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Reference for ultralytics/data/utils.py

Note

This file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/utils.py. If you spot a problem please help fix it by contributing a Pull Request 🛠️. Thank you 🙏!


ultralytics.data.utils.HUBDatasetStats

HUBDatasetStats(path='coco8.yaml', task='detect', autodownload=False)

A class for generating HUB dataset JSON and -hub dataset directory.

Parameters:

Name Type Description Default
path str

Path to data.yaml or data.zip (with data.yaml inside data.zip). Default is 'coco8.yaml'.

'coco8.yaml'
task str

Dataset task. Options are 'detect', 'segment', 'pose', 'classify'. Default is 'detect'.

'detect'
autodownload bool

Attempt to download dataset if not found locally. Default is False.

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.

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()

Source code in ultralytics/data/utils.py
def __init__(self, path="coco8.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, 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

get_json

get_json(save=False, verbose=False)

Return dataset JSON for Ultralytics HUB.

Source code in ultralytics/data/utils.py
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 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

            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") 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

process_images

process_images()

Compress images for Ultralytics HUB.

Source code in ultralytics/data/utils.py
def process_images(self):
    """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





ultralytics.data.utils.img2label_paths

img2label_paths(img_paths)

Define label paths as a function of image paths.

Source code in ultralytics/data/utils.py
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]





ultralytics.data.utils.get_hash

get_hash(paths)

Returns a single hash value of a list of paths (files or dirs).

Source code in ultralytics/data/utils.py
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





ultralytics.data.utils.exif_size

exif_size(img: Image.Image)

Returns exif-corrected PIL size.

Source code in ultralytics/data/utils.py
def exif_size(img: Image.Image):
    """Returns exif-corrected PIL size."""
    s = img.size  # (width, height)
    if img.format == "JPEG":  # only support JPEG images
        try:
            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]
        except:  # noqa E722
            pass
    return s





ultralytics.data.utils.verify_image

verify_image(args)

Verify one image.

Source code in ultralytics/data/utils.py
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}. {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}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





ultralytics.data.utils.verify_image_label

verify_image_label(args)

Verify one image-label pair.

Source code in ultralytics/data/utils.py
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}. {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}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"
                    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:]
                assert points.max() <= 1, f"non-normalized or out of bounds coordinates {points[points > 1]}"
                assert lb.min() >= 0, f"negative label values {lb[lb < 0]}"

                # All labels
                max_cls = 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}WARNING ⚠️ {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 keypoints 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}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}"
        return [None, None, None, None, None, nm, nf, ne, nc, msg]





ultralytics.data.utils.polygon2mask

polygon2mask(imgsz, polygons, color=1, downsample_ratio=1)

Convert a list of polygons to a binary mask of the specified image size.

Parameters:

Name Type Description Default
imgsz tuple

The size of the image as (height, width).

required
polygons list[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.

required
color int

The color value to fill in the polygons on the mask. Defaults to 1.

1
downsample_ratio int

Factor by which to downsample the mask. Defaults to 1.

1

Returns:

Type Description
ndarray

A binary mask of the specified image size with the polygons filled in.

Source code in ultralytics/data/utils.py
def polygon2mask(imgsz, polygons, color=1, downsample_ratio=1):
    """
    Convert a list of polygons to a binary mask of the specified image size.

    Args:
        imgsz (tuple): 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. Defaults to 1.
        downsample_ratio (int, optional): Factor by which to downsample the mask. Defaults to 1.

    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))





ultralytics.data.utils.polygons2masks

polygons2masks(imgsz, polygons, color, downsample_ratio=1)

Convert a list of polygons to a set of binary masks of the specified image size.

Parameters:

Name Type Description Default
imgsz tuple

The size of the image as (height, width).

required
polygons list[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.

required
color int

The color value to fill in the polygons on the masks.

required
downsample_ratio int

Factor by which to downsample each mask. Defaults to 1.

