imgutils.preprocess.transformers

Overview:

Convert transformers image processors to PillowCompose objects.

Supported Processors:

Name	Supported	Repos	Function
ViTImageProcessor	✅	5906 (33.24%)	create_transforms_from_vit_processor()
DonutImageProcessor	❌	1901 (10.70%)	N/A
DetrImageProcessor	❌	1575 (8.86%)	N/A
CLIPImageProcessor	✅	1374 (7.73%)	create_transforms_from_clip_processor()
VideoMAEImageProcessor	❌	1093 (6.15%)	N/A
ConvNextImageProcessor	✅	648 (3.65%)	create_transforms_from_convnext_processor()
SegformerImageProcessor	❌	533 (3.00%)	N/A
BeitImageProcessor	❌	468 (2.63%)	N/A
SiglipImageProcessor	✅	440 (2.48%)	create_transforms_from_siglip_processor()
LayoutLMv3ImageProcessor	❌	403 (2.27%)	N/A
LayoutLMv2ImageProcessor	❌	332 (1.87%)	N/A
MllamaImageProcessor	❌	332 (1.87%)	N/A
Qwen2VLImageProcessor	❌	314 (1.77%)	N/A
BlipImageProcessor	❌	276 (1.55%)	N/A
Idefics2ImageProcessor	❌	226 (1.27%)	N/A
LlavaNextImageProcessor	❌	215 (1.21%)	N/A
BitImageProcessor	❌	210 (1.18%)	N/A
Pix2StructImageProcessor	❌	113 (0.64%)	N/A
ConditionalDetrImageProcessor	❌	95 (0.53%)	N/A
SamImageProcessor	❌	92 (0.52%)	N/A
DeiTImageProcessor	❌	91 (0.51%)	N/A
Mask2FormerImageProcessor	❌	89 (0.50%)	N/A
VivitImageProcessor	❌	88 (0.50%)	N/A
YolosImageProcessor	❌	84 (0.47%)	N/A
ViltImageProcessor	❌	73 (0.41%)	N/A
DetaImageProcessor	❌	68 (0.38%)	N/A
PixtralImageProcessor	❌	68 (0.38%)	N/A
MobileNetV2ImageProcessor	❌	63 (0.35%)	N/A
MobileViTImageProcessor	❌	61 (0.34%)	N/A
DPTImageProcessor	❌	51 (0.29%)	N/A
MaskFormerImageProcessor	❌	49 (0.28%)	N/A
NougatImageProcessor	❌	48 (0.27%)	N/A
IdeficsImageProcessor	❌	47 (0.26%)	N/A
RTDetrImageProcessor	❌	45 (0.25%)	N/A
EfficientNetImageProcessor	❌	40 (0.23%)	N/A
DeformableDetrImageProcessor	❌	36 (0.20%)	N/A
Idefics3ImageProcessor	❌	32 (0.18%)	N/A
FuyuImageProcessor	❌	22 (0.12%)	N/A
VideoLlavaImageProcessor	❌	17 (0.10%)	N/A
PvtImageProcessor	❌	16 (0.09%)	N/A
OneFormerImageProcessor	❌	14 (0.08%)	N/A
MobileNetV1ImageProcessor	❌	12 (0.07%)	N/A
Owlv2ImageProcessor	❌	12 (0.07%)	N/A
ChineseCLIPImageProcessor	❌	9 (0.05%)	N/A
EfficientFormerImageProcessor	❌	8 (0.05%)	N/A
LlavaOnevisionImageProcessor	❌	8 (0.05%)	N/A
Swin2SRImageProcessor	❌	8 (0.05%)	N/A
ViTHybridImageProcessor	❌	8 (0.05%)	N/A
OwlViTImageProcessor	❌	7 (0.04%)	N/A
GroundingDinoImageProcessor	❌	6 (0.03%)	N/A
PerceiverImageProcessor	❌	6 (0.03%)	N/A
ChameleonImageProcessor	❌	5 (0.03%)	N/A
LevitImageProcessor	❌	5 (0.03%)	N/A
VitMatteImageProcessor	❌	5 (0.03%)	N/A

register_creators_for_transformers

imgutils.preprocess.transformers.register_creators_for_transformers()

Decorator for registering transform creator functions.

