/jddlmZddlZddlZddlZddlZddlZddlZddlm Z ddl m Z ddl m Z ddlmZddlZddlZddlmZmZddlmZdd lmZmZmZmZmZmZmZm Z m!Z!m"Z"m#Z#m$Z$dd l%m&Z&m'Z'm(Z(dd l)m*Z*m+Z+m,Z,dd l-m.Z.d Z/hdZ0hdZ1de0de1Z2dYdZ3 dZd[d"Z4d\d$Z5d]d(Z6d^d+Z7d_d-Z8d`d2Z9 dadbd:Z: dcdbd;Z; dcddd>ZdhdidJZ?GdKdLZ@djdkdSZAdldUZBdmdXZCdS)n) annotationsN) ThreadPool)Path) is_tarfile)Any)ImageImageOps)check_class_names) ASSETS_URL DATASETS_DIRLOGGER NUM_THREADSROOT SETTINGS_FILETQDMYAML clean_urlcolorstremojisis_dir_writeable) check_file check_fontis_ascii)download safe_download unzip_file)segments2boxeszJSee https://docs.ultralytics.com/datasets for dataset formatting guidance.>bmpdngjp2jpgmpopngtifavifheicheifjpegtiffwebpjpeg2000> tsasfavigifm4vmkvmovmp4mpgwmvmpegwebmzSupported formats are: images: z videos: img_paths list[str]returnctjdtjtjdtjcfd|DS)zaConvert image paths to label paths by replacing 'images' with 'labels' and extension with '.txt'.imageslabelscg|]H}|dddddzIS).rz.txt)joinrsplit).0xsasbs [/home/longshao/multi-rider-rag/.venv/lib/python3.11/site-packages/ultralytics/data/utils.py z#img2label_paths..?sK S S SABGGAHHROO $ $ + +C 3 3A 6 ? S S S)ossep)r8rErFs @@rGimg2label_pathsrL<sP & &bf & &26(A(A(A(AFB S S S S S S S SSrI 2files threshold_msfloat threshold_mb max_filesintprefixstrc |stj|ddStj|t |t |}g}g}g}|D]} t j} tj |j } | t j| z dz| | t j} t|d5} | } dddn #1swxYwYt j| z } | dkr| | dz | z #t$rYwxYw|stj|ddStj|}t |dkrtj|d nd}d tj|d z d d }d|d d|d d}|rKtj|}t |dkrtj|d nd}d|d d|d d}nd}||ks||kr tj|d|||ddStj|d|||ddS)awCheck dataset file access speed and provide performance feedback. This function tests the access speed of dataset files by measuring ping (stat call) time and read speed. It samples up to `max_files` files from the provided list and warns if access times exceed the threshold. Args: files (list[str]): List of file paths to check for access speed. threshold_ms (float, optional): Threshold in milliseconds for ping time warnings. threshold_mb (float, optional): Threshold in megabytes per second for read speed warnings. max_files (int, optional): The maximum number of files to check. prefix (str, optional): Prefix string to add to log messages. Examples: >>> from pathlib import Path >>> image_files = list(Path("dataset/images").glob("*.jpg")) >>> check_file_speeds(image_files, threshold_ms=15) z%Image speed checks: No files to checkNirbriz*Image speed checks: failed to access filesr?)ddofz, size: i.1fz KBzping: ±z msz, read: z MB/srPuFast image access ✅ ()zSlow image access detected (z). Use local storage instead of remote/mounted storage for better performance. See https://docs.ultralytics.com/guides/model-training-tips/)r warningrandomsampleminlentime