o Zh+5 @s(dZddlmZmZmZddlZddlmZm Z m Z ddl m Z m Z mZmZddlmZmZmZmZmZmZmZmZddlmZmZmZmZmZmZdd l m!Z!erWddl"Z"er^ddl#Z#e$e%Z&d d Z'  dd ej(d ee)dee)deee)effddZ*e!ddGdddeZ+dgZ,dS)z%Image processor class for LayoutLMv2.)DictOptionalUnionN)BaseImageProcessor BatchFeature get_size_dict)flip_channel_orderresizeto_channel_dimension_format to_pil_image)ChannelDimension ImageInputPILImageResamplinginfer_channel_dimension_formatmake_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypefilter_out_non_signature_kwargsis_pytesseract_availableis_vision_availableloggingrequires_backends)requirescCsLtd|d|td|d|td|d|td|d|gS)Nirr)int)boxwidthheightr"i/var/www/auris/lib/python3.10/site-packages/transformers/models/layoutlmv2/image_processing_layoutlmv2.py normalize_box6s r$imagelangtesseract_configinput_data_formatcsb|dur|nd}t||d}|j\}}tj||d|d}|d|d|d|d |d f\}} } } } d d t|Dfd d t|D}fdd t| D} fdd t| D} fdd t| D} fdd t| D} g} t| | | | D]\}}}}||||||g}| |q{g}| D] }|t|||qt|t|ksJd||fS)zdApplies Tesseract OCR on a document image, and returns recognized words + normalized bounding boxes.Nr(dict)r& output_typeconfigtextlefttopr r!cSsg|] \}}|s|qSr")strip.0idxwordr"r"r# Oz#apply_tesseract..cg|] \}}|vr|qSr"r"r2Zirrelevant_indicesr"r#r6Pr7cr8r"r"r3r4Zcoordr9r"r#r6Qr7cr8r"r"r:r9r"r#r6Rr7cr8r"r"r:r9r"r#r6Sr7cr8r"r"r:r9r"r#r6Tr7z-Not as many words as there are bounding boxes) r size pytesseractZ image_to_data enumeratezipappendr$len)r%r&r'r(Z pil_imageZ image_widthZ image_heightdatawordsr/r0r r!Z actual_boxesxywhZ actual_boxZnormalized_boxesrr"r9r#apply_tesseract?s(  , rG)Zvision)backendscsBeZdZdZdgZddejdddfdedee e e fded ed ee d ee d dffd d Z ejddfde jde e e fdedeee efdeee efd e jf ddZedddddddejdf dedeedee e e fded eed ee d ee deee efdedeee efd ejjfddZZS)LayoutLMv2ImageProcessora Constructs a LayoutLMv2 image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions to `(size["height"], size["width"])`. Can be overridden by `do_resize` in `preprocess`. size (`Dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`): Size of the image after resizing. Can be overridden by `size` in `preprocess`. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the `preprocess` method. apply_ocr (`bool`, *optional*, defaults to `True`): Whether to apply the Tesseract OCR engine to get words + normalized bounding boxes. Can be overridden by `apply_ocr` in `preprocess`. ocr_lang (`str`, *optional*): The language, specified by its ISO code, to be used by the Tesseract OCR engine. By default, English is used. Can be overridden by `ocr_lang` in `preprocess`. tesseract_config (`str`, *optional*, defaults to `""`): Any additional custom configuration flags that are forwarded to the `config` parameter when calling Tesseract. For example: '--psm 6'. Can be overridden by `tesseract_config` in `preprocess`. pixel_valuesTNr) do_resizer;resample apply_ocrocr_langr'returnc sXtjdi||dur|nddd}t|}||_||_||_||_||_||_dS)N)r!r r") super__init__rrKr;rLrMrNr')selfrKr;rLrMrNr'kwargs __class__r"r#rRs  z!LayoutLMv2ImageProcessor.__init__r% data_formatr(cKsTt|}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.BILINEAR`): `PILImageResampling` 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 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;rLrWr()r ValueErrorkeysr )rSr%r;rLrWr(rTZ output_sizer"r"r#r s#zLayoutLMv2ImageProcessor.resizeimagesreturn_tensorsc sn|dur|nj}durnjtdurnj|dur%|nj}|dur.|nj}|dur7|nj}t|}t|sFt dt |ddd|D}dur^t |d|rt dg} g} |D]} t | ||d\}}| || |qk|rfd d|D}fd d|D}fd d|D}td |i|d }|r| |d<| |d<|S)a Preprocess an image or batch of images. Args: images (`ImageInput`): Image to preprocess. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`Dict[str, int]`, *optional*, defaults to `self.size`): Desired size of the output image after resizing. resample (`PILImageResampling`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of the enum `PIL.Image` resampling filter. Only has an effect if `do_resize` is set to `True`. apply_ocr (`bool`, *optional*, defaults to `self.apply_ocr`): Whether to apply the Tesseract OCR engine to get words + normalized bounding boxes. ocr_lang (`str`, *optional*, defaults to `self.ocr_lang`): The language, specified by its ISO code, to be used by the Tesseract OCR engine. By default, English is used. tesseract_config (`str`, *optional*, defaults to `self.tesseract_config`): Any additional custom configuration flags that are forwarded to the `config` parameter when calling Tesseract. 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. NzkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.)rKr;rLcSsg|]}t|qSr")rr3r%r"r"r#r6 sz7LayoutLMv2ImageProcessor.preprocess..rr<r*csg|] }j|dqS))r%r;rLr()r r\)r(rLrSr;r"r#r6scsg|]}t|dqS)r*)r r\r*r"r#r6"scsg|] }t|dqS))Zinput_channel_dim)r r\)rWr(r"r#r6#srJ)rAZ tensor_typerBboxes)rKr;rrLrMrNr'rrrXrrrrGr?r)rSrZrKr;rLrMrNr'r[rWr(Z words_batchZ boxes_batchr%rBr]rAr")rWr(rLrSr;r# preprocesssR/     z#LayoutLMv2ImageProcessor.preprocess)__name__ __module__ __qualname____doc__Zmodel_input_namesrZBILINEARboolrrstrrrRnpndarrayrr r rZFIRSTrrPILZImager^ __classcell__r"r"rUr#rIfs   0    rI)NN)-rbtypingrrrnumpyreZimage_processing_utilsrrrZimage_transformsr r r r Z image_utilsr rrrrrrrutilsrrrrrrZutils.import_utilsrrgr<Z get_loggerr_loggerr$rfrdrGrI__all__r"r"r"r#s:(    ' I