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Referência para ultralytics/data/base.py

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ultralytics.data.base.BaseDataset

Bases: Dataset

Classe de conjunto de dados base para carregar e processar dados de imagem.

Parâmetros:

Nome Tipo Descrição Predefinição
img_path str

Caminho para a pasta que contém as imagens.

necessário
imgsz int

Tamanho da imagem. Usa como padrão 640.

640
cache bool

Armazena imagens em cache na RAM ou no disco durante o treino. A predefinição é Falso.

False
augment bool

Se for True, aplica o aumento de dados. A predefinição é Verdadeiro.

True
hyp dict

Hiperparâmetros para aplicar o aumento de dados. A predefinição é Nenhum.

DEFAULT_CFG
prefix str

Prefixo a imprimir nas mensagens de registo. Usa por defeito ''.

''
rect bool

Se for Verdadeiro, utiliza a formação retangular. A predefinição é Falso.

False
batch_size int

Tamanho dos lotes. Predefine-se como Nenhum.

16
stride int

Passa. Por defeito, escolhe 32.

32
pad float

Preenchimento. Predefine-se como 0,0.

0.5
single_cls bool

Se for Verdadeiro, utiliza a formação de classe única. A predefinição é Falso.

False
classes list

Lista de classes incluídas. A predefinição é Nenhum.

None
fraction float

Fração do conjunto de dados a utilizar. A predefinição é 1.0 (utiliza todos os dados).

1.0

Atributos:

Nome Tipo Descrição
im_files list

Lista de caminhos de ficheiros de imagem.

labels list

Lista de dicionários de dados de etiquetas.

ni int

Número de imagens no conjunto de dados.

ims list

Lista de imagens carregadas.

npy_files list

Lista de caminhos de ficheiros numpy.

transforms callable

Função de transformação de imagem.

Código fonte em ultralytics/data/base.py
class BaseDataset(Dataset):
    """
    Base dataset class for loading and processing image data.

    Args:
        img_path (str): Path to the folder containing images.
        imgsz (int, optional): Image size. Defaults to 640.
        cache (bool, optional): Cache images to RAM or disk during training. Defaults to False.
        augment (bool, optional): If True, data augmentation is applied. Defaults to True.
        hyp (dict, optional): Hyperparameters to apply data augmentation. Defaults to None.
        prefix (str, optional): Prefix to print in log messages. Defaults to ''.
        rect (bool, optional): If True, rectangular training is used. Defaults to False.
        batch_size (int, optional): Size of batches. Defaults to None.
        stride (int, optional): Stride. Defaults to 32.
        pad (float, optional): Padding. Defaults to 0.0.
        single_cls (bool, optional): If True, single class training is used. Defaults to False.
        classes (list): List of included classes. Default is None.
        fraction (float): Fraction of dataset to utilize. Default is 1.0 (use all data).

    Attributes:
        im_files (list): List of image file paths.
        labels (list): List of label data dictionaries.
        ni (int): Number of images in the dataset.
        ims (list): List of loaded images.
        npy_files (list): List of numpy file paths.
        transforms (callable): Image transformation function.
    """

    def __init__(
        self,
        img_path,
        imgsz=640,
        cache=False,
        augment=True,
        hyp=DEFAULT_CFG,
        prefix="",
        rect=False,
        batch_size=16,
        stride=32,
        pad=0.5,
        single_cls=False,
        classes=None,
        fraction=1.0,
    ):
        """Initialize BaseDataset with given configuration and options."""
        super().__init__()
        self.img_path = img_path
        self.imgsz = imgsz
        self.augment = augment
        self.single_cls = single_cls
        self.prefix = prefix
        self.fraction = fraction
        self.im_files = self.get_img_files(self.img_path)
        self.labels = self.get_labels()
        self.update_labels(include_class=classes)  # single_cls and include_class
        self.ni = len(self.labels)  # number of images
        self.rect = rect
        self.batch_size = batch_size
        self.stride = stride
        self.pad = pad
        if self.rect:
            assert self.batch_size is not None
            self.set_rectangle()

