# Ultralytics YOLO 🚀, AGPL-3.0 license

import contextlib
import hashlib
import json
import os
import random
import subprocess
import time
import zipfile
from multiprocessing.pool import ThreadPool
from pathlib import Path
from tarfile import is_tarfile

import cv2
import numpy as np
from PIL import ExifTags, Image, ImageOps
from tqdm import tqdm

from ultralytics.nn.autobackend import check_class_names
from ultralytics.utils import (DATASETS_DIR, LOGGER, NUM_THREADS, ROOT, SETTINGS_YAML, clean_url, colorstr, emojis,
                               yaml_load)
from ultralytics.utils.checks import check_file, check_font, is_ascii
from ultralytics.utils.downloads import download, safe_download, unzip_file
from ultralytics.utils.ops import segments2boxes

HELP_URL = 'See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data'
IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm'  # image suffixes
VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv', 'webm'  # video suffixes
PIN_MEMORY = str(os.getenv('PIN_MEMORY', True)).lower() == 'true'  # global pin_memory for dataloaders
IMAGENET_MEAN = 0.485, 0.456, 0.406  # RGB mean
IMAGENET_STD = 0.229, 0.224, 0.225  # RGB standard deviation

# Get orientation exif tag
for orientation in ExifTags.TAGS.keys():
    if ExifTags.TAGS[orientation] == 'Orientation':
        break


def img2label_paths(img_paths):
    """Define label paths as a function of image paths."""
    sa, sb = f'{os.sep}images{os.sep}', f'{os.sep}labels{os.sep}'  # /images/, /labels/ substrings
    return [sb.join(x.rsplit(sa, 1)).rsplit('.', 1)[0] + '.txt' for x in img_paths]


def get_hash(paths):
    """Returns a single hash value of a list of paths (files or dirs)."""
    size = sum(os.path.getsize(p) for p in paths if os.path.exists(p))  # sizes
    h = hashlib.sha256(str(size).encode())  # hash sizes
    h.update(''.join(paths).encode())  # hash paths
    return h.hexdigest()  # return hash


def exif_size(img):
    """Returns exif-corrected PIL size."""
    s = img.size  # (width, height)
    with contextlib.suppress(Exception):
        rotation = dict(img._getexif().items())[orientation]
        if rotation in [6, 8]:  # rotation 270 or 90
            s = (s[1], s[0])
    return s


def verify_image_label(args):
    """Verify one image-label pair."""
    im_file, lb_file, prefix, keypoint, num_cls, nkpt, ndim = args
    # Number (missing, found, empty, corrupt), message, segments, keypoints
    nm, nf, ne, nc, msg, segments, keypoints = 0, 0, 0, 0, '', [], None
    try:
        # Verify images
        im = Image.open(im_file)
        im.verify()  # PIL verify
        shape = exif_size(im)  # image size
        shape = (shape[1], shape[0])  # hw
        assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels'
        assert im.format.lower() in IMG_FORMATS, f'invalid image format {im.format}'
        if im.format.lower() in ('jpg', 'jpeg'):
            with open(im_file, 'rb') as f:
                f.seek(-2, 2)
                if f.read() != b'\xff\xd9':  # corrupt JPEG
                    ImageOps.exif_transpose(Image.open(im_file)).save(im_file, 'JPEG', subsampling=0, quality=100)
                    msg = f'{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved'

