general.py

# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""General utils."""

import contextlib
import glob
import inspect
import logging
import logging.config
import math
import os
import platform
import random
import re
import signal
import subprocess
import sys
import time
import urllib
from copy import deepcopy
from datetime import datetime
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from subprocess import check_output
from tarfile import is_tarfile
from typing import Optional
from zipfile import ZipFile, is_zipfile

import cv2
import numpy as np
import pandas as pd
import pkg_resources as pkg
import torch
import torchvision
import yaml
from ultralytics.utils.checks import check_requirements

from utils import TryExcept, emojis
from utils.downloads import curl_download, gsutil_getsize
from utils.metrics import box_iou, fitness

FILE = Path(__file__).resolve()
ROOT = FILE.parents[1]  # YOLOv3 root directory
RANK = int(os.getenv("RANK", -1))

# Settings
NUM_THREADS = min(8, max(1, os.cpu_count() - 1))  # number of YOLOv3 multiprocessing threads
DATASETS_DIR = Path(os.getenv("YOLOv5_DATASETS_DIR", ROOT.parent / "datasets"))  # global datasets directory
AUTOINSTALL = str(os.getenv("YOLOv5_AUTOINSTALL", True)).lower() == "true"  # global auto-install mode
VERBOSE = str(os.getenv("YOLOv5_VERBOSE", True)).lower() == "true"  # global verbose mode
TQDM_BAR_FORMAT = "{l_bar}{bar:10}{r_bar}"  # tqdm bar format
FONT = "Arial.ttf"  # https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf

torch.set_printoptions(linewidth=320, precision=5, profile="long")
np.set_printoptions(linewidth=320, formatter={"float_kind": "{:11.5g}".format})  # format short g, %precision=5
pd.options.display.max_columns = 10
cv2.setNumThreads(0)  # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
os.environ["NUMEXPR_MAX_THREADS"] = str(NUM_THREADS)  # NumExpr max threads
os.environ["OMP_NUM_THREADS"] = "1" if platform.system() == "darwin" else str(NUM_THREADS)  # OpenMP (PyTorch and SciPy)
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"  # suppress verbose TF compiler warnings in Colab


def is_ascii(s=""):
    """Checks if input string `s` is composed solely of ASCII characters; compatible with pre-Python 3.7 versions."""
    s = str(s)  # convert list, tuple, None, etc. to str
    return len(s.encode().decode("ascii", "ignore")) == len(s)


def is_chinese(s="人工智能"):
    """Determines if a string `s` contains any Chinese characters; returns a boolean."""
    return bool(re.search("[\u4e00-\u9fff]", str(s)))


def is_colab():
    """Checks if the current environment is a Google Colab instance; returns a boolean."""
    return "google.colab" in sys.modules


def is_jupyter():
    """
    Check if the current script is running inside a Jupyter Notebook. Verified on Colab, Jupyterlab, Kaggle, Paperspace.

    Returns:
        bool: True if running inside a Jupyter Notebook, False otherwise.
    """
    with contextlib.suppress(Exception):
        from IPython import get_ipython

        return get_ipython() is not None
    return False


def is_kaggle():
    """Determines if the environment is a Kaggle Notebook by checking environment variables."""
    return os.environ.get("PWD") == "/kaggle/working" and os.environ.get("KAGGLE_URL_BASE") == "https://www.kaggle.com"


def is_docker() -> bool:
    """Check if the process runs inside a docker container."""
    if Path("/.dockerenv").exists():
        return True
    try:  # check if docker is in control groups
        with open("/proc/self/cgroup") as file:
            return any("docker" in line for line in file)
    except OSError:
        return False


def is_writeable(dir, test=False):
    """Determines if a directory is writeable, optionally tests by writing a file if `test=True`."""
    if not test:
        return os.access(dir, os.W_OK)  # possible issues on Windows
    file = Path(dir) / "tmp.txt"
    try:
        with open(file, "w"):  # open file with write permissions
            pass
        file.unlink()  # remove file
        return True
    except OSError:
        return False


LOGGING_NAME = "yolov5"


def set_logging(name=LOGGING_NAME, verbose=True):
    """Configures logging with specified verbosity; 'name' sets logger identity, 'verbose' toggles logging level."""
    rank = int(os.getenv("RANK", -1))  # rank in world for Multi-GPU trainings
    level = logging.INFO if verbose and rank in {-1, 0} else logging.ERROR
    logging.config.dictConfig(
        {
            "version": 1,
            "disable_existing_loggers": False,
            "formatters": {name: {"format": "%(message)s"}},
            "handlers": {
                name: {
                    "class": "logging.StreamHandler",
                    "formatter": name,
                    "level": level,
                }
            },
            "loggers": {
                name: {
                    "level": level,
                    "handlers": [name],
                    "propagate": False,
                }
            },
        }
    )


set_logging(LOGGING_NAME)  # run before defining LOGGER
LOGGER = logging.getLogger(LOGGING_NAME)  # define globally (used in train.py, val.py, detect.py, etc.)
if platform.system() == "Windows":
    for fn in LOGGER.info, LOGGER.warning:
        setattr(LOGGER, fn.__name__, lambda x: fn(emojis(x)))  # emoji safe logging


def user_config_dir(dir="Ultralytics", env_var="YOLOV5_CONFIG_DIR"):
    """Returns user configuration directory path, prefers `env_var` if set, else uses OS-specific path, creates
    directory if needed.
    """
    if env := os.getenv(env_var):
        path = Path(env)  # use environment variable
    else:
        cfg = {"Windows": "AppData/Roaming", "Linux": ".config", "Darwin": "Library/Application Support"}  # 3 OS dirs
        path = Path.home() / cfg.get(platform.system(), "")  # OS-specific config dir
        path = (path if is_writeable(path) else Path("/tmp")) / dir  # GCP and AWS lambda fix, only /tmp is writeable
    path.mkdir(exist_ok=True)  # make if required
    return path