This decorator adds the decorated function to the list of available transform creators that will be tried when creating transforms from a transformers processor.

Returns:

Decorator function

Return type:

callable

Example:

>>> @register_creators_for_transformers()
>>> def my_transform_creator(processor):
...     # Create and return transforms
...     pass

NotProcessorTypeError

class imgutils.preprocess.transformers.NotProcessorTypeError

Exception raised when a processor type is not recognized or supported.

This error occurs when attempting to create transforms from an unsupported or unknown transformers processor type.

create_transforms_from_transformers

imgutils.preprocess.transformers.create_transforms_from_transformers(processor)

Create image transforms from a transformers processor.

This function attempts to create appropriate image transforms by trying each registered creator function until one succeeds.

Parameters:

processor (object) – A transformers processor object

Returns:

Image transforms appropriate for the given processor

Return type:

object

Raises:

NotProcessorTypeError – If no suitable creator is found for the processor

Example:

>>> from transformers import AutoImageProcessor
>>> from imgutils.preprocess.transformers import create_transforms_from_transformers
>>>
>>> processor = AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32")
>>> transforms = create_transforms_from_transformers(processor)
>>> transforms
PillowCompose(
    PillowConvertRGB(force_background='white')
    PillowResize(size=224, interpolation=bicubic, max_size=None, antialias=True)
    PillowCenterCrop(size=(224, 224))
    PillowToTensor()
    PillowNormalize(mean=[0.48145467 0.4578275  0.40821072], std=[0.26862955 0.2613026  0.2757771 ])
)

create_clip_transforms

imgutils.preprocess.transformers.create_clip_transforms(do_resize: bool = True, size=<object object>, resample=3, do_center_crop=True, crop_size=<object object>, do_rescale: bool = True, rescale_factor: float = 0.00392156862745098, do_normalize: bool = True, image_mean=<object object>, image_std=<object object>, do_convert_rgb: bool = True)

Creates a composition of image transforms typically used for CLIP models.

Parameters:

• do_resize (bool) – Whether to resize the image.

• size (dict) – Target size for resizing. Can be {“shortest_edge”: int} or {“height”: int, “width”: int}.

• resample (int) – PIL resampling filter to use for resizing.

• do_center_crop (bool) – Whether to center crop the image.

• crop_size (dict) – Size for center cropping in {“height”: int, “width”: int} format.

• do_rescale (bool) – Whether to rescale pixel values.

• rescale_factor (float) – Factor to use for rescaling pixels.

• do_normalize (bool) – Whether to normalize the image.

• image_mean (list or tuple) – Mean values for normalization.

• image_std (list or tuple) – Standard deviation values for normalization.

• do_convert_rgb (bool) – Whether to convert image to RGB.

Returns:

A composed transformation pipeline.

Return type:

PillowCompose

create_transforms_from_clip_processor

imgutils.preprocess.transformers.create_transforms_from_clip_processor(processor)

Creates image transforms from a CLIP processor configuration.

Parameters:

processor (Union[CLIPProcessor, CLIPImageProcessor]) – A CLIP processor or image processor instance from transformers library.

Returns:

A composed transformation pipeline matching the processor’s configuration.

Return type:

PillowCompose

Raises:

NotProcessorTypeError – If the provided processor is not a CLIP processor.

create_convnext_transforms

imgutils.preprocess.transformers.create_convnext_transforms(do_resize: bool = True, size=<object object>, crop_pct: float = <object object>, resample=2, do_rescale: bool = True, rescale_factor: float = 0.00392156862745098, do_normalize: bool = True, image_mean=<object object>, image_std=<object object>)

Create a composition of image transforms specifically tailored for ConvNext models.

This function creates a transformation pipeline that can include resizing, rescaling, and normalization operations. The transforms are applied in the following order:

1. Resize (optional)

2. Convert to tensor

3. Rescale (optional)

4. Normalize (optional)

Parameters:

• do_resize (bool) – Whether to resize the image

• size (dict) – Target size dictionary with ‘shortest_edge’ key

• crop_pct (float) – Center crop percentage, used to compute resize size

• resample (int) – PIL resampling filter to use for resizing

• do_rescale (bool) – Whether to rescale pixel values

• rescale_factor (float) – Factor to use for rescaling pixels

• do_normalize (bool) – Whether to normalize the image

• image_mean (tuple or list) – Mean values for normalization

• image_std (tuple or list) – Standard deviation values for normalization

Returns:

A composed transformation pipeline

Return type:

PillowCompose

create_transforms_from_convnext_processor

imgutils.preprocess.transformers.create_transforms_from_convnext_processor(processor)

Create image transforms from a ConvNext processor configuration.