perf_counterrJstatst_sizeappendopenread Exceptionnpmeanstdinfo)rQrRrTrUrW ping_times file_sizes read_speedsfstart file_sizefile_obj_ read_timeavg_pingstd_pingsize_msgping_msg avg_speed std_speed speed_msgs rGcheck_file_speedsrBsC( &GGGHHH M%YE !;!; < Return a single hash value of a list of paths (files or dirs).rhashlibrP) rJrfrgOSError __import__sha256rXencodeupdaterA hexdigest)rsizephs rGget_hashrs D   BGAJJ& &DD    H 9$$SYY%5%5%7%788AHHRWWU^^ " " $ $%%% ;;==s % 22img Image.Imagetuple[int, int]c|j}|jdkrR |x}r*|dd}|dvr|d|df}n#t$rYnwxYw|S)zReturn exif-corrected PIL size.JPEGiN>r?r)rformatgetexifgetrk)rsexifrotations rG exif_sizers A zV {{}}$t #88C..v%%!ad A    D  HsAA A"!A"argstuplec4|\\}}}d\}}} tj|}|t|}|d|df}|ddk|ddkzsJd|d|jt vsJd|jdt|jd vrt|d 5} | d d | d krDtj tj| |ddd||d}dddn #1swxYwYd}n"#t$r} d}||d| }Yd} ~ nd} ~ wwxYw||f|||fS)zVerify one image.)rrrPr?r image size <10 pixelszInvalid image format . >r!r(rZrd subsamplingquality!: corrupt JPEG restored and savedN : ignoring corrupt image/label: )rriverifyrrlower IMG_FORMATSFORMATS_HELP_MSGseekrjr exif_transposesaverk) rim_fileclsrWnfncmsgimshaperses rG verify_imagers$!NWcFKBCF Z  " q58$a1 qA.PP0Pe0P0P0PPP.y  K///1h1h1hVf1h1h/// 9??   / /gt$$ Pr1 6688{**+EJw,?,?@@EEgvcdnqErrr#OWOOOC  P P P P P P P P P P P P P P P  FFF EEE!EEF S>2r3 &&s=CE0#A3E" E0"E&&E0)E&*E00 F: F  Flistc 2 |\}}}}}}}}dddddgdf\} } } } } } tj|}|t|}|d|df}|ddk|ddkzsJd|d|jt vsJd|jd t|jd vrt|d 5}|d d | dkrDtj tj| |ddd||d} dddn #1swxYwYtj|rd} t|d5}d| D}t%d|Drp|snt'jd|Dt&j}d|Dt'j|ddt1fd}t'j|t&j}dddn #1swxYwYt3|x}r|rZ|jdd||zzksJdd||zzd|ddddfd|dddd f}n8|jddksJd|jdd|ddddf}|d ksJd!||d k|d"ksJd#||d"k|rdn|dddf}||ks#Jd$t;|d%|d&|dz t'j|dd'(\}}t3||kr2||}rfd)|D||d*|t3|z d+} n]d} t'jd|rd||zzndft&j}n.d} t'jd|rd||zzndft&j}|r|ddddfd||}|d krht'j |d,dk|d-dkzd.d/!t&j}t'j||d0gd1}|ddddf}||||| | | | | f S#tD$r!}d} ||d2|} ddddd| | | | | g cYd}~Sd}~wwxYw)3zVerify one image-label pair.rrPNr?rrrzinvalid image format r>r!r(rZrrrrrrrutf-8encodingcTg|]%}t||&S)rcsplitrCrDs rGrHz&verify_image_label..s+QQQA#a&&QaggiiQQQrIc3<K|]}t|dkVdS)rN)rcrs rG z%verify_image_label..s,..as1vvz......rIcg|] }|d S)rrrs rGrHz&verify_image_label..s'9'9'9!'9'9'9rIdtypecg|]>}tj|ddtjdd?S)r?Nrr)rlarrayfloat32reshapers rGrHz&verify_image_label..sB]]]UV122bj A A A I I"a P P]]]rIrrOzlabels require z columns eachzlabels require 5 columns, z columns detectedg)\(?z,non-normalized or out of bounds coordinates g{Gzz$negative class labels or coordinate z Label class z exceeds dataset class count z. Possible class labels are 0-T)axis return_indexc g|] }| Srr)rCrDsegmentss rGrHz&verify_image_label..s#;#;#;AHQK#;#;#;rI: z duplicate labels removed).r).r?g?).N)rr)#rrirrrrrrrrjr rrrJpathisfilestrip splitlinesanyrlrr concatenaterrrcrmaxrbrVuniquezeroswhereastyperk)rrlb_filerWkeypointnum_clsnkptndim single_clsnmrnerr keypointsrrrslbclassesnlpointsmax_clsrwikpt_maskrrs @rGverify_image_labelrs JNGGWfhtZ/0!Q2r4/G,BBC9@C Z  " q58$a1 qA.PP0Pe0P0P0PPP.y  K///1h1h1hVf1h1h/// 9??   / /gt$$ Pr1 6688{**+EJw,?,?@@EEgvcdnqErrr#OWOOOC  P P P P P P P P P P P P P P P 7>>' " "% WBg000 4AQQ)9)9)D)D)F)FQQQ..2.....__ h'9'9b'9'9'9LLLG]]Z\]]]HQ)?)?PXAYAY(Z\]^^BXb 333  4 4 4 4 4 4 4 4 4 4 4 4 4 4 4WW}r ['8A;1td{?;;;=oPQTX[_T_P_=o=o=o;;;122Y..r488BQB?FF8A;!+++-h"(ST+-h-h-h+++122YFzz||t+++-s\bcilpcp\q-s-s+++vvxx5(((*aQSTVY^T^Q_*a*a((( *=!!r!!!Q$x||~~(((A3w<<AAgAA3:Q;AA)((y!$???1q66B;;AB<#;#;#;#;#;#;#;#VWVVSVV VVVCXqx"F1td{??QGrzZZZB18Bq4$;C2:VVVB  V111abb5 ))"dD99Iqyy8Yv%6%:y?PST?T$UWZ\_``gghjhrssNIx 7J+KRTUUU 2A2YE8YBBKK CCC EEE!EEdD$b"b"cBBBBBBBCsiCU+2A3E1% U+1E55U+8E596U+/C,J' U+'J++U+.J+/J;U++ V5V VV image_pathtxt_path label_mapdict[int, str]c ddlm}ddlm}t jt j|}|jdd\}}g}t|d5} | D]q} tt| \} } } }}| |dz z |z}| |dz z |z}||z}||z}|j ||||t| fr dddn #1swxYwY|d\}}|D]\}}}}}td||d D}|||f||d|d }||d |dzd |dzzd|dzz}|||dz |||dkrdnd||||dS)aVisualize YOLO annotations (bounding boxes and class labels) on an image. This function reads an image and its corresponding annotation file in YOLO format, then draws bounding boxes around detected objects and labels them with their respective class names. The bounding box colors are assigned based on the class ID, and the text color is dynamically adjusted for readability, depending on the background color's luminance. Args: image_path (str): Path to the image file to annotate. The file must be readable by PIL. txt_path (str): Path to the annotation file in YOLO format, which should contain one line per object. label_map (dict[int, str]): A dictionary that maps class IDs (integers) to class labels (strings). Examples: >>> label_map = {0: "cat", 1: "dog", 2: "bird"} # Should include all annotated classes >>> visualize_image_annotations("path/to/image.jpg", "path/to/annotations.txt", label_map) rN)colorsrrrr?c3 K|] }|dz V dS)Nr)rCcs rGrz.visualize_image_annotations..-s&<.]s5eeeWX\%!))B--%AQRReeerI)rlr)rrrr s` ``rGpolygons2masksr/Ns3 8eeeeee\deee f ffrIrtuple[np.ndarray, np.ndarray]ctj|d|z|d|zft|dkr tjn tj}g}g}|D]}}t ||dg|d}|||j || ~tj |}tj | }tj ||}tt|D]/} || | dzz}||z}tj|d| dz}0||fS)z:Return a downsampled overlap mask and sorted area indices.rr?rrr)r r)a_mina_max)rlrrcr%r#r,rrhrrsumr$argsortrrangeclip) rrr masksareasmssegmentr)indexrs rGpolygons2masks_overlapr=`si H q% %uQx3C'CDh--#--bhh28   E E B!!  __R !