CONFIG_DIR = user_config_dir()  # Ultralytics settings dir


class Profile(contextlib.ContextDecorator):
    """Profiles code execution time, usable as a context manager or decorator for performance monitoring."""

    def __init__(self, t=0.0):
        """Initializes a profiling context for YOLOv3 with optional timing threshold `t` and checks CUDA
        availability.
        """
        self.t = t
        self.cuda = torch.cuda.is_available()

    def __enter__(self):
        """Starts the profiling timer, returning the profile instance for use with @Profile() decorator or 'with
        Profile():' context.
        """
        self.start = self.time()
        return self

    def __exit__(self, type, value, traceback):
        """Ends profiling, calculating time delta and updating total time, for use within 'with Profile():' context."""
        self.dt = self.time() - self.start  # delta-time
        self.t += self.dt  # accumulate dt

    def time(self):
        """Returns current time, ensuring CUDA operations are synchronized if on GPU."""
        if self.cuda:
            torch.cuda.synchronize()
        return time.time()


class Timeout(contextlib.ContextDecorator):
    """Enforces a timeout on code execution, raising TimeoutError on expiry."""

    def __init__(self, seconds, *, timeout_msg="", suppress_timeout_errors=True):
        """Initializes a timeout context/decorator with specified duration, custom message, and error handling
        option.
        """
        self.seconds = int(seconds)
        self.timeout_message = timeout_msg
        self.suppress = bool(suppress_timeout_errors)

    def _timeout_handler(self, signum, frame):
        """Raises a TimeoutError with a custom message upon timeout signal reception."""
        raise TimeoutError(self.timeout_message)

    def __enter__(self):
        """Starts a countdown for a signal alarm; not supported on Windows."""
        if platform.system() != "Windows":  # not supported on Windows
            signal.signal(signal.SIGALRM, self._timeout_handler)  # Set handler for SIGALRM
            signal.alarm(self.seconds)  # start countdown for SIGALRM to be raised

    def __exit__(self, exc_type, exc_val, exc_tb):
        """Cancels any scheduled SIGALRM on non-Windows platforms, optionally suppressing TimeoutError."""
        if platform.system() != "Windows":
            signal.alarm(0)  # Cancel SIGALRM if it's scheduled
            if self.suppress and exc_type is TimeoutError:  # Suppress TimeoutError
                return True


class WorkingDirectory(contextlib.ContextDecorator):
    """Context manager to temporarily change the working directory, reverting to the original on exit."""

    def __init__(self, new_dir):
        """Initializes context manager to temporarily change working directory, reverting on exit."""
        self.dir = new_dir  # new dir
        self.cwd = Path.cwd().resolve()  # current dir

    def __enter__(self):
        """Temporarily changes the current working directory to `new_dir`, reverting to the original on exit."""
        os.chdir(self.dir)

    def __exit__(self, exc_type, exc_val, exc_tb):
        """Reverts to the original working directory upon exiting the context manager."""
        os.chdir(self.cwd)


def methods(instance):
    """Returns a list of callable class/instance methods, excluding magic methods."""
    return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith("__")]


def print_args(args: Optional[dict] = None, show_file=True, show_func=False):
    """Prints function arguments; optionally specify args dict, show file and/or function name."""
    x = inspect.currentframe().f_back  # previous frame
    file, _, func, _, _ = inspect.getframeinfo(x)
    if args is None:  # get args automatically
        args, _, _, frm = inspect.getargvalues(x)
        args = {k: v for k, v in frm.items() if k in args}
    try:
        file = Path(file).resolve().relative_to(ROOT).with_suffix("")
    except ValueError:
        file = Path(file).stem
    s = (f"{file}: " if show_file else "") + (f"{func}: " if show_func else "")
    LOGGER.info(colorstr(s) + ", ".join(f"{k}={v}" for k, v in args.items()))


def init_seeds(seed=0, deterministic=False):
    """Initializes RNG seeds for reproducibility; `seed`: RNG seed, `deterministic`: enforces deterministic behavior if
    True.
    """
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)  # for Multi-GPU, exception safe
    # torch.backends.cudnn.benchmark = True  # AutoBatch problem https://github.com/ultralytics/yolov5/issues/9287
    if deterministic and check_version(torch.__version__, "1.12.0"):  # https://github.com/ultralytics/yolov5/pull/8213
        torch.use_deterministic_algorithms(True)
        torch.backends.cudnn.deterministic = True
        os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
        os.environ["PYTHONHASHSEED"] = str(seed)


def intersect_dicts(da, db, exclude=()):
    """Intersects two dicts by matching keys and shapes, excluding specified keys, and retains values from the first
    dict.
    """
    return {k: v for k, v in da.items() if k in db and all(x not in k for x in exclude) and v.shape == db[k].shape}


def get_default_args(func):
    """Returns a dict of `func`'s default arguments using inspection."""
    signature = inspect.signature(func)
    return {k: v.default for k, v in signature.parameters.items() if v.default is not inspect.Parameter.empty}


def get_latest_run(search_dir="."):
    """Returns path to the most recent 'last.pt' file within 'search_dir' for resuming, or an empty string if not
    found.
    """
    last_list = glob.glob(f"{search_dir}/**/last*.pt", recursive=True)
    return max(last_list, key=os.path.getctime) if last_list else ""


def file_age(path=__file__):
    """Returns the number of days since the last update of the file specified by 'path'."""
    dt = datetime.now() - datetime.fromtimestamp(Path(path).stat().st_mtime)  # delta
    return dt.days  # + dt.seconds / 86400  # fractional days


def file_date(path=__file__):
    """Returns file modification date in 'YYYY-M-D' format for the file at 'path'."""
    t = datetime.fromtimestamp(Path(path).stat().st_mtime)
    return f"{t.year}-{t.month}-{t.day}"


def file_size(path):
    """Returns the size of a file or total size of files in a directory at 'path' in MB."""
    mb = 1 << 20  # bytes to MiB (1024 ** 2)
    path = Path(path)
    if path.is_file():
        return path.stat().st_size / mb
    elif path.is_dir():
        return sum(f.stat().st_size for f in path.glob("**/*") if f.is_file()) / mb
    else:
        return 0.0


