def check_wrap_around(ndim, axis):
# create a ramp, but with the last pixel along axis equalling the first
elements = 100
ramp = np.linspace(0, 12 * np.pi, elements)
ramp[-1] = ramp[0]
image = ramp.reshape(
tuple([elements if n == axis else 1 for n in range(ndim)]))
image_wrapped = np.angle(np.exp(1j * image))
index_first = tuple([0] * ndim)
index_last = tuple([-1 if n == axis else 0 for n in range(ndim)])
# unwrap the image without wrap around
with warnings.catch_warnings():
# We do not want warnings about length 1 dimensions
warnings.simplefilter("ignore")
image_unwrap_no_wrap_around = unwrap_phase(image_wrapped)
print('endpoints without wrap_around:',
image_unwrap_no_wrap_around[index_first],
image_unwrap_no_wrap_around[index_last])
# without wrap around, the endpoints of the image should differ
assert abs(image_unwrap_no_wrap_around[index_first] -
image_unwrap_no_wrap_around[index_last]) > np.pi
# unwrap the image with wrap around
wrap_around = [n == axis for n in range(ndim)]
with warnings.catch_warnings():
# We do not want warnings about length 1 dimensions
warnings.simplefilter("ignore")
image_unwrap_wrap_around = unwrap_phase(image_wrapped, wrap_around)
print('endpoints with wrap_around:', image_unwrap_wrap_around[index_first],
image_unwrap_wrap_around[index_last])
# with wrap around, the endpoints of the image should be equal
assert_almost_equal(image_unwrap_wrap_around[index_first],
image_unwrap_wrap_around[index_last])
def test_mask():
length = 100
ramps = [
np.linspace(0, 4 * np.pi, length),
np.linspace(0, 8 * np.pi, length),
np.linspace(0, 6 * np.pi, length)
]
image = np.vstack(ramps)
mask_1d = np.ones((length, ), dtype=np.bool)
mask_1d[0] = mask_1d[-1] = False
for i in range(len(ramps)):
# mask all ramps but the i'th one
mask = np.zeros(image.shape, dtype=np.bool)
mask |= mask_1d.reshape(1, -1)
mask[i, :] = False # unmask i'th ramp
image_wrapped = np.ma.array(np.angle(np.exp(1j * image)), mask=mask)
image_unwrapped = unwrap_phase(image_wrapped)
image_unwrapped -= image_unwrapped[0, 0] # remove phase shift
# The end of the unwrapped array should have value equal to the
# endpoint of the unmasked ramp
assert_array_almost_equal(image_unwrapped[:, -1], image[i, -1])
# Same tests, but forcing use of the 3D unwrapper by reshaping
image_wrapped_3d = image_wrapped.reshape((1, ) + image_wrapped.shape)
image_unwrapped_3d = unwrap_phase(image_wrapped_3d)
image_unwrapped_3d -= image_unwrapped_3d[0, 0, 0] # remove phase shift
assert_array_almost_equal(image_unwrapped_3d[:, :, -1], image[i, -1])
def check_wrap_around(ndim, axis):
# create a ramp, but with the last pixel along axis equalling the first
elements = 100
ramp = np.linspace(0, 12 * np.pi, elements)
ramp[-1] = ramp[0]
image = ramp.reshape(tuple([elements if n == axis else 1
for n in range(ndim)]))
image_wrapped = np.angle(np.exp(1j * image))
index_first = tuple([0] * ndim)
index_last = tuple([-1 if n == axis else 0 for n in range(ndim)])
# unwrap the image without wrap around
with warnings.catch_warnings():
# We do not want warnings about length 1 dimensions
warnings.simplefilter("ignore")
image_unwrap_no_wrap_around = unwrap_phase(image_wrapped)
print('endpoints without wrap_around:',
image_unwrap_no_wrap_around[index_first],
image_unwrap_no_wrap_around[index_last])
