a yfy@s>ddlZddlZddlZddlZddlmZddlmZddlm Z m Z m Z ddl m Z mZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZm Z hdZ!hdZ"dd d d d d Z#dddddd Z$dddddd Z%dde"DZ&ej'pddgZ(de)dge(ddde"de!dZ*hd Z+hd!Z,hd"Z-hd#Z.d$d%Z/edfe e)ee efe d&d'd(Z0dKd*d+Z1dLd,d-Z2d.d/Z3dMe e d0d1d2Z4e e)e e)d3d4d5Z5e e)dd3d6d7Z6e e)dd3d8d9Z7e e)d:d;d<Z8d=d>Z9dNe)d@dAdBZ:dCdDZ;dOdEdFZdIkr:etrackexporttrainZ benchmarkpredictval>obbposesegmentclassifydetectz coco8.yamlzcoco8-seg.yamlZ imagenet10zcoco8-pose.yamlz dota8.yaml)r$r"r#r!r yolo11n.ptzyolo11n-seg.ptzyolo11n-cls.ptzyolo11n-pose.ptzyolo11n-obb.ptzmetrics/mAP50-95(B)zmetrics/mAP50-95(M)zmetrics/accuracy_top1zmetrics/mAP50-95(P)cCsh|] }t|qS) TASK2MODEL).0taskr&r&T/var/www/html/django/DPS/env/lib/python3.9/site-packages/ultralytics/cfg/__init__.py :r+z Arguments received: Zyoloz. Ultralytics 'yolo' commands use the following syntax: yolo TASK MODE ARGS Where TASK (optional) is one of z+ MODE (required) is one of a 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' 1. Train a detection model for 10 epochs with an initial learning_rate of 0.01 yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01 2. Predict a YouTube video using a pretrained segmentation model at image size 320: yolo predict model=yolo11n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320 3. Val a pretrained detection model at batch-size 1 and image size 640: yolo val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640 4. 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 5. Explore your datasets using semantic search and SQL with a simple GUI powered by Ultralytics Explorer API yolo explorer data=data.yaml model=yolo11n.pt 6. Streamlit real-time webcam inference GUI yolo streamlit-predict 7. 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 > batchZ warmup_epochsboxZshearZdflZ workspacetimedegreescls>Z perspective translateZlrfZdropoutZmixupscaleconfZbgrfractionZlabel_smoothingZ copy_pasteZ weight_decayZhsv_vZmomentumZlr0ZiouZwarmup_momentumZmosaicZfliplrZhsv_hZflipudZwarmup_bias_lrZhsv_s> ZpatienceZ save_periodZ mask_ratioseedZnbsZ vid_stride line_widthZ close_mosaicZmax_detworkersZepochs>"Z save_framesZnmsZhalfZkerasoptimizeZsave_txtZ agnostic_nmsZaugmentZcos_lrZ single_clsZdynamicrectZint8 show_confverbosesaveZ save_confZ retina_maskssimplifyZ save_jsonZ visualizeZ save_crop show_labels show_boxesZ multi_scaleZ deterministicZplotsexist_okZprofileZdnnZ save_hybridZ overlap_maskshowrcCs.t|ttfrt|}nt|tr*t|}|S)a Converts a configuration object to a dictionary. Args: cfg (str | Path | Dict | SimpleNamespace): Configuration object to be converted. Can be a file path, a string, a dictionary, or a SimpleNamespace object. Returns: (Dict): Configuration object in dictionary format. Examples: Convert a YAML file path to a dictionary: >>> config_dict = cfg2dict("config.yaml") Convert a SimpleNamespace to a dictionary: >>> from types import SimpleNamespace >>> config_sn = SimpleNamespace(param1="value1", param2="value2") >>> config_dict = cfg2dict(config_sn) Pass through an already existing dictionary: >>> config_dict = cfg2dict({"param1": "value1", "param2": "value2"}) Notes: - If cfg is a path or string, it's loaded as YAML and converted to a dictionary. - If cfg is a SimpleNamespace object, it's converted to a dictionary using vars(). - If cfg is already a dictionary, it's returned unchanged. ) isinstancestrrrrvarscfgr&r&r*cfg2dicts   rJ)rI overridescCst|}|r>t|}d|vr(|ddt||i||}dD].