Reference for ultralytics/data/utils.py

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).
        task (str): Dataset task. Options are 'detect', 'segment', 'pose', 'classify'.
        autodownload (bool): Attempt to download dataset if not found locally.

    Attributes:
        task (str): Dataset task type.
        hub_dir (Path): Directory path for HUB dataset files.
        im_dir (Path): Directory path for compressed images.
        stats (dict): Statistics dictionary containing dataset information.
        data (dict): Dataset configuration data.

    Methods:
        get_json: Return dataset JSON for Ultralytics HUB.
        process_images: Compress images for Ultralytics HUB.

    Examples:
        >>> 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/dota8.zip", task="obb")  # OBB dataset
        >>> stats = HUBDatasetStats("path/to/imagenet10.zip", task="classify")  # classification dataset
        >>> stats.get_json(save=True)
        >>> stats.process_images()

    Notes:
        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.
    """

    def __init__(self, path: str = "coco8.yaml", task: str = "detect", autodownload: bool = False):
        """Initialize class."""
        path = Path(path).resolve()
        LOGGER.info(f"Starting HUB dataset checks for {path}....")

        self.task = task  # detect, segment, pose, classify, obb
        if self.task == "classify":
            unzip_dir = unzip_file(path)
            data = check_cls_dataset(unzip_dir)
            data["path"] = unzip_dir
        else:  # detect, segment, pose, obb
            _, data_dir, yaml_path = self._unzip(Path(path))
            try:
                # Load YAML with checks
                data = YAML.load(yaml_path)
                data["path"] = ""  # strip path since YAML should be in dataset root for all HUB datasets
                YAML.save(yaml_path, data)
                data = check_det_dataset(yaml_path, autodownload)  # dict
                data["path"] = data_dir  # YAML path should be set to '' (relative) or parent (absolute)
            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.stats = {"nc": len(data["names"]), "names": list(data["names"].values())}  # statistics dictionary
        self.data = data

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

@staticmethod
def _unzip(path: Path) -> tuple[bool, str, 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. path/to/abc.zip MUST unzip to path/to/abc/"
    )
    return True, str(unzip_dir), find_dataset_yaml(unzip_dir)  # zipped, data_dir, yaml_path

def get_json(self, save: bool = False, verbose: bool = False) -> dict:
    """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 in {"segment", "obb"}:  # Segment and OBB use segments. OBB segments are normalized xyxyxyxy
            coordinates = [x.flatten() for x in labels["segments"]]
        elif self.task == "pose":
            n, nk, nd = labels["keypoints"].shape
            coordinates = np.concatenate((labels["bboxes"], labels["keypoints"].reshape(n, nk * nd)), 1)
        else:
            raise ValueError(f"Undefined dataset task={self.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  # scope for faster 'import ultralytics'

            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, task=self.task)
            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:
        self.hub_dir.mkdir(parents=True, exist_ok=True)  # makes dataset-hub/
        stats_path = self.hub_dir / "stats.json"
        LOGGER.info(f"Saving {stats_path.resolve()}...")
        with open(stats_path, "w", encoding="utf-8") 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) -> Path:
    """Compress images for Ultralytics HUB."""
    from ultralytics.data import YOLODataset  # ClassificationDataset

    self.im_dir.mkdir(parents=True, exist_ok=True)  # makes dataset-hub/images/
    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 img2label_paths(img_paths: list[str]) -> list[str]:
    """Convert image paths to label paths by replacing 'images' with 'labels' and extension with '.txt'."""
    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 check_file_speeds(
    files: list[str], threshold_ms: float = 10, threshold_mb: float = 50, max_files: int = 5, prefix: str = ""
):
    """Check dataset file access speed and provide performance feedback.

    This function tests the access speed of dataset files by measuring ping (stat call) time and read speed. It samples
    up to 5 files from the provided list and warns if access times exceed the threshold.

