j2 dZddlZ ddlmZmZmZmZnX#e$rPdhdej de dej fdZdej dej fd Zd ej d e dej fd Zd ej dej fd ZYnwxYwgdZ dZ dZddhZedee DZdee DZdee DZdeDZeZdZdZdZdej dej dedej fdZdej dej dej ded ed!edzd"e d#e deefd$Zd%eed&eed edefd'ZGd(d)Z e!d*kr~ddl"Z"ddl#Z#ddl$Z$ddl%Z%ddlZddl&Z'ddl(Z(e$j)d+,Z*e*+d-d./e*+d0d1/e*+d2e d3d45e*+d6edd75e*+d8ed9d:5e*,Z-e#j./e-j0s#e1d;e-j0e"j2d<e#j.3e-j4s#e1d=e-j4e"j2d<ej56d>Z7ej8ej9gd?gej:@e7;dAdBedHZ?dIZ@dJZAdKZBdLZCdMZDe1dNe-j0e'jEe-j0dOgPZFe eFZGe1dQdReFHDedSe#jIe-j4DZJeJs#e1dTe-j4e"j2de1dUe>>>>c8ddtj| zz S)z- Numerically stable sigmoid. ?)r r)rs rrr=scBFA2JJ&''rimagenew_sizec|jdd\}}|\}}tj|dz||z zdz }tj|dz||z zdz }tj|t }tj|t } tj|dzd|dz } tj| dzd|dz } ||z dddf} || z dddf} tj|d|dz }tj| d|dz } |jdkry|| dddf|dddff}|| dddf| dddff}|| dddf|dddff}|| dddf| dddff}nl|tj| |}|tj| | }|tj| |}|tj| | }d| z d| z z}| d| z z}d| z | z}| | z}|jdkr/|d|z|d|zz|d|zz|d|zzn||z||zz||zz||zz}tj|dd|j S) a Resize an image using bilinear interpolation with numpy (vectorized). Equivalent to cv2.resize(..., INTER_LINEAR). Parameters ---------- image : np.ndarray Input image (H, W) or (H, W, C). new_size : tuple (new_width, new_height) Returns ------- np.ndarray Resized image. Nr?r).N) shaper arangefloorastypeintclipndimix_dtype)rrhwnew_wnew_hryx0y0x1y1dxdyIaIbIcIdwawbwcwdouts rrrDs"{2A21 uYu   #E 2S 8 Yu   #E 2S 8 Xa[[   $ $ Xa[[   $ $ WR!VQA & & WR!VQA & &"fdAAAg "faaag WRAE " " WRAE " " :??r!!!T'{BtQQQwK/0Br!!!T'{BtQQQwK/0Br!!!T'{BtQQQwK/0Br!!!T'{BtQQQwK/0BBrvb"~~&Brvb"~~&Brvb"~~&Brvb"~~&B"fR  1r6]"f] "WrSs> !T """""""rc6g|]\}}|dk |tv|SrrMrOs r rVs: !T1uu000000rc.g|]\}}|tk|S)_PICTURE_CLASSrOs rrVrVs- !T0F0FA0F0F0Frc>g|]}t|tk|SrX) _CLASS_NAMESrY)rPrQs rrVrVs- LO~$E$EA$E$E$Erg333333?g?boxesscoresiou_thrc|jddkr tjdtjS|dddf|dddf|dddf|dddff\}}}}||z ||z z}|ddd}g} |jr|d} | | |jdkrn|dd} tj|| || } tj|| || } tj|| || }tj|| || }tjd|| z tjd|| z z}||| || z|z d zz }| ||k}|jtj | tjS) zxGreedy NMS on (N, 4) boxes sorted by descending score. Returns: keep : (K,) int array of kept indices. rr&Nrrrgư>) rr emptyint32argsortsizeappendmaximumminimumarray)r\r]r^r.r/x2y2areasorderkeeprQrestxx1yy1xx2yy2interious r _nms_numpyrws  {1~x****111a4[%1+uQQQT{E!!!Q$KGNBB2g"r' "E ^^  ddd #E D * % !H A :?? QRRyz"Q%D**z"Q%D**z"Q%D**z"Q%D** 3c **RZS3Y-G-GGqE$K/%7$>?SG^$ * % 8D ) ) ))r seg_logitsregctr score_thrnms_iouactive_class_indicesorig_horig_wc|j\}} } } t|dd} t|d} || z }|| z }tj| tjdz|z}tj| tjdz|z}tj||\}}|d|z}|d|z}|d|z}|d|z}||nttd | }ggg}}}|D]`}|dks|| kr| || z}||k}| s6tj ||z |d|}tj ||z |d|} tj ||z|d|}!tj ||z|d|}"tj || |!