        # Buffer thread for mosaic images
        self.buffer = []  # buffer size = batch size
        self.max_buffer_length = min((self.ni, self.batch_size * 8, 1000)) if self.augment else 0

        # Cache images (options are cache = True, False, None, "ram", "disk")
        self.ims, self.im_hw0, self.im_hw = [None] * self.ni, [None] * self.ni, [None] * self.ni
        self.npy_files = [Path(f).with_suffix(".npy") for f in self.im_files]
        self.cache = cache.lower() if isinstance(cache, str) else "ram" if cache is True else None
        if (self.cache == "ram" and self.check_cache_ram()) or self.cache == "disk":
            self.cache_images()

        # Transforms
        self.transforms = self.build_transforms(hyp=hyp)

    def get_img_files(self, img_path):
        """Read image files."""
        try:
            f = []  # image files
            for p in img_path if isinstance(img_path, list) else [img_path]:
                p = Path(p)  # os-agnostic
                if p.is_dir():  # dir
                    f += glob.glob(str(p / "**" / "*.*"), recursive=True)
                    # F = list(p.rglob('*.*'))  # pathlib
                elif p.is_file():  # file
                    with open(p) as t:
                        t = t.read().strip().splitlines()
                        parent = str(p.parent) + os.sep
                        f += [x.replace("./", parent) if x.startswith("./") else x for x in t]  # local to global path
                        # F += [p.parent / x.lstrip(os.sep) for x in t]  # local to global path (pathlib)
                else:
                    raise FileNotFoundError(f"{self.prefix}{p} does not exist")
            im_files = sorted(x.replace("/", os.sep) for x in f if x.split(".")[-1].lower() in IMG_FORMATS)
            # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS])  # pathlib
            assert im_files, f"{self.prefix}No images found in {img_path}. {FORMATS_HELP_MSG}"
        except Exception as e:
            raise FileNotFoundError(f"{self.prefix}Error loading data from {img_path}\n{HELP_URL}") from e
        if self.fraction < 1:
            im_files = im_files[: round(len(im_files) * self.fraction)]  # retain a fraction of the dataset
        return im_files

    def update_labels(self, include_class: Optional[list]):
        """Update labels to include only these classes (optional)."""
        include_class_array = np.array(include_class).reshape(1, -1)
        for i in range(len(self.labels)):
            if include_class is not None:
                cls = self.labels[i]["cls"]
                bboxes = self.labels[i]["bboxes"]
                segments = self.labels[i]["segments"]
                keypoints = self.labels[i]["keypoints"]
                j = (cls == include_class_array).any(1)
                self.labels[i]["cls"] = cls[j]
                self.labels[i]["bboxes"] = bboxes[j]
                if segments:
                    self.labels[i]["segments"] = [segments[si] for si, idx in enumerate(j) if idx]
                if keypoints is not None:
                    self.labels[i]["keypoints"] = keypoints[j]
            if self.single_cls:
                self.labels[i]["cls"][:, 0] = 0

    def load_image(self, i, rect_mode=True):
        """Loads 1 image from dataset index 'i', returns (im, resized hw)."""
        im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i]
        if im is None:  # not cached in RAM
            if fn.exists():  # load npy
                try:
                    im = np.load(fn)
                except Exception as e:
                    LOGGER.warning(f"{self.prefix}WARNING ⚠️ Removing corrupt *.npy image file {fn} due to: {e}")
                    Path(fn).unlink(missing_ok=True)
                    im = cv2.imread(f)  # BGR
            else:  # read image
                im = cv2.imread(f)  # BGR
            if im is None:
                raise FileNotFoundError(f"Image Not Found {f}")

            h0, w0 = im.shape[:2]  # orig hw
            if rect_mode:  # resize long side to imgsz while maintaining aspect ratio
                r = self.imgsz / max(h0, w0)  # ratio
                if r != 1:  # if sizes are not equal
                    w, h = (min(math.ceil(w0 * r), self.imgsz), min(math.ceil(h0 * r), self.imgsz))
                    im = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR)
            elif not (h0 == w0 == self.imgsz):  # resize by stretching image to square imgsz
                im = cv2.resize(im, (self.imgsz, self.imgsz), interpolation=cv2.INTER_LINEAR)