    Args:
        files (list[str]): List of file paths to check for access speed.
        threshold_ms (float, optional): Threshold in milliseconds for ping time warnings.
        threshold_mb (float, optional): Threshold in megabytes per second for read speed warnings.
        max_files (int, optional): The maximum number of files to check.
        prefix (str, optional): Prefix string to add to log messages.

    Examples:
        >>> from pathlib import Path
        >>> image_files = list(Path("dataset/images").glob("*.jpg"))
        >>> check_file_speeds(image_files, threshold_ms=15)
    """
    if not files:
        LOGGER.warning(f"{prefix}Image speed checks: No files to check")
        return

    # Sample files (max 5)
    files = random.sample(files, min(max_files, len(files)))

    # Test ping (stat time)
    ping_times = []
    file_sizes = []
    read_speeds = []

    for f in files:
        try:
            # Measure ping (stat call)
            start = time.perf_counter()
            file_size = os.stat(f).st_size
            ping_times.append((time.perf_counter() - start) * 1000)  # ms
            file_sizes.append(file_size)

            # Measure read speed
            start = time.perf_counter()
            with open(f, "rb") as file_obj:
                _ = file_obj.read()
            read_time = time.perf_counter() - start
            if read_time > 0:  # Avoid division by zero
                read_speeds.append(file_size / (1 << 20) / read_time)  # MB/s
        except Exception:
            pass

    if not ping_times:
        LOGGER.warning(f"{prefix}Image speed checks: failed to access files")
        return

    # Calculate stats with uncertainties
    avg_ping = np.mean(ping_times)
    std_ping = np.std(ping_times, ddof=1) if len(ping_times) > 1 else 0
    size_msg = f", size: {np.mean(file_sizes) / (1 << 10):.1f} KB"
    ping_msg = f"ping: {avg_ping:.1f}±{std_ping:.1f} ms"

    if read_speeds:
        avg_speed = np.mean(read_speeds)
        std_speed = np.std(read_speeds, ddof=1) if len(read_speeds) > 1 else 0
        speed_msg = f", read: {avg_speed:.1f}±{std_speed:.1f} MB/s"
    else:
        speed_msg = ""

    if avg_ping < threshold_ms or avg_speed < threshold_mb:
        LOGGER.info(f"{prefix}Fast image access ✅ ({ping_msg}{speed_msg}{size_msg})")
    else:
        LOGGER.warning(
            f"{prefix}Slow image access detected ({ping_msg}{speed_msg}{size_msg}). "
            f"Use local storage instead of remote/mounted storage for better performance. "
            f"See https://docs.ultralytics.com/guides/model-training-tips/"
        )

def get_hash(paths: list[str]) -> str:
    """Return a single hash value of a list of paths (files or dirs)."""
    size = 0
    for p in paths:
        try:
            size += os.stat(p).st_size
        except OSError:
            continue
    h = __import__("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) -> tuple[int, int]:
    """Return exif-corrected PIL size."""
    s = img.size  # (width, height)
    if img.format == "JPEG":  # only support JPEG images
        try:
            if exif := img.getexif():
                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]
        except Exception:
            pass
    return s

def verify_image(args: tuple) -> tuple:
    """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}. {FORMATS_HELP_MSG}"
        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}{im_file}: corrupt JPEG restored and saved"
        nf = 1
    except Exception as e:
        nc = 1
        msg = f"{prefix}{im_file}: ignoring corrupt image/label: {e}"
    return (im_file, cls), nf, nc, msg

def verify_image_label(args: tuple) -> list:
    """Verify one image-label pair."""
    im_file, lb_file, prefix, keypoint, num_cls, nkpt, ndim, single_cls = 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}. {FORMATS_HELP_MSG}"
        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}{im_file}: corrupt JPEG restored and saved"