def check_online():
    """Checks internet connectivity by attempting to connect to "1.1.1.1" on port 443 twice; returns True if
    successful.
    """
    import socket

    def run_once():
        """Attempts a single internet connectivity check to '1.1.1.1' on port 443 and returns True if successful."""
        try:
            socket.create_connection(("1.1.1.1", 443), 5)  # check host accessibility
            return True
        except OSError:
            return False

    return run_once() or run_once()  # check twice to increase robustness to intermittent connectivity issues


def git_describe(path=ROOT):  # path must be a directory
    """Returns human-readable git description of a directory if it's a git repository, otherwise an empty string."""
    try:
        assert (Path(path) / ".git").is_dir()
        return check_output(f"git -C {path} describe --tags --long --always", shell=True).decode()[:-1]
    except Exception:
        return ""


@TryExcept()
@WorkingDirectory(ROOT)
def check_git_status(repo="ultralytics/yolov5", branch="master"):
    """Checks YOLOv3 code update status against remote, suggests 'git pull' if outdated; requires internet and git
    repository.
    """
    url = f"https://github.com/{repo}"
    msg = f", for updates see {url}"
    s = colorstr("github: ")  # string
    assert Path(".git").exists(), s + "skipping check (not a git repository)" + msg
    assert check_online(), s + "skipping check (offline)" + msg

    splits = re.split(pattern=r"\s", string=check_output("git remote -v", shell=True).decode())
    matches = [repo in s for s in splits]
    if any(matches):
        remote = splits[matches.index(True) - 1]
    else:
        remote = "ultralytics"
        check_output(f"git remote add {remote} {url}", shell=True)
    check_output(f"git fetch {remote}", shell=True, timeout=5)  # git fetch
    local_branch = check_output("git rev-parse --abbrev-ref HEAD", shell=True).decode().strip()  # checked out
    n = int(check_output(f"git rev-list {local_branch}..{remote}/{branch} --count", shell=True))  # commits behind
    if n > 0:
        pull = "git pull" if remote == "origin" else f"git pull {remote} {branch}"
        s += f"⚠️ YOLOv3 is out of date by {n} commit{'s' * (n > 1)}. Use '{pull}' or 'git clone {url}' to update."
    else:
        s += f"up to date with {url} ✅"
    LOGGER.info(s)


@WorkingDirectory(ROOT)
def check_git_info(path="."):
    """
    Checks YOLOv3 git info (remote, branch, commit) in path, requires 'gitpython'.

    Returns dict.
    """
    check_requirements("gitpython")
    import git

    try:
        repo = git.Repo(path)
        remote = repo.remotes.origin.url.replace(".git", "")  # i.e. 'https://github.com/ultralytics/yolov5'
        commit = repo.head.commit.hexsha  # i.e. '3134699c73af83aac2a481435550b968d5792c0d'
        try:
            branch = repo.active_branch.name  # i.e. 'main'
        except TypeError:  # not on any branch
            branch = None  # i.e. 'detached HEAD' state
        return {"remote": remote, "branch": branch, "commit": commit}
    except git.exc.InvalidGitRepositoryError:  # path is not a git dir
        return {"remote": None, "branch": None, "commit": None}


def check_python(minimum="3.7.0"):
    """Checks if current Python version meets the specified minimum requirement, raising error if not."""
    check_version(platform.python_version(), minimum, name="Python ", hard=True)


def check_version(current="0.0.0", minimum="0.0.0", name="version ", pinned=False, hard=False, verbose=False):
    """Compares current and minimum version requirements, optionally enforcing minimum version and logging warnings."""
    current, minimum = (pkg.parse_version(x) for x in (current, minimum))
    result = (current == minimum) if pinned else (current >= minimum)  # bool
    s = f"WARNING ⚠️ {name}{minimum} is required by YOLOv3, but {name}{current} is currently installed"  # string
    if hard:
        assert result, emojis(s)  # assert min requirements met
    if verbose and not result:
        LOGGER.warning(s)
    return result


def check_img_size(imgsz, s=32, floor=0):
    """Adjusts image size to be divisible by `s`, ensuring it's above `floor`; returns int for single dim or list for
    dims.
    """
    if isinstance(imgsz, int):  # integer i.e. img_size=640
        new_size = max(make_divisible(imgsz, int(s)), floor)
    else:  # list i.e. img_size=[640, 480]
        imgsz = list(imgsz)  # convert to list if tuple
        new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz]
    if new_size != imgsz:
        LOGGER.warning(f"WARNING ⚠️ --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}")
    return new_size


def check_imshow(warn=False):
    """Checks if the environment supports image display; warns if `warn=True` and display is unsupported."""
    try:
        assert not is_jupyter()
        assert not is_docker()
        cv2.imshow("test", np.zeros((1, 1, 3)))
        cv2.waitKey(1)
        cv2.destroyAllWindows()
        cv2.waitKey(1)
        return True
    except Exception as e:
        if warn:
            LOGGER.warning(f"WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show()\n{e}")
        return False


def check_suffix(file="yolov5s.pt", suffix=(".pt",), msg=""):
    """Checks for acceptable file suffixes, supports batch checking for lists or tuples of filenames."""
    if file and suffix:
        if isinstance(suffix, str):
            suffix = [suffix]
        for f in file if isinstance(file, (list, tuple)) else [file]:
            s = Path(f).suffix.lower()  # file suffix
            if len(s):
                assert s in suffix, f"{msg}{f} acceptable suffix is {suffix}"


def check_yaml(file, suffix=(".yaml", ".yml")):
    """Searches/downloads a YAML file and returns its path, ensuring it has a .yaml or .yml suffix."""
    return check_file(file, suffix)


