9i"otSrSSKrSSKrSSKrSSKJr SSKrSSKr SSK r SSK r SSK r SSK Jr "SS5rg)z Created by Jaided AI Released Date: 18/08/2022 Description: DBNet text detection module. Many parts of the codes are adapted from https://github.com/MhLiao/DB N)Polygon) Configurablec\rSrSrSSjrSrSrSrSrSr SS jr S r S r SS jr SS jrSSjrSrSSjrSSjrSSjrSrSrSrSSjrSrg)DBNetNcfX`l[RR[RR [ 5SS5n[ US5n [R"U 5Ul SSS5 Ub!URURU5Ul XRR5;aXl OA[SRSRURR5555eUbX lOF[RR[RR [ 5S5UlU(aX0RUSR5;a@[RRURURUSU5n S n O,[RRURU5n S n [RR!U 5(d[#U RX:55eUR%URUS U 5 OSUl[(R*"URS 5UlURS UlURSUlg!,(df  GN7=f)a DBNet text detector class Parameters ---------- backbone : str, optional Backbone to use. Options are "resnet18" and "resnet50". The default is "resnet18". weight_dir : str, optional Path to directory that contains weight files. If set to None, the path will be set to "../weights/". The default is None. weight_name : str, optional Name of the weight to use as specified in DBNet_inference.yaml or a filename in weight_dir. The default is 'pretrained'. initialize_model : Boolean, optional If True, construct the model and load weight at class initialization. Otherwise, only initial the class without constructing the model. The default is True. dynamic_import_relative_path : str, optional Relative path to 'model/detector.py'. This option is for supporting integrating this module into other modules. For example, easyocr/DBNet This should be left as None when calling this module as a standalone. The default is None. device : str, optional Device to use. Options are "cuda" and "cpu". The default is 'cuda'. verbose : int, optional Verbosity level. The default is 0. Raises ------ ValueError Raised when backbone is invalid. FileNotFoundError Raised when weight file is not found. Returns ------- None. configszDBNet_inference.yamlrNz2Invalid backbone. Current support backbone are {}.,weightsweightzUA weight with a name {} is found in DBNet_inference.yaml but cannot be find file: {}.zYA weight with a name {} is not found in DBNet_inference.yaml and cannot be find file: {}.modelBGR_MEANmin_detection_sizemax_detection_size)deviceospathjoindirname__file__openyaml safe_loadr set_relative_import_pathkeysbackbone ValueErrorformat weight_dirisfileFileNotFoundErrorinitialize_modelrnparrayrrr) selfrr! weight_namer$dynamic_import_relative_pathrverbose config_pathfid weight_path error_messages S/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/easyocr/DBNet/DBNet.py__init__DBNet.__init__s\ ggll277??8#>#.DL$ ( 388GcdDL ||((* *$MQXXY\YaYabfbnbnbsbsbuYvwx x  !(O ggll277??8+DiPDO ll84X>CCEE ggll4??DLL>+..' (<(<[(VWW  ! !$,,x"8"A; ODJj!9: "&,,/C"D"&,,/C"DE$ #s J!! J0c FUceUR[R5nUR5HhupEUS:Xa5UR USR X5RS5-505 M@[ U[5(aURXR5nMhMj U$)a' Create relative import paths for modules specified in class. This method is recursive. Parameters ---------- configs : dict Configuration dictionary from .yaml file. dynamic_import_relative_path : str, optional Relative path to 'model/detector/'. This option is for supporting integrating this module into other modules. For example, easyocr/DBNet This should be left as None when calling this module as a standalone. The default is None. Returns ------- configs : dict Configuration dictionary with correct relative path. class.) splitrsepitemsupdater