        # Verify labels
        if os.path.isfile(lb_file):
            nf = 1  # label found
            with open(lb_file) as f:
                lb = [x.split() for x in f.read().strip().splitlines() if len(x)]
                if any(len(x) > 6 for x in lb) and (not keypoint):  # is segment
                    classes = np.array([x[0] for x in lb], dtype=np.float32)
                    segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb]  # (cls, xy1...)
                    lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1)  # (cls, xywh)
                lb = np.array(lb, dtype=np.float32)
            nl = len(lb)
            if nl:
                if keypoint:
                    assert lb.shape[1] == (5 + nkpt * ndim), f'labels require {(5 + nkpt * ndim)} columns each'
                    assert (lb[:, 5::ndim] <= 1).all(), 'non-normalized or out of bounds coordinate labels'
                    assert (lb[:, 6::ndim] <= 1).all(), 'non-normalized or out of bounds coordinate labels'
                else:
                    assert lb.shape[1] == 5, f'labels require 5 columns, {lb.shape[1]} columns detected'
                    assert (lb[:, 1:] <= 1).all(), \
                        f'non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}'
                    assert (lb >= 0).all(), f'negative label values {lb[lb < 0]}'
                # All labels
                max_cls = int(lb[:, 0].max())  # max label count
                assert max_cls <= num_cls, \
                    f'Label class {max_cls} exceeds dataset class count {num_cls}. ' \
                    f'Possible class labels are 0-{num_cls - 1}'
                _, i = np.unique(lb, axis=0, return_index=True)
                if len(i) < nl:  # duplicate row check
                    lb = lb[i]  # remove duplicates
                    if segments:
                        segments = [segments[x] for x in i]
                    msg = f'{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed'
            else:
                ne = 1  # label empty
                lb = np.zeros((0, (5 + nkpt * ndim)), dtype=np.float32) if keypoint else np.zeros(
                    (0, 5), dtype=np.float32)
        else:
            nm = 1  # label missing
            lb = np.zeros((0, (5 + nkpt * ndim)), dtype=np.float32) if keypoint else np.zeros((0, 5), dtype=np.float32)
        if keypoint:
            keypoints = lb[:, 5:].reshape(-1, nkpt, ndim)
            if ndim == 2:
                kpt_mask = np.ones(keypoints.shape[:2], dtype=np.float32)
                kpt_mask = np.where(keypoints[..., 0] < 0, 0.0, kpt_mask)
                kpt_mask = np.where(keypoints[..., 1] < 0, 0.0, kpt_mask)
                keypoints = np.concatenate([keypoints, kpt_mask[..., None]], axis=-1)  # (nl, nkpt, 3)
        lb = lb[:, :5]
        return im_file, lb, shape, segments, keypoints, nm, nf, ne, nc, msg
    except Exception as e:
        nc = 1
        msg = f'{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}'
        return [None, None, None, None, None, nm, nf, ne, nc, msg]


def polygon2mask(imgsz, polygons, color=1, downsample_ratio=1):
    """
    Args:
        imgsz (tuple): The image size.
        polygons (list[np.ndarray]): [N, M], N is the number of polygons, M is the number of points(Be divided by 2).
        color (int): color
        downsample_ratio (int): downsample ratio
    """
    mask = np.zeros(imgsz, dtype=np.uint8)
    polygons = np.asarray(polygons)
    polygons = polygons.astype(np.int32)
    shape = polygons.shape
    polygons = polygons.reshape(shape[0], -1, 2)
    cv2.fillPoly(mask, polygons, color=color)
    nh, nw = (imgsz[0] // downsample_ratio, imgsz[1] // downsample_ratio)
    # NOTE: fillPoly firstly then resize is trying the keep the same way
    # of loss calculation when mask-ratio=1.
    mask = cv2.resize(mask, (nw, nh))
    return mask


def polygons2masks(imgsz, polygons, color, downsample_ratio=1):
    """
    Args:
        imgsz (tuple): The image size.
        polygons (list[np.ndarray]): each polygon is [N, M], N is number of polygons, M is number of points (M % 2 = 0)
        color (int): color
        downsample_ratio (int): downsample ratio
    """
    masks = []
    for si in range(len(polygons)):
        mask = polygon2mask(imgsz, [polygons[si].reshape(-1)], color, downsample_ratio)
        masks.append(mask)
    return np.array(masks)


def polygons2masks_overlap(imgsz, segments, downsample_ratio=1):
    """Return a (640, 640) overlap mask."""
    masks = np.zeros((imgsz[0] // downsample_ratio, imgsz[1] // downsample_ratio),
                     dtype=np.int32 if len(segments) > 255 else np.uint8)
    areas = []
    ms = []
    for si in range(len(segments)):
        mask = polygon2mask(imgsz, [segments[si].reshape(-1)], downsample_ratio=downsample_ratio, color=1)
        ms.append(mask)
        areas.append(mask.sum())
    areas = np.asarray(areas)
    index = np.argsort(-areas)
    ms = np.array(ms)[index]
    for i in range(len(segments)):
        mask = ms[i] * (i + 1)
        masks = masks + mask
        masks = np.clip(masks, a_min=0, a_max=i + 1)
    return masks, index


def check_det_dataset(dataset, autodownload=True):
    """Download, check and/or unzip dataset if not found locally."""
    data = check_file(dataset)

    # Download (optional)
    extract_dir = ''
    if isinstance(data, (str, Path)) and (zipfile.is_zipfile(data) or is_tarfile(data)):
        new_dir = safe_download(data, dir=DATASETS_DIR, unzip=True, delete=False, curl=False)
        data = next((DATASETS_DIR / new_dir).rglob('*.yaml'))
        extract_dir, autodownload = data.parent, False

