o Gi@@sdZddlZddlZddlmZmZmZddlm Z m Z m Z ddl m Z mZmZmZmZmZmZmZmZmZddlmZmZmZddlmZerMddlZee Z!Gdd d eZ"dS) zImage processor class for BLIP.N)BaseImageProcessor BatchFeature get_size_dict)convert_to_rgbresizeto_channel_dimension_format) OPENAI_CLIP_MEANOPENAI_CLIP_STDChannelDimension ImageInputPILImageResamplinginfer_channel_dimension_formatis_scaled_imagemake_list_of_imagesto_numpy_array valid_images) TensorTypeis_vision_availablelogging) numpy_to_pilcseZdZdZdgZddejdddddddf dedee e fded ed e e Bd ed e e e BdBd e e e BdBdededdffdd Z ejddfdejdee e fdede eBdBde eBdBdejf ddZdddddddddddejdf dededBdee e fdBded edBdedBd e dBd edBd e e e BdBd e e e BdBde eBdBdedede eBdBdejjfddZd!dejde fdd ZZS)"BlipImageProcessora Constructs a BLIP 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. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.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`): Wwhether 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. pixel_valuesTNgp? do_resizesizeresample do_rescalerescale_factor do_normalize image_mean image_stddo_convert_rgbdo_center_cropreturnc  stjdi| |dur|nddd}t|dd}||_||_||_||_||_||_|dur2|nt |_ |dur;|nt |_ | |_ | |_dS)N)heightwidthTdefault_to_square)super__init__rrrrrrrrrr rr r!) selfrrrrrrrrr r!kwargs __class__r(l/home/ubuntu/.local/lib/python3.10/site-packages/diffusers/pipelines/blip_diffusion/blip_image_processing.pyr*Ts  zBlipImageProcessor.__init__image data_formatinput_data_formatcKsTt|}d|vs d|vrtd||d|df}t|f||||d|S)a Resize an image to `(size["height"], size["width"])`. Args: image (`np.ndarray`): Image to resize. size (`dict[str, int]`): Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image. 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"` or `ChannelDimension.NONE`: image in (height, width) format. 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. r$r%zFThe `size` dictionary must contain the keys `height` and `width`. Got )rrr1r2)r ValueErrorkeysr)r+r0rrr1r2r, output_sizer(r(r/rrs#zBlipImageProcessor.resizeimagesreturn_tensorsc s|dur|nj}durnj|dur|nj}dur!nj|dur*|nj}dur3njdur<nj| durE| nj} |durN|nj}durWnj t ddt |}t |slt d|rrdusvdurzt d|rdurt d|rdusdurt d| rdd |D}d d |D}t|d r|rtd durt|d |rLJfd d |D}|rԇfdd |D}|rfdd |D}|rfdd |D}fdd |D}td|i| d}|S)am 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. NFr&zkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.z9Size and resample must be specified if do_resize is True.z7Rescale factor must be specified if do_rescale is True.z=Image mean and std must be specified if do_normalize is True.cSg|]}t|qSr()r.0r0r(r(r/ z1BlipImageProcessor.preprocess..cSr8r()rr9r(r(r/r;r<rzIt 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.csg|] }j|dqS))r0rrr2)rr9)r2rr+rr(r/r;csg|] }j|dqS))r0scaler2)rescaler9)r2rr+r(r/r;scsg|] }j|dqS))r0meanstdr2) normalizer9)rrr2r+r(r/r;r=csg|] }j|dqS))r2) center_cropr9)r2r+rr(r/r;!scsg|] }t|dqS))input_channel_dim)rr9)r1r2r(r/r;#sr)data tensor_type)rrrrrrrr r!rrrrr3rlogger warning_oncer r)r+r6rrrrr!rrrrr7r r1r2r,encoded_outputsr()r1rrr2rrr+rr/ preprocesssdA    zBlipImageProcessor.preprocesspilsample output_typecCsh|dvr td|d|dddd}|dkr|S|ddd d}|d kr.|St|}|S) N)ptnprKz output_type=zD is not supported. Make sure to choose one of ['pt', 'np', or 'pil']g?rrNrO)r3clampcpupermutenumpyr)r+rLrMr(r(r/ postprocess+s zBlipImageProcessor.postprocess)rK)__name__ __module__ __qualname____doc__model_input_namesr BICUBICbooldictstrintfloatlistr*rOndarrayr rFIRSTr rPILImagerJtorchTensorrW __classcell__r(r(r-r/r/s"     "    3        r)#r[rVrOrh#transformers.image_processing_utilsrrrtransformers.image_transformsrrrtransformers.image_utilsrr r r r r rrrrtransformers.utilsrrrdiffusers.utilsr PIL.Imagerf get_loggerrXrGrr(r(r(r/s0