isinstancedictr)r'r r)preficeskeyvalues r/rDBNet.set_relative_import_pathjs(,777/55bff= ICg~SXXhS9I.I%JKLeT** 99%^E)cURc [S5eURR[R"XR S9SS9 URR 5 g)z Load weight to model. Parameters ---------- weight_path : str Path to trained weight. Raises ------ RuntimeError Raised when the model has not yet been contructed. Returns ------- None. Nz#model has not yet been constructed.) map_locationF)strict)r RuntimeErrorload_state_dicttorchloadreval)r'r-s r/ load_weightDBNet.load_weightsM$ :: DE E ""5::k #T]b"c r?c[R"U5RRR UR 5Ulg)z Contruct text detection model based on the configuration in .yaml file. Parameters ---------- config : dict Configuration dictionary. Returns ------- None. N)rconstruct_class_from_config structurebuilderbuildrr)r'configs r/construct_modelDBNet.construct_models4"==fEOOWW]]^b^i^ij r?cvURU5 URU5 [URR[R R 5(aZURS:XaIURRRRUR5URlggg)z Wrapper to initialize text detection model. This model includes contructing and weight loading. Parameters ---------- model_config : dict Configuration dictionary. weight_path : str Path to trained weight. Returns ------- None. cpuN) rPrHr9rrEnn DataParallelrmoduleto)r' model_configr-s r/r$DBNet.initialize_models \* % djj&&(=(= > >4;;RWCW#zz//6699$++FDJJ DX >r?c*[U[5(at[RR U5(a6[ R "U[ R5RS5nU$[SRU55e[U[R5(aURS5nU$[U[RR5(a%[R"U5SS2SS2SSS24nU$[!S5e)a Load or convert input to OpenCV BGR image numpy array. Parameters ---------- image : str, PIL.Image, or np.ndarray Image to load or convert. Raises ------ FileNotFoundError Raised when the input is a path to file (str), but the file is not found. TypeError Raised when the data type of the input is not supported. Returns ------- image : np.ndarray OpenCV BGR image. float32zCannot find {}NzYUnsupport image format. Only path-to-file, opencv BGR image, and PIL image are supported.)r9strrrr"cv2imread IMREAD_COLORastyper#r r%ndarrayPILImageasarray TypeErrorr'images r/ get_cv2_imageDBNet.get_cv2_images* eS ! !ww~~e$$ 5#*:*:;BB9M ((8(?(?(FGG rzz * *LL+E  syy / /JJu%aDbDj1E wx xr?cURup4nUc*[UR[X4UR55nX4:aP[ [ R"US- 5S-5n[ [ R"Xc- U-S- 5S-5nOO[ [ R"US- 5S-5n[ [ R"Xt- U-S- 5S-5n[R"XU45nXU44$)ar Resize image such that the shorter side of the image is equal to the closest multiple of 32 to the provided detection_size. If detection_size is not provided, it will be resized to the closest multiple of 32 each side. If the original size exceeds the min-/max-detection sizes (specified in configs.yaml), it will be resized to be within the min-/max-sizes. Parameters ---------- img : np.ndarray OpenCV BGR image. detection_size : int, optional Target detection size. The default is None. Returns ------- np.ndarray Resized OpenCV BGR image. The width and height of this image should be multiple of 32. ) shapemaxrminrintmathceilr^resize) r'imgdetection_sizeheightwidth_ new_height new_width resized_imgs r/ resize_imageDBNet.resize_images,99q  ! !8!8#fTMdMd:efN >TYY~':;b@AJDIIj&9E&AB&FG"LMIDIInr&9:R?