|h  ddlZddlZddlZddlZddlZddlZddlZddlZddl Z ddl Z ddl Z ddl Z ddl Z ddlmZddl mZddlmZddlmZddlmZddlZddlZddlZddlZddlmZddlmZm Z m!Z!m"Z"e#ejHd d Z%e#ejHd d Z&e jNxsd d gZ(ee)jUZ+e+jXd Z-e-dz Z.dZ/e-dz Z0e1de2d ejfd z Z4e5ejHddjmdk(Z7e5ejHddjmdk(Z8e8rdndZ9dZ:ddD\Z;Zd?Z`Gd@dAZaeaje0ZcecjZeeWdxiecZfdBe5fdCZgdBehfdDZidEZjdFZkdGZldHZmdBehfdIZndBehfdJZodBehfdKZpdBehfdLZqerfdMe5dBehfdNZsdOee5efdBehfdPZtdQZudBehfdRZvdSZwdTZxdUZydVZzdWZ{dXZ|dydYZ}egZ~eqZejZekZenZepZelZesZeoZewZexZeejHdZxse}Zed[z Zd\Zd]ZGd^d_ejZGd`daejZdbZdcZGdddeeOZGdfdgeZdzdhZdiZdjZd{dBe5fdkZedlZeZeedmz ZeednZeedoZeedpZerdqnerdrnerdsnerdtnej<ZeuxsevZee"e_e=ree!e ce_e_!e_ yy)|N)Path)Lock)SimpleNamespace)Union)unquote) __version__)imreadimshowimwrite torch_saveRANK LOCAL_RANKassetsz>https://github.com/ultralytics/assets/releases/download/v0.0.0zcfg/default.yamlYOLO_AUTOINSTALLTtrue YOLO_VERBOSEz{l_bar}{bar:10}{r_bar} ultralyticsc#JK|]}tj|k(ywN)platformsystem).0xs Y/var/www/html/test/engine/venv/lib/python3.12/site-packages/ultralytics/utils/__init__.py r.sXA*a/Xs!#)DarwinLinuxWindows>arm64aarch64 torchvision TERM_PROGRAMFvscode> rk2118rk3562rk3566rk3568rk3576rk3588rv1103rv1106rv1103brv1106ba Examples for running Ultralytics: 1. Install the ultralytics package: pip install ultralytics 2. Use the Python SDK: from ultralytics import YOLO # Load a model model = YOLO("yolo11n.yaml") # build a new model from scratch model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training) # Use the model results = model.train(data="coco8.yaml", epochs=3) # train the model results = model.val() # evaluate model performance on the validation set results = model("https://ultralytics.com/images/bus.jpg") # predict on an image success = model.export(format="onnx") # export the model to ONNX format 3. Use the command line interface (CLI): Ultralytics 'yolo' CLI commands use the following syntax: yolo TASK MODE ARGS Where TASK (optional) is one of [detect, segment, classify, pose, obb] MODE (required) is one of [train, val, predict, export, track, benchmark] ARGS (optional) are any number of custom "arg=value" pairs like "imgsz=320" that override defaults. See all ARGS at https://docs.ultralytics.com/usage/cfg or with "yolo cfg" - Train a detection model for 10 epochs with an initial learning_rate of 0.01 yolo detect train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01 - Predict a YouTube video using a pretrained segmentation model at image size 320: yolo segment predict model=yolo11n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320 - Val a pretrained detection model at batch-size 1 and image size 640: yolo detect val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640 - Export a YOLO11n classification model to ONNX format at image size 224 by 128 (no TASK required) yolo export model=yolo11n-cls.pt format=onnx imgsz=224,128 - Run special commands: yolo help yolo checks yolo version yolo settings yolo copy-cfg yolo cfg Docs: https://docs.ultralytics.com Community: https://community.ultralytics.com GitHub: https://github.com/ultralytics/ultralytics i@default) linewidth precisionprofilez{:11.5g}) float_kind)r4 formatterNUMEXPR_MAX_THREADS3TF_CPP_MIN_LOG_LEVELERRORTORCH_CPP_LOG_LEVEL5KINETO_LOG_LEVELYOLO_TQDM_RICH)richc,eZdZdZfdZfdZxZS)TQDMa A custom TQDM progress bar class that extends the original tqdm functionality. This class modifies the behavior of the original tqdm progress