|"gd }#||}$t|#|$|}%|%j dkr||#|%|tj|%j |tj||$|%b|rCtj|dtj|dtj|dd gStjd tjtjdtjtjdtjd gS) aDecode one FCOS head's ONNX outputs into bbox dicts (numpy-only). Normalisation convention (DocumentJsonDataset): l_norm = l_fmap_px / fmap_w Decoded: l_orig = l_norm * orig_w = (l_fmap_px / fmap_w) * orig_w Grid: grid_x_orig = (i + 0.5) * (orig_w / fmap_w) => x1 = grid_x_orig - l_orig correctly recovers orig-space coordinate. Args: seg_logits : (1, C, H, W) -- raw segmentation logits at fmap resolution. reg : (1, 4, H, W) -- normalised (l, t, r, b) per DocumentJsonDataset. ctr : (1, 1, H, W) -- centerness logits. score_thr : minimum score (seg_prob x centerness) to keep. nms_iou : IoU threshold for class-wise NMS. active_class_indices : foreground indices this head is responsible for. None -> all foreground classes. orig_h, orig_w : output coordinate space (page image size). Returns: List with one element (batch=1): {'boxes': (N, 4) float32 -- (x1, y1, x2, y2) in orig image px, 'labels': (N,) int32, 'scores': (N,) float32} rr rrr`r)rr)rr)rrNrr\labelsr])r)rrrr rr=meshgridlistrangeanyr#stackrwrfrgfullrd concatenaterc)&rxryrzr{r|r}r~r_CHW seg_probs ctr_scoresscale_xscale_yysxsgrid_xgrid_yltrbv cls_range all_boxes all_labels all_scoresclsscoremaskr.r/rkrlr\r]ros& r_fcos_decode_numpyrsF!JAq!Qjm!444Is4y))JqjGqjG )ARZ ( ( (3 .' 9B )ARZ ( ( (3 .' 9B[R((NFF TV A TV A TV A TV B(<(H$$%1++&&)+B:zI(( !88saxx #+ !xxzz   Wfqj$'F 3 3 Wfqj$'F 3 3 Wfqj$'F 3 3 Wfrk4(!V 4 42r2r*333t%11 9>> t%%%"'$)SAAABBB&,''''@>)1===>*1===>*1===??@ @xbj999xrx888xrz::: < < ==r dets_coarse dets_finec|d|d}}tj|d|dgd}tj|d|dgd}tj|d|dgd}|jddkr|||dSggg} } }tj|D]} || k} t || || |} ||| | | || | | || | tj|dtj| dtj| ddS)aConcatenate coarse + fine detections and apply a per-class NMS pass. Class-agnostic NMS would incorrectly suppress valid detections of different classes that happen to overlap (e.g. a section-header and the text block immediately below it). Per-class NMS only suppresses duplicate candidates within the same class, which matches the convention used in fcos_decode (PyTorch path). Args: dets_coarse, dets_fine : each a single-element list (batch=1) as returned by _fcos_decode_numpy. nms_iou : IoU threshold for per-class NMS. Returns: Single dict with keys 'boxes', 'labels', 'scores'. rr\rrr]r)r rruniquerwrg)rrr|dcdfrrr kept_boxes kept_labels kept_scoresrrros r_merge_detectionsrSs*^Yq\BGr'{ ;1EEEIHr(| <1EEEJHr(| <1EEEJqQ"jJOOO+-r2[ Jy$$33s"IdOZ-=wGG)D/$/000:d+D1222:d+D12222.1555.1555.1555  rceZdZdZdZdZdZddZdeded e j fd Z d Z d Z d ZdZd efdZd efdZd efdZ ddeded efdZ ddeded efdZd efdZdS)ImageFeatureExtractorV2a  Wraps a UNetThin2 ONNX session (exported with output_name='dec_all', use_fcos=True) to produce: - a combined decoder feature map (forwarded to the GNN pipeline) - picture-class bbox detections (lazy, via get_picture_detections()) - non-picture detections (lazy, via is_image_page()) The public interface is fully compatible with ImageFeatureExtractorV1 so existing callers (BoxRFDGNN, image_feature_extraction_task) require no changes. Internal changes