qi3@dZddlmZmZddlmZerddlmZddlZ ddl Z ddl m Z mZddlmZmZdd lmZmZmZmZmZmZmZmZmZmZdd lmZdd lm Z m!Z!m"Z"m#Z#erddl$Z$e"jJe&Z'Gd d edZ(edGdde Z)dgZ*y)zImage processor class for GLPN.) TYPE_CHECKINGUnion)requires)DepthEstimatorOutputN)BaseImageProcessor BatchFeature)resizeto_channel_dimension_format) ChannelDimensionPILImageResamplingget_image_sizeinfer_channel_dimension_formatis_scaled_imageis_torch_availablemake_flat_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) ImagesKwargs) TensorTypefilter_out_non_signature_kwargsloggingrequires_backendsc&eZdZUdZeed<eed<y)GLPNImageProcessorKwargsz size_divisor (`int`, *optional*, defaults to 32): When `do_resize` is `True`, images are resized so their height and width are rounded down to the closest multiple of `size_divisor`. size_divisorresampleN)__name__ __module__ __qualname____doc__int__annotations__r `/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/glpn/image_processing_glpn.pyrr4s   r&rF)total)vision)backendsceZdZdZdgZeZddejddfde de de d e d zd d f fd Z ejd d fd e jde deded zdeezd zd e jf dZed d d d d d ej&d fdedeedeefde d zde d zde d zd e d zdeezd zdedeezd zd efdZ ddddeeee e fzd zd zd eeeeffdZxZS)GLPNImageProcessora Constructs a GLPN image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions, rounding them down to the closest multiple of `size_divisor`. Can be overridden by `do_resize` in `preprocess`. size_divisor (`int`, *optional*, defaults to 32): When `do_resize` is `True`, images are resized so their height and width are rounded down to the closest multiple of `size_divisor`. Can be overridden by `size_divisor` in `preprocess`. resample (`PIL.Image` resampling filter, *optional*, defaults to `Resampling.BILINEAR`): Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`. do_rescale (`bool`, *optional*, defaults to `True`): Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.). Can be overridden by `do_rescale` in `preprocess`. rescale_factor (`float`, *optional*, defaults to `1 / 255`): The scaling factor to apply to the pixel values. Can be overridden by `rescale_factor` in `preprocess`. pixel_valuesT gp? do_resizer do_rescalerescale_factorNreturnc j||_||_||_||_||_t |di|y)Nr%)r/r0rrr1super__init__)selfr/rrr0r1kwargs __class__s r'r5zGLPNImageProcessor.__init__Ws:#$(  , "6"r&imager data_formatinput_data_formatc lt||\}}||z|z} ||z|z} t|| | ff|||d|}|S)a* Resize the image, rounding the (height, width) dimensions down to the closest multiple of size_divisor. If the image is of dimension (3, 260, 170) and size_divisor is 32, the image will be resized to (3, 256, 160). Args: image (`np.ndarray`): The image to resize. size_divisor (`int`): The image is resized so its height and width are rounded down to the closest multiple of `size_divisor`. resample: `PIL.Image` resampling filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`. data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the output image. If `None`, the channel dimension format of the input image is used. 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 of the input image. If not set, 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. Returns: `np.ndarray`: The resized image. ) channel_dim)rr:r;)rr ) r6r9rrr:r;r7heightwidthnew_hnew_ws r'r zGLPNImageProcessor.resizegsiH'u:KL ,&5% 4  EN #/     r&imageszPIL.Image.Imagereturn_tensorsc ||n |j}||n |j}||n |j}||n |j}||n |j}t |}t |s tdt||||D cgc] } t| }} |r#t|drtjd| t|d} |r"|D cgc]} |j| ||| }} |r!|D cgc]} |j| || }} |D cgc]} t!| || }} d|i} t#| | Scc} wcc} wcc} wcc} w) a Preprocess the given images. Args: images (`PIL.Image.Image` or `TensorType` or `list[np.ndarray]` or `list[TensorType]`): Images 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_normalize=False`. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the input such that the (height, width) dimensions are a multiple of `size_divisor`. size_divisor (`int`, *optional*, defaults to `self.size_divisor`): When `do_resize` is `True`, images are resized so their height and width are rounded down to the closest multiple of `size_divisor`. resample (`PIL.Image` resampling filter, *optional*, defaults to `self.resample`): `PIL.Image` resampling filter to use if resizing the image e.g. `PILImageResampling.BILINEAR`. Only has an effect if `do_resize` is set to `True`. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.). 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. zSInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, or torch.Tensor)r/sizerrzIt 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.)rrr;)scaler;)input_channel_dimr-)data tensor_type)r/r0r1rrrr ValueErrorrrrlogger warning_oncerr rescaler r ) r6rBr/rrr0r1rCr:r;imgr9rHs r' preprocesszGLPNImageProcessor.preprocesss\"+!6IDNN #-#9Zt +9+E4K^K^'3'?|TEVEV '38)&1F#rs s & 288#.%88 /&)4   s   $ >vay I  $ E xct uF lrch U.L] ^F ou ej '{N_ `  '>BB99  sEE7E Eoutputsr target_sizesc~t|d|j}|"t|t|k7r tdg}|dgt|zn|}t ||D]\\}}|B|d}t j jj||dd}|j}|jd|i^|S) a Converts the raw output of [`DepthEstimatorOutput`] into final depth predictions and depth PIL images. Only supports PyTorch. Args: outputs ([`DepthEstimatorOutput`]): Raw outputs of the model. target_sizes (`TensorType` or `list[tuple[int, int]]`, *optional*): Tensor of shape `(batch_size, 2)` or list of tuples (`tuple[int, int]`) containing the target size (height, width) of each image in the batch. If left to None, predictions will not be resized. Returns: `list[dict[str, TensorType]]`: A list of dictionaries of tensors representing the processed depth predictions. torchNz]Make sure that you pass in as many target sizes as the batch dimension of the predicted depth)NN.bicubicF)rEmode align_cornerspredicted_depth) rrWlenrJziprSnn functional interpolatesqueezeappend)r6rPrQrWresultsdepth target_sizes r'post_process_depth_estimationz0GLPNImageProcessor.post_process_depth_estimations( $(!11  $3+?3|CT+To 8D8LvO 44R^ "%o|"D 7 E;&o.++77KV_ot7u  NN-u5 6  7r&)N)rr r!r"model_input_namesr valid_kwargsr BILINEARboolr#floatr5npndarrayr strr rFIRSTrrlistr rOtupledictrb __classcell__)r8s@r'r,r,?s &((+L#,,'. ###  #  # #((:'B'B/3;? 0zz00% 0 &, 0 !11D8 0 0d%&"&#'"&'+26(8(>(>;?]C'T:K5LdS]N^^_]C$;]CDj ]C 4K ]C ]C#S(4/]C&]C!11D8]C ]C']CDJN'''!4c3h#884?$F' d3 ?# $ 'r&r,)+r"typingrrutils.import_utilsrmodeling_outputsrnumpyrh PIL.ImagePILimage_processing_utilsrr image_transformsr r image_utilsr r rrrrrrrrprocessing_utilsrutilsrrrrrS get_loggerrrKrr,__all__r%r&r'r}s&'*8FC   -\\   H %!|5! ;`+` `F r&