o ZhD@sdZddlmZmZmZmZddlZddlm Z m Z m Z ddl m Z mZmZddlmZmZmZmZmZmZmZmZmZmZmZmZddlmZmZm Z m!Z!dd l"m#Z#e r[ddl$Z$e!%e&Z'e#d d Gd d d e Z(d gZ)dS)z$Image processor class for Perceiver.)DictListOptionalUnionN)BaseImageProcessor BatchFeature get_size_dict) center_cropresizeto_channel_dimension_format) IMAGENET_DEFAULT_MEANIMAGENET_DEFAULT_STDChannelDimension ImageInputPILImageResamplingget_image_sizeinfer_channel_dimension_formatis_scaled_imagemake_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypefilter_out_non_signature_kwargsis_vision_availablelogging)requires)Zvision)backendsc!s eZdZdZdgZddddejdddddf dedee e e fded ee e e fd ed ed e e e fd edee e ee fdee e ee fddffdd Z   ddejde e e fd ee dee e efdee e efdejf ddZejddfdejd e e e fd edee e efdee e efdejf ddZedddddddddddejdf dedeedee e e fdeed ee e e fd ed eed ee d eedee e ee fdee e ee fdee e efdedee e efdejjfddZZS)PerceiverImageProcessora/ Constructs a Perceiver image processor. Args: do_center_crop (`bool`, `optional`, defaults to `True`): Whether or not to center crop the image. If the input size if smaller than `crop_size` along any edge, the image will be padded with zeros and then center cropped. Can be overridden by the `do_center_crop` parameter in the `preprocess` method. crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 256, "width": 256}`): Desired output size when applying center-cropping. Can be overridden by the `crop_size` parameter in the `preprocess` method. do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image to `(size["height"], size["width"])`. Can be overridden by the `do_resize` parameter in the `preprocess` method. size (`Dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`): Size of the image after resizing. Can be overridden by the `size` parameter in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Defines the resampling filter to use if resizing the image. Can be overridden by the `resample` 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. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Defines the scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the `preprocess` method. do_normalize: 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_center_crop crop_size do_resizesizeresample do_rescalerescale_factor do_normalize image_mean image_stdreturnc  stjdi| |dur|nddd}t|dd}|dur |nddd}t|}||_||_||_||_||_||_||_ ||_ | durG| nt |_ | durS| |_dSt |_dS)N)heightwidthr" param_name)super__init__r r!r"r#r$r%r&r'r(r r)rr*) selfr!r"r#r$r%r&r'r(r)r*kwargs __class__r2g/var/www/auris/lib/python3.10/site-packages/transformers/models/perceiver/image_processing_perceiver.pyr4Xs z PerceiverImageProcessor.__init__image data_formatinput_data_formatc Ks|dur|jn|}t|}t|dd}t||d\}}t||} |d|d| } |d|d| } t|f| | f||d|S)a Center crop an image to `(size["height"] / crop_size["height"] * min_dim, size["width"] / crop_size["width"] * min_dim)`. Where `min_dim = min(size["height"], size["width"])`. If the input size is smaller than `crop_size` along any edge, the image will be padded with zeros and then center cropped. Args: image (`np.ndarray`): Image to center crop. crop_size (`Dict[str, int]`): Desired output size after applying the center crop. size (`Dict[str, int]`, *optional*): Size of the image after resizing. If not provided, the self.size attribute will be used. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. input_data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. Nr"r/)Z channel_dimr-r.)r$r;r<)r$r rminr ) r5r:r"r$r;r<r6r-r.Zmin_dimZcropped_heightZ cropped_widthr2r2r9r ws   z#PerceiverImageProcessor.center_cropcKsTt|}d|vs d|vrtd||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. r-r.zFThe `size` dictionary must contain the keys `height` and `width`. Got )r$r%r;r<)r ValueErrorkeysr )r5r:r$r%r;r<r6Z output_sizer2r2r9r s#zPerceiverImageProcessor.resizeimagesreturn_tensorsc s|dur|nj}durnjtdd|dur|nj}dur'njtdur4nj|dur=|nj}durFnj| durO| nj} durXnj duranj t |}t |spt dt|| ||d dd|D}|rt|drtd durt|d|rfd d|D}|rfd d|D}|rŇfd d|D}| rӇfd d|D}fdd|D}d|i}t|| dS)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_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`): Whether to center crop the image to `crop_size`. crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`): Desired output size after applying the center crop. 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 resizing. resample (`int`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, 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. 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.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. Nr"r/zkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.) r&r'r(r)r*r!r"r#r$r%cSsg|]}t|qSr2)r.0r:r2r2r9 6sz6PerceiverImageProcessor.preprocess..rzIt looks like you are trying to rescale already rescaled images. If the input images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again.csg|] }j|dqS))r$r<)r rB)r"r<r5r$r2r9rDCscsg|] }j|dqS))r:r$r%r<)r rB)r<r%r5r$r2r9rDHcsg|] }j|dqS))r:scaler<)ZrescalerB)r<r'r5r2r9rDNscsg|] }j|dqS))r:meanZstdr<) normalizerB)r)r*r<r5r2r9rDTrEcsg|] }t|dqS))Zinput_channel_dim)r rB)r;r<r2r9rDYsr )dataZ tensor_type)r!r"r r#r$r%r&r'r(r)r*rrr>rrloggerZ warning_oncerr)r5r@r!r"r#r$r%r&r'r(r)r*rAr;r<rIr2) r"r;r)r*r<r%r'r5r$r9 preprocesssr@    z"PerceiverImageProcessor.preprocess)NNN)__name__ __module__ __qualname____doc__Zmodel_input_namesrZBICUBICboolrrstrintrfloatrr4npZndarrayrr r rZFIRSTrrPILZImagerK __classcell__r2r2r7r9r0s$     #  1  0     r)*rOtypingrrrrnumpyrTZimage_processing_utilsrrr Zimage_transformsr r r Z image_utilsr rrrrrrrrrrrutilsrrrrZutils.import_utilsrrUZ get_loggerrLrJr__all__r2r2r2r9s 8   2