}||vrBt||ttfrBt||||<qB|ddkr|dddd|d<t d |dd t |t fi|S) a1 Load and merge configuration data from a file or dictionary, with optional overrides. Args: cfg (str | Path | Dict | SimpleNamespace): Configuration data source. Can be a file path, dictionary, or SimpleNamespace object. overrides (Dict | None): Dictionary containing key-value pairs to override the base configuration. Returns: (SimpleNamespace): Namespace containing the merged configuration arguments. Examples: >>> from ultralytics.cfg import get_cfg >>> config = get_cfg() # Load default configuration >>> config = get_cfg("path/to/config.yaml", overrides={"epochs": 50, "batch_size": 16}) Notes: - If both `cfg` and `overrides` are provided, the values in `overrides` will take precedence. - Special handling ensures alignment and correctness of the configuration, such as converting numeric `project` and `name` to strings and validating configuration keys and values. - The function performs type and value checks on the configuration data. save_dirN)projectnamerNmodelr-.ru;WARNING ⚠️ 'name=model' automatically updated to 'name='.) rJpopcheck_dict_alignmentrEintfloatrFgetsplitr warning check_cfgr)rIrKkr&r&r*get_cfgs   r[TcCs|D]\}}|dur|tvrvt|ttfsv|rhtd|d|dt|jd|d|d|d t|||<q|tvr t|ttfs|rtd|d|dt|jd|d|d|d t|||<}d |krd ksnt d|d|d |d q|t vrdt|tsd|rVtd|d|dt|jd |d|d t|||<q|t vrt|t s|rtd|d|dt|jd |d|d|d t |||<qdS)a Checks configuration argument types and values for the Ultralytics library. This function validates the types and values of configuration arguments, ensuring correctness and converting them if necessary. It checks for specific key types defined in global variables such as CFG_FLOAT_KEYS, CFG_FRACTION_KEYS, CFG_INT_KEYS, and CFG_BOOL_KEYS. Args: cfg (Dict): Configuration dictionary to validate. hard (bool): If True, raises exceptions for invalid types and values; if False, attempts to convert them. Examples: >>> config = { ... "epochs": 50, # valid integer ... "lr0": 0.01, # valid float ... "momentum": 1.2, # invalid float (out of 0.0-1.0 range) ... "save": "true", # invalid bool ... } >>> check_cfg(config, hard=False) >>> print(config) {'epochs': 50, 'lr0': 0.01, 'momentum': 1.2, 'save': False} # corrected 'save' key Notes: - The function modifies the input dictionary in-place. - None values are ignored as they may be from optional arguments. - Fraction keys are checked to be within the range [0.0, 1.0]. N'=z' is of invalid type z . Valid 'z' types are int (i.e. 'z=0') or float (i.e. 'z=0.5')gg?z' is an invalid value. Valid 'z!' values are between 0.0 and 1.0.z. 'z' must be an int (i.e. 'z=8')z' must be a bool (i.e. 'z =True' or 'z=False')) itemsCFG_FLOAT_KEYSrErTrU TypeErrortype__name__CFG_FRACTION_KEYS ValueError CFG_INT_KEYS CFG_BOOL_KEYSbool)rIhardrZvr&r&r*rYs^ (rYcCszt|ddr|j}n^ddlm}|jp>> from types import SimpleNamespace >>> args = SimpleNamespace(project="my_project", task="detect", mode="train", exist_ok=True) >>> save_dir = get_save_dir(args) >>> print(save_dir) my_project/detect/train rLNr)increment_pathztests/tmp/runs>rT)rC)getattrrLZultralytics.utils.filesrjrMrrparentrr)rNmoderr rC)argsrNrLrjrMr&r&r* get_save_dirOs  "rpcCs|D]}|dkr0t|d|d|d<|dkrTt|d|ddk|d<|dkrxt|d|ddk|d<|dkr t|d |d|d <q |S) a Handles deprecated configuration keys by mapping them to current equivalents with deprecation warnings. Args: custom (Dict): Configuration dictionary potentially containing deprecated keys. Examples: >>> custom_config = {"boxes": True, "hide_labels": "False", "line_thickness": 2} >>> _handle_deprecation(custom_config) >>> print(custom_config) {'show_boxes': True, 'show_labels': True, 'line_width': 2} Notes: This function modifies the input dictionary in-place, replacing deprecated keys with their current equivalents. It also handles value conversions where necessary, such as inverting boolean values for 'hide_labels' and 'hide_conf'. ZboxesrBZ hide_labelsrAFalseZ hide_confr=Zline_thicknessr9)copykeysrrR)customkeyr&r&r*_handle_deprecationos    rvbasertc st|}dd|fD\}fdd|D}|rddlm}d}|D]R}||}fdd|D}|rxd |d nd} |d td d |d| d7}qHt|t|dS)av Checks alignment between custom and base configuration dictionaries, handling deprecated keys and providing error messages for mismatched keys. Args: base (Dict): The base configuration dictionary containing valid keys. custom (Dict): The custom configuration dictionary to be checked for alignment. e (Exception | None): Optional error instance passed by the calling function. Raises: SystemExit: If mismatched keys are found between the custom and base dictionaries. Examples: >>> base_cfg = {"epochs": 50, "lr0": 0.01, "batch_size": 16} >>> custom_cfg = {"epoch": 100, "lr": 0.02, "batch_size": 32} >>> try: ... check_dict_alignment(base_cfg, custom_cfg) ... except SystemExit: ... print("Mismatched keys found") Notes: - Suggests corrections for mismatched keys based on similarity to valid keys. - Automatically replaces deprecated keys in the custom configuration with updated equivalents. - Prints detailed error messages for each mismatched key to help users correct their configurations. css|]}t|VqdSN)setrs)r(xr&r&r* r,z'check_dict_alignment..csg|]}|vr|qSr&r&r(rZ) base_keysr&r* r,z(check_dict_alignment..r)get_close_matchesr-cs0g|](}|dur(|d|n|qS)Nr])rVr})rxr&r*rr,zSimilar arguments are i.e. rPr\redboldz ' is not a valid YOLO argument.  N)rvdifflibrr SyntaxError CLI_HELP_MSG) rxrteZ custom_keysZ mismatchedrstringr{matchesZ match_strr&)rxr~r*rSs   rS)roreturncCsg}t|D]\}}|dkrfd|kr8t|dkrfnn*|dd||d7<||d=q |dr|t|dkrd||dvr||||d||d=q |dr|dkr|d|7<q ||q |S)ae Merges arguments around isolated '=' in a list of strings, handling three cases: 1. ['arg', '=', 'val'] becomes ['arg=val'], 2. ['arg=', 'val'] becomes ['arg=val'], 3. ['arg', '=val'] becomes ['arg=val']. Args: args (List[str]): A list of strings where each element represents an argument. Returns: (List[str]): A list of strings where the arguments around isolated '=' are merged. Examples: >>> args = ["arg1", "=", "value", "arg2=", "value2", "arg3", "=value3"] >>> merge_equals_args(args) ['arg1=value', 'arg2=value2', 'arg3=value3'] r]rr.rk) enumeratelenendswithappend startswith)ronew_argsiargr&r&r*merge_equals_argss( *  rcCsTddlm}|ddkr>> handle_yolo_settings(["reset"]) # Reset YOLO settings >>> handle_yolo_settings(["default_cfg_path=yolo11n.yaml"]) # Update a specific setting Notes: - If no arguments are provided, the function will display the current settings. - The 'reset' command will delete the existing settings file and create new default settings. - Other arguments are treated as key-value pairs to update specific settings. - The function will check for alignment between the provided settings and the existing ones. - After processing, the updated settings will be displayed. - For more information on handling YOLO settings, visit: https://docs.ultralytics.com/quickstart/#ultralytics-settings z=https://docs.ultralytics.com/quickstart/#ultralytics-settingsrresetzSettings reset successfullycss|]}t|VqdSryparse_key_value_pairr(ar&r&r*r|r,z'handle_yolo_settings..u.💡 Learn more about Ultralytics Settings at u WARNING ⚠️ settings error: 'z'. Please see z for help.N) anyrunlinkrrr infodictrSupdateprint ExceptionrX)rourlnewrr&r&r*handle_yolo_settingss    rrocCsztdtdddtdddg}tdd |D}td d d D|d |D]\}}|||g7}qVt |dS)a Launches a graphical user interface that provides tools for interacting with and analyzing datasets using the Ultralytics Explorer API. It checks for the required 'streamlit' package and informs the user that the Explorer dashboard is loading. Args: args (List[str]): A list of optional command line arguments. Examples: ```bash yolo explorer