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ultralytics.utils.instance.Bboxes

Una classe per gestire i rettangoli di selezione.

La classe supporta diversi formati di rettangolo di selezione come 'xyxy', 'xywh' e 'ltwh'. I dati dei riquadri di delimitazione devono essere forniti in array numpy.

Attributi:

Nome Tipo Descrizione
bboxes ndarray

I riquadri di delimitazione memorizzati in un array numpy 2D.

format str

Il formato delle caselle di delimitazione ('xyxy', 'xywh' o 'ltwh').

Nota

Questa classe non gestisce la normalizzazione o la denormalizzazione dei rettangoli di selezione.

Codice sorgente in ultralytics/utils/instance.py
class Bboxes:
    """
    A class for handling bounding boxes.

    The class supports various bounding box formats like 'xyxy', 'xywh', and 'ltwh'.
    Bounding box data should be provided in numpy arrays.

    Attributes:
        bboxes (numpy.ndarray): The bounding boxes stored in a 2D numpy array.
        format (str): The format of the bounding boxes ('xyxy', 'xywh', or 'ltwh').

    Note:
        This class does not handle normalization or denormalization of bounding boxes.
    """

    def __init__(self, bboxes, format="xyxy") -> None:
        """Initializes the Bboxes class with bounding box data in a specified format."""
        assert format in _formats, f"Invalid bounding box format: {format}, format must be one of {_formats}"
        bboxes = bboxes[None, :] if bboxes.ndim == 1 else bboxes
        assert bboxes.ndim == 2
        assert bboxes.shape[1] == 4
        self.bboxes = bboxes
        self.format = format
        # self.normalized = normalized

    def convert(self, format):
        """Converts bounding box format from one type to another."""
        assert format in _formats, f"Invalid bounding box format: {format}, format must be one of {_formats}"
        if self.format == format:
            return
        elif self.format == "xyxy":
            func = xyxy2xywh if format == "xywh" else xyxy2ltwh
        elif self.format == "xywh":
            func = xywh2xyxy if format == "xyxy" else xywh2ltwh
        else:
            func = ltwh2xyxy if format == "xyxy" else ltwh2xywh
        self.bboxes = func(self.bboxes)
        self.format = format

    def areas(self):
        """Return box areas."""
        self.convert("xyxy")
        return (self.bboxes[:, 2] - self.bboxes[:, 0]) * (self.bboxes[:, 3] - self.bboxes[:, 1])

    # def denormalize(self, w, h):
    #    if not self.normalized:
    #         return
    #     assert (self.bboxes <= 1.0).all()
    #     self.bboxes[:, 0::2] *= w
    #     self.bboxes[:, 1::2] *= h
    #     self.normalized = False
    #
    # def normalize(self, w, h):
    #     if self.normalized:
    #         return
    #     assert (self.bboxes > 1.0).any()
    #     self.bboxes[:, 0::2] /= w
    #     self.bboxes[:, 1::2] /= h
    #     self.normalized = True

    def mul(self, scale):
        """
        Args:
            scale (tuple | list | int): the scale for four coords.
        """
        if isinstance(scale, Number):
            scale = to_4tuple(scale)
        assert isinstance(scale, (tuple, list))
        assert len(scale) == 4
        self.bboxes[:, 0] *= scale[0]
        self.bboxes[:, 1] *= scale[1]
        self.bboxes[:, 2] *= scale[2]
        self.bboxes[:, 3] *= scale[3]

    def add(self, offset):
        """
        Args:
            offset (tuple | list | int): the offset for four coords.
        """
        if isinstance(offset, Number):
            offset = to_4tuple(offset)
        assert isinstance(offset, (tuple, list))
        assert len(offset) == 4
        self.bboxes[:, 0] += offset[0]
        self.bboxes[:, 1] += offset[1]
        self.bboxes[:, 2] += offset[2]
        self.bboxes[:, 3] += offset[3]

    def __len__(self):
        """Return the number of boxes."""
        return len(self.bboxes)

    @classmethod
    def concatenate(cls, boxes_list: List["Bboxes"], axis=0) -> "Bboxes":
        """
        Concatenate a list of Bboxes objects into a single Bboxes object.

        Args:
            boxes_list (List[Bboxes]): A list of Bboxes objects to concatenate.
            axis (int, optional): The axis along which to concatenate the bounding boxes.
                                   Defaults to 0.

