本文整理汇总了Python中menpo.transform.Translation.compose_after方法的典型用法代码示例。如果您正苦于以下问题：Python Translation.compose_after方法的具体用法？Python Translation.compose_after怎么用？Python Translation.compose_after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menpo.transform.Translation的用法示例。
在下文中一共展示了Translation.compose_after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: noisy_alignment_similarity_transform
# 需要导入模块: from menpo.transform import Translation [as 别名]
# 或者: from menpo.transform.Translation import compose_after [as 别名]
def noisy_alignment_similarity_transform(source, target, noise_type='uniform',
noise_percentage=0.1,
allow_alignment_rotation=False):
r"""
Constructs and perturbs the optimal similarity transform between the source
and target shapes by adding noise to its parameters.
Parameters
----------
source : `menpo.shape.PointCloud`
The source pointcloud instance used in the alignment
target : `menpo.shape.PointCloud`
The target pointcloud instance used in the alignment
noise_type : ``{'uniform', 'gaussian'}``, optional
The type of noise to be added.
noise_percentage : `float` in ``(0, 1)`` or `list` of `len` `3`, optional
The standard percentage of noise to be added. If `float`, then the same
amount of noise is applied to the scale, rotation and translation
parameters of the optimal similarity transform. If `list` of
`float` it must have length 3, where the first, second and third elements
denote the amount of noise to be applied to the scale, rotation and
translation parameters, respectively.
allow_alignment_rotation : `bool`, optional
If ``False``, then the rotation is not considered when computing the
optimal similarity transform between source and target.
Returns
-------
noisy_alignment_similarity_transform : `menpo.transform.Similarity`
The noisy Similarity Transform between source and target.
"""
if isinstance(noise_percentage, float):
noise_percentage = [noise_percentage] * 3
elif len(noise_percentage) == 1:
noise_percentage *= 3
similarity = AlignmentSimilarity(source, target,
rotation=allow_alignment_rotation)
if noise_type is 'gaussian':
s = noise_percentage[0] * (0.5 / 3) * np.asscalar(np.random.randn(1))
r = noise_percentage[1] * (180 / 3) * np.asscalar(np.random.randn(1))
t = noise_percentage[2] * (target.range() / 3) * np.random.randn(2)
s = scale_about_centre(target, 1 + s)
r = rotate_ccw_about_centre(target, r)
t = Translation(t, source.n_dims)
elif noise_type is 'uniform':
s = noise_percentage[0] * 0.5 * (2 * np.asscalar(np.random.randn(1)) - 1)
r = noise_percentage[1] * 180 * (2 * np.asscalar(np.random.rand(1)) - 1)
t = noise_percentage[2] * target.range() * (2 * np.random.rand(2) - 1)
s = scale_about_centre(target, 1. + s)
r = rotate_ccw_about_centre(target, r)
t = Translation(t, source.n_dims)
else:
raise ValueError('Unexpected noise type. '
'Supported values are {gaussian, uniform}')
return similarity.compose_after(t.compose_after(s.compose_after(r)))示例2: test_translation_compose_after_homog
# 需要导入模块: from menpo.transform import Translation [as 别名]
# 或者: from menpo.transform.Translation import compose_after [as 别名]
def test_translation_compose_after_homog():
# can't do this inplace - so should just give transform chain
homog = Homogeneous(np.array([[0, 1, 0],
[1, 0, 0],
[0, 0, 1]]))
t = Translation([3, 4])
res = t.compose_after(homog)
assert(type(res) == Homogeneous)示例3: test_translation_compose_after_uniformscale
# 需要导入模块: from menpo.transform import Translation [as 别名]
# 或者: from menpo.transform.Translation import compose_after [as 别名]
def test_translation_compose_after_uniformscale():
t = Translation([3, 4])
s = UniformScale(2, 2)
res = t.compose_after(s)
assert(type(res) == Similarity)