o 0j@sZddlZddlZddlmZmZmZmZmZmZddl Z ddl m Z ddl mZmZmZddlmZddlmZdd lmZmZdd lmZdd lmZd d lmZd dlmZd dl m!Z!d dl"m#Z#d dl$m%Z%d dl&m'Z'ddl(m)Z)m*Z*ddl+m,Z-ddl.m,Z,ddl m/Z/edrddl0m1Z1ej2GdddeZ3edGdddeZ4dS)N)AnyDictListOptionalTupleUnion)logging)function_requires_depsis_dep_availablepipeline_requires_extra)ImageBatchSampler) ReadImage) HPIConfigPaddlePredictorOption) DetResult) benchmark)(AutoParallelImageSimpleInferencePipeline) BasePipeline) CropByBoxes)DocPreprocessorResult)get_sub_regions_ocr_res) OCRResult)SingleTableRecognitionResultTableRecognitionResult)get_table_recognition_res)get_neighbor_boxes_idx scikit-learn)KMeansc)sJeZdZdZddddddddedeedeedeeeefd eed e d ee eeefe fd dffd dZ dee dee dee dee d e f ddZdededed e fddZdejde d eeejffddZddZ dHd!d"Zd#d$Zd%d&Z 'dId(d)Zd*d+Zd,d-Zd.d/Zd0d1Zd2d3Z     4 4dJdejded5e!d6e d7e d8e d9e d:e d;e d e"fde ee$eeje$ejfdee dee dee dee deedeed?ee%d@eedAee&dBee&dCee&dDee&d6e d7e d8e d9e dEe d:e d e'f(dFdGZ(Z)S)L_TableRecognitionPipelineV2zTable Recognition PipelineNFdeviceengine engine_config pp_optionuse_hpip hpi_configconfigr$r%r&r'r(r)returnc stjd ||||||d||dd|_|jr,|didddi} || |_|dd|_|jrH|d id d d i} || |_|d id d di} || |_ |d idd di} || |_ |d idd di} || |_ |d idd di}|||_ |d idd di}|||_ |dd|_d|_|jr|didddi}|||_n |didd|_d|_|d idd|_t|_tdd|_tdd|_dS)!aInitializes the table recognition pipeline v2. Args: config (Dict): Configuration dictionary containing various settings. device (Optional[str], optional): The device to use for prediction. Defaults to `None`. engine (Optional[str], optional): Inference engine. Defaults to `None`. engine_config (Optional[Dict[str, Any]], optional): Engine-specific config. Defaults to `None`. pp_option (Optional[PaddlePredictorOption], optional): Paddle predictor options. Defaults to `None`. use_hpip (bool, optional): Whether to use HPIP. Defaults to `False`. hpi_config (Optional[Union[Dict[str, Any], HPIConfig]], optional): HPIP configuration. Defaults to `None`. r#use_doc_preprocessorTZ SubPipelinesZDocPreprocessorZpipeline_config_errorz+config error for doc_preprocessor_pipeline!use_layout_detectionZ SubModulesZLayoutDetectionZmodel_config_errorz"config error for layout_det_model!ZTableClassificationz,config error for table_classification_model!ZWiredTableStructureRecognitionz-config error for wired_table_structure_model!Z!WirelessTableStructureRecognitionz0config error for wireless_table_structure_model!ZWiredTableCellsDetectionz3config error for wired_table_cells_detection_model!ZWirelessTableCellsDetectionz6config error for wireless_table_cells_detection_model! use_ocr_modelNZ GeneralOCRz&config error for general_ocr_pipeline!ZTableOrientationClassifyr)Z batch_sizeZBGR)format)super__init__getr,create_pipelinedoc_preprocessor_pipeliner- create_modellayout_det_modeltable_cls_modelwired_table_rec_modelwireless_table_rec_model!wired_table_cells_detection_model$wireless_table_cells_detection_modelr.general_ocr_pipelinegeneral_ocr_config_bak table_orientation_classify_model!table_orientation_classify_configr_crop_by_boxesr batch_samplerr img_reader)selfr*r$r%r&r'r(r)kwargsZdoc_preprocessor_configZlayout_det_configZtable_cls_configZwired_table_rec_configZwireless_table_rec_configZwired_table_cells_det_configZwireless_table_cells_det_configZgeneral_ocr_config __class__r0z/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddlex/inference/pipelines/table_recognition/pipeline_v2.pyr25s                z$_TableRecognitionPipelineV2.