        Returns:
            Bboxes: A new Bboxes object containing the concatenated bounding boxes.

        Note:
            The input should be a list or tuple of Bboxes objects.
        """
        assert isinstance(boxes_list, (list, tuple))
        if not boxes_list:
            return cls(np.empty(0))
        assert all(isinstance(box, Bboxes) for box in boxes_list)

        if len(boxes_list) == 1:
            return boxes_list[0]
        return cls(np.concatenate([b.bboxes for b in boxes_list], axis=axis))

    def __getitem__(self, index) -> "Bboxes":
        """
        Retrieve a specific bounding box or a set of bounding boxes using indexing.

        Args:
            index (int, slice, or np.ndarray): The index, slice, or boolean array to select
                                               the desired bounding boxes.

        Returns:
            Bboxes: A new Bboxes object containing the selected bounding boxes.

        Raises:
            AssertionError: If the indexed bounding boxes do not form a 2-dimensional matrix.

        Note:
            When using boolean indexing, make sure to provide a boolean array with the same
            length as the number of bounding boxes.
        """
        if isinstance(index, int):
            return Bboxes(self.bboxes[index].view(1, -1))
        b = self.bboxes[index]
        assert b.ndim == 2, f"Indexing on Bboxes with {index} failed to return a matrix!"
        return Bboxes(b)

__getitem__(index)

Recupera un riquadro di delimitazione specifico o un insieme di riquadri di delimitazione utilizzando l'indicizzazione.

Parametri:

Nome Tipo Descrizione Predefinito
index int, slice, or np.ndarray

L'indice, la slice o l'array booleano per selezionare le caselle di delimitazione desiderate.

richiesto

Restituzione:

Nome Tipo Descrizione
Bboxes Bboxes

Un nuovo oggetto Bboxes contenente i rettangoli di selezione.

Aumenta:

Tipo Descrizione
AssertionError

Se le caselle di delimitazione indicizzate non formano una matrice bidimensionale.

Nota

Quando si utilizza l'indicizzazione booleana, assicurati di fornire un array booleano della stessa lunghezza del numero di bounding box. lunghezza del numero di bounding box.

Codice sorgente in ultralytics/utils/instance.py
def __getitem__(self, index) -> "Bboxes":
    """
    Retrieve a specific bounding box or a set of bounding boxes using indexing.

    Args:
        index (int, slice, or np.ndarray): The index, slice, or boolean array to select
                                           the desired bounding boxes.

    Returns:
        Bboxes: A new Bboxes object containing the selected bounding boxes.

    Raises:
        AssertionError: If the indexed bounding boxes do not form a 2-dimensional matrix.

    Note:
        When using boolean indexing, make sure to provide a boolean array with the same
        length as the number of bounding boxes.
    """
    if isinstance(index, int):
        return Bboxes(self.bboxes[index].view(1, -1))
    b = self.bboxes[index]
    assert b.ndim == 2, f"Indexing on Bboxes with {index} failed to return a matrix!"
    return Bboxes(b)

__init__(bboxes, format='xyxy')

Inizializza la classe Bboxes con i dati dei rettangoli di selezione nel formato specificato.

Codice sorgente in ultralytics/utils/instance.py
def __init__(self, bboxes, format="xyxy") -> None:
    """Initializes the Bboxes class with bounding box data in a specified format."""
    assert format in _formats, f"Invalid bounding box format: {format}, format must be one of {_formats}"
    bboxes = bboxes[None, :] if bboxes.ndim == 1 else bboxes
    assert bboxes.ndim == 2
    assert bboxes.shape[1] == 4
    self.bboxes = bboxes
    self.format = format

__len__()

Restituisce il numero di caselle.

Codice sorgente in ultralytics/utils/instance.py
def __len__(self):
    """Return the number of boxes."""
    return len(self.bboxes)

add(offset)

Parametri:

Nome Tipo Descrizione Predefinito
offset tuple | list | int

l'offset per quattro coordinate.

richiesto
Codice sorgente in ultralytics/utils/instance.py
def add(self, offset):
    """
    Args:
        offset (tuple | list | int): the offset for four coords.
    """
    if isinstance(offset, Number):
        offset = to_4tuple(offset)
    assert isinstance(offset, (tuple, list))
    assert len(offset) == 4
    self.bboxes[:, 0] += offset[0]
    self.bboxes[:, 1] += offset[1]
    self.bboxes[:, 2] += offset[2]
    self.bboxes[:, 3] += offset[3]

areas()

Aree della scatola di ritorno.