1

Returns:

Type Description
ndarray

A set of binary masks of the specified image size with the polygons filled in.

Source code in ultralytics/data/utils.py
def polygons2masks(imgsz, polygons, color, downsample_ratio=1):
    """
    Convert a list of polygons to a set of binary masks of the specified image size.

    Args:
        imgsz (tuple): 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. Defaults to 1.

    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])





ultralytics.data.utils.polygons2masks_overlap

polygons2masks_overlap(imgsz, segments, downsample_ratio=1)

Return a (640, 640) overlap mask.

Source code in ultralytics/data/utils.py
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.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





ultralytics.data.utils.find_dataset_yaml

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. An AssertionError is raised if no YAML file is found or if multiple YAML files are found.

Parameters:

Name Type Description Default
path Path

The directory path to search for the YAML file.

required

Returns:

Type Description
Path

The path of the found YAML file.

Source code in ultralytics/data/utils.py
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. An AssertionError
    is raised if no YAML file is found or if multiple YAML files are found.

    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]





ultralytics.data.utils.check_det_dataset

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.

Parameters:

Name Type Description Default
dataset str

Path to the dataset or dataset descriptor (like a YAML file).

required
autodownload bool

Whether to automatically download the dataset if not found. Defaults to True.

True

Returns:

Type Description
dict

Parsed dataset information and paths.

Source code in ultralytics/data/utils.py
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.
    """
    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.info("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"])

    # 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()

    # 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
            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_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} ...")
                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





ultralytics.data.utils.check_cls_dataset

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.

Parameters:

Name Type Description Default
dataset str | Path

The name of the dataset.

required
split str

The split of the dataset. Either 'val', 'test', or ''. Defaults to ''.

''

Returns:

Type Description
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.

Source code in ultralytics/data/utils.py
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.
    """
    # 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 Path(dataset).suffix in {".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():
        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/assets/releases/download/v0.0.0/{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}





ultralytics.data.utils.compress_one_image

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.

Parameters:

Name Type Description Default
f str

The path to the input image file.

required
f_new str

The path to the output image file. If not specified, the input file will be overwritten.

None
max_dim int

The maximum dimension (width or height) of the output image. Default is 1920 pixels.

1920
quality int

The image compression quality as a percentage. Default is 50%.

50
Example
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)
Source code in ultralytics/data/utils.py
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)





ultralytics.data.utils.autosplit

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.

Parameters:

Name Type Description Default
path Path

Path to images directory. Defaults to DATASETS_DIR / 'coco8/images'.

DATASETS_DIR / 'coco8/images'
weights list | tuple

Train, validation, and test split fractions. Defaults to (0.9, 0.1, 0.0).

(0.9, 0.1, 0.0)
annotated_only bool

If True, only images with an associated txt file are used. Defaults to False.

False
Example
from ultralytics.data.utils import autosplit

autosplit()
Source code in ultralytics/data/utils.py
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





ultralytics.data.utils.load_dataset_cache_file

load_dataset_cache_file(path)

Load an Ultralytics *.cache dictionary from path.

Source code in ultralytics/data/utils.py
def load_dataset_cache_file(path):
    """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





ultralytics.data.utils.save_dataset_cache_file

save_dataset_cache_file(prefix, path, x, version)

Save an Ultralytics dataset *.cache dictionary x to path.

Source code in ultralytics/data/utils.py
def save_dataset_cache_file(prefix, path, x, version):
    """Save an Ultralytics dataset *.cache dictionary x to path."""
    x["version"] = version  # add cache version
    if is_dir_writeable(path.parent):
        if path.exists():
            path.unlink()  # remove *.cache file if exists
        np.save(str(path), x)  # save cache for next time
        path.with_suffix(".cache.npy").rename(path)  # remove .npy suffix
        LOGGER.info(f"{prefix}New cache created: {path}")
    else:
        LOGGER.warning(f"{prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable, cache not saved.")




📅 Created 11 months ago ✏️ Updated 1 month ago