def check_file(file, suffix=""):
    """Checks for file's existence locally, downloads if a URL, supports ClearML dataset IDs, and enforces optional
    suffix.
    """
    check_suffix(file, suffix)  # optional
    file = str(file)  # convert to str()
    if os.path.isfile(file) or not file:  # exists
        return file
    elif file.startswith(("http:/", "https:/")):  # download
        url = file  # warning: Pathlib turns :// -> :/
        file = Path(urllib.parse.unquote(file).split("?")[0]).name  # '%2F' to '/', split https://url.com/file.txt?auth
        if os.path.isfile(file):
            LOGGER.info(f"Found {url} locally at {file}")  # file already exists
        else:
            LOGGER.info(f"Downloading {url} to {file}...")
            torch.hub.download_url_to_file(url, file)
            assert Path(file).exists() and Path(file).stat().st_size > 0, f"File download failed: {url}"  # check
        return file
    elif file.startswith("clearml://"):  # ClearML Dataset ID
        assert "clearml" in sys.modules, (
            "ClearML is not installed, so cannot use ClearML dataset. Try running 'pip install clearml'."
        )
        return file
    else:  # search
        files = []
        for d in "data", "models", "utils":  # search directories
            files.extend(glob.glob(str(ROOT / d / "**" / file), recursive=True))  # find file
        assert len(files), f"File not found: {file}"  # assert file was found
        assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}"  # assert unique
        return files[0]  # return file


def check_font(font=FONT, progress=False):
    """Checks and downloads the specified font to CONFIG_DIR if not present, with optional download progress."""
    font = Path(font)
    file = CONFIG_DIR / font.name
    if not font.exists() and not file.exists():
        url = f"https://github.com/ultralytics/assets/releases/download/v0.0.0/{font.name}"
        LOGGER.info(f"Downloading {url} to {file}...")
        torch.hub.download_url_to_file(url, str(file), progress=progress)


def check_dataset(data, autodownload=True):
    """Verifies and prepares dataset by downloading if absent, checking, and unzipping; supports auto-downloading."""
    # Download (optional)
    extract_dir = ""
    if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)):
        download(data, dir=f"{DATASETS_DIR}/{Path(data).stem}", unzip=True, delete=False, curl=False, threads=1)
        data = next((DATASETS_DIR / Path(data).stem).rglob("*.yaml"))
        extract_dir, autodownload = data.parent, False

    # Read yaml (optional)
    if isinstance(data, (str, Path)):
        data = yaml_load(data)  # dictionary

    # Checks
    for k in "train", "val", "names":
        assert k in data, emojis(f"data.yaml '{k}:' field missing ❌")
    if isinstance(data["names"], (list, tuple)):  # old array format
        data["names"] = dict(enumerate(data["names"]))  # convert to dict
    assert all(isinstance(k, int) for k in data["names"].keys()), "data.yaml names keys must be integers, i.e. 2: car"
    data["nc"] = len(data["names"])

    # Resolve paths
    path = Path(extract_dir or data.get("path") or "")  # optional 'path' default to '.'
    if not path.is_absolute():
        path = (ROOT / path).resolve()
        data["path"] = path  # download scripts
    for k in "train", "val", "test":
        if data.get(k):  # prepend path
            if isinstance(data[k], str):
                x = (path / data[k]).resolve()
                if not x.exists() and data[k].startswith("../"):
                    x = (path / data[k][3:]).resolve()
                data[k] = str(x)
            else:
                data[k] = [str((path / x).resolve()) for x in data[k]]

    # Parse yaml
    train, val, test, s = (data.get(x) for x in ("train", "val", "test", "download"))
    if val:
        val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])]  # val path
        if not all(x.exists() for x in val):
            LOGGER.info("\nDataset not found ⚠️, missing paths %s" % [str(x) for x in val if not x.exists()])
            if not s or not autodownload:
                raise Exception("Dataset not found ❌")
            t = time.time()
            if s.startswith("http") and s.endswith(".zip"):  # URL
                f = Path(s).name  # filename
                LOGGER.info(f"Downloading {s} to {f}...")
                torch.hub.download_url_to_file(s, f)
                Path(DATASETS_DIR).mkdir(parents=True, exist_ok=True)  # create root
                unzip_file(f, path=DATASETS_DIR)  # unzip
                Path(f).unlink()  # remove zip
                r = None  # success
            elif s.startswith("bash "):  # bash script
                LOGGER.info(f"Running {s} ...")
                r = subprocess.run(s, shell=True)
            else:  # python script
                r = exec(s, {"yaml": data})  # return None
            dt = f"({round(time.time() - t, 1)}s)"
            s = f"success ✅ {dt}, saved to {colorstr('bold', DATASETS_DIR)}" if r in (0, None) else f"failure {dt} ❌"
            LOGGER.info(f"Dataset download {s}")
    check_font("Arial.ttf" if is_ascii(data["names"]) else "Arial.Unicode.ttf", progress=True)  # download fonts
    return data  # dictionary


def check_amp(model):
    """Checks PyTorch AMP functionality with model and sample image, returning True if AMP operates correctly."""
    from models.common import AutoShape, DetectMultiBackend

    def amp_allclose(model, im):
        """Compares FP32 and AMP inference results for a model and image, ensuring outputs are within 10% tolerance."""
        m = AutoShape(model, verbose=False)  # model
        a = m(im).xywhn[0]  # FP32 inference
        m.amp = True
        b = m(im).xywhn[0]  # AMP inference
        return a.shape == b.shape and torch.allclose(a, b, atol=0.1)  # close to 10% absolute tolerance

    prefix = colorstr("AMP: ")
    device = next(model.parameters()).device  # get model device
    if device.type in ("cpu", "mps"):
        return False  # AMP only used on CUDA devices
    f = ROOT / "data" / "images" / "bus.jpg"  # image to check
    im = f if f.exists() else "https://ultralytics.com/images/bus.jpg" if check_online() else np.ones((640, 640, 3))
    try:
        assert amp_allclose(deepcopy(model), im) or amp_allclose(DetectMultiBackend("yolov5n.pt", device), im)
        LOGGER.info(f"{prefix}checks passed ✅")
        return True
    except Exception:
        help_url = "https://github.com/ultralytics/yolov5/issues/7908"
        LOGGER.warning(f"{prefix}checks failed ❌, disabling Automatic Mixed Precision. See {help_url}")
        return False