Codice sorgente in ultralytics/utils/instance.py
def areas(self):
    """Return box areas."""
    self.convert("xyxy")
    return (self.bboxes[:, 2] - self.bboxes[:, 0]) * (self.bboxes[:, 3] - self.bboxes[:, 1])

concatenate(boxes_list, axis=0) classmethod

Concatena un elenco di oggetti Bboxes in un unico oggetto Bboxes.

Parametri:

Nome Tipo Descrizione Predefinito
boxes_list List[Bboxes]

Un elenco di oggetti Bbox da concatenare.

richiesto
axis int

L'asse lungo il quale concatenare i rettangoli di selezione. Il valore predefinito è 0.

0

Restituzione:

Nome Tipo Descrizione
Bboxes Bboxes

Un nuovo oggetto Bboxes contenente i rettangoli di selezione concatenati.

Nota

L'input deve essere un elenco o una tupla di oggetti Bboxes.

Codice sorgente in ultralytics/utils/instance.py
@classmethod
def concatenate(cls, boxes_list: List["Bboxes"], axis=0) -> "Bboxes":
    """
    Concatenate a list of Bboxes objects into a single Bboxes object.

    Args:
        boxes_list (List[Bboxes]): A list of Bboxes objects to concatenate.
        axis (int, optional): The axis along which to concatenate the bounding boxes.
                               Defaults to 0.

    Returns:
        Bboxes: A new Bboxes object containing the concatenated bounding boxes.

    Note:
        The input should be a list or tuple of Bboxes objects.
    """
    assert isinstance(boxes_list, (list, tuple))
    if not boxes_list:
        return cls(np.empty(0))
    assert all(isinstance(box, Bboxes) for box in boxes_list)

    if len(boxes_list) == 1:
        return boxes_list[0]
    return cls(np.concatenate([b.bboxes for b in boxes_list], axis=axis))

convert(format)

Converte il formato del rettangolo di selezione da un tipo all'altro.

Codice sorgente in ultralytics/utils/instance.py
def convert(self, format):
    """Converts bounding box format from one type to another."""
    assert format in _formats, f"Invalid bounding box format: {format}, format must be one of {_formats}"
    if self.format == format:
        return
    elif self.format == "xyxy":
        func = xyxy2xywh if format == "xywh" else xyxy2ltwh
    elif self.format == "xywh":
        func = xywh2xyxy if format == "xyxy" else xywh2ltwh
    else:
        func = ltwh2xyxy if format == "xyxy" else ltwh2xywh
    self.bboxes = func(self.bboxes)
    self.format = format

mul(scale)

Parametri:

Nome Tipo Descrizione Predefinito
scale tuple | list | int

la scala per quattro coordinate.

richiesto
Codice sorgente in ultralytics/utils/instance.py
def mul(self, scale):
    """
    Args:
        scale (tuple | list | int): the scale for four coords.
    """
    if isinstance(scale, Number):
        scale = to_4tuple(scale)
    assert isinstance(scale, (tuple, list))
    assert len(scale) == 4
    self.bboxes[:, 0] *= scale[0]
    self.bboxes[:, 1] *= scale[1]
    self.bboxes[:, 2] *= scale[2]
    self.bboxes[:, 3] *= scale[3]



ultralytics.utils.instance.Instances

Contenitore per le caselle di delimitazione, i segmenti e i punti chiave degli oggetti rilevati in un'immagine.

Attributi:

Nome Tipo Descrizione
_bboxes Bboxes

Oggetto interno per gestire le operazioni di bounding box.

keypoints ndarray

keypoints(x, y, visible) con forma [N, 17, 3]. Il valore predefinito è Nessuno.

normalized bool

Flag che indica se le coordinate del rettangolo di selezione sono normalizzate.

segments ndarray

Schiera di segmenti con forma [N, 1000, 2] dopo il ricampionamento.

Parametri:

Nome Tipo Descrizione Predefinito
bboxes ndarray

Un array di caselle di delimitazione con forma [N, 4].

richiesto
segments list | ndarray

Un elenco o un array di segmenti di oggetti. Il valore predefinito è Nessuno.

None
keypoints ndarray

Un array di punti chiave con forma [N, 17, 3]. Il valore predefinito è Nessuno.

