qiu>(dZddlZddlmZmZmZddlmZm Z m Z m Z ddl m Z mZmZmZmZmZmZmZmZmZmZddlmZddlmZmZmZmZdd lm Z erddl!Z!ejDe#Z$Gd d ed Z%e dGddeZ&dgZ'y)z#Image processor class for ConvNeXT.N)BaseImageProcessor BatchFeature get_size_dict) center_cropget_resize_output_image_sizeresizeto_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_availablelogging)requiresceZdZUdZeed<y)ConvNextImageProcessorKwargsz crop_pct (`float`, *optional*): Percentage of the image to crop. Only has an effect if size < 384. Can be overridden by `crop_pct` in the`preprocess` method. crop_pctN)__name__ __module__ __qualname____doc__float__annotations__h/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/convnext/image_processing_convnext.pyrr2s Or&rF)total)vision)backendscbeZdZdZdgZeZdddejdddddf de de e e fdzde dzd ed e d e e zd e d e ee zdzde ee zdzddffd Zejddfdej"de e e fde d ede ezdzde ezdzdej"fdZeddddddddddej*df dede dzde e e fdzde dzd edzd e dzd e dzd e dzd e ee zdzde ee zdzde ezdzdede ezdzdej2j2fdZxZS)ConvNextImageProcessora: Constructs a ConvNeXT image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Controls whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by `do_resize` in the `preprocess` method. size (`dict[str, int]` *optional*, defaults to `{"shortest_edge": 384}`): Resolution of the output image after `resize` is applied. If `size["shortest_edge"]` >= 384, the image is resized to `(size["shortest_edge"], size["shortest_edge"])`. Otherwise, the smaller edge of the image will be matched to `int(size["shortest_edge"]/crop_pct)`, after which the image is cropped to `(size["shortest_edge"], size["shortest_edge"])`. Only has an effect if `do_resize` is set to `True`. Can be overridden by `size` in the `preprocess` method. crop_pct (`float` *optional*, defaults to 224 / 256): Percentage of the image to crop. Only has an effect if `do_resize` is `True` and size < 384. Can be overridden by `crop_pct` in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`): Resampling filter to use if resizing the image. Can be overridden by `resample` 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 `do_rescale` 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 `rescale_factor` 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. pixel_valuesTNgp? do_resizesizerresample do_rescalerescale_factor do_normalize image_mean image_stdreturnc  t |di| ||nddi}t|d}||_||_||nd|_||_||_||_||_ ||nt|_ | | |_ yt|_ y)N shortest_edgeFdefault_to_squareg?r%)super__init__rr.r/rr0r1r2r3r r4r r5) selfr.r/rr0r1r2r3r4r5kwargs __class__s r'r=zConvNextImageProcessor.__init__ds "6"'tos-CTU;" $,$8i   $,((2(>*DZ&/&;AVr&image data_formatinput_data_formatc t|d}d|vrtd|j|d}|dkr@t||z } t || d|} t d || |||d|}t d |||f||d|St |f||f|||d |S) a Resize an image. Args: image (`np.ndarray`): Image to resize. size (`dict[str, int]`): Dictionary of the form `{"shortest_edge": int}`, specifying the size of the output image. If `size["shortest_edge"]` >= 384 image is resized to `(size["shortest_edge"], size["shortest_edge"])`. Otherwise, the smaller edge of the image will be matched to `int(size["shortest_edge"] / crop_pct)`, after which the image is cropped to `(size["shortest_edge"], size["shortest_edge"])`. crop_pct (`float`): Percentage of the image to crop. Only has an effect if size < 384. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Resampling filter to use when resizing the image. 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 from the input image. Fr:r8z6Size dictionary must contain 'shortest_edge' key. Got r9)r/r;rC)rAr/r0rBrC)rAr/rBrC)r/r0rBrCr%)r ValueErrorkeysintrr r) r>rAr/rr0rBrCr?r8resize_shortest_edge resize_sizes r'r zConvNextImageProcessor.resizes>TU; $ &UVZV_V_VaUbcd d_- 3 #&}x'?#@ 60E]nK !'"3   E#]3'"3    #]3!'"3    r&imagesreturn_tensorsc ||n |j}||n |j}||n |j}||n |j}||n |j}||n |j }| | n |j } | | n |j} ||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]}|j+|| | | }}|Dcgc]}t-|| | }}d |i}t/|| Scc}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 output image after `resize` has been applied. If `size["shortest_edge"]` >= 384, the image is resized to `(size["shortest_edge"], size["shortest_edge"])`. Otherwise, the smaller edge of the image will be matched to `int(size["shortest_edge"]/ crop_pct)`, after which the image is cropped to `(size["shortest_edge"], size["shortest_edge"])`. Only has an effect if `do_resize` is set to `True`. crop_pct (`float`, *optional*, defaults to `self.crop_pct`): Percentage of the image to crop if size < 384. resample (`int`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of `PILImageResampling`, filters. 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. image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`): Image standard deviation. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - Unset: 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: - `"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. Fr:zSInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, or torch.Tensor)r1r2r3r4r5r.r/r0rzIt 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.)rAr/rr0rC)rAscalerC)rAmeanstdrC)input_channel_dimr-)data tensor_type)r.rr0r1r2r3r4r5r/rrrrErrrlogger warning_oncerr rescale normalizer r)r>rJr.r/rr0r1r2r3r4r5rKrBrCrArQs r' preprocessz!ConvNextImageProcessor.preprocesss:~"+!6IDNN '38'38#-#9Zt +9+E4K^K^'3'?|TEVEV #-#9Zt !*!6IDNN 'tTYYTU;)&1F#rs s%!)%! 6<vay I   $  dXduF $ 5Rc dF $U^opF ou ej '{N_ `  '>BBK=   sF7%F< G-GG )rr r!r"model_input_namesr valid_kwargsrBICUBICbooldictstrrGr#listr=npndarrayr r rFIRSTrrPILImagerW __classcell__)r@s@r'r,r,<s!F((/L&*!%'9'A'A&-!1504WW38nt#W$, W % W  We WWDK'$.W4;&-W WB(:'A'A59;?CzzC38nC C % C ++d2 C!11D8C CJ%&"&&*!%.2"&'+$(150426(8(>(>;?@C@C$;@C38nt# @C $, @C %t+ @C4K@C @CTk@CDK'$.@C4;&-@Cj(4/@C&@C!11D8@C @C'@Cr&r,)(r"numpyr_image_processing_utilsrrrimage_transformsrrr r image_utilsr r r rrrrrrrrprocessing_utilsrutilsrrrrutils.import_utilsrrb get_loggerrrSrr,__all__r%r&r'rns*UU    -^^*   H %