-     $++ek**+++ TXXZZ    Ju  E Jv  E "e B 3x== ! !55!uA Qa!e444 %<rIrrctdp!td}|s Jddt |dkrfd|D}t |dks2Jddt |d||d S) aFind and return the YAML file associated with a Detect, Segment or Pose dataset. This function searches for a YAML file at the root level of the provided directory first, and if not found, it performs a recursive search. It prefers YAML files that have the same stem as the provided path. Args: path (Path): The directory path to search for the YAML file. Returns: (Path): The path of the found YAML file. z*.yamlzNo YAML file found in ''r?c4g|]}|jjk|Sr)stem)rCrsrs rGrHz%find_dataset_yaml..s'999qQVty%8%8%8%8%8rIzExpected 1 YAML file in 'z ', but found z. r)rglobrglobresolverc)rrQs` rGfind_dataset_yamlrE}s 8$$ % % Cdjj.B.B)C)CE ===DLLNN=====5 5zzA~~9999E999 u::???k kkUXY^U_U_kkdikk??? 8OrITdataset autodownloadbooldict[str, Any]c  t|}d}tj|st|r8t |t dd}t t |z }|jd}}tj |ddD]^}|vrX|dksdvr"tt|d |d tj d dd<_d vr#d vrtt|dd vrad vr]td d kr>tt|dtd dd dd vr#dt!d Dd <ntd d <t#d d <ddd<t'|p<dp't'ddjs0st z d<dD]}|rt/|t0r|z }|s@|dr%|ddz }t1||<fd|D|<ΈfddD\}}|rdt/|t4r|n|gD}t7d|Dst9|} tjdd| d t=d!|Dd"} |r|rtj | n%| d#t d$t>d"z } tA| tCj!} d} |d%r-|"d&rt |t d'nh|d(rAtjd)|d*tGj$|%d+ntM|d,id-tOtCj!| z d.d/} | d0vrd1| d2tQd3t nd4| d5}tjd6|d7tStUd rd8nd9S):amDownload, verify, and/or unzip a dataset if not found locally. This function checks the availability of a specified dataset, and if not found, it has the option to download and unzip the dataset. It then reads and parses the accompanying YAML data, ensuring key requirements are met and also resolves paths related to the dataset. Args: dataset (str): Path to the dataset or dataset descriptor (like a YAML file). autodownload (bool, optional): Whether to automatically download the dataset if not found. Returns: (dict[str, Any]): Parsed dataset information and paths. rPTFdirunzipdelete)append_filename)trainvalrQ validation 'uE:' key missing ❌. 'train' and 'val' are required in all data YAMLs.zBrenaming data YAML 'validation' key to 'val' to match YOLO format.namesruI key missing ❌. either 'names' or 'nc' are required in all data YAMLs.z 'names' length z and 'nc: z ' must match.cg|]}d|S)class_r)rCrs rGrHz%check_det_dataset..sAAA!!AAArIchannelsr yaml_file)rPrQtestminivalz../NcXg|]&}t|z 'Sr)rXrD)rCrDrs rGrHz%check_det_dataset..s1FFF3q113344FFFrIc3BK|]}|VdSN)r)rCrDdatas rGrz$check_det_dataset..s- 7 7adhhqkk 7 7 7 7 7 7rI)rQrcPg|]#}t|$Sr)rrDrs rGrHz%check_det_dataset..s(TTTQtAww  TTTrIc3>K|]}|VdSr^existsrs rGrz$check_det_dataset..s*++!188::++++++rIz Dataset 'z"' images not found, missing path 'c3BK|]}||VdSr^rbrs rGrz$check_det_dataset..s4HjHjq_`_g_g_i_iHjHjHjHjHjHjHjrIr?z% Note dataset download directory is 'z'. You can update this in 'http.zip)urlrLrNzbash zRunning z ...checkyaml(r?zs)>Nru