__init__use_doc_orientation_classifyuse_doc_unwarpingr-r.cCs\|dur |dur |j}n |dus|durd}nd}|dur |j}|dur'|j}t|||dS)a Get the model settings based on the provided parameters or default values. Args: use_doc_orientation_classify (Optional[bool]): Whether to use document orientation classification. use_doc_unwarping (Optional[bool]): Whether to use document unwarping. use_layout_detection (Optional[bool]): Whether to use layout detection. use_ocr_model (Optional[bool]): Whether to use OCR model. Returns: dict: A dictionary containing the model settings. NTF)r,r-r.)r,r-r.dict)rDrIrJr-r.r,r0r0rHget_model_settingssz._TableRecognitionPipelineV2.get_model_settingsmodel_settingsoverall_ocr_reslayout_det_rescCs|dr|jstddS|dr'|durtddS|js'tddS|drB|dur6td dS|js@td dSd S|durMtd dSd S) a0 Check if the input parameters are valid based on the initialized models. Args: model_settings (Dict): A dictionary containing input parameters. overall_ocr_res (OCRResult): Overall OCR result obtained after running the OCR pipeline. The overall OCR result with convert_points_to_boxes information. layout_det_res (DetResult): The layout detection result. Returns: bool: True if all required models are initialized according to input parameters, False otherwise. r,zRSet use_doc_preprocessor, but the models for doc preprocessor are not initialized.Fr-Nz^The layout detection model has already been initialized, please set use_layout_detection=FalsezRSet use_layout_detection, but the models for layout detection are not initialized.r.zLThe OCR models have already been initialized, please set use_ocr_model=Falsez>Set use_ocr_model, but the models for OCR are not initialized.z7Set use_ocr_model=False, but no OCR results were found.T)r,r errorr-r.)rDrMrNrOr0r0rHcheck_model_settings_valids@ z6_TableRecognitionPipelineV2.check_model_settings_valid image_array input_paramscCsP|dr |d}|d}t|j|||dd}|d}||fSi}|}||fS)a Preprocess the document image based on input parameters. Args: image_array (np.ndarray): The input image array. input_params (dict): Dictionary containing preprocessing parameters. Returns: tuple[DocPreprocessorResult, np.ndarray]: A tuple containing the preprocessing result dictionary and the processed image array. r,rIrJrIrJr output_img)listr5)rDrRrSrIrJdoc_preprocessor_resdoc_preprocessor_imager0r0rHpredict_doc_preprocessor_ress"z8_TableRecognitionPipelineV2.predict_doc_preprocessor_resc Cs|dkr|dt|dS|dkrWd}g}g}d|vrSt|dtrS|dD]*}t|trRd|vrRd|vrR|d}|d}t|trR||krR||||q(||fS|d kr_|d SdS) Ncls label_namesscoresdetboxesscore coordinate table_stru structure)npZargmax isinstancerVrKfloatappend) rDpredtask thresholdresultZ cell_scoreboxr`rar0r0rHextract_results$s&   z+_TableRecognitionPipelineV2.extract_results333333?cCst|}t|}g}|dddf}|dddf}|dddf} |dddf} | || |} |ddd} | jdkr| d} || t|| || dd}t|| || dd}t| | | | dd}t| | | | dd}td||}td||}||}|| | | | dd|}t||kd}| |d} | jdksB||}||}||fS)a Apply Non-Maximum Suppression (NMS) on detection results to remove redundant overlapping bounding boxes. Args: cells_det_results (list): List of bounding boxes, each box is in format [x1, y1, x2, y2]. cells_det_scores (list): List of confidence scores corresponding to the bounding boxes. cells_det_threshold (float): IoU threshold for suppression. Boxes with IoU greater than this threshold will be suppressed. Default is 0.5. Returns: Tuple[list, list]: A tuple containing the list of bounding boxes and confidence scores after NMS, while maintaining one-to-one correspondence. Nrrrr r^) rdarrayargsortsizergmaximumminimumwheretolist)rDcells_det_resultscells_det_scoresZcells_det_thresholdr_r\Zpicked_indicesx1y1x2y2ZareasorderiZxx1Zyy1Zxx2Zyy2whinterZovrZindsZ final_boxesZ final_scoresr0r0rHcells_det_results_nms9s8       z1_TableRecognitionPipelineV2.cells_det_results_nmscCsd}|\}}}}g}|D]3} | \} } } } | ||kr?| ||kr?| ||kr?| ||kr?| || || || |g}||q |S)aAdjust the coordinates of ocr_det_boxes that are fully inside table_box relative to table_box. Args: ocr_det_boxes (list of list): List of bounding boxes [x1, y1, x2, y2] in the original image. table_box (list): Bounding box [x1, y1, x2, y2] of the target region in the original image. Returns: list of list: List of adjusted bounding boxes relative to table_box, for boxes fully inside table_box. r)rg)rD ocr_det_boxes table_boxZtolZx_min_tZy_min_tZx_max_tZy_max_tZadjusted_boxesrlZx_min_bZy_min_bZx_max_bZy_max_bZ adjusted_boxr0r0rHget_region_ocr_det_boxesrs"       z4_TableRecognitionPipelineV2.get_region_ocr_det_boxescCshdd}tddd}t|}t|}t|}d}t||kr%|St||kr=d}t| d|}||}d } g} |D].} g} d} |D]}|| |}|d kr[| ||| kset| | krid} nqK| sq| | qCt| d kr|dkr~|n|}n"|dkr||| |}n|t|}|| |}tj||fd d }t|d |kr|||}|S) ao Process and filter cells_det_results based on ocr_det_results and html_pred_boxes_nums. Args: cells_det_results (List[List[float]]): List of detected cell rectangles [[x1, y1, x2, y2], ...]. cells_det_scores (List[float]): List of confidence scores for each rectangle in cells_det_results. ocr_det_results (List[List[float]]): List of OCR detected rectangles [[x1, y1, x2, y2], ...]. html_pred_boxes_nums (int): The desired number of rectangles in the final output. Returns: List[List[float]]: The processed list of rectangles. c Sst|d|d}t|d|d}t|d|d}t|d|d}||ks,||kr.dS||||}|d|d|d|d}|d|d|d|d|t|}|S)aX Compute the Intersection over Union (IoU) between two rectangles. Args: box1 (array-like): [x1, y1, x2, y2] of the first rectangle. box2 (array-like): [x1, y1, x2, y2] of the second rectangle. Returns: float: The IoU between the two rectangles. rrrr r^)maxminrf) box1box2x_lefty_topx_righty_bottomintersection_area box1_areaiour0r0rH compute_ious    zO_TableRecognitionPipelineV2.cells_det_results_reprocessing..compute_iour