@ITYYy'86'AB'FG"LMJjj*&=> UO++r?c[R"U5RSSS5R5R S5$)z Convert image array (assuming OpenCV BGR format) to image tensor. Parameters ---------- image : np.ndarray OpenCV BGR image. Returns ------- torch.tensor Tensor image with 4 dimension [batch, channel, width, height]. rr)rE from_numpypermutefloat unsqueezergs r/image_array2tensorDBNet.image_array2tensor s8&..q!Q7==?II!LLr?c$XR- S- $)z Normalize image by substracting BGR mean and divided by 255 Parameters ---------- image : np.ndarray OpenCV BGR image. Returns ------- np.ndarray OpenCV BGR image. go@)rrgs r/normalize_imageDBNet.normalize_images %u,,r?cURU5nURX2S9up4URU5nURU5nX44$)a Wrapper to load and convert an image to an image tensor Parameters ---------- image : path-to-file, PIL.Image, or np.ndarray Image to load or convert. detection_size : int, optional Target detection size. The default is None. Returns ------- img : torch.tensor Tensor image with 4 dimension [batch, channel, width, height].. original_shape : tuple A tuple (height, width) of the original input image before resizing. ru)rir|rr)r' image_pathrurtoriginal_shapes r/ load_imageDBNet.load_image,sS$   +"///U""3'%%c*""r?c [UVs/sHnURX2S9PM sn6up[R"USS9U4$s snf)a( Wrapper to load or convert list of multiple images to a single image tensor. Multiple images are concatenated together on the first dimension. Parameters ---------- images : a list of path-to-file, PIL.Image, or np.ndarray Image to load or convert. detection_size : int, optional Target detection size. The default is None. Returns ------- img : torch.tensor A single tensor image with 4 dimension [batch, channel, width, height]. original_shape : tuple A list of tuples (height, width) of the original input image before resizing. rr)dim)ziprrEcat)r'imagesrurhoriginal_shapess r/ load_imagesDBNet.load_imagesEsR&#&5;(=5;E)-(_5;(=#>yyq)?::(=sAc DURX4S9n /n /n [URS55Hkn X,upU(aURX<XUU UUUS9unnOUR X<XUU UUUS9unnU R U5 U R U5 Mm [ [ X5VVVVs/sHOunn[US:5(a2[ [ UU5VVs/sHunnUS:dMUU4PM snn6OSS/PMQ snnnn6upX4$s snnfs snnnnf)a Translate probability heatmap tensor to text region boudning boxes. Parameters ---------- image_tensor : torch.tensor Image tensor. original_shapes : tuple Original size of the image (height, width) of the input image (before rounded to the closest multiple of 32). hmap : torch.tensor Probability heatmap tensor. text_threshold : float, optional Minimum probability for each pixel of heatmap tensor to be considered as a valid text pixel. The default is 0.2. bbox_min_score : float, optional Minimum score for each detected bounding box to be considered as a valid text bounding box. The default is 0.2. bbox_min_size : int, optional Minimum size for each detected bounding box to be considered as a valid text bounding box. The default is 3. max_candidates : int, optional Maximum number of detected bounding boxes to be considered as candidates for valid text bounding box. Setting it to 0 implies no maximum. The default is 0. as_polygon : boolean, optional If True, return the bounding box as polygon (fine vertrices), otherwise return as rectangular. The default is False. Returns ------- boxes_batch : list of lists Bounding boxes of each text box. scores_batch : list of floats Confidence scores of each text box. ) thresholdr)bbox_min_score bbox_min_sizemax_candidates)binarizerangesizepolygons_from_bitmapboxes_from_bitmapappendrany)r' image_tensorrhmaptext_thresholdrrr as_polygon segmentation boxes_batch scores_batch batch_indexrvrwboxesscoresboxscores r/ hmap2bboxDBNet.hmap2bbox\s\}}T}F    !2!21!56K+8MF $ 9 9(,(9(4(A(-(.9G8E9G!:!I v!