success ✅ z , saved to boldzfailure u ❌zDataset download  z Arial.ttfzArial.Unicode.ttf)+rzipfile is_zipfilerrr rEparentrload SyntaxErrorrr r_poprcr6r rrrc is_absoluterD isinstancerX startswithrallrronextrFileNotFoundErrorrdendswith subprocessrunrexecroundrrr)rFrGr extract_dirnew_dirkrDrQrnamemtrdtr_rs @@rGcheck_det_datasetrs g  DK$7:d#3#37,d5QQQ !788$(K\  9T4 0 0 0D11 D==Ezz\55!grrrrrss N_ ` ` `((<00DKdt4//&G!w!w!wxxyyy$44<,>$t*,L,L&G!t!tSg=O=O!t!t[_`d[e!t!t!tuuvvvdAAuT$Z/@/@AAAW g''T %d7m44DMxx A..D  Ytxx//Y4b8Q8Q3R3R3Y Z ZD ;;==/!1!1!3!3/t#,,..DL .GG 88A;; G$q'3'' GDG^,,..xxzz7d1g&8&8&?&?7Q +4466Aa&&QFFFFd1gFFFQ8 7 7 7#6 7 7 7FC 3TT*S$2G2G+R33cUTTT++s+++++ 3W%%D KOOOmDmmDHjHjTWHjHjHjDjDjmmmA +\ +q!!!!wlwwgtwwww'*** AA||F## ( 6(:(: (!dCCCCCg&& ( .q...///qwwyy55555Q'''2U49;;?A..222BRSW`R`R`NrNNhv|.L.LNNNfyqsfyfyfyA K1A111 2 2 2htG}55N{{;NOOO KrI str | Pathrc  t|drt|tdd}nIt|dr't |}t|tdd}t |}|r|n t|z }|s|j dkrtd|dtj dtj d |d tj}t|d kr.tjd tt"d z gdn!t%t&d|d|jtj dtj|z ddt+d|d|dz }|stj d|t-|dt-|dzx}r>ddlm}tj dt5|d||d }|dz }ntjd!|d"|d#z r|d#z n9|d$z r|d$z n|d%z r|d%z nd&}|d'z r|d'z nd&} |d#kr|stj d(| }n|d'kr| stj d)|} t5d*|dz d+D} d,|dz D} t?tAtC| } ||| d-"D]\} } t+| d.d/| d0}| tj |5d1| d2D}t5|}t5d3|D}|dkr9| dkrtG|d4| d5tj |d6|d7|d8|| kr$tj|d6|d7|d9| d:|d; tj |d6|d7|d<||| | | d=d>S)?aCheck a classification dataset such as Imagenet. This function accepts a `dataset` name and attempts to retrieve the corresponding dataset information. If the dataset is not found locally, it attempts to download the dataset from the internet and save it locally. Args: dataset (str | Path): The name of the dataset. split (str, optional): The split of the dataset. Either 'val', 'test', or ''. Returns: (dict[str, Any]): A dictionary containing the following keys: - 'train' (Path): The directory path containing the training set of the dataset. - 'val' (Path): The directory path containing the validation set of the dataset. - 'test' (Path): The directory path containing the test set of the dataset. - 'nc' (int): The number of classes in the dataset. - 'names' (dict[int, str]): A dictionary of class names in the dataset. )zhttp:/zhttps:/TFrK)rfz.tarz.gzrPzSClassification datasets must be a directory (data="path/to/dir") not a file (data="z7"), See https://docs.ultralytics.com/datasets/classify/z Dataset not found, missing path z, attempting download...imagenetbashzdata/scripts/get_imagenet.shrh/rf)rLuDataset download success ✅ (r\z s), saved to rlrmrPz#Dataset 'split=train' not found at z*.jpgz*.pngr)split_classify_datasetzFound z1 images in subdirectories. Attempting to split...g?) train_ratiozNo images found in z or its subdirectories.rQrRvalidNrZz:Dataset 'split=val' not found, using 'split=test' instead.z:Dataset 'split=test' not found, using 'split=val' instead.c:g|]}||Sr)is_dirrs rGrHz%check_cls_dataset..5s% F F FA188:: Fa F F FrI*cDg|]}||jSr)rrrs rGrHz%check_cls_dataset..6s' J J Jqxxzz JQV J J JrIrPrQrZ: ...cbg|],}|jddtv*|-Sr?Nsuffixrr)rCrs rGrHz%check_cls_dataset..?s9___d ABB8M8M8O8OS^8^8^T8^8^8^rI*.