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Bboxes


Now only numpy is supported.

Source code in ultralytics/yolo/utils/instance.py
class Bboxes:
    """Now only numpy is supported."""

    def __init__(self, bboxes, format='xyxy') -> None:
        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):
    #     assert format in _formats
    #     if self.format == format:
    #         bboxes = self.bboxes
    #     elif self.format == "xyxy":
    #         if format == "xywh":
    #             bboxes = xyxy2xywh(self.bboxes)
    #         else:
    #             bboxes = xyxy2ltwh(self.bboxes)
    #     elif self.format == "xywh":
    #         if format == "xyxy":
    #             bboxes = xywh2xyxy(self.bboxes)
    #         else:
    #             bboxes = xywh2ltwh(self.bboxes)
    #     else:
    #         if format == "xyxy":
    #             bboxes = ltwh2xyxy(self.bboxes)
    #         else:
    #             bboxes = ltwh2xywh(self.bboxes)
    #
    #     return Bboxes(bboxes, format)

    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':
            bboxes = xyxy2xywh(self.bboxes) if format == 'xywh' else xyxy2ltwh(self.bboxes)
        elif self.format == 'xywh':
            bboxes = xywh2xyxy(self.bboxes) if format == 'xyxy' else xywh2ltwh(self.bboxes)
        else:
            bboxes = ltwh2xyxy(self.bboxes) if format == 'xyxy' else ltwh2xywh(self.bboxes)
        self.bboxes = 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) or (list) or (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) or (list) or (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)

Retrieve a specific bounding box or a set of bounding boxes using indexing.

Parameters:

Name Type Description Default
index int, slice, or np.ndarray

The index, slice, or boolean array to select the desired bounding boxes.

required

Returns:

Name Type Description
Bboxes Bboxes

A new Bboxes object containing the selected bounding boxes.

Raises:

Type Description
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.

Source code in ultralytics/yolo/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)

__len__()

Return the number of boxes.

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

add(offset)

Parameters:

Name Type Description Default
offset tuple) or (list) or (int

the offset for four coords.

required
Source code in ultralytics/yolo/utils/instance.py
def add(self, offset):
    """
    Args:
        offset (tuple) or (list) or (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()

Return box areas.

Source code in ultralytics/yolo/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

Concatenate a list of Bboxes objects into a single Bboxes object.

Parameters:

Name Type Description Default
boxes_list List[Bboxes]

A list of Bboxes objects to concatenate.

required
axis int

The axis along which to concatenate the bounding boxes. Defaults to 0.

0

Returns:

Name Type Description
Bboxes Bboxes

A new Bboxes object containing the concatenated bounding boxes.

Note

The input should be a list or tuple of Bboxes objects.

Source code in ultralytics/yolo/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)

Converts bounding box format from one type to another.

Source code in ultralytics/yolo/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':
        bboxes = xyxy2xywh(self.bboxes) if format == 'xywh' else xyxy2ltwh(self.bboxes)
    elif self.format == 'xywh':
        bboxes = xywh2xyxy(self.bboxes) if format == 'xyxy' else xywh2ltwh(self.bboxes)
    else:
        bboxes = ltwh2xyxy(self.bboxes) if format == 'xyxy' else ltwh2xywh(self.bboxes)
    self.bboxes = bboxes
    self.format = format

mul(scale)

Parameters:

Name Type Description Default
scale tuple) or (list) or (int

the scale for four coords.

required
Source code in ultralytics/yolo/utils/instance.py
def mul(self, scale):
    """
    Args:
        scale (tuple) or (list) or (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]



Instances


Source code in ultralytics/yolo/utils/instance.py
class Instances:

    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].
        """
        if segments is None:
            segments = []
        self._bboxes = Bboxes(bboxes=bboxes, format=bbox_format)
        self.keypoints = keypoints
        self.normalized = normalized

        if len(segments) > 0:
            # list[np.array(1000, 2)] * num_samples
            segments = resample_segments(segments)
            # (N, 1000, 2)
            segments = np.stack(segments, axis=0)
        else:
            segments = np.zeros((0, 1000, 2), dtype=np.float32)
        self.segments = segments

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

    def bbox_areas(self):
        """Calculate the area of bounding boxes."""
        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._bboxes.areas() > 0
        if not all(good):
            self._bboxes = Bboxes(self._bboxes.bboxes[good], format=self._bboxes.format)
            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

bboxes property

Return bounding boxes.

__getitem__(index)

Retrieve a specific instance or a set of instances using indexing.

Parameters:

Name Type Description Default
index int, slice, or np.ndarray

The index, slice, or boolean array to select the desired instances.

required

Returns:

Name Type Description
Instances 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.

Source code in ultralytics/yolo/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)

Parameters:

Name Type Description Default
bboxes ndarray

bboxes with shape [N, 4].

required
segments list | ndarray

segments.

None
keypoints ndarray

keypoints(x, y, visible) with shape [N, 17, 3].

None
Source code in ultralytics/yolo/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].
    """
    if segments is None:
        segments = []
    self._bboxes = Bboxes(bboxes=bboxes, format=bbox_format)
    self.keypoints = keypoints
    self.normalized = normalized

    if len(segments) > 0:
        # list[np.array(1000, 2)] * num_samples
        segments = resample_segments(segments)
        # (N, 1000, 2)
        segments = np.stack(segments, axis=0)
    else:
        segments = np.zeros((0, 1000, 2), dtype=np.float32)
    self.segments = segments

__len__()

Return the length of the instance list.

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

add_padding(padw, padh)

Handle rect and mosaic situation.

Source code in ultralytics/yolo/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

bbox_areas()

Calculate the area of bounding boxes.

Source code in ultralytics/yolo/utils/instance.py
def bbox_areas(self):
    """Calculate the area of bounding boxes."""
    self._bboxes.areas()

clip(w, h)

Clips bounding boxes, segments, and keypoints values to stay within image boundaries.

Source code in ultralytics/yolo/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

Concatenates a list of Instances objects into a single Instances object.

Parameters:

Name Type Description Default
instances_list List[Instances]

A list of Instances objects to concatenate.

required
axis int

The axis along which the arrays will be concatenated. Defaults to 0.

0

Returns:

Name Type Description
Instances 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.

Source code in ultralytics/yolo/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)

Convert bounding box format.

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

denormalize(w, h)

Denormalizes boxes, segments, and keypoints from normalized coordinates.

Source code in ultralytics/yolo/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)

Reverses the order of the bounding boxes and segments horizontally.

Source code in ultralytics/yolo/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)

Flips the coordinates of bounding boxes, segments, and keypoints vertically.

Source code in ultralytics/yolo/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)

Normalize bounding boxes, segments, and keypoints to image dimensions.

Source code in ultralytics/yolo/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()

Remove zero-area boxes, i.e. after clipping some boxes may have zero width or height. This removes them.

Source code in ultralytics/yolo/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._bboxes.areas() > 0
    if not all(good):
        self._bboxes = Bboxes(self._bboxes.bboxes[good], format=self._bboxes.format)
        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)

this might be similar with denormalize func but without normalized sign.

Source code in ultralytics/yolo/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)

Updates instance variables.

Source code in ultralytics/yolo/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



_ntuple


From PyTorch internals.

Source code in ultralytics/yolo/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




Created 2023-04-16, Updated 2023-05-17
Authors: Glenn Jocher (3)