# coding=utf-8 # Copyright 2025 The ZhipuAI Inc. team and 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 GLM-4.1V.""" import math from typing import Optional, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature from ...image_transforms import ( convert_to_rgb, resize, to_channel_dimension_format, ) from ...image_utils import ( OPENAI_CLIP_MEAN, OPENAI_CLIP_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, logging from ...video_utils import VideoInput logger = logging.get_logger(__name__) def smart_resize( num_frames: int, height: int, width: int, temporal_factor: int = 2, factor: int = 28, min_pixels: int = 112 * 112, max_pixels: int = 14 * 14 * 2 * 2 * 2 * 6144, ): if num_frames < temporal_factor: raise ValueError(f"t:{num_frames} must be larger than temporal_factor:{temporal_factor}") if height < factor or width < factor: raise ValueError(f"height:{height} or width:{width} must be larger than factor:{factor}") elif max(height, width) / min(height, width) > 200: raise ValueError( f"absolute aspect ratio must be smaller than 200, got {max(height, width) / min(height, width)}" ) h_bar = round(height / factor) * factor w_bar = round(width / factor) * factor t_bar = round(num_frames / temporal_factor) * temporal_factor if t_bar * h_bar * w_bar > max_pixels: beta = math.sqrt((num_frames * height * width) / max_pixels) h_bar = max(factor, math.floor(height / beta / factor) * factor) w_bar = max(factor, math.floor(width / beta / factor) * factor) elif t_bar * h_bar * w_bar < min_pixels: beta = math.sqrt(min_pixels / (num_frames * height * width)) h_bar = math.ceil(height * beta / factor) * factor w_bar = math.ceil(width * beta / factor) * factor return h_bar, w_bar class Glm4vImageProcessor(BaseImageProcessor): r""" Constructs a GLM-4V image processor that dynamically resizes images based on the original images. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions. size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 112 * 112, "longest_edge": 28 * 28 * 15000}`): Size of the image's `(height, width)` dimensions after resizing. Can be overridden by the `size` parameter in the `preprocess` method. Available options are: - `{"height": int, "width": int}`: The image will be resized to the exact size `(height, width)`. Do NOT keep the aspect ratio. - `{"shortest_edge": int, "longest_edge": int}`: The image will be resized to a maximum size respecting the aspect ratio and keeping the shortest edge less or equal to `shortest_edge` and the longest edge less or equal to `longest_edge`. - `{"max_height": int, "max_width": int}`: The image will be resized to the maximum size respecting the aspect ratio and keeping the height less or equal to `max_height` and the width less or equal to `max_width`. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`): Resampling filter to use when resizing the image. do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the image by the specified scale `rescale_factor`. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Scale factor to use if rescaling the image. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image. image_mean (`float` or `List[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`): Mean to use if normalizing the image. This is a float or list of floats for each channel in the image. image_std (`float` or `List[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`): Standard deviation to use if normalizing the image. This is a float or list of floats for each channel in the image. do_convert_rgb (`bool`, *optional*, defaults to `True`): Whether to convert the image to RGB. patch_size (`int`, *optional*, defaults to 14): The spatial patch size of the vision encoder. temporal_patch_size (`int`, *optional*, defaults to 2): The temporal patch size of the vision encoder. merge_size (`int`, *optional*, defaults to 2): The merge size of the vision encoder to llm encoder. """ model_input_names = ["pixel_values", "image_grid_thw"] def __init__( self, do_resize: bool = True, size: Optional[dict[str, int]] = None, resample: PILImageResampling = PILImageResampling.BICUBIC, 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: bool = True, patch_size: int = 14, temporal_patch_size: int = 2, merge_size: int = 2, **kwargs, ) -> None: super().__init__(**kwargs) if size is not None and ("shortest_edge" not in size or "longest_edge" not in size): raise ValueError("size must contain 'shortest_edge' and 'longest_edge' keys.") elif size is None: size = {"shortest_edge": 112 * 112, "longest_edge": 28 * 28 * 15000} self.size = size self.do_resize = do_resize self.resample = resample self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_normalize = do_normalize self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD self.patch_size = patch_size self.temporal_patch_size = temporal_patch_size self.merge_size = merge_size self.do_convert_rgb = do_convert_rgb def _preprocess( self, images: Union[ImageInput, VideoInput], do_resize: Optional[bool] = None, size: Optional[dict[str, int]] = None, resample: Optional[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, patch_size: Optional[int] = None, temporal_patch_size: Optional[int] = None, merge_size: Optional[int] = None, do_convert_rgb: Optional[bool] = None, data_format: Optional[ChannelDimension] = ChannelDimension.FIRST, input_data_format: Optional[Union[str, ChannelDimension]] = None, ): """ Preprocess an image or batch of images. Copy of the `preprocess` method from `CLIPImageProcessor`. Args: images (`ImageInput`): Image or batch of images to preprocess. Expects pixel values ranging from 0 to 255. If pixel values range from 0 to 1, set `do_rescale=False`. vision_info (`List[Dict]`, *optional*): Optional list of dictionaries containing additional information about vision inputs. 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. `shortest_edge` and `longest_edge` keys must be present. resample (`PILImageResampling`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of the `PILImageResampling` enums. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Scale factor to use if rescaling the image. 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`): Mean to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image. image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): Standard deviation to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image. patch_size (`int`, *optional*, defaults to `self.patch_size`): The spatial patch size of the vision encoder. temporal_patch_size (`int`, *optional*, defaults to `self.temporal_patch_size`): The temporal patch size of the vision encoder. merge_size (`int`, *optional*, defaults to `self.merge_size`): The merge size of the vision encoder to llm encoder. do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`): Whether to convert the image to RGB. data_format (`ChannelDimension`, *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. 