This function takes a Hugging Face ConvNextImageProcessor and creates a corresponding transformation pipeline that matches its configuration settings.

Parameters:

processor (ConvNextImageProcessor) – The ConvNext image processor to create transforms from

Returns:

A composed transformation pipeline matching the processor’s configuration

Return type:

PillowCompose

Raises:

NotProcessorTypeError – If the provided processor is not a ConvNextImageProcessor

create_vit_transforms

imgutils.preprocess.transformers.create_vit_transforms(do_resize: bool = True, size=<object object>, resample: int = 2, do_rescale: bool = True, rescale_factor: float = 0.00392156862745098, do_normalize: bool = True, image_mean=<object object>, image_std=<object object>)

Create a composition of image transforms typically used for ViT models.

This function creates a transform pipeline that can include resizing, tensor conversion, rescaling, and normalization operations. The transforms are applied in sequence to prepare images for ViT model input.

Parameters:

• do_resize (bool) – Whether to resize the input images

• size (dict) – Target size for resizing, should be dict with ‘height’ and ‘width’ keys

• resample (int) – PIL resampling filter to use for resizing

• do_rescale (bool) – Whether to rescale pixel values

• rescale_factor (float) – Factor to use for rescaling pixel values

• do_normalize (bool) – Whether to normalize the image

• image_mean (tuple or list) – Mean values for normalization

• image_std (tuple or list) – Standard deviation values for normalization

Returns:

A composition of image transforms

Return type:

PillowCompose

create_transforms_from_vit_processor

imgutils.preprocess.transformers.create_transforms_from_vit_processor(processor)

Create image transforms from a Hugging Face ViT processor configuration.

This function takes a ViT image processor from the transformers library and creates a matching transform pipeline that replicates the processor’s preprocessing steps.

Parameters:

processor (ViTImageProcessor) – A ViT image processor from Hugging Face transformers

Returns:

A composition of image transforms matching the processor’s configuration

Return type:

PillowCompose

Raises:

NotProcessorTypeError – If the provided processor is not a ViTImageProcessor

create_siglip_transforms

imgutils.preprocess.transformers.create_siglip_transforms(do_resize: bool = True, size=<object object>, resample: int = 3, do_rescale: bool = True, rescale_factor: float = 0.00392156862745098, do_normalize: bool = True, image_mean=<object object>, image_std=<object object>, do_convert_rgb: bool = True)

Creates a composition of image transformations for SigLIP model input processing.

This function builds a pipeline of image transformations that can include:

• RGB conversion

• Image resizing

• Tensor conversion

• Image rescaling

• Normalization

Parameters:

• do_resize (bool) – Whether to resize the image

• size (dict) – Target size dictionary with ‘height’ and ‘width’ keys

• resample (int) – PIL image resampling filter to use for resizing

• do_rescale (bool) – Whether to rescale pixel values

• rescale_factor (float) – Factor to use for pixel value rescaling

• do_normalize (bool) – Whether to normalize the image

• image_mean (tuple or list) – Mean values for normalization

• image_std (tuple or list) – Standard deviation values for normalization

• do_convert_rgb (bool) – Whether to convert image to RGB

Returns:

A composed transformation pipeline

Return type:

PillowCompose

create_transforms_from_siglip_processor

imgutils.preprocess.transformers.create_transforms_from_siglip_processor(processor)

Creates image transformations from a SigLIP processor configuration.

This function extracts transformation parameters from a HuggingFace SigLIP image processor and creates a corresponding transformation pipeline.

Parameters:

processor (SiglipImageProcessor) – A HuggingFace SigLIP image processor instance

Returns:

A composed transformation pipeline

Return type:

PillowCompose

Raises:

NotProcessorTypeError – If the processor is not a SiglipImageProcessor