From c61eb55213f6f3c54e271276e4e79025b40afd8b Mon Sep 17 00:00:00 2001 From: "deepsource-autofix[bot]" <62050782+deepsource-autofix[bot]@users.noreply.github.com> Date: Sun, 19 Sep 2021 16:48:19 +0000 Subject: [PATCH] Format code with autopep8 This commit fixes the style issues introduced in b47228d according to the output from autopep8. Details: https://deepsource.io/gh/mkleshchenok/EasyOCR/transform/2fcc439f-9c87-435b-bafb-9d95bda1d25f/ --- easyocr/cli.py | 66 ++--- easyocr/config.py | 72 ++--- easyocr/craft.py | 12 +- easyocr/craft_utils.py | 120 +++++--- easyocr/detection.py | 13 +- easyocr/easyocr.py | 314 +++++++++++--------- easyocr/export.py | 9 +- easyocr/imgproc.py | 27 +- easyocr/model/model.py | 14 +- easyocr/model/modules.py | 76 +++-- easyocr/model/vgg_model.py | 11 +- easyocr/recognition.py | 102 ++++--- easyocr/utils.py | 412 +++++++++++++++----------- setup.py | 2 + trainer/dataset.py | 68 +++-- trainer/model.py | 34 ++- trainer/modules/feature_extraction.py | 66 +++-- trainer/modules/prediction.py | 39 ++- trainer/modules/sequence_modeling.py | 6 +- trainer/modules/transformation.py | 68 +++-- trainer/test.py | 38 ++- trainer/train.py | 104 ++++--- trainer/utils.py | 152 ++++++---- 23 files changed, 1110 insertions(+), 715 deletions(-) diff --git a/easyocr/cli.py b/easyocr/cli.py index 4d18ce7fb..f4b7b80a5 100644 --- a/easyocr/cli.py +++ b/easyocr/cli.py @@ -233,40 +233,40 @@ def parse_args(): def main(): args = parse_args() - reader = easyocr.Reader(lang_list=args.lang,\ - gpu=args.gpu,\ - model_storage_directory=args.model_storage_directory,\ - user_network_directory=args.user_network_directory,\ - recog_network=args.recog_network,\ - download_enabled=args.download_enabled,\ - detector=args.detector,\ - recognizer=args.recognizer,\ - verbose=args.verbose,\ + reader = easyocr.Reader(lang_list=args.lang, + gpu=args.gpu, + model_storage_directory=args.model_storage_directory, + user_network_directory=args.user_network_directory, + recog_network=args.recog_network, + download_enabled=args.download_enabled, + detector=args.detector, + recognizer=args.recognizer, + verbose=args.verbose, quantize=args.quantize) - for line in reader.readtext(args.file,\ - decoder=args.decoder,\ - beamWidth=args.beamWidth,\ - batch_size=args.batch_size,\ - workers=args.workers,\ - allowlist=args.allowlist,\ - blocklist=args.blocklist,\ - detail=args.detail,\ - rotation_info=args.rotation_info,\ - paragraph=args.paragraph,\ - min_size=args.min_size,\ - contrast_ths=args.contrast_ths,\ - adjust_contrast=args.adjust_contrast,\ - text_threshold=args.text_threshold,\ - low_text=args.low_text,\ - link_threshold=args.link_threshold,\ - canvas_size=args.canvas_size,\ - mag_ratio=args.mag_ratio,\ - slope_ths=args.slope_ths,\ - ycenter_ths=args.ycenter_ths,\ - height_ths=args.height_ths,\ - width_ths=args.width_ths,\ - y_ths=args.y_ths,\ - x_ths=args.x_ths,\ + for line in reader.readtext(args.file, + decoder=args.decoder, + beamWidth=args.beamWidth, + batch_size=args.batch_size, + workers=args.workers, + allowlist=args.allowlist, + blocklist=args.blocklist, + detail=args.detail, + rotation_info=args.rotation_info, + paragraph=args.paragraph, + min_size=args.min_size, + contrast_ths=args.contrast_ths, + adjust_contrast=args.adjust_contrast, + text_threshold=args.text_threshold, + low_text=args.low_text, + link_threshold=args.link_threshold, + canvas_size=args.canvas_size, + mag_ratio=args.mag_ratio, + slope_ths=args.slope_ths, + ycenter_ths=args.ycenter_ths, + height_ths=args.height_ths, + width_ths=args.width_ths, + y_ths=args.y_ths, + x_ths=args.x_ths, add_margin=args.add_margin): print(line) diff --git a/easyocr/config.py b/easyocr/config.py index 6d8c612cd..6ac29ceb4 100644 --- a/easyocr/config.py +++ b/easyocr/config.py @@ -4,12 +4,12 @@ BASE_PATH = os.path.dirname(__file__) MODULE_PATH = os.environ.get("EASYOCR_MODULE_PATH") or \ - os.environ.get("MODULE_PATH") or \ - os.path.expanduser("~/.EasyOCR/") + os.environ.get("MODULE_PATH") or \ + os.path.expanduser("~/.EasyOCR/") # detector parameters detection_models = { - 'craft' : { + 'craft': { 'filename': 'craft_mlt_25k.pth', 'url': 'https://github.com/JaidedAI/EasyOCR/releases/download/pre-v1.1.6/craft_mlt_25k.zip', 'filesize': '2f8227d2def4037cdb3b34389dcf9ec1' @@ -17,20 +17,20 @@ } # recognizer parameters -latin_lang_list = ['af','az','bs','cs','cy','da','de','en','es','et','fr','ga',\ - 'hr','hu','id','is','it','ku','la','lt','lv','mi','ms','mt',\ - 'nl','no','oc','pi','pl','pt','ro','rs_latin','sk','sl','sq',\ - 'sv','sw','tl','tr','uz','vi'] -arabic_lang_list = ['ar','fa','ug','ur'] -bengali_lang_list = ['bn','as','mni'] -cyrillic_lang_list = ['ru','rs_cyrillic','be','bg','uk','mn','abq','ady','kbd',\ - 'ava','dar','inh','che','lbe','lez','tab','tjk'] -devanagari_lang_list = ['hi','mr','ne','bh','mai','ang','bho','mah','sck','new',\ - 'gom','sa','bgc'] -other_lang_list = ['th','ch_sim','ch_tra','ja','ko','ta','te','kn'] +latin_lang_list = ['af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'en', 'es', 'et', 'fr', 'ga', + 'hr', 'hu', 'id', 'is', 'it', 'ku', 'la', 'lt', 'lv', 'mi', 'ms', 'mt', + 'nl', 'no', 'oc', 'pi', 'pl', 'pt', 'ro', 'rs_latin', 'sk', 'sl', 'sq', + 'sv', 'sw', 'tl', 'tr', 'uz', 'vi'] +arabic_lang_list = ['ar', 'fa', 'ug', 'ur'] +bengali_lang_list = ['bn', 'as', 'mni'] +cyrillic_lang_list = ['ru', 'rs_cyrillic', 'be', 'bg', 'uk', 'mn', 'abq', 'ady', 'kbd', + 'ava', 'dar', 'inh', 'che', 'lbe', 'lez', 'tab', 'tjk'] +devanagari_lang_list = ['hi', 'mr', 'ne', 'bh', 'mai', 'ang', 'bho', 'mah', 'sck', 'new', + 'gom', 'sa', 'bgc'] +other_lang_list = ['th', 'ch_sim', 'ch_tra', 'ja', 'ko', 'ta', 'te', 'kn'] -all_lang_list = latin_lang_list + arabic_lang_list+ cyrillic_lang_list +\ - devanagari_lang_list + bengali_lang_list + other_lang_list +all_lang_list = latin_lang_list + arabic_lang_list + cyrillic_lang_list +\ + devanagari_lang_list + bengali_lang_list + other_lang_list imgH = 64 separator_list = { 'th': ['\xa2', '\xa3'], @@ -41,8 +41,8 @@ separator_char += sep recognition_models = { - 'gen1' : { - 'latin_g1':{ + 'gen1': { + 'latin_g1': { 'filename': 'latin.pth', 'model_script': 'latin', 'url': 'https://github.com/JaidedAI/EasyOCR/releases/download/pre-v1.1.6/latin.zip', @@ -50,7 +50,7 @@ 'characters': "0123456789!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyzÀÁÂÃÄÅÆÇÈÉÊËÍÎÑÒÓÔÕÖØÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõöøùúûüýþÿąęĮįıŁłŒœŠšųŽž", 'symbols': "0123456789!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~ " }, - 'zh_tra_g1':{ + 'zh_tra_g1': { 'filename': 'chinese.pth', 'model_script': 'chinese_tra', 'url': 'https://github.com/JaidedAI/EasyOCR/releases/download/pre-v1.1.6/chinese.zip', @@ -58,7 +58,7 @@ 'characters': '0123456789!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~ 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靄靈青靖靚靛靜非靠靡面靨革靳靴靶靼鞅鞋鞍鞏鞘鞠鞣鞭韁韃韋韌韓韜韞韭音韶韻響頁頂頃項順須頊頌頏預頑頒頓頗領頜頡頤頦頭頰頷頸頹頻顆題額顎顏顒顓願顙顛類顥顧顫顯顱風颯颱颶飄飆飛食飢飩飪飭飯飲飴飼飽飾餅餉養餌餐餒餓餘餚餛餞餡館餬餵餽餾饅饈饋饌饑饒饕饗首馗香馥馨馬馭馮馳馴駁駐駒駕駙駛駝駭駱駿騁騎騏騑騙騧騫騰騷騾驁驃驄驅驊驍驕驗驚驛驟驢驤驩驪骨骰骷骸骼髀髏髒髓體髖高髦髮髯髻鬃鬆鬍鬘鬚鬟鬣鬥鬧鬩鬯鬱鬲鬻鬼魁魂魄魅魋魏魔魚魨魯魴魷鮆鮑鮚鮠鮨鮪鮫鮭鮮鮸鯁鯉鯊鯔鯖鯛鯡鯧鯨鯰鯽鰈鰍鰓鰨鰭鰲鰻鱄鱈鱔鱗鱘鱠鱧鱨鱷鱸鱺鳥鳩鳳鳴鳶鴉鴒鴛鴞鴦鴨鴻鴿鵝鵠鵡鵪鵬鵰鵲鶉鶩鶯鶴鶿鷗鷳鷸鷹鷺鸕鸚鸛鸞鹵鹹鹼鹽鹿麂麇麋麒麓麗麝麟麥麩麴麵麻麼麾黃黎黏黑黔默黛黜點黠黧黨黯黴黷黼鼎鼐鼓鼠鼬鼻齊齋齒齡齣齦齧龍龐龔龕龜龢', 'symbols': "0123456789!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~ " }, - 'zh_sim_g1':{ + 'zh_sim_g1': { 'filename': 'chinese_sim.pth', 'model_script': 'chinese_sim', 'url': 'https://github.com/JaidedAI/EasyOCR/releases/download/pre-v1.1.6/chinese_sim.zip', @@ -66,7 +66,7 @@ 'characters': "0123456789!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~ 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'symbols': "0123456789!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~ " }, - 'japanese_g1':{ + 'japanese_g1': { 'filename': 'japanese.pth', 'model_script': 'japanese', 'url': 'https://github.com/JaidedAI/EasyOCR/releases/download/pre-v1.1.6/japanese.zip', @@ -74,7 +74,7 @@ 'symbols': "0123456789!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~ ", 'characters': '0123456789!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~ 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}, - 'korean_g1':{ + 'korean_g1': { 'filename': 'korean.pth', 'model_script': 'korean', 'url': 'https://github.com/JaidedAI/EasyOCR/releases/download/pre-v1.1.6/korean.zip', @@ -82,7 +82,7 @@ 'symbols': "0123456789!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~ ", 'characters': '0123456789!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~ abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZㆍ가각간갇갈갉감갑값갓갔강갖같갚개객갤갭갯갱갸걀거걱건걷걸검겁것겉게겐겔겟겠겨격겪견결겸겹겼경곁계고곡곤곧골곰곱곳공곶과곽관괄괌광괘괜괭괴괼굉교굘구국군굳굴굵굶굽굿궁궂궈권궐궤귀귄귈규균귤그극근글긁금급긋긍기긴길김깁깃깊까깍깎깐깔깜깝깡깥깨깬꺼꺾껄껌껍껏껐껑께껴꼈꼬꼭꼴꼼꼽꽁꽂꽃꽈꽉꽝꽤꾀꾸꾼꿀꿇꿈꿔꿨꿰뀌뀐뀔끄끈끊끌끓끔끗끝끼낀낄낌나낙낚난날낡남납낫났낭낮낯낱낳내낸낼냄냅냇냈냉냐냥너넉넌널넓넘넛넣네넥넨넬넵넷녀녁년념녔녕녘녜녠노녹논놀놈놉농높놓놔놨뇌뇨뇰뇽누눅눈눌눔눕눙눠뉘뉜뉴늄느늑는늘늙늠능늦늪늬니닉닌닐님닙닛닝다닥닦단닫달닭닮닳담답닷당닻닿대댁댄댈댐댑댓더덕던덜덟덤덥덧덩덫덮데덱덴델뎀뎅뎌뎬도독돈돋돌돔돕돗동돛돼됐되된될됨됩두둑둔둘둠둡둥둬뒀뒤뒷듀듈듐드득든듣들듬듭듯등디딕딘딛딜딥딧딩딪따딱딴딸땀땃땄땅때땐땡떠떡떤떨떻떼또똑똔똘똥뚜뚝뚫뚱뛰뛴뛸뜀뜨뜩뜬뜯뜰뜸뜻띄띈띠띤라락란랄람랍랏랐랑랗래랙랜랠램랩랫랬랭랴략랸량러럭런럴럼럽럿렀렁렇레렉렌렐렘렙렛렝려력련렬렴렵렷렸령례롄로록론롤롬롭롯롱롸뢰룀료룡루룩룬룰룸룹룻룽뤄뤘뤼뤽륀륄류륙륜률륨륭르륵른를름릅릇릉릎리릭린릴림립릿링마막만많맏말맑맘맙맛망맞맡맣매맥맨맬맴맵맷맹맺먀머먹먼멀멈멋멍메멕멘멜멤멧멩며멱면멸몄명몇모목몫몬몰몸몹못몽뫼묀묘무묵묶문묻물뭄뭇뭉뭐뭔뭘뮌뮐뮤뮬므믈믐미믹민믿밀밈밋밍및밑바박밖반받발밝밟밤밥밧방밭배백밴밸뱀뱃뱅뱉뱌버벅번벌범법벗벙벚베벡벤벨벰벳벵벼벽변별볍병볕보복볶본볼봄봅봇봉봐봤뵈뵙뵤부북분불붉붐붓붕붙뷔뷘뷰브븐블비빅빈빌빔빕빗빙빚빛빠빡빤빨빵빼빽뺀뺏뺑뺨뻐뻔뻗뻘뻤뼈뽀뽑뾰뿌뿐뿔뿜쁘쁜쁨삐사삭산살삶삼삽삿샀상새색샌샐샘샛생샤샨샬샴샵샷샹섀서석섞선섣설섬섭섯섰성세섹센셀셈셉셋셍셔션셜셤셨셰셴셸소속손솔솜솟송솽쇄쇠쇼숀숄숍숏숑수숙순술숨숫숭숱숲숴쉐쉬쉴쉼쉽슈슐슘슛슝스슨슬슭슴습슷승시식신싣실싫심십싯싱싶싸싹싼쌀쌈쌌쌍쌓써썩썬썰썸썹썼썽쎄쏘쏙쏜쏟쏠쏴쐈쐐쑈쑤쑥쑨쑹쓰쓴쓸씀씌씨씩씬씹씻씽아악안앉않알앓암압앗았앙앞애액앤앨앰앱앳앴앵야약얀얄얇얌얏양얕얘어억언얹얻얼얽엄업없엇었엉엌엎에엑엔엘엠엡엣엥여역엮연열엷염엽엿였영옅옆예옌옐옙옛오옥온올옭옮옳옴옵옷옹옻와왁완왈왓왔왕왜외왼요욕욘욜용우욱운울움웁웃웅워웍원월웜웠웨웬웰웸웹위윅윈윌윔윗윙유육윤율윳융으은을음읍응의이익인일읽잃임입잇있잉잊잎자작잔잖잘잠잡잣장잦재잭잼잿쟁쟈쟝저적전절젊점접젓정젖제젝젠젤젭젯젱져젼졌조족존졸좀좁종좇좋좌좡죄죠주죽준줄줌줍중줘줬쥐쥔쥘쥬즈즉즌즐즘즙증지직진질짊짐집짓징짖짙짚짜짝짠짤짧짱째쨌쩌쩍쩐쩔쩡쪼쪽쫓쬐쭈쭉쭝쭤쯔쯤찌찍찐찔찢차착찬찮찰참찹찻창찾채책챈챌챔챗챙챠처척천철첨첩첫청체첸첼쳇쳐쳤초촉촌촐촘촛총촨촬최쵸추축춘출춤춥춧충춰췄췌취츄츠측츨츰츳층치칙친칠침칩칭카칸칼캄캅캇캉캐캔캘캠캡캣캥캬커컨컫컬컴컵컷컸컹케켁켄켈켐켓켜켰코콕콘콜콤콥콧콩콰콴콸쾌쾨쾰쿄쿠쿡쿤쿨쿰쿼퀀퀄퀘퀴퀸퀼큐큘크큰클큼키킥킨킬킴킵킷킹타탁탄탈탐탑탓탔탕태택탠탤탬탭탱탸터턱턴털텀텁텃텅테텍텐텔템텝텟텡톈토톡톤톨톰톱통퇴투툭툰툴툼퉁튀튕튜튠튬트특튼튿틀틈티틱틴틸팀팁팅파팍팎판팔팜팝팟팡팥패팩팬팰팹팻팽퍼퍽펀펄펌펑페펙펜펠펨펩펫펴편펼폄폈평폐포폭폰폴폼퐁푀표푸푹푼풀품풋풍퓌퓨퓰프픈플픔피픽핀필핌핍핏핑하학한할핥함합핫항해핵핸핼햄햇했행햐향허헉헌헐험헛헝헤헥헨헬헴헵혀혁현혈혐협혔형혜호혹혼홀홈홉홋홍화확환활황홰횃회획횟횡효후훅훈훌훑훔훗훙훤훨훼휘휜휠휩휴흉흐흑흔흘흙흠흡흥흩희흰히힉힌힐힘힙' }, - 'thai_g1':{ + 'thai_g1': { 'filename': 'thai.pth', 'model_script': 'thai', 'url': 'https://github.com/JaidedAI/EasyOCR/releases/download/pre-v1.1.6/thai.zip', @@ -90,7 +90,7 @@ 'symbols': "0123456789!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~ ", 'characters': ''.join(separator_char)+'!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~ abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZกขคฆงจฉชซฌญฎฏฐฑฒณดตถทธนบปผฝพฟภมยรลวศษสหฬอฮฤเแโใไะาุูิีืึั่้๊๋็์ำํฺฯๆ0123456789๑๒๓๔๕๖๗๘๙' }, - 'devanagari_g1':{ + 'devanagari_g1': { 'filename': 'devanagari.pth', 'model_script': 'devanagari', 'url': 'https://github.com/JaidedAI/EasyOCR/releases/download/pre-v1.1.6/devanagari.zip', @@ -98,7 +98,7 @@ 'symbols': "0123456789!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~ ", 'characters': '0123456789!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~ abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ.ँंःअअंअःआइईउऊऋएऐऑओऔकखगघङचछजझञटठडढणतथदधनऩपफबभमयरऱलळवशषसह़ािीुूृॅेैॉोौ्ॐ॒क़ख़ग़ज़ड़ढ़फ़ॠ।०१२३४५६७८९॰' }, - 'cyrillic_g1':{ + 'cyrillic_g1': { 'filename': 'cyrillic.pth', 'model_script': 'cyrillic', 'url': 'https://github.com/JaidedAI/EasyOCR/releases/download/pre-v1.1.6/cyrillic.zip', @@ -106,7 +106,7 @@ 'symbols': "0123456789!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~ ", 'characters': '0123456789!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~ abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZЁЂЄІЇЈЉЊЋЎЏАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЪЫЬЭЮЯабвгдежзийклмнопрстуфхцчшщъыьэюяёђєіїјљњћўџҐґҮүө' }, - 'arabic_g1':{ + 'arabic_g1': { 'filename': 'arabic.pth', 'model_script': 'arabic', 'url': 'https://github.com/JaidedAI/EasyOCR/releases/download/pre-v1.1.6/arabic.zip', @@ -114,7 +114,7 @@ 'symbols': '«»؟،؛٠١٢٣٤٥٦٧٨٩' + '0123456789!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~ ', 'characters': '0123456789!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~ abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ٠١٢٣٤٥٦٧٨٩«»؟،؛ءآأؤإئااًبةتثجحخدذرزسشصضطظعغفقكلمنهوىيًٌٍَُِّْٰٓٔٱٹپچڈڑژکڭگںھۀہۂۃۆۇۈۋیېےۓە' }, - 'tamil_g1':{ + 'tamil_g1': { 'filename': 'tamil.pth', 'model_script': 'tamil', 'url': 'https://github.com/JaidedAI/EasyOCR/releases/download/v1.1.7/tamil.zip', @@ -122,7 +122,7 @@ 'symbols': "0123456789!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~ ", 'characters': '0123456789!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~ abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZஃஅஆஇஈஉஊஎஏஐஒஓஔகஙசஜஞடணதநனபமயரறலளழவஷஸஹாிீுூெேைொோௌ்' }, - 'bengali_g1':{ + 'bengali_g1': { 'filename': 'bengali.pth', 'model_script': 'bengali', 'url': 'https://github.com/JaidedAI/EasyOCR/releases/download/v1.1.8/bengali.zip', @@ -131,8 +131,8 @@ 'characters': '0123456789!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~ abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ।ঁংঃঅআইঈউঊঋঌএঐওঔকখগঘঙচছজঝঞটঠডঢণতথদধনপফবভমযরলশষসহ়ািীুূৃেৈোৌ্ৎড়ঢ়য়০১২৩৪৫৬৭৮৯' }, }, - 'gen2' : { - 'english_g2':{ + 'gen2': { + 'english_g2': { 'filename': 'english_g2.pth', 'model_script': 'english', 'url': 'https://github.com/JaidedAI/EasyOCR/releases/download/v1.3/english_g2.zip', @@ -140,7 +140,7 @@ 'symbols': "0123456789!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~ €", 'characters': "0123456789!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~ €ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" }, - 'latin_g2':{ + 'latin_g2': { 'filename': 'latin_g2.pth', 'model_script': 'latin', 'url': 'https://github.com/JaidedAI/EasyOCR/releases/download/v1.3/latin_g2.zip', @@ -148,7 +148,7 @@ 'symbols': " !\"#$%&'()*+,-./0123456789:;<=>?@[\\]^_`{|}~ €", 'characters': " !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~ªÀÁÂÃÄÅÆÇÈÉÊËÌÍÎÏÐÑÒÓÔÕÖØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõöøùúûüýþÿĀāĂ㥹ĆćČčĎďĐđĒēĖėĘęĚěĞğĨĩĪīĮįİıĶķĹĺĻļĽľŁłŃńŅņŇňŒœŔŕŘřŚśŞşŠšŤťŨũŪūŮůŲųŸŹźŻżŽžƏƠơƯưȘșȚțə̇ḌḍḶḷṀṁṂṃṄṅṆṇṬṭẠạẢảẤấẦầẨẩẪẫẬậẮắẰằẲẳẴẵẶặẸẹẺẻẼẽẾếỀềỂểỄễỆệỈỉỊịỌọỎỏỐốỒồỔổỖỗỘộỚớỜờỞởỠỡỢợỤụỦủỨứỪừỬửỮữỰựỲỳỴỵỶỷỸỹ€" }, - 'zh_sim_g2':{ + 'zh_sim_g2': { 'filename': 'zh_sim_g2.pth', 'model_script': 'chinese_sim', 'url': 'https://github.com/JaidedAI/EasyOCR/releases/download/v1.3/zh_sim_g2.zip', @@ -156,7 +156,7 @@ 'symbols': " !\"#$%&'()*+,-./0123456789:;<=>?@[\\]^_`{|}~。〈〉《》「」『』", 'characters': " 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}, - 'japanese_g2':{ + 'japanese_g2': { 'filename': 'japanese_g2.pth', 'model_script': 'japanese', 'url': 'https://github.com/JaidedAI/EasyOCR/releases/download/v1.3/japanese_g2.zip', @@ -164,7 +164,7 @@ 'symbols': " !\"#$%&'()*+,-./0123456789:;<=>?@[\\]^_`{|}~‥…※♪、。々〈〉《》「」『』【】〔〕", 'characters': " !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~‥…※♪、。々〈〉《》「」『』【】〔〕あぃいうぇえおかがきぎくぐけげこごさざしじすずせぜそぞただちぢっつづてでとどなにぬねのはばぱひびぴふぶぷへべぺほぼぽまみむめもゃやゅゆょよらりるれろわをんァアィイゥウェエォオカガキギクグケゲコゴサザシジスズセゼソゾタダチヂッツヅテデトドナニヌネノハバパヒビピフブプヘベペホボポマミムメモャヤュユョヨラリルレロワヱヲンヴヶ・ー一丁七万丈三上下不与丑世丘丙丞両並中串丸丹主乃久之乗乙九也乱乳乾亀了予争事二互五井亘亜亡交亥亨享京亭亮人仁今介仏仕他付仙代令以仮仰仲件任企伊伍伎伏休会伝伯伴伶伸似伽位低住佐佑体何余作佳使例侍侑供依価便係俊俗保信俣修俵倉個倍倒倖候借値倫倭倶偉偏健側偵偽傍傑備債傷働像僧儀優允元兄充先光克免兎児党入全八公六共兵其具典兼内円再写冠冨冬冴冶冷凍凡処凪出刀刃分切刈刑列初判別利制刷券刺刻則削前剛剣剤剥副割創劇力功加助労効勅勇勉動勘務勝勢勤勧勲包化北匠匡区医十千升午半卒卓協南単博占卯印危即卵卸厚原厨厳去参又叉及友双反収取受叡口古句只叫召可台史右叶号司各合吉同名后吏吐向君吟否吸吹吾呂呉告呑周味呼命和咲哀品哉員哲唄唐唯唱商問啓善喜喬喰営嗣嘉噌器四回因団囲図固国國園土圧在圭地坂均坊坐坪垂型垢垣埋城埜域執基埼堀堂堅堤堰報場堺塔塗塚塩塵境墓増墨墳壁壇壊士壬壮声壱売壷変夏夕外多夜夢大天太夫央失夷奇奈奉奏契奥奨女奴好如妃妙妹妻姉始姓委姥姫姿威娘婆婚婦嫌嬉子孔字存孝孟季孤学孫宅宇守安完宏宗官宙定宜宝実客宣室宥宮宰害家容宿寂寄寅密富寒寛寝察寧審寸寺対寿封専射将尊尋導小少尚尭就尺尻尼尽尾尿局居屈屋展属層屯山岐岡岩岬岱岳岸峠峡峨峯峰島峻崇崎崩嵐嵩嵯嶋嶺巌川州巡巣工左巧巨差己巳巴巻巾市布帆希帝師席帯帰帳常帽幅幌幕幡幣干平年幸幹幽幾庁広庄床序底店府度座庫庭庵康庸廃廉廣延建廻弁式弐弓引弘弟弥弦弱張強弾当形彦彩彪彫彬彰影役彼往征径待律後徒従得御復微徳徹心必忌忍志応忠快念怒怜思急性恋恐恒恩恭息恵悌悟悠患悦悪悲情惇惑惟惣想意愚愛感慈態慎慣慧慶憂憲憶懐懸戎成我戒戦戯戸戻房所扇手才打払扶承技投抗折抜抱押担拓拝拡拳拾持指挙振捕捨捷掃排掘掛採探接推掻提揖揚換揮援揺損摂摘摩摺撃撫播撮操擦擬支改攻放政故敏救敗教敢散敦敬数整敵敷文斉斎斐斑斗料斜斤断斯新方於施旅旋族旗日旦旧旨早旬旭旺昂昆昇昌明易星映春昭是昼時晃晋晩普景晴晶智暁暖暗暢暦暮暴曇曙曜曲曳更書曹曽曾替最月有朋服朔朗望朝期木未末本札朱朴杉李杏材村杖杜束条来杭東杵松板析枕林枚果枝枯架柄柊柏柑染柔柚柱柳柴査柿栃栄栖栗校株核根格桂桃案桐桑桜桝桧桶梁梅梓梢梨梯械梶棄棒棚棟森椋植椎検椿楊楓楠楢業楯極楼楽榊榎榛構槌様槙槻樋標模権横樫樹樺樽橋橘機檀櫛欠次欣欧欲欽歌歓止正此武歩歯歳歴死殊残殖段殺殻殿毅母毎毒比毛氏民気水氷永汀汁求汐汗汚江池汰汲決沈沓沖沙沢河油治沼泉泊法波泣泥注泰洋洗洞津洪洲活派流浄浅浜浦浩浪浮浴海消涌涙液涼淀淑淡深淳淵混添清済渉渋渓渕渚減渡渥温測港湊湖湧湯湾湿満源準溜溝溶滅滋滑滝漁漆漏演漢漬潔潜潟潤潮潰澄澤激濃濱瀧瀬灘火灯灰災炉炎炭点為烈烏無焦然焼煙照煮熊熟熱燃燈燕燦燭爆爪父爽爾片版牛牟牧物特犬犯状狂狐狗狩独狭狼猛猪猫献猿獄獅獣玄率玉王玖玲珍珠現球理琉琢琳琴瑚瑛瑞瑠瑳璃環瓜瓦瓶甘甚生産用甫田由甲申男町画界畑畔留畜畝畠略番異畳疾病症痛療発登白百的皆皇皮皿盆益盗盛盟監盤目盲直相省眉看県眞真眠眼着督睦瞬瞳矢知矩短石砂研砲破硫硬碑碧碩確磁磐磨磯礁示礼社祇祈祉祐祖祝神祢祥票祭禁禄禅禎福禰秀私秋科秘秦秩称移稀程税稔稗稚種稲穂積穏穴究空突窓窪立竜章童竪端競竹笑笛笠符第笹筆等筋筑筒答策箇箕算管箱箸節範築篠篤篭簡簾籍米粉粒粕粗粟粥精糖糞糠糸系紀約紅紋納純紗紘紙級素紡索紫細紳紹紺終組経結絡絢給統絵絶絹継続綜維綱網綾綿緋総緑緒線締編緩練縁縄縦縫縮績繁織繰罪置羅羊美群義羽翁習翔翠翼耀老考者耐耕耳耶聖聞聡聴職肇肉肌肝股肥肩育肺背胞胡胤胸能脂脇脈脚脱脳腐腕腫腰腸腹膜膳臣臥臨自臭至致臼興舌舎舘舛舜舞舟航般船艦良色艶芋芙芝芥芦花芳芸芹芽苅苑苔苗若苦苫英茂茄茅茉茜茨茶草荒荘荷荻莉菅菊菌菓菖菜華菱萌萩萱落葉葛葦葵蒔蒲蒸蒼蓋蓑蓬蓮蔦蔭蔵蕗薄薩薫薬薮藁藍藤藻蘇蘭虎虚虫虹虻蚊蛇蛍蛭蜂蜜蝦蝶融螺蟹蟻血衆行術街衛衝衡衣表袋袖被裁裂装裏裕補裟裸製複西要覆覇見規視覚覧親観角解触言計訓託記訪設許訳訴診証評詞詠試詩詰話誉誌認誓誘語誠誤説読課調談請諏論諭諸謙講謝謹識警議譲護讃谷豆豊豚象豪貝貞負財貢貧貨販貫責貯貴買貸費賀賃資賞賢質赤赦走起超越足跡路跳踏身車軌軍軒軟転軸軽載輔輝輪輸辛辞辰農辺辻込迎近返迦迫述迷追退送逃逆透途通速造逢連週進逸遅遊運過道達違遠遣遥適選遺遼避邑那邦邪郁郎郡部郭郵郷都配酒酔酢酸醍醐采釈里重野量金釘釜針釣鈴鉄鉛鉢鉱鉾銀銃銅銘銭鋭鋼錦録鍋鍛鍬鍵鎌鎖鎮鏡鐘鑑長門閉開閑間関閣闇闘阪防阿陀附降限院陣除陰陳陵陶陸険陽隅隆隈隊階随隔際障隠隣隼雀雁雄雅集雑雛離難雨雪雲零雷電震霊霜霞霧露青靖静非面革鞍鞠韓音響頂頃項順須預頓領頭頼題額顔顕願類風飛食飯飲飼飽飾餅養館首香馨馬駄駅駆駐駒駿騎験骨高髪鬼魁魂魅魔魚鮎鮫鮮鯉鯨鳥鳩鳳鳴鴨鴻鵜鶏鶴鷲鷹鷺鹿麓麗麦麻麿黄黒黙鼓鼠鼻齢龍＊" }, - 'korean_g2':{ + 'korean_g2': { 'filename': 'korean_g2.pth', 'model_script': 'korean', 'url': 'https://github.com/JaidedAI/EasyOCR/releases/download/v1.3/korean_g2.zip', @@ -172,7 +172,7 @@ 'symbols': " !\"#$%&'()*+,-./0123456789:;<=>?@[\\]^_`{|}~", 'characters': " !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~가각간갇갈감갑값강갖같갚갛개객걀거걱건걷걸검겁것겉게겨격겪견결겹경곁계고곡곤곧골곰곱곳공과관광괜괴굉교구국군굳굴굵굶굽궁권귀규균그극근글긁금급긋긍기긴길김깅깊까깎깐깔깜깝깥깨꺼꺾껍껏껑께껴꼬꼭꼴꼼꼽꽂꽃꽉꽤꾸꿀꿈뀌끄끈끊끌끓끔끗끝끼낌나낙낚난날낡남납낫낭낮낯낱낳내냄냉냐냥너넉널넓넘넣네넥넷녀녁년념녕노녹논놀놈농높놓놔뇌뇨누눈눕뉘뉴늄느늑는늘늙능늦늬니닐님다닥닦단닫달닭닮담답닷당닿대댁댐더덕던덜덤덥덧덩덮데델도독돈돌돕동돼되된두둑둘둠둡둥뒤뒷드득든듣들듬듭듯등디딩딪따딱딴딸땀땅때땜떠떡떤떨떻떼또똑뚜뚫뚱뛰뜨뜩뜯뜰뜻띄라락란람랍랑랗래랜램랫략량러럭런럴럼럽럿렁렇레렉렌려력련렬렵령례로록론롬롭롯료루룩룹룻뤄류륙률륭르른름릇릎리릭린림립릿마막만많말맑맘맙맛망맞맡맣매맥맨맵맺머먹먼멀멈멋멍멎메멘멩며면멸명몇모목몰몸몹못몽묘무묵묶문묻물뭄뭇뭐뭣므미민믿밀밉밌및밑바박밖반받발밝밟밤밥방밭배백뱀뱃뱉버번벌범법벗베벤벼벽변별볍병볕보복볶본볼봄봇봉뵈뵙부북분불붉붐붓붕붙뷰브블비빌빗빚빛빠빨빵빼뺨뻐뻔뻗뼈뽑뿌뿐쁘쁨사삭산살삶삼상새색샌생서석섞선설섬섭섯성세센셈셋션소속손솔솜솟송솥쇄쇠쇼수숙순술숨숫숲쉬쉽슈스슨슬슴습슷승시식신싣실싫심십싱싶싸싹쌀쌍쌓써썩썰썹쎄쏘쏟쑤쓰쓸씀씌씨씩씬씹씻아악안앉않알앓암압앗앙앞애액야약얇양얗얘어억언얹얻얼엄업없엇엉엌엎에엔엘여역연열엷염엽엿영옆예옛오옥온올옮옳옷와완왕왜왠외왼요욕용우욱운울움웃웅워원월웨웬위윗유육율으윽은을음응의이익인일읽잃임입잇있잊잎자작잔잖잘잠잡장잦재쟁저적전절젊점접젓정젖제젠젯져조족존졸좀좁종좋좌죄주죽준줄줌줍중쥐즈즉즌즐즘증지직진질짐집짓징짙짚짜짝짧째쨌쩌쩍쩐쪽쫓쭈쭉찌찍찢차착찬찮찰참창찾채책챔챙처척천철첫청체쳐초촉촌총촬최추축춘출춤춥춧충취츠측츰층치칙친칠침칭카칸칼캐캠커컨컬컴컵컷켓켜코콜콤콩쾌쿠퀴크큰클큼키킬타탁탄탈탑탓탕태택탤터턱털텅테텍텔템토톤톱통퇴투툼퉁튀튜트특튼튿틀틈티틱팀팅파팎판팔패팩팬퍼퍽페펴편펼평폐포폭표푸푹풀품풍퓨프플픔피픽필핏핑하학한할함합항해핵핸햄햇행향허헌험헤헬혀현혈협형혜호혹혼홀홍화확환활황회획횟효후훈훌훔훨휘휴흉흐흑흔흘흙흡흥흩희흰히힘" }, - 'telugu_g2':{ + 'telugu_g2': { 'filename': 'telugu.pth', 'model_script': 'telugu', 'url': 'https://github.com/JaidedAI/EasyOCR/releases/download/v1.2/telugu.zip', @@ -180,7 +180,7 @@ 'symbols': '0123456789!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~ ', 'characters': '0123456789!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~ abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZఁంఃఅఆఇఈఉఊఋఌఎఏఐఒఓఔకఖగఘఙచఛజఝఞటఠడఢణతథదధనపఫబభమయరఱలళవశషసహాిీుూృౄెేైొోౌ్ౠౡౢౣ' }, - 'kannada_g2':{ + 'kannada_g2': { 'filename': 'kannada.pth', 'model_script': 'kannada', 'url': 'https://github.com/JaidedAI/EasyOCR/releases/download/v1.2/kannada.zip', diff --git a/easyocr/craft.py b/easyocr/craft.py index 074d7e4b4..76208ff4c 100644 --- a/easyocr/craft.py +++ b/easyocr/craft.py @@ -10,6 +10,7 @@ from .model.modules import vgg16_bn, init_weights + class double_conv(nn.Module): def __init__(self, in_ch, mid_ch, out_ch): super(double_conv, self).