*ch|] }|j Sr)rp)rCrs rG z$check_cls_dataset..As444ddk444rIrSz:' no training images foundz found z images in z classes (no images found)z classes (requires z classes, not r^u classes ✅ rX)rPrQrZrrTrW)$rXrvrr rzrrrrDr ValueErrorr ror_rdr{r|rrr rprrrCultralytics.data.splitrrcerrorrcrBiterdirdict enumeratesorteditemsry)rFrrdata_dirr train_set image_filesrval_settest_setrrTrvrWrQrnds rGcheck_cls_datasetrs( 7||455R\eTTT W  6 7 7R'"",d5QQQ7mmG">>++I,2HRRTTH ??   w ?b FfmFFF   B\(\\\]]] IKK w<<: % % NFC/M(M$N$NOW[ \ \ \ \ \  22W222 H H H H uTY[[1_uuuW_`fhpWqWquuuvvv7"I      RHYHHIIIx~~g6677$x~~g?V?V:W:WW W; R E E E E E E Kd[!1!1ddd e e e--hCHHHH 7*II LPxPPP Q Q Q u  $ $ & & 5 | # + + - -X $ $ w  & & ( (X    &.%6$>$>$@$@Jx&  dH ~~g~STTT &STTT F F(W,22377 F F F G GB J Jh099;; J J JE 6%==)) * *E$GXFFLLNNPP1WWW%%..... 9 K    __aggenn___EUB44e44455BQww<<+w,X,X!,X,X,XYYYNf#b#bR#b#bB#b#b#bccccr llrllbllUWllgilllmmmm vNNbNNRNNNOOOOwUZhi j jjrIcNeZdZdZddd ZeddZddZdddZddZ dS)HUBDatasetStatsaDA class for generating HUB dataset JSON and `-hub` dataset directory. Args: path (str): Path to data.yaml or data.zip (with data.yaml inside data.zip). task (str): Dataset task. Options are 'detect', 'segment', 'pose', 'classify', 'obb'. autodownload (bool): Attempt to download dataset if not found locally. Attributes: task (str): Dataset task type. hub_dir (Path): Directory path for HUB dataset files. im_dir (Path): Directory path for compressed images. stats (dict): Statistics dictionary containing dataset information. data (dict): Dataset configuration data. Methods: get_json: Return dataset JSON for Ultralytics HUB. process_images: Compress images for Ultralytics HUB. Examples: >>> from ultralytics.data.utils import HUBDatasetStats >>> stats = HUBDatasetStats("path/to/coco8.zip", task="detect") # detect dataset >>> stats = HUBDatasetStats("path/to/coco8-seg.zip", task="segment") # segment dataset >>> stats = HUBDatasetStats("path/to/coco8-pose.zip", task="pose") # pose dataset >>> stats = HUBDatasetStats("path/to/dota8.zip", task="obb") # OBB dataset >>> stats = HUBDatasetStats("path/to/imagenet10.zip", task="classify") # classification dataset >>> stats.get_json(save=True) >>> stats.process_images() Notes: Download *.zip files from https://github.com/ultralytics/hub/tree/main/example_datasets i.e. https://github.com/ultralytics/hub/raw/main/example_datasets/coco8.zip for coco8.zip. coco8.yamldetectFrrXtaskrGrHct|}tjd|d||_|jdkr$t |}t |}||d<n|t|\}}} tj |}d|d<tj ||t||}||d<n"#t$r} td| d} ~ wwxYwt|dd|_ |j d z |_t|d t!