def test_multiple_dimensional_integer_shift(self):
shape = array([2, 2, 3, 2])
x = zeros(shape)
x[0, 0, 0, 0] = 1
shift = []
for max_shift in shape:
shift.append(randint(max_shift))
xx = mm.circshift(x, shift)
i = unravel_index(argmax(xx), x.shape)
self.assertListEqual(list(shift), list(i))
def test_multiple_dimensional_integer_shift(self):
shape = array([2, 2, 3, 2])
x = zeros(shape)
x[0, 0, 0, 0] = 1
shift = []
for max_shift in shape:
shift.append(randint(max_shift))
xx = mm.circshift(x, shift)
i = unravel_index(argmax(xx), x.shape)
self.assertListEqual(list(shift), list(i))
def test_upsample_1000(self):
img0 = scipy.misc.lena()
ny, nx = img0.shape
row_shift = (rand() - 0.5)*ny
col_shift = (rand() - 0.5)*nx
img1 = mm.circshift(img0, (row_shift, col_shift))
val, row, col = mm.register(img0, img1, upsample=105)
self.assertAlmostEqual(row, row_shift, places=2)
self.assertAlmostEqual(col, col_shift, places=2)
def test_upsample_1000(self):
img0 = scipy.misc.lena()
ny, nx = img0.shape
row_shift = (rand() - 0.5) * ny
col_shift = (rand() - 0.5) * nx
img1 = mm.circshift(img0, (row_shift, col_shift))
val, row, col = mm.register(img0, img1, upsample=105)
self.assertAlmostEqual(row, row_shift, places=2)
self.assertAlmostEqual(col, col_shift, places=2)
def setUp(self):
img0 = scipy.misc.lena()
ny, nx = img0.shape
row_shift = randint(-ny/2, ny/2)
col_shift = randint(-nx/2, nx/2)
img1 = mm.circshift(img0, (row_shift, col_shift))
self.img0 = img0
self.img1 = img1
self.nx = nx
self.ny = ny
self.col_shift = col_shift
self.row_shift = row_shift
def setUp(self):
img0 = scipy.misc.lena()
ny, nx = img0.shape
row_shift = randint(-ny / 2, ny / 2)
col_shift = randint(-nx / 2, nx / 2)
img1 = mm.circshift(img0, (row_shift, col_shift))
self.img0 = img0
self.img1 = img1
self.nx = nx
self.ny = ny
self.col_shift = col_shift
self.row_shift = row_shift
def test_upsample_1(self):
"""Test algorithm without subpixel registration."""
img0 = scipy.misc.lena()
ny, nx = img0.shape
row_shift = randint(-ny/2, ny/2)
col_shift = randint(-nx/2, nx/2)
row_shift = 10
col_shift = 27
img1 = mm.circshift(img0, (row_shift, col_shift))
img1 = abs(img1)
if PLOTTING:
subplot(211)
imshow(img0)
title('original')
subplot(212)
imshow(img1)
title('shifted by {}x{}'.format(row_shift, col_shift))
show()
val, row, col = mm.register(img0, img1)
self.assertEqual(row, row_shift)
self.assertEqual(col, col_shift)
def test_upsample_1(self):
"""Test algorithm without subpixel registration."""
img0 = scipy.misc.lena()
ny, nx = img0.shape
row_shift = randint(-ny / 2, ny / 2)
col_shift = randint(-nx / 2, nx / 2)
row_shift = 10
col_shift = 27
img1 = mm.circshift(img0, (row_shift, col_shift))
img1 = abs(img1)
if PLOTTING:
subplot(211)
imshow(img0)
title('original')
subplot(212)
imshow(img1)
title('shifted by {}x{}'.format(row_shift, col_shift))
show()
val, row, col = mm.register(img0, img1)
self.assertEqual(row, row_shift)
self.assertEqual(col, col_shift)
def test_allones(self):
N = randint(2, 30)
shift = 2*N*rand()
x = ones([N])
xx = mm.circshift(x, shift)
self.assertArraysClose(x, xx)
def test_single_dim_integer_shift(self):
a = arange(6)
shift = 1
a_s = around(mm.circshift(a, shift))
self.assertArraysClose(a_s, array([5, 0, 1, 2, 3, 4]))
def test_allones(self):
N = randint(2, 30)
shift = 2 * N * rand()
x = ones([N])
xx = mm.circshift(x, shift)
self.assertArraysClose(x, xx)
def test_single_dim_integer_shift(self):
a = arange(6)
shift = 1
a_s = around(mm.circshift(a, shift))
self.assertArraysClose(a_s, array([5, 0, 1, 2, 3, 4]))