o i\7@sdZddlmZmZddlZddlmZmZddl m Z m Z ddl m Z mZmZmZmZmZmZmZmZmZmZddlmZmZmZdd lmZeeZ Gd d d eZ!d gZ"dS) z#Image processor class for ViTMatte.)OptionalUnionN)BaseImageProcessor BatchFeature)padto_channel_dimension_format) IMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDChannelDimension ImageInputget_image_sizeinfer_channel_dimension_formatis_scaled_imagemake_flat_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypefilter_out_non_signature_kwargslogging)deprecate_kwargcseZdZdZdgZ       d!dedeeefd ed e eee efd e eee efd ed eddffdd Z e ddZ e jddZ   d"dejdede eeefde eeefdejf ddZeeddd dddddddddejdf dedede ede ed e ed e eee efd e eee efd e ed e ede eeefdeeefde eeeffdd ZZS)#VitMatteImageProcessora Constructs a ViTMatte image processor. Args: 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. 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. 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. 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. do_pad (`bool`, *optional*, defaults to `True`): Whether to pad the image to make the width and height divisible by `size_divisor`. Can be overridden by the `do_pad` parameter in the `preprocess` method. size_divisor (`int`, *optional*, defaults to 32): The width and height of the image will be padded to be divisible by this number. pixel_valuesTp?N do_rescalerescale_factor do_normalize image_mean image_stddo_pad size_divisorreturnc  sttjdi|||_||_||_||_|dur|nt|_|dur$|nt|_ | d} | dur5| |_ dS||_ dS)Nsize_divisibility) super__init__rrr!rr rr r getr") selfrrrrr r!r"kwargsr$ __class__r%j/home/ubuntu/.local/lib/python3.10/site-packages/transformers/models/vitmatte/image_processing_vitmatte.pyr'Hs  zVitMatteImageProcessor.__init__cCstd|jSNzk`self.size_divisibility` attribute is deprecated and will be removed in v5. Use `self.size_divisor` insteadloggerwarningr")r)r%r%r-r$]sz(VitMatteImageProcessor.size_divisibilitycCstd||_dSr.r/)r)valuer%r%r-r$ds imager$ data_formatinput_data_formatc Cs|durt|}t||\}}||dkrdn|||}||dkr%dn|||}||dkrAd|fd|ff} t|| ||d}|durKt|||}|S)a Args: image (`np.ndarray`): Image to pad. size_divisibility (`int`, *optional*, defaults to 32): The width and height of the image will be padded to be divisible by this number. 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. Nr)paddingr4r5)rr rr) r)r3r$r4r5heightwidth pad_height pad_widthr6r%r%r- pad_imageks  z VitMatteImageProcessor.pad_imagev5)versionnew_nameimagestrimapsreturn_tensorsc s|dur|nj}|dur|nj}|dur|nj}dur!njdur*njdur3njdur<njt|}t|dd}t|sQt dt|sYt dt ||ddd|D}d d|D}|r}t |d r}t d durt|d |rfd d|D}fd d|D}|rfdd|D}tjkrdnd fddt||D}|rχfdd|D}fdd|D}d|i} t| | dS)a 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`. trimaps (`ImageInput`): Trimap to preprocess. 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 use if `do_normalize` is set to `True`. image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`): Image standard deviation to use if `do_normalize` is set to `True`. do_pad (`bool`, *optional*, defaults to `self.do_pad`): Whether to pad the image. size_divisor (`int`, *optional*, defaults to `self.size_divisor`): The size divisibility to pad the image to if `do_pad` 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. N)expected_ndimszlInvalid trimap type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.zkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.)rrrrr cSg|]}t|qSr%r.0r3r%r%r- z5VitMatteImageProcessor.preprocess..cSrDr%rErGtrimapr%r%r-rHrIrzIt 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.cg|] }j|dqS)r3scaler5rescalerFr5rr)r%r-rHcrLrMrOrJrQr%r-rHrRcsg|] }j|dqS))r3meanstdr5) normalizerF)rr r5r)r%r-rHscs,g|]\}}tj|tj|dgdqS)axis)np concatenate expand_dims)rGr3rKrWr%r-rH scsg|] }j|dqS))r$r5)r;rF)r5r)r"r%r-rHrRcsg|] }t|dqS))r3 channel_diminput_channel_dim)rrF)r4r5r%r-rHs r)data tensor_type)rrr!rrr r"rr ValueErrorrrr0 warning_oncerr LASTzipr)r)r?r@rrrrr r!r"rAr4r5r^r%)rXr4rr r5rr)r"r- preprocesssp;      z!VitMatteImageProcessor.preprocess)TrTNNTr)rNN)__name__ __module__ __qualname____doc__model_input_namesboolrintfloatrlistr'propertyr$setterrYndarraystrr r;rrFIRSTr rrd __classcell__r%r%r+r-r+s      )      r)#rhtypingrrnumpyrYimage_processing_utilsrrimage_transformsrr image_utilsr r r r r rrrrrrutilsrrrutils.deprecationr get_loggerrer0r__all__r%r%r%r-s4   w