bar based on global settings and provides additional customization options for Ultralytics projects. The progress bar is automatically disabled when VERBOSE is False or when explicitly disabled. Attributes: disable (bool): Whether to disable the progress bar. Determined by the global VERBOSE setting and any passed 'disable' argument. bar_format (str): The format string for the progress bar. Uses the global TQDM_BAR_FORMAT if not explicitly set. Methods: __init__: Initialize the TQDM object with custom settings. __iter__: Return self as iterator to satisfy Iterable interface. Examples: >>> from ultralytics.utils import TQDM >>> for i in TQDM(range(100)): ... # Your processing code here ... pass ctjdtjt xs|j dd|d<|j dtt|$|i|y)a Initialize a custom TQDM progress bar with Ultralytics-specific settings. Args: *args (Any): Variable length argument list to be passed to the original tqdm constructor. **kwargs (Any): Arbitrary keyword arguments to be passed to the original tqdm constructor. Notes: - The progress bar is disabled if VERBOSE is False or if 'disable' is explicitly set to True in kwargs. - The default bar format is set to TQDM_BAR_FORMAT unless overridden in kwargs. Examples: >>> from ultralytics.utils import TQDM >>> for i in TQDM(range(100)): ... # Your code here ... pass ignore)categorydisableF bar_formatN) warningsfilterwarningstqdmTqdmExperimentalWarningVERBOSEget setdefaultTQDM_BAR_FORMATsuper__init__selfargskwargs __class__s rrRz TQDM.__init__sY$ 43O3OP 'KG6::i+Gy,8 $)&)c t|S)z6Return self as iterator to satisfy Iterable interface.)rQ__iter__rTrWs rrZz TQDM.__iter__sw!!rX)__name__ __module__ __qualname____doc__rRrZ __classcell__rWs@rrCrCs0*.""rXrCcHeZdZdZd dZd dZd dZd dZd dZd dZ d dZ y ) DataExportMixina Mixin class for exporting validation metrics or prediction results in various formats. This class provides utilities to export performance metrics (e.g., mAP, precision, recall) or prediction results from classification, object detection, segmentation, or pose estimation tasks into various formats: Pandas DataFrame, CSV, XML, HTML, JSON and SQLite (SQL). Methods: to_df: Convert summary to a Pandas DataFrame. to_csv: Export results as a CSV string. to_xml: Export results as an XML string (requires `lxml`). to_html: Export results as an HTML table. to_json: Export results as a JSON string. tojson: Deprecated alias for `to_json()`. to_sql: Export results to an SQLite database. Examples: >>> model = YOLO("yolo11n.pt") >>> results = model("image.jpg") >>> df = results.to_df() >>> print(df) >>> csv_data = results.to_csv() >>> results.to_sql(table_name="yolo_results") cNddl}|j|j||S)ag Create a pandas DataFrame from the prediction results summary or validation metrics. Args: normalize (bool, optional): Normalize numerical values for easier comparison. decimals (int, optional): Decimal places to round floats. Returns: (DataFrame): DataFrame containing the summary data. rN normalizedecimals)pandas DataFramesummary)rTrfrgpds rto_dfzDataExportMixin.to_dfs# ||DLL9xLPQQrXcD|j||jS)z Export results to CSV string format. Args: normalize (bool, optional): Normalize numeric values. decimals (int, optional): Decimal precision. Returns: (str): CSV content as string. re)rlto_csvrTrfrgs rrnzDataExportMixin.to_csvs zzIzAHHJJrXch|j||}|jrdS|jdS)a/ Export results to XML format. Args: normalize (bool, optional): Normalize numeric values. decimals (int, optional): Decimal precision. Returns: (str): XML string. Notes: Requires `lxml` package to be installed. rez4 etree)parser)rlemptyto_xml)rTrfrgdfs rrtzDataExportMixin.to_xmls8ZZ)hZ ?JL((FqXZXaXaipXaXqqrXch|j||}|jrdS|j|S)a` Export results to HTML table format. Args: normalize (bool, optional): Normalize numeric values. decimals (int, optional): Decimal precision. index (bool, optional): Whether to include index column in the HTML table. Returns: (str): HTML representation of the results. rez
)index)rlrsto_html)rTrfrgrwrus rrxzDataExportMixin.to_htmls3ZZ)hZ ?$&HH I"**5*2IIrXcPtjd|j||S)z Deprecated version of to_json().zA'result.tojson()' is deprecated, replace with 'result.to_json()'.)LOGGERwarningto_jsonros rtojsonzDataExportMixin.tojsons Z[||Ix00rXcJ|j||jddS)a Export results to JSON format. Args: normalize (bool, optional): Normalize numeric values. decimals (int, optional): Decimal precision. Returns: (str): JSON-formatted string of the results. rerecords)orientindent)rlr|ros rr|zDataExportMixin.to_jsons)zzIzAIIQZcdIeerXc 8|j||}|js|jjryddl}|j |}|j }g} |jD]} || j js || j jdnd} t| trd} nt| ttfrd} nd} | jd| d| |jd|d|jd |d d j| d |jD]\} }|Dcgc])}t|trt!j"|n|+}}d jd |jD}d jd|jD}|jd|d|d|d ||j%|j't(j+d|d|dycc}w)a> Save results to an SQLite database. Args: normalize (bool, optional): Normalize numeric values. decimals (int, optional): Decimal precision. table_name (str, optional): Name of the SQL table. db_path (str, optional): SQLite database file path. NrrTEXTREAL"z" zDROP TABLE IF EXISTS "zCREATE TABLE "z)" (id INTEGER PRIMARY KEY AUTOINCREMENT, z, )c3(K|] }d|d yw)rN)rcols rrz)DataExportMixin.to_sql..Ks$FCqQZ$Fsc3 K|]}dyw)?Nr)r_s rrz)DataExportMixin.to_sql..Ls$=QS$=s z INSERT INTO "z" (z ) VALUES (zResults saved to SQL table 'z' in 'z'.)rlrscolumnssqlite3connectcursordropnailoc isinstancedictfloatintappendexecutejoiniterrowsjsondumpscommitcloserzinfo)rTrfrg table_namedb_pathrurconnrrr sample_valcol_typerrowvvalues column_names placeholderss rto_sqlzDataExportMixin.to_sql&sZZ 8 , 88rzz'' w':: 2C9;C9I9O9OC)..q1UWJ*d+!J 5!! NNQse2hZ0 1 2 / |1=> |3\]a]f]fgn]o\ppqrskkm kFAsKNOaz!T':djjmAOFO99$F2::$FFL99$="**$==L NN]:,c,zR^Q__`aci j  k   2:,fWIRPQPs.HN)F)FrF)Frresultsz results.db) r\r]r^r_rlrnrtrxr}r|rrrXrrcrcs02 R Kr" J1 f+RrXrcc"eZdZdZdZdZdZy) SimpleClassa A simple base class for creating objects with string representations of their attributes. This class provides a foundation for creating objects that can be easily printed or represented as strings, showing all their non-callable attributes. It's useful for debugging and introspection of object states. Methods: __str__: Return a human-readable string representation of the object. __repr__: Return a machine-readable string representation of the object. __getattr__: Provide a custom attribute access error message with helpful information. Examples: >>> class MyClass(SimpleClass): ... def __init__(self): ... self.x = 10 ... self.y = "hello" >>> obj = MyClass() >>> print(obj) __main__.MyClass object with attributes: x: 10 y: 'hello' Notes: - This class is designed to be subclassed. It provides a convenient way to inspect object attributes. - The string representation includes the module and class name of the object. - Callable attributes and attributes starting with an underscore are excluded from the string representation. cg}t|D]}t||}t|r|jdr-t |t r*|d|j d|jjd}n|dt|}|j||j d|jjddj|zS)Return a machine-readable string representation of the object.)rrTs r__repr__zSimpleClass.