            # Add to buffer if training with augmentations
            if self.augment:
                self.ims[i], self.im_hw0[i], self.im_hw[i] = im, (h0, w0), im.shape[:2]  # im, hw_original, hw_resized
                self.buffer.append(i)
                if 1 < len(self.buffer) >= self.max_buffer_length:  # prevent empty buffer
                    j = self.buffer.pop(0)
                    if self.cache != "ram":
                        self.ims[j], self.im_hw0[j], self.im_hw[j] = None, None, None

            return im, (h0, w0), im.shape[:2]

        return self.ims[i], self.im_hw0[i], self.im_hw[i]

    def cache_images(self):
        """Cache images to memory or disk."""
        b, gb = 0, 1 << 30  # bytes of cached images, bytes per gigabytes
        fcn, storage = (self.cache_images_to_disk, "Disk") if self.cache == "disk" else (self.load_image, "RAM")
        with ThreadPool(NUM_THREADS) as pool:
            results = pool.imap(fcn, range(self.ni))
            pbar = TQDM(enumerate(results), total=self.ni, disable=LOCAL_RANK > 0)
            for i, x in pbar:
                if self.cache == "disk":
                    b += self.npy_files[i].stat().st_size
                else:  # 'ram'
                    self.ims[i], self.im_hw0[i], self.im_hw[i] = x  # im, hw_orig, hw_resized = load_image(self, i)
                    b += self.ims[i].nbytes
                pbar.desc = f"{self.prefix}Caching images ({b / gb:.1f}GB {storage})"
            pbar.close()

    def cache_images_to_disk(self, i):
        """Saves an image as an *.npy file for faster loading."""
        f = self.npy_files[i]
        if not f.exists():
            np.save(f.as_posix(), cv2.imread(self.im_files[i]), allow_pickle=False)

    def check_cache_ram(self, safety_margin=0.5):
        """Check image caching requirements vs available memory."""
        b, gb = 0, 1 << 30  # bytes of cached images, bytes per gigabytes
        n = min(self.ni, 30)  # extrapolate from 30 random images
        for _ in range(n):
            im = cv2.imread(random.choice(self.im_files))  # sample image
            ratio = self.imgsz / max(im.shape[0], im.shape[1])  # max(h, w)  # ratio
            b += im.nbytes * ratio**2
        mem_required = b * self.ni / n * (1 + safety_margin)  # GB required to cache dataset into RAM
        mem = psutil.virtual_memory()
        success = mem_required < mem.available  # to cache or not to cache, that is the question
        if not success:
            self.cache = None
            LOGGER.info(
                f"{self.prefix}{mem_required / gb:.1f}GB RAM required to cache images "
                f"with {int(safety_margin * 100)}% safety margin but only "
                f"{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, not caching images ⚠️"
            )
        return success

    def set_rectangle(self):
        """Sets the shape of bounding boxes for YOLO detections as rectangles."""
        bi = np.floor(np.arange(self.ni) / self.batch_size).astype(int)  # batch index
        nb = bi[-1] + 1  # number of batches

        s = np.array([x.pop("shape") for x in self.labels])  # hw
        ar = s[:, 0] / s[:, 1]  # aspect ratio
        irect = ar.argsort()
        self.im_files = [self.im_files[i] for i in irect]
        self.labels = [self.labels[i] for i in irect]
        ar = ar[irect]

