# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/janus/modular_janus.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_janus.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # coding=utf-8 # Copyright 2025 Deepseek AI and The HuggingFace 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. from typing import Dict, Iterable, List, Optional, Tuple, 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 ( OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, ChannelDimension, ImageInput, PILImageResampling, get_image_size, infer_channel_dimension_format, is_scaled_image, make_flat_list_of_images, make_list_of_images, to_numpy_array, valid_images, validate_preprocess_arguments, ) from ...utils import ( TensorType, filter_out_non_signature_kwargs, is_vision_available, logging, ) if is_vision_available(): import PIL logger = logging.get_logger(__name__) class JanusImageProcessor(BaseImageProcessor): r""" Constructs a JANUS 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 the `do_resize` parameter in the `preprocess` method. size (`dict`, *optional*, defaults to `{"height": 384, "width": 384}`): Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess` method. min_size (`int`, *optional*, defaults to 14): The minimum allowed size for the resized image. Ensures that neither the height nor width falls below this value after resizing. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`): Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`. Can be overridden by the `resample` parameter 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 the `do_rescale` parameter in the `preprocess` method. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Scale factor to use if rescaling the image. Only has an effect if `do_rescale` is set to `True`. Can be overridden by the `rescale_factor` parameter in the `preprocess` method. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` method. Can be overridden by the `do_normalize` parameter in the `preprocess` method. image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`): 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. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`): 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. """ model_input_names = ["pixel_values"] def __init__( self, do_resize: bool = True, size: Optional[Dict[str, int]] = None, min_size: int = 14, 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: Optional[bool] = None, **kwargs, ) -> None: super().__init__(**kwargs) size = size if size is not None else {"height": 384, "width": 384} size = get_size_dict(size, default_to_square=True) 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 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.do_convert_rgb = do_convert_rgb self.min_size = min_size if image_mean is None: self.background_color = (127, 127, 127) else: self.background_color = tuple([int(x * 255) for x in image_mean]) def resize( self, image: np.ndarray, size: Union[Dict[str, int], int], resample: PILImageResampling = PILImageResampling.BICUBIC, data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs, ) -> np.ndarray: """ Resize an image to dynamically calculated size. Args: image (`np.ndarray`): Image to resize. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BICUBIC`. data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the output image. If unset, the channel dimension format of the input image is used. 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`: will be inferred from input 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. Returns: `np.ndarray`: The resized image. """ if input_data_format is None: input_data_format = infer_channel_dimension_format(image) height, width = get_image_size(image, input_data_format) max_size = max(height, width) size = get_size_dict(size, default_to_square=True) if size["height"] != size["width"]: raise ValueError( f"Output height and width must be the same. Got height={size['height']} and width={size['width']}" ) size = size["height"] delta = size / max_size # Largest side becomes `size` and the other side is scaled according to the aspect ratio. output_size_nonpadded = [ max(int(height * delta), self.min_size), max(int(width * delta), self.min_size), ] image = resize( image, size=output_size_nonpadded, resample=resample, data_format=data_format, input_data_format=input_data_format, **kwargs, ) # Expand and pad the images to obtain a square image of dimensions `size x size` image = self.pad_to_square( image=image, background_color=self.background_color, input_data_format=input_data_format, ) return image @filter_out_non_signature_kwargs() def preprocess( self, images: ImageInput, do_resize: Optional[bool] = None, size: Optional[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, do_convert_rgb: Optional[bool] = None, data_format: ChannelDimension = ChannelDimension.FIRST, input_data_format: Optional[Union[str, ChannelDimension]] = 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`): Controls the size of the image after `resize`. The shortest edge of the image is resized to `size["shortest_edge"]` whilst preserving the aspect ratio. If the longest edge of this resized image is > `int(size["shortest_edge"] * (1333 / 800))`, then the image is resized again to make the longest edge equal to `int(size["shortest_edge"] * (1333 / 800))`. resample (`PILImageResampling`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. 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 values between [0 - 1]. 