cst}||kr StddD}t|ddd}||}g}t|D]S}t||kd}t|dkr8q&tfdd|D} t| dddf} t| dddf} t| ddd f} t| ddd f} | | | | | gq&|S) a} Combine rectangles into N rectangles based on geometric proximity. Args: rectangles (list of list of int): A list of rectangles, each represented by [x1, y1, x2, y2]. N (int): The desired number of combined rectangles. Returns: list of list of int: A list of N combined rectangles. cSs4g|]}|d|dd|d|ddgqS)rrrr r0.0rectr0r0rH s zj_TableRecognitionPipelineV2.cells_det_results_reprocessing..combine_rectangles..rauto)Z n_clustersZ random_stateZn_initcg|]}|qSr0r0)ridx rectanglesr0rHrNrrr ) lenrdrpr!Z fit_predictrangerurrrg)rNZ num_rectsZcentersZkmeanslabelsZcombined_rectanglesr~indicesZ cluster_rectsZx1_minZy1_minZx2_maxZy2_maxr0rrHcombine_rectangless,    zV_TableRecognitionPipelineV2.cells_det_results_reprocessing..combine_rectanglesFTNg333333?r)Zaxis) r rdrprrqrvrgsumZ concatenate)rDrwrxocr_det_resultsZhtml_pred_boxes_numsrrZmore_cells_flagZ top_indicesZ iou_thresholdZocr_miss_boxesZocr_rectZmerge_ocr_box_iouZ has_large_iouZ cell_rectrZ final_resultsrZocr_supp_boxesr0r0rHcells_det_results_reprocessings`  2            z:_TableRecognitionPipelineV2.cells_det_results_reprocessingrc sddfdd}dd}t|ddr|d}n|d}|d }g} g} d } | t|kr|| } || } || |}t||kr|| ||}g}|D]^}t|d t|d t|d t|d f\}}}}||d kr||d kr||jd kr||jd krt|j|||||ddfd }d|vr|d}nd}nd}||qM| || |n | | | | | d 7} | t|ks0| |d<| |d <|S)a Split OCR bounding boxes based on table cell boundaries when they span multiple cells horizontally. Args: cells_det_results (list): List of cell bounding boxes in format [x1, y1, x2, y2] overall_ocr_res (dict): Dictionary containing OCR results with keys: - 'rec_boxes': OCR bounding boxes (will be converted to list) - 'rec_texts': OCR recognized texts ori_img (np.array): Original input image array k (int): Threshold for determining when to split (minimum number of cells spanned) Returns: dict: Modified overall_ocr_res with split boxes and texts c Sst|d|d}t|d|d}t|d|d}t|d|d}||ks,||kr.dS||||}|d|d|d|d}|d|d|d|d}||S)a Calculate Intersection over Union (IoU) between two bounding boxes. Args: box1 (list): [x1, y1, x2, y2] box2 (list): [x1, y1, x2, y2] Returns: float: IoU value rrrr r^)rr) rrrrrrrrZ box2_arear0r0rH calculate_iou@s   zR_TableRecognitionPipelineV2.split_ocr_bboxes_by_table_cells..calculate_ioucsFg}tD]\}}||dkr||q|jfddd|S)aM Find cells that overlap significantly with the OCR box (IoU > 0.5). Args: ocr_box (list): OCR bounding box [x1, y1, x2, y2] cells (list): List of cell bounding boxes Returns: list: Indices of overlapping cells, sorted by x-coordinate g?cs |dS)Nrr0r~cellsr0rHis zl_TableRecognitionPipelineV2.split_ocr_bboxes_by_table_cells..get_overlapping_cells..key) enumeratergsort)ocr_boxrZ overlappingrcellrrrHget_overlapping_cellsYs  zZ_TableRecognitionPipelineV2.split_ocr_bboxes_by_table_cells..get_overlapping_cellsc sn|s|gSg}fdd|D}|d|ddkr-||d|d|dd|dgtt|D]H}||}|t|d|d|dt|d|d|dg|t|dkr{||d}|d|dkr{||d|d|d|dgq3|d}|d|dkr||d|d|d|dgg} t} |D]} t| } | | vr| | | | q| S)aS Split