% 6 6(,(9(4(A(-(.9G8E9G!7!I v   u %    '-70%(ADK@^*`A__eV36fqj//+.CFufCU0dCUKSY^abYb1=e CU0d+/HJ2w+OA_*`%)! (( 0d*`s20D" D3D; DDc X:$)z Apply threshold to return boolean tensor. Parameters ---------- tensor : torch.tensor input tensor. threshold : float Threshold. Returns ------- torch.tensor Boolean tensor. r)r'tensorrs r/rDBNet.binarizes "!!r?cURS5S:XdeUR5R5SnUR5R5R5SnURup/n /n [ R "US-R[R5[ R[ R5upUS:aU SUn U GHnS[ R"US5-n[ R"UUS5nURS5nURSS:aM\URUURS S 55nUU:aMURSS :a"UR!US S 9n[#U5S:aMOMURS S 5nUR%URS 55unnUUS -:aM['U[(5(d UR+5nUR+5n[R,"[R."USS2S4U - U-5SU5USS2S4'[R,"[R."USS2S4U - U-5SU5USS2S4'U R1UR355 U R1U5 GM X4$)a Translate boolean tensor to fine polygon indicating text bounding boxes Parameters ---------- hmap : torch.tensor Probability heatmap tensor. segmentation : torch.tensor Segmentataion tensor. dest_width : TYPE target width of the output. dest_height : TYPE target width of the output. bbox_min_score : float, optional Minimum score for each detected bounding box to be considered as a valid text bounding box. The default is 0.2. bbox_min_size : int, optional Minimum size for each detected bounding box to be considered as a valid text bounding box. The default is 3. max_candidates : int, optional Maximum number of detected bounding boxes to be considered as candidates for valid text bounding box. Setting it to 0 implies no maximum. The default is 0. Returns ------- boxes_batch : list of lists Polygon bounding boxes of each text box. scores_batch : list of floats Confidence scores of each text box. rrNgMb`?T)r\rr\rg@) unclip_ratio)r\rr)rrSnumpydetachrmr^ findContoursrar%uint8 RETR_LISTCHAIN_APPROX_SIMPLE arcLength approxPolyDPreshapebox_score_fastuncliplenget_mini_boxesr9rpitemcliproundrtolist)r'rr dest_width dest_heightrrrbitmaprvrwrrcontoursrxcontourepsilonapproxpointsrrssides r/rDBNet.polygons_from_bitmapstP  #q(((!!#))+A.xxz  "((*1- && CZ   ) MM3224  A 0HGcmmGT::G%%gw=F^^G,F||A"''fnnR.CDE~%||A"kk&sk;s8a< ++b!$C**3;;z+BCHAu}q((j#..'__. )..0 QTU*Z78!ZIC1IQTV+k9:A{LC1I LL & MM% C F}r?cURS5S:XdeUR5R5SnUR5R5R5SnURup[ R "US-R[R5[ R[ R5upUS:a[[U 5U5n O [U 5n [R"U SS4[RS9n[R"U 4[R S9n[#U 5GHnU UnUR%U5unnUU:aM%[R&"U5nUR)UUR+SS55nUU:aMeUR-U5R+SSS5nUR%U5unnUUS-:aM[R&"U5n[/U[05(d UR35nUR35n[R4"[R6"USS2S4U - U-5SU5USS2S4'[R4"[R6"USS2S4U - U-5SU5USS2S4'UR[R5UUSS2SS24'UUU'GM UR95U4$) a Translate boolean tensor to fine polygon indicating text bounding boxes Parameters ---------- hmap : torch.tensor Probability heatmap tensor. segmentation : torch.tensor Segmentataion tensor. dest_width : TYPE target width of the output. dest_height : TYPE target width of the output. bbox_min_score : float, optional Minimum score for each detected bounding box to be considered as a valid text bounding box. The default is 0.2. bbox_min_size : int, optional Minimum size for each detected bounding box to be considered as a valid text bounding box. The default is 3. max_candidates : int, optional Maximum number of detected bounding boxes to be considered as candidates for valid text bounding box. Setting it to 0 implies no maximum. The default is 0. Returns ------- boxes_batch : list of lists Polygon bounding boxes of each text box. scores_batch : list of floats Confidence scores of each text box. rrrrrdtyper\N)rrSrrrmr^rrar%rrrrorzerosint16r[rrr&rrrr9rprrrr)r'rrrrrrrrrvrwrrx num_contoursrrindexrrrrrs r/rDBNet.boxes_from_bitmapsN  #q(((!!