        # Set training image shapes
        shapes = [[1, 1]] * nb
        for i in range(nb):
            ari = ar[bi == i]
            mini, maxi = ari.min(), ari.max()
            if maxi < 1:
                shapes[i] = [maxi, 1]
            elif mini > 1:
                shapes[i] = [1, 1 / mini]

        self.batch_shapes = np.ceil(np.array(shapes) * self.imgsz / self.stride + self.pad).astype(int) * self.stride
        self.batch = bi  # batch index of image

    def __getitem__(self, index):
        """Returns transformed label information for given index."""
        return self.transforms(self.get_image_and_label(index))

    def get_image_and_label(self, index):
        """Get and return label information from the dataset."""
        label = deepcopy(self.labels[index])  # requires deepcopy() https://github.com/ultralytics/ultralytics/pull/1948
        label.pop("shape", None)  # shape is for rect, remove it
        label["img"], label["ori_shape"], label["resized_shape"] = self.load_image(index)
        label["ratio_pad"] = (
            label["resized_shape"][0] / label["ori_shape"][0],
            label["resized_shape"][1] / label["ori_shape"][1],
        )  # for evaluation
        if self.rect:
            label["rect_shape"] = self.batch_shapes[self.batch[index]]
        return self.update_labels_info(label)

    def __len__(self):
        """Returns the length of the labels list for the dataset."""
        return len(self.labels)

    def update_labels_info(self, label):
        """Custom your label format here."""
        return label

    def build_transforms(self, hyp=None):
        """
        Users can customize augmentations here.

        Example:
            ```python
            if self.augment:
                # Training transforms
                return Compose([])
            else:
                # Val transforms
                return Compose([])
            ```
        """
        raise NotImplementedError

    def get_labels(self):
        """
        Users can customize their own format here.

        Note:
            Ensure output is a dictionary with the following keys:
            ```python
            dict(
                im_file=im_file,
                shape=shape,  # format: (height, width)
                cls=cls,
                bboxes=bboxes, # xywh
                segments=segments,  # xy
                keypoints=keypoints, # xy
                normalized=True, # or False
                bbox_format="xyxy",  # or xywh, ltwh
            )
            ```
        """
        raise NotImplementedError

__getitem__(index)

Devolve a informação da etiqueta transformada para um determinado índice.

Código fonte em ultralytics/data/base.py
def __getitem__(self, index):
    """Returns transformed label information for given index."""
    return self.transforms(self.get_image_and_label(index))

__init__(img_path, imgsz=640, cache=False, augment=True, hyp=DEFAULT_CFG, prefix='', rect=False, batch_size=16, stride=32, pad=0.5, single_cls=False, classes=None, fraction=1.0)

Inicializa o BaseDataset com a configuração e as opções dadas.

Código fonte em ultralytics/data/base.py
def __init__(
    self,
    img_path,
    imgsz=640,
    cache=False,
    augment=True,
    hyp=DEFAULT_CFG,
    prefix="",
    rect=False,
    batch_size=16,
    stride=32,
    pad=0.5,
    single_cls=False,
    classes=None,
    fraction=1.0,
):
    """Initialize BaseDataset with given configuration and options."""
    super().__init__()
    self.img_path = img_path
    self.imgsz = imgsz
    self.augment = augment
    self.single_cls = single_cls
    self.prefix = prefix
    self.fraction = fraction
    self.im_files = self.get_img_files(self.img_path)
    self.labels = self.get_labels()
    self.update_labels(include_class=classes)  # single_cls and include_class
    self.ni = len(self.labels)  # number of images
    self.rect = rect
    self.batch_size = batch_size
    self.stride = stride
    self.pad = pad
    if self.rect:
        assert self.batch_size is not None
        self.set_rectangle()

    # Buffer thread for mosaic images
    self.buffer = []  # buffer size = batch size
    self.max_buffer_length = min((self.ni, self.batch_size * 8, 1000)) if self.augment else 0