__init__() @@ -63,18 +64,21 @@ def forward(self, x): y = torch.cat([sources[0], sources[1]], dim=1) y = self.upconv1(y) - y = F.interpolate(y, size=sources[2].size()[2:], mode='bilinear', align_corners=False) + y = F.interpolate(y, size=sources[2].size()[ + 2:], mode='bilinear', align_corners=False) y = torch.cat([y, sources[2]], dim=1) y = self.upconv2(y) - y = F.interpolate(y, size=sources[3].size()[2:], mode='bilinear', align_corners=False) + y = F.interpolate(y, size=sources[3].size()[ + 2:], mode='bilinear', align_corners=False) y = torch.cat([y, sources[3]], dim=1) y = self.upconv3(y) - y = F.interpolate(y, size=sources[4].size()[2:], mode='bilinear', align_corners=False) + y = F.interpolate(y, size=sources[4].size()[ + 2:], mode='bilinear', align_corners=False) y = torch.cat([y, sources[4]], dim=1) feature = self.upconv4(y) y = self.conv_cls(feature) - return y.permute(0,2,3,1), feature + return y.permute(0, 2, 3, 1), feature diff --git a/easyocr/craft_utils.py b/easyocr/craft_utils.py index 40209d230..9691adc11 100644 --- a/easyocr/craft_utils.py +++ b/easyocr/craft_utils.py @@ -11,9 +11,13 @@ """ auxilary functions """ # unwarp corodinates + + def warpCoord(Minv, pt): out = np.matmul(Minv, (pt[0], pt[1], 1)) return np.array([out[0]/out[2], out[1]/out[2]]) + + """ end of auxilary functions """ @@ -28,42 +32,53 @@ def getDetBoxes_core(textmap, linkmap, text_threshold, link_threshold, low_text, ret, link_score = cv2.threshold(linkmap, link_threshold, 1, 0) text_score_comb = np.clip(text_score + link_score, 0, 1) - nLabels, labels, stats, centroids = cv2.connectedComponentsWithStats(text_score_comb.astype(np.uint8), connectivity=4) + nLabels, labels, stats, centroids = cv2.connectedComponentsWithStats( + text_score_comb.astype(np.uint8), connectivity=4) det = [] mapper = [] - for k in range(1,nLabels): + for k in range(1, nLabels): # size filtering size = stats[k, cv2.CC_STAT_AREA] - if size < 10: continue + if size < 10: + continue # thresholding - if np.max(textmap[labels==k]) < text_threshold: continue + if np.max(textmap[labels == k]) < text_threshold: + continue # make segmentation map segmap = np.zeros(textmap.shape, dtype=np.uint8) - segmap[labels==k] = 255 + segmap[labels == k] = 255 if estimate_num_chars: - _, character_locs = cv2.threshold((textmap - linkmap) * segmap /255., text_threshold, 1, 0) + _, character_locs = cv2.threshold( + (textmap - linkmap) * segmap / 255., text_threshold, 1, 0) _, n_chars = label(character_locs) mapper.append(n_chars) else: mapper.append(k) - segmap[np.logical_and(link_score==1, text_score==0)] = 0 # remove link area + # remove link area + segmap[np.logical_and(link_score == 1, text_score == 0)] = 0 x, y = stats[k, cv2.CC_STAT_LEFT], stats[k, cv2.CC_STAT_TOP] w, h = stats[k, cv2.CC_STAT_WIDTH], stats[k, cv2.CC_STAT_HEIGHT] niter = int(math.sqrt(size * min(w, h) / (w * h)) * 2) sx, ex, sy, ey = x - niter, x + w + niter + 1, y - niter, y + h + niter + 1 # boundary check - if sx < 0 : sx = 0 - if sy < 0 : sy = 0 - if ex >= img_w: ex = img_w - if ey >= img_h: ey = img_h - kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(1 + niter, 1 + niter)) + if sx < 0: + sx = 0 + if sy < 0: + sy = 0 + if ex >= img_w: + ex = img_w + if ey >= img_h: + ey = img_h + kernel = cv2.getStructuringElement( + cv2.MORPH_RECT, (1 + niter, 1 + niter)) segmap[sy:ey, sx:ex] = cv2.dilate(segmap[sy:ey, sx:ex], kernel) # make box - np_contours = np.roll(np.array(np.where(segmap!=0)),1,axis=0).transpose().reshape(-1,2) + np_contours = np.roll(np.array(np.where(segmap != 0)), + 1, axis=0).transpose().reshape(-1, 2) rectangle = cv2.minAreaRect(np_contours) box = cv2.boxPoints(rectangle) @@ -71,8 +86,8 @@ def getDetBoxes_core(textmap, linkmap, text_threshold, link_threshold, low_text, w, h = np.linalg.norm(box[0] - box[1]), np.linalg.norm(box[1] - box[2]) box_ratio = max(w, h) / (min(w, h) + 1e-5) if abs(1 - box_ratio) <= 0.1: - l, r = min(np_contours[:,0]), max(np_contours[:,0]) - t, b = min(np_contours[:,1]), max(np_contours[:,1]) + l, r = min(np_contours[:, 0]), max(np_contours[:, 0]) + t, b = min(np_contours[:, 1]), max(np_contours[:, 1]) box = np.array([[l, t], [r, t], [r, b], [l, b]], dtype=np.float32) # make clock-wise order @@ -84,6 +99,7 @@ def getDetBoxes_core(textmap, linkmap, text_threshold, link_threshold, low_text, return det, labels, mapper + def getPoly_core(boxes, labels, mapper, linkmap): # configs num_cp = 5 @@ -92,21 +108,25 @@ def getPoly_core(boxes, labels, mapper, linkmap): max_r = 2.0 step_r = 0.2 - polys = [] + polys = [] for k, box in enumerate(boxes): # size filter for small instance - w, h = int(np.linalg.norm(box[0] - box[1]) + 1), int(np.linalg.norm(box[1] - box[2]) + 1) + w, h = int(np.linalg.norm(box[0] - box[1]) + + 1), int(np.linalg.norm(box[1] - box[2]) + 1) if w < 10 or h < 10: - polys.append(None); continue + polys.append(None) + continue # warp image - tar = np.float32([[0,0],[w,0],[w,h],[0,h]]) + tar = np.float32([[0, 0], [w, 0], [w, h], [0, h]]) M = cv2.getPerspectiveTransform(box, tar) - word_label = cv2.warpPerspective(labels, M, (w, h), flags=cv2.INTER_NEAREST) + word_label = cv2.warpPerspective( + labels, M, (w, h), flags=cv2.INTER_NEAREST) try: Minv = np.linalg.inv(M) except: - polys.append(None); continue + polys.append(None) + continue # binarization for selected label cur_label = mapper[k] @@ -118,15 +138,18 @@ def getPoly_core(boxes, labels, mapper, linkmap): cp = [] max_len = -1 for i in range(w): - region = np.where(word_label[:,i] != 0)[0] - if len(region) < 2 : continue + region = np.where(word_label[:, i] != 0)[0] + if len(region) < 2: + continue cp.append((i, region[0], region[-1])) length = region[-1] - region[0] + 1 - if length > max_len: max_len = length + if length > max_len: + max_len = length # pass if max_len is similar to h if h * max_len_ratio < max_len: - polys.append(None); continue + polys.append(None) + continue # get pivot points with fixed length tot_seg = num_cp * 2 + 1 @@ -137,12 +160,14 @@ def getPoly_core(boxes, labels, mapper, linkmap): seg_num = 0 num_sec = 0 prev_h = -1 - for i in range(0,len(cp)): + for i in range(0, len(cp)): (x, sy, ey) = cp[i] if (seg_num + 1) * seg_w <= x and seg_num <= tot_seg: # average previous segment - if num_sec == 0: break - cp_section[seg_num] = [cp_section[seg_num][0] / num_sec, cp_section[seg_num][1] / num_sec] + if num_sec == 0: + break + cp_section[seg_num] = [cp_section[seg_num][0] / + num_sec, cp_section[seg_num][1] / num_sec] num_sec = 0 # reset variables @@ -152,10 +177,12 @@ def getPoly_core(boxes, labels, mapper, linkmap): # accumulate center points cy = (sy + ey) * 0.5 cur_h = ey - sy + 1 - cp_section[seg_num] = [cp_section[seg_num][0] + x, cp_section[seg_num][1] + cy] + cp_section[seg_num] = [cp_section[seg_num] + [0] + x, cp_section[seg_num][1] + cy] num_sec += 1 - if seg_num % 2 == 0: continue # No polygon area + if seg_num % 2 == 0: + continue # No polygon area if prev_h < cur_h: pp[int((seg_num - 1)/2)] = (x, cy) @@ -164,11 +191,13 @@ def getPoly_core(boxes, labels, mapper, linkmap): # processing last segment if num_sec != 0: - cp_section[-1] = [cp_section[-1][0] / num_sec, cp_section[-1][1] / num_sec] + cp_section[-1] = [cp_section[-1][0] / + num_sec, cp_section[-1][1] / num_sec] - # pass if num of pivots is not sufficient or segment width is smaller than character height + # pass if num of pivots is not sufficient or segment width is smaller than character height if None in pp or seg_w < np.max(seg_height) * 0.25: - polys.append(None); continue + polys.append(None) + continue # calc median maximum of pivot points half_char_h = np.median(seg_height) * expand_ratio / 2 @@ -187,15 +216,18 @@ def getPoly_core(boxes, labels, mapper, linkmap): # get edge points to cover character heatmaps isSppFound, isEppFound = False, False - grad_s = (pp[1][1] - pp[0][1]) / (pp[1][0] - pp[0][0]) + (pp[2][1] - pp[1][1]) / (pp[2][0] - pp[1][0]) - grad_e = (pp[-2][1] - pp[-1][1]) / (pp[-2][0] - pp[-1][0]) + (pp[-3][1] - pp[-2][1]) / (pp[-3][0] - pp[-2][0]) + grad_s = (pp[1][1] - pp[0][1]) / (pp[1][0] - pp[0][0]) + \ + (pp[2][1] - pp[1][1]) / (pp[2][0] - pp[1][0]) + grad_e = (pp[-2][1] - pp[-1][1]) / (pp[-2][0] - pp[-1][0]) + \ + (pp[-3][1] - pp[-2][1]) / (pp[-3][0] - pp[-2][0]) for r in np.arange(0.5, max_r, step_r): dx = 2 * half_char_h * r if not isSppFound: line_img = np.zeros(word_label.shape, dtype=np.uint8) dy = grad_s * dx p = np.array(new_pp[0]) - np.array([dx, dy, dx, dy]) - cv2.line(line_img, (int(p[0]), int(p[1])), (int(p[2]), int(p[3])), 1, thickness=1) + cv2.line(line_img, (int(p[0]), int(p[1])), + (int(p[2]), int(p[3])), 1, thickness=1) if np.sum(np.logical_and(word_label, line_img)) == 0 or r + 2 * step_r >= max_r: spp = p isSppFound = True @@ -203,7 +235,8 @@ def getPoly_core(boxes, labels, mapper, linkmap): line_img = np.zeros(word_label.shape, dtype=np.uint8) dy = grad_e * dx p = np.array(new_pp[-1]) + np.array([dx, dy, dx, dy]) - cv2.line(line_img, (int(p[0]), int(p[1])), (int(p[2]), int(p[3])), 1, thickness=1) + cv2.line(line_img, (int(p[0]), int(p[1])), + (int(p[2]), int(p[3])), 1, thickness=1) if np.sum(np.logical_and(word_label, line_img)) == 0 or r + 2 * step_r >= max_r: epp = p isEppFound = True @@ -212,7 +245,8 @@ def getPoly_core(boxes, labels, mapper, linkmap): # pass if boundary of polygon is not found if not (isSppFound and isEppFound): - polys.append(None); continue + polys.append(None) + continue # make final polygon poly = [] @@ -230,10 +264,13 @@ def getPoly_core(boxes, labels, mapper, linkmap): return polys + def getDetBoxes(textmap, linkmap, text_threshold, link_threshold, low_text, poly=False, estimate_num_chars=False): if poly and estimate_num_chars: - raise Exception("Estimating the number of characters not currently supported with poly.") - boxes, labels, mapper = getDetBoxes_core(textmap, linkmap, text_threshold, link_threshold, low_text, estimate_num_chars) + raise Exception( + "Estimating the number of characters not currently supported with poly.") + boxes, labels, mapper = getDetBoxes_core( + textmap, linkmap, text_threshold, link_threshold, low_text, estimate_num_chars) if poly: polys = getPoly_core(boxes, labels, mapper, linkmap) @@ -242,7 +279,8 @@ def getDetBoxes(textmap, linkmap, text_threshold, link_threshold, low_text, poly return boxes, polys, mapper -def adjustResultCoordinates(polys, ratio_w, ratio_h, ratio_net = 2): + +def adjustResultCoordinates(polys, ratio_w, ratio_h, ratio_net=2): if len(polys) > 0: polys = np.array(polys) for k in range(len(polys)): diff --git a/easyocr/detection.py b/easyocr/detection.py index 6435443cd..30a237b24 100644 --- a/easyocr/detection.py +++ b/easyocr/detection.py @@ -10,6 +10,7 @@ from .imgproc import resize_aspect_ratio, normalizeMeanVariance from .craft import CRAFT + def copyStateDict(state_dict): if list(state_dict.keys())[0].startswith("module"): start_idx = 1 @@ -21,6 +22,7 @@ def copyStateDict(state_dict): new_state_dict[name] = v return new_state_dict + def test_net(canvas_size, mag_ratio, net, image, text_threshold, link_threshold, low_text, poly, device, estimate_num_chars=False): if isinstance(image, np.ndarray) and len(image.shape) == 4: # image is batch of np arrays image_arrs = image @@ -71,24 +73,29 @@ def test_net(canvas_size, mag_ratio, net, image, text_threshold, link_threshold, return boxes_list, polys_list + def get_detector(trained_model, device='cpu', quantize=True, cudnn_benchmark=False): net = CRAFT() if device == 'cpu': - net.load_state_dict(copyStateDict(torch.load(trained_model, map_location=device))) + net.load_state_dict(copyStateDict( + torch.load(trained_model, map_location=device))) if quantize: try: - torch.quantization.quantize_dynamic(net, dtype=torch.qint8, inplace=True) + torch.quantization.quantize_dynamic( + net, dtype=torch.qint8, inplace=True) except: pass else: - net.load_state_dict(copyStateDict(torch.load(trained_model, map_location=device))) + net.load_state_dict(copyStateDict( + torch.load(trained_model, map_location=device))) net = torch.nn.DataParallel(net).to(device) cudnn.benchmark = cudnn_benchmark net.eval() return net + def get_textbox(detector, image, canvas_size, mag_ratio, text_threshold, link_threshold, low_text, poly, device, optimal_num_chars=None): result = [] estimate_num_chars = optimal_num_chars is not None diff --git a/easyocr/easyocr.py b/easyocr/easyocr.py index 9e2d51413..e1df749b3 100644 --- a/easyocr/easyocr.py +++ b/easyocr/easyocr.py @@ -3,9 +3,9 @@ from .detection import get_detector, get_textbox from .recognition import get_recognizer, get_text from .utils import group_text_box, get_image_list, calculate_md5, get_paragraph,\ - download_and_unzip, printProgressBar, diff, reformat_input,\ - make_rotated_img_list, set_result_with_confidence,\ - reformat_input_batched + download_and_unzip, printProgressBar, diff, reformat_input,\ + make_rotated_img_list, set_result_with_confidence,\ + reformat_input_batched from .config import * from bidi.algorithm import get_display import numpy as np @@ -27,10 +27,11 @@ LOGGER = getLogger(__name__) + class Reader(object): def __init__(self, lang_list, gpu=True, model_storage_directory=None, - user_network_directory=None, recog_network = 'standard', + user_network_directory=None, recog_network='standard', download_enabled=True, detector=True, recognizer=True, verbose=True, quantize=True, cudnn_benchmark=False): """Create an EasyOCR Reader @@ -66,11 +67,13 @@ def __init__(self, lang_list, gpu=True, model_storage_directory=None, if gpu is False: self.device = 'cpu' if verbose: - LOGGER.warning('Using CPU. Note: This module is much faster with a GPU.') + LOGGER.warning( + 'Using CPU. Note: This module is much faster with a GPU.') elif not torch.cuda.is_available(): self.device = 'cpu' if verbose: - LOGGER.warning('CUDA not available - defaulting to CPU. Note: This module is much faster with a GPU.') + LOGGER.warning( + 'CUDA not available - defaulting to CPU. Note: This module is much faster with a GPU.') elif gpu is True: self.device = 'cuda' else: @@ -80,25 +83,32 @@ def __init__(self, lang_list, gpu=True, model_storage_directory=None, # check and download detection model detector_model = 'craft' corrupt_msg = 'MD5 hash mismatch, possible file corruption' - detector_path = os.path.join(self.model_storage_directory, detection_models[detector_model]['filename']) + detector_path = os.path.join( + self.model_storage_directory, detection_models[detector_model]['filename']) if detector: if os.path.isfile(detector_path) == False: if not self.download_enabled: - raise FileNotFoundError("Missing %s and downloads disabled" % detector_path) + raise FileNotFoundError( + "Missing %s and downloads disabled" % detector_path) LOGGER.warning('Downloading detection model, please wait. ' 'This may take several minutes depending upon your network connection.') - download_and_unzip(detection_models[detector_model]['url'], detection_models[detector_model]['filename'], self.model_storage_directory, verbose) - assert calculate_md5(detector_path) == detection_models[detector_model]['filesize'], corrupt_msg + download_and_unzip(detection_models[detector_model]['url'], + detection_models[detector_model]['filename'], self.model_storage_directory, verbose) + assert calculate_md5( + detector_path) == detection_models[detector_model]['filesize'], corrupt_msg LOGGER.info('Download complete') elif calculate_md5(detector_path) != detection_models[detector_model]['filesize']: if not self.download_enabled: - raise FileNotFoundError("MD5 mismatch for %s and downloads disabled" % detector_path) + raise FileNotFoundError( + "MD5 mismatch for %s and downloads disabled" % detector_path) LOGGER.warning(corrupt_msg) os.remove(detector_path) LOGGER.warning('Re-downloading the detection model, please wait. ' 'This may take several minutes depending upon your network connection.') - download_and_unzip(detection_models[detector_model]['url'], detection_models[detector_model]['filename'], self.model_storage_directory, verbose) - assert calculate_md5(detector_path) == detection_models[detector_model]['filesize'], corrupt_msg + download_and_unzip(detection_models[detector_model]['url'], + detection_models[detector_model]['filename'], self.model_storage_directory, verbose) + assert calculate_md5( + detector_path) == detection_models[detector_model]['filesize'], corrupt_msg # recognition model separator_list = {} @@ -112,57 +122,69 @@ def __init__(self, lang_list, gpu=True, model_storage_directory=None, model = recognition_models['gen2'][recog_network] recog_network = 'generation2' self.model_lang = model['model_script'] - else: # auto-detect + else: # auto-detect unknown_lang = set(lang_list) - set(all_lang_list) if unknown_lang != set(): raise ValueError(unknown_lang, 'is not supported') # choose recognition model if lang_list == ['en']: - self.setModelLanguage('english', lang_list, ['en'], '["en"]') + self.setModelLanguage( + 'english', lang_list, ['en'], '["en"]') model = recognition_models['gen2']['english_g2'] recog_network = 'generation2' elif 'th' in lang_list: - self.setModelLanguage('thai', lang_list, ['th','en'], '["th","en"]') + self.setModelLanguage('thai', lang_list, [ + 'th', 'en'], '["th","en"]') model = recognition_models['gen1']['thai_g1'] recog_network = 'generation1' elif 'ch_tra' in lang_list: - self.setModelLanguage('chinese_tra', lang_list, ['ch_tra','en'], '["ch_tra","en"]') + self.setModelLanguage('chinese_tra', lang_list, [ + 'ch_tra', 'en'], '["ch_tra","en"]') model = recognition_models['gen1']['zh_tra_g1'] recog_network = 'generation1' elif 'ch_sim' in lang_list: - self.setModelLanguage('chinese_sim', lang_list, ['ch_sim','en'], '["ch_sim","en"]') + self.setModelLanguage('chinese_sim', lang_list, [ + 'ch_sim', 'en'], '["ch_sim","en"]') model = recognition_models['gen2']['zh_sim_g2'] recog_network = 'generation2' elif 'ja' in lang_list: - self.setModelLanguage('japanese', lang_list, ['ja','en'], '["ja","en"]') + self.setModelLanguage('japanese', lang_list, [ + 'ja', 'en'], '["ja","en"]') model = recognition_models['gen2']['japanese_g2'] recog_network = 'generation2' elif 'ko' in lang_list: - self.setModelLanguage('korean', lang_list, ['ko','en'], '["ko","en"]') + self.setModelLanguage('korean', lang_list, [ + 'ko', 'en'], '["ko","en"]') model = recognition_models['gen2']['korean_g2'] recog_network = 'generation2' elif 'ta' in lang_list: - self.setModelLanguage('tamil', lang_list, ['ta','en'], '["ta","en"]') + self.setModelLanguage('tamil', lang_list, [ + 'ta', 'en'], '["ta","en"]') model = recognition_models['gen1']['tamil_g1'] recog_network = 'generation1' elif 'te' in lang_list: - self.setModelLanguage('telugu', lang_list, ['te','en'], '["te","en"]') + self.setModelLanguage('telugu', lang_list, [ + 'te', 'en'], '["te","en"]') model = recognition_models['gen2']['telugu_g2'] recog_network = 'generation2' elif 'kn' in lang_list: - self.setModelLanguage('kannada', lang_list, ['kn','en'], '["kn","en"]') + self.setModelLanguage('kannada', lang_list, [ + 'kn', 'en'], '["kn","en"]') model = recognition_models['gen2']['kannada_g2'] recog_network = 'generation2' elif set(lang_list) & set(bengali_lang_list): - self.setModelLanguage('bengali', lang_list, bengali_lang_list+['en'], '["bn","as","en"]') + self.setModelLanguage( + 'bengali', lang_list, bengali_lang_list+['en'], '["bn","as","en"]') model = recognition_models['gen1']['bengali_g1'] recog_network = 'generation1' elif set(lang_list) & set(arabic_lang_list): - self.setModelLanguage('arabic', lang_list, arabic_lang_list+['en'], '["ar","fa","ur","ug","en"]') + self.setModelLanguage( + 'arabic', lang_list, arabic_lang_list+['en'], '["ar","fa","ur","ug","en"]') model = recognition_models['gen1']['arabic_g1'] recog_network = 'generation1' elif set(lang_list) & set(devanagari_lang_list): - self.setModelLanguage('devanagari', lang_list, devanagari_lang_list+['en'], '["hi","mr","ne","en"]') + self.setModelLanguage( + 'devanagari', lang_list, devanagari_lang_list+['en'], '["hi","mr","ne","en"]') model = recognition_models['gen1']['devanagari_g1'] recog_network = 'generation1' elif set(lang_list) & set(cyrillic_lang_list): @@ -176,38 +198,46 @@ def __init__(self, lang_list, gpu=True, model_storage_directory=None, recog_network = 'generation2' self.character = model['characters'] - model_path = os.path.join(self.model_storage_directory, model['filename']) + model_path = os.path.join( + self.model_storage_directory, model['filename']) # check recognition model file if recognizer: if os.path.isfile(model_path) == False: if not self.download_enabled: - raise FileNotFoundError("Missing %s and downloads disabled" % model_path) + raise FileNotFoundError( + "Missing %s and downloads disabled" % model_path) LOGGER.warning('Downloading recognition model, please wait. ' 'This may take several minutes depending upon your network connection.') - download_and_unzip(model['url'], model['filename'], self.model_storage_directory, verbose) - assert calculate_md5(model_path) == model['filesize'], corrupt_msg + download_and_unzip( + model['url'], model['filename'], self.model_storage_directory, verbose) + assert calculate_md5( + model_path) == model['filesize'], corrupt_msg LOGGER.info('Download complete.') elif calculate_md5(model_path) != model['filesize']: if not self.download_enabled: - raise FileNotFoundError("MD5 mismatch for %s and downloads disabled" % model_path) + raise FileNotFoundError( + "MD5 mismatch for %s and downloads disabled" % model_path) LOGGER.warning(corrupt_msg) os.remove(model_path) LOGGER.warning('Re-downloading the recognition model, please wait. ' 'This may take several minutes depending upon your network connection.') - download_and_unzip(model['url'], model['filename'], self.model_storage_directory, verbose) - assert calculate_md5(model_path) == model['filesize'], corrupt_msg + download_and_unzip( + model['url'], model['filename'], self.model_storage_directory, verbose) + assert calculate_md5( + model_path) == model['filesize'], corrupt_msg LOGGER.info('Download complete') self.setLanguageList(lang_list, model) - else: # user-defined model - with open(os.path.join(self.user_network_directory, recog_network+ '.yaml'), encoding='utf8') as file: + else: # user-defined model + with open(os.path.join(self.user_network_directory, recog_network + '.yaml'), encoding='utf8') as file: recog_config = yaml.load(file, Loader=yaml.FullLoader) imgH = recog_config['imgH'] available_lang = recog_config['lang_list'] - self.setModelLanguage(recog_network, lang_list, available_lang, available_lang) + self.setModelLanguage(recog_network, lang_list, + available_lang, available_lang) #char_file = os.path.join(self.user_network_directory, recog_network+ '.txt') self.character = recog_config['character_list'] - model_file = recog_network+ '.pth' + model_file = recog_network + '.pth' model_path = os.path.join(self.model_storage_directory, model_file) self.setLanguageList(lang_list, None) @@ -216,32 +246,34 @@ def __init__(self, lang_list, gpu=True, model_storage_directory=None, dict_list[lang] = os.path.join(BASE_PATH, 'dict', lang + ".txt") if detector: - self.detector = get_detector(detector_path, self.device, quantize, cudnn_benchmark=cudnn_benchmark) + self.detector = get_detector( + detector_path, self.device, quantize, cudnn_benchmark=cudnn_benchmark) if recognizer: if recog_network == 'generation1': network_params = { 'input_channel': 1, 'output_channel': 512, 'hidden_size': 512 - } + } elif recog_network == 'generation2': network_params = { 'input_channel': 1, 'output_channel': 256, 'hidden_size': 256 - } + } else: network_params = recog_config['network_params'] - self.recognizer, self.converter = get_recognizer(recog_network, network_params,\ - self.character, separator_list,\ - dict_list, model_path, device = self.device, quantize=quantize) + self.recognizer, self.converter = get_recognizer(recog_network, network_params, + self.character, separator_list, + dict_list, model_path, device=self.device, quantize=quantize) def setModelLanguage(self, language, lang_list, list_lang, list_lang_string): self.model_lang = language if set(lang_list) - set(list_lang) != set(): if language == 'ch_tra' or language == 'ch_sim': language = 'chinese' - raise ValueError(language.capitalize() + ' is only compatible with English, try lang_list=' + list_lang_string) + raise ValueError(language.capitalize( + ) + ' is only compatible with English, try lang_list=' + list_lang_string) def getChar(self, fileName): char_file = os.path.join(BASE_PATH, 'character', fileName) @@ -253,9 +285,10 @@ def getChar(self, fileName): def setLanguageList(self, lang_list, model): self.lang_char = [] for lang in lang_list: - char_file = os.path.join(BASE_PATH, 'character', lang + "_char.txt") - with open(char_file, "r", encoding = "utf-8-sig") as input_file: - char_list = input_file.read().splitlines() + char_file = os.path.join( + BASE_PATH, 'character', lang + "_char.txt") + with open(char_file, "r", encoding="utf-8-sig") as input_file: + char_list = input_file.read().splitlines() self.lang_char += char_list if model: symbol = model['symbols'] @@ -264,10 +297,10 @@ def setLanguageList(self, lang_list, model): self.lang_char = set(self.lang_char).union(set(symbol)) self.lang_char = ''.join(self.lang_char) - def detect(self, img, min_size = 20, text_threshold = 0.7, low_text = 0.4,\ - link_threshold = 0.4,canvas_size = 2560, mag_ratio = 1.,\ - slope_ths = 0.1, ycenter_ths = 0.5, height_ths = 0.5,\ - width_ths = 0.5, add_margin = 0.1, reformat=True, optimal_num_chars=None): + def detect(self, img, min_size=20, text_threshold=0.7, low_text=0.4, + link_threshold=0.4, canvas_size=2560, mag_ratio=1., + slope_ths=0.1, ycenter_ths=0.5, height_ths=0.5, + width_ths=0.5, add_margin=0.1, reformat=True, optimal_num_chars=None): if reformat: img, img_cv_grey = reformat_input(img) @@ -292,12 +325,12 @@ def detect(self, img, min_size = 20, text_threshold = 0.7, low_text = 0.4,\ return horizontal_list_agg, free_list_agg - def recognize(self, img_cv_grey, horizontal_list=None, free_list=None,\ - decoder = 'greedy', beamWidth= 5, batch_size = 1,\ - workers = 0, allowlist = None, blocklist = None, detail = 1,\ - rotation_info = None,paragraph = False,\ - contrast_ths = 0.1,adjust_contrast = 0.5, filter_ths = 0.003,\ - y_ths = 0.5, x_ths = 1.0, reformat=True, output_format='standard'): + def recognize(self, img_cv_grey, horizontal_list=None, free_list=None, + decoder='greedy', beamWidth=5, batch_size=1, + workers=0, allowlist=None, blocklist=None, detail=1, + rotation_info=None, paragraph=False, + contrast_ths=0.1, adjust_contrast=0.5, filter_ths=0.003, + y_ths=0.5, x_ths=1.0, reformat=True, output_format='standard'): if reformat: img, img_cv_grey = reformat_input(img_cv_grey) @@ -309,9 +342,10 @@ def recognize(self, img_cv_grey, horizontal_list=None, free_list=None,\ else: ignore_char = ''.join(set(self.character)-set(self.lang_char)) - if self.model_lang in ['chinese_tra','chinese_sim']: decoder = 'greedy' + if self.model_lang in ['chinese_tra', 'chinese_sim']: + decoder = 'greedy' - if (horizontal_list==None) and (free_list==None): + if (horizontal_list == None) and (free_list == None): y_max, x_max = img_cv_grey.shape horizontal_list = [[0, x_max, 0, y_max]] free_list = [] @@ -322,30 +356,33 @@ def recognize(self, img_cv_grey, horizontal_list=None, free_list=None,\ for bbox in horizontal_list: h_list = [bbox] f_list = [] - image_list, max_width = get_image_list(h_list, f_list, img_cv_grey, model_height = imgH) - result0 = get_text(self.character, imgH, int(max_width), self.recognizer, self.converter, image_list,\ - ignore_char, decoder, beamWidth, batch_size, contrast_ths, adjust_contrast, filter_ths,\ - workers, self.device) + image_list, max_width = get_image_list( + h_list, f_list, img_cv_grey, model_height=imgH) + result0 = get_text(self.character, imgH, int(max_width), self.recognizer, self.converter, image_list, + ignore_char, decoder, beamWidth, batch_size, contrast_ths, adjust_contrast, filter_ths, + workers, self.device) result += result0 for bbox in free_list: h_list = [] f_list = [bbox] - image_list, max_width = get_image_list(h_list, f_list, img_cv_grey, model_height = imgH) - result0 = get_text(self.character, imgH, int(max_width), self.recognizer, self.converter, image_list,\ - ignore_char, decoder, beamWidth, batch_size, contrast_ths, adjust_contrast, filter_ths,\ - workers, self.device) + image_list, max_width = get_image_list( + h_list, f_list, img_cv_grey, model_height=imgH) + result0 = get_text(self.character, imgH, int(max_width), self.recognizer, self.converter, image_list, + ignore_char, decoder, beamWidth, batch_size, contrast_ths, adjust_contrast, filter_ths, + workers, self.device) result += result0 # default mode will try to process multiple boxes at the same time else: - image_list, max_width = get_image_list(horizontal_list, free_list, img_cv_grey, model_height = imgH) + image_list, max_width = get_image_list( + horizontal_list, free_list, img_cv_grey, model_height=imgH) image_len = len(image_list) if rotation_info and image_list: image_list = make_rotated_img_list(rotation_info, image_list) max_width = max(max_width, imgH) - result = get_text(self.character, imgH, int(max_width), self.recognizer, self.converter, image_list,\ - ignore_char, decoder, beamWidth, batch_size, contrast_ths, adjust_contrast, filter_ths,\ - workers, self.device) + result = get_text(self.character, imgH, int(max_width), self.recognizer, self.converter, image_list, + ignore_char, decoder, beamWidth, batch_size, contrast_ths, adjust_contrast, filter_ths, + workers, self.device) if rotation_info and (horizontal_list+free_list): result = set_result_with_confidence(result, image_len) @@ -359,110 +396,112 @@ def recognize(self, img_cv_grey, horizontal_list=None, free_list=None,\ direction_mode = 'ltr' if paragraph: - result = get_paragraph(result, x_ths=x_ths, y_ths=y_ths, mode = direction_mode) + result = get_paragraph(result, x_ths=x_ths, + y_ths=y_ths, mode=direction_mode) if detail == 0: return [item[1] for item in result] elif output_format == 'dict': - return [ {'boxes':item[0],'text':item[1],'confident':item[2]} for item in result] + return [{'boxes': item[0], 'text':item[1], 'confident':item[2]} for item in result] else: return result - def readtext(self, image, decoder = 'greedy', beamWidth= 5, batch_size = 1,\ - workers = 0, allowlist = None, blocklist = None, detail = 1,\ - rotation_info = None, paragraph = False, min_size = 20,\ - contrast_ths = 0.1,adjust_contrast = 0.5, filter_ths = 0.003,\ - text_threshold = 0.7, low_text = 0.4, link_threshold = 0.4,\ - canvas_size = 2560, mag_ratio = 1.,\ - slope_ths = 0.1, ycenter_ths = 0.5, height_ths = 0.5,\ - width_ths = 0.5, y_ths = 0.5, x_ths = 1.0, add_margin = 0.1, output_format='standard'): + def readtext(self, image, decoder='greedy', beamWidth=5, batch_size=1, + workers=0, allowlist=None, blocklist=None, detail=1, + rotation_info=None, paragraph=False, min_size=20, + contrast_ths=0.1, adjust_contrast=0.5, filter_ths=0.003, + text_threshold=0.7, low_text=0.4, link_threshold=0.4, + canvas_size=2560, mag_ratio=1., + slope_ths=0.1, ycenter_ths=0.5, height_ths=0.5, + width_ths=0.5, y_ths=0.5, x_ths=1.0, add_margin=0.1, output_format='standard'): ''' Parameters: image: file path or numpy-array or a byte stream object ''' img, img_cv_grey = reformat_input(image) - horizontal_list, free_list = self.detect(img, min_size, text_threshold,\ - low_text, link_threshold,\ - canvas_size, mag_ratio,\ - slope_ths, ycenter_ths,\ - height_ths,width_ths,\ + horizontal_list, free_list = self.detect(img, min_size, text_threshold, + low_text, link_threshold, + canvas_size, mag_ratio, + slope_ths, ycenter_ths, + height_ths, width_ths, add_margin, False) # get the 1st result from hor & free list as self.detect returns a list of depth 3 horizontal_list, free_list = horizontal_list[0], free_list[0] - result = self.recognize(img_cv_grey, horizontal_list, free_list,\ - decoder, beamWidth, batch_size,\ - workers, allowlist, blocklist, detail, rotation_info,\ - paragraph, contrast_ths, adjust_contrast,\ + result = self.recognize(img_cv_grey, horizontal_list, free_list, + decoder, beamWidth, batch_size, + workers, allowlist, blocklist, detail, rotation_info, + paragraph, contrast_ths, adjust_contrast, filter_ths, y_ths, x_ths, False, output_format) return result - - def readtextlang(self, image, decoder = 'greedy', beamWidth= 5, batch_size = 1,\ - workers = 0, allowlist = None, blocklist = None, detail = 1,\ - rotation_info = None, paragraph = False, min_size = 20,\ - contrast_ths = 0.1,adjust_contrast = 0.5, filter_ths = 0.003,\ - text_threshold = 0.7, low_text = 0.4, link_threshold = 0.4,\ - canvas_size = 2560, mag_ratio = 1.,\ - slope_ths = 0.1, ycenter_ths = 0.5, height_ths = 0.5,\ - width_ths = 0.5, y_ths = 0.5, x_ths = 1.0, add_margin = 0.1, output_format='standard'): + + def readtextlang(self, image, decoder='greedy', beamWidth=5, batch_size=1, + workers=0, allowlist=None, blocklist=None, detail=1, + rotation_info=None, paragraph=False, min_size=20, + contrast_ths=0.1, adjust_contrast=0.5, filter_ths=0.003, + text_threshold=0.7, low_text=0.4, link_threshold=0.4, + canvas_size=2560, mag_ratio=1., + slope_ths=0.1, ycenter_ths=0.5, height_ths=0.5, + width_ths=0.5, y_ths=0.5, x_ths=1.0, add_margin=0.1, output_format='standard'): ''' Parameters: image: file path or numpy-array or a byte stream object ''' img, img_cv_grey = reformat_input(image) - horizontal_list, free_list = self.detect(img, min_size, text_threshold,\ - low_text, link_threshold,\ - canvas_size, mag_ratio,\ - slope_ths, ycenter_ths,\ - height_ths,width_ths,\ + horizontal_list, free_list = self.detect(img, min_size, text_threshold, + low_text, link_threshold, + canvas_size, mag_ratio, + slope_ths, ycenter_ths, + height_ths, width_ths, add_margin, False) # get the 1st result from hor & free list as self.detect returns a list of depth 3 horizontal_list, free_list = horizontal_list[0], free_list[0] - result = self.recognize(img_cv_grey, horizontal_list, free_list,\ - decoder, beamWidth, batch_size,\ - workers, allowlist, blocklist, detail, rotation_info,\ - paragraph, contrast_ths, adjust_contrast,\ + result = self.recognize(img_cv_grey, horizontal_list, free_list, + decoder, beamWidth, batch_size, + workers, allowlist, blocklist, detail, rotation_info, + paragraph, contrast_ths, adjust_contrast, filter_ths, y_ths, x_ths, False, output_format) - + char = [] directory = 'characters/' for i in range(len(result)): char.append(result[i][1]) - - def search(arr,x): + + def search(arr, x): g = False for i in range(len(arr)): - if arr[i]==x: + if arr[i] == x: g = True return 1 if g == False: return -1 + def tupleadd(i): a = result[i] b = a + (filename[0:2],) return b - + for filename in os.listdir(directory): if filename.endswith(".txt"): - with open ('characters/'+ filename,'rt',encoding="utf8") as myfile: - chartrs = str(myfile.read().splitlines()).replace('\n','') + with open('characters/' + filename, 'rt', encoding="utf8") as myfile: + chartrs = str(myfile.read().splitlines()).replace('\n', '') for i in range(len(char)): - res = search(chartrs,char[i]) + res = search(chartrs, char[i]) if res != -1: - if filename[0:2]=="en" or filename[0:2]=="ch": + if filename[0:2] == "en" or filename[0:2] == "ch": print(tupleadd(i)) - def readtext_batched(self, image, n_width=None, n_height=None,\ - decoder = 'greedy', beamWidth= 5, batch_size = 1,\ - workers = 0, allowlist = None, blocklist = None, detail = 1,\ - rotation_info = None, paragraph = False, min_size = 20,\ - contrast_ths = 0.1,adjust_contrast = 0.5, filter_ths = 0.003,\ - text_threshold = 0.7, low_text = 0.4, link_threshold = 0.4,\ - canvas_size = 2560, mag_ratio = 1.,\ - slope_ths = 0.1, ycenter_ths = 0.5, height_ths = 0.5,\ - width_ths = 0.5, y_ths = 0.5, x_ths = 1.0, add_margin = 0.1, output_format='standard'): + def readtext_batched(self, image, n_width=None, n_height=None, + decoder='greedy', beamWidth=5, batch_size=1, + workers=0, allowlist=None, blocklist=None, detail=1, + rotation_info=None, paragraph=False, min_size=20, + contrast_ths=0.1, adjust_contrast=0.5, filter_ths=0.003, + text_threshold=0.7, low_text=0.4, link_threshold=0.4, + canvas_size=2560, mag_ratio=1., + slope_ths=0.1, ycenter_ths=0.5, height_ths=0.5, + width_ths=0.5, y_ths=0.5, x_ths=1.0, add_margin=0.1, output_format='standard'): ''' Parameters: image: file path or numpy-array or a byte stream object @@ -473,20 +512,21 @@ def readtext_batched(self, image, n_width=None, n_height=None,\ ''' img, img_cv_grey = reformat_input_batched(image, n_width, n_height) - horizontal_list_agg, free_list_agg = self.detect(img, min_size, text_threshold,\ - low_text, link_threshold,\ - canvas_size, mag_ratio,\ - slope_ths, ycenter_ths,\ - height_ths, width_ths,\ + horizontal_list_agg, free_list_agg = self.detect(img, min_size, text_threshold, + low_text, link_threshold, + canvas_size, mag_ratio, + slope_ths, ycenter_ths, + height_ths, width_ths, add_margin, False) result_agg = [] # put img_cv_grey in a list if its a single img - img_cv_grey = [img_cv_grey] if len(img_cv_grey.shape) == 2 else img_cv_grey + img_cv_grey = [img_cv_grey] if len( + img_cv_grey.shape) == 2 else img_cv_grey for grey_img, horizontal_list, free_list in zip(img_cv_grey, horizontal_list_agg, free_list_agg): - result_agg.append(self.recognize(grey_img, horizontal_list, free_list,\ - decoder, beamWidth, batch_size,\ - workers, allowlist, blocklist, detail, rotation_info,\ - paragraph, contrast_ths, adjust_contrast,\ - filter_ths, y_ths, x_ths, False, output_format)) + result_agg.append(self.recognize(grey_img, horizontal_list, free_list, + decoder, beamWidth, batch_size, + workers, allowlist, blocklist, detail, rotation_info, + paragraph, contrast_ths, adjust_contrast, + filter_ths, y_ths, x_ths, False, output_format)) return result_agg diff --git a/easyocr/export.py b/easyocr/export.py index 937253ab4..4540b26ac 100644 --- a/easyocr/export.py +++ b/easyocr/export.py @@ -72,15 +72,18 @@ def to_numpy(tensor): ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(dummy_input)} y_onnx_out, feature_onnx_out = ort_session.run(None, ort_inputs) - print(f"torch outputs: y_torch_out.shape={y_torch_out.shape} feature_torch_out.shape={feature_torch_out.shape}") - print(f"onnx outputs: y_onnx_out.shape={y_onnx_out.shape} feature_onnx_out.shape={feature_onnx_out.shape}") + print( + f"torch outputs: y_torch_out.shape={y_torch_out.shape} feature_torch_out.shape={feature_torch_out.shape}") + print( + f"onnx outputs: y_onnx_out.shape={y_onnx_out.shape} feature_onnx_out.shape={feature_onnx_out.shape}") # compare ONNX Runtime and PyTorch results np.testing.assert_allclose( to_numpy(y_torch_out), y_onnx_out, rtol=1e-03, atol=1e-05) np.testing.assert_allclose( to_numpy(feature_torch_out), feature_onnx_out, rtol=1e-03, atol=1e-05) - print(f"Model exported to {detector_onnx_save_path} and tested with ONNXRuntime, and the result looks good!") + print( + f"Model exported to {detector_onnx_save_path} and tested with ONNXRuntime, and the result looks good!") def parse_args(): diff --git a/easyocr/imgproc.py b/easyocr/imgproc.py index ab09d6f81..e0ed66fca 100644 --- a/easyocr/imgproc.py +++ b/easyocr/imgproc.py @@ -8,23 +8,31 @@ from skimage import io import cv2 + def loadImage(img_file): img = io.imread(img_file) # RGB order - if img.shape[0] == 2: img = img[0] - if len(img.shape) == 2 : img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) - if img.shape[2] == 4: img = img[:,:,:3] + if img.shape[0] == 2: + img = img[0] + if len(img.shape) == 2: + img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) + if img.shape[2] == 4: + img = img[:, :, :3] img = np.array(img) return img + def normalizeMeanVariance(in_img, mean=(0.485, 0.456, 0.406), variance=(0.229, 0.224, 0.225)): # should be RGB order img = in_img.copy().astype(np.float32) - img -= np.array([mean[0] * 255.0, mean[1] * 255.0, mean[2] * 255.0], dtype=np.float32) - img /= np.array([variance[0] * 255.0, variance[1] * 255.0, variance[2] * 255.0], dtype=np.float32) + img -= np.array([mean[0] * 255.0, mean[1] * 255.0, + mean[2] * 255.0], dtype=np.float32) + img /= np.array([variance[0] * 255.0, variance[1] * 255.0, + variance[2] * 255.0], dtype=np.float32) return img + def denormalizeMeanVariance(in_img, mean=(0.485, 0.456, 0.406), variance=(0.229, 0.224, 0.225)): # should be RGB order img = in_img.copy() @@ -34,6 +42,7 @@ def denormalizeMeanVariance(in_img, mean=(0.485, 0.456, 0.406), variance=(0.229, img = np.clip(img, 0, 255).astype(np.uint8) return img + def resize_aspect_ratio(img, square_size, interpolation, mag_ratio=1): height, width, channel = img.shape @@ -43,12 +52,11 @@ def resize_aspect_ratio(img, square_size, interpolation, mag_ratio=1): # set original image size if target_size > square_size: target_size = square_size - - ratio = target_size / max(height, width) - target_h, target_w = int(height * ratio), int(width * ratio) - proc = cv2.resize(img, (target_w, target_h), interpolation = interpolation) + ratio = target_size / max(height, width) + target_h, target_w = int(height * ratio), int(width * ratio) + proc = cv2.resize(img, (target_w, target_h), interpolation=interpolation) # make canvas and paste image target_h32, target_w32 = target_h, target_w @@ -64,6 +72,7 @@ def resize_aspect_ratio(img, square_size, interpolation, mag_ratio=1): return resized, ratio, size_heatmap + def cvt2HeatmapImg(img): img = (np.clip(img, 0, 1) * 255).astype(np.uint8) img = cv2.applyColorMap(img, cv2.COLORMAP_JET) diff --git a/easyocr/model/model.py b/easyocr/model/model.py index ed3f3fb96..ccf0d5c50 100644 --- a/easyocr/model/model.py +++ b/easyocr/model/model.py @@ -1,29 +1,33 @@ import torch.nn as nn from .modules import ResNet_FeatureExtractor, BidirectionalLSTM + class Model(nn.Module): def __init__(self, input_channel, output_channel, hidden_size, num_class): super(Model, self).__init__() """ FeatureExtraction """ - self.FeatureExtraction = ResNet_FeatureExtractor(input_channel, output_channel) + self.FeatureExtraction = ResNet_FeatureExtractor( + input_channel, output_channel) self.FeatureExtraction_output = output_channel # int(imgH/16-1) * 512 - self.AdaptiveAvgPool = nn.AdaptiveAvgPool2d((None, 1)) # Transform final (imgH/16-1) -> 1 + self.AdaptiveAvgPool = nn.AdaptiveAvgPool2d( + (None, 1)) # Transform final (imgH/16-1) -> 1 """ Sequence modeling""" self.SequenceModeling = nn.Sequential( - BidirectionalLSTM(self.FeatureExtraction_output, hidden_size, hidden_size), + BidirectionalLSTM(self.FeatureExtraction_output, + hidden_size, hidden_size), BidirectionalLSTM(hidden_size, hidden_size, hidden_size)) self.SequenceModeling_output = hidden_size """ Prediction """ self.Prediction = nn.Linear(self.SequenceModeling_output, num_class) - def forward(self, input, text): """ Feature extraction stage """ visual_feature = self.FeatureExtraction(input) - visual_feature = self.AdaptiveAvgPool(visual_feature.permute(0, 3, 1, 2)) # [b, c, h, w] -> [b, w, c, h] + visual_feature = self.AdaptiveAvgPool( + visual_feature.permute(0, 3, 1, 2)) # [b, c, h, w] -> [b, w, c, h] visual_feature = visual_feature.squeeze(3) """ Sequence modeling stage """ diff --git a/easyocr/model/modules.py b/easyocr/model/modules.py index d8b6d92eb..4977d2f8c 100644 --- a/easyocr/model/modules.py +++ b/easyocr/model/modules.py @@ -6,6 +6,7 @@ from torchvision.models.vgg import model_urls from collections import namedtuple + def init_weights(modules): for m in modules: if isinstance(m, nn.Conv2d): @@ -19,11 +20,14 @@ def init_weights(modules): m.weight.data.normal_(0, 0.01) m.bias.data.zero_() + class vgg16_bn(torch.nn.Module): def __init__(self, pretrained=True, freeze=True): super(vgg16_bn, self).__init__() - model_urls['vgg16_bn'] = model_urls['vgg16_bn'].replace('https://', 'http://') - vgg_pretrained_features = models.vgg16_bn(pretrained=pretrained).features + model_urls['vgg16_bn'] = model_urls['vgg16_bn'].replace( + 'https://', 'http://') + vgg_pretrained_features = models.vgg16_bn( + pretrained=pretrained).features self.slice1 = torch.nn.Sequential() self.slice2 = torch.nn.Sequential() self.slice3 = torch.nn.Sequential() @@ -40,9 +44,9 @@ def __init__(self, pretrained=True, freeze=True): # fc6, fc7 without atrous conv self.slice5 = torch.nn.Sequential( - nn.MaxPool2d(kernel_size=3, stride=1, padding=1), - nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6), - nn.Conv2d(1024, 1024, kernel_size=1) + nn.MaxPool2d(kernel_size=3, stride=1, padding=1), + nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6), + nn.Conv2d(1024, 1024, kernel_size=1) ) if not pretrained: @@ -51,11 +55,12 @@ def __init__(self, pretrained=True, freeze=True): init_weights(self.slice3.modules()) init_weights(self.slice4.modules()) - init_weights(self.slice5.modules()) # no pretrained model for fc6 and fc7 + # no pretrained model for fc6 and fc7 + init_weights(self.slice5.modules()) if freeze: for param in self.slice1.parameters(): # only first conv - param.requires_grad= False + param.requires_grad = False def forward(self, X): h = self.slice1(X) @@ -68,15 +73,18 @@ def forward(self, X): h_relu5_3 = h h = self.slice5(h) h_fc7 = h - vgg_outputs = namedtuple("VggOutputs", ['fc7', 'relu5_3', 'relu4_3', 'relu3_2', 'relu2_2']) + vgg_outputs = namedtuple( + "VggOutputs", ['fc7', 'relu5_3', 'relu4_3', 'relu3_2', 'relu2_2']) out = vgg_outputs(h_fc7, h_relu5_3, h_relu4_3, h_relu3_2, h_relu2_2) return out + class BidirectionalLSTM(nn.Module): def __init__(self, input_size, hidden_size, output_size): super(BidirectionalLSTM, self).__init__() - self.rnn = nn.LSTM(input_size, hidden_size, bidirectional=True, batch_first=True) + self.rnn = nn.LSTM(input_size, hidden_size, + bidirectional=True, batch_first=True) self.linear = nn.Linear(hidden_size * 2, output_size) def forward(self, input): @@ -84,14 +92,16 @@ def forward(self, input): input : visual feature [batch_size x T x input_size] output : contextual feature [batch_size x T x output_size] """ - try: # multi gpu needs this + try: # multi gpu needs this self.rnn.flatten_parameters() - except: # quantization doesn't work with this + except: # quantization doesn't work with this pass - recurrent, _ = self.rnn(input) # batch_size x T x input_size -> batch_size x T x (2*hidden_size) + # batch_size x T x input_size -> batch_size x T x (2*hidden_size) + recurrent, _ = self.rnn(input) output = self.linear(recurrent) # batch_size x T x output_size return output + class VGG_FeatureExtractor(nn.Module): def __init__(self, input_channel, output_channel=256): @@ -99,16 +109,22 @@ def __init__(self, input_channel, output_channel=256): self.output_channel = [int(output_channel / 8), int(output_channel / 4), int(output_channel / 2), output_channel] self.ConvNet = nn.Sequential( - nn.Conv2d(input_channel, self.output_channel[0], 3, 1, 1), nn.ReLU(True), + nn.Conv2d(input_channel, + self.output_channel[0], 3, 1, 1), nn.ReLU(True), nn.MaxPool2d(2, 2), - nn.Conv2d(self.output_channel[0], self.output_channel[1], 3, 1, 1), nn.ReLU(True), + nn.Conv2d( + self.output_channel[0], self.output_channel[1], 3, 1, 1), nn.ReLU(True), nn.MaxPool2d(2, 2), - nn.Conv2d(self.output_channel[1], self.output_channel[2], 3, 1, 1), nn.ReLU(True), - nn.Conv2d(self.output_channel[2], self.output_channel[2], 3, 1, 1), nn.ReLU(True), + nn.Conv2d( + self.output_channel[1], self.output_channel[2], 3, 1, 1), nn.ReLU(True), + nn.Conv2d( + self.output_channel[2], self.output_channel[2], 3, 1, 1), nn.ReLU(True), nn.MaxPool2d((2, 1), (2, 1)), - nn.Conv2d(self.output_channel[2], self.output_channel[3], 3, 1, 1, bias=False), + nn.Conv2d( + self.output_channel[2], self.output_channel[3], 3, 1, 1, bias=False), nn.BatchNorm2d(self.output_channel[3]), nn.ReLU(True), - nn.Conv2d(self.output_channel[3], self.output_channel[3], 3, 1, 1, bias=False), + nn.Conv2d( + self.output_channel[3], self.output_channel[3], 3, 1, 1, bias=False), nn.BatchNorm2d(self.output_channel[3]), nn.ReLU(True), nn.MaxPool2d((2, 1), (2, 1)), nn.Conv2d(self.output_channel[3], self.output_channel[3], 2, 1, 0), nn.ReLU(True)) @@ -116,16 +132,19 @@ def __init__(self, input_channel, output_channel=256): def forward(self, input): return self.ConvNet(input) + class ResNet_FeatureExtractor(nn.Module): """ FeatureExtractor of FAN (http://openaccess.thecvf.com/content_ICCV_2017/papers/Cheng_Focusing_Attention_Towards_ICCV_2017_paper.pdf) """ def __init__(self, input_channel, output_channel=512): super(ResNet_FeatureExtractor, self).__init__() - self.ConvNet = ResNet(input_channel, output_channel, BasicBlock, [1, 2, 5, 3]) + self.ConvNet = ResNet(input_channel, output_channel, + BasicBlock, [1, 2, 5, 3]) def forward(self, input): return self.ConvNet(input) + class BasicBlock(nn.Module): expansion = 1 @@ -161,12 +180,14 @@ def forward(self, x): return out + class ResNet(nn.Module): def __init__(self, input_channel, output_channel, block, layers): super(ResNet, self).__init__() - self.output_channel_block = [int(output_channel / 4), int(output_channel / 2), output_channel, output_channel] + self.output_channel_block = [ + int(output_channel / 4), int(output_channel / 2), output_channel, output_channel] self.inplanes = int(output_channel / 8) self.conv0_1 = nn.Conv2d(input_channel, int(output_channel / 16), @@ -178,24 +199,29 @@ def __init__(self, input_channel, output_channel, block, layers): self.relu = nn.ReLU(inplace=True) self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0) - self.layer1 = self._make_layer(block, self.output_channel_block[0], layers[0]) + self.layer1 = self._make_layer( + block, self.output_channel_block[0], layers[0]) self.conv1 = nn.Conv2d(self.output_channel_block[0], self.output_channel_block[ 0], kernel_size=3, stride=1, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(self.output_channel_block[0]) self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0) - self.layer2 = self._make_layer(block, self.output_channel_block[1], layers[1], stride=1) + self.layer2 = self._make_layer( + block, self.output_channel_block[1], layers[1], stride=1) self.conv2 = nn.Conv2d(self.output_channel_block[1], self.output_channel_block[ 1], kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(self.output_channel_block[1]) - self.maxpool3 = nn.MaxPool2d(kernel_size=2, stride=(2, 1), padding=(0, 1)) - self.layer3 = self._make_layer(block, self.output_channel_block[2], layers[2], stride=1) + self.maxpool3 = nn.MaxPool2d( + kernel_size=2, stride=(2, 1), padding=(0, 1)) + self.layer3 = self._make_layer( + block, self.output_channel_block[2], layers[2], stride=1) self.conv3 = nn.Conv2d(self.output_channel_block[2], self.output_channel_block[ 2], kernel_size=3, stride=1, padding=1, bias=False) self.bn3 = nn.BatchNorm2d(self.output_channel_block[2]) - self.layer4 = self._make_layer(block, self.output_channel_block[3], layers[3], stride=1) + self.layer4 = self._make_layer( + block, self.output_channel_block[3], layers[3], stride=1) self.conv4_1 = nn.Conv2d(self.output_channel_block[3], self.output_channel_block[ 3], kernel_size=2, stride=(2, 1), padding=(0, 1), bias=False) self.bn4_1 = nn.BatchNorm2d(self.output_channel_block[3]) diff --git a/easyocr/model/vgg_model.py b/easyocr/model/vgg_model.py index b0f2ae1c6..94e7a8636 100644 --- a/easyocr/model/vgg_model.py +++ b/easyocr/model/vgg_model.py @@ -1,29 +1,32 @@ import torch.nn as nn from .modules import VGG_FeatureExtractor, BidirectionalLSTM + class Model(nn.Module): def __init__(self, input_channel, output_channel, hidden_size, num_class): super(Model, self).__init__() """ FeatureExtraction """ - self.FeatureExtraction = VGG_FeatureExtractor(input_channel, output_channel) + self.FeatureExtraction = VGG_FeatureExtractor( + input_channel, output_channel) self.FeatureExtraction_output = output_channel self.AdaptiveAvgPool = nn.AdaptiveAvgPool2d((None, 1)) """ Sequence modeling""" self.SequenceModeling = nn.Sequential( - BidirectionalLSTM(self.FeatureExtraction_output, hidden_size, hidden_size), + BidirectionalLSTM(self.FeatureExtraction_output, + hidden_size, hidden_size), BidirectionalLSTM(hidden_size, hidden_size, hidden_size)) self.SequenceModeling_output = hidden_size """ Prediction """ self.Prediction = nn.Linear(self.SequenceModeling_output, num_class) - def forward(self, input, text): """ Feature extraction stage """ visual_feature = self.FeatureExtraction(input) - visual_feature = self.AdaptiveAvgPool(visual_feature.permute(0, 3, 1, 2)) + visual_feature = self.AdaptiveAvgPool( + visual_feature.permute(0, 3, 1, 2)) visual_feature = visual_feature.squeeze(3) """ Sequence modeling stage """ diff --git a/easyocr/recognition.py b/easyocr/recognition.py index 147370f7d..e9511f9fd 100644 --- a/easyocr/recognition.py +++ b/easyocr/recognition.py @@ -10,23 +10,28 @@ from .utils import CTCLabelConverter import math + def custom_mean(x): return x.prod()**(2.0/np.sqrt(len(x))) + def contrast_grey(img): high = np.percentile(img, 90) - low = np.percentile(img, 10) + low = np.percentile(img, 10) return (high-low)/np.maximum(10, high+low), high, low -def adjust_contrast_grey(img, target = 0.4): + +def adjust_contrast_grey(img, target=0.4): contrast, high, low = contrast_grey(img) if contrast < target: img = img.astype(int) ratio = 200./np.maximum(10, high-low) img = (img - low + 25)*ratio - img = np.maximum(np.full(img.shape, 0) ,np.minimum(np.full(img.shape, 255), img)).astype(np.uint8) + img = np.maximum(np.full(img.shape, 0), np.minimum( + np.full(img.shape, 255), img)).astype(np.uint8) return img + class NormalizePAD(object): def __init__(self, max_size, PAD_type='right'): @@ -42,10 +47,12 @@ def __call__(self, img): Pad_img = torch.FloatTensor(*self.max_size).fill_(0) Pad_img[:, :, :w] = img # right pad if self.max_size[2] != w: # add border Pad - Pad_img[:, :, w:] = img[:, :, w - 1].unsqueeze(2).expand(c, h, self.max_size[2] - w) + Pad_img[:, :, w:] = img[:, :, w - + 1].unsqueeze(2).expand(c, h, self.max_size[2] - w) return Pad_img + class ListDataset(torch.utils.data.Dataset): def __init__(self, image_list): @@ -59,9 +66,10 @@ def __getitem__(self, index): img = self.image_list[index] return Image.fromarray(img, 'L') + class AlignCollate(object): - def __init__(self, imgH=32, imgW=100, keep_ratio_with_pad=False, adjust_contrast = 0.): + def __init__(self, imgH=32, imgW=100, keep_ratio_with_pad=False, adjust_contrast=0.): self.imgH = imgH self.imgW = imgW self.keep_ratio_with_pad = keep_ratio_with_pad @@ -78,10 +86,11 @@ def __call__(self, batch): resized_images = [] for image in images: w, h = image.size - #### augmentation here - change contrast + # augmentation here - change contrast if self.adjust_contrast > 0: image = np.array(image.convert("L")) - image = adjust_contrast_grey(image, target = self.adjust_contrast) + image = adjust_contrast_grey( + image, target=self.adjust_contrast) image = Image.fromarray(image, 'L') ratio = w / float(h) @@ -96,8 +105,9 @@ def __call__(self, batch): image_tensors = torch.cat([t.unsqueeze(0) for t in resized_images], 0) return image_tensors -def recognizer_predict(model, converter, test_loader, batch_max_length,\ - ignore_idx, char_group_idx, decoder = 'greedy', beamWidth= 5, device = 'cpu'): + +def recognizer_predict(model, converter, test_loader, batch_max_length, + ignore_idx, char_group_idx, decoder='greedy', beamWidth=5, device='cpu'): model.eval() result = [] with torch.no_grad(): @@ -105,18 +115,20 @@ def recognizer_predict(model, converter, test_loader, batch_max_length,\ batch_size = image_tensors.size(0) image = image_tensors.to(device) # For max length prediction - length_for_pred = torch.IntTensor([batch_max_length] * batch_size).to(device) - text_for_pred = torch.LongTensor(batch_size, batch_max_length + 1).fill_(0).to(device) + length_for_pred = torch.IntTensor( + [batch_max_length] * batch_size).to(device) + text_for_pred = torch.LongTensor( + batch_size, batch_max_length + 1).fill_(0).to(device) preds = model(image, text_for_pred) # Select max probabilty (greedy decoding) then decode index to character preds_size = torch.IntTensor([preds.size(1)] * batch_size) - ######## filter ignore_char, rebalance + # filter ignore_char, rebalance preds_prob = F.softmax(preds, dim=2) preds_prob = preds_prob.cpu().detach().numpy() - preds_prob[:,:,ignore_idx] = 0. + preds_prob[:, :, ignore_idx] = 0. pred_norm = preds_prob.sum(axis=2) preds_prob = preds_prob/np.expand_dims(pred_norm, axis=-1) preds_prob = torch.from_numpy(preds_prob).float().to(device) @@ -125,21 +137,23 @@ def recognizer_predict(model, converter, test_loader, batch_max_length,\ # Select max probabilty (greedy decoding) then decode index to character _, preds_index = preds_prob.max(2) preds_index = preds_index.view(-1) - preds_str = converter.decode_greedy(preds_index.data.cpu().detach().numpy(), preds_size.data) + preds_str = converter.decode_greedy( + preds_index.data.cpu().detach().numpy(), preds_size.data) elif decoder == 'beamsearch': k = preds_prob.cpu().detach().numpy() preds_str = converter.decode_beamsearch(k, beamWidth=beamWidth) elif decoder == 'wordbeamsearch': k = preds_prob.cpu().detach().numpy() - preds_str = converter.decode_wordbeamsearch(k, beamWidth=beamWidth) + preds_str = converter.decode_wordbeamsearch( + k, beamWidth=beamWidth) preds_prob = preds_prob.cpu().detach().numpy() values = preds_prob.max(axis=2) indices = preds_prob.argmax(axis=2) preds_max_prob = [] - for v,i in zip(values, indices): - max_probs = v[i!=0] - if len(max_probs)>0: + for v, i in zip(values, indices): + max_probs = v[i != 0] + if len(max_probs) > 0: preds_max_prob.append(max_probs) else: preds_max_prob.append(np.array([0])) @@ -150,9 +164,10 @@ def recognizer_predict(model, converter, test_loader, batch_max_length,\ return result -def get_recognizer(recog_network, network_params, character,\ - separator_list, dict_list, model_path,\ - device = 'cpu', quantize = True): + +def get_recognizer(recog_network, network_params, character, + separator_list, dict_list, model_path, + device='cpu', quantize=True): converter = CTCLabelConverter(character, separator_list, dict_list) num_class = len(converter.character) @@ -174,7 +189,8 @@ def get_recognizer(recog_network, network_params, character,\ model.load_state_dict(new_state_dict) if quantize: try: - torch.quantization.quantize_dynamic(model, dtype=torch.qint8, inplace=True) + torch.quantization.quantize_dynamic( + model, dtype=torch.qint8, inplace=True) except: pass else: @@ -183,51 +199,57 @@ def get_recognizer(recog_network, network_params, character,\ return model, converter -def get_text(character, imgH, imgW, recognizer, converter, image_list,\ - ignore_char = '',decoder = 'greedy', beamWidth =5, batch_size=1, contrast_ths=0.1,\ - adjust_contrast=0.5, filter_ths = 0.003, workers = 1, device = 'cpu'): + +def get_text(character, imgH, imgW, recognizer, converter, image_list, + ignore_char='', decoder='greedy', beamWidth=5, batch_size=1, contrast_ths=0.1, + adjust_contrast=0.5, filter_ths=0.003, workers=1, device='cpu'): batch_max_length = int(imgW/10) char_group_idx = {} ignore_idx = [] for char in ignore_char: - try: ignore_idx.append(character.index(char)+1) - except: pass + try: + ignore_idx.append(character.index(char)+1) + except: + pass coord = [item[0] for item in image_list] img_list = [item[1] for item in image_list] - AlignCollate_normal = AlignCollate(imgH=imgH, imgW=imgW, keep_ratio_with_pad=True) + AlignCollate_normal = AlignCollate( + imgH=imgH, imgW=imgW, keep_ratio_with_pad=True) test_data = ListDataset(img_list) test_loader = torch.utils.data.DataLoader( test_data, batch_size=batch_size, shuffle=False, num_workers=int(workers), collate_fn=AlignCollate_normal, pin_memory=True) # predict first round - result1 = recognizer_predict(recognizer, converter, test_loader,batch_max_length,\ - ignore_idx, char_group_idx, decoder, beamWidth, device = device) + result1 = recognizer_predict(recognizer, converter, test_loader, batch_max_length, + ignore_idx, char_group_idx, decoder, beamWidth, device=device) # predict second round - low_confident_idx = [i for i,item in enumerate(result1) if (item[1] < contrast_ths)] + low_confident_idx = [i for i, item in enumerate( + result1) if (item[1] < contrast_ths)] if len(low_confident_idx) > 0: img_list2 = [img_list[i] for i in low_confident_idx] - AlignCollate_contrast = AlignCollate(imgH=imgH, imgW=imgW, keep_ratio_with_pad=True, adjust_contrast=adjust_contrast) + AlignCollate_contrast = AlignCollate( + imgH=imgH, imgW=imgW, keep_ratio_with_pad=True, adjust_contrast=adjust_contrast) test_data = ListDataset(img_list2) test_loader = torch.utils.data.DataLoader( - test_data, batch_size=batch_size, shuffle=False, - num_workers=int(workers), collate_fn=AlignCollate_contrast, pin_memory=True) - result2 = recognizer_predict(recognizer, converter, test_loader, batch_max_length,\ - ignore_idx, char_group_idx, decoder, beamWidth, device = device) + test_data, batch_size=batch_size, shuffle=False, + num_workers=int(workers), collate_fn=AlignCollate_contrast, pin_memory=True) + result2 = recognizer_predict(recognizer, converter, test_loader, batch_max_length, + ignore_idx, char_group_idx, decoder, beamWidth, device=device) result = [] for i, zipped in enumerate(zip(coord, result1)): box, pred1 = zipped if i in low_confident_idx: pred2 = result2[low_confident_idx.index(i)] - if pred1[1]>pred2[1]: - result.append( (box, pred1[0], pred1[1]) ) + if pred1[1] > pred2[1]: + result.append((box, pred1[0], pred1[1])) else: - result.append( (box, pred2[0], pred2[1]) ) + result.append((box, pred2[0], pred2[1])) else: - result.append( (box, pred1[0], pred1[1]) ) + result.append((box, pred1[0], pred1[1])) return result diff --git a/easyocr/utils.py b/easyocr/utils.py index 7c3a0875e..a40af93a1 100644 --- a/easyocr/utils.py +++ b/easyocr/utils.py @@ -8,7 +8,8 @@ from PIL import Image, JpegImagePlugin from scipy import ndimage import hashlib -import sys, os +import sys +import os from zipfile import ZipFile from .imgproc import loadImage @@ -17,59 +18,70 @@ else: from urllib.request import urlretrieve -def consecutive(data, mode ='first', stepsize=1): + +def consecutive(data, mode='first', stepsize=1): group = np.split(data, np.where(np.diff(data) != stepsize)[0]+1) - group = [item for item in group if len(item)>0] + group = [item for item in group if len(item) > 0] - if mode == 'first': result = [l[0] for l in group] - elif mode == 'last': result = [l[-1] for l in group] + if mode == 'first': + result = [l[0] for l in group] + elif mode == 'last': + result = [l[-1] for l in group] return result -def word_segmentation(mat, separator_idx = {'th': [1,2],'en': [3,4]}, separator_idx_list = [1,2,3,4]): + +def word_segmentation(mat, separator_idx={'th': [1, 2], 'en': [3, 4]}, separator_idx_list=[1, 2, 3, 4]): result = [] sep_list = [] start_idx = 0 sep_lang = '' for sep_idx in separator_idx_list: - if sep_idx % 2 == 0: mode ='first' - else: mode ='last' - a = consecutive( np.argwhere(mat == sep_idx).flatten(), mode) - new_sep = [ [item, sep_idx] for item in a] + if sep_idx % 2 == 0: + mode = 'first' + else: + mode = 'last' + a = consecutive(np.argwhere(mat == sep_idx).flatten(), mode) + new_sep = [[item, sep_idx] for item in a] sep_list += new_sep sep_list = sorted(sep_list, key=lambda x: x[0]) for sep in sep_list: for lang in separator_idx.keys(): - if