本文整理汇总了Python中object_detection.core.box_list_ops.area方法的典型用法代码示例。如果您正苦于以下问题：Python box_list_ops.area方法的具体用法？Python box_list_ops.area怎么用？Python box_list_ops.area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.box_list_ops的用法示例。
在下文中一共展示了box_list_ops.area方法的5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_sort_by_field_invalid_inputs
# 需要导入模块: from object_detection.core import box_list_ops [as 别名]
# 或者: from object_detection.core.box_list_ops import area [as 别名]
def test_sort_by_field_invalid_inputs(self):
corners = tf.constant([4 * [0.0], 4 * [0.5], 4 * [1.0], 4 * [2.0], 4 *
[3.0], 4 * [4.0]])
misc = tf.constant([[.95, .9], [.5, .3]], tf.float32)
weights = tf.constant([.1, .2], tf.float32)
boxes = box_list.BoxList(corners)
boxes.add_field('misc', misc)
boxes.add_field('weights', weights)
with self.test_session() as sess:
with self.assertRaises(ValueError):
box_list_ops.sort_by_field(boxes, 'area')
with self.assertRaises(ValueError):
box_list_ops.sort_by_field(boxes, 'misc')
with self.assertRaisesWithPredicateMatch(errors.InvalidArgumentError,
'Incorrect field size'):
sess.run(box_list_ops.sort_by_field(boxes, 'weights').get()) 示例2: test_sort_by_field_invalid_inputs
# 需要导入模块: from object_detection.core import box_list_ops [as 别名]
# 或者: from object_detection.core.box_list_ops import area [as 别名]
def test_sort_by_field_invalid_inputs(self):
corners = tf.constant([4 * [0.0], 4 * [0.5], 4 * [1.0], 4 * [2.0], 4 *
[3.0], 4 * [4.0]])
misc = tf.constant([[.95, .9], [.5, .3]], tf.float32)
weights = tf.constant([.1, .2], tf.float32)
boxes = box_list.BoxList(corners)
boxes.add_field('misc', misc)
boxes.add_field('weights', weights)
with self.assertRaises(ValueError):
box_list_ops.sort_by_field(boxes, 'area')
with self.assertRaises(ValueError):
box_list_ops.sort_by_field(boxes, 'misc')
with self.assertRaises(ValueError):
box_list_ops.sort_by_field(boxes, 'weights') 示例3: test_sort_by_field_invalid_inputs
# 需要导入模块: from object_detection.core import box_list_ops [as 别名]
# 或者: from object_detection.core.box_list_ops import area [as 别名]
def test_sort_by_field_invalid_inputs(self):
corners = tf.constant([4 * [0.0], 4 * [0.5], 4 * [1.0], 4 * [2.0], 4 *
[3.0], 4 * [4.0]])
misc = tf.constant([[.95, .9], [.5, .3]], tf.float32)
weights = tf.constant([.1, .2], tf.float32)
boxes = box_list.BoxList(corners)
boxes.add_field('misc', misc)
boxes.add_field('weights', weights)
with self.test_session() as sess:
with self.assertRaises(ValueError):
box_list_ops.sort_by_field(boxes, 'area')
with self.assertRaises(ValueError):
box_list_ops.sort_by_field(boxes, 'misc')
if ops._USE_C_API:
with self.assertRaises(ValueError):
box_list_ops.sort_by_field(boxes, 'weights')
else:
with self.assertRaisesWithPredicateMatch(errors.InvalidArgumentError,
'Incorrect field size'):
sess.run(box_list_ops.sort_by_field(boxes, 'weights').get()) 示例4: test_sort_by_field_invalid_inputs
# 需要导入模块: from object_detection.core import box_list_ops [as 别名]
# 或者: from object_detection.core.box_list_ops import area [as 别名]
def test_sort_by_field_invalid_inputs(self):
corners = tf.constant([4 * [0.0], 4 * [0.5], 4 * [1.0], 4 * [2.0], 4 *
[3.0], 4 * [4.0]])
misc = tf.constant([[.95, .9], [.5, .3]], tf.float32)
weights = tf.constant([[.1, .2]], tf.float32)
boxes = box_list.BoxList(corners)
boxes.add_field('misc', misc)
boxes.add_field('weights', weights)
with self.assertRaises(ValueError):
box_list_ops.sort_by_field(boxes, 'area')
with self.assertRaises(ValueError):
box_list_ops.sort_by_field(boxes, 'misc')
with self.assertRaises(ValueError):
box_list_ops.sort_by_field(boxes, 'weights') 示例5: assign_boxes_to_layers
# 需要导入模块: from object_detection.core import box_list_ops [as 别名]
# 或者: from object_detection.core.box_list_ops import area [as 别名]
def assign_boxes_to_layers(self, absolute_boxes):
"""Assigns boxes to feature pyramid layers.
Args:
absolute_boxes: A float32 tensor with shape [batch_size,
num_proposals, box_code_size] containing boxes in
absolute coordinates.
Returns:
layer_indices: A int32 tensor of shape [N],
ranging from lowest resolution (0) to highest (num_resolutions - 1)
"""
def log2(x):
numerator = tf.log(x)
denominator = tf.log(tf.constant(2, dtype=numerator.dtype))
return numerator / denominator
min_scale = self._base_anchor_size[0] * min(self._pyramid_scales)
box_scales = tf.sqrt(box_list_ops.area(box_list.BoxList(absolute_boxes)))
max_k = len(self._pyramid_scales) - 1
k = tf.round(log2(box_scales / min_scale))
k = tf.minimum(tf.maximum(k, 0), max_k)
return max_k - tf.to_int32(k)