__repr__s||~rXcj|jj}td|d|d|j)IProvide a custom attribute access error message with helpful information.'' object has no attribute 'z'. See valid attributes below. )rWr\AttributeErrorr_rTrnames r __getattr__zSimpleClass.__getattr__s9~~&&q&A$GghlhthtguvwwrXN)r\r]r^r_rrrrrXrrrTs: lxrXrc*eZdZdZdZdZdZddZy)IterableSimpleNamespacea An iterable SimpleNamespace class that provides enhanced functionality for attribute access and iteration. This class extends the SimpleNamespace class with additional methods for iteration, string representation, and attribute access. It is designed to be used as a convenient container for storing and accessing configuration parameters. Methods: __iter__: Return an iterator of key-value pairs from the namespace's attributes. __str__: Return a human-readable string representation of the object. __getattr__: Provide a custom attribute access error message with helpful information. get: Retrieve the value of a specified key, or a default value if the key doesn't exist. Examples: >>> cfg = IterableSimpleNamespace(a=1, b=2, c=3) >>> for k, v in cfg: ... print(f"{k}: {v}") a: 1 b: 2 c: 3 >>> print(cfg) a=1 b=2 c=3 >>> cfg.get("b") 2 >>> cfg.get("d", "default") 'default' Notes: This class is particularly useful for storing configuration parameters in a more accessible and iterable format compared to a standard dictionary. cFtt|jS)zFReturn an iterator of key-value pairs from the namespace's attributes.)itervarsitemsrs rrZz IterableSimpleNamespace.__iter__sDJ$$&''rXc`djdt|jDS)rrc30K|]\}}|d|yw)=Nr)rkrs rrz2IterableSimpleNamespace.__str__..sC1A3asCs)rrrrs rrzIterableSimpleNamespace.__str__s$yyCT 0@0@0BCCCrXc `|jj}td|d|dtd)rz 'rz'. This may be caused by a modified or out of date ultralytics 'default.yaml' file. Please update your code with 'pip install -U ultralytics' and if necessary replace z with the latest version from https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/default.yaml )rWr\rDEFAULT_CFG_PATHrs rrz#IterableSimpleNamespace.__getattr__sD~~&&V.tf5      rXNct|||S)zXReturn the value of the specified key if it exists; otherwise, return the default value.)r)rTkeyr3s rrNzIterableSimpleNamespace.getstS'**rXr)r\r]r^r_rZrrrNrrXrrrs D(D  +rXrc"ddifd}|S)a Decorator to temporarily set rc parameters and the backend for a plotting function. Args: rcparams (dict, optional): Dictionary of rc parameters to set. backend (str, optional): Name of the backend to use. Returns: (Callable): Decorated function with temporarily set rc parameters and backend. Examples: >>> @plt_settings({"font.size": 12}) >>> def plot_function(): ... plt.figure() ... plt.plot([1, 2, 3]) ... plt.show() >>> with plt_settings({"font.size": 12}): ... plt.figure() ... plt.plot([1, 2, 3]) ... plt.show() z font.size cfd}|S)zEDecorator to apply temporary rc parameters and backend to a function.cddlm}|j}j|jk7}|r"|j d|j  |j 5|i|}ddd|r"|j d|j |S#1swY/xYw#|r#|j d|j |wwxYw)zTSet rc parameters and backend, call the original function, and restore the settings.rNall)matplotlib.pyplotpyplot get_backendlowerrswitch_backend rc_context) rUrVpltoriginal_backendswitchresultbackendfuncrcparamss rwrapperz0plt_settings..decorator..wrappers +"0 ]]_(8(>(>(@@F % ""7+ 9^^H-3!4262F3IIe$&&'78M 33IIe$&&'78s$B2/ B&8B2&B/+B22'Cr)rrrrs` r decoratorzplt_settings..decorators (rXr)rrrs`` r plt_settingsrs".