sep[1] == separator_idx[lang][0]: # start lang + if sep[1] == separator_idx[lang][0]: # start lang sep_lang = lang sep_start_idx = sep[0] - elif sep[1] == separator_idx[lang][1]: # end lang - if sep_lang == lang: # check if last entry if the same start lang + elif sep[1] == separator_idx[lang][1]: # end lang + if sep_lang == lang: # check if last entry if the same start lang new_sep_pair = [lang, [sep_start_idx+1, sep[0]-1]] if sep_start_idx > start_idx: - result.append( ['', [start_idx, sep_start_idx-1] ] ) + result.append(['', [start_idx, sep_start_idx-1]]) start_idx = sep[0]+1 result.append(new_sep_pair) - sep_lang = ''# reset + sep_lang = '' # reset if start_idx <= len(mat)-1: - result.append( ['', [start_idx, len(mat)-1] ] ) + result.append(['', [start_idx, len(mat)-1]]) return result # code is based from https://github.com/githubharald/CTCDecoder/blob/master/src/BeamSearch.py + + class BeamEntry: "information about one single beam at specific time-step" + def __init__(self): - self.prTotal = 0 # blank and non-blank - self.prNonBlank = 0 # non-blank - self.prBlank = 0 # blank - self.prText = 1 # LM score - self.lmApplied = False # flag if LM was already applied to this beam - self.labeling = () # beam-labeling + self.prTotal = 0 # blank and non-blank + self.prNonBlank = 0 # non-blank + self.prBlank = 0 # blank + self.prText = 1 # LM score + self.lmApplied = False # flag if LM was already applied to this beam + self.labeling = () # beam-labeling self.simplified = True # To run simplyfiy label + class BeamState: "information about the beams at specific time-step" + def __init__(self): self.entries = {} @@ -77,27 +89,32 @@ def norm(self): "length-normalise LM score" for (k, _) in self.entries.items(): labelingLen = len(self.entries[k].labeling) - self.entries[k].prText = self.entries[k].prText ** (1.0 / (labelingLen if labelingLen else 1.0)) + self.entries[k].prText = self.entries[k].prText ** ( + 1.0 / (labelingLen if labelingLen else 1.0)) def sort(self): "return beam-labelings, sorted by probability" beams = [v for (_, v) in self.entries.items()] - sortedBeams = sorted(beams, reverse=True, key=lambda x: x.prTotal*x.prText) + sortedBeams = sorted(beams, reverse=True, + key=lambda x: x.prTotal*x.prText) return [x.labeling for x in sortedBeams] def wordsearch(self, classes, ignore_idx, maxCandidate, dict_list): beams = [v for (_, v) in self.entries.items()] - sortedBeams = sorted(beams, reverse=True, key=lambda x: x.prTotal*x.prText) - if len(sortedBeams) > maxCandidate: sortedBeams = sortedBeams[:maxCandidate] + sortedBeams = sorted(beams, reverse=True, + key=lambda x: x.prTotal*x.prText) + if len(sortedBeams) > maxCandidate: + sortedBeams = sortedBeams[:maxCandidate] for j, candidate in enumerate(sortedBeams): idx_list = candidate.labeling text = '' - for i,l in enumerate(idx_list): + for i, l in enumerate(idx_list): if l not in ignore_idx and (not (i > 0 and idx_list[i - 1] == idx_list[i])): text += classes[l] - if j == 0: best_text = text + if j == 0: + best_text = text if text in dict_list: #print('found text: ', text) best_text = text @@ -107,33 +124,42 @@ def wordsearch(self, classes, ignore_idx, maxCandidate, dict_list): #print('not in dict: ', text) return best_text + def applyLM(parentBeam, childBeam, classes, lm): "calculate LM score of child beam by taking score from parent beam and bigram probability of last two chars" if lm and not childBeam.lmApplied: - c1 = classes[parentBeam.labeling[-1] if parentBeam.labeling else classes.index(' ')] # first char - c2 = classes[childBeam.labeling[-1]] # second char - lmFactor = 0.01 # influence of language model - bigramProb = lm.getCharBigram(c1, c2) ** lmFactor # probability of seeing first and second char next to each other - childBeam.prText = parentBeam.prText * bigramProb # probability of char sequence - childBeam.lmApplied = True # only apply LM once per beam entry - -def simplify_label(labeling, blankIdx = 0): + c1 = classes[parentBeam.labeling[-1] + if parentBeam.labeling else classes.index(' ')] # first char + c2 = classes[childBeam.labeling[-1]] # second char + lmFactor = 0.01 # influence of language model + # probability of seeing first and second char next to each other + bigramProb = lm.getCharBigram(c1, c2) ** lmFactor + childBeam.prText = parentBeam.prText * \ + bigramProb # probability of char sequence + childBeam.lmApplied = True # only apply LM once per beam entry + + +def simplify_label(labeling, blankIdx=0): labeling = np.array(labeling) # collapse blank - idx = np.where(~((np.roll(labeling,1) == labeling) & (labeling == blankIdx)))[0] + idx = np.where(~((np.roll(labeling, 1) == labeling) + & (labeling == blankIdx)))[0] labeling = labeling[idx] # get rid of blank between different characters - idx = np.where( ~((np.roll(labeling,1) != np.roll(labeling,-1)) & (labeling == blankIdx)) )[0] + idx = np.where(~((np.roll(labeling, 1) != np.roll( + labeling, -1)) & (labeling == blankIdx)))[0] if len(labeling) > 0: last_idx = len(labeling)-1 - if last_idx not in idx: idx = np.append(idx, [last_idx]) + if last_idx not in idx: + idx = np.append(idx, [last_idx]) labeling = labeling[idx] return tuple(labeling) + def fast_simplify_label(labeling, c, blankIdx=0): # Adding BlankIDX after Non-Blank IDX @@ -173,12 +199,14 @@ def fast_simplify_label(labeling, c, blankIdx=0): return newLabeling + def addBeam(beamState, labeling): "add beam if it does not yet exist" if labeling not in beamState.entries: beamState.entries[labeling] = BeamEntry() -def ctcBeamSearch(mat, classes, ignore_idx, lm, beamWidth=25, dict_list = []): + +def ctcBeamSearch(mat, classes, ignore_idx, lm, beamWidth=25, dict_list=[]): blankIdx = 0 maxT, maxC = mat.shape @@ -201,7 +229,8 @@ def ctcBeamSearch(mat, classes, ignore_idx, lm, beamWidth=25, dict_list = []): # in case of non-empty beam if labeling: # probability of paths with repeated last char at the end - prNonBlank = last.entries[labeling].prNonBlank * mat[t, labeling[-1]] + prNonBlank = last.entries[labeling].prNonBlank * \ + mat[t, labeling[-1]] # probability of paths ending with a blank prBlank = (last.entries[labeling].prTotal) * mat[t, blankIdx] @@ -222,13 +251,14 @@ def ctcBeamSearch(mat, classes, ignore_idx, lm, beamWidth=25, dict_list = []): curr.entries[labeling].prText = last.entries[prev_labeling].prText # beam-labeling not changed, therefore also LM score unchanged from - #curr.entries[labeling].lmApplied = True # LM already applied at previous time-step for this beam-labeling + # curr.entries[labeling].lmApplied = True # LM already applied at previous time-step for this beam-labeling # extend current beam-labeling # char_highscore = np.argpartition(mat[t, :], -5)[-5:] # run through 5 highest probability - char_highscore = np.where(mat[t, :] >= 0.5/maxC)[0] # run through all probable characters + # run through all probable characters + char_highscore = np.where(mat[t, :] >= 0.5/maxC)[0] for c in char_highscore: - #for c in range(maxC - 1): + # for c in range(maxC - 1): # add new char to current beam-labeling # newLabeling = labeling + (c,) # newLabeling = simplify_label(newLabeling, blankIdx) @@ -236,9 +266,11 @@ def ctcBeamSearch(mat, classes, ignore_idx, lm, beamWidth=25, dict_list = []): # if new labeling contains duplicate char at the end, only consider paths ending with a blank if labeling and labeling[-1] == c: - prNonBlank = mat[t, c] * last.entries[prev_labeling].prBlank + prNonBlank = mat[t, c] * \ + last.entries[prev_labeling].prBlank else: - prNonBlank = mat[t, c] * last.entries[prev_labeling].prTotal + prNonBlank = mat[t, c] * \ + last.entries[prev_labeling].prTotal # add beam at current time-step if needed addBeam(curr, newLabeling) @@ -259,9 +291,9 @@ def ctcBeamSearch(mat, classes, ignore_idx, lm, beamWidth=25, dict_list = []): last.norm() if dict_list == []: - bestLabeling = last.sort()[0] # get most probable labeling + bestLabeling = last.sort()[0] # get most probable labeling res = '' - for i,l in enumerate(bestLabeling): + for i, l in enumerate(bestLabeling): # removing repeated characters and blank. if l not in ignore_idx and (not (i > 0 and bestLabeling[i - 1] == bestLabeling[i])): res += classes[l] @@ -273,7 +305,7 @@ def ctcBeamSearch(mat, classes, ignore_idx, lm, beamWidth=25, dict_list = []): class CTCLabelConverter(object): """ Convert between text-label and text-index """ - def __init__(self, character, separator_list = {}, dict_pathlist = {}): + def __init__(self, character, separator_list={}, dict_pathlist={}): # character (str): set of the possible characters. dict_character = list(character) @@ -281,29 +313,30 @@ def __init__(self, character, separator_list = {}, dict_pathlist = {}): for i, char in enumerate(dict_character): self.dict[char] = i + 1 - self.character = ['[blank]'] + dict_character # dummy '[blank]' token for CTCLoss (index 0) + # dummy '[blank]' token for CTCLoss (index 0) + self.character = ['[blank]'] + dict_character self.separator_list = separator_list separator_char = [] for lang, sep in separator_list.items(): separator_char += sep - self.ignore_idx = [0] + [i+1 for i,item in enumerate(separator_char)] + self.ignore_idx = [0] + [i+1 for i, item in enumerate(separator_char)] - ####### latin dict + # latin dict if len(separator_list) == 0: dict_list = [] for lang, dict_path in dict_pathlist.items(): try: - with open(dict_path, "r", encoding = "utf-8-sig") as input_file: - word_count = input_file.read().splitlines() + with open(dict_path, "r", encoding="utf-8-sig") as input_file: + word_count = input_file.read().splitlines() dict_list += word_count except: pass else: dict_list = {} for lang, dict_path in dict_pathlist.items(): - with open(dict_path, "r", encoding = "utf-8-sig") as input_file: - word_count = input_file.read().splitlines() + with open(dict_path, "r", encoding="utf-8-sig") as input_file: + word_count = input_file.read().splitlines() dict_list[lang] = word_count self.dict_list = dict_list @@ -331,9 +364,9 @@ def decode_greedy(self, text_index, length): for l in length: t = text_index[index:index + l] # Returns a boolean array where true is when the value is not repeated - a = np.insert(~((t[1:]==t[:-1])),0,True) + a = np.insert(~((t[1:] == t[:-1])), 0, True) # Returns a boolean array where true is when the value is not in the ignore_idx list - b = ~np.isin(t,np.array(self.ignore_idx)) + b = ~np.isin(t, np.array(self.ignore_idx)) # Combine the two boolean array c = a & b # Gets the corresponding character according to the saved indexes @@ -345,13 +378,14 @@ def decode_greedy(self, text_index, length): def decode_beamsearch(self, mat, beamWidth=5): texts = [] for i in range(mat.shape[0]): - t = ctcBeamSearch(mat[i], self.character, self.ignore_idx, None, beamWidth=beamWidth) + t = ctcBeamSearch(mat[i], self.character, + self.ignore_idx, None, beamWidth=beamWidth) texts.append(t) return texts def decode_wordbeamsearch(self, mat, beamWidth=5): texts = [] - argmax = np.argmax(mat, axis = 2) + argmax = np.argmax(mat, axis=2) for i in range(mat.shape[0]): string = '' @@ -359,30 +393,36 @@ def decode_wordbeamsearch(self, mat, beamWidth=5): if len(self.separator_list) == 0: space_idx = self.dict[' '] - data = np.argwhere(argmax[i]!=space_idx).flatten() + data = np.argwhere(argmax[i] != space_idx).flatten() group = np.split(data, np.where(np.diff(data) != 1)[0]+1) - group = [ list(item) for item in group if len(item)>0] + group = [list(item) for item in group if len(item) > 0] for j, list_idx in enumerate(group): - matrix = mat[i, list_idx,:] - t = ctcBeamSearch(matrix, self.character, self.ignore_idx, None,\ + matrix = mat[i, list_idx, :] + t = ctcBeamSearch(matrix, self.character, self.ignore_idx, None, beamWidth=beamWidth, dict_list=self.dict_list) - if j == 0: string += t - else: string += ' '+t + if j == 0: + string += t + else: + string += ' '+t # with separators else: words = word_segmentation(argmax[i]) for word in words: - matrix = mat[i, word[1][0]:word[1][1]+1,:] - if word[0] == '': dict_list = [] - else: dict_list = self.dict_list[word[0]] - t = ctcBeamSearch(matrix, self.character, self.ignore_idx, None, beamWidth=beamWidth, dict_list=dict_list) + matrix = mat[i, word[1][0]:word[1][1]+1, :] + if word[0] == '': + dict_list = [] + else: + dict_list = self.dict_list[word[0]] + t = ctcBeamSearch(matrix, self.character, self.ignore_idx, + None, beamWidth=beamWidth, dict_list=dict_list) string += t texts.append(string) return texts + def four_point_transform(image, rect): (tl, tr, br, bl) = rect @@ -397,7 +437,8 @@ def four_point_transform(image, rect): heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2)) maxHeight = max(int(heightA), int(heightB)) - dst = np.array([[0, 0],[maxWidth - 1, 0],[maxWidth - 1, maxHeight - 1],[0, maxHeight - 1]], dtype = "float32") + dst = np.array([[0, 0], [maxWidth - 1, 0], [maxWidth - 1, + maxHeight - 1], [0, maxHeight - 1]], dtype="float32") # compute the perspective transform matrix and then apply it M = cv2.getPerspectiveTransform(rect, dst) @@ -405,27 +446,31 @@ def four_point_transform(image, rect): return warped -def group_text_box(polys, slope_ths = 0.1, ycenter_ths = 0.5, height_ths = 0.5, width_ths = 1.0, add_margin = 0.05, sort_output = True): + +def group_text_box(polys, slope_ths=0.1, ycenter_ths=0.5, height_ths=0.5, width_ths=1.0, add_margin=0.05, sort_output=True): # poly top-left, top-right, low-right, low-left - horizontal_list, free_list,combined_list, merged_list = [],[],[],[] + horizontal_list, free_list, combined_list, merged_list = [], [], [], [] for poly in polys: slope_up = (poly[3]-poly[1])/np.maximum(10, (poly[2]-poly[0])) slope_down = (poly[5]-poly[7])/np.maximum(10, (poly[4]-poly[6])) if max(abs(slope_up), abs(slope_down)) < slope_ths: - x_max = max([poly[0],poly[2],poly[4],poly[6]]) - x_min = min([poly[0],poly[2],poly[4],poly[6]]) - y_max = max([poly[1],poly[3],poly[5],poly[7]]) - y_min = min([poly[1],poly[3],poly[5],poly[7]]) - horizontal_list.append([x_min, x_max, y_min, y_max, 0.5*(y_min+y_max), y_max-y_min]) + x_max = max([poly[0], poly[2], poly[4], poly[6]]) + x_min = min([poly[0], poly[2], poly[4], poly[6]]) + y_max = max([poly[1], poly[3], poly[5], poly[7]]) + y_min = min([poly[1], poly[3], poly[5], poly[7]]) + horizontal_list.append( + [x_min, x_max, y_min, y_max, 0.5*(y_min+y_max), y_max-y_min]) else: - height = np.linalg.norm([poly[6]-poly[0],poly[7]-poly[1]]) - width = np.linalg.norm([poly[2]-poly[0],poly[3]-poly[1]]) + height = np.linalg.norm([poly[6]-poly[0], poly[7]-poly[1]]) + width = np.linalg.norm([poly[2]-poly[0], poly[3]-poly[1]]) margin = int(1.44*add_margin*min(width, height)) - theta13 = abs(np.arctan( (poly[1]-poly[5])/np.maximum(10, (poly[0]-poly[4])))) - theta24 = abs(np.arctan( (poly[3]-poly[7])/np.maximum(10, (poly[2]-poly[6])))) + theta13 = abs( + np.arctan((poly[1]-poly[5])/np.maximum(10, (poly[0]-poly[4])))) + theta24 = abs( + np.arctan((poly[3]-poly[7])/np.maximum(10, (poly[2]-poly[6])))) # do I need to clip minimum, maximum value here? x1 = poly[0] - np.cos(theta13)*margin y1 = poly[1] - np.sin(theta13)*margin @@ -436,7 +481,7 @@ def group_text_box(polys, slope_ths = 0.1, ycenter_ths = 0.5, height_ths = 0.5, x4 = poly[6] - np.cos(theta24)*margin y4 = poly[7] + np.sin(theta24)*margin - free_list.append([[x1,y1],[x2,y2],[x3,y3],[x4,y4]]) + free_list.append([[x1, y1], [x2, y2], [x3, y3], [x4, y4]]) if sort_output: horizontal_list = sorted(horizontal_list, key=lambda item: item[4]) @@ -463,21 +508,23 @@ def group_text_box(polys, slope_ths = 0.1, ycenter_ths = 0.5, height_ths = 0.5, # merge list use sort again for boxes in combined_list: - if len(boxes) == 1: # one box per line + if len(boxes) == 1: # one box per line box = boxes[0] - margin = int(add_margin*min(box[1]-box[0],box[5])) - merged_list.append([box[0]-margin,box[1]+margin,box[2]-margin,box[3]+margin]) - else: # multiple boxes per line + margin = int(add_margin*min(box[1]-box[0], box[5])) + merged_list.append( + [box[0]-margin, box[1]+margin, box[2]-margin, box[3]+margin]) + else: # multiple boxes per line boxes = sorted(boxes, key=lambda item: item[0]) - merged_box, new_box = [],[] + merged_box, new_box = [], [] for box in boxes: if len(new_box) == 0: b_height = [box[5]] x_max = box[1] new_box.append(box) else: - if (abs(np.mean(b_height) - box[5]) < height_ths*np.mean(b_height)) and (abs(box[0]-x_max) < width_ths *(box[3]-box[2])): # merge boxes + # merge boxes + if (abs(np.mean(b_height) - box[5]) < height_ths*np.mean(b_height)) and (abs(box[0]-x_max) < width_ths * (box[3]-box[2])): b_height.append(box[5]) x_max = box[1] new_box.append(box) @@ -486,10 +533,11 @@ def group_text_box(polys, slope_ths = 0.1, ycenter_ths = 0.5, height_ths = 0.5, x_max = box[1] merged_box.append(new_box) new_box = [box] - if len(new_box) >0: merged_box.append(new_box) + if len(new_box) > 0: + merged_box.append(new_box) for mbox in merged_box: - if len(mbox) != 1: # adjacent box in same line + if len(mbox) != 1: # adjacent box in same line # do I need to add margin here? x_min = min(mbox, key=lambda x: x[0])[0] x_max = max(mbox, key=lambda x: x[1])[1] @@ -500,76 +548,86 @@ def group_text_box(polys, slope_ths = 0.1, ycenter_ths = 0.5, height_ths = 0.5, box_height = y_max - y_min margin = int(add_margin * (min(box_width, box_height))) - merged_list.append([x_min-margin, x_max+margin, y_min-margin, y_max+margin]) - else: # non adjacent box in same line + merged_list.append( + [x_min-margin, x_max+margin, y_min-margin, y_max+margin]) + else: # non adjacent box in same line box = mbox[0] box_width = box[1] - box[0] box_height = box[3] - box[2] margin = int(add_margin * (min(box_width, box_height))) - merged_list.append([box[0]-margin,box[1]+margin,box[2]-margin,box[3]+margin]) + merged_list.append( + [box[0]-margin, box[1]+margin, box[2]-margin, box[3]+margin]) # may need to check if box is really in image return merged_list, free_list -def calculate_ratio(width,height): + +def calculate_ratio(width, height): ''' Calculate aspect ratio for normal use case (w>h) and vertical text (h>w) ''' ratio = width/height - if ratio<1.0: + if ratio < 1.0: ratio = 1./ratio return ratio -def compute_ratio_and_resize(img,width,height,model_height): + +def compute_ratio_and_resize(img, width, height, model_height): ''' Calculate ratio and resize correctly for both horizontal text and vertical case ''' ratio = width/height - if ratio<1.0: - ratio = calculate_ratio(width,height) - img = cv2.resize(img,(model_height,int(model_height*ratio)), interpolation=Image.ANTIALIAS) + if ratio < 1.0: + ratio = calculate_ratio(width, height) + img = cv2.resize(img, (model_height, int( + model_height*ratio)), interpolation=Image.ANTIALIAS) else: - img = cv2.resize(img,(int(model_height*ratio),model_height),interpolation=Image.ANTIALIAS) - return img,ratio + img = cv2.resize(img, (int(model_height*ratio), + model_height), interpolation=Image.ANTIALIAS) + return img, ratio -def get_image_list(horizontal_list, free_list, img, model_height = 64, sort_output = True): +def get_image_list(horizontal_list, free_list, img, model_height=64, sort_output=True): image_list = [] - maximum_y,maximum_x = img.shape + maximum_y, maximum_x = img.shape - max_ratio_hori, max_ratio_free = 1,1 + max_ratio_hori, max_ratio_free = 1, 1 for box in free_list: - rect = np.array(box, dtype = "float32") + rect = np.array(box, dtype="float32") transformed_img = four_point_transform(img, rect) - ratio = calculate_ratio(transformed_img.shape[1],transformed_img.shape[0]) + ratio = calculate_ratio( + transformed_img.shape[1], transformed_img.shape[0]) new_width = int(model_height*ratio) if new_width == 0: pass else: - crop_img,ratio = compute_ratio_and_resize(transformed_img,transformed_img.shape[1],transformed_img.shape[0],model_height) - image_list.append( (box,crop_img) ) # box = [[x1,y1],[x2,y2],[x3,y3],[x4,y4]] + crop_img, ratio = compute_ratio_and_resize( + transformed_img, transformed_img.shape[1], transformed_img.shape[0], model_height) + # box = [[x1,y1],[x2,y2],[x3,y3],[x4,y4]] + image_list.append((box, crop_img)) max_ratio_free = max(ratio, max_ratio_free) - max_ratio_free = math.ceil(max_ratio_free) for box in horizontal_list: - x_min = max(0,box[0]) - x_max = min(box[1],maximum_x) - y_min = max(0,box[2]) - y_max = min(box[3],maximum_y) - crop_img = img[y_min : y_max, x_min:x_max] + x_min = max(0, box[0]) + x_max = min(box[1], maximum_x) + y_min = max(0, box[2]) + y_max = min(box[3], maximum_y) + crop_img = img[y_min: y_max, x_min:x_max] width = x_max - x_min height = y_max - y_min - ratio = calculate_ratio(width,height) + ratio = calculate_ratio(width, height) new_width = int(model_height*ratio) if new_width == 0: pass else: - crop_img,ratio = compute_ratio_and_resize(crop_img,width,height,model_height) - image_list.append( ( [[x_min,y_min],[x_max,y_min],[x_max,y_max],[x_min,y_max]] ,crop_img) ) + crop_img, ratio = compute_ratio_and_resize( + crop_img, width, height, model_height) + image_list.append( + ([[x_min, y_min], [x_max, y_min], [x_max, y_max], [x_min, y_max]], crop_img)) max_ratio_hori = max(ratio, max_ratio_hori) max_ratio_hori = math.ceil(max_ratio_hori) @@ -577,17 +635,21 @@ def get_image_list(horizontal_list, free_list, img, model_height = 64, sort_outp max_width = math.ceil(max_ratio)*model_height if sort_output: - image_list = sorted(image_list, key=lambda item: item[0][0][1]) # sort by vertical position + # sort by vertical position + image_list = sorted(image_list, key=lambda item: item[0][0][1]) return image_list, max_width + def download_and_unzip(url, filename, model_storage_directory, verbose=True): zip_path = os.path.join(model_storage_directory, 'temp.zip') - reporthook = printProgressBar(prefix='Progress:', suffix='Complete', length=50) if verbose else None + reporthook = printProgressBar( + prefix='Progress:', suffix='Complete', length=50) if verbose else None urlretrieve(url, zip_path, reporthook=reporthook) with ZipFile(zip_path, 'r') as zipObj: zipObj.extract(filename, model_storage_directory) os.remove(zip_path) + def calculate_md5(fname): hash_md5 = hashlib.md5() with open(fname, "rb") as f: @@ -595,10 +657,12 @@ def calculate_md5(fname): hash_md5.update(chunk) return hash_md5.hexdigest() + def diff(input_list): return max(input_list)-min(input_list) -def get_paragraph(raw_result, x_ths=1, y_ths=0.5, mode = 'ltr'): + +def get_paragraph(raw_result, x_ths=1, y_ths=0.5, mode='ltr'): # create basic attributes box_group = [] for box in raw_result: @@ -609,37 +673,48 @@ def get_paragraph(raw_result, x_ths=1, y_ths=0.5, mode = 'ltr'): min_y = min(all_y) max_y = max(all_y) height = max_y - min_y - box_group.append([box[1], min_x, max_x, min_y, max_y, height, 0.5*(min_y+max_y), 0]) # last element indicates group + # last element indicates group + box_group.append([box[1], min_x, max_x, min_y, max_y, + height, 0.5*(min_y+max_y), 0]) # cluster boxes into paragraph current_group = 1 - while len([box for box in box_group if box[7]==0]) > 0: - box_group0 = [box for box in box_group if box[7]==0] # group0 = non-group + while len([box for box in box_group if box[7] == 0]) > 0: + # group0 = non-group + box_group0 = [box for box in box_group if box[7] == 0] # new group - if len([box for box in box_group if box[7]==current_group]) == 0: - box_group0[0][7] = current_group # assign first box to form new group + if len([box for box in box_group if box[7] == current_group]) == 0: + # assign first box to form new group + box_group0[0][7] = current_group # try to add group else: - current_box_group = [box for box in box_group if box[7]==current_group] + current_box_group = [ + box for box in box_group if box[7] == current_group] mean_height = np.mean([box[5] for box in current_box_group]) - min_gx = min([box[1] for box in current_box_group]) - x_ths*mean_height - max_gx = max([box[2] for box in current_box_group]) + x_ths*mean_height - min_gy = min([box[3] for box in current_box_group]) - y_ths*mean_height - max_gy = max([box[4] for box in current_box_group]) + y_ths*mean_height + min_gx = min([box[1] + for box in current_box_group]) - x_ths*mean_height + max_gx = max([box[2] + for box in current_box_group]) + x_ths*mean_height + min_gy = min([box[3] + for box in current_box_group]) - y_ths*mean_height + max_gy = max([box[4] + for box in current_box_group]) + y_ths*mean_height add_box = False for box in box_group0: - same_horizontal_level = (min_gx<=box[1]<=max_gx) or (min_gx<=box[2]<=max_gx) - same_vertical_level = (min_gy<=box[3]<=max_gy) or (min_gy<=box[4]<=max_gy) + same_horizontal_level = (min_gx <= box[1] <= max_gx) or ( + min_gx <= box[2] <= max_gx) + same_vertical_level = (min_gy <= box[3] <= max_gy) or ( + min_gy <= box[4] <= max_gy) if same_horizontal_level and same_vertical_level: box[7] = current_group add_box = True break # cannot add more box, go to next group - if add_box==False: + if add_box == False: current_group += 1 # arrage order in paragraph result = [] for i in set(box[7] for box in box_group): - current_box_group = [box for box in box_group if box[7]==i] + current_box_group = [box for box in box_group if box[7] == i] mean_height = np.mean([box[5] for box in current_box_group]) min_gx = min([box[1] for box in current_box_group]) max_gx = max([box[2] for box in current_box_group]) @@ -649,25 +724,29 @@ def get_paragraph(raw_result, x_ths=1, y_ths=0.5, mode = 'ltr'): text = '' while len(current_box_group) > 0: highest = min([box[6] for box in current_box_group]) - candidates = [box for box in current_box_group if box[6]= len(result[1])): result = result_to_split[rot][i] - confidence = result_to_split[rot][i][2] + confidence = result_to_split[rot][i][2] final_result.append(result) diff --git a/setup.py b/setup.py index 22007ea5c..9fc5b2e91 100644 --- a/setup.py +++ b/setup.py @@ -8,11 +8,13 @@ with open('requirements.txt', encoding="utf-8-sig") as f: requirements = f.readlines() + def readme(): with open('README.md', encoding="utf-8-sig") as f: README = f.read() return README + setup( name='easyocr', packages=['easyocr'], diff --git a/trainer/dataset.py b/trainer/dataset.py index a6485eea3..24a8dc535 100644 --- a/trainer/dataset.py +++ b/trainer/dataset.py @@ -4,7 +4,7 @@ import six import math import torch -import pandas as pd +import pandas as pd from natsort import natsorted from PIL import Image @@ -13,18 +13,21 @@ from torch._utils import _accumulate import torchvision.transforms as transforms + def contrast_grey(img): high = np.percentile(img, 90) - low = np.percentile(img, 10) + low = np.percentile(img, 10) return (high-low)/(high+low), high, low -def adjust_contrast_grey(img, target = 0.4): + +def adjust_contrast_grey(img, target=0.4): contrast, high, low = contrast_grey(img) if contrast < target: img = img.astype(int) ratio = 200./(high-low) img = (img - low + 25)*ratio - img = np.maximum(np.full(img.shape, 0) ,np.minimum(np.full(img.shape, 255), img)).astype(np.uint8) + img = np.maximum(np.full(img.shape, 0), np.minimum( + np.full(img.shape, 255), img)).astype(np.uint8) return img @@ -36,15 +39,19 @@ def __init__(self, opt): For example, when select_data is "MJ-ST" and batch_ratio is "0.5-0.5", the 50% of the batch is filled with MJ and the other 50% of the batch is filled with ST. """ - log = open(f'./saved_models/{opt.experiment_name}/log_dataset.txt', 'a') + log = open( + f'./saved_models/{opt.experiment_name}/log_dataset.txt', 'a') dashed_line = '-' * 80 print(dashed_line) log.write(dashed_line + '\n') - print(f'dataset_root: {opt.train_data}\nopt.select_data: {opt.select_data}\nopt.batch_ratio: {opt.batch_ratio}') - log.write(f'dataset_root: {opt.train_data}\nopt.select_data: {opt.select_data}\nopt.batch_ratio: {opt.batch_ratio}\n') + print( + f'dataset_root: {opt.train_data}\nopt.select_data: {opt.select_data}\nopt.batch_ratio: {opt.batch_ratio}') + log.write( + f'dataset_root: {opt.train_data}\nopt.select_data: {opt.select_data}\nopt.batch_ratio: {opt.batch_ratio}\n') assert len(opt.select_data) == len(opt.batch_ratio) - _AlignCollate = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD, contrast_adjust = opt.contrast_adjust) + _AlignCollate = AlignCollate( + imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD, contrast_adjust=opt.contrast_adjust) self.data_loader_list = [] self.dataloader_iter_list = [] batch_size_list = [] @@ -53,7 +60,8 @@ def __init__(self, opt): _batch_size = max(round(opt.batch_size * float(batch_ratio_d)), 1) print(dashed_line) log.write(dashed_line + '\n') - _dataset, _dataset_log = hierarchical_dataset(root=opt.train_data, opt=opt, select_data=[selected_d]) + _dataset, _dataset_log = hierarchical_dataset( + root=opt.train_data, opt=opt, select_data=[selected_d]) total_number_dataset = len(_dataset) log.write(_dataset_log) @@ -62,8 +70,10 @@ def __init__(self, opt): ex) opt.total_data_usage_ratio = 1 indicates 100% usage, and 0.2 indicates 20% usage. See 4.2 section in our paper. """ - number_dataset = int(total_number_dataset * float(opt.total_data_usage_ratio)) - dataset_split = [number_dataset, total_number_dataset - number_dataset] + number_dataset = int(total_number_dataset * + float(opt.total_data_usage_ratio)) + dataset_split = [number_dataset, + total_number_dataset - number_dataset] indices = range(total_number_dataset) _dataset, _ = [Subset(_dataset, indices[offset - length:offset]) for offset, length in zip(_accumulate(dataset_split), dataset_split)] @@ -77,7 +87,8 @@ def __init__(self, opt): _data_loader = torch.utils.data.DataLoader( _dataset, batch_size=_batch_size, shuffle=True, - num_workers=int(opt.workers), #prefetch_factor=2,persistent_workers=True, + # prefetch_factor=2,persistent_workers=True, + num_workers=int(opt.workers), collate_fn=_AlignCollate, pin_memory=True) self.data_loader_list.append(_data_loader) self.dataloader_iter_list.append(iter(_data_loader)) @@ -139,6 +150,7 @@ def hierarchical_dataset(root, opt, select_data='/'): return concatenated_dataset, dataset_log + class OCRDataset(Dataset): def __init__(self, root, opt): @@ -146,15 +158,17 @@ def __init__(self, root, opt): self.root = root self.opt = opt print(root) - self.df = pd.read_csv(os.path.join(root,'labels.csv'), sep='^([^,]+),', engine='python', usecols=['filename', 'words'], keep_default_na=False) + self.df = pd.read_csv(os.path.join( + root, 'labels.csv'), sep='^([^,]+),', engine='python', usecols=['filename', 'words'], keep_default_na=False) self.nSamples = len(self.df) if self.opt.data_filtering_off: - self.filtered_index_list = [index + 1 for index in range(self.nSamples)] + self.filtered_index_list = [ + index + 1 for index in range(self.nSamples)] else: self.filtered_index_list = [] for index in range(self.nSamples): - label = self.df.at[index,'words'] + label = self.df.at[index, 'words'] try: if len(label) > self.opt.batch_max_length: continue @@ -171,9 +185,9 @@ def __len__(self): def __getitem__(self, index): index = self.filtered_index_list[index] - img_fname = self.df.at[index,'filename'] + img_fname = self.df.at[index, 'filename'] img_fpath = os.path.join(self.root, img_fname) - label = self.df.at[index,'words'] + label = self.df.at[index, 'words'] if self.opt.rgb: img = Image.open(img_fpath).convert('RGB') # for color image @@ -189,6 +203,7 @@ def __getitem__(self, index): return (img, label) + class ResizeNormalize(object): def __init__(self, size, interpolation=Image.BICUBIC): @@ -218,14 +233,15 @@ def __call__(self, img): Pad_img = torch.FloatTensor(*self.max_size).fill_(0) Pad_img[:, :, :w] = img # right pad if self.max_size[2] != w: # add border Pad - Pad_img[:, :, w:] = img[:, :, w - 1].unsqueeze(2).expand(c, h, self.max_size[2] - w) + Pad_img[:, :, w:] = img[:, :, w - + 1].unsqueeze(2).expand(c, h, self.max_size[2] - w) return Pad_img class AlignCollate(object): - def __init__(self, imgH=32, imgW=100, keep_ratio_with_pad=False, contrast_adjust = 0.): + def __init__(self, imgH=32, imgW=100, keep_ratio_with_pad=False, contrast_adjust=0.): self.imgH = imgH self.imgW = imgW self.keep_ratio_with_pad = keep_ratio_with_pad @@ -244,10 +260,11 @@ def __call__(self, batch): for image in images: w, h = image.size - #### augmentation here - change contrast + # augmentation here - change contrast if self.contrast_adjust > 0: image = np.array(image.convert("L")) - image = adjust_contrast_grey(image, target = self.contrast_adjust) + image = adjust_contrast_grey( + image, target=self.contrast_adjust) image = Image.fromarray(image, 'L') ratio = w / float(h) @@ -256,16 +273,19 @@ def __call__(self, batch): else: resized_w = math.ceil(self.imgH * ratio) - resized_image = image.resize((resized_w, self.imgH), Image.BICUBIC) + resized_image = image.resize( + (resized_w, self.imgH), Image.BICUBIC) resized_images.append(transform(resized_image)) # resized_image.save('./image_test/%d_test.jpg' % w) - image_tensors = torch.cat([t.unsqueeze(0) for t in resized_images], 0) + image_tensors = torch.cat([t.unsqueeze(0) + for t in resized_images], 0) else: transform = ResizeNormalize((self.imgW, self.imgH)) image_tensors = [transform(image) for image in images] - image_tensors = torch.cat([t.unsqueeze(0) for t in image_tensors], 0) + image_tensors = torch.cat([t.unsqueeze(0) + for t in image_tensors], 0) return image_tensors, labels diff --git a/trainer/model.py b/trainer/model.py index 19ca71114..1fffdfc5b 100644 --- a/trainer/model.py +++ b/trainer/model.py @@ -4,6 +4,7 @@ from modules.sequence_modeling import BidirectionalLSTM from modules.prediction import Attention + class Model(nn.Module): def __init__(self, opt): @@ -21,20 +22,26 @@ def __init__(self, opt): """ FeatureExtraction """ if opt.FeatureExtraction == 'VGG': - self.FeatureExtraction = VGG_FeatureExtractor(opt.input_channel, opt.output_channel) + self.FeatureExtraction = VGG_FeatureExtractor( + opt.input_channel, opt.output_channel) elif opt.FeatureExtraction == 'RCNN': - self.FeatureExtraction = RCNN_FeatureExtractor(opt.input_channel, opt.output_channel) + self.FeatureExtraction = RCNN_FeatureExtractor( + opt.input_channel, opt.output_channel) elif opt.FeatureExtraction == 'ResNet': - self.FeatureExtraction = ResNet_FeatureExtractor(opt.input_channel, opt.output_channel) + self.FeatureExtraction = ResNet_FeatureExtractor( + opt.input_channel, opt.output_channel) else: raise Exception('No FeatureExtraction module specified') - self.FeatureExtraction_output = opt.output_channel # int(imgH/16-1) * 512 - self.AdaptiveAvgPool = nn.AdaptiveAvgPool2d((None, 1)) # Transform final (imgH/16-1) -> 1 + # int(imgH/16-1) * 512 + self.FeatureExtraction_output = opt.output_channel + self.AdaptiveAvgPool = nn.AdaptiveAvgPool2d( + (None, 1)) # Transform final (imgH/16-1) -> 1 """ Sequence modeling""" if opt.SequenceModeling == 'BiLSTM': self.SequenceModeling = nn.Sequential( - BidirectionalLSTM(self.FeatureExtraction_output, opt.hidden_size, opt.hidden_size), + BidirectionalLSTM(self.FeatureExtraction_output, + opt.hidden_size, opt.hidden_size), BidirectionalLSTM(opt.hidden_size, opt.hidden_size, opt.hidden_size)) self.SequenceModeling_output = opt.hidden_size else: @@ -43,9 +50,11 @@ def __init__(self, opt): """ Prediction """ if opt.Prediction == 'CTC': - self.Prediction = nn.Linear(self.SequenceModeling_output, opt.num_class) + self.Prediction = nn.Linear( + self.SequenceModeling_output, opt.num_class) elif opt.Prediction == 'Attn': - self.Prediction = Attention(self.SequenceModeling_output, opt.hidden_size, opt.num_class) + self.Prediction = Attention( + self.SequenceModeling_output, opt.hidden_size, opt.num_class) else: raise Exception('Prediction is neither CTC or Attn') @@ -56,19 +65,22 @@ def forward(self, input, text, is_train=True): """ Feature extraction stage """ visual_feature = self.FeatureExtraction(input) - visual_feature = self.AdaptiveAvgPool(visual_feature.permute(0, 3, 1, 2)) # [b, c, h, w] -> [b, w, c, h] + visual_feature = self.AdaptiveAvgPool( + visual_feature.permute(0, 3, 1, 2)) # [b, c, h, w] -> [b, w, c, h] visual_feature = visual_feature.squeeze(3) """ Sequence modeling stage """ if self.stages['Seq'] == 'BiLSTM': contextual_feature = self.SequenceModeling(visual_feature) else: - contextual_feature = visual_feature # for convenience. this is NOT contextually modeled by BiLSTM + # for convenience. this is NOT contextually modeled by BiLSTM + contextual_feature = visual_feature """ Prediction stage """ if self.stages['Pred'] == 'CTC': prediction = self.Prediction(contextual_feature.contiguous()) else: - prediction = self.Prediction(contextual_feature.contiguous(), text, is_train, batch_max_length=self.opt.batch_max_length) + prediction = self.Prediction(contextual_feature.contiguous( + ), text, is_train, batch_max_length=self.opt.batch_max_length) return prediction diff --git a/trainer/modules/feature_extraction.py b/trainer/modules/feature_extraction.py index b5f300420..55130cafc 100644 --- a/trainer/modules/feature_extraction.py +++ b/trainer/modules/feature_extraction.py @@ -10,16 +10,22 @@ def __init__(self, input_channel, output_channel=512): self.output_channel = [int(output_channel / 8), int(output_channel / 4), int(output_channel / 2), output_channel] # [64, 128, 256, 512] self.ConvNet = nn.Sequential( - nn.Conv2d(input_channel, self.output_channel[0], 3, 1, 1), nn.ReLU(True), + nn.Conv2d(input_channel, + self.output_channel[0], 3, 1, 1), nn.ReLU(True), nn.MaxPool2d(2, 2), # 64x16x50 - nn.Conv2d(self.output_channel[0], self.output_channel[1], 3, 1, 1), nn.ReLU(True), + nn.Conv2d( + self.output_channel[0], self.output_channel[1], 3, 1, 1), nn.ReLU(True), nn.MaxPool2d(2, 2), # 128x8x25 - nn.Conv2d(self.output_channel[1], self.output_channel[2], 3, 1, 1), nn.ReLU(True), # 256x8x25 - nn.Conv2d(self.output_channel[2], self.output_channel[2], 3, 1, 1), nn.ReLU(True), + nn.Conv2d(self.output_channel[1], self.output_channel[2], 3, 1, 1), nn.ReLU( + True), # 256x8x25 + nn.Conv2d( + self.output_channel[2], self.output_channel[2], 3, 1, 1), nn.ReLU(True), nn.MaxPool2d((2, 1), (2, 1)), # 256x4x25 - nn.Conv2d(self.output_channel[2], self.output_channel[3], 3, 1, 1, bias=False), + nn.Conv2d( + self.output_channel[2], self.output_channel[3], 3, 1, 1, bias=False), nn.BatchNorm2d(self.output_channel[3]), nn.ReLU(True), # 512x4x25 - nn.Conv2d(self.output_channel[3], self.output_channel[3], 3, 1, 1, bias=False), + nn.Conv2d( + self.output_channel[3], self.output_channel[3], 3, 1, 1, bias=False), nn.BatchNorm2d(self.output_channel[3]), nn.ReLU(True), nn.MaxPool2d((2, 1), (2, 1)), # 512x2x25 nn.Conv2d(self.output_channel[3], self.output_channel[3], 2, 1, 0), nn.ReLU(True)) # 512x1x24 @@ -36,15 +42,20 @@ def __init__(self, input_channel, output_channel=512): self.output_channel = [int(output_channel / 8), int(output_channel / 4), int(output_channel / 2), output_channel] # [64, 128, 256, 512] self.ConvNet = nn.Sequential( - nn.Conv2d(input_channel, self.output_channel[0], 3, 1, 1), nn.ReLU(True), + nn.Conv2d(input_channel, + self.output_channel[0], 3, 1, 1), nn.ReLU(True), nn.MaxPool2d(2, 2), # 64 x 16 x 50 - GRCL(self.output_channel[0], self.output_channel[0], num_iteration=5, kernel_size=3, pad=1), + GRCL(self.output_channel[0], self.output_channel[0], + num_iteration=5, kernel_size=3, pad=1), nn.MaxPool2d(2, 2), # 64 x 8 x 25 - GRCL(self.output_channel[0], self.output_channel[1], num_iteration=5, kernel_size=3, pad=1), + GRCL(self.output_channel[0], self.output_channel[1], + num_iteration=5, kernel_size=3, pad=1), nn.MaxPool2d(2, (2, 1), (0, 1)), # 128 x 4 x 26 - GRCL(self.output_channel[1], self.output_channel[2], num_iteration=5, kernel_size=3, pad=1), + GRCL(self.output_channel[1], self.output_channel[2], + num_iteration=5, kernel_size=3, pad=1), nn.MaxPool2d(2, (2, 1), (0, 1)), # 256 x 2 x 27 - nn.Conv2d(self.output_channel[2], self.output_channel[3], 2, 1, 0, bias=False), + nn.Conv2d( + self.output_channel[2], self.output_channel[3], 2, 1, 0, bias=False), nn.BatchNorm2d(self.output_channel[3]), nn.ReLU(True)) # 512 x 1 x 26 def forward(self, input): @@ -56,7 +67,8 @@ class ResNet_FeatureExtractor(nn.Module): def __init__(self, input_channel, output_channel=512): super(ResNet_FeatureExtractor, self).__init__() - self.ConvNet = ResNet(input_channel, output_channel, BasicBlock, [1, 2, 5, 3]) + self.ConvNet = ResNet(input_channel, output_channel, + BasicBlock, [1, 2, 5, 3]) def forward(self, input): return self.ConvNet(input) @@ -67,10 +79,14 @@ class GRCL(nn.Module): def __init__(self, input_channel, output_channel, num_iteration, kernel_size, pad): super(GRCL, self).__init__() - self.wgf_u = nn.Conv2d(input_channel, output_channel, 1, 1, 0, bias=False) - self.wgr_x = nn.Conv2d(output_channel, output_channel, 1, 1, 0, bias=False) - self.wf_u = nn.Conv2d(input_channel, output_channel, kernel_size, 1, pad, bias=False) - self.wr_x = nn.Conv2d(output_channel, output_channel, kernel_size, 1, pad, bias=False) + self.wgf_u = nn.Conv2d( + input_channel, output_channel, 1, 1, 0, bias=False) + self.wgr_x = nn.Conv2d( + output_channel, output_channel, 1, 1, 0, bias=False) + self.wf_u = nn.Conv2d(input_channel, output_channel, + kernel_size, 1, pad, bias=False) + self.wr_x = nn.Conv2d(output_channel, output_channel, + kernel_size, 1, pad, bias=False) self.BN_x_init = nn.BatchNorm2d(output_channel) @@ -155,7 +171,8 @@ class ResNet(nn.Module): def __init__(self, input_channel, output_channel, block, layers): super(ResNet, self).