    # Cache images (options are cache = True, False, None, "ram", "disk")
    self.ims, self.im_hw0, self.im_hw = [None] * self.ni, [None] * self.ni, [None] * self.ni
    self.npy_files = [Path(f).with_suffix(".npy") for f in self.im_files]
    self.cache = cache.lower() if isinstance(cache, str) else "ram" if cache is True else None
    if (self.cache == "ram" and self.check_cache_ram()) or self.cache == "disk":
        self.cache_images()

    # Transforms
    self.transforms = self.build_transforms(hyp=hyp)

__len__()

Devolve o comprimento da lista de etiquetas para o conjunto de dados.

Código fonte em ultralytics/data/base.py
def __len__(self):
    """Returns the length of the labels list for the dataset."""
    return len(self.labels)

build_transforms(hyp=None)

Os utilizadores podem personalizar as ampliações aqui.

Exemplo
if self.augment:
    # Training transforms
    return Compose([])
else:
    # Val transforms
    return Compose([])
Código fonte em ultralytics/data/base.py
def build_transforms(self, hyp=None):
    """
    Users can customize augmentations here.

    Example:
        ```python
        if self.augment:
            # Training transforms
            return Compose([])
        else:
            # Val transforms
            return Compose([])
        ```
    """
    raise NotImplementedError

cache_images()

Armazena imagens em cache na memória ou no disco.

Código fonte em ultralytics/data/base.py
def cache_images(self):
    """Cache images to memory or disk."""
    b, gb = 0, 1 << 30  # bytes of cached images, bytes per gigabytes
    fcn, storage = (self.cache_images_to_disk, "Disk") if self.cache == "disk" else (self.load_image, "RAM")
    with ThreadPool(NUM_THREADS) as pool:
        results = pool.imap(fcn, range(self.ni))
        pbar = TQDM(enumerate(results), total=self.ni, disable=LOCAL_RANK > 0)
        for i, x in pbar:
            if self.cache == "disk":
                b += self.npy_files[i].stat().st_size
            else:  # 'ram'
                self.ims[i], self.im_hw0[i], self.im_hw[i] = x  # im, hw_orig, hw_resized = load_image(self, i)
                b += self.ims[i].nbytes
            pbar.desc = f"{self.prefix}Caching images ({b / gb:.1f}GB {storage})"
        pbar.close()

cache_images_to_disk(i)

Guarda uma imagem como um ficheiro *.npy para um carregamento mais rápido.

Código fonte em ultralytics/data/base.py
def cache_images_to_disk(self, i):
    """Saves an image as an *.npy file for faster loading."""
    f = self.npy_files[i]
    if not f.exists():
        np.save(f.as_posix(), cv2.imread(self.im_files[i]), allow_pickle=False)

check_cache_ram(safety_margin=0.5)

Verifica os requisitos de cache de imagem em relação à memória disponível.

Código fonte em ultralytics/data/base.py
def check_cache_ram(self, safety_margin=0.5):
    """Check image caching requirements vs available memory."""
    b, gb = 0, 1 << 30  # bytes of cached images, bytes per gigabytes
    n = min(self.ni, 30)  # extrapolate from 30 random images
    for _ in range(n):
        im = cv2.imread(random.choice(self.im_files))  # sample image
        ratio = self.imgsz / max(im.shape[0], im.shape[1])  # max(h, w)  # ratio
        b += im.nbytes * ratio**2
    mem_required = b * self.ni / n * (1 + safety_margin)  # GB required to cache dataset into RAM
    mem = psutil.virtual_memory()
    success = mem_required < mem.available  # to cache or not to cache, that is the question
    if not success:
        self.cache = None
        LOGGER.info(
            f"{self.prefix}{mem_required / gb:.1f}GB RAM required to cache images "
            f"with {int(safety_margin * 100)}% safety margin but only "
            f"{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, not caching images ⚠️"
        )
    return success

get_image_and_label(index)

Obtém e devolve informações de etiquetas do conjunto de dados.

