yolov8-to/ultralytics/data/utils.py

# 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 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/datasets/detect for dataset formatting guidance.'
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


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: Image.Image):
    """Returns exif-corrected PIL size."""
    s = img.size  # (width, height)
    if img.format == 'JPEG':  # only support JPEG images
        with contextlib.suppress(Exception):
            exif = img.getexif()
            if exif:
                rotation = exif.get(274, None)  # the EXIF key for the orientation tag is 274
                if rotation in [6, 8]:  # rotation 270 or 90
                    s = s[1], s[0]
    return s


def verify_image(args):
    """Verify one image."""
    (im_file, cls), prefix = args
    # Number (found, corrupt), message
    nf, nc, msg = 0, 0, ''
    try:
        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'
        nf = 1
    except Exception as e:
        nc = 1
        msg = f'{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}'
    return (im_file, cls), nf, nc, msg


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, regression_vars = 0, 0, 0, 0, '', [], None, 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)
                    regression_vars = np.array([x[1:7] for x in lb], dtype=np.float32)  # Extract regression variables
                    segments = [np.array(x[7:], dtype=np.float32).reshape(-1, 2) for x in lb]
                    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.where((keypoints[..., 0] < 0) | (keypoints[..., 1] < 0), 0.0, 1.0).astype(np.float32)
                keypoints = np.concatenate([keypoints, kpt_mask[..., None]], axis=-1)  # (nl, nkpt, 3)
        lb = lb[:, :5]
        return im_file, lb, shape, segments, regression_vars, 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,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, dtype=np.int32)
    polygons = polygons.reshape((polygons.shape[0], -1, 2))
    cv2.fillPoly(mask, polygons, color=color)
    nh, nw = (imgsz[0] // downsample_ratio, imgsz[1] // downsample_ratio)
    # NOTE: fillPoly first then resize is trying to keep the same way of loss calculation when mask-ratio=1.
    return cv2.resize(mask, (nw, nh))


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
    """
    return np.array([polygon2mask(imgsz, [x.reshape(-1)], color, downsample_ratio) for x in polygons])


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, verify, and/or unzip a dataset if not found locally.

    This function checks the availability of a specified dataset, and if not found, it has the option to download and
    unzip the dataset. It then reads and parses the accompanying YAML data, ensuring key requirements are met and also
    resolves paths related to the dataset.

    Args:
        dataset (str): Path to the dataset or dataset descriptor (like a YAML file).
        autodownload (bool, optional): Whether to automatically download the dataset if not found. Defaults to True.

    Returns:
        (dict): Parsed dataset information and paths.
    """

    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:
            if k == 'val' and 'validation' in data:
                LOGGER.info("WARNING ⚠️ renaming data YAML 'validation' key to 'val' to match YOLO format.")
                data['val'] = data.pop('validation')  # replace 'validation' key with 'val' key
            else:
                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 path '{[x for x in val if not x.exists()][0]}'"
            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()
            r = None  # success
            if s.startswith('http') and s.endswith('.zip'):  # URL
                safe_download(url=s, dir=DATASETS_DIR, delete=True)
            elif s.startswith('bash '):  # bash script
                LOGGER.info(f'Running {s} ...')
                r = os.system(s)
            else:  # python script
                exec(s, {'yaml': data})
            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, 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 | Path): 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.
    """

    dataset = Path(dataset)
    data_dir = (dataset if dataset.is_dir() else (DATASETS_DIR / dataset)).resolve()
    if not data_dir.is_dir():
        LOGGER.warning(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 data_dir / 'validation' if (
        data_dir / 'validation').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.warning("WARNING ⚠️ Dataset 'split=val' not found, using 'split=test' instead.")
    elif split == 'test' and not test_set:
        LOGGER.warning("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)))