__init__() - self.output_channel_block = [int(output_channel / 4), int(output_channel / 2), output_channel, output_channel] + self.output_channel_block = [ + int(output_channel / 4), int(output_channel / 2), output_channel, output_channel] self.inplanes = int(output_channel / 8) self.conv0_1 = nn.Conv2d(input_channel, int(output_channel / 16), @@ -167,24 +184,29 @@ def __init__(self, input_channel, output_channel, block, layers): self.relu = nn.ReLU(inplace=True) self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0) - self.layer1 = self._make_layer(block, self.output_channel_block[0], layers[0]) + self.layer1 = self._make_layer( + block, self.output_channel_block[0], layers[0]) self.conv1 = nn.Conv2d(self.output_channel_block[0], self.output_channel_block[ 0], kernel_size=3, stride=1, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(self.output_channel_block[0]) self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0) - self.layer2 = self._make_layer(block, self.output_channel_block[1], layers[1], stride=1) + self.layer2 = self._make_layer( + block, self.output_channel_block[1], layers[1], stride=1) self.conv2 = nn.Conv2d(self.output_channel_block[1], self.output_channel_block[ 1], kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(self.output_channel_block[1]) - self.maxpool3 = nn.MaxPool2d(kernel_size=2, stride=(2, 1), padding=(0, 1)) - self.layer3 = self._make_layer(block, self.output_channel_block[2], layers[2], stride=1) + self.maxpool3 = nn.MaxPool2d( + kernel_size=2, stride=(2, 1), padding=(0, 1)) + self.layer3 = self._make_layer( + block, self.output_channel_block[2], layers[2], stride=1) self.conv3 = nn.Conv2d(self.output_channel_block[2], self.output_channel_block[ 2], kernel_size=3, stride=1, padding=1, bias=False) self.bn3 = nn.BatchNorm2d(self.output_channel_block[2]) - self.layer4 = self._make_layer(block, self.output_channel_block[3], layers[3], stride=1) + self.layer4 = self._make_layer( + block, self.output_channel_block[3], layers[3], stride=1) self.conv4_1 = nn.Conv2d(self.output_channel_block[3], self.output_channel_block[ 3], kernel_size=2, stride=(2, 1), padding=(0, 1), bias=False) self.bn4_1 = nn.BatchNorm2d(self.output_channel_block[3]) diff --git a/trainer/modules/prediction.py b/trainer/modules/prediction.py index c8a40af0e..8fcc5a7db 100644 --- a/trainer/modules/prediction.py +++ b/trainer/modules/prediction.py @@ -8,7 +8,8 @@ class Attention(nn.Module): def __init__(self, input_size, hidden_size, num_classes): super(Attention, self).__init__() - self.attention_cell = AttentionCell(input_size, hidden_size, num_classes) + self.attention_cell = AttentionCell( + input_size, hidden_size, num_classes) self.hidden_size = hidden_size self.num_classes = num_classes self.generator = nn.Linear(hidden_size, num_classes) @@ -30,26 +31,34 @@ def forward(self, batch_H, text, is_train=True, batch_max_length=25): batch_size = batch_H.size(0) num_steps = batch_max_length + 1 # +1 for [s] at end of sentence. - output_hiddens = torch.FloatTensor(batch_size, num_steps, self.hidden_size).fill_(0).to(device) + output_hiddens = torch.FloatTensor( + batch_size, num_steps, self.hidden_size).fill_(0).to(device) hidden = (torch.FloatTensor(batch_size, self.hidden_size).fill_(0).to(device), torch.FloatTensor(batch_size, self.hidden_size).fill_(0).to(device)) if is_train: for i in range(num_steps): # one-hot vectors for a i-th char. in a batch - char_onehots = self._char_to_onehot(text[:, i], onehot_dim=self.num_classes) + char_onehots = self._char_to_onehot( + text[:, i], onehot_dim=self.num_classes) # hidden : decoder's hidden s_{t-1}, batch_H : encoder's hidden H, char_onehots : one-hot(y_{t-1}) - hidden, alpha = self.attention_cell(hidden, batch_H, char_onehots) - output_hiddens[:, i, :] = hidden[0] # LSTM hidden index (0: hidden, 1: Cell) + hidden, alpha = self.attention_cell( + hidden, batch_H, char_onehots) + # LSTM hidden index (0: hidden, 1: Cell) + output_hiddens[:, i, :] = hidden[0] probs = self.generator(output_hiddens) else: - targets = torch.LongTensor(batch_size).fill_(0).to(device) # [GO] token - probs = torch.FloatTensor(batch_size, num_steps, self.num_classes).fill_(0).to(device) + targets = torch.LongTensor(batch_size).fill_( + 0).to(device) # [GO] token + probs = torch.FloatTensor( + batch_size, num_steps, self.num_classes).fill_(0).to(device) for i in range(num_steps): - char_onehots = self._char_to_onehot(targets, onehot_dim=self.num_classes) - hidden, alpha = self.attention_cell(hidden, batch_H, char_onehots) + char_onehots = self._char_to_onehot( + targets, onehot_dim=self.num_classes) + hidden, alpha = self.attention_cell( + hidden, batch_H, char_onehots) probs_step = self.generator(hidden[0]) probs[:, i, :] = probs_step _, next_input = probs_step.max(1) @@ -63,7 +72,8 @@ class AttentionCell(nn.Module): def __init__(self, input_size, hidden_size, num_embeddings): super(AttentionCell, self).__init__() self.i2h = nn.Linear(input_size, hidden_size, bias=False) - self.h2h = nn.Linear(hidden_size, hidden_size) # either i2i or h2h should have bias + # either i2i or h2h should have bias + self.h2h = nn.Linear(hidden_size, hidden_size) self.score = nn.Linear(hidden_size, 1, bias=False) self.rnn = nn.LSTMCell(input_size + num_embeddings, hidden_size) self.hidden_size = hidden_size @@ -72,10 +82,13 @@ def forward(self, prev_hidden, batch_H, char_onehots): # [batch_size x num_encoder_step x num_channel] -> [batch_size x num_encoder_step x hidden_size] batch_H_proj = self.i2h(batch_H) prev_hidden_proj = self.h2h(prev_hidden[0]).unsqueeze(1) - e = self.score(torch.tanh(batch_H_proj + prev_hidden_proj)) # batch_size x num_encoder_step * 1 + # batch_size x num_encoder_step * 1 + e = self.score(torch.tanh(batch_H_proj + prev_hidden_proj)) alpha = F.softmax(e, dim=1) - context = torch.bmm(alpha.permute(0, 2, 1), batch_H).squeeze(1) # batch_size x num_channel - concat_context = torch.cat([context, char_onehots], 1) # batch_size x (num_channel + num_embedding) + context = torch.bmm(alpha.permute(0, 2, 1), batch_H).squeeze( + 1) # batch_size x num_channel + # batch_size x (num_channel + num_embedding) + concat_context = torch.cat([context, char_onehots], 1) cur_hidden = self.rnn(concat_context, prev_hidden) return cur_hidden, alpha diff --git a/trainer/modules/sequence_modeling.py b/trainer/modules/sequence_modeling.py index 7320ce267..9414f52ec 100644 --- a/trainer/modules/sequence_modeling.py +++ b/trainer/modules/sequence_modeling.py @@ -5,7 +5,8 @@ class BidirectionalLSTM(nn.Module): def __init__(self, input_size, hidden_size, output_size): super(BidirectionalLSTM, self).__init__() - self.rnn = nn.LSTM(input_size, hidden_size, bidirectional=True, batch_first=True) + self.rnn = nn.LSTM(input_size, hidden_size, + bidirectional=True, batch_first=True) self.linear = nn.Linear(hidden_size * 2, output_size) def forward(self, input): @@ -17,6 +18,7 @@ def forward(self, input): self.rnn.flatten_parameters() except: pass - recurrent, _ = self.rnn(input) # batch_size x T x input_size -> batch_size x T x (2*hidden_size) + # batch_size x T x input_size -> batch_size x T x (2*hidden_size) + recurrent, _ = self.rnn(input) output = self.linear(recurrent) # batch_size x T x output_size return output diff --git a/trainer/modules/transformation.py b/trainer/modules/transformation.py index 7c4dd5a7c..1909b3a80 100644 --- a/trainer/modules/transformation.py +++ b/trainer/modules/transformation.py @@ -23,14 +23,18 @@ def __init__(self, F, I_size, I_r_size, I_channel_num=1): self.I_size = I_size self.I_r_size = I_r_size # = (I_r_height, I_r_width) self.I_channel_num = I_channel_num - self.LocalizationNetwork = LocalizationNetwork(self.F, self.I_channel_num) + self.LocalizationNetwork = LocalizationNetwork( + self.F, self.I_channel_num) self.GridGenerator = GridGenerator(self.F, self.I_r_size) def forward(self, batch_I): batch_C_prime = self.LocalizationNetwork(batch_I) # batch_size x K x 2 - build_P_prime = self.GridGenerator.build_P_prime(batch_C_prime) # batch_size x n (= I_r_width x I_r_height) x 2 - build_P_prime_reshape = build_P_prime.reshape([build_P_prime.size(0), self.I_r_size[0], self.I_r_size[1], 2]) - batch_I_r = F.grid_sample(batch_I, build_P_prime_reshape, padding_mode='border') + # batch_size x n (= I_r_width x I_r_height) x 2 + build_P_prime = self.GridGenerator.build_P_prime(batch_C_prime) + build_P_prime_reshape = build_P_prime.reshape( + [build_P_prime.size(0), self.I_r_size[0], self.I_r_size[1], 2]) + batch_I_r = F.grid_sample( + batch_I, build_P_prime_reshape, padding_mode='border') return batch_I_r @@ -46,15 +50,19 @@ def __init__(self, F, I_channel_num): nn.Conv2d(in_channels=self.I_channel_num, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False), nn.BatchNorm2d(64), nn.ReLU(True), nn.MaxPool2d(2, 2), # batch_size x 64 x I_height/2 x I_width/2 - nn.Conv2d(64, 128, 3, 1, 1, bias=False), nn.BatchNorm2d(128), nn.ReLU(True), + nn.Conv2d(64, 128, 3, 1, 1, bias=False), nn.BatchNorm2d( + 128), nn.ReLU(True), nn.MaxPool2d(2, 2), # batch_size x 128 x I_height/4 x I_width/4 - nn.Conv2d(128, 256, 3, 1, 1, bias=False), nn.BatchNorm2d(256), nn.ReLU(True), + nn.Conv2d(128, 256, 3, 1, 1, bias=False), nn.BatchNorm2d( + 256), nn.ReLU(True), nn.MaxPool2d(2, 2), # batch_size x 256 x I_height/8 x I_width/8 - nn.Conv2d(256, 512, 3, 1, 1, bias=False), nn.BatchNorm2d(512), nn.ReLU(True), + nn.Conv2d(256, 512, 3, 1, 1, bias=False), nn.BatchNorm2d( + 512), nn.ReLU(True), nn.AdaptiveAvgPool2d(1) # batch_size x 512 ) - self.localization_fc1 = nn.Sequential(nn.Linear(512, 256), nn.ReLU(True)) + self.localization_fc1 = nn.Sequential( + nn.Linear(512, 256), nn.ReLU(True)) self.localization_fc2 = nn.Linear(256, self.F * 2) # Init fc2 in LocalizationNetwork @@ -66,7 +74,8 @@ def __init__(self, F, I_channel_num): ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1) ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1) initial_bias = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0) - self.localization_fc2.bias.data = torch.from_numpy(initial_bias).float().view(-1) + self.localization_fc2.bias.data = torch.from_numpy( + initial_bias).float().view(-1) def forward(self, batch_I): """ @@ -75,7 +84,8 @@ def forward(self, batch_I): """ batch_size = batch_I.size(0) features = self.conv(batch_I).view(batch_size, -1) - batch_C_prime = self.localization_fc2(self.localization_fc1(features)).view(batch_size, self.F, 2) + batch_C_prime = self.localization_fc2( + self.localization_fc1(features)).view(batch_size, self.F, 2) return batch_C_prime @@ -90,12 +100,14 @@ def __init__(self, F, I_r_size): self.F = F self.C = self._build_C(self.F) # F x 2 self.P = self._build_P(self.I_r_width, self.I_r_height) - ## for multi-gpu, you need register buffer - self.register_buffer("inv_delta_C", torch.tensor(self._build_inv_delta_C(self.F, self.C)).float()) # F+3 x F+3 - self.register_buffer("P_hat", torch.tensor(self._build_P_hat(self.F, self.C, self.P)).float()) # n x F+3 - ## for fine-tuning with different image width, you may use below instead of self.register_buffer - #self.inv_delta_C = torch.tensor(self._build_inv_delta_C(self.F, self.C)).float().cuda() # F+3 x F+3 - #self.P_hat = torch.tensor(self._build_P_hat(self.F, self.C, self.P)).float().cuda() # n x F+3 + # for multi-gpu, you need register buffer + self.register_buffer("inv_delta_C", torch.tensor( + self._build_inv_delta_C(self.F, self.C)).float()) # F+3 x F+3 + self.register_buffer("P_hat", torch.tensor( + self._build_P_hat(self.F, self.C, self.P)).float()) # n x F+3 + # for fine-tuning with different image width, you may use below instead of self.register_buffer + # self.inv_delta_C = torch.tensor(self._build_inv_delta_C(self.F, self.C)).float().cuda() # F+3 x F+3 + # self.P_hat = torch.tensor(self._build_P_hat(self.F, self.C, self.P)).float().cuda() # n x F+3 def _build_C(self, F): """ Return coordinates of fiducial points in I_r; C """ @@ -121,8 +133,10 @@ def _build_inv_delta_C(self, F, C): delta_C = np.concatenate( # F+3 x F+3 [ np.concatenate([np.ones((F, 1)), C, hat_C], axis=1), # F x F+3 - np.concatenate([np.zeros((2, 3)), np.transpose(C)], axis=1), # 2 x F+3 - np.concatenate([np.zeros((1, 3)), np.ones((1, F))], axis=1) # 1 x F+3 + np.concatenate( + [np.zeros((2, 3)), np.transpose(C)], axis=1), # 2 x F+3 + np.concatenate( + [np.zeros((1, 3)), np.ones((1, F))], axis=1) # 1 x F+3 ], axis=0 ) @@ -130,8 +144,10 @@ def _build_inv_delta_C(self, F, C): return inv_delta_C # F+3 x F+3 def _build_P(self, I_r_width, I_r_height): - I_r_grid_x = (np.arange(-I_r_width, I_r_width, 2) + 1.0) / I_r_width # self.I_r_width - I_r_grid_y = (np.arange(-I_r_height, I_r_height, 2) + 1.0) / I_r_height # self.I_r_height + I_r_grid_x = (np.arange(-I_r_width, I_r_width, 2) + + 1.0) / I_r_width # self.I_r_width + I_r_grid_y = (np.arange(-I_r_height, I_r_height, 2) + + 1.0) / I_r_height # self.I_r_height P = np.stack( # self.I_r_width x self.I_r_height x 2 np.meshgrid(I_r_grid_x, I_r_grid_y), axis=2 @@ -140,11 +156,14 @@ def _build_P(self, I_r_width, I_r_height): def _build_P_hat(self, F, C, P): n = P.shape[0] # n (= self.I_r_width x self.I_r_height) - P_tile = np.tile(np.expand_dims(P, axis=1), (1, F, 1)) # n x 2 -> n x 1 x 2 -> n x F x 2 + P_tile = np.tile(np.expand_dims(P, axis=1), (1, F, 1) + ) # n x 2 -> n x 1 x 2 -> n x F x 2 C_tile = np.expand_dims(C, axis=0) # 1 x F x 2 P_diff = P_tile - C_tile # n x F x 2 - rbf_norm = np.linalg.norm(P_diff, ord=2, axis=2, keepdims=False) # n x F - rbf = np.multiply(np.square(rbf_norm), np.log(rbf_norm + self.eps)) # n x F + rbf_norm = np.linalg.norm( + P_diff, ord=2, axis=2, keepdims=False) # n x F + rbf = np.multiply(np.square(rbf_norm), np.log( + rbf_norm + self.eps)) # n x F P_hat = np.concatenate([np.ones((n, 1)), P, rbf], axis=1) return P_hat # n x F+3 @@ -155,6 +174,7 @@ def build_P_prime(self, batch_C_prime): batch_P_hat = self.P_hat.repeat(batch_size, 1, 1) batch_C_prime_with_zeros = torch.cat((batch_C_prime, torch.zeros( batch_size, 3, 2).float().to(device)), dim=1) # batch_size x F+3 x 2 - batch_T = torch.bmm(batch_inv_delta_C, batch_C_prime_with_zeros) # batch_size x F+3 x 2 + # batch_size x F+3 x 2 + batch_T = torch.bmm(batch_inv_delta_C, batch_C_prime_with_zeros) batch_P_prime = torch.bmm(batch_P_hat, batch_T) # batch_size x n x 2 return batch_P_prime # batch_size x n x 2 diff --git a/trainer/test.py b/trainer/test.py index 85c08fda9..3e594381f 100644 --- a/trainer/test.py +++ b/trainer/test.py @@ -14,6 +14,7 @@ from dataset import hierarchical_dataset, AlignCollate from model import Model + def validation(model, criterion, evaluation_loader, converter, opt, device): """ validation or evaluation """ n_correct = 0 @@ -27,11 +28,14 @@ def validation(model, criterion, evaluation_loader, converter, opt, device): length_of_data = length_of_data + batch_size image = image_tensors.to(device) # For max length prediction - length_for_pred = torch.IntTensor([opt.batch_max_length] * batch_size).to(device) - text_for_pred = torch.LongTensor(batch_size, opt.batch_max_length + 1).fill_(0).to(device) + length_for_pred = torch.IntTensor( + [opt.batch_max_length] * batch_size).to(device) + text_for_pred = torch.LongTensor( + batch_size, opt.batch_max_length + 1).fill_(0).to(device) + + text_for_loss, length_for_loss = converter.encode( + labels, batch_max_length=opt.batch_max_length) - text_for_loss, length_for_loss = converter.encode(labels, batch_max_length=opt.batch_max_length) - start_time = time.time() if 'CTC' in opt.Prediction: preds = model(image, text_for_pred) @@ -40,13 +44,15 @@ def validation(model, criterion, evaluation_loader, converter, opt, device): # Calculate evaluation loss for CTC deocder. preds_size = torch.IntTensor([preds.size(1)] * batch_size) # permute 'preds' to use CTCloss format - cost = criterion(preds.log_softmax(2).permute(1, 0, 2), text_for_loss, preds_size, length_for_loss) + cost = criterion(preds.log_softmax(2).permute( + 1, 0, 2), text_for_loss, preds_size, length_for_loss) if opt.decode == 'greedy': # Select max probabilty (greedy decoding) then decode index to character _, preds_index = preds.max(2) preds_index = preds_index.view(-1) - preds_str = converter.decode_greedy(preds_index.data, preds_size.data) + preds_str = converter.decode_greedy( + preds_index.data, preds_size.data) elif opt.decode == 'beamsearch': preds_str = converter.decode_beamsearch(preds, beamWidth=2) @@ -56,7 +62,8 @@ def validation(model, criterion, evaluation_loader, converter, opt, device): preds = preds[:, :text_for_loss.shape[1] - 1, :] target = text_for_loss[:, 1:] # without [GO] Symbol - cost = criterion(preds.contiguous().view(-1, preds.shape[-1]), target.contiguous().view(-1)) + cost = criterion(preds.contiguous().view(-1, + preds.shape[-1]), target.contiguous().view(-1)) # select max probabilty (greedy decoding) then decode index to character _, preds_index = preds.max(2) @@ -70,12 +77,13 @@ def validation(model, criterion, evaluation_loader, converter, opt, device): preds_prob = F.softmax(preds, dim=2) preds_max_prob, _ = preds_prob.max(dim=2) confidence_score_list = [] - + for gt, pred, pred_max_prob in zip(labels, preds_str, preds_max_prob): if 'Attn' in opt.Prediction: gt = gt[:gt.find('[s]')] pred_EOS = pred.find('[s]') - pred = pred[:pred_EOS] # prune after "end of sentence" token ([s]) + # prune after "end of sentence" token ([s]) + pred = pred[:pred_EOS] pred_max_prob = pred_max_prob[:pred_EOS] if pred == gt: @@ -89,9 +97,9 @@ def validation(model, criterion, evaluation_loader, converter, opt, device): else: norm_ED += edit_distance(pred, gt) / len(gt) ''' - - # ICDAR2019 Normalized Edit Distance - if len(gt) == 0 or len(pred) ==0: + + # ICDAR2019 Normalized Edit Distance + if len(gt) == 0 or len(pred) == 0: norm_ED += 0 elif len(gt) > len(pred): norm_ED += 1 - edit_distance(pred, gt) / len(gt) @@ -102,11 +110,13 @@ def validation(model, criterion, evaluation_loader, converter, opt, device): try: confidence_score = pred_max_prob.cumprod(dim=0)[-1] except: - confidence_score = 0 # for empty pred case, when prune after "end of sentence" token ([s]) + # for empty pred case, when prune after "end of sentence" token ([s]) + confidence_score = 0 confidence_score_list.append(confidence_score) # print(pred, gt, pred==gt, confidence_score) accuracy = n_correct / float(length_of_data) * 100 - norm_ED = norm_ED / float(length_of_data) # ICDAR2019 Normalized Edit Distance + # ICDAR2019 Normalized Edit Distance + norm_ED = norm_ED / float(length_of_data) return valid_loss_avg.val(), accuracy, norm_ED, preds_str, confidence_score_list, labels, infer_time, length_of_data diff --git a/trainer/train.py b/trainer/train.py index e0066f3d0..3d8aa045b 100644 --- a/trainer/train.py +++ b/trainer/train.py @@ -17,19 +17,22 @@ from test import validation device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + def count_parameters(model): print("Modules, Parameters") total_params = 0 for name, parameter in model.named_parameters(): - if not parameter.requires_grad: continue + if not parameter.requires_grad: + continue param = parameter.numel() #table.add_row([name, param]) - total_params+=param + total_params += param print(name, param) print(f"Total Trainable Params: {total_params}") return total_params -def train(opt, show_number = 2, amp=False): + +def train(opt, show_number=2, amp=False): """ dataset preparation """ if not opt.data_filtering_off: print('Filtering the images containing characters which are not in opt.character') @@ -39,19 +42,23 @@ def train(opt, show_number = 2, amp=False): opt.batch_ratio = opt.batch_ratio.split('-') train_dataset = Batch_Balanced_Dataset(opt) - log = open(f'./saved_models/{opt.experiment_name}/log_dataset.txt', 'a', encoding="utf8") - AlignCollate_valid = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD, contrast_adjust=opt.contrast_adjust) - valid_dataset, valid_dataset_log = hierarchical_dataset(root=opt.valid_data, opt=opt) + log = open( + f'./saved_models/{opt.experiment_name}/log_dataset.txt', 'a', encoding="utf8") + AlignCollate_valid = AlignCollate( + imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD, contrast_adjust=opt.contrast_adjust) + valid_dataset, valid_dataset_log = hierarchical_dataset( + root=opt.valid_data, opt=opt) valid_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=min(32, opt.batch_size), - shuffle=True, # 'True' to check training progress with validation function. + # 'True' to check training progress with validation