def yaml_load(file="data.yaml"):
    """Safely loads a YAML file, ignoring file errors; default file is 'data.yaml'."""
    with open(file, errors="ignore") as f:
        return yaml.safe_load(f)


def yaml_save(file="data.yaml", data=None):
    """Safely saves data to a YAML file, converting `Path` objects to strings; defaults to 'data.yaml'."""
    if data is None:
        data = {}
    with open(file, "w") as f:
        yaml.safe_dump({k: str(v) if isinstance(v, Path) else v for k, v in data.items()}, f, sort_keys=False)


def unzip_file(file, path=None, exclude=(".DS_Store", "__MACOSX")):
    """Unzips '*.zip' to `path` (default: file's parent), excluding files matching `exclude` (`('.DS_Store',
    '__MACOSX')`).
    """
    if path is None:
        path = Path(file).parent  # default path
    with ZipFile(file) as zipObj:
        for f in zipObj.namelist():  # list all archived filenames in the zip
            if all(x not in f for x in exclude):
                zipObj.extract(f, path=path)


def url2file(url):
    """Converts a URL to a filename by extracting the last path segment and removing query parameters."""
    url = str(Path(url)).replace(":/", "://")  # Pathlib turns :// -> :/
    return Path(urllib.parse.unquote(url)).name.split("?")[0]  # '%2F' to '/', split https://url.com/file.txt?auth


def download(url, dir=".", unzip=True, delete=True, curl=False, threads=1, retry=3):
    """Downloads files from URLs into a specified directory, optionally unzips, and supports multithreading and
    retries.
    """

    def download_one(url, dir):
        """Downloads a file from a URL into the specified directory, supporting retries and using curl or torch
        methods.
        """
        success = True
        if os.path.isfile(url):
            f = Path(url)  # filename
        else:  # does not exist
            f = dir / Path(url).name
            LOGGER.info(f"Downloading {url} to {f}...")
            for i in range(retry + 1):
                if curl:
                    success = curl_download(url, f, silent=(threads > 1))
                else:
                    torch.hub.download_url_to_file(url, f, progress=threads == 1)  # torch download
                    success = f.is_file()
                if success:
                    break
                elif i < retry:
                    LOGGER.warning(f"⚠️ Download failure, retrying {i + 1}/{retry} {url}...")
                else:
                    LOGGER.warning(f"❌ Failed to download {url}...")

        if unzip and success and (f.suffix == ".gz" or is_zipfile(f) or is_tarfile(f)):
            LOGGER.info(f"Unzipping {f}...")
            if is_zipfile(f):
                unzip_file(f, dir)  # unzip
            elif is_tarfile(f):
                subprocess.run(["tar", "xf", f, "--directory", f.parent], check=True)  # unzip
            elif f.suffix == ".gz":
                subprocess.run(["tar", "xfz", f, "--directory", f.parent], check=True)  # unzip
            if delete:
                f.unlink()  # remove zip

    dir = Path(dir)
    dir.mkdir(parents=True, exist_ok=True)  # make directory
    if threads > 1:
        pool = ThreadPool(threads)
        pool.imap(lambda x: download_one(*x), zip(url, repeat(dir)))  # multithreaded
        pool.close()
        pool.join()
    else:
        for u in [url] if isinstance(url, (str, Path)) else url:
            download_one(u, dir)


def make_divisible(x, divisor):
    """Adjusts `x` to be nearest and greater than or equal to value divisible by `divisor`."""
    if isinstance(divisor, torch.Tensor):
        divisor = int(divisor.max())  # to int
    return math.ceil(x / divisor) * divisor


def clean_str(s):
    """Cleans a string by replacing special characters with underscores, e.g., 'test@string!' to 'test_string_'."""
    return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s)


def one_cycle(y1=0.0, y2=1.0, steps=100):
    """Generates a lambda for a sinusoidal ramp from y1 to y2 over 'steps'; usage: `lambda x: ((1 - math.cos(x *
    math.pi / steps)) / 2) * (y2 - y1) + y1`.
    """
    return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1


def colorstr(*input):
    """
    Colors strings using ANSI escape codes; see usage example `colorstr('blue', 'hello world')`.

    [https://en.wikipedia.org/wiki/ANSI_escape_code]
    """
    *args, string = input if len(input) > 1 else ("blue", "bold", input[0])  # color arguments, string
    colors = {
        "black": "\033[30m",  # basic colors
        "red": "\033[31m",
        "green": "\033[32m",
        "yellow": "\033[33m",
        "blue": "\033[34m",
        "magenta": "\033[35m",
        "cyan": "\033[36m",
        "white": "\033[37m",
        "bright_black": "\033[90m",  # bright colors
        "bright_red": "\033[91m",
        "bright_green": "\033[92m",
        "bright_yellow": "\033[93m",
        "bright_blue": "\033[94m",
        "bright_magenta": "\033[95m",
        "bright_cyan": "\033[96m",
        "bright_white": "\033[97m",
        "end": "\033[0m",  # misc
        "bold": "\033[1m",
        "underline": "\033[4m",
    }
    return "".join(colors[x] for x in args) + f"{string}" + colors["end"]


def labels_to_class_weights(labels, nc=80):
    """Calculates class weights from labels to counteract dataset imbalance; `labels` is a list of numpy arrays with
    shape `(n, 5)`.
    """
    if labels[0] is None:  # no labels loaded
        return torch.Tensor()

    labels = np.concatenate(labels, 0)  # labels.shape = (866643, 5) for COCO
    classes = labels[:, 0].astype(int)  # labels = [class xywh]
    weights = np.bincount(classes, minlength=nc)  # occurrences per class

