation pipeline configured according to the processor's settings. :rtype: PillowCompose :raises NotProcessorTypeError: If the provided processor is not a BlipImageProcessor. """ _check_transformers() from transformers import BlipImageProcessor Loading