OCR box vertically at cell boundaries. Args: ocr_box (list): Original OCR box [x1, y1, x2, y2] cell_indices (list): Indices of cells to split by cells (list): All cell bounding boxes Returns: list: List of split boxes crr0r0)rr~rr0rHr{rzk_TableRecognitionPipelineV2.split_ocr_bboxes_by_table_cells..split_box_by_cells..rrr rro)rgrrrrsettupleadd) rZ cell_indicesr split_boxesZcells_to_splitr~Z current_cellZ next_cell last_cellZ unique_boxesseenrlZ box_tupler0rrHsplit_box_by_cellslsH  "  zW_TableRecognitionPipelineV2.split_ocr_bboxes_by_table_cells..split_box_by_cells rec_boxesrv rec_textsrrrr NZrec_text) hasattrrvrintshaperVr=Ztext_rec_modelrgextend)rDrwrNori_imgkrrrZ ocr_textsZ new_boxesZ new_textsr~rtextZoverlapping_cellsrZ split_textsrlryrzr{r|Z ocr_resultrkr0rrHsplit_ocr_bboxes_by_table_cells.s` 2   4        .z;_TableRecognitionPipelineV2.split_ocr_bboxes_by_table_cellsc Cst|ts |}g}tt|D]9}dd||D\}}}}||dkrJ||dkrJt||||||ddfd} |d| dq|S)at Splits OCR bounding boxes by table cells and retrieves text. Args: ori_img (ndarray): The original image from which text regions will be extracted. cells_bboxes (list or ndarray): Detected cell bounding boxes to extract text from. Returns: list: A list containing the recognized texts from each cell. cSsg|]}t|qSr0)mathceil)rrr0r0rHrzH_TableRecognitionPipelineV2.gen_ocr_with_table_cells..rNrrr)rerVrvrrr=rgjoin) rDrZ cells_bboxesZ texts_listr~ryrzr{r|Zrec_ter0r0rHgen_ocr_with_table_cellss (z4_TableRecognitionPipelineV2.gen_ocr_with_table_cellscCsg}tt|D]f}||d||d||d||df\}}} } |dkr7||| } } | ||} }n)|dkrN|| || } } ||||} }n|dkr`|| |} } ||| } }t| | | |g}||q|S)a Map bounding boxes from the rotated image back to the original image. Parameters: - detections: list of numpy arrays, each containing bounding box coordinates [x1, y1, x2, y2] - table_angle: rotation angle in degrees (90, 180, or 270) - width_orig: width of the original image (img1) - height_orig: height of the original image (img1) Returns: - mapped_detections: list of numpy arrays with mapped bounding box coordinates rrrr 27018090)rrrdrprg)rDZ detections table_angle img_width img_heightZmapped_detectionsr~tbx1tby1tbx2tby2new_x1new_y1new_x2new_y2Znew_boxr0r0rHmap_cells_to_original_images&      z7_TableRecognitionPipelineV2.map_cells_to_original_imagecCsBgd}ddd|D}td|d|}dd|D}|S) z Split HTML string by keywords. Args: html_string (str): The HTML string. Returns: split_html (list): The list of html keywords. )0zzzzzz colspan="2"z colspan="3"z colspan="4"z colspan="5"z colspan="6"z colspan="7"z colspan="8"z colspan="9"z colspan="10"z colspan="11"z colspan="12"z colspan="13"z colspan="14"z colspan="15"z colspan="16"z colspan="17"z colspan="18"z colspan="19"z colspan="20"z rowspan="2"z rowspan="3"z rowspan="4"z rowspan="5"z rowspan="6"z rowspan="7"z rowspan="8"z rowspan="9"z rowspan="10"z rowspan="11"z rowspan="12"z rowspan="13"z rowspan="14"z rowspan="15"z rowspan="16"z rowspan="17"z rowspan="18"z rowspan="19"z rowspan="20"|css|]}t|VqdSN)reescape)rkeywordr0r0rH ZszG_TableRecognitionPipelineV2.split_string_by_keywords..