`i14dZddlmZmZmZddlmZerddlmZddl Z ddl Z ddl mZmZddlmZmZdd lmZmZmZmZmZmZmZmZmZmZdd lmZm Z m!Z!m"Z"erddl#Z#e!j$e%Z&ed Gd deZ'dgZ(dS)zImage processor class for GLPN.) TYPE_CHECKINGOptionalUnion)requires)DepthEstimatorOutputN)BaseImageProcessor BatchFeature)resizeto_channel_dimension_format) ChannelDimensionPILImageResamplingget_image_sizeinfer_channel_dimension_formatis_scaled_imageis_torch_availablemake_flat_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypefilter_out_non_signature_kwargsloggingrequires_backends)vision)backendsceZdZdZdgZddejdfdedededd ffd Z ejd d fd e j ded ed e e de eee fde j f dZed d d d d e jd fdedeedeefde ede ede ede eeefd e de eee fdefdZ dddde eeeeeefd fdeeeeffdZxZS)GLPNImageProcessora Constructs a GLPN image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions, rounding them down to the closest multiple of `size_divisor`. Can be overridden by `do_resize` in `preprocess`. size_divisor (`int`, *optional*, defaults to 32): When `do_resize` is `True`, images are resized so their height and width are rounded down to the closest multiple of `size_divisor`. Can be overridden by `size_divisor` in `preprocess`. resample (`PIL.Image` resampling filter, *optional*, defaults to `Resampling.BILINEAR`): Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`. do_rescale (`bool`, *optional*, defaults to `True`): Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.). Can be overridden by `do_rescale` in `preprocess`. pixel_valuesT do_resize size_divisor do_rescalereturnNc r||_||_||_||_t jdi|dS)N)r!r#r"resamplesuper__init__)selfr!r"r'r#kwargs __class__s /home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/transformers/models/glpn/image_processing_glpn.pyr)zGLPNImageProcessor.__init__IsE#$(  ""6"""""imager' data_formatinput_data_formatc vt||\}}||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)r'r0r1)rr ) r*r/r"r'r0r1r+heightwidthnew_hnew_ws r-r zGLPNImageProcessor.resizeWsyH'u:KLLL ,&5% 4  EN #/       r.imageszPIL.Image.Imagereturn_tensorsc ||nj}||nj}njnjt |}t |st dt|d|D}|r/t|drt dt|d|rfd|D}|rfd|D}fd |D}d |i} t| | S) 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.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: - `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. NzkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.)r!sizer'c,g|]}t|Sr&)r).0imgs r- z1GLPNImageProcessor.preprocess..s 888#.%%888r.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.cBg|]}|S))r"r'r1)r )r=r/r1r'r*r"s r-r?z1GLPNImageProcessor.preprocess..s> E xct uur.c@g|]}|dS)gp?)scaler1)rescale)r=r/r1r*s r-r?z1GLPNImageProcessor.preprocess..s-rrrbgdll5K\l]]rrrr.c4g|]}t|S))input_channel_dim)r )r=r/r0r1s r-r?z1GLPNImageProcessor.preprocess..s7   ej '{N_ ` ` `   r.r)data tensor_type) r!r#r"r'rr ValueErrorrrlogger warning_oncerr ) r*r8r!r"r'r#r9r0r1rFs ` `` `` r- preprocesszGLPNImageProcessor.preprocesss^"+!6IIDN #-#9ZZt '3'?||TEV '388)&11F## :  &    98888  /&)44    s     $ >vay I I   #F  srrrrrkqrrrF     nt   '>BBBBr.outputsr target_sizesct|d|j}|/t|t|krtdg}|dgt|zn|}t ||D]b\}}|D|d}t jj||dd}| }| d|ic|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)r;mode align_cornerspredicted_depth) rrSlenrHziprOnn functional interpolatesqueezeappend)r*rLrMrSresultsdepth target_sizes r-post_process_depth_estimationz0GLPNImageProcessor.post_process_depth_estimations( $(((!1  $3+?+?3|CTCT+T+To 8D8LvO 4 444R^ "%o|"D"D 7 7 E;&o.+77KV_ot7uu  NN-u5 6 6 6 6r.)N)__name__ __module__ __qualname____doc__model_input_namesrBILINEARboolintr)npndarrayrr rstrr rFIRSTrlistr rKtupledictr^ __classcell__)r,s@r-rr4si"((#, # # # #  #  # # # # # #$(:'B26DH 00z00% 0 ./ 0 $E#/?*?$@A 0 0000d%$&&%)&*%);?(8(>DH^C^C'T:K5LdS]N^^_^CD>^Csm ^C TN ^C!z3!78^C&^C$E#/?*?$@A^C ^C^C^C'&^CFRV''''uZeCHo1F%LMN' d3 ?# $ ''''''''r.r))rbtypingrrrutils.import_utilsrmodeling_outputsrnumpyrg PIL.ImagePILimage_processing_utilsr r image_transformsr r image_utilsr rrrrrrrrrutilsrrrrrO get_loggerr_rIr__all__r&r.r-r{s&%1111111111******9888888FFFFFFFFCCCCCCCC                        ]\\\\\\\\\\\LLL  H % % ;\\\\\+\\ \~ r.