#))+A.xxz  "((*1- &&#CZ//9MM3+B+BD  A s8}n=Lx=L,1-RXX>&DBNet.get_mini_boxes..zs1r?)r<)rrrrrrr)r^ minAreaRectsortedlist boxPointsro) r'r bounding_boxrindex_1index_2index_3index_4rs r/rDBNet.get_mini_boxesxsw/ S]]<89~N-7*' !9Q<&)A, &GGGG !9Q<&)A, &GGGG1C Q(((r?cURSSup4UR5n[R"[R"USS2S4R 55R [R5SUS- 5n[R"[R"USS2S4R55R [R5SUS- 5n[R"[R"USS2S4R 55R [R5SUS- 5n[R"[R"USS2S4R55R [R5SUS- 5n [R"X- S-Xv- S-4[RS9n USS2S4U- USS2S4'USS2S4U- USS2S4'[R"XRSSS5R [R5S5 [R"XU S-2XgS-24U 5S$)z Calculate total score of each bounding box Parameters ---------- hmap : torch.tensor Probability heatmap tensor. box_ : list Rectanguar bounding box. Returns ------- float Confidence score. Nrrrrr\)rmcopyr%rfloorroraint32rrrnrrr^fillPolyrmean) r'rbox_hwrxminxmaxyminymaxmasks r/rDBNet.box_score_fasts zz"1~iikwwrxxAqD 077A1a!eLwwrwws1a4y}}/66rxx@!QUKwwrxxAqD 077A1a!eLwwrwws1a4y}}/66rxx@!QUKxxq$+/:"((K1I$AqD 1I$AqD  T;;q"a077A1Exx$q&[$Av+56=a@@r?c6URRUSS9$)a Run the model to obtain a heatmap tensor from a image tensor. The heatmap tensor indicates the probability of each pixel being a part of text area. Parameters ---------- image_tensor : torch.tensor Image tensor. Returns ------- torch.tensor Probability heatmap tensor. F)training)rforward)r'rs r/ image2hmapDBNet.image2hmapszz!!,!??r?c [U[5(dU/nURXS9up[R"5 UR U 5n UR U U U UUUUUS9upSSS5 U(aW W 4$W $!,(df  N=f)ar Wrapper to run the model on an input image to get text bounding boxes. Parameters ---------- image : path-to-file, PIL.Image, or np.ndarray Image to load or convert. text_threshold : float, optional Minimum probability for each pixel of heatmap tensor to be considered as a valid text pixel. The default is 0.2. bbox_min_score : float, optional Minimum score for each detected bounding box to be considered as a valid text bounding box. The default is 0.2. bbox_min_size : int, optional Minimum size for each detected bounding box to be considered as a valid text bounding box. The default is 3. max_candidates : int, optional Maximum number of detected bounding boxes to be considered as candidates for valid text bounding box. Setting it to 0 implies no maximum. The default is 0. detection_size : int, optional Target detection size. Please see docstring under method resize_image() for explanation. The default is None. as_polygon : boolean, optional If true, return the bounding boxes as find polygons, otherwise, return as rectagular. The default is False. return_scores : boolean, optional If true, return confidence score along with the text bounding boxes. The default is False. Returns ------- list of lists Text bounding boxes. If return_scores is set to true, another list of lists will also be returned. r)rrrrrN)r9rrrEno_gradr r)r'rhrrrrrur return_scoresrrr batch_boxes batch_scoress r/ inferenceDBNet.inferences\%&&GE(,(8(8(8(`% ]]_??<0D(,|7F7;HVHVGTHVBL)7)N %K  , , _s *A>> B )rrr rrrrr!)resnet18N pretrainedTNcudarr)r)皙?rrrF)rrr)g?)rrrrNFF)__name__ __module__ __qualname____firstlineno__r0rrHrPr$rir|rrrrrrrrrrrr r__static_attributes__rr?r/rrs&"+$(04 SEj@. kG*!F",HM - #2;6$'#&"##$"O)b"0/2-../Y@,/*++,R&h).A<@&$'#&"##$#'$"'@r?r)__doc__rrqrshapely.geometryr PIL.Imagercrr%r^rrEmodel.constructorrrrr?r/r#s6 $  +j j r?