    # Prepend gridpoint count (for uCE training)
    # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum()  # gridpoints per image
    # weights = np.hstack([gpi * len(labels)  - weights.sum() * 9, weights * 9]) ** 0.5  # prepend gridpoints to start

    weights[weights == 0] = 1  # replace empty bins with 1
    weights = 1 / weights  # number of targets per class
    weights /= weights.sum()  # normalize
    return torch.from_numpy(weights).float()


def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
    """Calculates image weights from labels using class weights, for balanced sampling."""
    # Usage: index = random.choices(range(n), weights=image_weights, k=1)  # weighted image sample
    class_counts = np.array([np.bincount(x[:, 0].astype(int), minlength=nc) for x in labels])
    return (class_weights.reshape(1, nc) * class_counts).sum(1)


def coco80_to_coco91_class():  # converts 80-index (val2014) to 91-index (paper)
    """
    Converts COCO 80-class index to COCO 91-class index.

    Reference: https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
    """
    # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
    # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
    # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)]  # darknet to coco
    # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)]  # coco to darknet
    return [
        1,
        2,
        3,
        4,
        5,
        6,
        7,
        8,
        9,
        10,
        11,
        13,
        14,
        15,
        16,
        17,
        18,
        19,
        20,
        21,
        22,
        23,
        24,
        25,
        27,
        28,
        31,
        32,
        33,
        34,
        35,
        36,
        37,
        38,
        39,
        40,
        41,
        42,
        43,
        44,
        46,
        47,
        48,
        49,
        50,
        51,
        52,
        53,
        54,
        55,
        56,
        57,
        58,
        59,
        60,
        61,
        62,
        63,
        64,
        65,
        67,
        70,
        72,
        73,
        74,
        75,
        76,
        77,
        78,
        79,
        80,
        81,
        82,
        84,
        85,
        86,
        87,
        88,
        89,
        90,
    ]


def xyxy2xywh(x):
    """Converts nx4 bounding boxes from corners [x1, y1, x2, y2] to center format [x, y, w, h]."""
    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
    y[..., 0] = (x[..., 0] + x[..., 2]) / 2  # x center
    y[..., 1] = (x[..., 1] + x[..., 3]) / 2  # y center
    y[..., 2] = x[..., 2] - x[..., 0]  # width
    y[..., 3] = x[..., 3] - x[..., 1]  # height
    return y


def xywh2xyxy(x):
    """Converts bbox format from [x, y, w, h] to [x1, y1, x2, y2], supporting torch.Tensor and np.ndarray."""
    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
    y[..., 0] = x[..., 0] - x[..., 2] / 2  # top left x
    y[..., 1] = x[..., 1] - x[..., 3] / 2  # top left y
    y[..., 2] = x[..., 0] + x[..., 2] / 2  # bottom right x
    y[..., 3] = x[..., 1] + x[..., 3] / 2  # bottom right y
    return y


def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
    """Converts boxes from normalized [x, y, w, h] to [x1, y1, x2, y2] format, applies padding."""
    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
    y[..., 0] = w * (x[..., 0] - x[..., 2] / 2) + padw  # top left x
    y[..., 1] = h * (x[..., 1] - x[..., 3] / 2) + padh  # top left y
    y[..., 2] = w * (x[..., 0] + x[..., 2] / 2) + padw  # bottom right x
    y[..., 3] = h * (x[..., 1] + x[..., 3] / 2) + padh  # bottom right y
    return y


def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
    """Converts bounding boxes from [x1, y1, x2, y2] format to normalized [x, y, w, h] format."""
    if clip:
        clip_boxes(x, (h - eps, w - eps))  # warning: inplace clip
    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
    y[..., 0] = ((x[..., 0] + x[..., 2]) / 2) / w  # x center
    y[..., 1] = ((x[..., 1] + x[..., 3]) / 2) / h  # y center
    y[..., 2] = (x[..., 2] - x[..., 0]) / w  # width
    y[..., 3] = (x[..., 3] - x[..., 1]) / h  # height
    return y


def xyn2xy(x, w=640, h=640, padw=0, padh=0):
    """Converts normalized segments to pixel segments, shape (n,2), adjusting for width `w`, height `h`, and padding."""
    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
    y[..., 0] = w * x[..., 0] + padw  # top left x
    y[..., 1] = h * x[..., 1] + padh  # top left y
    return y


def segment2box(segment, width=640, height=640):
    """Converts a single segment to a bounding box using image dimensions, output shape (4,), ensuring coordinates stay
    within image boundaries.
    """
    x, y = segment.T  # segment xy
    inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
    (
        x,
        y,
    ) = x[inside], y[inside]
    return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4))  # xyxy


def segments2boxes(segments):
    """Converts segmentation labels to bounding box labels in format (cls, xywh) from (cls, xy1, xy2, ...)."""
    boxes = []
    for s in segments:
        x, y = s.T  # segment xy
        boxes.append([x.min(), y.min(), x.max(), y.max()])  # cls, xyxy
    return xyxy2xywh(np.array(boxes))  # cls, xywh


def resample_segments(segments, n=1000):
    """Resamples segments to a fixed number of points (n), returning up-sampled (n,2) segment arrays."""
    for i, s in enumerate(segments):
        s = np.concatenate((s, s[0:1, :]), axis=0)
        x = np.linspace(0, len(s) - 1, n)
        xp = np.arange(len(s))
        segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T  # segment xy
    return segments


def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):
    """Rescales bounding boxes from one image shape to another, optionally with ratio and padding adjustments."""
    if ratio_pad is None:  # calculate from img0_shape
        gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1])  # gain  = old / new
        pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh padding
    else:
        gain = ratio_pad[0][0]
        pad = ratio_pad[1]

    boxes[..., [0, 2]] -= pad[0]  # x padding
    boxes[..., [1, 3]] -= pad[1]  # y padding
    boxes[..., :4] /= gain
    clip_boxes(boxes, img0_shape)
    return boxes