        # Verify labels
        if os.path.isfile(lb_file):
            nf = 1  # label found
            with open(lb_file, encoding="utf-8") 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)
            if nl := len(lb):
                if keypoint:
                    assert lb.shape[1] == (5 + nkpt * ndim), f"labels require {(5 + nkpt * ndim)} columns each"
                    points = lb[:, 5:].reshape(-1, ndim)[:, :2]
                else:
                    assert lb.shape[1] == 5, f"labels require 5 columns, {lb.shape[1]} columns detected"
                    points = lb[:, 1:]
                # Coordinate points check with 1% tolerance
                assert points.max() <= 1.01, f"non-normalized or out of bounds coordinates {points[points > 1.01]}"
                assert lb.min() >= -0.01, f"negative class labels or coordinate {lb[lb < -0.01]}"

                # All labels
                max_cls = 0 if single_cls else lb[:, 0].max()  # max label count
                assert max_cls < num_cls, (
                    f"Label class {int(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}{im_file}: {nl - len(i)} duplicate labels removed"
            else:
                ne = 1  # label empty
                lb = np.zeros((0, (5 + nkpt * ndim) if keypoint else 5), dtype=np.float32)
        else:
            nm = 1  # label missing
            lb = np.zeros((0, (5 + nkpt * ndim) if keypoint else 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, keypoints, nm, nf, ne, nc, msg
    except Exception as e:
        nc = 1
        msg = f"{prefix}{im_file}: ignoring corrupt image/label: {e}"
        return [None, None, None, None, None, nm, nf, ne, nc, msg]

def visualize_image_annotations(image_path: str, txt_path: str, label_map: dict[int, str]):
    """Visualize YOLO annotations (bounding boxes and class labels) on an image.

    This function reads an image and its corresponding annotation file in YOLO format, then draws bounding boxes around
    detected objects and labels them with their respective class names. The bounding box colors are assigned based on
    the class ID, and the text color is dynamically adjusted for readability, depending on the background color's
    luminance.

    Args:
        image_path (str): The path to the image file to annotate, and it can be in formats supported by PIL.
        txt_path (str): The path to the annotation file in YOLO format, that should contain one line per object.
        label_map (dict[int, str]): A dictionary that maps class IDs (integers) to class labels (strings).

    Examples:
        >>> label_map = {0: "cat", 1: "dog", 2: "bird"}  # It should include all annotated classes details
        >>> visualize_image_annotations("path/to/image.jpg", "path/to/annotations.txt", label_map)
    """
    import matplotlib.pyplot as plt

    from ultralytics.utils.plotting import colors

    img = np.array(Image.open(image_path))
    img_height, img_width = img.shape[:2]
    annotations = []
    with open(txt_path, encoding="utf-8") as file:
        for line in file:
            class_id, x_center, y_center, width, height = map(float, line.split())
            x = (x_center - width / 2) * img_width
            y = (y_center - height / 2) * img_height
            w = width * img_width
            h = height * img_height
            annotations.append((x, y, w, h, int(class_id)))
    _, ax = plt.subplots(1)  # Plot the image and annotations
    for x, y, w, h, label in annotations:
        color = tuple(c / 255 for c in colors(label, True))  # Get and normalize the RGB color
        rect = plt.Rectangle((x, y), w, h, linewidth=2, edgecolor=color, facecolor="none")  # Create a rectangle
        ax.add_patch(rect)
        luminance = 0.2126 * color[0] + 0.7152 * color[1] + 0.0722 * color[2]  # Formula for luminance
        ax.text(x, y - 5, label_map[label], color="white" if luminance < 0.5 else "black", backgroundcolor=color)
    ax.imshow(img)
    plt.show()

def polygon2mask(
    imgsz: tuple[int, int], polygons: list[np.ndarray], color: int = 1, downsample_ratio: int = 1
) -> np.ndarray:
    """Convert a list of polygons to a binary mask of the specified image size.

    Args:
        imgsz (tuple[int, int]): The size of the image as (height, width).
        polygons (list[np.ndarray]): A list of polygons. Each polygon is an array with shape (N, M), where N is the
            number of polygons, and M is the number of points such that M % 2 = 0.
        color (int, optional): The color value to fill in the polygons on the mask.
        downsample_ratio (int, optional): Factor by which to downsample the mask.