()cSsg|]}|r|qSr0r0)rpartr0r0rHr\rzH_TableRecognitionPipelineV2.split_string_by_keywords..)rrsplit)rDZ html_stringkeywordsZ regex_patternZ split_resultZ split_htmlr0r0rHsplit_string_by_keywordss  2z4_TableRecognitionPipelineV2.split_string_by_keywordscCs|sgStt|}g}|dg}|ddD] }t||d|kr)||q|t|t||g}q|t|t||S)Nrrro)sortedrabsrgrr)rDZ positions toleranceZ clusteredZcurrent_clusterposr0r0rHcluster_positions_s   z-_TableRecognitionPipelineV2.cluster_positionsc sd}dd|D}dd|D}||||||ddtD}ddtD}td}tdfd dt|D}g} d } i} t|D]\} } | \tttfd d d }tttfdd d }tttfdd d }tttfdd d }t||}t||}t||}t||}||}||}|d krd}|d krd}| ||||ddt|||D]}t|||D]}||f}|| vrq| | |<qqqOd}t|D]v}|d7}d }|krb||f}|| vrV| |} | | }|d|krO|d|krO|d}|d}|dkr/d|dnd}|dkr.cSr)rr r0)rryr0r0rHrzrcSi|]\}}||qSr0r0)rr~rr0r0rH }rzO_TableRecognitionPipelineV2.trans_cells_det_results_to_html..cSrr0r0)rr~rr0r0rHr~rrcsg|] }ddtDqS)cSsg|]}dqSrr0r_r0r0rHrzZ_TableRecognitionPipelineV2.trans_cells_det_results_to_html...)rr)num_colsr0rHrsrct|Srrr)ry x_positionsr0rHrrzM_TableRecognitionPipelineV2.trans_cells_det_results_to_html..rcrrrr)r{rr0rHrrcrrrr)rz y_positionsr0rHrrcrrrr)r|rr0rHrrr) row_start col_startrowspancolspancontentzrrrrrz rowspan=""z colspan="rrrrz rz
)rrrrrrrgr) rDrwrZx_coordsZy_coordsZx_position_to_indexZy_position_to_indexZnum_rowsgridZ cells_infoZ cell_indexZcell_mapindexrZx1_idxZx2_idxZy1_idxZy2_idxrZcol_endrZrow_endrrrcrhtmlZ cell_infoZ rowspan_attrZ colspan_attrrr0)rryr{rrzr|rrHtrans_cells_det_results_to_htmlns                 z;_TableRecognitionPipelineV2.trans_cells_det_results_to_htmlTruse_e2e_wired_table_rec_model use_e2e_wireless_table_rec_model#use_wired_table_cells_trans_to_html&use_wireless_table_cells_trans_to_html use_ocr_results_with_table_cellsflag_find_nei_textc  Cspt||d} || d} d} d} | dkrG|dkrd} n t||d}|dkr;d} | dkr:t||d}n?t|j|ddd}n3| dkrz|dkrRd} n t||d}|dkrod} | dkrnt||d}n t|j|ddd}| dkr||d \}}|||\}}| dkr||}n||d }| |d  |}| |||t |d }|dkr|j dkrt|g}t||}||||}g}n |||}i}ni}g}t||||||||j }n#g}d}|d }d d|D}| dkr|||d<t|||||}d}| r2t|d |}t |dkr2|D] }||d|d7}q$||d<|S)a Predict table recognition results from an image array, layout detection results, and OCR results. Args: image_array (np.ndarray): The input image represented as a numpy array. overall_ocr_res (OCRResult): Overall OCR result obtained after running the OCR pipeline. The overall OCR results containing text recognition information. table_box (list): The table box coordinates. use_e2e_wired_table_rec_model (bool): Whether to use end-to-end wired table recognition model. use_e2e_wireless_table_rec_model (bool): Whether to use end-to-end wireless table recognition model. use_wired_table_cells_trans_to_html (bool): Whether to use wired table cells trans to HTML. use_wireless_table_cells_trans_to_html (bool): Whether to use wireless table cells trans to HTML. use_ocr_results_with_table_cells (bool): Whether to use OCR results processed by table cells. flag_find_nei_text (bool): Whether to find neighboring text. Returns: SingleTableRecognitionResult: single table recognition