本文整理汇总了Python中object_detection.utils.np_box_list_ops.gather方法的典型用法代码示例。如果您正苦于以下问题：Python np_box_list_ops.gather方法的具体用法？Python np_box_list_ops.gather怎么用？Python np_box_list_ops.gather使用的例子？那么恭喜您, 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_list_ops的用法示例。
在下文中一共展示了np_box_list_ops.gather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: test_gather_without_fields_specified
# 需要导入模块: from object_detection.utils import np_box_list_ops [as 别名]
# 或者: from object_detection.utils.np_box_list_ops import gather [as 别名]
def test_gather_without_fields_specified(self):
indices = np.array([2, 0, 1], dtype=int)
boxlist = self.boxlist
subboxlist = np_box_list_ops.gather(boxlist, indices)
expected_scores = np.array([0.9, 0.5, 0.7], dtype=float)
self.assertAllClose(expected_scores, subboxlist.get_field('scores'))
expected_boxes = np.array([[0.0, 0.0, 20.0, 20.0], [3.0, 4.0, 6.0, 8.0],
[14.0, 14.0, 15.0, 15.0]],
dtype=float)
self.assertAllClose(expected_boxes, subboxlist.get())
expected_labels = np.array([[0, 0, 0, 0, 1], [0, 0, 0, 1, 0],
[0, 1, 0, 0, 0]],
dtype=int)
self.assertAllClose(expected_labels, subboxlist.get_field('labels')) 示例2: test_gather_with_fields_specified
# 需要导入模块: from object_detection.utils import np_box_list_ops [as 别名]
# 或者: from object_detection.utils.np_box_list_ops import gather [as 别名]
def test_gather_with_fields_specified(self):
indices = np.array([2, 0, 1], dtype=int)
boxlist = self.boxlist
subboxlist = np_box_list_ops.gather(boxlist, indices, ['labels'])
self.assertFalse(subboxlist.has_field('scores'))
expected_boxes = np.array([[0.0, 0.0, 20.0, 20.0], [3.0, 4.0, 6.0, 8.0],
[14.0, 14.0, 15.0, 15.0]],
dtype=float)
self.assertAllClose(expected_boxes, subboxlist.get())
expected_labels = np.array([[0, 0, 0, 0, 1], [0, 0, 0, 1, 0],
[0, 1, 0, 0, 0]],
dtype=int)
self.assertAllClose(expected_labels, subboxlist.get_field('labels')) 示例3: prune_non_overlapping_masks
# 需要导入模块: from object_detection.utils import np_box_list_ops [as 别名]
# 或者: from object_detection.utils.np_box_list_ops import gather [as 别名]
def prune_non_overlapping_masks(box_mask_list1, box_mask_list2, minoverlap=0.0):
"""Prunes the boxes in list1 that overlap less than thresh with list2.
For each mask in box_mask_list1, we want its IOA to be more than minoverlap
with at least one of the masks in box_mask_list2. If it does not, we remove
it. If the masks are not full size image, we do the pruning based on boxes.
Args:
box_mask_list1: np_box_mask_list.BoxMaskList holding N boxes and masks.
box_mask_list2: np_box_mask_list.BoxMaskList holding M boxes and masks.
minoverlap: Minimum required overlap between boxes, to count them as
overlapping.
Returns:
A pruned box_mask_list with size [N', 4].
"""
intersection_over_area = ioa(box_mask_list2, box_mask_list1) # [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_box_mask_list1 = gather(box_mask_list1, keep_inds)
return new_box_mask_list1