def scale_segments(img1_shape, segments, img0_shape, ratio_pad=None, normalize=False):
    """Rescales segment coordinates from img1_shape to img0_shape, optionally normalizing, with support for padding
    adjustments.
    """
    if ratio_pad is None:  # calculate from img0_shape
        gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1])  # gain  = old / new
        pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh padding
    else:
        gain = ratio_pad[0][0]
        pad = ratio_pad[1]

    segments[:, 0] -= pad[0]  # x padding
    segments[:, 1] -= pad[1]  # y padding
    segments /= gain
    clip_segments(segments, img0_shape)
    if normalize:
        segments[:, 0] /= img0_shape[1]  # width
        segments[:, 1] /= img0_shape[0]  # height
    return segments


def clip_boxes(boxes, shape):
    """Clips bounding boxes to within the specified image shape; supports both torch.Tensor and np.array."""
    if isinstance(boxes, torch.Tensor):  # faster individually
        boxes[..., 0].clamp_(0, shape[1])  # x1
        boxes[..., 1].clamp_(0, shape[0])  # y1
        boxes[..., 2].clamp_(0, shape[1])  # x2
        boxes[..., 3].clamp_(0, shape[0])  # y2
    else:  # np.array (faster grouped)
        boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1])  # x1, x2
        boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0])  # y1, y2


def clip_segments(segments, shape):
    """Clips segments to within image shape (height, width), supporting torch.Tensor and np.array inputs."""
    if isinstance(segments, torch.Tensor):  # faster individually
        segments[:, 0].clamp_(0, shape[1])  # x
        segments[:, 1].clamp_(0, shape[0])  # y
    else:  # np.array (faster grouped)
        segments[:, 0] = segments[:, 0].clip(0, shape[1])  # x
        segments[:, 1] = segments[:, 1].clip(0, shape[0])  # y


def non_max_suppression(
    prediction,
    conf_thres=0.25,
    iou_thres=0.45,
    classes=None,
    agnostic=False,
    multi_label=False,
    labels=(),
    max_det=300,
    nm=0,  # number of masks
):
    """
    Non-Maximum Suppression (NMS) on inference results to reject overlapping detections.

    Returns:
         list of detections, on (n,6) tensor per image [xyxy, conf, cls]
    """
    # Checks
    assert 0 <= conf_thres <= 1, f"Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0"
    assert 0 <= iou_thres <= 1, f"Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0"
    if isinstance(prediction, (list, tuple)):  # YOLOv3 model in validation model, output = (inference_out, loss_out)
        prediction = prediction[0]  # select only inference output

    device = prediction.device
    mps = "mps" in device.type  # Apple MPS
    if mps:  # MPS not fully supported yet, convert tensors to CPU before NMS
        prediction = prediction.cpu()
    bs = prediction.shape[0]  # batch size
    nc = prediction.shape[2] - nm - 5  # number of classes
    xc = prediction[..., 4] > conf_thres  # candidates

    # Settings
    # min_wh = 2  # (pixels) minimum box width and height
    max_wh = 7680  # (pixels) maximum box width and height
    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
    time_limit = 0.5 + 0.05 * bs  # seconds to quit after
    redundant = True  # require redundant detections
    multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)
    merge = False  # use merge-NMS

    t = time.time()
    mi = 5 + nc  # mask start index
    output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs
    for xi, x in enumerate(prediction):  # image index, image inference
        # Apply constraints
        # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height
        x = x[xc[xi]]  # confidence

        # Cat apriori labels if autolabelling
        if labels and len(labels[xi]):
            lb = labels[xi]
            v = torch.zeros((len(lb), nc + nm + 5), device=x.device)
            v[:, :4] = lb[:, 1:5]  # box
            v[:, 4] = 1.0  # conf
            v[range(len(lb)), lb[:, 0].long() + 5] = 1.0  # cls
            x = torch.cat((x, v), 0)

        # If none remain process next image
        if not x.shape[0]:
            continue

        # Compute conf
        x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf

        # Box/Mask
        box = xywh2xyxy(x[:, :4])  # center_x, center_y, width, height) to (x1, y1, x2, y2)
        mask = x[:, mi:]  # zero columns if no masks

        # Detections matrix nx6 (xyxy, conf, cls)
        if multi_label:
            i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T
            x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1)
        else:  # best class only
            conf, j = x[:, 5:mi].max(1, keepdim=True)
            x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres]

        # Filter by class
        if classes is not None:
            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]

        # Apply finite constraint
        # if not torch.isfinite(x).all():
        #     x = x[torch.isfinite(x).all(1)]

        # Check shape
        n = x.shape[0]  # number of boxes
        if not n:  # no boxes
            continue
        x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence and remove excess boxes

        # Batched NMS
        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes
        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
        i = i[:max_det]  # limit detections
        if merge and (1 < n < 3e3):  # Merge NMS (boxes merged using weighted mean)
            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
            weights = iou * scores[None]  # box weights
            x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes
            if redundant:
                i = i[iou.sum(1) > 1]  # require redundancy

        output[xi] = x[i]
        if mps:
            output[xi] = output[xi].to(device)
        if (time.time() - t) > time_limit:
            LOGGER.warning(f"WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded")
            break  # time limit exceeded

    return output


def strip_optimizer(f="best.pt", s=""):  # from utils.general import *; strip_optimizer()
    """Strips optimizer from a checkpoint file 'f', optionally saving as 's', to finalize training."""
    x = torch.load(f, map_location=torch.device("cpu"))
    if x.get("ema"):
        x["model"] = x["ema"]  # replace model with ema
    for k in "optimizer", "best_fitness", "ema", "updates":  # keys
        x[k] = None
    x["epoch"] = -1
    x["model"].half()  # to FP16
    for p in x["model"].parameters():
        p.requires_grad = False
    torch.save(x, s or f)
    mb = os.path.getsize(s or f) / 1e6  # filesize
    LOGGER.info(f"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB")


def print_mutation(keys, results, hyp, save_dir, bucket, prefix=colorstr("evolve: ")):
    """Logs mutation results, updates evolve CSV/YAML, optionally syncs with cloud storage."""
    evolve_csv = save_dir / "evolve.csv"
    evolve_yaml = save_dir / "hyp_evolve.yaml"
    keys = tuple(keys) + tuple(hyp.keys())  # [results + hyps]
    keys = tuple(x.strip() for x in keys)
    vals = results + tuple(hyp.values())
    n = len(keys)