None
bbox_format str

Il formato delle caselle di delimitazione ('xywh' o 'xyxy'). Il valore predefinito è 'xywh'.

'xywh'
normalized bool

Se le coordinate del rettangolo di selezione sono normalizzate. Il valore predefinito è Vero.

True

Esempi:

# Create an Instances object
instances = Instances(
    bboxes=np.array([[10, 10, 30, 30], [20, 20, 40, 40]]),
    segments=[np.array([[5, 5], [10, 10]]), np.array([[15, 15], [20, 20]])],
    keypoints=np.array([[[5, 5, 1], [10, 10, 1]], [[15, 15, 1], [20, 20, 1]]])
)
Nota

Il formato del riquadro di delimitazione è 'xywh' o 'xyxy' ed è determinato dal parametro bbox_format argomento. Questa classe non esegue la convalida degli input e presuppone che gli input siano ben formati.

Codice sorgente in ultralytics/utils/instance.py
class Instances:
    """
    Container for bounding boxes, segments, and keypoints of detected objects in an image.

    Attributes:
        _bboxes (Bboxes): Internal object for handling bounding box operations.
        keypoints (ndarray): keypoints(x, y, visible) with shape [N, 17, 3]. Default is None.
        normalized (bool): Flag indicating whether the bounding box coordinates are normalized.
        segments (ndarray): Segments array with shape [N, 1000, 2] after resampling.

    Args:
        bboxes (ndarray): An array of bounding boxes with shape [N, 4].
        segments (list | ndarray, optional): A list or array of object segments. Default is None.
        keypoints (ndarray, optional): An array of keypoints with shape [N, 17, 3]. Default is None.
        bbox_format (str, optional): The format of bounding boxes ('xywh' or 'xyxy'). Default is 'xywh'.
        normalized (bool, optional): Whether the bounding box coordinates are normalized. Default is True.

    Examples:
        ```python
        # Create an Instances object
        instances = Instances(
            bboxes=np.array([[10, 10, 30, 30], [20, 20, 40, 40]]),
            segments=[np.array([[5, 5], [10, 10]]), np.array([[15, 15], [20, 20]])],
            keypoints=np.array([[[5, 5, 1], [10, 10, 1]], [[15, 15, 1], [20, 20, 1]]])
        )
        ```

    Note:
        The bounding box format is either 'xywh' or 'xyxy', and is determined by the `bbox_format` argument.
        This class does not perform input validation, and it assumes the inputs are well-formed.
    """

    def __init__(self, bboxes, segments=None, keypoints=None, bbox_format="xywh", normalized=True) -> None:
        """
        Args:
            bboxes (ndarray): bboxes with shape [N, 4].
            segments (list | ndarray): segments.
            keypoints (ndarray): keypoints(x, y, visible) with shape [N, 17, 3].
        """
        self._bboxes = Bboxes(bboxes=bboxes, format=bbox_format)
        self.keypoints = keypoints
        self.normalized = normalized
        self.segments = segments

    def convert_bbox(self, format):
        """Convert bounding box format."""
        self._bboxes.convert(format=format)

    @property
    def bbox_areas(self):
        """Calculate the area of bounding boxes."""
        return self._bboxes.areas()

    def scale(self, scale_w, scale_h, bbox_only=False):
        """This might be similar with denormalize func but without normalized sign."""
        self._bboxes.mul(scale=(scale_w, scale_h, scale_w, scale_h))
        if bbox_only:
            return
        self.segments[..., 0] *= scale_w
        self.segments[..., 1] *= scale_h
        if self.keypoints is not None:
            self.keypoints[..., 0] *= scale_w
            self.keypoints[..., 1] *= scale_h

    def denormalize(self, w, h):
        """Denormalizes boxes, segments, and keypoints from normalized coordinates."""
        if not self.normalized:
            return
        self._bboxes.mul(scale=(w, h, w, h))
        self.segments[..., 0] *= w
        self.segments[..., 1] *= h
        if self.keypoints is not None:
            self.keypoints[..., 0] *= w
            self.keypoints[..., 1] *= h
        self.normalized = False

    def normalize(self, w, h):
        """Normalize bounding boxes, segments, and keypoints to image dimensions."""
        if self.normalized:
            return
        self._bboxes.mul(scale=(1 / w, 1 / h, 1 / w, 1 / h))
        self.segments[..., 0] /= w
        self.segments[..., 1] /= h
        if self.keypoints is not None:
            self.keypoints[..., 0] /= w
            self.keypoints[..., 1] /= h
        self.normalized = True

    def add_padding(self, padw, padh):
        """Handle rect and mosaic situation."""
        assert not self.normalized, "you should add padding with absolute coordinates."
        self._bboxes.add(offset=(padw, padh, padw, padh))
        self.segments[..., 0] += padw
        self.segments[..., 1] += padh
        if self.keypoints is not None:
            self.keypoints[..., 0] += padw
            self.keypoints[..., 1] += padh

    def __getitem__(self, index) -> "Instances":
        """
        Retrieve a specific instance or a set of instances using indexing.