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

    # Checks
    for k in 'train', 'val':
        if k not in data:
            raise SyntaxError(
                emojis(f"{dataset} '{k}:' key missing ❌.\n'train' and 'val' are required in all data YAMLs."))
    if 'names' not in data and 'nc' not in data:
        raise SyntaxError(emojis(f"{dataset} key missing ❌.\n either 'names' or 'nc' are required in all data YAMLs."))
    if 'names' in data and 'nc' in data and len(data['names']) != data['nc']:
        raise SyntaxError(emojis(f"{dataset} 'names' length {len(data['names'])} and 'nc: {data['nc']}' must match."))
    if 'names' not in data:
        data['names'] = [f'class_{i}' for i in range(data['nc'])]
    else:
        data['nc'] = len(data['names'])

    data['names'] = check_class_names(data['names'])

    # Resolve paths
    path = Path(extract_dir or data.get('path') or Path(data.get('yaml_file', '')).parent)  # dataset root

    if not path.is_absolute():
        path = (DATASETS_DIR / 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):
            name = clean_url(dataset)  # dataset name with URL auth stripped
            m = f"\nDataset '{name}' images not found ⚠️, missing paths %s" % [str(x) for x in val if not x.exists()]
            if s and autodownload:
                LOGGER.warning(m)
            else:
                m += f"\nNote dataset download directory is '{DATASETS_DIR}'. You can update this in '{SETTINGS_YAML}'"
                raise FileNotFoundError(m)
            t = time.time()
            if s.startswith('http') and s.endswith('.zip'):  # URL
                safe_download(url=s, dir=DATASETS_DIR, delete=True)
                r = None  # success
            elif s.startswith('bash '):  # bash script
                LOGGER.info(f'Running {s} ...')
                r = os.system(s)
            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}\n')
    check_font('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf')  # download fonts

    return data  # dictionary


def check_cls_dataset(dataset: str, split=''):
    """
    Checks 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): The name of the dataset.
        split (str, optional): The split of the dataset. Either 'val', 'test', or ''. Defaults to ''.

    Returns:
        (dict): 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): A dictionary of class names in the dataset.

    Raises:
        FileNotFoundError: If the specified dataset is not found and cannot be downloaded.
    """

    dataset = Path(dataset)
    data_dir = (dataset if dataset.is_dir() else (DATASETS_DIR / dataset)).resolve()
    if not data_dir.is_dir():
        LOGGER.info(f'\nDataset not found ⚠️, missing path {data_dir}, attempting download...')
        t = time.time()
        if str(dataset) == 'imagenet':
            subprocess.run(f"bash {ROOT / 'data/scripts/get_imagenet.sh'}", shell=True, check=True)
        else:
            url = f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{dataset}.zip'
            download(url, dir=data_dir.parent)
        s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n"
        LOGGER.info(s)
    train_set = data_dir / 'train'
    val_set = data_dir / 'val' if (data_dir / 'val').exists() else None  # data/test or data/val
    test_set = data_dir / 'test' if (data_dir / 'test').exists() else None  # data/val or data/test
    if split == 'val' and not val_set:
        LOGGER.info("WARNING ⚠️ Dataset 'split=val' not found, using 'split=test' instead.")
    elif split == 'test' and not test_set:
        LOGGER.info("WARNING ⚠️ Dataset 'split=test' not found, using 'split=val' instead.")

    nc = len([x for x in (data_dir / 'train').glob('*') if x.is_dir()])  # number of classes
    names = [x.name for x in (data_dir / 'train').iterdir() if x.is_dir()]  # class names list
    names = dict(enumerate(sorted(names)))
    return {'train': train_set, 'val': val_set or test_set, 'test': test_set or val_set, 'nc': nc, 'names': names}


class HUBDatasetStats():
    """
    A 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). Default is 'coco128.yaml'.
        task (str): Dataset task. Options are 'detect', 'segment', 'pose', 'classify'. Default is 'detect'.
        autodownload (bool): Attempt to download dataset if not found locally. Default is False.

