# Copyright (c) 2025 PaddlePaddle Authors. 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 PaddleOCR-VL.""" import math from typing import Dict, List, Optional, Union import numpy as np import torch from transformers.image_processing_utils import BaseImageProcessor, BatchFeature from torchvision.transforms import functional as TF from transformers.image_transforms import ( convert_to_rgb, resize, to_channel_dimension_format, ) from transformers.image_utils import ( OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, ChannelDimension, PILImageResampling, get_image_size, infer_channel_dimension_format, is_scaled_image, is_valid_image, make_list_of_images, to_numpy_array, valid_images, validate_preprocess_arguments, ) from transformers.utils import TensorType, is_vision_available, logging logger = logging.get_logger(__name__) if is_vision_available(): from PIL import Image ImageInput = Union[ "PIL.Image.Image", np.ndarray, "torch.Tensor", List["PIL.Image.Image"], List[np.ndarray], List["torch.Tensor"], ] # noqa VideoInput = Union[ List["PIL.Image.Image"], "np.ndarray", "torch.Tensor", List["np.ndarray"], List["torch.Tensor"], List[List["PIL.Image.Image"]], List[List["np.ndarrray"]], List[List["torch.Tensor"]], ] # noqa def make_batched_images(images) -> List[List[ImageInput]]: """ Accepts images in list or nested list format, and makes a list of images for preprocessing. Args: images (`Union[List[List[ImageInput]], List[ImageInput], ImageInput]`): The input image. Returns: list: A list of images. """ if ( isinstance(images, (list, tuple)) and isinstance(images[0], (list, tuple)) and is_valid_image(images[0][0]) ): return [img for img_list in images for img in img_list] elif isinstance(images, (list, tuple)) and is_valid_image(images[0]): return images elif is_valid_image(images): return [images] raise ValueError(f"Could not make batched images from {images}") def adjust_size(size, patch_size): num_patches = size // patch_size if num_patches % 2 != 0: # 如果是奇数，减1 num_patches -= 1 return num_patches * patch_size def make_batched_videos(videos) -> List[VideoInput]: if ( isinstance(videos, (list, tuple)) and isinstance(videos[0], (list, tuple)) and is_valid_image(videos[0][0]) ): return videos elif isinstance(videos, (list, tuple)) and is_valid_image(videos[0]): if isinstance(videos[0], Image.Image): return [videos] elif len(videos[0].shape) == 4: return [list(video) for video in videos] elif is_valid_image(videos) and len(videos.shape) == 4: return [list(videos)] raise ValueError(f"Could not make batched video from {videos}") def smart_resize( height: int, width: int, factor: int = 28, min_pixels: int = 28 * 28 * 130, max_pixels: int = 28 * 28 * 1280, ): """Rescales the image so that the following conditions are met: 1. Both dimensions (height and width) are divisible by 'factor'. 2. The total number of pixels is within the range ['min_pixels', 'max_pixels']. 3. The aspect ratio of the image is maintained as closely as possible. """ # if height < factor or width < factor: # raise ValueError(f"height:{height} or width:{width} must be larger than factor:{factor}") # if int(height < factor//4) + int(width < factor//4): # raise ValueError(f"height:{height} or width:{width} must be larger than factor:{factor//4}") if height < factor: print(f"smart_resize: height={height} < factor={factor}, reset height=factor") width = round((width * factor) / height) height = factor if width < factor: print(f"smart_resize: width={width} < factor={factor}, reset width=factor") height = round((height * factor) / width) width = factor if 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 if h_bar * w_bar > max_pixels: beta = math.sqrt((height * width) / max_pixels) h_bar = math.floor(height / beta / factor) * factor w_bar = math.floor(width / beta / factor) * factor elif h_bar * w_bar < min_pixels: beta = math.sqrt(min_pixels / (height * width)) h_bar = math.ceil(height * beta / factor) * factor w_bar = math.ceil(width * beta / factor) * factor return h_bar, w_bar class PaddleOCRVLImageProcessor(BaseImageProcessor): r""" Constructs a Siglip 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. 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. min_pixels (`int`, *optional*, defaults to `28 * 28 * 130`): The min pixels of the image to resize the image. max_pixels (`int`, *optional*, defaults to `28 * 28 * 1670`): The max pixels of the image to resize the image. patch_size (`int`, *optional*, defaults to 14): The spacial 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", "pixel_values_videos", "video_grid_thw", ] def __init__( self, do_resize: bool = True, 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, min_pixels: int = 28 * 28 * 130, max_pixels: int = 28 * 28 * 1280, patch_size: int = 14, temporal_patch_size: int = 1, merge_size: int = 2, **kwargs, ) -> None: super().