vs. V1: - CCL (connected-component labelling) is replaced by FCOS decoding. - ONNX session produces 5 outputs instead of 1; all are stored. - Detection results are split by head assignment (coarse / fine) and merged with a final NMS pass. c||_d|_d|_d|_d|_d|_d|_d|_d|_d|_ d|_ d|_ d|_ d|_ d|_d|_d|_dS)ai Args: onnx_session : onnxruntime.InferenceSession for a UNetThin2 model exported with output_name='dec_all', use_fcos=True. Expected output order: [0] combined, [1] reg_coarse, [2] ctr_coarse, [3] reg_fine, [4] ctr_fine NFr)_session _combined _reg_coarse _ctr_coarse _reg_fine _ctr_fine_logits _text_logits _thresh_map_db_map_cached_detections_picture_detections_non_picture_page_h_page_w _target_h _target_w)self onnx_sessions r__init__z ImageFeatureExtractorV2.__init__s%   !    (, '+$  rcd|_dS)z4Signal that predict() results should be reused once.TNrrs r mark_cachedz#ImageFeatureExtractorV2.mark_cacheds  rc&|jr d|_dSdS)z Return True (and clear the flag) if a cached result exists. Called by image_feature_extraction_task() to skip predict(). FTrrs r consume_cachez%ImageFeatureExtractorV2.consume_caches <  DL4urNc6|jdd\|_|_|jdj}|d|dc|_|_t||j|jf}t| tj }| | }}||z ||z dzz }tj|d}|tj||gd}tj|d}|jdj} d|jD} |jd| |i} t't)| | } | d|_| d |_| d |_| d |_| d |_| d |_| d|_| d|_| d|_d|_d|_ dS)a Run ONNX inference and store all five head outputs. FCOS decoding is NOT performed here -- results are computed lazily. Calling predict() invalidates all previously cached detection results so that get_picture_detections() and is_image_page() always reflect the current page. Args: page_img : np.ndarray (H, W, C) uint8 aug_fetmap : optional extra channel map concatenated before inference Side effects: self._combined / _logits / _reg_coarse / _ctr_coarse / _reg_fine / _ctr_fine <- set from ONNX outputs self._detections_picture / _non_picture <- reset to None Nrrrg:0yE>rcg|] }|j SrXrRrPos rrVz3ImageFeatureExtractorV2.predict..sDDD1DDDrcombined reg_coarse ctr_coarsereg_finectr_finelogits text_logits thresh_mapdb_map)!rrrr get_inputsrrrrr!r r=minr expand_dimsrrR get_outputsrundictzipgetrrrrrrrrrrr) rpage_img aug_fetmap input_shape img_resizedimg_graymin_valmax_valnn_input input_name output_names ort_outputsout_maps rpredictzImageFeatureExtractorV2.predicts<$&.^BQB%7" dlm..0039 )4QQ&#8dndn-MNN k**11"*==#<<>>8<<>>w&7W+(333  !~x&<1EEEH>(333m..0038 DD (A(A(C(CDDD m''z8.DEE s<5566";;z22";;|44";;|44";;z22";;z22";;x00 $KK 66#KK 55#KK11 (, '+$$$rfmap_hfmap_wr c V|jj\}}}}||kr ||kr|jS ddl}tjd|||ftj}t |D].