        Args:
            index (int, slice, or np.ndarray): The index, slice, or boolean array to select
                                               the desired instances.

        Returns:
            Instances: A new Instances object containing the selected bounding boxes,
                       segments, and keypoints if present.

        Note:
            When using boolean indexing, make sure to provide a boolean array with the same
            length as the number of instances.
        """
        segments = self.segments[index] if len(self.segments) else self.segments
        keypoints = self.keypoints[index] if self.keypoints is not None else None
        bboxes = self.bboxes[index]
        bbox_format = self._bboxes.format
        return Instances(
            bboxes=bboxes,
            segments=segments,
            keypoints=keypoints,
            bbox_format=bbox_format,
            normalized=self.normalized,
        )

    def flipud(self, h):
        """Flips the coordinates of bounding boxes, segments, and keypoints vertically."""
        if self._bboxes.format == "xyxy":
            y1 = self.bboxes[:, 1].copy()
            y2 = self.bboxes[:, 3].copy()
            self.bboxes[:, 1] = h - y2
            self.bboxes[:, 3] = h - y1
        else:
            self.bboxes[:, 1] = h - self.bboxes[:, 1]
        self.segments[..., 1] = h - self.segments[..., 1]
        if self.keypoints is not None:
            self.keypoints[..., 1] = h - self.keypoints[..., 1]

    def fliplr(self, w):
        """Reverses the order of the bounding boxes and segments horizontally."""
        if self._bboxes.format == "xyxy":
            x1 = self.bboxes[:, 0].copy()
            x2 = self.bboxes[:, 2].copy()
            self.bboxes[:, 0] = w - x2
            self.bboxes[:, 2] = w - x1
        else:
            self.bboxes[:, 0] = w - self.bboxes[:, 0]
        self.segments[..., 0] = w - self.segments[..., 0]
        if self.keypoints is not None:
            self.keypoints[..., 0] = w - self.keypoints[..., 0]

    def clip(self, w, h):
        """Clips bounding boxes, segments, and keypoints values to stay within image boundaries."""
        ori_format = self._bboxes.format
        self.convert_bbox(format="xyxy")
        self.bboxes[:, [0, 2]] = self.bboxes[:, [0, 2]].clip(0, w)
        self.bboxes[:, [1, 3]] = self.bboxes[:, [1, 3]].clip(0, h)
        if ori_format != "xyxy":
            self.convert_bbox(format=ori_format)
        self.segments[..., 0] = self.segments[..., 0].clip(0, w)
        self.segments[..., 1] = self.segments[..., 1].clip(0, h)
        if self.keypoints is not None:
            self.keypoints[..., 0] = self.keypoints[..., 0].clip(0, w)
            self.keypoints[..., 1] = self.keypoints[..., 1].clip(0, h)

    def remove_zero_area_boxes(self):
        """
        Remove zero-area boxes, i.e. after clipping some boxes may have zero width or height.

        This removes them.
        """
        good = self.bbox_areas > 0
        if not all(good):
            self._bboxes = self._bboxes[good]
            if len(self.segments):
                self.segments = self.segments[good]
            if self.keypoints is not None:
                self.keypoints = self.keypoints[good]
        return good

    def update(self, bboxes, segments=None, keypoints=None):
        """Updates instance variables."""
        self._bboxes = Bboxes(bboxes, format=self._bboxes.format)
        if segments is not None:
            self.segments = segments
        if keypoints is not None:
            self.keypoints = keypoints

    def __len__(self):
        """Return the length of the instance list."""
        return len(self.bboxes)

    @classmethod
    def concatenate(cls, instances_list: List["Instances"], axis=0) -> "Instances":
        """
        Concatenates a list of Instances objects into a single Instances object.

