本文整理汇总了Python中object_detection.utils.np_box_ops.ioa方法的典型用法代码示例。如果您正苦于以下问题：Python np_box_ops.ioa方法的具体用法？Python np_box_ops.ioa怎么用？Python np_box_ops.ioa使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类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个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: prune_non_overlapping_boxes
# 需要导入模块: from object_detection.utils import np_box_ops [as 别名]
# 或者: from object_detection.utils.np_box_ops import ioa [as 别名]
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
# 需要导入模块: from object_detection.utils import np_box_ops [as 别名]
# 或者: from object_detection.utils.np_box_ops import ioa [as 别名]
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
# 需要导入模块: from object_detection.utils import np_box_ops [as 别名]
# 或者: from object_detection.utils.np_box_ops import ioa [as 别名]
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())