qi&HdZddlZddlmZmZmZddlmZm Z m Z ddl m Z m Z mZmZmZmZmZmZmZmZmZddlmZddlmZmZmZmZerddlZej>e Z!Gd d ed Z"Gd deZ#dgZ$y)z'Image processor class for EfficientNet.N)BaseImageProcessor BatchFeature get_size_dict)rescaleresizeto_channel_dimension_format) IMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDChannelDimension ImageInputPILImageResamplinginfer_channel_dimension_formatis_scaled_imagemake_flat_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) ImagesKwargs) TensorTypefilter_out_non_signature_kwargsis_vision_availableloggingc&eZdZUdZeed<eed<y) EfficientNetImageProcessorKwargsak rescale_offset (`bool`, *optional*, defaults to `self.rescale_offset`): Whether to rescale the image between [-max_range/2, scale_range/2] instead of [0, scale_range]. include_top (`bool`, *optional*, defaults to `self.include_top`): Normalize the image again with the standard deviation only for image classification if set to True. rescale_offset include_topN)__name__ __module__ __qualname____doc__bool__annotations__p/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/efficientnet/image_processing_efficientnet.pyrr,sr%rF)totalc"eZdZdZdgZeZddejdddddddddf de de e e fdzd ed e d e e e fdzd e e zd e de de de ee zdzde ee zdzde ddffd Zejddfdej"de e e fd ede ezdzde ezdzdej"f dZ ddej"de e zde de ezdzde ezdzf dZedddddddddddddej,dfdede dzde e e fdzd e dzd e e e fdzde dzd e dzd e dzde dzde ee zdzde ee zdzde dzde ezdzdede ezdzdej4j4f dZxZS) EfficientNetImageProcessoraa Constructs a EfficientNet 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 `do_resize` in `preprocess`. size (`dict[str, int]` *optional*, defaults to `{"height": 346, "width": 346}`): Size of the image after `resize`. Can be overridden by `size` in `preprocess`. resample (`PILImageResampling` filter, *optional*, defaults to `Resampling.BICUBIC`): Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`. do_center_crop (`bool`, *optional*, defaults to `False`): Whether to center crop the image. If the input size is smaller than `crop_size` along any edge, the image is padded with 0's and then center cropped. Can be overridden by `do_center_crop` in `preprocess`. crop_size (`dict[str, int]`, *optional*, defaults to `{"height": 289, "width": 289}`): Desired output size when applying center-cropping. Can be overridden by `crop_size` in `preprocess`. 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. rescale_offset (`bool`, *optional*, defaults to `False`): Whether to rescale the image between [-scale_range, scale_range] instead of [0, scale_range]. Can be overridden by the `rescale_factor` 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. 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. include_top (`bool`, *optional*, defaults to `True`): Whether to rescale the image again. Should be set to True if the inputs are used for image classification. pixel_valuesTNFgp? do_resizesizeresampledo_center_crop crop_sizerescale_factorr do_rescale do_normalize image_mean image_stdrreturnc  @t|di| ||nddd}t|}||nddd}t|d}||_||_||_||_||_||_||_ ||_ | |_ | | nt|_ | | nt|_| |_y)NiZ)heightwidthi!r/ param_namer$)super__init__rr+r,r-r.r/r1r0rr2r r3r r4r)selfr+r,r-r.r/r0rr1r2r3r4rkwargs __class__s r&r<z#EfficientNetImageProcessor.__init__bs "6"'tc-JT"!