    Usage
        from ultralytics.data.utils import HUBDatasetStats
        stats = HUBDatasetStats('/Users/glennjocher/Downloads/coco8.zip', task='detect')  # detect dataset
        stats = HUBDatasetStats('/Users/glennjocher/Downloads/coco8-seg.zip', task='segment')  # segment dataset
        stats = HUBDatasetStats('/Users/glennjocher/Downloads/coco8-pose.zip', task='pose')  # pose dataset
        stats.get_json(save=False)
        stats.process_images()
    """

    def __init__(self, path='coco128.yaml', task='detect', autodownload=False):
        """Initialize class."""
        LOGGER.info(f'Starting HUB dataset checks for {path}....')
        zipped, data_dir, yaml_path = self._unzip(Path(path))
        try:
            # data = yaml_load(check_yaml(yaml_path))  # data dict
            data = check_det_dataset(yaml_path, autodownload)  # data dict
            if zipped:
                data['path'] = data_dir
        except Exception as e:
            raise Exception('error/HUB/dataset_stats/yaml_load') from e

        self.hub_dir = Path(str(data['path']) + '-hub')
        self.im_dir = self.hub_dir / 'images'
        self.im_dir.mkdir(parents=True, exist_ok=True)  # makes /images
        self.stats = {'nc': len(data['names']), 'names': list(data['names'].values())}  # statistics dictionary
        self.data = data
        self.task = task  # detect, segment, pose, classify

    @staticmethod
    def _find_yaml(dir):
        """Return data.yaml file."""
        files = list(dir.glob('*.yaml')) or list(dir.rglob('*.yaml'))  # try root level first and then recursive
        assert files, f'No *.yaml file found in {dir}'
        if len(files) > 1:
            files = [f for f in files if f.stem == dir.stem]  # prefer *.yaml files that match dir name
            assert files, f'Multiple *.yaml files found in {dir}, only 1 *.yaml file allowed'
        assert len(files) == 1, f'Multiple *.yaml files found: {files}, only 1 *.yaml file allowed in {dir}'
        return files[0]

    def _unzip(self, path):
        """Unzip data.zip."""
        if not str(path).endswith('.zip'):  # path is data.yaml
            return False, None, path
        unzip_dir = unzip_file(path, path=path.parent)
        assert unzip_dir.is_dir(), f'Error unzipping {path}, {unzip_dir} not found. ' \
                                   f'path/to/abc.zip MUST unzip to path/to/abc/'
        return True, str(unzip_dir), self._find_yaml(unzip_dir)  # zipped, data_dir, yaml_path

    def _hub_ops(self, f):
        """Saves a compressed image for HUB previews."""
        compress_one_image(f, self.im_dir / Path(f).name)  # save to dataset-hub

    def get_json(self, save=False, verbose=False):
        """Return dataset JSON for Ultralytics HUB."""
        from ultralytics.data import YOLODataset  # ClassificationDataset

        def _round(labels):
            """Update labels to integer class and 4 decimal place floats."""
            if self.task == 'detect':
                coordinates = labels['bboxes']
            elif self.task == 'segment':
                coordinates = [x.flatten() for x in labels['segments']]
            elif self.task == 'pose':
                n = labels['keypoints'].shape[0]
                coordinates = np.concatenate((labels['bboxes'], labels['keypoints'].reshape(n, -1)), 1)
            else:
                raise ValueError('Undefined dataset task.')
            zipped = zip(labels['cls'], coordinates)
            return [[int(c), *(round(float(x), 4) for x in points)] for c, points in zipped]

        for split in 'train', 'val', 'test':
            if self.data.get(split) is None:
                self.stats[split] = None  # i.e. no test set
                continue

            dataset = YOLODataset(img_path=self.data[split],
                                  data=self.data,
                                  use_segments=self.task == 'segment',
                                  use_keypoints=self.task == 'pose')
            x = np.array([
                np.bincount(label['cls'].astype(int).flatten(), minlength=self.data['nc'])
                for label in tqdm(dataset.labels, total=len(dataset), desc='Statistics')])  # shape(128x80)
            self.stats[split] = {
                'instance_stats': {
                    'total': int(x.sum()),
                    'per_class': x.sum(0).tolist()},
                'image_stats': {
                    'total': len(dataset),
                    'unlabelled': int(np.all(x == 0, 1).sum()),
                    'per_class': (x > 0).sum(0).tolist()},
                'labels': [{
                    Path(k).name: _round(v)} for k, v in zip(dataset.im_files, dataset.labels)]}

        # Save, print and return
        if save:
            stats_path = self.hub_dir / 'stats.json'
            LOGGER.info(f'Saving {stats_path.resolve()}...')
            with open(stats_path, 'w') as f:
                json.dump(self.stats, f)  # save stats.json
        if verbose:
            LOGGER.info(json.dumps(self.stats, indent=2, sort_keys=False))
        return self.stats

    def process_images(self):
        """Compress images for Ultralytics HUB."""
        from ultralytics.data import YOLODataset  # ClassificationDataset

        for split in 'train', 'val', 'test':
            if self.data.get(split) is None:
                continue
            dataset = YOLODataset(img_path=self.data[split], data=self.data)
            with ThreadPool(NUM_THREADS) as pool:
                for _ in tqdm(pool.imap(self._hub_ops, dataset.im_files), total=len(dataset), desc=f'{split} images'):
                    pass
        LOGGER.info(f'Done. All images saved to {self.im_dir}')
        return self.im_dir


def compress_one_image(f, f_new=None, max_dim=1920, quality=50):
    """
    Compresses 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. Default is 1920 pixels.
        quality (int, optional): The image compression quality as a percentage. Default is 50%.