    # Print to console
    for k, v in {'train': train_set, 'val': val_set, 'test': test_set}.items():
        prefix = f'{colorstr(f"{k}:")} {v}...'
        if v is None:
            LOGGER.info(prefix)
        else:
            files = [path for path in v.rglob('*.*') if path.suffix[1:].lower() in IMG_FORMATS]
            nf = len(files)  # number of files
            nd = len({file.parent for file in files})  # number of directories
            if nf == 0:
                if k == 'train':
                    raise FileNotFoundError(emojis(f"{dataset} '{k}:' no training images found ❌ "))
                else:
                    LOGGER.warning(f'{prefix} found {nf} images in {nd} classes: WARNING ⚠️ no images found')
            elif nd != nc:
                LOGGER.warning(f'{prefix} found {nf} images in {nd} classes: ERROR ❌️ requires {nc} classes, not {nd}')
            else:
                LOGGER.info(f'{prefix} found {nf} images in {nd} classes ✅ ')

    return {'train': train_set, 'val': val_set, 'test': test_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.

    Example:
        Download *.zip files from https://github.com/ultralytics/hub/tree/main/example_datasets
            i.e. https://github.com/ultralytics/hub/raw/main/example_datasets/coco8.zip for coco8.zip.
        ```python
        from ultralytics.data.utils import HUBDatasetStats

        stats = HUBDatasetStats('path/to/coco8.zip', task='detect')  # detect dataset
        stats = HUBDatasetStats('path/to/coco8-seg.zip', task='segment')  # segment dataset
        stats = HUBDatasetStats('path/to/coco8-pose.zip', task='pose')  # pose dataset
        stats = HUBDatasetStats('path/to/imagenet10.zip', task='classify')  # classification dataset

        stats.get_json(save=True)
        stats.process_images()
        ```
    """

    def __init__(self, path='coco128.yaml', task='detect', autodownload=False):
        """Initialize class."""
        path = Path(path).resolve()
        LOGGER.info(f'Starting HUB dataset checks for {path}....')

        self.task = task  # detect, segment, pose, classify
        if self.task == 'classify':
            unzip_dir = unzip_file(path)
            data = check_cls_dataset(unzip_dir)
            data['path'] = unzip_dir
        else:  # detect, segment, pose
            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/init') from e

        self.hub_dir = Path(f'{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

    @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.resolve()}'"
        if len(files) > 1:
            files = [f for f in files if f.stem == dir.stem]  # prefer *.yaml files that match dir name
        assert len(files) == 1, f"Expected 1 *.yaml file in '{dir.resolve()}', but found {len(files)}.\n{files}"
        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."""

        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[0]), *(round(float(x), 4) for x in points)] for c, points in zipped]

        for split in 'train', 'val', 'test':
            self.stats[split] = None  # predefine
            path = self.data.get(split)

            # Check split
            if path is None:  # no split
                continue
            files = [f for f in Path(path).rglob('*.*') if f.suffix[1:].lower() in IMG_FORMATS]  # image files in split
            if not files:  # no images
                continue

            # Get dataset statistics
            if self.task == 'classify':
                from torchvision.datasets import ImageFolder

                dataset = ImageFolder(self.data[split])

                x = np.zeros(len(dataset.classes)).astype(int)
                for im in dataset.imgs:
                    x[im[1]] += 1

                self.stats[split] = {
                    'instance_stats': {
                        'total': len(dataset),
                        'per_class': x.tolist()},
                    'image_stats': {
                        'total': len(dataset),
                        'unlabelled': 0,
                        'per_class': x.tolist()},
                    'labels': [{
                        Path(k).name: v} for k, v in dataset.imgs]}
            else:
                from ultralytics.data import YOLODataset

                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%.

    Example:
        ```python
        from pathlib import Path
        from ultralytics.data.utils import compress_one_image

        for f in Path('path/to/dataset').rglob('*.jpg'):
            compress_one_image(f)
        ```
    """

    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 autosplit(path=DATASETS_DIR / 'coco8/images', weights=(0.9, 0.1, 0.0), annotated_only=False):
    """
    Automatically split 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 / 'coco8/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.

    Example:
        ```python
        from ultralytics.data.utils 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