result. rrZFZ wired_tableTrn)rjZwireless_tabler]rbrZbboxcSs(g|]}|d|d|d|dgqS)rrrrr0rr0r0rHrJszT_TableRecognitionPipelineV2.predict_single_table_recognition_res..rcrrz; Zneighbor_texts)rVr8rmr9r;r:r<rrrrvrrcells_split_ocrrdrprrrrget_table_recognition_res_e2er)rDrRrNrr r r r r rZtable_cls_predZtable_cls_resultZ use_e2e_modelZcells_trans_to_htmlZtable_structure_predZtable_cells_predZtable_cells_resultZtable_cells_scoreZtable_structure_resultrZtable_box_copyZtable_ocr_predZcells_texts_listZsingle_table_recognition_resZtable_cells_result_e2eZ neighbor_textZmatch_idx_listrr0r0rH$predict_single_table_recognition_ress          z@_TableRecognitionPipelineV2.predict_single_table_recognition_resinputtext_det_limit_side_lentext_det_limit_typetext_det_threshtext_det_box_threshtext_det_unclip_ratiotext_rec_score_threshuse_table_orientation_classifyc0 ksd|_|dkr|jdur|jdksJ||j|_|||||}||||s/ddiVt||D]\}}||j d}|drTt |j |||dd}nd|i}|d}|d rqt |j ||| | | | | d d}n |j dur|dkr|jd ks|dkr|j dksJ||j |_ |d krd }g}d }|ds|dur|jdd\}}dd|d |d g} |dkrt ||ddd}|dkrtj|d d}n|dkrtj|dd}n |dkrtj|dd}|dvrXt |j ||| | | | | d d}| d| d | d| df\}!}"}#}$|dkr'|"||#}%}&|$||!}'}(n+|dkr?||#||$}%}&||!||"}'}(n|dkrR||$|!}%}&||"|#}'}(|%|&|'|(g} |j||| |||||d d })||)d<|dkr|d kr|jdd\}}||)d||||)d<||)|d 7}n=|drt ||d}|jdd\}}|dD] }*|*ddvr|||*g}+|+d}+|+d} |dkr|},t ||+dddd}|dkrtj|+dd d|+d<tj|,d d},n/|dkrtj|+ddd|+d<tj|,dd},n|dkr)tj|+ddd|+d<tj|,dd},|dvrt |j |,|| | | | | d d}| d| d | d| df\}!}"}#}$|dkrd|"||#}%}&|$||!}'}(n+|dkr|||#||$}%}&||!||"}'}(n|dkr||$|!}%}&||"|#}'}(|%|&|'|(g} ||+d|| |||||})||)d<|dkr|d kr|,jdd\}-}.||)d||.|-|)d<||)|d 7}q|jd|jd|||||d}/t|/Vq6dS) a9 This function predicts the layout parsing result for the given input. Args: input (Union[str, list[str], np.ndarray, list[np.ndarray]]): The input image(s) of pdf(s) to be processed. use_layout_detection (bool): Whether to use layout detection. use_doc_orientation_classify (bool): Whether to use document orientation classification. use_doc_unwarping (bool): Whether to use document unwarping. overall_ocr_res (OCRResult): The overall OCR result with convert_points_to_boxes information. It will be used if it is not None and use_ocr_model is False. layout_det_res (DetResult): The layout detection result. It will be used if it is not None and use_layout_detection is False. use_e2e_wired_table_rec_model (bool): Whether to use end-to-end wired table recognition model. use_e2e_wireless_table_rec_model (bool): Whether to use end-to-end wireless table recognition model. use_wired_table_cells_trans_to_html (bool): Whether to use wired table cells trans to HTML. use_wireless_table_cells_trans_to_html (bool): Whether to use wireless table cells trans to HTML. use_table_orientation_classify (bool): Whether to use table orientation classification. use_ocr_results_with_table_cells (bool): Whether to use OCR results processed by table cells. **kwargs: Additional keyword arguments. Returns: TableRecognitionResult: The predicted table recognition result. 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