        Args:
            instances_list (List[Instances]): A list of Instances objects to concatenate.
            axis (int, optional): The axis along which the arrays will be concatenated. Defaults to 0.

        Returns:
            Instances: A new Instances object containing the concatenated bounding boxes,
                       segments, and keypoints if present.

        Note:
            The `Instances` objects in the list should have the same properties, such as
            the format of the bounding boxes, whether keypoints are present, and if the
            coordinates are normalized.
        """
        assert isinstance(instances_list, (list, tuple))
        if not instances_list:
            return cls(np.empty(0))
        assert all(isinstance(instance, Instances) for instance in instances_list)

        if len(instances_list) == 1:
            return instances_list[0]

        use_keypoint = instances_list[0].keypoints is not None
        bbox_format = instances_list[0]._bboxes.format
        normalized = instances_list[0].normalized

        cat_boxes = np.concatenate([ins.bboxes for ins in instances_list], axis=axis)
        cat_segments = np.concatenate([b.segments for b in instances_list], axis=axis)
        cat_keypoints = np.concatenate([b.keypoints for b in instances_list], axis=axis) if use_keypoint else None
        return cls(cat_boxes, cat_segments, cat_keypoints, bbox_format, normalized)

    @property
    def bboxes(self):
        """Return bounding boxes."""
        return self._bboxes.bboxes

bbox_areas property

Calcola l'area delle caselle di delimitazione.

bboxes property

Restituisce le caselle di delimitazione.

__getitem__(index)

Recupera un'istanza specifica o un insieme di istanze utilizzando l'indicizzazione.

Parametri:

Nome Tipo Descrizione Predefinito
index int, slice, or np.ndarray

L'indice, la fetta o l'array booleano per selezionare le istanze desiderate.

richiesto

Restituzione:

Nome Tipo Descrizione
Instances Instances

Un nuovo oggetto Instances contenente i rettangoli di selezione, segmenti e punti chiave, se presenti.

Nota

Quando si utilizza l'indicizzazione booleana, assicurati di fornire un array booleano con la stessa lunghezza del numero di istanze. lunghezza del numero di istanze.

Codice sorgente in ultralytics/utils/instance.py
def __getitem__(self, index) -> "Instances":
    """
    Retrieve a specific instance or a set of instances using indexing.

    Args:
        index (int, slice, or np.ndarray): The index, slice, or boolean array to select
                                           the desired instances.

    Returns:
        Instances: A new Instances object containing the selected bounding boxes,
                   segments, and keypoints if present.

    Note:
        When using boolean indexing, make sure to provide a boolean array with the same
        length as the number of instances.
    """
    segments = self.segments[index] if len(self.segments) else self.segments
    keypoints = self.keypoints[index] if self.keypoints is not None else None
    bboxes = self.bboxes[index]
    bbox_format = self._bboxes.format
    return Instances(
        bboxes=bboxes,
        segments=segments,
        keypoints=keypoints,
        bbox_format=bbox_format,
        normalized=self.normalized,
    )

__init__(bboxes, segments=None, keypoints=None, bbox_format='xywh', normalized=True)

Parametri:

Nome Tipo Descrizione Predefinito
bboxes ndarray

bbox con forma [N, 4].

richiesto
segments list | ndarray

segmenti.

None
keypoints ndarray

keypoints(x, y, visibile) con forma [N, 17, 3].

None
Codice sorgente in ultralytics/utils/instance.py
def __init__(self, bboxes, segments=None, keypoints=None, bbox_format="xywh", normalized=True) -> None:
    """
    Args:
        bboxes (ndarray): bboxes with shape [N, 4].
        segments (list | ndarray): segments.
        keypoints (ndarray): keypoints(x, y, visible) with shape [N, 17, 3].
    """
    self._bboxes = Bboxes(bboxes=bboxes, format=bbox_format)
    self.keypoints = keypoints
    self.normalized = normalized
    self.segments = segments

__len__()

Restituisce la lunghezza dell'elenco di istanze.

Codice sorgente in ultralytics/utils/instance.py
def __len__(self):
    """Return the length of the instance list."""
    return len(self.bboxes)

add_padding(padw, padh)

Gestisci le situazioni rettangolari e mosaiche.