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 normalize the image by if `do_normalize` is set to `True`. image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): Image standard deviation to normalize the image by 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 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 size = size if size is not None else self.size size = get_size_dict(size, default_to_square=False) 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, ) # PIL RGBA images are converted to RGB 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_resize: images = [ self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format) for image in images ] if do_rescale: images = [ self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format) for image in images ] if do_normalize: images = [ self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format) for image in images ] images = [ to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images ] encoded_outputs = BatchFeature(data={"pixel_values": images}, tensor_type=return_tensors) return encoded_outputs def pad_to_square( self, image: np.ndarray, background_color: Union[int, Tuple[int, int, int]] = 0, data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, ) -> np.array: """ Pads an image to a square based on the longest edge. Args: image (`np.ndarray`): The image to pad. background_color (`int` or `Tuple[int, int, int]`, *optional*, defaults to 0): The color to use for the padding. Can be an integer for single channel or a tuple of integers representing for multi-channel images. If passed as integer in mutli-channel mode, it will default to `0` in subsequent channels. data_format (`str` or `ChannelDimension`, *optional*): 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. If unset, will use same as the input image. input_data_format (`str` or `ChannelDimension`, *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. Returns: `np.ndarray`: The padded image. """ height, width = get_image_size(image, input_data_format) num_channels = image.shape[0] if input_data_format == ChannelDimension.FIRST else image.shape[-1] if height == width: image = ( to_channel_dimension_format(image, data_format, input_data_format) if data_format is not None else image ) return image max_dim = max(height, width) # Ensure background_color is the correct shape if isinstance(background_color, int): background_color = [background_color] elif len(background_color) != num_channels: raise ValueError( f"background_color must have no more than {num_channels} elements to match the number of channels" ) if input_data_format == ChannelDimension.FIRST: result = np.zeros((num_channels, max_dim, max_dim), dtype=image.dtype) for i, color in enumerate(background_color): result[i, :, :] = color if width > height: start = (max_dim - height) // 2 result[:, start : start + height, :] = image else: start = (max_dim - width) // 2 result[:, :, start : start + width] = image else: result = np.zeros((max_dim, max_dim, num_channels), dtype=image.dtype) for i, color in enumerate(background_color): result[:, :, i] = color if width > height: start = (max_dim - height) // 2 result[start : start + height, :, :] = image else: start = (max_dim - width) // 2 result[:, start : start + width, :] = image return result def postprocess( self, images: ImageInput, do_rescale: Optional[bool] = None, rescale_factor: Optional[float] = None, do_normalize: Optional[bool] = None, image_mean: Optional[List[float]] = None, image_std: Optional[List[float]] = None, input_data_format: Optional[str] = None, return_tensors: Optional[str] = None, ): """Applies post-processing to the decoded image tokens by reversing transformations applied during preprocessing.""" do_rescale = do_rescale if do_rescale is not None else self.do_rescale rescale_factor = 1.0 / self.rescale_factor if rescale_factor is None else 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 images = make_list_of_images(images) # Ensures input is a list if isinstance(images[0], PIL.Image.Image): return images if len(images) > 1 else images[0] if input_data_format is None: input_data_format = infer_channel_dimension_format(images[0]) # Determine format dynamically pixel_values = [] for image in images: image = to_numpy_array(image) # Ensure NumPy format if do_normalize: image = self.unnormalize( image=image, image_mean=image_mean, image_std=image_std, input_data_format=input_data_format ) if do_rescale: image = self.rescale(image, scale=rescale_factor, input_data_format=input_data_format) image = image.clip(0, 255).astype(np.uint8) if do_normalize and do_rescale and return_tensors == "PIL.Image.Image": image = to_channel_dimension_format(image, ChannelDimension.LAST, input_channel_dim=input_data_format) image = PIL.Image.fromarray(image) pixel_values.append(image) data = {"pixel_values": pixel_values} return_tensors = return_tensors if return_tensors != "PIL.Image.Image" else None return BatchFeature(data=data, tensor_type=return_tensors) def unnormalize( self, image: np.array, image_mean: Union[float, Iterable[float]], image_std: Union[float, Iterable[float]], input_data_format: Optional[Union[str, ChannelDimension]] = None, ) -> np.array: """ Unnormalizes `image` using the mean and standard deviation specified by `mean` and `std`. image = (image * image_std) + image_mean Args: image (`torch.Tensor` of shape `(batch_size, num_channels, image_size, image_size)` or `(num_channels, image_size, image_size)`): Batch of pixel values to postprocess. image_mean (`float` or `Iterable[float]`): The mean to use for unnormalization. image_std (`float` or `Iterable[float]`): The standard deviation to use for unnormalization. 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. """ num_channels = 3 if isinstance(image_mean, Iterable): if len(image_mean) != num_channels: raise ValueError(f"mean must have {num_channels} elements if it is an iterable, got {len(image_mean)}") else: image_mean = [image_mean] * num_channels if isinstance(image_std, Iterable): if len(image_std) != num_channels: raise ValueError(f"std must have {num_channels} elements if it is an iterable, got {len(image_std)}") else: image_std = [image_std] * num_channels rev_image_mean = tuple(-mean / std for mean, std in zip(image_mean, image_std)) rev_image_std = tuple(1 / std for std in image_std) image = self.normalize( image=image, mean=rev_image_mean, std=rev_image_std, input_data_format=input_data_format ) return image __all__ = ["JanusImageProcessor"]