本文整理汇总了Python中menpo.image.Image.normalize_norm方法的典型用法代码示例。如果您正苦于以下问题：Python Image.normalize_norm方法的具体用法？Python Image.normalize_norm怎么用？Python Image.normalize_norm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.image.Image的用法示例。
在下文中一共展示了Image.normalize_norm方法的4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_normalize_norm_image
# 需要导入模块: from menpo.image import Image [as 别名]
# 或者: from menpo.image.Image import normalize_norm [as 别名]
def test_normalize_norm_image():
pixels = np.ones((3, 120, 120))
pixels[0] = 0.5
pixels[1] = 0.2345
image = Image(pixels)
new_image = image.normalize_norm()
assert_allclose(np.mean(new_image.pixels), 0, atol=1e-10)
assert_allclose(np.linalg.norm(new_image.pixels), 1)示例2: test_normalize_norm_image
# 需要导入模块: from menpo.image import Image [as 别名]
# 或者: from menpo.image.Image import normalize_norm [as 别名]
def test_normalize_norm_image():
pixels = np.ones((3, 120, 120))
pixels[0] = 0.5
pixels[1] = 0.2345
image = Image(pixels)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
new_image = image.normalize_norm()
assert_allclose(np.mean(new_image.pixels), 0, atol=1e-10)
assert_allclose(np.linalg.norm(new_image.pixels), 1)示例3: test_normalize_norm_image_per_channel
# 需要导入模块: from menpo.image import Image [as 别名]
# 或者: from menpo.image.Image import normalize_norm [as 别名]
def test_normalize_norm_image_per_channel():
pixels = np.random.randn(3, 120, 120)
pixels[1] *= 17
pixels[0] += -114
pixels[2] /= 30
image = Image(pixels)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
new_image = image.normalize_norm(mode="per_channel")
assert_allclose(np.mean(new_image.as_vector(keep_channels=True), axis=1), 0, atol=1e-10)
assert_allclose(np.linalg.norm(new_image.as_vector(keep_channels=True), axis=1), 1)示例4: test_normalize_norm_image_per_channel
# 需要导入模块: from menpo.image import Image [as 别名]
# 或者: from menpo.image.Image import normalize_norm [as 别名]
def test_normalize_norm_image_per_channel():
pixels = np.random.randn(3, 120, 120)
pixels[1] *= 17
pixels[0] += -114
pixels[2] /= 30
image = Image(pixels)
new_image = image.normalize_norm(mode='per_channel')
assert_allclose(
np.mean(new_image.as_vector(keep_channels=True), axis=1), 0,
atol=1e-10)
assert_allclose(
np.linalg.norm(new_image.as_vector(keep_channels=True), axis=1), 1)