本文整理汇总了Python中object_detection.core.keypoint_ops.flip_vertical方法的典型用法代码示例。如果您正苦于以下问题：Python keypoint_ops.flip_vertical方法的具体用法？Python keypoint_ops.flip_vertical怎么用？Python keypoint_ops.flip_vertical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.core.keypoint_ops的用法示例。
在下文中一共展示了keypoint_ops.flip_vertical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_flip_vertical
# 需要导入模块: from object_detection.core import keypoint_ops [as 别名]
# 或者: from object_detection.core.keypoint_ops import flip_vertical [as 别名]
def test_flip_vertical(self):
keypoints = tf.constant([
[[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]],
[[0.4, 0.4], [0.5, 0.5], [0.6, 0.6]]
])
flip_permutation = [0, 2, 1]
expected_keypoints = tf.constant([
[[0.9, 0.1], [0.7, 0.3], [0.8, 0.2]],
[[0.6, 0.4], [0.4, 0.6], [0.5, 0.5]],
])
output = keypoint_ops.flip_vertical(keypoints, 0.5, flip_permutation)
with self.test_session() as sess:
output_, expected_keypoints_ = sess.run([output, expected_keypoints])
self.assertAllClose(output_, expected_keypoints_) 示例2: test_flip_vertical
# 需要导入模块: from object_detection.core import keypoint_ops [as 别名]
# 或者: from object_detection.core.keypoint_ops import flip_vertical [as 别名]
def test_flip_vertical(self):
def graph_fn():
keypoints = tf.constant([
[[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]],
[[0.4, 0.4], [0.5, 0.5], [0.6, 0.6]]
])
expected_keypoints = tf.constant([
[[0.9, 0.1], [0.8, 0.2], [0.7, 0.3]],
[[0.6, 0.4], [0.5, 0.5], [0.4, 0.6]],
])
output = keypoint_ops.flip_vertical(keypoints, 0.5)
return output, expected_keypoints
output, expected_keypoints = self.execute(graph_fn, [])
self.assertAllClose(output, expected_keypoints) 示例3: test_flip_vertical_permutation
# 需要导入模块: from object_detection.core import keypoint_ops [as 别名]
# 或者: from object_detection.core.keypoint_ops import flip_vertical [as 别名]
def test_flip_vertical_permutation(self):
def graph_fn():
keypoints = tf.constant([[[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]],
[[0.4, 0.4], [0.5, 0.5], [0.6, 0.6]]])
flip_permutation = [0, 2, 1]
expected_keypoints = tf.constant([
[[0.9, 0.1], [0.7, 0.3], [0.8, 0.2]],
[[0.6, 0.4], [0.4, 0.6], [0.5, 0.5]],
])
output = keypoint_ops.flip_vertical(keypoints, 0.5, flip_permutation)
return output, expected_keypoints
output, expected_keypoints = self.execute(graph_fn, [])
self.assertAllClose(output, expected_keypoints)