$2 rXcV|rtdvrtjntj}Gddtj}|d}t rt tjdrtjjdk7r t tjdr!tjjdnRt tjd r8d d l }|jtjjdt_tjtj}|j!||j#|tj$|}|j#||j'|d |_|S#t$rYwxYw) a Set up logging with UTF-8 encoding and configurable verbosity. This function configures logging for the Ultralytics library, setting the appropriate logging level and formatter based on the verbosity flag and the current process rank. It handles special cases for Windows environments where UTF-8 encoding might not be the default. Args: name (str): Name of the logger. verbose (bool): Flag to set logging level to INFO if True, ERROR otherwise. Returns: (logging.Logger): Configured logger object. Examples: >>> set_logging(name="ultralytics", verbose=True) >>> logger = logging.getLogger("ultralytics") >>> logger.info("This is an info message") Notes: - On Windows, this function attempts to reconfigure stdout to use UTF-8 encoding if possible. - If reconfiguration is not possible, it falls back to a custom formatter that handles non-UTF-8 environments. - The function sets up a StreamHandler with the appropriate formatter and level. - The logger's propagate flag is set to False to prevent duplicate logging in parent loggers. rrceZdZfdZxZS)$set_logging..PrefixFormatterc.|jtjk(r!tsdnd}|d|j|_n=|jtj k(r tsdnd}|d|j|_t ||}t|S)z0Format log records with prefixes based on level.uWARNING ⚠️WARNING u ERROR ❌r<) levelnologgingrWINDOWSmsgr<rQformatemojis)rTrecordprefixformatted_messagerWs rrz+set_logging..PrefixFormatter.formats~~018)i &xq 5 7==0,3 &xq 5 !&v 6 +, ,rX)r\r]r^rr`ras@rPrefixFormatterrs  - -rXrz %(message)sencodingutf-8 reconfigurerbufferrNF)r rINFOr< Formatterrhasattrsysstdoutrrio TextIOWrapperr Exception StreamHandler setFormattersetLevel getLogger addHandler propagate)rverboselevelrr8rstream_handlerloggers r set_loggingrs54$GLLW]]E -'++ - .I73::z2szz7J7Jg7U szz=1 &&&8X.--cjj.?.?'-R **3::6N *E"  t $F OOE n%F M   s B F F('F()r) sentry_sdkzurllib3.connectionpoolcRtr |jjddS|S)z7Return platform-dependent emoji-safe version of string.asciirE)rencodedecode)strings rrrJs"8?6==? ! !'8 4KVKrXceZdZdZdZdZy)ThreadingLockeda) A decorator class for ensuring thread-safe execution of a function or method. This class can be used as a decorator to make sure that if the decorated function is called from multiple threads, only one thread at a time will be able to execute the function. Attributes: lock (threading.Lock): A lock object used to manage access to the decorated function. Examples: >>> from ultralytics.utils import ThreadingLocked >>> @ThreadingLocked() >>> def my_function(): ... # Your code here c6tj|_y)z5Initialize the decorator class with a threading lock.N) threadingrlockrs rrRzThreadingLocked.__init__`sNN$ rXc8ddlm}|fd}|S)z0Run thread-safe execution of function or method.r)wrapscXj5|i|cdddS#1swYyxYw)z8Apply thread-safety to the decorated function or method.N)r)rUrVfrTs r decoratedz+ThreadingLocked.__call__..decoratedhs. *$)&) * * *s )) functoolsr!)rTr#r!r$s`` r__call__zThreadingLocked.