Código fonte em ultralytics/data/base.py
def get_image_and_label(self, index):
    """Get and return label information from the dataset."""
    label = deepcopy(self.labels[index])  # requires deepcopy() https://github.com/ultralytics/ultralytics/pull/1948
    label.pop("shape", None)  # shape is for rect, remove it
    label["img"], label["ori_shape"], label["resized_shape"] = self.load_image(index)
    label["ratio_pad"] = (
        label["resized_shape"][0] / label["ori_shape"][0],
        label["resized_shape"][1] / label["ori_shape"][1],
    )  # for evaluation
    if self.rect:
        label["rect_shape"] = self.batch_shapes[self.batch[index]]
    return self.update_labels_info(label)

get_img_files(img_path)

Lê ficheiros de imagem.

Código fonte em ultralytics/data/base.py
def get_img_files(self, img_path):
    """Read image files."""
    try:
        f = []  # image files
        for p in img_path if isinstance(img_path, list) else [img_path]:
            p = Path(p)  # os-agnostic
            if p.is_dir():  # dir
                f += glob.glob(str(p / "**" / "*.*"), recursive=True)
                # F = list(p.rglob('*.*'))  # pathlib
            elif p.is_file():  # file
                with open(p) as t:
                    t = t.read().strip().splitlines()
                    parent = str(p.parent) + os.sep
                    f += [x.replace("./", parent) if x.startswith("./") else x for x in t]  # local to global path
                    # F += [p.parent / x.lstrip(os.sep) for x in t]  # local to global path (pathlib)
            else:
                raise FileNotFoundError(f"{self.prefix}{p} does not exist")
        im_files = sorted(x.replace("/", os.sep) for x in f if x.split(".")[-1].lower() in IMG_FORMATS)
        # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS])  # pathlib
        assert im_files, f"{self.prefix}No images found in {img_path}. {FORMATS_HELP_MSG}"
    except Exception as e:
        raise FileNotFoundError(f"{self.prefix}Error loading data from {img_path}\n{HELP_URL}") from e
    if self.fraction < 1:
        im_files = im_files[: round(len(im_files) * self.fraction)]  # retain a fraction of the dataset
    return im_files

get_labels()

Os utilizadores podem personalizar o seu próprio formato aqui.

Nota

Certifica-te de que o resultado é um dicionário com as seguintes chaves:

dict(
    im_file=im_file,
    shape=shape,  # format: (height, width)
    cls=cls,
    bboxes=bboxes, # xywh
    segments=segments,  # xy
    keypoints=keypoints, # xy
    normalized=True, # or False
    bbox_format="xyxy",  # or xywh, ltwh
)

Código fonte em ultralytics/data/base.py
def get_labels(self):
    """
    Users can customize their own format here.

    Note:
        Ensure output is a dictionary with the following keys:
        ```python
        dict(
            im_file=im_file,
            shape=shape,  # format: (height, width)
            cls=cls,
            bboxes=bboxes, # xywh
            segments=segments,  # xy
            keypoints=keypoints, # xy
            normalized=True, # or False
            bbox_format="xyxy",  # or xywh, ltwh
        )
        ```
    """
    raise NotImplementedError

load_image(i, rect_mode=True)

Carrega 1 imagem do conjunto de dados de índice 'i', devolve (im, resized hw).

