(src_bbox, loc)
return dst_bbox
Decode bounding boxes from bounding box offsets and scales.

    Given bounding box offsets and scales computed by
    :meth:`bbox2loc`, this function decodes the representation to
    coordinates in 2D image coordinates.

    Given scales and offsets :math:`t_y, t_x, t_h, t_w` and a bounding
    box whose center is :math:`(y, x) = p_y, p_x` and size :math:`p_h, p_w`,
    the decoded bounding box's center :math:`\\hat{g}_y`, :math:`\\hat{g}_x`
    and size :math:`\\hat{g}_h`, :math:`\\hat{g}_w` are calculated
    by the following formulas.

    * :math:`\\hat{g}_y = p_h t_y + p_y`
    * :math:`\\hat{g}_x = p_w t_x + p_x`
    * :math:`\\hat{g}_h = p_h \\exp(t_h)`
    * :math:`\\hat{g}_w = p_w \\exp(t_w)`

    The decoding formulas are used in works such as R-CNN [#]_.

    The output is same type as the type of the inputs.

    .. [#] Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik. \
    Rich feature hierarchies for accurate object detection and semantic \
    segmentation. CVPR 2014.

    Args:
        src_bbox (array): A coordinates of bounding boxes.
            Its shape is :math:`(R, 4)`. These coordinates are
            :math:`p_{ymin}, p_{xmin}, p_{ymax}, p_{xmax}`.
        loc (array): An array with offsets and scales.
            The shapes of :obj:`src_bbox` and :obj:`loc` should be same.
            This contains values :math:`t_y, t_x, t_h, t_w`.

    Returns:
        array:
        Decoded bounding box coordinates. Its shape is :math:`(R, 4)`. \
        The second axis contains four values \
        :math:`\\hat{g}_{ymin}, \\hat{g}_{xmin},
        \\hat{g}_{ymax}, \\hat{g}_{xmax}`.
Decode bounding boxes from bounding box offsets and scales.
Decode bounding boxes from bounding box offsets and scales .
def loc2bbox(src_bbox, loc):
    """Decode bounding boxes from bounding box offsets and scales.

    Given bounding box offsets and scales computed by
    :meth:`bbox2loc`, this function decodes the representation to
    coordinates in 2D image coordinates.

    Given scales and offsets :math:`t_y, t_x, t_h, t_w` and a bounding
    box whose center is :math:`(y, x) = p_y, p_x` and size :math:`p_h, p_w`,
    the decoded bounding box's center :math:`\\hat{g}_y`, :math:`\\hat{g}_x`
    and size :math:`\\hat{g}_h`, :math:`\\hat{g}_w` are calculated
    by the following formulas.

    * :math:`\\hat{g}_y = p_h t_y + p_y`
    * :math:`\\hat{g}_x = p_w t_x + p_x`
    * :math:`\\hat{g}_h = p_h \\exp(t_h)`
    * :math:`\\hat{g}_w = p_w \\exp(t_w)`

    The decoding formulas are used in works such as R-CNN [#]_.

    The output is same type as the type of the inputs.

    .. [#] Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik. \
    Rich feature hierarchies for accurate object detection and semantic \
    segmentation. CVPR 2014.

    Args:
        src_bbox (array): A coordinates of bounding boxes.
            Its shape is :math:`(R, 4)`. These coordinates are
            :math:`p_{ymin}, p_{xmin}, p_{ymax}, p_{xmax}`.
        loc (array): An array with offsets and scales.
            The shapes of :obj:`src_bbox` and :obj:`loc` should be same.
            This contains values :math:`t_y, t_x, t_h, t_w`.

    Returns:
        array:
        Decoded bounding box coordinates. Its shape is :math:`(R, 4)`. \
        The second axis contains four values \
        :math:`\\hat{g}_{ymin}, \\hat{g}_{xmin},
        \\hat{g}_{ymax}, \\hat{g}_{xmax}`.

    """
    xp = cuda.get_array_module(src_bbox)

    if src_bbox.shape[0] == 0:
        return xp.zeros((0, 4), dtype=loc.dtype)

    src_bbox = src_bbox.astype(src_bbox.dtype, copy=False)

    src_height = src_bbox[:, 2] - src_bbox[:, 0]
    src_width = src_bbox[:, 3] - src_bbox[:, 1]
    src_ctr_y = src_bbox[:, 0] + 0.5 * src_height
    src_ctr_x = src_bbox[:, 1] + 0.5 * src_width

    dy = loc[:, 0::4]
    dx = loc[:, 1::4]
    dh = loc[:, 2::4]
    dw = loc[:, 3::4]

    ctr_y = dy * src_height[:, xp.newaxis] + src_ctr_y[:, xp.newaxis]
    ctr_x = dx * src_width[:, xp.newaxis] + src_ctr_x[:, xp.newaxis]
    h = xp.exp(dh) * src_height[:, xp.newaxis]
    w = xp.exp(dw) * src_width[:, xp.newaxis]

    dst_bbox = xp.zeros(loc.shape, dtype=loc.dtype)
    dst_bbox[:, 0::4] = ctr_y - 0.5 * h
    dst_bbox[:, 1::4] = ctr_x - 0.5 * w
    dst_bbox[:, 2::4] = ctr_y + 0.5 * h
    dst_bbox[:, 3::4] = ctr_x + 0.5 * w

    return dst_bbox
def loc2bbox ( src_bbox , loc ) : xp = cuda . get_array_module ( src_bbox ) if src_bbox . shape [ 0 ] == 0 : return xp . zeros ( ( 0 , 4 ) , dtype = loc . dtype ) src_bbox = src_bbox . astype ( src_bbox . dtype , copy = False ) src_height = src_bbox [ : , 2 ] - src_bbox [ : , 0 ] src_width = src_bbox [ : , 3 ] - src_bbox [ : , 1 ] src_ctr_y = src_bbox [ : , 0 ] + 0.5 * src_height src_ctr_x = src_bbox [ : , 1 ] + 0.5 * src_width dy = loc [ : , 0 : : 4 ] dx = loc [ : , 1 : : 4 ] dh = loc [ : , 2 : : 4 ] dw = loc [ : , 3 : : 4 ] ctr_y = dy * src_height [ : , xp . newaxis ] + src_ctr_y [ : , xp . newaxis ] ctr_x = dx * src_width [ : , xp . newaxis ] + src_ctr_x [ : , xp . newaxis ] h = xp . exp ( dh ) * src_height [ : , xp . newaxis ] w = xp . exp ( dw ) * src_width [ : , xp . newaxis ] dst_bbox = xp . zeros ( loc . shape , dtype = loc . dtype ) dst_bbox [ : , 0 : : 4 ] = ctr_y - 0.5 * h dst_bbox [ : , 1 : : 4 ] = ctr_x - 0.5 * w dst_bbox [ : , 2 : : 4 ] = ctr_y + 0.5 * h dst_bbox [ : , 3 : : 4 ] = ctr_x + 0.5 * w return dst_bbox