def test_fast_homography():
img = rgb2gray(data.lena()).astype(np.uint8)
img = img[:, :100]
theta = np.deg2rad(30)
scale = 0.5
tx, ty = 50, 50
H = np.eye(3)
S = scale * np.sin(theta)
C = scale * np.cos(theta)
H[:2, :2] = [[C, -S], [S, C]]
H[:2, 2] = [tx, ty]
for mode in ('constant', 'mirror', 'wrap'):
print 'Transform mode:', mode
p0 = homography(img, H, mode=mode, order=1)
p1 = fast_homography(img, H, mode=mode)
p1 = np.round(p1)
## import matplotlib.pyplot as plt
## f, (ax0, ax1, ax2, ax3) = plt.subplots(1, 4)
## ax0.imshow(img)
## ax1.imshow(p0, cmap=plt.cm.gray)
## ax2.imshow(p1, cmap=plt.cm.gray)
## ax3.imshow(np.abs(p0 - p1), cmap=plt.cm.gray)
## plt.show()
d = np.mean(np.abs(p0 - p1))
print "delta=", d
assert d < 0.2
def test_tv_denoise_2d(self):
"""
Apply the TV denoising algorithm on the lena image provided
by scipy
"""
# lena image
lena = color.rgb2gray(data.lena())
# add noise to lena
lena += 0.5 * lena.std()*np.random.randn(*lena.shape)
# denoise
denoised_lena = filter.tv_denoise(lena, weight=60.0)
# which dtype?
assert denoised_lena.dtype in [np.float, np.float32, np.float64]
from scipy import ndimage
grad = ndimage.morphological_gradient(lena, size=((3,3)))
grad_denoised = ndimage.morphological_gradient(denoised_lena, size=((3,3)))
# test if the total variation has decreased
assert np.sqrt((grad_denoised**2).sum()) < np.sqrt((grad**2).sum()) / 2
denoised_lena_int = filter.tv_denoise(lena.astype(np.int32), \
weight=60.0, keep_type=True)
assert denoised_lena_int.dtype is np.dtype('int32')
norm of the gradient of the image, and minimizing the total variation
typically produces "posterized" images with flat domains separated by
sharp edges.
It is possible to change the degree of posterization by controlling
the tradeoff between denoising and faithfulness to the original image.
"""
import numpy as np
import matplotlib.pyplot as plt
from scikits.image import data, color, img_as_ubyte
from scikits.image.filter import tv_denoise
l = img_as_ubyte(color.rgb2gray(data.lena()))
l = l[230:290, 220:320]
noisy = l + 0.4 * l.std() * np.random.random(l.shape)
tv_denoised = tv_denoise(noisy, weight=10)
plt.figure(figsize=(12,2.8))
plt.subplot(131)
plt.imshow(noisy, cmap=plt.cm.gray, vmin=40, vmax=220)
plt.axis('off')
plt.title('noisy', fontsize=20)
plt.subplot(132)
plt.imshow(tv_denoised, cmap=plt.cm.gray, vmin=40, vmax=220)
plt.axis('off')
def test_lena():
""" Test that "Lena" image can be loaded. """
lena = data.lena()
assert_equal(lena.shape, (512, 512, 3))