Código fonte em ultralytics/data/base.py
def load_image(self, i, rect_mode=True):
    """Loads 1 image from dataset index 'i', returns (im, resized hw)."""
    im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i]
    if im is None:  # not cached in RAM
        if fn.exists():  # load npy
            try:
                im = np.load(fn)
            except Exception as e:
                LOGGER.warning(f"{self.prefix}WARNING ⚠️ Removing corrupt *.npy image file {fn} due to: {e}")
                Path(fn).unlink(missing_ok=True)
                im = cv2.imread(f)  # BGR
        else:  # read image
            im = cv2.imread(f)  # BGR
        if im is None:
            raise FileNotFoundError(f"Image Not Found {f}")

        h0, w0 = im.shape[:2]  # orig hw
        if rect_mode:  # resize long side to imgsz while maintaining aspect ratio
            r = self.imgsz / max(h0, w0)  # ratio
            if r != 1:  # if sizes are not equal
                w, h = (min(math.ceil(w0 * r), self.imgsz), min(math.ceil(h0 * r), self.imgsz))
                im = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR)
        elif not (h0 == w0 == self.imgsz):  # resize by stretching image to square imgsz
            im = cv2.resize(im, (self.imgsz, self.imgsz), interpolation=cv2.INTER_LINEAR)

        # Add to buffer if training with augmentations
        if self.augment:
            self.ims[i], self.im_hw0[i], self.im_hw[i] = im, (h0, w0), im.shape[:2]  # im, hw_original, hw_resized
            self.buffer.append(i)
            if 1 < len(self.buffer) >= self.max_buffer_length:  # prevent empty buffer
                j = self.buffer.pop(0)
                if self.cache != "ram":
                    self.ims[j], self.im_hw0[j], self.im_hw[j] = None, None, None

        return im, (h0, w0), im.shape[:2]

    return self.ims[i], self.im_hw0[i], self.im_hw[i]

set_rectangle()

Define a forma das caixas delimitadoras para YOLO detecções como rectângulos.

Código fonte em ultralytics/data/base.py
def set_rectangle(self):
    """Sets the shape of bounding boxes for YOLO detections as rectangles."""
    bi = np.floor(np.arange(self.ni) / self.batch_size).astype(int)  # batch index
    nb = bi[-1] + 1  # number of batches

    s = np.array([x.pop("shape") for x in self.labels])  # hw
    ar = s[:, 0] / s[:, 1]  # aspect ratio
    irect = ar.argsort()
    self.im_files = [self.im_files[i] for i in irect]
    self.labels = [self.labels[i] for i in irect]
    ar = ar[irect]

    # Set training image shapes
    shapes = [[1, 1]] * nb
    for i in range(nb):
        ari = ar[bi == i]
        mini, maxi = ari.min(), ari.max()
        if maxi < 1:
            shapes[i] = [maxi, 1]
        elif mini > 1:
            shapes[i] = [1, 1 / mini]

    self.batch_shapes = np.ceil(np.array(shapes) * self.imgsz / self.stride + self.pad).astype(int) * self.stride
    self.batch = bi  # batch index of image

update_labels(include_class)

Actualiza as etiquetas para incluir apenas estas classes (opcional).

Código fonte em ultralytics/data/base.py
def update_labels(self, include_class: Optional[list]):
    """Update labels to include only these classes (optional)."""
    include_class_array = np.array(include_class).reshape(1, -1)
    for i in range(len(self.labels)):
        if include_class is not None:
            cls = self.labels[i]["cls"]
            bboxes = self.labels[i]["bboxes"]
            segments = self.labels[i]["segments"]
            keypoints = self.labels[i]["keypoints"]
            j = (cls == include_class_array).any(1)
            self.labels[i]["cls"] = cls[j]
            self.labels[i]["bboxes"] = bboxes[j]
            if segments:
                self.labels[i]["segments"] = [segments[si] for si, idx in enumerate(j) if idx]
            if keypoints is not None:
                self.labels[i]["keypoints"] = keypoints[j]
        if self.single_cls:
            self.labels[i]["cls"][:, 0] = 0

update_labels_info(label)

Personaliza o formato da tua etiqueta aqui.

Código fonte em ultralytics/data/base.py
def update_labels_info(self, label):
    """Custom your label format here."""
    return label





Criado em 2023-11-12, Atualizado em 2024-05-18
Autores: glenn-jocher (4), Burhan-Q (1)