*!6IsUX*DZ&/&;AV&r%image data_formatinput_data_formatc t|}d|vsd|vrtd|j|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. r7r8zFThe `size` dictionary must contain the keys `height` and `width`. Got )r,r-rArB)r ValueErrorkeysr)r=r@r,r-rArBr> output_sizes r&rz!EfficientNetImageProcessor.resizesyFT" 4 7$#6efjfofofqerst tH~tG}5   #/     r%scaleoffsetc 4t|f|||d|}|r|dz }|S)a Rescale an image by a scale factor. If `offset` is `True`, the image has its values rescaled by `scale` and then offset by 1. If `scale` is 1/127.5, the image is rescaled between [-1, 1]. image = image * scale - 1 If `offset` is `False`, and `scale` is 1/255, the image is rescaled between [0, 1]. image = image * scale Args: image (`np.ndarray`): Image to rescale. scale (`int` or `float`): Scale to apply to the image. offset (`bool`, *optional*): Whether to scale the image in both negative and positive directions. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. )rGrArB)r)r=r@rGrHrArBr>rescaled_images r&rz"EfficientNetImageProcessor.rescales;>!  KK\ `f  +a/Nr%imagesreturn_tensorsc ||n |j}||n |j}||n |j}||n |j}||n |j}| | n |j } | | n |j } | | n |j} | | n |j} | | n |j} ||n |j}t|}||n |j}t|d}t|}t|s tdt!||| | | ||||| |Dcgc] }t#|}}|r#t%|drt&j)d|t+|d}|r"|Dcgc]}|j-||||}}|r!|Dcgc]}|j/|||}}|r"|Dcgc]}|j1||| | }}| r"|Dcgc]}|j3|| | | }}| r"|Dcgc]}|j3|d| | }}|Dcgc]}t5||| }}d |i}t7|| Scc}wcc}wcc}wcc}wcc}wcc}wcc}w)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`. 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 `resize`. resample (`PILImageResampling`, *optional*, defaults to `self.resample`): PILImageResampling filter to use if resizing the image Only has an effect if `do_resize` is set to `True`. do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`): Whether to center crop the image. crop_size (`dict[str, int]`, *optional*, defaults to `self.crop_size`): Size of the image after center crop. If one edge the image is smaller than `crop_size`, it will be padded with zeros and then cropped 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`. rescale_offset (`bool`, *optional*, defaults to `self.rescale_offset`): Whether to rescale the image between [-scale_range, scale_range] instead of [0, scale_range]. 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. image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`): Image standard deviation. include_top (`bool`, *optional*, defaults to `self.include_top`): Rescales the image again for image classification if set to True. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - `None`: Return a list of `np.ndarray`. - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output image. Can be one of: - `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `ChannelDimension.LAST`: image in (height, width, num_channels) 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. r/r9zSInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, or torch.Tensor) r1r0r2r3r4r.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.)r@r,r-rB)r@r,rB)r@rGrHrB)r@meanstdrB)input_channel_dimr*)data tensor_type)r+r-r.r1r0rr2r3r4rr,rr/rrrDrrrlogger warning_oncerr center_cropr normalizer r)r=rLr+r,r-r.r/r1r0rr2r3r4rrMrArBr@rRs r& preprocessz%EfficientNetImageProcessor.preprocesssJ"+!6IDNN '38+9+E4K^K^#-#9Zt +9+E4K^K^+9+E4K^K^'3'?|TEVEV #-#9Zt !*!6IDNN %0%vay I  $ %dXYj kF pvgl  u9Pa bF  $  ~n`qF $U^opF $U UfgF ou ej '{N_ `  '>BBa=     s*H;"II)I  I1II)TNN)rrr r!model_input_namesr valid_kwargsrBICUBICr"dictstrintfloatlistr<npndarrayr rrrFIRSTr rPILImagerX __classcell__)r?s@r&r)r)8sX$L((3L&*'9'A'A$+/&-$!1504 !'!'38nt#!'% !'  !' S>D( !'e !'!'!'!'DK'$.!'4;&-!'!' !'N(:'A'A59;? . zz. 38n. % . ++d2 . !11D8 .  . h59;? &zz&U{& & ++d2 & !11D8 &P%&"&&*&*+/"&'+&*$(1504#'26(8(>(>;?#UCUC$;UC38nt# UC t UCS>D(UC4KUC UCt UCTkUCDK'$.UC4;&-UCD[UCj(4/UC &!UC"!11D8#UC$ %UC'UCr%r))%r!numpyraimage_processing_utilsrrrimage_transformsrrr image_utilsr r r r rrrrrrrprocessing_utilsrutilsrrrrrd get_loggerrrTrr)__all__r$r%r&ros.UULL    -^^   H % |5 {C!3{C| ( (r%