    # Download (optional)
    if bucket:
        url = f"gs://{bucket}/evolve.csv"
        if gsutil_getsize(url) > (evolve_csv.stat().st_size if evolve_csv.exists() else 0):
            subprocess.run(["gsutil", "cp", f"{url}", f"{save_dir}"])  # download evolve.csv if larger than local

    # Log to evolve.csv
    s = "" if evolve_csv.exists() else (("%20s," * n % keys).rstrip(",") + "\n")  # add header
    with open(evolve_csv, "a") as f:
        f.write(s + ("%20.5g," * n % vals).rstrip(",") + "\n")

    # Save yaml
    with open(evolve_yaml, "w") as f:
        data = pd.read_csv(evolve_csv, skipinitialspace=True)
        data = data.rename(columns=lambda x: x.strip())  # strip keys
        i = np.argmax(fitness(data.values[:, :4]))  #
        generations = len(data)
        f.write(
            "# YOLOv3 Hyperparameter Evolution Results\n"
            + f"# Best generation: {i}\n"
            + f"# Last generation: {generations - 1}\n"
            + "# "
            + ", ".join(f"{x.strip():>20s}" for x in keys[:7])
            + "\n"
            + "# "
            + ", ".join(f"{x:>20.5g}" for x in data.values[i, :7])
            + "\n\n"
        )
        yaml.safe_dump(data.loc[i][7:].to_dict(), f, sort_keys=False)

    # Print to screen
    LOGGER.info(
        prefix
        + f"{generations} generations finished, current result:\n"
        + prefix
        + ", ".join(f"{x.strip():>20s}" for x in keys)
        + "\n"
        + prefix
        + ", ".join(f"{x:20.5g}" for x in vals)
        + "\n\n"
    )

    if bucket:
        subprocess.run(["gsutil", "cp", f"{evolve_csv}", f"{evolve_yaml}", f"gs://{bucket}"])  # upload


def apply_classifier(x, model, img, im0):
    """Applies a second stage classifier to YOLO outputs, adjusting box shapes and filtering class matches."""
    # Example model = torchvision.models.__dict__['efficientnet_b0'](pretrained=True).to(device).eval()
    im0 = [im0] if isinstance(im0, np.ndarray) else im0
    for i, d in enumerate(x):  # per image
        if d is not None and len(d):
            d = d.clone()

            # Reshape and pad cutouts
            b = xyxy2xywh(d[:, :4])  # boxes
            b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1)  # rectangle to square
            b[:, 2:] = b[:, 2:] * 1.3 + 30  # pad
            d[:, :4] = xywh2xyxy(b).long()

            # Rescale boxes from img_size to im0 size
            scale_boxes(img.shape[2:], d[:, :4], im0[i].shape)

            # Classes
            pred_cls1 = d[:, 5].long()
            ims = []
            for a in d:
                cutout = im0[i][int(a[1]) : int(a[3]), int(a[0]) : int(a[2])]
                im = cv2.resize(cutout, (224, 224))  # BGR

                im = im[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
                im = np.ascontiguousarray(im, dtype=np.float32)  # uint8 to float32
                im /= 255  # 0 - 255 to 0.0 - 1.0
                ims.append(im)

            pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1)  # classifier prediction
            x[i] = x[i][pred_cls1 == pred_cls2]  # retain matching class detections

    return x


def increment_path(path, exist_ok=False, sep="", mkdir=False):
    """
    Increments file or directory path, optionally creating the directory, not thread-safe.

    Args: path (str/Path), exist_ok (bool), sep (str), mkdir (bool).
    """
    path = Path(path)  # os-agnostic
    if path.exists() and not exist_ok:
        path, suffix = (path.with_suffix(""), path.suffix) if path.is_file() else (path, "")

        # Method 1
        for n in range(2, 9999):
            p = f"{path}{sep}{n}{suffix}"  # increment path
            if not os.path.exists(p):  #
                break
        path = Path(p)

        # Method 2 (deprecated)
        # dirs = glob.glob(f"{path}{sep}*")  # similar paths
        # matches = [re.search(rf"{path.stem}{sep}(\d+)", d) for d in dirs]
        # i = [int(m.groups()[0]) for m in matches if m]  # indices
        # n = max(i) + 1 if i else 2  # increment number
        # path = Path(f"{path}{sep}{n}{suffix}")  # increment path

    if mkdir:
        path.mkdir(parents=True, exist_ok=True)  # make directory

    return path


# OpenCV Multilanguage-friendly functions
# ------------------------------------------------------------------------------------
imshow_ = cv2.imshow  # copy to avoid recursion errors


def imread(filename, flags=cv2.IMREAD_COLOR):
    """Reads an image from a file, supporting multilanguage paths, and returns it in the specified color scheme."""
    return cv2.imdecode(np.fromfile(filename, np.uint8), flags)


def imwrite(filename, img):
    """
    Writes an image to a file; returns True on success, False on failure.

    Args: filename (str), img (ndarray).
    """
    try:
        cv2.imencode(Path(filename).suffix, img)[1].tofile(filename)
        return True
    except Exception:
        return False


def imshow(path, im):
    """Displays an image; accepts a path (str) and image data (ndarray) as arguments."""
    imshow_(path.encode("unicode_escape").decode(), im)


if Path(inspect.stack()[0].filename).parent.parent.as_posix() in inspect.stack()[-1].filename:
    cv2.imread, cv2.imwrite, cv2.imshow = imread, imwrite, imshow  # redefine

# Variables ------------------------------------------------------------------------------------------------------------