__call__ds$# q *  * rXNr\r]r^r_rRr&rrXrrrOs % rXrc^eZdZdZdZedZdZeddZed dZ edZ y) YAMLa YAML utility class for efficient file operations with automatic C-implementation detection. This class provides optimized YAML loading and saving operations using PyYAML's fastest available implementation (C-based when possible). It implements a singleton pattern with lazy initialization, allowing direct class method usage without explicit instantiation. The class handles file path creation, validation, and character encoding issues automatically. The implementation prioritizes performance through: - Automatic C-based loader/dumper selection when available - Singleton pattern to reuse the same instance - Lazy initialization to defer import costs until needed - Fallback mechanisms for handling problematic YAML content Attributes: _instance: Internal singleton instance storage. yaml: Reference to the PyYAML module. SafeLoader: Best available YAML loader (CSafeLoader if available). SafeDumper: Best available YAML dumper (CSafeDumper if available). Examples: >>> data = YAML.load("config.yaml") >>> data["new_value"] = 123 >>> YAML.save("updated_config.yaml", data) >>> YAML.print(data) NcJ|j ||_|jS)z+Initialize singleton instance on first use.) _instance)clss r _get_instancezYAML._get_instances! == ECM}}rXcddl}||_ |j|_|j|_y#t t f$r%|j|_|j|_YywxYw)zEInitialize with optimal YAML implementation (C-based when available).rN)yaml CSafeLoader SafeLoader CSafeDumper SafeDumperr ImportError)rTr/s rrRz YAML.__init__sU  ."..DO"..DO , ."ooDO"ooDO .s"01A$#A$c |j}|i}t|}|jjddtt t tttttdf}|jD] \}}t||rt |||<"t|ddd5}|r|j||j j#||dd|j$ dddy#1swYyxYw) z Save Python object as YAML file. Args: file (str | Path): Path to save YAML file. data (dict | None): Dict or compatible object to save. header (str): Optional string to add at file beginning. 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Returns: (dict): Loaded YAML content. )z.yamlz.ymlzNot a YAML file: rErr:N)LoaderzJ[^\x09\x0A\x0D\x20-\x7E\x85\xA0-\uD7FF\uE000-\uFFFD\U00010000-\U0010ffff]+rNone yaml_file) r-rAendswithrFreadr/loadr1r resubrr) r,rIappend_filenamerLr#rrJrrs rrUz YAML.loads2$$&4y!!"34P8I$6PP4$x' : aA  K==%%a0C0C%DJD T[[] ">BjjlKdaAqF{t1KDK  #D D  #    KdfhjklA==%%a0C0C%DJD KLs%C+C"-D3C"A D0/D0c|j}t|ttfr|j |n|}|j j |ddd|j}tjdtdd|d|y ) z Pretty print YAML file or object to console. Args: yaml_file (str | Path | dict): Path to YAML file or dict to print. FTr)r<r=widthr>z Printing 'boldblackz' N) r-rrArrUr/rHr3rzrcolorstr)r,rRrL yaml_dictrHs rprintz YAML.prints~$$&,6i#t+MCHHY'S\ }}!!)uDXZckcvcv!w j&'9!E FeD6RSrX) data.yamlNr)r`F) r\r]r^r_r+ classmethodr-rRrNrUr_rrXrr)r)qsf6I .ii:  DTTrXr)returncFtjjS)z Read the device model information from the system and cache it for quick access. Returns: (str): Kernel release information. )rreleaserrrXrread_device_modelres     # # %%rXc td5}d|jvcdddS#1swYyxYw#t$rYywxYw)zi Check if the OS is Ubuntu. Returns: (bool): True if OS is Ubuntu, False otherwise. /etc/os-releasez ID=ubuntuNF)rFrTFileNotFoundErrorr#s r is_ubunturjsF # $ +!&&(* + + + s# 5) 5255 AAcNdtjvxsdtjvS)z Check if the current script is running inside a Google Colab notebook. Returns: (bool): True if running inside a Colab notebook, False otherwise. COLAB_RELEASE_TAGCOLAB_BACKEND_VERSIONosenvironrrXris_colabrqs" "** , U0G2::0UUrXctjjddk(xr"tjjddk(S)z Check if the current script is running inside a Kaggle kernel. Returns: (bool): True if running inside a Kaggle kernel, False otherwise. PWDz/kaggle/workingKAGGLE_URL_BASEzhttps://www.kaggle.com)rorprNrrXr is_kagglerus6 ::>>% $5 5 w"**..IZ:[_w:wwrXctxstS)a Check if the current script is running inside a Jupyter Notebook. Returns: (bool): True if running inside a Jupyter Notebook, False otherwise. 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