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Referencia para ultralytics/data/utils.py

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Este archivo está disponible en https://github.com/ultralytics/ ultralytics/blob/main/ ultralytics/data/utils .py. Si detectas algún problema, por favor, ayuda a solucionarlo contribuyendo con una Pull Request 🛠️. ¡Gracias 🙏!



ultralytics.data.utils.HUBDatasetStats

Una clase para generar conjuntos de datos HUB JSON y -hub directorio del conjunto de datos.

Parámetros:

Nombre Tipo Descripción Por defecto
path str

Ruta a data.yaml o data.zip (con data.yaml dentro de data.zip). Por defecto es 'coco8.yaml'.

'coco8.yaml'
task str

Tarea del conjunto de datos. Las opciones son "detectar", "segmentar", "plantear", "clasificar". Por defecto es "detectar".

'detect'
autodownload bool

Intentar descargar el conjunto de datos si no se encuentra localmente. Por defecto es Falso.

False
Ejemplo

Descarga los archivos *.zip de https://github.com/ultralytics/hub/tree/main/example_datasets es decir, https://github.com/ultralytics/hub/raw/main/example_datasets/coco8.zip para 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()

Código fuente en 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). Default is 'coco8.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/dota8.zip', task='obb')  # OBB dataset
        stats = HUBDatasetStats('path/to/imagenet10.zip', task='classify')  # classification dataset

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

    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
        if self.task == "classify":
            unzip_dir = unzip_file(path)
            data = check_cls_dataset(unzip_dir)
            data["path"] = unzip_dir
        else:  # detect, segment, pose
            _, 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

    @staticmethod
    def _unzip(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), find_dataset_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 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

    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

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

Inicializa la clase.

Código fuente en 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
    if self.task == "classify":
        unzip_dir = unzip_file(path)
        data = check_cls_dataset(unzip_dir)
        data["path"] = unzip_dir
    else:  # detect, segment, pose
        _, 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(save=False, verbose=False)

Devuelve el conjunto de datos JSON para Ultralytics HUB.

Código fuente en 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()

Comprime imágenes para Ultralytics HUB.

Código fuente en 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(img_paths)

Define las trayectorias de las etiquetas en función de las trayectorias de las imágenes.

Código fuente en 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(paths)

Devuelve un único valor hash de una lista de rutas (archivos o dirs).

Código fuente en 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(img)

Devuelve el tamaño PIL corregido exif.

Código fuente en 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
        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



ultralytics.data.utils.verify_image(args)

Verifica una imagen.

Código fuente en 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(args)

Verifica un par imagen-etiqueta.

Código fuente en 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(imgsz, polygons, color=1, downsample_ratio=1)

Convierte una lista de polígonos en una máscara binaria del tamaño de imagen especificado.

Parámetros:

Nombre Tipo Descripción Por defecto
imgsz tuple

El tamaño de la imagen como (alto, ancho).

necesario
polygons list[ndarray]

Una lista de polígonos. Cada polígono es una matriz con forma [N, M], donde N es el número de polígonos, y M es el número de puntos tales que M % 2 = 0.

necesario
color int

El valor de color para rellenar los polígonos de la máscara. Por defecto es 1.

1
downsample_ratio int

Factor por el que reducir la máscara. Por defecto es 1.

1

Devuelve:

Tipo Descripción
ndarray

Una máscara binaria del tamaño de imagen especificado con los polígonos rellenos.

Código fuente en 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(imgsz, polygons, color, downsample_ratio=1)

Convierte una lista de polígonos en un conjunto de máscaras binarias del tamaño de imagen especificado.

Parámetros:

Nombre Tipo Descripción Por defecto
imgsz tuple

El tamaño de la imagen como (alto, ancho).

necesario
polygons list[ndarray]

Una lista de polígonos. Cada polígono es una matriz con forma [N, M], donde N es el número de polígonos, y M es el número de puntos tales que M % 2 = 0.

necesario
color int

El valor de color para rellenar los polígonos de las máscaras.

necesario
downsample_ratio int

Factor por el que reducir la muestra de cada máscara. Por defecto es 1.

1

Devuelve:

Tipo Descripción
ndarray

Un conjunto de máscaras binarias del tamaño de imagen especificado con los polígonos rellenos.

Código fuente en 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(imgsz, segments, downsample_ratio=1)

Devuelve una máscara de solapamiento (640, 640).

Código fuente en 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)
        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(path)

Busca y devuelve el archivo YAML asociado a un conjunto de datos Detectar, Segmento o Pose.