    Returns:
        (np.ndarray): A binary mask of the specified image size with the polygons filled in.
    """
    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 loss calculation method when mask-ratio=1
    return cv2.resize(mask, (nw, nh))

def polygons2masks(
    imgsz: tuple[int, int], polygons: list[np.ndarray], color: int, downsample_ratio: int = 1
) -> np.ndarray:
    """Convert a list of polygons to a set of binary masks of the specified image size.

    Args:
        imgsz (tuple[int, int]): The size of the image as (height, width).
        polygons (list[np.ndarray]): A list of polygons. Each polygon is an array with shape (N, M), where N is the
            number of polygons, and M is the number of points such that M % 2 = 0.
        color (int): The color value to fill in the polygons on the masks.
        downsample_ratio (int, optional): Factor by which to downsample each mask.

    Returns:
        (np.ndarray): A set of binary masks of the specified image size with the polygons filled in.
    """
    return np.array([polygon2mask(imgsz, [x.reshape(-1)], color, downsample_ratio) for x in polygons])

def polygons2masks_overlap(
    imgsz: tuple[int, int], segments: list[np.ndarray], downsample_ratio: int = 1
) -> tuple[np.ndarray, np.ndarray]:
    """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 segment in segments:
        mask = polygon2mask(
            imgsz,
            [segment.reshape(-1)],
            downsample_ratio=downsample_ratio,
            color=1,
        )
        ms.append(mask.astype(masks.dtype))
        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 find_dataset_yaml(path: Path) -> Path:
    """Find and return the YAML file associated with a Detect, Segment or Pose dataset.

    This function searches for a YAML file at the root level of the provided directory first, and if not found, it
    performs a recursive search. It prefers YAML files that have the same stem as the provided path.

    Args:
        path (Path): The directory path to search for the YAML file.

    Returns:
        (Path): The path of the found YAML file.
    """
    files = list(path.glob("*.yaml")) or list(path.rglob("*.yaml"))  # try root level first and then recursive
    assert files, f"No YAML file found in '{path.resolve()}'"
    if len(files) > 1:
        files = [f for f in files if f.stem == path.stem]  # prefer *.yaml files that match
    assert len(files) == 1, f"Expected 1 YAML file in '{path.resolve()}', but found {len(files)}.\n{files}"
    return files[0]

def check_det_dataset(dataset: str, autodownload: bool = True) -> dict[str, Any]:
    """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.

    Returns:
        (dict[str, Any]): Parsed dataset information and paths.
    """
    file = check_file(dataset)

    # Download (optional)
    extract_dir = ""
    if zipfile.is_zipfile(file) or is_tarfile(file):
        new_dir = safe_download(file, dir=DATASETS_DIR, unzip=True, delete=False)
        file = find_dataset_yaml(DATASETS_DIR / new_dir)
        extract_dir, autodownload = file.parent, False

    # Read YAML
    data = YAML.load(file, append_filename=True)  # dictionary

    # Checks
    for k in "train", "val":
        if k not in data:
            if k != "val" or "validation" not in data:
                raise SyntaxError(
                    emojis(f"{dataset} '{k}:' key missing ❌.\n'train' and 'val' are required in all data YAMLs.")
                )
            LOGGER.warning("renaming data YAML 'validation' key to 'val' to match YOLO format.")
            data["val"] = data.pop("validation")  # replace 'validation' key with 'val' key
    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"])
    data["channels"] = data.get("channels", 3)  # get image channels, default to 3

    # Resolve paths
    path = Path(extract_dir or data.get("path") or Path(data.get("yaml_file", "")).parent)  # dataset root
    if not path.exists() and not path.is_absolute():
        path = (DATASETS_DIR / path).resolve()  # path relative to DATASETS_DIR

    # Set paths
    data["path"] = path  # download scripts
    for k in "train", "val", "test", "minival":
        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
    val, s = (data.get(x) for x in ("val", "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
            LOGGER.info("")
            m = f"Dataset '{name}' images not found, missing path '{next(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_FILE}'"
                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} ...")
                subprocess.run(s.split(), check=True)
            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: str | Path, split: str = "") -> dict[str, Any]:
    """Check 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 ''.

