本文整理汇总了Python中menpo.image.MaskedImage类的典型用法代码示例。如果您正苦于以下问题：Python MaskedImage类的具体用法？Python MaskedImage怎么用？Python MaskedImage使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了MaskedImage类的15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_zero_norm_exception
def test_normalize_norm_zero_norm_exception():
pixels = np.zeros((3, 120, 120))
image = MaskedImage(pixels)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
with raises(ValueError):
image.normalize_norm(mode='per_channel')示例2: test_masked_image_as_unmasked_fill_tuple
def test_masked_image_as_unmasked_fill_tuple():
m_img = MaskedImage(np.random.rand(3, 3, 3), copy=False)
m_img.mask.pixels[0, 0, 0] = False
img = m_img.as_unmasked(fill=(1, 2, 3))
assert(type(img) == Image)
assert_allclose(m_img.pixels[0, 1:, 1:], img.pixels[0, 1:, 1:])
assert_allclose(img.pixels[:, 0, 0], (1, 2, 3))示例3: test_masked_image_as_unmasked_fill
def test_masked_image_as_unmasked_fill():
m_img = MaskedImage(np.random.rand(1, 3, 3), copy=False)
m_img.mask.pixels[0, 0, 0] = False
img = m_img.as_unmasked(fill=8)
assert type(img) == Image
assert_allclose(m_img.pixels[0, 1:, 1:], img.pixels[0, 1:, 1:])
assert_allclose(img.pixels[0, 0, 0], 8.0)示例4: test_normalize_std_no_variance_exception
def test_normalize_std_no_variance_exception():
pixels = np.ones((3, 120, 120))
pixels[0] = 0.5
pixels[1] = 0.2345
image = MaskedImage(pixels)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
image.normalize_std(mode='per_channel')示例5: test_2d_crop_with_mask
def test_2d_crop_with_mask():
pixels = np.ones((120, 120, 3))
mask = np.zeros_like(pixels[..., 0])
mask[10:100, 20:30] = 1
im = MaskedImage(pixels, mask=mask)
cropped_im = im.cropped_copy([0, 0], [20, 60])
assert (cropped_im.shape == (20, 60))
assert (np.alltrue(cropped_im.shape))示例6: test_normalize_norm_default
def test_normalize_norm_default():
pixels = np.ones((120, 120, 3))
pixels[..., 0] = 0.5
pixels[..., 1] = 0.2345
image = MaskedImage(pixels)
image.normalize_norm_inplace()
assert_allclose(np.mean(image.pixels), 0, atol=1e-10)
assert_allclose(np.linalg.norm(image.pixels), 1)示例7: test_as_greyscale_average
def test_as_greyscale_average():
ones = np.ones([3, 120, 120])
image = MaskedImage(ones)
image.pixels[0] *= 0.5
new_image = image.as_greyscale(mode='average')
assert (new_image.shape == image.shape)
assert (new_image.n_channels == 1)
assert_allclose(new_image.pixels[0], ones[0] * 0.83333333)示例8: test_as_greyscale_luminosity
def test_as_greyscale_luminosity():
ones = np.ones([3, 120, 120])
image = MaskedImage(ones)
image.pixels[0] *= 0.5
new_image = image.as_greyscale(mode='luminosity')
assert (new_image.shape == image.shape)
assert (new_image.n_channels == 1)
assert_allclose(new_image.pixels[0], ones[0] * 0.850532)示例9: test_normalize_norm_per_channel
def test_normalize_norm_per_channel():
pixels = np.random.randn(120, 120, 3)
pixels[..., 1] *= 7
pixels[..., 0] += -14
pixels[..., 2] /= 130
image = MaskedImage(pixels)
image.normalize_norm_inplace(mode="per_channel")
assert_allclose(np.mean(image.as_vector(keep_channels=True), axis=0), 0, atol=1e-10)
assert_allclose(np.linalg.norm(image.as_vector(keep_channels=True), axis=0), 1)示例10: test_2d_crop_without_mask
def test_2d_crop_without_mask():
pixels = np.ones((120, 120, 3))
im = MaskedImage(pixels)
cropped_im = im.cropped_copy([10, 50], [20, 60])
assert (cropped_im.shape == (10, 10))
assert (cropped_im.n_channels == 3)
assert (np.alltrue(cropped_im.shape))示例11: test_maskedimage_copy
def test_maskedimage_copy():
pixels = np.ones([1, 10, 10])
landmarks = PointCloud(np.ones([3, 2]), copy=False)
im = MaskedImage(pixels, copy=False)
im.landmarks['test'] = landmarks
im_copy = im.copy()
assert (not is_same_array(im.pixels, im_copy.pixels))
assert (not is_same_array(im_copy.landmarks['test'].points,
im.landmarks['test'].points))示例12: rebuild_feature_image_with_centres
def rebuild_feature_image_with_centres(image, f_pixels, centres):
if hasattr(image, 'mask'):
mask = sample_mask_for_centres(image.mask.mask, centres)
new_image = MaskedImage(f_pixels, mask=mask, copy=False)
else:
new_image = Image(f_pixels, copy=False)
if image.has_landmarks:
t = lm_centres_correction(centres)
new_image.landmarks = t.apply(image.landmarks)
return new_image示例13: test_normalize_norm_masked_per_channel
def test_normalize_norm_masked_per_channel():
pixels = np.random.randn(3, 120, 120)
pixels[1] *= 7
pixels[0] += -14
pixels[2] /= 130
image = MaskedImage(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14: test_normalize_norm_masked
def test_normalize_norm_masked():
pixels = np.random.randn(120, 120, 3)
pixels[..., 1] *= 7
pixels[..., 0] += -14
pixels[..., 2] /= 130
mask = np.zeros((120, 120))
mask[30:50, 20:30] = 1
image = MaskedImage(pixels, mask=mask)
image.normalize_norm_inplace(mode="per_channel", limit_to_mask=True)
assert_allclose(np.mean(image.as_vector(keep_channels=True), axis=0), 0, atol=1e-10)
assert_allclose(np.linalg.norm(image.as_vector(keep_channels=True), axis=0), 1)示例15: test_normalize_std_masked_per_channel
def test_normalize_std_masked_per_channel():
pixels = np.random.randn(3, 120, 120)
pixels[0] *= 7
pixels[1] += -14
pixels[2] /= 130
image = MaskedImage(pixels)
new_image = image.normalize_std(mode='per_channel')
assert_allclose(
np.mean(new_image.as_vector(keep_channels=True), axis=1), 0, atol=1e-10)
assert_allclose(
np.std(new_image.as_vector(keep_channels=True), axis=1), 1)