本文整理匯總了Python中object_detection.utils.np_box_ops.ioa方法的典型用法代碼示例。如果您正苦於以下問題：Python np_box_ops.ioa方法的具體用法？Python np_box_ops.ioa怎麽用？Python np_box_ops.ioa使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類object_detection.utils.np_box_ops的用法示例。
在下文中一共展示了np_box_ops.ioa方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: prune_non_overlapping_boxes
# 需要導入模塊: from object_detection.utils import np_box_ops [as 別名]
# 或者: from object_detection.utils.np_box_ops import ioa [as 別名]
def prune_non_overlapping_boxes(boxlist1, boxlist2, minoverlap=0.0):
"""Prunes the boxes in boxlist1 that overlap less than thresh with boxlist2.
For each box in boxlist1, we want its IOA to be more than minoverlap with
at least one of the boxes in boxlist2. If it does not, we remove it.
Args:
boxlist1: BoxList holding N boxes.
boxlist2: BoxList holding M boxes.
minoverlap: Minimum required overlap between boxes, to count them as
overlapping.
Returns:
A pruned boxlist with size [N', 4].
"""
intersection_over_area = ioa(boxlist2, boxlist1) # [M, N] tensor
intersection_over_area = np.amax(intersection_over_area, axis=0) # [N] tensor
keep_bool = np.greater_equal(intersection_over_area, np.array(minoverlap))
keep_inds = np.nonzero(keep_bool)[0]
new_boxlist1 = gather(boxlist1, keep_inds)
return new_boxlist1 示例2: testIOA
# 需要導入模塊: from object_detection.utils import np_box_ops [as 別名]
# 或者: from object_detection.utils.np_box_ops import ioa [as 別名]
def testIOA(self):
boxes1 = np.array([[0.25, 0.25, 0.75, 0.75],
[0.0, 0.0, 0.5, 0.75]],
dtype=np.float32)
boxes2 = np.array([[0.5, 0.25, 1.0, 1.0],
[0.0, 0.0, 1.0, 1.0]],
dtype=np.float32)
ioa21 = np_box_ops.ioa(boxes2, boxes1)
expected_ioa21 = np.array([[0.5, 0.0],
[1.0, 1.0]],
dtype=np.float32)
self.assertAllClose(ioa21, expected_ioa21) 示例3: ioa
# 需要導入模塊: from object_detection.utils import np_box_ops [as 別名]
# 或者: from object_detection.utils.np_box_ops import ioa [as 別名]
def ioa(boxlist1, boxlist2):
"""Computes pairwise intersection-over-area between box collections.
Intersection-over-area (ioa) between two boxes box1 and box2 is defined as
their intersection area over box2's area. Note that ioa is not symmetric,
that is, IOA(box1, box2) != IOA(box2, box1).
Args:
boxlist1: BoxList holding N boxes
boxlist2: BoxList holding M boxes
Returns:
a numpy array with shape [N, M] representing pairwise ioa scores.
"""
return np_box_ops.ioa(boxlist1.get(), boxlist2.get())