    Returns:
        (dict[str, Any]): 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[int, str]): A dictionary of class names in the dataset.
    """
    # Download (optional if dataset=https://file.zip is passed directly)
    if str(dataset).startswith(("http:/", "https:/")):
        dataset = safe_download(dataset, dir=DATASETS_DIR, unzip=True, delete=False)
    elif str(dataset).endswith((".zip", ".tar", ".gz")):
        file = check_file(dataset)
        dataset = safe_download(file, dir=DATASETS_DIR, unzip=True, delete=False)

    dataset = Path(dataset)
    data_dir = (dataset if dataset.is_dir() else (DATASETS_DIR / dataset)).resolve()
    if not data_dir.is_dir():
        if data_dir.suffix != "":
            raise ValueError(
                f'Classification datasets must be a directory (data="path/to/dir") not a file (data="{dataset}"), '
                "See https://docs.ultralytics.com/datasets/classify/"
            )
        LOGGER.info("")
        LOGGER.warning(f"Dataset not found, missing path {data_dir}, attempting download...")
        t = time.time()
        if str(dataset) == "imagenet":
            subprocess.run(["bash", str(ROOT / "data/scripts/get_imagenet.sh")], check=True)
        else:
            download(f"{ASSETS_URL}/{dataset}.zip", dir=data_dir.parent)
        LOGGER.info(f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n")
    train_set = data_dir / "train"
    if not train_set.is_dir():
        LOGGER.warning(f"Dataset 'split=train' not found at {train_set}")
        if image_files := list(data_dir.rglob("*.jpg")) + list(data_dir.rglob("*.png")):
            from ultralytics.data.split import split_classify_dataset

            LOGGER.info(f"Found {len(image_files)} images in subdirectories. Attempting to split...")
            data_dir = split_classify_dataset(data_dir, train_ratio=0.8)
            train_set = data_dir / "train"
        else:
            LOGGER.error(f"No images found in {data_dir} or its subdirectories.")
    val_set = (
        data_dir / "val"
        if (data_dir / "val").exists()
        else data_dir / "validation"
        if (data_dir / "validation").exists()
        else data_dir / "valid"
        if (data_dir / "valid").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("Dataset 'split=val' not found, using 'split=test' instead.")
        val_set = test_set
    elif split == "test" and not test_set:
        LOGGER.warning("Dataset 'split=test' not found, using 'split=val' instead.")
        test_set = val_set

    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(f"{dataset} '{k}:' no training images found")
                else:
                    LOGGER.warning(f"{prefix} found {nf} images in {nd} classes (no images found)")
            elif nd != nc:
                LOGGER.error(f"{prefix} found {nf} images in {nd} classes (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, "channels": 3}

def compress_one_image(f: str, f_new: str | None = None, max_dim: int = 1920, quality: int = 50):
    """Compress 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.
        quality (int, optional): The image compression quality as a percentage.

    Examples:
        >>> 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
        Image.MAX_IMAGE_PIXELS = None  # Fix DecompressionBombError, allow optimization of image > ~178.9 million pixels
        im = Image.open(f)
        if im.mode in {"RGBA", "LA"}:  # Convert to RGB if needed (for JPEG)
            im = im.convert("RGB")
        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.warning(f"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 load_dataset_cache_file(path: Path) -> dict:
    """Load an Ultralytics *.cache dictionary from path."""
    import gc

    gc.disable()  # reduce pickle load time https://github.com/ultralytics/ultralytics/pull/1585
    cache = np.load(str(path), allow_pickle=True).item()  # load dict
    gc.enable()
    return cache