Codice sorgente in ultralytics/utils/instance.py
def add_padding(self, padw, padh):
    """Handle rect and mosaic situation."""
    assert not self.normalized, "you should add padding with absolute coordinates."
    self._bboxes.add(offset=(padw, padh, padw, padh))
    self.segments[..., 0] += padw
    self.segments[..., 1] += padh
    if self.keypoints is not None:
        self.keypoints[..., 0] += padw
        self.keypoints[..., 1] += padh

clip(w, h)

Ritaglia le caselle di delimitazione, i segmenti e i valori dei punti chiave per rimanere all'interno dei confini dell'immagine.

Codice sorgente in ultralytics/utils/instance.py
def clip(self, w, h):
    """Clips bounding boxes, segments, and keypoints values to stay within image boundaries."""
    ori_format = self._bboxes.format
    self.convert_bbox(format="xyxy")
    self.bboxes[:, [0, 2]] = self.bboxes[:, [0, 2]].clip(0, w)
    self.bboxes[:, [1, 3]] = self.bboxes[:, [1, 3]].clip(0, h)
    if ori_format != "xyxy":
        self.convert_bbox(format=ori_format)
    self.segments[..., 0] = self.segments[..., 0].clip(0, w)
    self.segments[..., 1] = self.segments[..., 1].clip(0, h)
    if self.keypoints is not None:
        self.keypoints[..., 0] = self.keypoints[..., 0].clip(0, w)
        self.keypoints[..., 1] = self.keypoints[..., 1].clip(0, h)

concatenate(instances_list, axis=0) classmethod

Concatena un elenco di oggetti Istanze in un unico oggetto Istanze.

Parametri:

Nome Tipo Descrizione Predefinito
instances_list List[Instances]

Un elenco di oggetti Istanze da concatenare.

richiesto
axis int

L'asse lungo il quale gli array saranno concatenati. Il valore predefinito è 0.

0

Restituzione:

Nome Tipo Descrizione
Instances Instances

Un nuovo oggetto Instances contenente i rettangoli di selezione concatenati, segmenti e punti chiave, se presenti.

Nota

Il Instances Gli oggetti dell'elenco devono avere le stesse proprietà, come ad esempio il formato dei riquadri di delimitazione, la presenza di punti chiave e la normalizzazione delle coordinate. coordinate sono normalizzate.

Codice sorgente in ultralytics/utils/instance.py
@classmethod
def concatenate(cls, instances_list: List["Instances"], axis=0) -> "Instances":
    """
    Concatenates a list of Instances objects into a single Instances object.

    Args:
        instances_list (List[Instances]): A list of Instances objects to concatenate.
        axis (int, optional): The axis along which the arrays will be concatenated. Defaults to 0.

    Returns:
        Instances: A new Instances object containing the concatenated bounding boxes,
                   segments, and keypoints if present.

    Note:
        The `Instances` objects in the list should have the same properties, such as
        the format of the bounding boxes, whether keypoints are present, and if the
        coordinates are normalized.
    """
    assert isinstance(instances_list, (list, tuple))
    if not instances_list:
        return cls(np.empty(0))
    assert all(isinstance(instance, Instances) for instance in instances_list)

    if len(instances_list) == 1:
        return instances_list[0]

    use_keypoint = instances_list[0].keypoints is not None
    bbox_format = instances_list[0]._bboxes.format
    normalized = instances_list[0].normalized

    cat_boxes = np.concatenate([ins.bboxes for ins in instances_list], axis=axis)
    cat_segments = np.concatenate([b.segments for b in instances_list], axis=axis)
    cat_keypoints = np.concatenate([b.keypoints for b in instances_list], axis=axis) if use_keypoint else None
    return cls(cat_boxes, cat_segments, cat_keypoints, bbox_format, normalized)

convert_bbox(format)

Convertire il formato del rettangolo di selezione.

Codice sorgente in ultralytics/utils/instance.py
def convert_bbox(self, format):
    """Convert bounding box format."""
    self._bboxes.convert(format=format)

denormalize(w, h)

Denormalizza caselle, segmenti e punti chiave a partire da coordinate normalizzate.

Codice sorgente in ultralytics/utils/instance.py
def denormalize(self, w, h):
    """Denormalizes boxes, segments, and keypoints from normalized coordinates."""
    if not self.normalized:
        return
    self._bboxes.mul(scale=(w, h, w, h))
    self.segments[..., 0] *= w
    self.segments[..., 1] *= h
    if self.keypoints is not None:
        self.keypoints[..., 0] *= w
        self.keypoints[..., 1] *= h
    self.normalized = False

fliplr(w)

Inverte l'ordine dei rettangoli di selezione e dei segmenti in orizzontale.