} |j|jd| f||f|j|d| f</|S#t$rtj ||z|z t} tj ||z|z t} |jdddd| dddf| dddffcYSwxYw)aFResize the real segmentation logits to the given feature-map size. Uses self._logits, which is the actual seg_head output (index 5 from the ONNX session). This is identical to the logits used in check_unethin2_result, so FCOS score computation is consistent between the PyTorch and ONNX inference paths. Args: fmap_h, fmap_w : target spatial size matching the reg/ctr tensors (H/8 for coarse head, H/4 for fine head). Returns: np.ndarray (1, C, fmap_h, fmap_w) float32 rNrr` interpolation) rrcv2r zerosr=rresize INTER_LINEAR ImportErrorrr!r") rrrrrrrrr:crrs r_logits_for_fmapz(ImageFeatureExtractorV2._logits_for_fmaps`\' 1a ;;1;;<  @ JJJ(Aq&&1DDDC1XX  &CJLA&V$"%"2AqD J @ @ @)F##a'&088==B)F##a'&088==B<111bDk2dAAAg; >? ? ? ? @sA'BBD('D(c |jdS|jjd}|jjd}|||}t ||j|jt tt|j |j }|d|_dS)zCRun picture FCOS decoding if not yet computed this predict() cycle.Nrrrxryrzr{r|r}r~rr) rrrrrr_SCORE_COARSE_THR_NMS_IOU_PICTURE_CLASS_IDXrr)rfmap_h_cfmap_w_clogits_cdetss r_ensure_picture_detectionsz2ImageFeatureExtractorV2._ensure_picture_detections5s  # / F#)!,#)!,((8<<!  '!3<<    $(7   rc &|jdS|jjd}|jjd}|||}t ||j|jt ttpd|j |j }|j jd}|j jd}|||}t ||j |j tttpd|j |j }t||t|_dS)a3Run non-picture FCOS decoding if not yet computed this predict() cycle. Non-picture classes span both heads: - coarse head : table (non-picture coarse class) - fine head : text, title, list-item, page-header, ... Both results are merged with a final NMS pass. Nrrr)rrrrrrrr_NON_PICTURE_COARSE_IDXrrrr_SCORE_FINE_THR_NON_PICTURE_FINE_IDXr) rrrrrfmap_h_ffmap_w_flogits_frs r_ensure_non_picture_detectionsz6ImageFeatureExtractorV2._ensure_non_picture_detectionsKs  ' 3 F#)!,#)!,((8<<(  '!8!@D<<    >'*>'*((8<<&%!6!>$<<    (9 H( ( $$$rc|jS)aD Return the combined decoder feature map from the last predict() call. This is the continuous embedding output only -- it no longer includes the class logits (see get_class_logits() for those). Returns: np.ndarray (1, 5*F, H, W) float32, or None if predict() not called. )rrs rget_feature_mapz'ImageFeatureExtractorV2.get_feature_map~s ~rc|jS)a Return the per-class segmentation logits from the last predict() call. Raw (pre-softmax) scores; apply softmax before pooling ops that expect a probability distribution (e.g. entropy, margin -- see roi_pooling.extract_bbox_features_by_roi_pooling). Returns: np.ndarray (1, C, H, W) float32, or None if predict() not called. )rrs rget_class_logitsz(ImageFeatureExtractorV2.get_class_logitss |rc|sdS|drdS|}t j|jtj}||j |j |j }| || ||jdjddkS)a Determine whether the page is an image-only PDF page that requires OCR. Returns True only when ALL three conditions hold: 1. The page contains at least one raster image (pymupdf). 2. The page contains no embedded selectable text (pymupdf). 