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. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. """ images = make_flat_list_of_images(images) 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]) height, width = get_image_size(images[0], channel_dim=input_data_format) resized_height, resized_width = height, width processed_images = [] for image in images: if do_resize: resized_height, resized_width = smart_resize( num_frames=temporal_patch_size, height=height, width=width, temporal_factor=temporal_patch_size, factor=patch_size * merge_size, min_pixels=size["shortest_edge"], max_pixels=size["longest_edge"], ) image = resize( image, size=(resized_height, resized_width), resample=resample, input_data_format=input_data_format ) if do_rescale: image = self.rescale(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) patches = np.array(processed_images) if data_format == ChannelDimension.LAST: patches = patches.transpose(0, 3, 1, 2) if patches.shape[0] % temporal_patch_size != 0: repeats = np.repeat( patches[-1][np.newaxis], temporal_patch_size - (patches.shape[0] % temporal_patch_size), axis=0 ) patches = np.concatenate([patches, repeats], axis=0) channel = patches.shape[1] grid_t = patches.shape[0] // temporal_patch_size grid_h, grid_w = resized_height // patch_size, resized_width // patch_size patches = patches.reshape( grid_t, temporal_patch_size, channel, grid_h // merge_size, merge_size, patch_size, grid_w // merge_size, merge_size, patch_size, ) patches = patches.transpose(0, 3, 6, 4, 7, 2, 1, 5, 8) flatten_patches = patches.reshape( grid_t * grid_h * grid_w, channel * temporal_patch_size * patch_size * patch_size ) return flatten_patches, (grid_t, grid_h, grid_w) def preprocess( self, images: ImageInput, videos: Optional[VideoInput] = None, do_resize: Optional[bool] = None, size: Optional[dict[str, int]] = None, resample: Optional[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, patch_size: Optional[int] = None, temporal_patch_size: Optional[int] = None, merge_size: Optional[int] = None, do_convert_rgb: Optional[bool] = None, return_tensors: Optional[Union[str, TensorType]] = None, data_format: Optional[ChannelDimension] = ChannelDimension.FIRST, input_data_format: Optional[Union[str, ChannelDimension]] = None, ): """ 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`. videos (`VideoInput`): Video to preprocess. Expects a single or batch of videos with pixel values ranging from 0 to 255. If passing in videos 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. Shortest edge of the image is resized to size["shortest_edge"], with the longest edge resized to keep the input aspect ratio. 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`. The max pixels of the image to resize the image. patch_size (`int`, *optional*, defaults to `self.patch_size`): The spatial patch size of the vision encoder. temporal_patch_size (`int`, *optional*, defaults to `self.temporal_patch_size`): The temporal patch size of the vision encoder. merge_size (`int`, *optional*, defaults to `self.merge_size`): The merge size of the vision encoder to llm encoder. do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`): Whether to convert the image to RGB. 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. """ # Try to use config values if set, otherwise fallback to global defaults size = size if size is not None else self.size if size is not None and ("shortest_edge" not in size or "longest_edge" not in size): raise ValueError("size must contain 'shortest_edge' and 'longest_edge' keys.") elif size is None: size = {"shortest_edge": 112 * 112, "longest_edge": 28 * 28 * 15000} do_resize = do_resize if do_resize is not None else self.do_resize 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 patch_size = patch_size if patch_size is not None else self.patch_size temporal_patch_size = temporal_patch_size if temporal_patch_size is not None else self.temporal_patch_size merge_size = merge_size if merge_size is not None else self.merge_size do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb if images is not None: images = self.fetch_images(images) images = make_flat_list_of_images(images) if images is not None and 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( rescale_factor=rescale_factor, do_normalize=do_normalize, image_mean=image_mean, image_std=image_std, do_resize=do_resize, size=size, resample=resample, ) data = {} if images is not None: pixel_values, vision_grid_thws = [], [] for image in images: patches, image_grid_thw = self._preprocess( image, do_resize=do_resize, size=size, resample=resample, do_rescale=do_rescale, rescale_factor=rescale_factor, do_normalize=do_normalize, image_mean=image_mean, image_std=image_std, patch_size=patch_size, temporal_patch_size=temporal_patch_size, merge_size=merge_size, data_format=data_format, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, ) pixel_values.extend(patches) vision_grid_thws.append(image_grid_thw) pixel_values = np.array(pixel_values) vision_grid_thws = np.array(vision_grid_thws) data.update({"pixel_values": pixel_values, "image_grid_thw": vision_grid_thws}) return BatchFeature(data=data, tensor_type=return_tensors) def get_number_of_image_patches(self, height: int, width: int, images_kwargs=None): """ A utility that returns number of image patches for a given image size. Args: height (`int`): Height of the input image. width (`int`): Width of the input image. images_kwargs (`dict`, *optional*) Any kwargs to override defaults of the image processor. Returns: `int`: Number of image patches per image. """ patch_size = images_kwargs.get("patch_size", self.patch_size) merge_size = images_kwargs.get("merge_size", self.merge_size) size = images_kwargs.get("size", {"shortest_edge": 112 * 112, "longest_edge": 28 * 28 * 15000}) factor = patch_size * merge_size resized_height, resized_width = smart_resize( num_frames=self.temporal_patch_size, height=height, width=width, factor=factor, min_pixels=size["shortest_edge"], max_pixels=size["longest_edge"], temporal_factor=self.temporal_patch_size, ) grid_h, grid_w = resized_height // patch_size, resized_width // patch_size return grid_h * grid_w __all__ = ["Glm4vImageProcessor"]