    Usage:
        from pathlib import Path
        from ultralytics.data.utils import compress_one_image
        for f in Path('/Users/glennjocher/Downloads/dataset').rglob('*.jpg'):
            compress_one_image(f)
    """
    try:  # use PIL
        im = Image.open(f)
        r = max_dim / max(im.height, im.width)  # ratio
        if r < 1.0:  # image too large
            im = im.resize((int(im.width * r), int(im.height * r)))
        im.save(f_new or f, 'JPEG', quality=quality, optimize=True)  # save
    except Exception as e:  # use OpenCV
        LOGGER.info(f'WARNING ⚠️ HUB ops PIL failure {f}: {e}')
        im = cv2.imread(f)
        im_height, im_width = im.shape[:2]
        r = max_dim / max(im_height, im_width)  # ratio
        if r < 1.0:  # image too large
            im = cv2.resize(im, (int(im_width * r), int(im_height * r)), interpolation=cv2.INTER_AREA)
        cv2.imwrite(str(f_new or f), im)


def delete_dsstore(path):
    """
    Deletes all ".DS_store" files under a specified directory.

    Args:
        path (str, optional): The directory path where the ".DS_store" files should be deleted.

    Usage:
        from ultralytics.data.utils import delete_dsstore
        delete_dsstore('/Users/glennjocher/Downloads/dataset')

    Note:
        ".DS_store" files are created by the Apple operating system and contain metadata about folders and files. They
        are hidden system files and can cause issues when transferring files between different operating systems.
    """
    # Delete Apple .DS_store files
    files = list(Path(path).rglob('.DS_store'))
    LOGGER.info(f'Deleting *.DS_store files: {files}')
    for f in files:
        f.unlink()


def zip_directory(dir, use_zipfile_library=True):
    """
    Zips a directory and saves the archive to the specified output path.

    Args:
        dir (str): The path to the directory to be zipped.
        use_zipfile_library (bool): Whether to use zipfile library or shutil for zipping.

    Usage:
        from ultralytics.data.utils import zip_directory
        zip_directory('/Users/glennjocher/Downloads/playground')

        zip -r coco8-pose.zip coco8-pose
    """
    delete_dsstore(dir)
    if use_zipfile_library:
        dir = Path(dir)
        with zipfile.ZipFile(dir.with_suffix('.zip'), 'w', zipfile.ZIP_DEFLATED) as zip_file:
            for file_path in dir.glob('**/*'):
                if file_path.is_file():
                    zip_file.write(file_path, file_path.relative_to(dir))
    else:
        import shutil
        shutil.make_archive(dir, 'zip', dir)


def autosplit(path=DATASETS_DIR / 'coco128/images', weights=(0.9, 0.1, 0.0), annotated_only=False):
    """
    Autosplit a dataset into train/val/test splits and save the resulting splits into autosplit_*.txt files.

    Args:
        path (Path, optional): Path to images directory. Defaults to DATASETS_DIR / 'coco128/images'.
        weights (list | tuple, optional): Train, validation, and test split fractions. Defaults to (0.9, 0.1, 0.0).
        annotated_only (bool, optional): If True, only images with an associated txt file are used. Defaults to False.

    Usage:
        from utils.dataloaders import autosplit
        autosplit()
    """

    path = Path(path)  # images dir
    files = sorted(x for x in path.rglob('*.*') if x.suffix[1:].lower() in IMG_FORMATS)  # image files only
    n = len(files)  # number of files
    random.seed(0)  # for reproducibility
    indices = random.choices([0, 1, 2], weights=weights, k=n)  # assign each image to a split

    txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt']  # 3 txt files
    for x in txt:
        if (path.parent / x).exists():
            (path.parent / x).unlink()  # remove existing

    LOGGER.info(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only)
    for i, img in tqdm(zip(indices, files), total=n):
        if not annotated_only or Path(img2label_paths([str(img)])[0]).exists():  # check label
            with open(path.parent / txt[i], 'a') as f:
                f.write(f'./{img.relative_to(path.parent).as_posix()}' + '\n')  # add image to txt file