3. The FCOS model detects at least one non-picture region (text, table, header, etc.) with score > _SCORE_FINE_THR. Calls predict() internally and marks the result as cached so the immediately following BoxRFDGNN.predict() skips re-running inference. Picture detection is deferred to get_picture_detections() if needed. Args: page : PyMuPDF page object Returns: bool FrDr`r\r)get_image_infoget_textstrip get_pixmapr frombuffersamplesr>reshapeheightwidthnrrr rr)rpagepix bytes_datars r is_image_pagez%ImageFeatureExtractorV2.is_image_pages*""$$ 5 == & & ( ( 5__&&]3;bh??? '' CIsuEE X  ++---+G4:1=AArc|jtd||j}|djddkrgS|dS)a Return picture-class detections from the last predict() call in page pixel coordinates. Runs picture FCOS decoding on the first call after each predict(). Subsequent calls within the same predict() cycle are free (cached). Returns: list of [x1, y1, x2, y2] in page pixel space (float). Raises: RuntimeError if predict() has not been called. Nz8predict() must be called before get_picture_detections()r\r)r RuntimeErrorrrrtolist)rdets rget_picture_detectionsz.ImageFeatureExtractorV2.get_picture_detectionsso > !J  '')))& w< a A % %I7|""$$$rc0|jtd|||j}|j}t j|d|dgd}t j|d|dgd}t j|d|dgd}|jddkrggggdS| | | d |DdS) ai Return all FCOS layout detections (both heads, merged) from the last predict() call in page pixel coordinates. Useful for downstream consumers that need the full detection set rather than the picture-only subset. Returns: dict with keys: 'boxes' : list of [x1, y1, x2, y2] 'labels' : list of int (1-based foreground class indices) 'scores' : list of float 'names' : list of str (class name for each detection) Raises: RuntimeError if predict() has not been called. Nz7predict() must be called before get_layout_detections()r\rrrr]r\rr]namescvg|]6}|ttkr t|nt|7SrX)lenr[str)rPlbls rrVzAImageFeatureExtractorV2.get_layout_detections..sJ&)3|+<+<%<%< S!!#c((r) rr!rr rrr rrr")rpicnopxrrrs rget_layout_detectionsz-ImageFeatureExtractorV2.get_layout_detectionss8$ > !I  ''))) ++---'+^S\DM$B!LLL ^S]DN$C!LLL ^S]DN$C!LLL ?1  " "2bII I &&(( '')) ''))%    rr db_threshmin_areac |jtd|}|ds|S |||}n#t$r|cYSwxYw|s|Str tdnd}t dt tDd}t|d}tj |tj }|dddf|ddd fzd z} |dddf|ddd fzd z} tj t|dtj } t t|d|d D]?\} \} }||kr| \}}}}| |k| |kz| |kz| |kz}|| dkz}| | |<@g}g}g}g}t t|d|d |d|dD]\} \} }}}||krU|| ||||||f|| | k}|jddkr|t#|dddft#|dddft#|ddd ft#|ddd fg|||||||| dk}|D]h}|||||d|di||||dS)a Refine layout detections by grouping text boxes into layout regions. Algorithm: 1. For each text detection box, check which layout bbox contains its centre point. 2. If it belongs to a layout region, add it to that region's group. The final bbox for that region is the union of all its text boxes. The original layout class and score are preserved. picture regions are excluded from this process (kept as-is). 3. Text boxes whose centre does not fall inside any layout region are assigned as independent 'text' class detections. 