本文整理汇总了Python中object_detection.core.keypoint_ops.scale方法的典型用法代码示例。如果您正苦于以下问题：Python keypoint_ops.scale方法的具体用法？Python keypoint_ops.scale怎么用？Python keypoint_ops.scale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.scale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_scale
# 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 scale [as 别名]
def test_scale(self):
keypoints = tf.constant([
[[0.0, 0.0], [100.0, 200.0]],
[[50.0, 120.0], [100.0, 140.0]]
])
y_scale = tf.constant(1.0 / 100)
x_scale = tf.constant(1.0 / 200)
expected_keypoints = tf.constant([
[[0., 0.], [1.0, 1.0]],
[[0.5, 0.6], [1.0, 0.7]]
])
output = keypoint_ops.scale(keypoints, y_scale, x_scale)
with self.test_session() as sess:
output_, expected_keypoints_ = sess.run([output, expected_keypoints])
self.assertAllClose(output_, expected_keypoints_) 示例2: test_scale
# 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 scale [as 别名]
def test_scale(self):
def graph_fn():
keypoints = tf.constant([
[[0.0, 0.0], [100.0, 200.0]],
[[50.0, 120.0], [100.0, 140.0]]
])
y_scale = tf.constant(1.0 / 100)
x_scale = tf.constant(1.0 / 200)
expected_keypoints = tf.constant([
[[0., 0.], [1.0, 1.0]],
[[0.5, 0.6], [1.0, 0.7]]
])
output = keypoint_ops.scale(keypoints, y_scale, x_scale)
return output, expected_keypoints
output, expected_keypoints = self.execute(graph_fn, [])
self.assertAllClose(output, expected_keypoints) 示例3: scale_boxes_to_pixel_coordinates
# 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 scale [as 别名]
def scale_boxes_to_pixel_coordinates(image, boxes, keypoints=None):
"""Scales boxes from normalized to pixel coordinates.
Args:
image: A 3D float32 tensor of shape [height, width, channels].
boxes: A 2D float32 tensor of shape [num_boxes, 4] containing the bounding
boxes in normalized coordinates. Each row is of the form
[ymin, xmin, ymax, xmax].
keypoints: (optional) rank 3 float32 tensor with shape
[num_instances, num_keypoints, 2]. The keypoints are in y-x normalized
coordinates.
Returns:
image: unchanged input image.
scaled_boxes: a 2D float32 tensor of shape [num_boxes, 4] containing the
bounding boxes in pixel coordinates.
scaled_keypoints: a 3D float32 tensor with shape
[num_instances, num_keypoints, 2] containing the keypoints in pixel
coordinates.
"""
boxlist = box_list.BoxList(boxes)
image_height = tf.shape(image)[0]
image_width = tf.shape(image)[1]
scaled_boxes = box_list_ops.scale(boxlist, image_height, image_width).get()
result = [image, scaled_boxes]
if keypoints is not None:
scaled_keypoints = keypoint_ops.scale(keypoints, image_height, image_width)
result.append(scaled_keypoints)
return tuple(result)
# pylint: disable=g-doc-return-or-yield