# coding=utf-8 # Copyright 2025 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Image processor class for Gemma3.""" import itertools import math from typing import Dict, List, Optional, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import ( convert_to_rgb, resize, to_channel_dimension_format, ) from ...image_utils import ( IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, ChannelDimension, ImageInput, PILImageResampling, get_image_size, infer_channel_dimension_format, is_scaled_image, make_flat_list_of_images, to_numpy_array, valid_images, validate_preprocess_arguments, ) from ...utils import TensorType, filter_out_non_signature_kwargs, is_vision_available, logging logger = logging.get_logger(__name__) if is_vision_available(): import PIL class Gemma3ImageProcessor(BaseImageProcessor): r""" Constructs a SigLIP image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by `do_resize` in the `preprocess` method. size (`Dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`): Size of the image after resizing. Can be overridden by `size` in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method. do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by `do_rescale` in the `preprocess` method. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess` method. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image by the specified mean and standard deviation. Can be overridden by `do_normalize` in the `preprocess` method. image_mean (`float` or `List[float]`, *optional*, defaults to `[0.5, 0.5, 0.5]`): Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`float` or `List[float]`, *optional*, defaults to `[0.5, 0.5, 0.5]`): Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. Can be overridden by the `image_std` parameter in the `preprocess` method. do_convert_rgb (`bool`, *optional*, defaults to `True`): Whether to convert the image to RGB. do_pan_and_scan (`bool`, *optional*): Whether to apply `pan_and_scan` to images. pan_and_scan_min_crop_size (`int`, *optional*): Minimum size of each crop in pan and scan. pan_and_scan_max_num_crops (`int`, *optional*): Maximum number of crops per image in pan and scan. pan_and_scan_min_ratio_to_activate (`float`, *optional*): Minimum aspect ratio to activate pan and scan. """ model_input_names = ["pixel_values", "num_crops"] def __init__( self, do_resize: bool = True, size: Dict[str, int] = None, resample: PILImageResampling = PILImageResampling.BILINEAR, do_rescale: bool = True, rescale_factor: Union[int, float] = 1 / 255, do_normalize: bool = True, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, do_convert_rgb: Optional[bool] = None, do_pan_and_scan: Optional[bool] = None, pan_and_scan_min_crop_size: Optional[int] = None, pan_and_scan_max_num_crops: Optional[int] = None, pan_and_scan_min_ratio_to_activate: Optional[float] = None, **kwargs, ) -> None: super().__init__(**kwargs) size = size if size is not None else {"height": 224, "width": 224} size = get_size_dict(size, default_to_square=True) image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD self.do_resize = do_resize self.size = size self.resample = resample self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std self.do_convert_rgb = do_convert_rgb self.do_pan_and_scan = do_pan_and_scan self.pan_and_scan_min_crop_size = pan_and_scan_min_crop_size self.pan_and_scan_max_num_crops = pan_and_scan_max_num_crops self.pan_and_scan_min_ratio_to_activate = pan_and_scan_min_ratio_to_activate def pan_and_scan( self, image: np.ndarray, pan_and_scan_min_crop_size: int, pan_and_scan_max_num_crops: int, pan_and_scan_min_ratio_to_activate: float, data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, ): """ Pan and Scan and image, by cropping into smaller images when the aspect ratio exceeds minumum allowed ratio. Args: image (`np.ndarray`): Image to resize. pan_and_scan_min_crop_size (`int`, *optional*): Minimum size of each crop in pan and scan. pan_and_scan_max_num_crops (`int`, *optional*): Maximum number of crops per image in pan and scan. pan_and_scan_min_ratio_to_activate (`float`, *optional*): Minimum aspect ratio to activate pan and scan. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. """ height, width = get_image_size(image) # Square or landscape image. if width >= height: # Only apply PaS if the image is sufficiently exaggerated if width / height < pan_and_scan_min_ratio_to_activate: return [] # Select ideal number of crops close to the image aspect ratio and such that crop_size > min_crop_size. num_crops_w = int(math.floor(width / height + 0.5)) # Half round up rounding. num_crops_w = min(int(math.floor(width / pan_and_scan_min_crop_size)), num_crops_w) # Make sure the number of crops is in range [2, pan_and_scan_max_num_crops]. num_crops_w = max(2, num_crops_w) num_crops_w = min(pan_and_scan_max_num_crops, num_crops_w) num_crops_h = 1 # Portrait image. else: # Only apply PaS if the image is sufficiently exaggerated if height / width < pan_and_scan_min_ratio_to_activate: return [] # Select ideal number of crops close to the image aspect ratio and such that crop_size > min_crop_size. num_crops_h = int(math.floor(height / width + 0.5)) num_crops_h = min(int(math.floor(height / pan_and_scan_min_crop_size)), num_crops_h) # Make sure the number of crops is in range [2, pan_and_scan_max_num_crops]. num_crops_h = max(2, num_crops_h) num_crops_h = min(pan_and_scan_max_num_crops, num_crops_h) num_crops_w = 1 crop_size_w = int(math.ceil(width / num_crops_w)) crop_size_h = int(math.ceil(height / num_crops_h)) # Don't apply PaS if crop size is too small. if min(crop_size_w, crop_size_h) < pan_and_scan_min_crop_size: return [] crop_positions_w = [crop_size_w * i for i in range(num_crops_w)] crop_positions_h = [crop_size_h * i for i in range(num_crops_h)] if input_data_format == ChannelDimension.LAST: image_crops = [ image[pos_h : pos_h + crop_size_h, pos_w : pos_w + crop_size_w] for pos_h, pos_w in itertools.product(crop_positions_h, crop_positions_w) ] else: image_crops = [ image[:, pos_h : pos_h + crop_size_h, pos_w : pos_w + crop_size_w] for pos_h, pos_w in itertools.product(crop_positions_h, crop_positions_w) ] return image_crops def _process_images_for_pan_and_scan( self, images: List[np.ndarray], do_pan_and_scan: bool, pan_and_scan_min_crop_size: int, pan_and_scan_max_num_crops: int, pan_and_scan_min_ratio_to_activate: float, data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, ): pas_images_list = [] num_crops = [] for image in images: pas_images = self.pan_and_scan( image=image, pan_and_scan_min_crop_size=pan_and_scan_min_crop_size, pan_and_scan_max_num_crops=pan_and_scan_max_num_crops, pan_and_scan_min_ratio_to_activate=pan_and_scan_min_ratio_to_activate, data_format=data_format, input_data_format=input_data_format, ) pas_images_list.extend([image] + pas_images) num_crops.append(len(pas_images)) return pas_images_list, num_crops @filter_out_non_signature_kwargs() def preprocess( self, images: ImageInput, do_resize: Optional[bool] = None, size: Dict[str, int] = None, resample: PILImageResampling = None, do_rescale: Optional[bool] = None, rescale_factor: Optional[float] = None, do_normalize: Optional[bool] = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, return_tensors: Optional[Union[str, TensorType]] = None, data_format: Optional[ChannelDimension] = ChannelDimension.FIRST, input_data_format: Optional[Union[str, ChannelDimension]] = None, do_convert_rgb: Optional[bool] = None, do_pan_and_scan: Optional[bool] = None, pan_and_scan_min_crop_size: Optional[int] = None, pan_and_scan_max_num_crops: Optional[int] = None, pan_and_scan_min_ratio_to_activate: Optional[float] = None, ) -> PIL.Image.Image: """ Preprocess an image or batch of images. Args: images (`ImageInput`): Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`Dict[str, int]`, *optional*, defaults to `self.size`): Size of the image after resizing. resample (`int`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only has an effect if `do_resize` is set to `True`. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Rescale factor to rescale the image by if `do_rescale` is set to `True`. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image. image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`. image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to `True`. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - Unset: Return a list of `np.ndarray`. - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): 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. - Unset: Use the channel dimension format of the input image. 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. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`): Whether to convert the image to RGB. do_pan_and_scan (`bool`, *optional*, defaults to `self.do_convert_rgb`): Whether to apply `pan_and_scan` to images. pan_and_scan_min_crop_size (`int`, *optional*, defaults to `self.pan_and_scan_min_crop_size`): Minimum size of each crop in pan and scan. pan_and_scan_max_num_crops (`int`, *optional*, defaults to `self.pan_and_scan_max_num_crops`): Maximum number of crops per image in pan and scan. pan_and_scan_min_ratio_to_activate (`float`, *optional*, defaults to `self.pan_and_scan_min_ratio_to_activate`): Minimum aspect ratio to activate pan and scan. """ do_resize = do_resize if do_resize is not None else self.do_resize size = size if size is not None else self.size size = get_size_dict(size, param_name="size", default_to_square=False) resample = resample if resample is not None else self.resample do_rescale = do_rescale if do_rescale is not None else self.do_rescale rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor do_normalize = do_normalize if do_normalize is not None else self.do_normalize image_mean = image_mean if image_mean is not None else self.image_mean image_std = image_std if image_std is not None else self.image_std do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb do_pan_and_scan = do_pan_and_scan if do_pan_and_scan is not None else self.do_pan_and_scan pan_and_scan_min_crop_size = ( pan_and_scan_min_crop_size if pan_and_scan_min_crop_size is not None else self.pan_and_scan_min_crop_size ) pan_and_scan_max_num_crops = ( pan_and_scan_max_num_crops if pan_and_scan_max_num_crops is not None else self.pan_and_scan_max_num_crops ) pan_and_scan_min_ratio_to_activate = ( pan_and_scan_min_ratio_to_activate if pan_and_scan_min_ratio_to_activate is not None else self.pan_and_scan_min_ratio_to_activate ) images = make_flat_list_of_images(images) if not valid_images(images): raise ValueError( "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " "torch.Tensor, tf.Tensor or jax.ndarray." ) validate_preprocess_arguments( do_rescale=do_rescale, rescale_factor=rescale_factor, do_normalize=do_normalize, image_mean=image_mean, image_std=image_std, do_resize=do_resize, size=size, resample=resample, ) if do_convert_rgb: images = [convert_to_rgb(image) for image in images] # All transformations expect numpy arrays. images = [to_numpy_array(image) for image in images] if do_rescale and is_scaled_image(images[0]): logger.warning_once( "It looks like you are trying to rescale already rescaled images. If the input" " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again." ) if input_data_format is None: # We assume that all images have the same channel dimension format. input_data_format = infer_channel_dimension_format(images[0]) if do_pan_and_scan: images, num_crops = self._process_images_for_pan_and_scan( images=images, do_pan_and_scan=do_pan_and_scan, pan_and_scan_min_crop_size=pan_and_scan_min_crop_size, pan_and_scan_max_num_crops=pan_and_scan_max_num_crops, pan_and_scan_min_ratio_to_activate=pan_and_scan_min_ratio_to_activate, data_format=data_format, input_data_format=input_data_format, ) else: num_crops = [0 for _ in images] processed_images = [] for image in images: if do_resize: height, width = size["height"], size["width"] image = resize( image=image, size=(height, width), resample=resample, input_data_format=input_data_format ) if do_rescale: image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format) if do_normalize: image = self.normalize( image=image, mean=image_mean, std=image_std, input_data_format=input_data_format ) image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) processed_images.append(image) data = {"pixel_values": processed_images, "num_crops": num_crops} return BatchFeature(data=data, tensor_type=return_tensors) __all__ = ["Gemma3ImageProcessor"]