__init__(**kwargs) 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.min_pixels = min_pixels self.max_pixels = max_pixels self.patch_size = patch_size self.temporal_patch_size = temporal_patch_size self.merge_size = merge_size self.size = {"min_pixels": min_pixels, "max_pixels": max_pixels} # not used self.do_convert_rgb = do_convert_rgb def mvit_rescale(self, image: Image.Image, merge_size: int = 2) -> Image.Image: try: w, h = image.size except: raise ValueError(str((type(image), image))) patch_size = self.patch_size if (w // patch_size) * (h // patch_size) > self.in_token_limit: scale = math.sqrt( self.in_token_limit / ((w // patch_size) * (h // patch_size)) ) new_w, new_h = int(w * scale), int(h * scale) image = image.resize((new_w, new_h), Image.Resampling.BICUBIC) if self.pad_input: new_w, new_h = image.size pad_size_h = merge_size * patch_size pad_size_w = merge_size * patch_size pad_h = (pad_size_h - new_h % pad_size_h) % pad_size_h pad_w = (pad_size_w - new_w % pad_size_w) % pad_size_w image = TF.pad(image, (0, 0, pad_w, pad_h)) else: new_w, new_h = image.size new_w = new_w - new_w % patch_size new_h = new_h - new_h % patch_size new_w = adjust_size(new_w, patch_size) new_h = adjust_size(new_h, patch_size) image = TF.center_crop(image, (new_h, new_w)) w, h = image.size if w // patch_size >= 512 or h // patch_size >= 512: new_h = min(patch_size * 510, h) new_w = min(patch_size * 510, w) image = TF.center_crop(image, (new_h, new_w)) # raise ValueError("Exceed pos emb") return image def _preprocess( self, images: Union[ImageInput, VideoInput], do_resize: bool = None, resample: PILImageResampling = None, do_rescale: bool = None, rescale_factor: float = None, do_normalize: bool = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, do_convert_rgb: 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. 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. 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_list_of_images(images) if do_convert_rgb: images = [convert_to_rgb(image) for image in images] # Determine source size without forcing a numpy conversion for PIL inputs. first = images[0] first_is_pil = isinstance(first, Image.Image) if first_is_pil: src_width, src_height = first.size src_format = ChannelDimension.LAST # np.asarray(PIL) -> HWC else: if input_data_format is None: input_data_format = infer_channel_dimension_format(first) src_format = input_data_format src_height, src_width = get_image_size(first, channel_dim=src_format) if is_scaled_image(first) and do_rescale: 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 do_resize: resized_height, resized_width = smart_resize( src_height, src_width, factor=self.patch_size * self.merge_size, min_pixels=self.min_pixels, max_pixels=self.max_pixels, ) else: resized_height, resized_width = src_height, src_width # Fast path: single PIL RGB image, default flags, temporal_patch_size=1. # Fuses resize->uint8->float32, normalize, channel-reorder, and patch-flatten # into a single np.subtract write pass (one allocation of the output tensor). if ( len(images) == 1 and first_is_pil and do_rescale and do_normalize and data_format == ChannelDimension.FIRST and self.temporal_patch_size == 1 ): img = images[0] if do_resize and img.size != (resized_width, resized_height): img = img.resize( (resized_width, resized_height), resample=resample ) hwc = np.asarray(img) if hwc.ndim == 3 and hwc.shape[-1] == 3: p = self.patch_size gh = resized_height // p gw = resized_width // p mean_arr = np.asarray(image_mean, dtype=np.float32) std_arr = np.asarray(image_std, dtype=np.float32) bias = (mean_arr / rescale_factor).reshape(3, 1, 1) scale = (rescale_factor / std_arr).reshape(3, 1, 1) # (H, W, 3) -> view (gh, p, gw, p, 3) -> (gh, gw, 3, p, p) non-contig. src = hwc.reshape(gh, p, gw, p, 3).transpose(0, 2, 4, 1, 3) out = np.subtract(src, bias, dtype=np.float32) out *= scale flatten_patches = out.reshape(gh * gw, 3, p, p) return flatten_patches, (1, gh, gw) # Precompute fused normalize constants once per call. # Math: (x * rf - mean) / std == (x - mean/rf) * (rf/std) fuse_norm = do_rescale and do_normalize if fuse_norm: mean_arr = np.asarray(image_mean, dtype=np.float32) std_arr = np.asarray(image_std, dtype=np.float32) fused_bias = mean_arr / rescale_factor fused_scale = rescale_factor / std_arr if src_format == ChannelDimension.FIRST: fused_bias = fused_bias.reshape(-1, 1, 1) fused_scale = fused_scale.reshape(-1, 1, 1) processed_images = [] for image in images: # Resize while still PIL (single PIL->numpy conversion) when possible. if isinstance(image, Image.Image): if do_resize and image.size != (resized_width, resized_height): image = image.resize( (resized_width, resized_height), resample=resample ) image = np.asarray(image) image_format = ChannelDimension.LAST else: if do_resize: image = resize( image, size=(resized_height, resized_width), resample=resample, input_data_format=src_format, ) image_format = src_format if fuse_norm: # Single allocation, two in-place passes. image = image.astype(np.float32, copy=True) image -= fused_bias image *= fused_scale else: if do_rescale: image = self.rescale( image, scale=rescale_factor, input_data_format=image_format ) if do_normalize: image = self.normalize( image=image, mean=image_mean, std=image_std, input_data_format=image_format, ) image = to_channel_dimension_format( image, data_format, input_channel_dim=image_format ) processed_images.append(image) # Avoid np.array(list) for the common single-image case. if len(processed_images) == 1: patches = processed_images[0][None] else: patches = np.stack(processed_images, axis=0) if data_format == ChannelDimension.LAST: patches = patches.transpose(0, 3, 1, 2) # np.tile with all-1 reps still allocates a fresh copy; skip it. if patches.shape[0] == 1 and self.temporal_patch_size != 1: patches = np.tile(patches, (self.temporal_patch_size, 1, 1, 1)) channel = patches.shape[1] grid_t = patches.shape[0] // self.temporal_patch_size grid_h, grid_w = ( resized_height // self.patch_size, resized_width // self.patch_size, ) patches = patches.reshape( grid_t, self.temporal_patch_size, channel, grid_h, self.patch_size, grid_w, self.patch_size, ) patches = patches.transpose(0, 3, 5, 2, 1, 4, 6) assert self.temporal_patch_size == 1 flatten_patches = patches.reshape( grid_t * grid_h * grid_w, channel, self.patch_size, self.patch_size ) return flatten_patches, (grid_t, grid_h, grid_w) def preprocess( self, images: ImageInput, videos: VideoInput = None, do_resize: bool = None, size: Dict[str, int] = None, resample: PILImageResampling = None, do_rescale: bool = None, rescale_factor: float = None, do_normalize: bool = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, do_convert_rgb: 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`. 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. """ do_resize = do_resize if do_resize is not None else self.do_resize size = size if size is not None else self.size 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 ) if images is not None: images = make_batched_images(images) if videos is not None: videos = make_batched_videos(videos) 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, ) if images is not None: pixel_chunks, vision_grid_thws = [], [] for image in images: patches, image_grid_thw = self._preprocess( image, do_resize=do_resize, resample=resample, do_rescale=do_rescale, rescale_factor=rescale_factor, do_normalize=do_normalize, image_mean=image_mean, image_std=image_std, data_format=data_format, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, ) pixel_chunks.append(patches) vision_grid_thws.append(image_grid_thw) # Avoid extend+np.array(list-of-rows) — concatenate already-contiguous # per-image patch blocks instead. pixel_values = ( pixel_chunks[0] if len(pixel_chunks) == 1 else np.concatenate(pixel_chunks, axis=0) ) vision_grid_thws = np.array(vision_grid_thws) data = {"pixel_values": pixel_values, "image_grid_thw": vision_grid_thws} if videos is not None: pixel_chunks, vision_grid_thws = [], [] for images in videos: patches, video_grid_thw = self._preprocess( images, do_resize=do_resize, resample=resample, do_rescale=do_rescale, rescale_factor=rescale_factor, do_normalize=do_normalize, image_mean=image_mean, image_std=image_std, data_format=data_format, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, ) pixel_chunks.append(patches) vision_grid_thws.append(video_grid_thw) pixel_values = ( pixel_chunks[0] if len(pixel_chunks) == 1 else np.concatenate(pixel_chunks, axis=0) ) vision_grid_thws = np.array(vision_grid_thws) data = { "pixel_values_videos": pixel_values, "video_grid_thw": vision_grid_thws, } return BatchFeature(data=data, tensor_type=return_tensors)