data=data.yaml model=yolo11n.pt ``` Notes: - Requires 'streamlit' package version 1.29.0 or higher. - The function does not take any arguments or return any values. - It is typically called from the command line interface using the 'yolo explorer' command. streamlit>=1.29.0u"💡 Loading Explorer dashboard... streamlitrunzdata/explorer/gui/dash.pyz--server.maxMessageSizeZ2048css|]}t|VqdSryrrr&r&r*r|;r,z"handle_explorer..cSsi|]}|t|qSr&)r r}r&r&r* <r,z#handle_explorer..)rOdatarwN) rcheck_requirementsr rrrrSr^ subprocessr)rocmdrrZrir&r&r*handle_explorer%s  rcCs0tdtdtddtdddgdS) a8 Open the Ultralytics Live Inference Streamlit app for real-time object detection. This function initializes and runs a Streamlit application designed for performing live object detection using Ultralytics models. It checks for the required Streamlit package and launches the app. Examples: >>> handle_streamlit_inference() Notes: - Requires Streamlit version 1.29.0 or higher. - The app is launched using the 'streamlit run' command. - The Streamlit app file is located in the Ultralytics package directory. ru.💡 Loading Ultralytics Live Inference app...rrz solutions/streamlit_inference.pyz--server.headlesstrueN)rrr rrrrr&r&r&r*handle_streamlit_inferenceBs  r key=value)paircCsB|dd\}}||}}|s6Jd|d|t|fS)a Parses a key-value pair string into separate key and value components. Args: pair (str): A string containing a key-value pair in the format "key=value". Returns: (tuple): A tuple containing two elements: - key (str): The parsed key. - value (str): The parsed value. Raises: AssertionError: If the value is missing or empty. Examples: >>> key, value = parse_key_value_pair("model=yolo11n.pt") >>> print(f"Key: {key}, Value: {value}") Key: model, Value: yolo11n.pt >>> key, value = parse_key_value_pair("epochs=100") >>> print(f"Key: {key}, Value: {value}") Key: epochs, Value: 100 Notes: - The function splits the input string on the first '=' character. - Leading and trailing whitespace is removed from both key and value. - An assertion error is raised if the value is empty after stripping. r]r.z missing 'z' value)rWstrip smart_value)rrZrir&r&r*rVsrcCsf|}|dkrdS|dkr dS|dkr,dSttt|WdS1sT0Y|SdS)a3 Converts a string representation of a value to its appropriate Python type. This function attempts to convert a given string into a Python object of the most appropriate type. It handles conversions to None, bool, int, float, and other types that can be evaluated safely. Args: v (str): The string representation of the value to be converted. Returns: (Any): The converted value. The type can be None, bool, int, float, or the original string if no conversion is applicable. Examples: >>> smart_value("42") 42 >>> smart_value("3.14") 3.14 >>> smart_value("True") True >>> smart_value("None") None >>> smart_value("some_string") 'some_string' Notes: - The function uses a case-insensitive comparison for boolean and None values. - For other types, it attempts to use Python's eval() function, which can be unsafe if used on untrusted input. - If no conversion is possible, the original string is returned. noneNrTfalseF)lower contextlibsuppressreval)riZv_lowerr&r&r*rys &rc s|r|dntdds,ttdSddtjddfddddfd dfd dfd dtfd dd dd }itddt Dddt D|}| dd| D| dd| Di|dd| Ddd| D}i}t D]}|drPtd|d|ddd|dd}|drtd|d|ddd|dd}d|vr4z`t|\}}|dkr|durtdtd |d!dtt| D}n|||<Wn>ttttfy0}zt||d"i|WYd}~n d}~00n|t vrH||d#<n|t vr\||d$<n||vr~||dS|tvrtt|trd%||<nF|tvrtd&td'd(|d)|dt|d*tnt||d"iqt|||d$}|dur0tj pd+}td,t d-|dn$|t vrTtd.|d/t d0t|!d#d} | r| t vrtd1| d2t d0td3|vrt"| |d3<|!d3tj#} | durd4} td5| d| |d3<t$| j%} d6| vrd7d8l&m'} | | } nbd9| vr$d7d:l&m(} | | } nBd;| vs8d<| vrNd7d=l&m)}|| } nd7d>l&m*}|| | d?