o Zh1@sdZddlmZmZmZmZmZmZddlm Z er ddl m Z ddl Z ddlZddlmZmZddlmZmZdd lmZmZmZmZmZmZmZmZmZm Z dd l!m"Z"m#Z#m$Z$m%Z%ercddl&Z&e$'e(Z)e d d Gd ddeZ*dgZ+dS)zImage processor class for GLPN.) TYPE_CHECKINGDictListOptionalTupleUnion)requires)DepthEstimatorOutputN)BaseImageProcessor BatchFeature)resizeto_channel_dimension_format) ChannelDimensionPILImageResamplingget_image_sizeinfer_channel_dimension_formatis_scaled_imageis_torch_availablemake_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypefilter_out_non_signature_kwargsloggingrequires_backends)Zvision)backendscs>eZdZdZdgZddejdfdedededd ffd d Z ejd d fd e j ded ede e de eee fde j f ddZed d d d d e jd fdedeedeefde ede ede ede eeefde de eee fdefddZ dddde eeeeeefd fdeeeeffddZZS)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 s.||_||_||_||_tjdi|dS)N)r!r#r"resamplesuper__init__)selfr!r"r&r#kwargs __class__r%]/var/www/auris/lib/python3.10/site-packages/transformers/models/glpn/image_processing_glpn.pyr(Is zGLPNImageProcessor.__init__imager& data_formatinput_data_formatc KsJt||d\}}|||} |||} 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. )Z channel_dim)r&r/r0)rr ) r)r.r"r&r/r0r*heightwidthZnew_hZnew_wr%r%r-r Ws$  zGLPNImageProcessor.resizeimageszPIL.Image.Imagereturn_tensorsc s|dur|nj}|dur|nj}durnjdur!njt|}t|s0tdt|ddd|D}|rKt|drKt ddurUt |d|rcfdd|D}|rofd d|D}fd d|D}d |i} t | |d 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&cSsg|]}t|qSr%)r).0Zimgr%r%r- sz1GLPNImageProcessor.preprocess..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))r"r&r0)r r6r.)r0r&r)r"r%r-r7scsg|] }j|ddqS)gp?)scaler0)Zrescaler8)r0r)r%r-r7scsg|] }t|dqS))Zinput_channel_dim)rr8)r/r0r%r-r7sr)dataZ tensor_type) r!r#r"r&rr ValueErrorrrloggerZ warning_oncerr ) r)r3r!r"r&r#r4r/r0r:r%)r/r0r&r)r"r- preprocesss@/   zGLPNImageProcessor.preprocessoutputsr target_sizescCst|d|j}|durt|t|krtdg}|dur%dgt|n|}t||D]"\}}|durG|d}tjjj||ddd}| }| d|iq,|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.ZbicubicF)r5modeZ align_cornerspredicted_depth) rrBlenr;zipr@nnZ functionalZ interpolateZsqueezeappend)r)r>r?rBresultsdepthZ target_sizer%r%r-post_process_depth_estimations z0GLPNImageProcessor.post_process_depth_estimation)N)__name__ __module__ __qualname____doc__Zmodel_input_namesrZBILINEARboolintr(npZndarrayrrrstrr rZFIRSTrrr r=rrrI __classcell__r%r%r+r-r4s 2  cr),rMtypingrrrrrrZutils.import_utilsr Zmodeling_outputsr numpyrPZ PIL.ImageZPILZimage_processing_utilsr r Zimage_transformsr rZ image_utilsrrrrrrrrrrutilsrrrrr@Z get_loggerrJr<r__all__r%r%r%r-s$   0   `