# without wrap around, the endpoints of the image should differ
assert abs(image_unwrap_no_wrap_around[index_first]
- image_unwrap_no_wrap_around[index_last]) > np.pi
# unwrap the image with wrap around
wrap_around = [n == axis for n in range(ndim)]
with warnings.catch_warnings():
# We do not want warnings about length 1 dimensions
warnings.simplefilter("ignore")
image_unwrap_wrap_around = unwrap_phase(image_wrapped, wrap_around)
print('endpoints with wrap_around:',
image_unwrap_wrap_around[index_first],
image_unwrap_wrap_around[index_last])
# with wrap around, the endpoints of the image should be equal
assert_almost_equal(image_unwrap_wrap_around[index_first],
image_unwrap_wrap_around[index_last])
def test_mask():
length = 100
ramps = [np.linspace(0, 4 * np.pi, length),
np.linspace(0, 8 * np.pi, length),
np.linspace(0, 6 * np.pi, length)]
image = np.vstack(ramps)
mask_1d = np.ones((length,), dtype=np.bool)
mask_1d[0] = mask_1d[-1] = False
for i in range(len(ramps)):
# mask all ramps but the i'th one
mask = np.zeros(image.shape, dtype=np.bool)
mask |= mask_1d.reshape(1, -1)
mask[i, :] = False # unmask i'th ramp
image_wrapped = np.ma.array(np.angle(np.exp(1j * image)), mask=mask)
image_unwrapped = unwrap_phase(image_wrapped)
image_unwrapped -= image_unwrapped[0, 0] # remove phase shift
# The end of the unwrapped array should have value equal to the
# endpoint of the unmasked ramp
assert_array_almost_equal(image_unwrapped[:, -1], image[i, -1])
# Same tests, but forcing use of the 3D unwrapper by reshaping
image_wrapped_3d = image_wrapped.reshape((1,) + image_wrapped.shape)
image_unwrapped_3d = unwrap_phase(image_wrapped_3d)
image_unwrapped_3d -= image_unwrapped_3d[0, 0, 0] # remove phase shift
assert_array_almost_equal(image_unwrapped_3d[:, :, -1], image[i, -1])
def check_unwrap(image, mask=None):
image_wrapped = np.angle(np.exp(1j * image))
if not mask is None:
print('Testing a masked image')
image = np.ma.array(image, mask=mask)
image_wrapped = np.ma.array(image_wrapped, mask=mask)
image_unwrapped = unwrap_phase(image_wrapped)
assert_phase_almost_equal(image_unwrapped, image)
def check_unwrap(image, mask=None):
image_wrapped = np.angle(np.exp(1j * image))
if not mask is None:
print('Testing a masked image')
image = np.ma.array(image, mask=mask)
image_wrapped = np.ma.array(image_wrapped, mask=mask)
image_unwrapped = unwrap_phase(image_wrapped)
assert_phase_almost_equal(image_unwrapped, image)
"""
import numpy as np
from matplotlib import pyplot as plt
from skimage import data, img_as_float, color, exposure
from skimage.exposure import unwrap_phase
# Load an image as a floating-point grayscale
image = color.rgb2gray(img_as_float(data.chelsea()))
# Scale the image to [0, 4*pi]
image = exposure.rescale_intensity(image, out_range=(0, 4 * np.pi))
# Create a phase-wrapped image in the interval [-pi, pi)
image_wrapped = np.angle(np.exp(1j * image))
# Perform phase unwrapping
image_unwrapped = unwrap_phase(image_wrapped)
plt.figure()
plt.subplot(221)
plt.title('Original')
plt.imshow(image, cmap='gray', vmin=0, vmax=4 * np.pi)
plt.colorbar()
plt.subplot(222)
plt.title('Wrapped phase')
plt.imshow(image_wrapped, cmap='gray', vmin=-np.pi, vmax=np.pi)
plt.colorbar()
plt.subplot(223)
plt.title('After phase unwrapping')
plt.imshow(image_unwrapped, cmap='gray')