Codice sorgente in ultralytics/utils/instance.py
def fliplr(self, w):
    """Reverses the order of the bounding boxes and segments horizontally."""
    if self._bboxes.format == "xyxy":
        x1 = self.bboxes[:, 0].copy()
        x2 = self.bboxes[:, 2].copy()
        self.bboxes[:, 0] = w - x2
        self.bboxes[:, 2] = w - x1
    else:
        self.bboxes[:, 0] = w - self.bboxes[:, 0]
    self.segments[..., 0] = w - self.segments[..., 0]
    if self.keypoints is not None:
        self.keypoints[..., 0] = w - self.keypoints[..., 0]

flipud(h)

Capovolge verticalmente le coordinate dei rettangoli di selezione, dei segmenti e dei punti chiave.

Codice sorgente in ultralytics/utils/instance.py
def flipud(self, h):
    """Flips the coordinates of bounding boxes, segments, and keypoints vertically."""
    if self._bboxes.format == "xyxy":
        y1 = self.bboxes[:, 1].copy()
        y2 = self.bboxes[:, 3].copy()
        self.bboxes[:, 1] = h - y2
        self.bboxes[:, 3] = h - y1
    else:
        self.bboxes[:, 1] = h - self.bboxes[:, 1]
    self.segments[..., 1] = h - self.segments[..., 1]
    if self.keypoints is not None:
        self.keypoints[..., 1] = h - self.keypoints[..., 1]

normalize(w, h)

Normalizza i rettangoli di selezione, i segmenti e i punti chiave alle dimensioni dell'immagine.

Codice sorgente in ultralytics/utils/instance.py
def normalize(self, w, h):
    """Normalize bounding boxes, segments, and keypoints to image dimensions."""
    if self.normalized:
        return
    self._bboxes.mul(scale=(1 / w, 1 / h, 1 / w, 1 / h))
    self.segments[..., 0] /= w
    self.segments[..., 1] /= h
    if self.keypoints is not None:
        self.keypoints[..., 0] /= w
        self.keypoints[..., 1] /= h
    self.normalized = True

remove_zero_area_boxes()

Rimuove le caselle ad area zero, cioè dopo il ritaglio alcune caselle potrebbero avere larghezza o altezza pari a zero.

Questo li rimuove.

Codice sorgente in ultralytics/utils/instance.py
def remove_zero_area_boxes(self):
    """
    Remove zero-area boxes, i.e. after clipping some boxes may have zero width or height.

    This removes them.
    """
    good = self.bbox_areas > 0
    if not all(good):
        self._bboxes = self._bboxes[good]
        if len(self.segments):
            self.segments = self.segments[good]
        if self.keypoints is not None:
            self.keypoints = self.keypoints[good]
    return good

scale(scale_w, scale_h, bbox_only=False)

Potrebbe essere simile alla funzione denormalize ma senza segno normalizzato.

Codice sorgente in ultralytics/utils/instance.py
def scale(self, scale_w, scale_h, bbox_only=False):
    """This might be similar with denormalize func but without normalized sign."""
    self._bboxes.mul(scale=(scale_w, scale_h, scale_w, scale_h))
    if bbox_only:
        return
    self.segments[..., 0] *= scale_w
    self.segments[..., 1] *= scale_h
    if self.keypoints is not None:
        self.keypoints[..., 0] *= scale_w
        self.keypoints[..., 1] *= scale_h

update(bboxes, segments=None, keypoints=None)

Aggiorna le variabili dell'istanza.

Codice sorgente in ultralytics/utils/instance.py
def update(self, bboxes, segments=None, keypoints=None):
    """Updates instance variables."""
    self._bboxes = Bboxes(bboxes, format=self._bboxes.format)
    if segments is not None:
        self.segments = segments
    if keypoints is not None:
        self.keypoints = keypoints



ultralytics.utils.instance._ntuple(n)

Da PyTorch internals.

Codice sorgente in ultralytics/utils/instance.py
def _ntuple(n):
    """From PyTorch internals."""

    def parse(x):
        """Parse bounding boxes format between XYWH and LTWH."""
        return x if isinstance(x, abc.Iterable) else tuple(repeat(x, n))

    return parse





Creato 2023-11-12, Aggiornato 2023-11-25
Autori: glenn-jocher (3), Laughing-q (1)