} t|d@t+r| ,|d@| | j-kr| rtdA| dB| j-dC| dD| j-dE | j-} |dFvrdG|vrtj.pt/|dG<tdH|dGdn|dIvrZdJ|vrdK|vrtj0p>> entrypoint("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: >>> entrypoint("predict model=yolo11n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320") Validate a pretrained detection model at batch-size 1 and image size 640: >>> entrypoint("val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640") Notes: - If no arguments are passed, the function will display the usage help message. - For a list of all available commands and their arguments, see the provided help messages and the Ultralytics documentation at https://docs.ultralytics.com.  r.NcSs ttSry)r rrr&r&r&r*r,zentrypoint..cSs ttSry)r rrr&r&r&r*rr,cstddSNr.)rr&rr&r*rr,cSsttSry)rr r&r&r&r*rr,cstddSrrr&rr&r*rr,cstSryrr&rr&r*rr,cstSryrr&rr&r*rr,cstddSr)rr&rr&r*rr,cSstSry)rr&r&r&r*rr,) helprversionsettingsrIrrrzcopy-cfgZexplorerzstreamlit-predictcSsi|] }|dqSryr&r}r&r&r*rr,zentrypoint..cSsi|]\}}|d|qS)rr&r(rZrir&r&r*rr,cSs4i|],\}}t|dkr|dr|dd|qS)r.sNrk)rrrr&r&r*rr,cSsi|]\}}d||qS)-r&rr&r&r*rr,cSsi|]\}}d||qS)--r&rr&r&r*rr,ruWARNING ⚠️ argument 'z5' does not require leading dashes '--', updating to 'rQ,z4' does not require trailing comma ',', updating to 'rkr]rIz Overriding z with cSsi|]\}}|dkr||qSrHr&)r(rZrr&r&r*rr,r-r)rnTr\rrzR' is a valid YOLO argument but is missing an '=' sign to set its value, i.e. try 'z' ru;WARNING ⚠️ 'mode' argument is missing. Valid modes are z. Using default 'mode=zInvalid 'mode=z'. Valid modes are z. zInvalid 'task=z'. Valid tasks are rOr%uAWARNING ⚠️ 'model' argument is missing. Using default 'model=Zrtdetrr)RTDETRZfastsam)FastSAMZsam_Zsam2_)SAM)YOLO)r)Z pretrainedu!WARNING ⚠️ conflicting 'task=z' passed with 'task=z' model. Ignoring 'task=z' and updating to 'task=z' to match model.>rrsourceuCWARNING ⚠️ 'source' argument is missing. Using default 'source=>rrrresumeu?WARNING ⚠️ 'data' argument is missing. Using default 'data=rformatZ torchscriptuCWARNING ⚠️ 'format' argument is missing. Using default 'format=u6💡 Learn more at https://docs.ultralytics.com/modes/r)7rWARGVr rrrZcollect_system_infocopy_default_cfgr TASKSMODESrr^rrrXrrr rZ check_yaml NameErrorrrdAssertionErrorrSrrErgrrVrrnrRr'rOrstemrrrrrrFloadr)rrr TASK2DATArrlr rr)debugZspecialZfull_args_dictrKrrZrirrnr)rOrrrrrr&rr* entrypoints       (,         (                            "  rcCsBttjdd}tt|ttd|d|ddS)a# Copies the default configuration file and creates a new one with '_copy' appended to its name. This function duplicates the existing default configuration file (DEFAULT_CFG_PATH) and saves it with '_copy' appended to its name in the current working directory. It provides a convenient way to create a custom configuration file based on the default settings. Examples: >>> copy_default_cfg() # Output: default.yaml copied to /path/to/current/directory/default_copy.yaml # Example YOLO command with this new custom cfg: # yolo cfg='/path/to/current/directory/default_copy.yaml' imgsz=320 batch=8 Notes: - The new configuration file is created in the current working directory. - After copying, the function prints a message with the new file's location and an example YOLO command demonstrating how to use the new configuration file. - This function is useful for users who want to modify the default configuration without altering the original file. z.yamlz _copy.yamlz copied to z> Example YOLO command with this new custom cfg: yolo cfg='z' imgsz=320 batch=8N) rcwdr rNreplaceshutilcopy2r r)new_filer&r&r*rJs  r__main__)r)T)N)N)r)r-)?rrrsyspathlibrtypesrtypingrrrZultralytics.utilsrrr r r r r rrrrrrrrrrrrrrrrr'Z TASK2METRICZMODELSargvrrFrr_rcrerfrJr[rYrprvrSrrrrrrrrrrbr&r&r&r*st  X*  &#"0 > #)! )#, &