4. Layout regions that received no text boxes keep their original FCOS bbox unchanged. Requires use_text_seg=True export. Falls back to get_layout_detections() when text outputs are absent. Args: db_thresh : binarisation threshold passed to get_text_detection(). min_area : minimum contour area passed to get_text_detection(). Returns: Same dict structure as get_layout_detections(). Raises: RuntimeError if predict() has not been called. Nz?predict() must be called before get_layout_detections_refined()r\)r0r1rrac3,K|]\}}|dk |VdS)rDNrX)rPrQrs rrSzHImageFeatureExtractorV2.get_layout_detections_refined..<s7,,$!Q F{{*{{{,,rrr`rrrrr]r'rbrDr&)rr!r.get_text_detectionrnext enumerater[r)r rjr=rrdrrgrfloatrrr")rr0r1layout text_boxes picture_idxtext_idxn_layouttbcxcy assignmentrQboxr+r.r/rkrlinsideunassigned_inside out_boxes out_labels out_scores out_namesrrRmembersorphansrows rget_layout_detections_refinedz5ImageFeatureExtractorV2.get_layout_detections_refined s|< > !Q ++--g M 009:B1DDJJ   MMM  M/AI(++r ,,)L*A*A,,,-.00vg''8Jbj999qqq!tHr!!!Q$x'3.qqq!tHr!!!Q$x'3.WS__bAAA &s6'?F8 findContours RETR_EXTERNALCHAIN_APPROX_SIMPLE contourArea boundingRectr7r#rg)rr0r1_cv2probtlfeat_hfeat_wrrbinarycontoursrr\cntrr+r(r'r.r/rkrls rr4z*ImageFeatureExtractorV2.get_text_detections+6 > !F    $G   U      U U USTT T U < #<%DD$Q'B !,,,Q/D,',')#++BH55;'' D&(@  ! + +C$$x//**3//JAq!Qrwq7{aFFGGBrwq7{aFFGGBrwA0aFFGGBrwA0aFFGGB LL"b"b) * * * * s 3A c|jtd|jtd|jd}t|dd}|j |jdnd}|j |jdnd}|||dS) a Return text segmentation outputs from the last predict() call. Available only when the model was exported with use_text_seg=True. Returns raw numpy arrays so the caller can apply any threshold strategy. Output keys: 'text_prob' : (H, W) float32 -- per-pixel text probability in [0, 1]. Derived from text_logits via softmax class-1 channel. Always present when use_text_seg=True. 'thresh_map' : (H, W) float32 -- adaptive threshold map in [0, 1]. Present only when use_db_thresh=True; None otherwise. 'db_map' : (H, W) float32 -- differentiable binary map in [0, 1]. Present only when use_db_thresh=True; None otherwise. Returns: dict with the keys above. Raises: RuntimeError if predict() has not been called. RuntimeError if the model was not exported with use_text_seg=True. Nz7predict() must be called before get_text_segmentation()rMrrrr) text_probrr)rr!rrrr)rrUr\rrs rget_text_segmentationz-ImageFeatureExtractorV2.get_text_segmentations. > !I    $G   q !!"1---a0 /3/?/KT%d++QU /3|/KT\$''QU$$    rrL)rr/)__name__ __module__ __qualname____doc__rrrrr"r ndarrayrrr rrboolrrr$rr.r7rKr4r]rXrrrrs&$$$T9,9,9,9,| @s @C @BJ @ @ @ @F+++,- - - f      'BT'B'B'B'BR%%%%%>, t, , , , \@C68s s us 03s =As s s s l58+-HHEH%(H26HHHHT, t, , , , , , rr__main__z7Visualise ImageFeatureExtractorV2 results on PDF files.) description onnx_pathz&Path to the exported .onnx model file.)helppdf_dirz*Directory containing PDF files to process.z--dpi`z*Rendering