Esta función busca primero un archivo YAML en el nivel raíz del directorio proporcionado, y si no lo encuentra realiza una búsqueda recursiva. Prefiere los archivos YAML que tengan la misma raíz que la ruta proporcionada. Si no hay ningún archivo YAML si no se encuentra ningún archivo YAML o si se encuentran varios archivos YAML.

Parámetros:

Nombre Tipo Descripción Por defecto
path Path

La ruta del directorio donde buscar el archivo YAML.

necesario

Devuelve:

Tipo Descripción
Path

La ruta del archivo YAML encontrado.

Código fuente en 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(dataset, autodownload=True)

Descarga, verifica y/o descomprime un conjunto de datos si no se encuentra localmente.

Esta función comprueba la disponibilidad de un conjunto de datos especificado y, si no lo encuentra, tiene la opción de descargar y descomprimir el conjunto de datos. A continuación, lee y analiza los datos YAML que lo acompañan, asegurándose de que se cumplen los requisitos clave y también resuelve las rutas relacionadas con el conjunto de datos.

Parámetros:

Nombre Tipo Descripción Por defecto
dataset str

Ruta al conjunto de datos o descriptor del conjunto de datos (como un archivo YAML).

necesario
autodownload bool

Si se descarga automáticamente el conjunto de datos si no se encuentra. Por defecto es Verdadero.

True

Devuelve:

Tipo Descripción
dict

Información y rutas del conjunto de datos analizado.

Código fuente en 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_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



ultralytics.data.utils.check_cls_dataset(dataset, split='')

Comprueba un conjunto de datos de clasificación como Imagenet.

Esta función acepta una dataset e intenta recuperar la información del conjunto de datos correspondiente. Si el conjunto de datos no se encuentra localmente, intenta descargarlo de Internet y guardarlo localmente.

Parámetros:

Nombre Tipo Descripción Por defecto
dataset str | Path

El nombre del conjunto de datos.

necesario
split str

La división del conjunto de datos. Puede ser 'val', 'test' o ''. Por defecto es ''.

''

Devuelve:

Tipo Descripción
dict

Un diccionario que contiene las siguientes claves - 'entrenar' (Ruta): La ruta del directorio que contiene el conjunto de datos de entrenamiento. - 'val' (Ruta): La ruta del directorio que contiene el conjunto de validación del conjunto de datos. - 'test' (Ruta): La ruta del directorio que contiene el conjunto de pruebas del conjunto de datos. - 'nc' (int): El número de clases del conjunto de datos. - 'nombres' (dict): Un diccionario con los nombres de las clases del conjunto de datos.

Código fuente en 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/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}



ultralytics.data.utils.compress_one_image(f, f_new=None, max_dim=1920, quality=50)

Comprime un único archivo de imagen a un tamaño reducido, conservando su relación de aspecto y calidad, utilizando la biblioteca Python Imaging Library (PIL) o la biblioteca OpenCV. Si la imagen de entrada es menor que la dimensión máxima, no se redimensionará. redimensionada.

Parámetros:

Nombre Tipo Descripción Por defecto
f str

La ruta al archivo de imagen de entrada.

necesario
f_new str

La ruta al archivo de imagen de salida. Si no se especifica, se sobrescribirá el archivo de entrada.

None
max_dim int

La dimensión máxima (anchura o altura) de la imagen de salida. Por defecto es 1920 píxeles.

1920
quality int

La calidad de compresión de la imagen en porcentaje. Por defecto es 50%.

50
Ejemplo
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)
Código fuente en 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(path=DATASETS_DIR / 'coco8/images', weights=(0.9, 0.1, 0.0), annotated_only=False)

Divide automáticamente un conjunto de datos en divisiones tren/val/prueba y guarda las divisiones resultantes en archivos autosplit_*.txt.

Parámetros:

Nombre Tipo Descripción Por defecto
path Path

Ruta al directorio de imágenes. Por defecto es DATASETS_DIR / 'coco8/images'.

DATASETS_DIR / 'coco8/images'
weights list | tuple

Fracciones de división de entrenamiento, validación y prueba. Por defecto (0,9, 0,1, 0,0).

(0.9, 0.1, 0.0)
annotated_only bool

Si es Verdadero, sólo se utilizan las imágenes con un archivo txt asociado. Por defecto es Falso.

False
Ejemplo
from ultralytics.data.utils import autosplit

autosplit()
Código fuente en 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(path)

Carga un diccionario Ultralytics *.cache desde la ruta.

Código fuente en 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(prefix, path, x, version)

Guarda un conjunto de datos Ultralytics *.cache diccionario x en la ruta.

Código fuente en 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.")





Creado 2023-11-12, Actualizado 2024-05-18
Autores: glenn-jocher (4), Burhan-Q (1), Laughing-q (1)