|d d |_||_dS) zInitialize class.z Starting HUB dataset checks for z....classifyrrPzerror/HUB/dataset_stats/initNz-hubr<rT)rrT)rrDr rorrr_unziprrqrrrkhub_dirim_dirrcrvaluesstatsr_) selfrrrG unzip_dirr_rwr yaml_pathrs rG__init__zHUBDatasetStats.__init__qstDzz!!## AtAAABBB 9 " ""4((I$Y//D$DLL%)[[d%<%< "Ax Gy++!V  )T***(LAA'V  G G G >??QF GtF|11122 lX- W ..d7m>R>R>T>T9U9UVV  sAC C:%C55C:rr:tuple[bool, str, Path]ct|dsdd|fSt||j}|sJd|d|ddt|t |fS) zUnzip data.zip.rfFN)rzError unzipping z, z6 not found. path/to/abc.zip MUST unzip to path/to/abc/T)rXrzrrprrE)rrs rGrzHUBDatasetStats._unzips4yy!!&)) %$$ $t$+666 !!  ht h hy h h h  !S^^%6y%A%AAArIrscZt||jt|jz dS)z)Save a compressed image for HUB previews.N)compress_one_imagerrr)rrss rG_hub_opszHUBDatasetStats._hub_opss'1dkDGGL899999rIrverboserc  fd dD]}dj|<j|}|*dt|dD}|sYjdkrddlm}|j|}tj t|j  t}|jD]} || d xxd z cc<t||d t|d|d d |jDd j|<Bddlm} | j|jj}tjfdt'|jt|dD}t||dd t|ttj|dkd |dkdd  fdt/|j|jDd j|<|rjddjdz } t7jd| dt=| dd5} t?j j| dddn #1swxYwY|r.t7jt?j!jddjS)z(Return dataset JSON for Ultralytics HUB.cjdkr |d}njdvrd|dD}nojdkrL|dj\}}}tj|d|d|||zfd}nt d jd t |d |}d |DS) z:Update labels to integer class and 4 decimal place floats.rbboxes>obbr;c6g|]}|Sr)flattenrs rGrHz._round..s GGGqqyy{{GGGrIrposerr?zUndefined dataset task=r@rcXg|]'\}}t|dgd|D(S)rc3PK|]!}tt|dV"dS)N)r~rSrs rGrzFHUBDatasetStats.get_json.._round...s2!E!E%a!"4"4!E!E!E!E!E!ErI)rV)rCrrs rGrHz._round..s=___91fS1YYF!E!Ef!E!E!EF___rI)rrrlrrrzip)r= coordinatesnnkrzippedrs rG_roundz(HUBDatasetStats.get_json.._roundsyH$$$X. 000GGF:4FGGG f$$";/5 2r nfX.>{@S@[@[\]_adf_f@g@g-hjkll  !G49!G!G!GHHH 44F__X^___ _rIrNcbg|],}|jddtv*|-Srr)rCrss rGrHz,HUBDatasetStats.get_json..s9___118ABB<;M;M;O;OS^;^;^Q;^;^;^rIrrr) ImageFolderr?)total per_class)r unlabelledrc@g|]\}}t|j|iSrrr)rCrrs rGrHz,HUBDatasetStats.get_json..s)JJJTQQ a0JJJrI)instance_stats image_statsr= YOLODataset)img_pathr_rcg|]S}tj|dtjdTS)rr) minlength)rlbincountrrVrr_)rCrrs rGrHz,HUBDatasetStats.get_json..s`! E%L$7$7$<$<$D$D$F$FRVR[\`RabbbrI StatisticsrdesccTg|]$\}}t|j|i%Srr)rCrrrs rGrHz,HUBDatasetStats.get_json..s2kkkTQQ ffQii8kkkrITparentsexist_okz stats.jsonzSaving rrrrrF)indent sort_keys)"rr_rrrCrtorchvision.datasetsrrlrrcrrrVimgstolistultralytics.datarrrr=r4rwrim_filesrmkdirr rorDrijsondumpdumps)rrrrrrQrrFrDrr stats_pathrsrs` @rGget_jsonzHUBDatasetStats.get_jsons ` ` ` ` `,, , E $DJu 9==''D|__T 0 0 7 7___E yJ&&<<<<<<%+di&677HS112299#>>!,""BbeHHHMHHHH14G 188::&V&V-0\\YZYaYaYcYc#d#dJJW\JJJ%% 5!! 