# 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. # This file is based on https://github.com/Kwai-Keye/Keye/blob/main/keye-vl-8b-preview/image_processing_keye.py # Original header: # 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 Keye.""" # TODO: Support videos import json import math from pathlib import Path from typing import Dict, List, Optional, Union import numpy as np from ......utils import logging from ..common import ( BatchFeature, convert_to_rgb, make_batched_images, make_list_of_images, to_numpy_array, ) _OPENAI_CLIP_MEAN = [0.48145466, 0.4578275, 0.40821073] _OPENAI_CLIP_STD = [0.26862954, 0.26130258, 0.27577711] def adjust_size(size, patch_size): num_patches = size // patch_size if num_patches % 2 != 0: num_patches -= 1 return num_patches * patch_size 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: logging.debug( f"smart_resize: height={height} < factor={factor}, reset height=factor" ) width = round((width * factor) / height) height = factor if width < factor: logging.debug( 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 SiglipImageProcessor(object): model_input_names = [ "pixel_values", "image_grid_thw", "pixel_values_videos", "video_grid_thw", ] def __init__( self, do_resize: bool = True, resample: int = 3, 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 = 147384, max_pixels: int = 28 * 28 * 3600, patch_size: int = 14, temporal_patch_size: int = 1, merge_size: int = 2, **kwargs, ) -> None: super().__init__() 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 @classmethod def from_pretrained(cls, pretrained_model_dir): pretrained_model_dir = Path(pretrained_model_dir) image_processor_config_path = pretrained_model_dir / "preprocessor_config.json" with open(image_processor_config_path, "r", encoding="utf-8") as f: image_processor_config = json.load(f) return cls(**image_processor_config) def _preprocess( self, images, do_resize: Optional[bool] = 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, do_convert_rgb: Optional[bool] = None, ): images = make_list_of_images(images) if do_convert_rgb: images = [convert_to_rgb(image) for image in images] width, height = images[0].size resized_height, resized_width = height, width processed_images = [] for image in images: if do_resize: resized_height, resized_width = smart_resize( height, width, factor=self.patch_size * self.merge_size, min_pixels=self.min_pixels, max_pixels=self.max_pixels, ) image = image.resize( (resized_width, resized_height), resample=self.resample ) image = to_numpy_array(image) if do_rescale: image = (image * rescale_factor).astype(np.float32) if do_normalize: image = image.astype(np.float32) image -= np.array(image_mean, dtype=np.float32) image /= np.array(image_std, dtype=np.float32) processed_images.append(image) patches = np.array(processed_images) patches = patches.transpose(0, 3, 1, 2) if patches.shape[0] == 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 __call__( self, images, videos=None, do_resize: Optional[bool] = None, size: Optional[Dict[str, int]] = 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, do_convert_rgb: Optional[bool] = None, return_tensors=None, ): do_resize = do_resize if do_resize is not None else self.do_resize size = size if size is not None else self.size 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: raise NotImplementedError("Videos are not yet supported") 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, do_rescale=do_rescale, rescale_factor=rescale_factor, do_normalize=do_normalize, image_mean=image_mean, image_std=image_std, do_convert_rgb=do_convert_rgb, ) 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 = {"pixel_values": pixel_values, "image_grid_thw": vision_grid_thws} return BatchFeature(data=data, tensor_type=return_tensors)