DPI for PDF pages (default: 96).)typedefaultrgz --score_thrz0Layout detection score threshold (default: 0.3).z--db_thrrz-DB map binarisation threshold (default: 0.5).zERROR: ONNX file not found: rz ERROR: PDF directory not found: *)rmrmr`<r)rfr&ct|t|jddz |t|jddz d}|dkrtj|d||}|S)Nrrrr)fxfy)rrrrr)imgmax_wmax_hscales r _scale_dispry&shEC ! a000C ! a000#77 3;;*S&Uu===C rc ,|}t|d|d|d|dD]S\\}}}}}}} t|t|t|t|f\}}}}tdtt |t tdz D} tj|||f||f| d| d|d } tj | tj d d\\} } }t|d z | dz}tj|||| z dz f|| zdz|dzf| d tj || |dz|ftj d d dtj U|S)z%Draw get_layout_detections() results.r\rr]r'c34K|]}t|VdSrL)r")rPrs rrSz%_draw_layout_boxes..4s@KKQ#a&&KKKKKKrrr z.2fg?rrarrr)copyrr"tuple _class_colorsrr)r rectangle getTextSizeFONT_HERSHEY_SIMPLEXrputTextLINE_AA)canvas detectionsr:r.r/rkrlr+rrRcolorlabeltwthrtys r_draw_layout_boxesr-skkmm257#Z%98$j&93;3; 9 9 . RRc5$!WWc"ggs2wwB?NBBKK'C]1C1Ca1G(H(HIKKKKKE M#Bx"b5! < < <))e)))E/s/q::KHRaR!VR!V$$B M#BGaK027Q;Q2G % % % KUR!VRL0$'CK 9 9 9 9 rc |}|D]W\}}}}tj|t|t|ft|t|fddX|S)z5Draw get_text_detection() results (green rectangles).)rrr)r~rrr")rr\r:r.r/rkrls r_draw_text_boxesrBsrkkmm % * * RR M#BR1CGGSWW3E%q * * * * rc,tj|tj||ftj}tj|dddztj}tj|tj S)z@Render a float32 (H,W) map in [0,1] as a JET colormap BGR image.rrbrr) rrr!r r=rr#r> applyColorMap COLORMAP_JET)arrr'r(resizedr>s r _map_to_jetrJsr*SZZ 33aV+.+;===#s++c199"(CC (8999rctj|ddtj}tjg|jdRtj}tttD]9}tt|ttdz |||k<:tj |||ftjS)z=Render segmentation logits (1,C,Hf,Wf) as a colour class map.rrrr`rr)r argmaxr!rdrrr>rr)r[rrrr INTER_NEAREST)rr'r(predr:rs r _seg_to_colorrQsy+++2228<<x($*(a((999s<(()) K KA*3q#m2D2Dq2H+I+IJC NNz#1vS5FGGGGrc |}tj||dtjdddtjtj||dtjdddtj|S)z%Stamp a label in the top-left corner.)333333?)rrrrr}r)r~rrrr)rurDr:s r_labelrYsnhhjj Cw,c93; P P P Cw,c?As{ T T T rc x|jdd\}}||_||_||||}t ||}t jdtt|dt|j ||}t|d} | } tt| d||d} | d Ktt| d ||d } tt| d ||d } ntj||d ftj}|} |} t j| ddt jdddt j| ddt jdddtj| | | | gd}t jdt|||}t-||}t jdtt|dt/|n2#t0$r%t jdt| YnwxYwt/|dS)aRun inference and display results in separate windows per row. Window 1 - Layout Detection: [ original | layout detection ] Window 2 - Segmentation Maps: [ layout seg | text prob | thresh map | db map ] (text panels only when use_text_seg=True) Window 3 - Text Detection: [ original | text detection ] (only when use_text_seg=True) predict() internally resizes the image to model input size, runs ONNX, then decodes coordinates back into the original page pixel space using _page_h/_page_w. So all returned coordinates are already in bgr_orig space with maximum precision. Returns n_layout (int). Nr)r0z1. Layout Detectionzlayout detection (refined)z layout segr\z text probrz thresh maprzdb maprr`z thresh: N/A)r/rmr)rrrzdb: N/Arz2. Segmentation Mapsz3. 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