988888%+ty/?diVZV_```H%)'.G S_%`%`%`14AEEGG 155QR88??K\K\&]&]!$W&)"&a*;*;*?*?*A*A&B&B&'!e[[^^%:%:%<%<$$ lkkkSIY[b[iEjEjkkk%% 5!!  ) L  td  ; ; ; 4J K;*"4"4"6"6;;; < < <j#888 )A $*a((( ) ) ) ) ) ) ) ) ) ) ) ) ) ) )  K K 4:a5III J J JzsL--L14L1cddlm}|jdddD]}|j|||j||j}t t5}t| |j |j t||d D]} dddn #1swxYwYtjd |j|jS) z$Compress images for Ultralytics HUB.rrTrrN)rr_z imagesrzDone. All images saved to )rrrrr_rrrrimaprrrcr ro)rrrrFpoolrws rGprocess_imageszHUBDatasetStats.process_imagessE000000 $666+  Ey}}U##+!k49U+;$)LLLGK(( Ddii w7GHHPST[P\P\fkctctctuuuA                 >>>???{s3ACC C N)rrF)rrXrrXrGrH)rrr:r)rsrX)FF)rrHrrHr:r)r:r) __name__ __module__ __qualname____doc__r staticmethodrrrrrrIrGrrOsB6BBB\B::::HHHHHT      rIrrsf_new str | Nonemax_dimrc 2 dt_tj|}|jdvr|d}|t |j|jz }|dkrA|t|j|zt|j|zf}| |p|d|ddS#t$r}tj d|d |tj|}|jdd \}}|t ||z }|dkrCtj|t||zt||zftj }tjt'|p||Yd}~dSd}~wwxYw) a6Compress a single image file to reduced size while preserving its aspect ratio and quality using either the Python Imaging Library (PIL) or OpenCV library. If the input image is smaller than the maximum dimension, it will not be resized. Args: f (str): The path to the input image file. f_new (str, optional): The path to the output image file. If not specified, the input file will be overwritten. max_dim (int, optional): The maximum dimension (width or height) of the output image. quality (int, optional): The image compression quality as a percentage. Examples: >>> from pathlib import Path >>> from ultralytics.data.utils import compress_one_image >>> for f in Path("path/to/dataset").rglob("*.jpg"): >>> compress_one_image(f) N>LARGBARGBrrT)roptimizezHUB ops PIL failure rr) interpolation)rMAX_IMAGE_PIXELSrimodeconvertrrrr(rVrrkr r_r&imreadr INTER_AREAimwriterX) rsrrrrrr im_heightim_widths rGrrs")!% Z]] 7n $ $E""B c")RX.. . s77C1 --s29q=/A/ABCCB  FGdCCCCC )))6a66166777 Z]] hrrl 8 c)X.. . s77BX\!2!2C A 4F4F GWZWefffB C OOR((((((((()sB=C F CFFrcddl}|tjt |d}||S)z1Load an Ultralytics *.cache dictionary from path.rNT) allow_pickle)gcdisablerlrqrXitemenable)rrcaches rGload_dataset_cache_filersP IIIJJLLL GCIID 1 1 1 6 6 8 8EIIKKK LrIrDversionc@||d<t|jr|r| t t |d5}t j||dddn #1swxYwYtj |d|dS#t$rJ}t|dtj |d|d|Yd}~dSd}~wwxYwtj |d |jd dS) z9Save an Ultralytics dataset *.cache dictionary x to path.rwbNzNew cache created: T) missing_oku'WARNING ⚠️ Failed to save cache to rzCache directory z" is not writable, cache not saved.) rrprcunlinkrirXrlrr rorkrr_)rWrrDrrrs rGsave_dataset_cache_filer #sAiL $$ c ;;==  KKMMM Zc$ii&& !$a    ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 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