function. + shuffle=True, num_workers=int(opt.workers), prefetch_factor=512, collate_fn=AlignCollate_valid, pin_memory=True) log.write(valid_dataset_log) print('-' * 80) log.write('-' * 80 + '\n') log.close() - + """ model configuration """ if 'CTC' in opt.Prediction: converter = CTCLabelConverter(opt.character) @@ -69,22 +76,24 @@ def train(opt, show_number = 2, amp=False): if opt.saved_model != '': pretrained_dict = torch.load(opt.saved_model) if opt.new_prediction: - model.Prediction = nn.Linear(model.SequenceModeling_output, len(pretrained_dict['module.Prediction.weight'])) - - model = torch.nn.DataParallel(model).to(device) + model.Prediction = nn.Linear(model.SequenceModeling_output, len( + pretrained_dict['module.Prediction.weight'])) + + model = torch.nn.DataParallel(model).to(device) print(f'loading pretrained model from {opt.saved_model}') if opt.FT: model.load_state_dict(pretrained_dict, strict=False) else: model.load_state_dict(pretrained_dict) if opt.new_prediction: - model.module.Prediction = nn.Linear(model.module.SequenceModeling_output, opt.num_class) + model.module.Prediction = nn.Linear( + model.module.SequenceModeling_output, opt.num_class) for name, param in model.module.Prediction.named_parameters(): if 'bias' in name: init.constant_(param, 0.0) elif 'weight' in name: init.kaiming_normal_(param) - model = model.to(device) + model = model.to(device) else: # weight initialization for name, param in model.named_parameters(): @@ -101,17 +110,18 @@ def train(opt, show_number = 2, amp=False): param.data.fill_(1) continue model = torch.nn.DataParallel(model).to(device) - - model.train() + + model.train() print("Model:") print(model) count_parameters(model) - + """ setup loss """ if 'CTC' in opt.Prediction: criterion = torch.nn.CTCLoss(zero_infinity=True).to(device) else: - criterion = torch.nn.CrossEntropyLoss(ignore_index=0).to(device) # ignore [GO] token = ignore index 0 + criterion = torch.nn.CrossEntropyLoss(ignore_index=0).to( + device) # ignore [GO] token = ignore index 0 # loss averager loss_avg = Averager() @@ -125,7 +135,7 @@ def train(opt, show_number = 2, amp=False): param.requires_grad = False except: pass - + # filter that only require gradient decent filtered_parameters = [] params_num = [] @@ -136,11 +146,12 @@ def train(opt, show_number = 2, amp=False): # [print(name, p.numel()) for name, p in filter(lambda p: p[1].requires_grad, model.named_parameters())] # setup optimizer - if opt.optim=='adam': + if opt.optim == 'adam': #optimizer = optim.Adam(filtered_parameters, lr=opt.lr, betas=(opt.beta1, 0.999)) optimizer = optim.Adam(filtered_parameters) else: - optimizer = optim.Adadelta(filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps) + optimizer = optim.Adadelta( + filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps) print("Optimizer:") print(optimizer) @@ -170,17 +181,18 @@ def train(opt, show_number = 2, amp=False): i = start_iter scaler = GradScaler() - t1= time.time() - + t1 = time.time() + while(True): # train part optimizer.zero_grad(set_to_none=True) - + if amp: with autocast(): image_tensors, labels = train_dataset.get_batch() image = image_tensors.to(device) - text, length = converter.encode(labels, batch_max_length=opt.batch_max_length) + text, length = converter.encode( + labels, batch_max_length=opt.batch_max_length) batch_size = image.size(0) if 'CTC' in opt.Prediction: @@ -188,12 +200,15 @@ def train(opt, show_number = 2, amp=False): preds_size = torch.IntTensor([preds.size(1)] * batch_size) preds = preds.permute(1, 0, 2) torch.backends.cudnn.enabled = False - cost = criterion(preds, text.to(device), preds_size.to(device), length.to(device)) + cost = criterion(preds, text.to(device), + preds_size.to(device), length.to(device)) torch.backends.cudnn.enabled = True else: - preds = model(image, text[:, :-1]) # align with Attention.forward + # align with Attention.forward + preds = model(image, text[:, :-1]) target = text[:, 1:] # without [GO] Symbol - cost = criterion(preds.view(-1, preds.shape[-1]), target.contiguous().view(-1)) + cost = criterion( + preds.view(-1, preds.shape[-1]), target.contiguous().view(-1)) scaler.scale(cost).backward() scaler.unscale_(optimizer) torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip) @@ -202,35 +217,40 @@ def train(opt, show_number = 2, amp=False): else: image_tensors, labels = train_dataset.get_batch() image = image_tensors.to(device) - text, length = converter.encode(labels, batch_max_length=opt.batch_max_length) + text, length = converter.encode( + labels, batch_max_length=opt.batch_max_length) batch_size = image.size(0) if 'CTC' in opt.Prediction: preds = model(image, text).log_softmax(2) preds_size = torch.IntTensor([preds.size(1)] * batch_size) preds = preds.permute(1, 0, 2) torch.backends.cudnn.enabled = False - cost = criterion(preds, text.to(device), preds_size.to(device), length.to(device)) + cost = criterion(preds, text.to(device), + preds_size.to(device), length.to(device)) torch.backends.cudnn.enabled = True else: - preds = model(image, text[:, :-1]) # align with Attention.forward + # align with Attention.forward + preds = model(image, text[:, :-1]) target = text[:, 1:] # without [GO] Symbol - cost = criterion(preds.view(-1, preds.shape[-1]), target.contiguous().view(-1)) + cost = criterion( + preds.view(-1, preds.shape[-1]), target.contiguous().view(-1)) cost.backward() - torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip) + torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip) optimizer.step() loss_avg.add(cost) # validation part - if (i % opt.valInterval == 0) and (i!=0): + if (i % opt.valInterval == 0) and (i != 0): print('training time: ', time.time()-t1) - t1=time.time() + t1 = time.time() elapsed_time = time.time() - start_time # for log with open(f'./saved_models/{opt.experiment_name}/log_train.txt', 'a', encoding="utf8") as log: model.eval() with torch.no_grad(): valid_loss, current_accuracy, current_norm_ED, preds, confidence_score, labels,\ - infer_time, length_of_data = validation(model, criterion, valid_loader, converter, opt, device) + infer_time, length_of_data = validation( + model, criterion, valid_loader, converter, opt, device) model.train() # training loss and validation loss @@ -242,10 +262,12 @@ def train(opt, show_number = 2, amp=False): # keep best accuracy model (on valid dataset) if current_accuracy > best_accuracy: best_accuracy = current_accuracy - torch.save(model.state_dict(), f'./saved_models/{opt.experiment_name}/best_accuracy.pth') + torch.save( + model.state_dict(), f'./saved_models/{opt.experiment_name}/best_accuracy.pth') if current_norm_ED > best_norm_ED: best_norm_ED = current_norm_ED - torch.save(model.state_dict(), f'./saved_models/{opt.experiment_name}/best_norm_ED.pth') + torch.save( + model.state_dict(), f'./saved_models/{opt.experiment_name}/best_norm_ED.pth') best_model_log = f'{"Best_accuracy":17s}: {best_accuracy:0.3f}, {"Best_norm_ED":17s}: {best_norm_ED:0.4f}' loss_model_log = f'{loss_log}\n{current_model_log}\n{best_model_log}' @@ -256,10 +278,10 @@ def train(opt, show_number = 2, amp=False): dashed_line = '-' * 80 head = f'{"Ground Truth":25s} | {"Prediction":25s} | Confidence Score & T/F' predicted_result_log = f'{dashed_line}\n{head}\n{dashed_line}\n' - + #show_number = min(show_number, len(labels)) - - start = random.randint(0,len(labels) - show_number ) + + start = random.randint(0, len(labels) - show_number) for gt, pred, confidence in zip(labels[start:start+show_number], preds[start:start+show_number], confidence_score[start:start+show_number]): if 'Attn' in opt.Prediction: gt = gt[:gt.find('[s]')] @@ -270,7 +292,7 @@ def train(opt, show_number = 2, amp=False): print(predicted_result_log) log.write(predicted_result_log + '\n') print('validation time: ', time.time()-t1) - t1=time.time() + t1 = time.time() # save model per 1e+4 iter. if (i + 1) % 1e+4 == 0: torch.save( diff --git a/trainer/utils.py b/trainer/utils.py index 4cf7dac8c..6e7b7e12c 100644 --- a/trainer/utils.py +++ b/trainer/utils.py @@ -3,24 +3,30 @@ import numpy as np device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + class AttrDict(dict): def __init__(self, *args, **kwargs): super(AttrDict, self).__init__(*args, **kwargs) self.__dict__ = self -##### https://github.com/githubharald/CTCDecoder/blob/master/src/BeamSearch.py +# https://github.com/githubharald/CTCDecoder/blob/master/src/BeamSearch.py + + class BeamEntry: "information about one single beam at specific time-step" + def __init__(self): - self.prTotal = 0 # blank and non-blank - self.prNonBlank = 0 # non-blank - self.prBlank = 0 # blank - self.prText = 1 # LM score - self.lmApplied = False # flag if LM was already applied to this beam - self.labeling = () # beam-labeling + self.prTotal = 0 # blank and non-blank + self.prNonBlank = 0 # non-blank + self.prBlank = 0 # blank + self.prText = 1 # LM score + self.lmApplied = False # flag if LM was already applied to this beam + self.labeling = () # beam-labeling + class BeamState: "information about the beams at specific time-step" + def __init__(self): self.entries = {} @@ -28,26 +34,31 @@ def norm(self): "length-normalise LM score" for (k, _) in self.entries.items(): labelingLen = len(self.entries[k].labeling) - self.entries[k].prText = self.entries[k].prText ** (1.0 / (labelingLen if labelingLen else 1.0)) + self.entries[k].prText = self.entries[k].prText ** ( + 1.0 / (labelingLen if labelingLen else 1.0)) def sort(self): "return beam-labelings, sorted by probability" beams = [v for (_, v) in self.entries.items()] - sortedBeams = sorted(beams, reverse=True, key=lambda x: x.prTotal*x.prText) + sortedBeams = sorted(beams, reverse=True, + key=lambda x: x.prTotal*x.prText) return [x.labeling for x in sortedBeams] def wordsearch(self, classes, ignore_idx, beamWidth, dict_list): beams = [v for (_, v) in self.entries.items()] - sortedBeams = sorted(beams, reverse=True, key=lambda x: x.prTotal*x.prText)[:beamWidth] + sortedBeams = sorted(beams, reverse=True, + key=lambda x: x.prTotal*x.prText)[:beamWidth] for j, candidate in enumerate(sortedBeams): idx_list = candidate.labeling text = '' - for i,l in enumerate(idx_list): - if l not in ignore_idx and (not (i > 0 and idx_list[i - 1] == idx_list[i])): # removing repeated characters and blank. + for i, l in enumerate(idx_list): + # removing repeated characters and blank. + if l not in ignore_idx and (not (i > 0 and idx_list[i - 1] == idx_list[i])): text += classes[l] - if j == 0: best_text = text + if j == 0: + best_text = text if text in dict_list: print('found text: ', text) best_text = text @@ -56,22 +67,28 @@ def wordsearch(self, classes, ignore_idx, beamWidth, dict_list): print('not in dict: ', text) return best_text + def applyLM(parentBeam, childBeam, classes, lm): "calculate LM score of child beam by taking score from parent beam and bigram probability of last two chars" if lm and not childBeam.lmApplied: - c1 = classes[parentBeam.labeling[-1] if parentBeam.labeling else classes.index(' ')] # first char - c2 = classes[childBeam.labeling[-1]] # second char - lmFactor = 0.01 # influence of language model - bigramProb = lm.getCharBigram(c1, c2) ** lmFactor # probability of seeing first and second char next to each other - childBeam.prText = parentBeam.prText * bigramProb # probability of char sequence - childBeam.lmApplied = True # only apply LM once per beam entry + c1 = classes[parentBeam.labeling[-1] + if parentBeam.labeling else classes.index(' ')] # first char + c2 = classes[childBeam.labeling[-1]] # second char + lmFactor = 0.01 # influence of language model + # probability of seeing first and second char next to each other + bigramProb = lm.getCharBigram(c1, c2) ** lmFactor + childBeam.prText = parentBeam.prText * \ + bigramProb # probability of char sequence + childBeam.lmApplied = True # only apply LM once per beam entry + def addBeam(beamState, labeling): "add beam if it does not yet exist" if labeling not in beamState.entries: beamState.entries[labeling] = BeamEntry() -def ctcBeamSearch(mat, classes, ignore_idx, lm, beamWidth=25, dict_list = []): + +def ctcBeamSearch(mat, classes, ignore_idx, lm, beamWidth=25, dict_list=[]): "beam search as described by the paper of Hwang et al. and the paper of Graves et al." #blankIdx = len(classes) @@ -100,7 +117,8 @@ def ctcBeamSearch(mat, classes, ignore_idx, lm, beamWidth=25, dict_list = []): # in case of non-empty beam if labeling: # probability of paths with repeated last char at the end - prNonBlank = last.entries[labeling].prNonBlank * mat[t, labeling[-1]] + prNonBlank = last.entries[labeling].prNonBlank * \ + mat[t, labeling[-1]] # probability of paths ending with a blank prBlank = (last.entries[labeling].prTotal) * mat[t, blankIdx] @@ -113,8 +131,10 @@ def ctcBeamSearch(mat, classes, ignore_idx, lm, beamWidth=25, dict_list = []): curr.entries[labeling].prNonBlank += prNonBlank curr.entries[labeling].prBlank += prBlank curr.entries[labeling].prTotal += prBlank + prNonBlank - curr.entries[labeling].prText = last.entries[labeling].prText # beam-labeling not changed, therefore also LM score unchanged from - curr.entries[labeling].lmApplied = True # LM already applied at previous time-step for this beam-labeling + # beam-labeling not changed, therefore also LM score unchanged from + curr.entries[labeling].prText = last.entries[labeling].prText + # LM already applied at previous time-step for this beam-labeling + curr.entries[labeling].lmApplied = True # extend current beam-labeling for c in range(maxC - 1): @@ -145,19 +165,20 @@ def ctcBeamSearch(mat, classes, ignore_idx, lm, beamWidth=25, dict_list = []): last.norm() # sort by probability - #bestLabeling = last.sort()[0] # get most probable labeling + # bestLabeling = last.sort()[0] # get most probable labeling # map labels to chars #res = '' - #for idx,l in enumerate(bestLabeling): + # for idx,l in enumerate(bestLabeling): # if l not in ignore_idx and (not (idx > 0 and bestLabeling[idx - 1] == bestLabeling[idx])): # removing repeated characters and blank. # res += classes[l] if dict_list == []: - bestLabeling = last.sort()[0] # get most probable labeling + bestLabeling = last.sort()[0] # get most probable labeling res = '' - for i,l in enumerate(bestLabeling): - if l not in ignore_idx and (not (i > 0 and bestLabeling[i - 1] == bestLabeling[i])): # removing repeated characters and blank. + for i, l in enumerate(bestLabeling): + # removing repeated characters and blank. + if l not in ignore_idx and (not (i > 0 and bestLabeling[i - 1] == bestLabeling[i])): res += classes[l] else: res = last.wordsearch(classes, ignore_idx, beamWidth, dict_list) @@ -165,50 +186,57 @@ def ctcBeamSearch(mat, classes, ignore_idx, lm, beamWidth=25, dict_list = []): return res ##### -def consecutive(data, mode ='first', stepsize=1): + +def consecutive(data, mode='first', stepsize=1): group = np.split(data, np.where(np.diff(data) != stepsize)[0]+1) - group = [item for item in group if len(item)>0] + group = [item for item in group if len(item) > 0] - if mode == 'first': result = [l[0] for l in group] - elif mode == 'last': result = [l[-1] for l in group] + if mode == 'first': + result = [l[0] for l in group] + elif mode == 'last': + result = [l[-1] for l in group] return result -def word_segmentation(mat, separator_idx = {'th': [1,2],'en': [3,4]}, separator_idx_list = [1,2,3,4]): + +def word_segmentation(mat, separator_idx={'th': [1, 2], 'en': [3, 4]}, separator_idx_list=[1, 2, 3, 4]): result = [] sep_list = [] start_idx = 0 for sep_idx in separator_idx_list: - if sep_idx % 2 == 0: mode ='first' - else: mode ='last' - a = consecutive( np.argwhere(mat == sep_idx).flatten(), mode) - new_sep = [ [item, sep_idx] for item in a] + if sep_idx % 2 == 0: + mode = 'first' + else: + mode = 'last' + a = consecutive(np.argwhere(mat == sep_idx).flatten(), mode) + new_sep = [[item, sep_idx] for item in a] sep_list += new_sep sep_list = sorted(sep_list, key=lambda x: x[0]) for sep in sep_list: for lang in separator_idx.keys(): - if sep[1] == separator_idx[lang][0]: # start lang + if sep[1] == separator_idx[lang][0]: # start lang sep_lang = lang sep_start_idx = sep[0] - elif sep[1] == separator_idx[lang][1]: # end lang - if sep_lang == lang: # check if last entry if the same start lang + elif sep[1] == separator_idx[lang][1]: # end lang + if sep_lang == lang: # check if last entry if the same start lang new_sep_pair = [lang, [sep_start_idx+1, sep[0]-1]] if sep_start_idx > start_idx: - result.append( ['', [start_idx, sep_start_idx-1] ] ) + result.append(['', [start_idx, sep_start_idx-1]]) start_idx = sep[0]+1 result.append(new_sep_pair) - else: # reset + else: # reset sep_lang = '' if start_idx <= len(mat)-1: - result.append( ['', [start_idx, len(mat)-1] ] ) + result.append(['', [start_idx, len(mat)-1]]) return result + class CTCLabelConverter(object): """ Convert between text-label and text-index """ - #def __init__(self, character, separator = []): - def __init__(self, character, separator_list = {}, dict_pathlist = {}): + # def __init__(self, character, separator = []): + def __init__(self, character, separator_list={}, dict_pathlist={}): # character (str): set of the possible characters. dict_character = list(character) @@ -216,20 +244,21 @@ def __init__(self, character, separator_list = {}, dict_pathlist = {}): #self.separator_char = special_character[:len(separator)] self.dict = {} - #for i, char in enumerate(self.separator_char + dict_character): + # for i, char in enumerate(self.separator_char + dict_character): for i, char in enumerate(dict_character): # NOTE: 0 is reserved for 'blank' token required by CTCLoss self.dict[char] = i + 1 - self.character = ['[blank]'] + dict_character # dummy '[blank]' token for CTCLoss (index 0) - #self.character = ['[blank]']+ self.separator_char + dict_character # dummy '[blank]' token for CTCLoss (index 0) + # dummy '[blank]' token for CTCLoss (index 0) + self.character = ['[blank]'] + dict_character + # self.character = ['[blank]']+ self.separator_char + dict_character # dummy '[blank]' token for CTCLoss (index 0) self.separator_list = separator_list separator_char = [] for lang, sep in separator_list.items(): separator_char += sep - self.ignore_idx = [0] + [i+1 for i,item in enumerate(separator_char)] + self.ignore_idx = [0] + [i+1 for i, item in enumerate(separator_char)] dict_list = {} for lang, dict_path in dict_pathlist.items(): @@ -263,8 +292,9 @@ def decode_greedy(self, text_index, length): char_list = [] for i in range(l): - if t[i] not in self.ignore_idx and (not (i > 0 and t[i - 1] == t[i])): # removing repeated characters and blank (and separator). - #if (t[i] != 0) and (not (i > 0 and t[i - 1] == t[i])): # removing repeated characters and blank (and separator). + # removing repeated characters and blank (and separator). + if t[i] not in self.ignore_idx and (not (i > 0 and t[i - 1] == t[i])): + # if (t[i] != 0) and (not (i > 0 and t[i - 1] == t[i])): # removing repeated characters and blank (and separator). char_list.append(self.character[t[i]]) text = ''.join(char_list) @@ -276,25 +306,30 @@ def decode_beamsearch(self, mat, beamWidth=5): texts = [] for i in range(mat.shape[0]): - t = ctcBeamSearch(mat[i], self.character, self.ignore_idx, None, beamWidth=beamWidth) + t = ctcBeamSearch(mat[i], self.character, + self.ignore_idx, None, beamWidth=beamWidth) texts.append(t) return texts def decode_wordbeamsearch(self, mat, beamWidth=5): texts = [] - argmax = np.argmax(mat, axis = 2) + argmax = np.argmax(mat, axis=2) for i in range(mat.shape[0]): words = word_segmentation(argmax[i]) string = '' for word in words: - matrix = mat[i, word[1][0]:word[1][1]+1,:] - if word[0] == '': dict_list = [] - else: dict_list = self.dict_list[word[0]] - t = ctcBeamSearch(matrix, self.character, self.ignore_idx, None, beamWidth=beamWidth, dict_list=dict_list) + matrix = mat[i, word[1][0]:word[1][1]+1, :] + if word[0] == '': + dict_list = [] + else: + dict_list = self.dict_list[word[0]] + t = ctcBeamSearch(matrix, self.character, self.ignore_idx, + None, beamWidth=beamWidth, dict_list=dict_list) string += t texts.append(string) return texts + class AttnLabelConverter(object): """ Convert between text-label and text-index """ @@ -330,7 +365,8 @@ def encode(self, text, batch_max_length=25): text = list(t) text.append('[s]') text = [self.dict[char] for char in text] - batch_text[i][1:1 + len(text)] = torch.LongTensor(text) # batch_text[:, 0] = [GO] token + # batch_text[:, 0] = [GO] token + batch_text[i][1:1 + len(text)] = torch.LongTensor(text) return (batch_text.to(device), torch.IntTensor(length).to(device)) def decode(self, text_index, length):