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 test_normalization(self):
img0 = zeros([30, 30])
img0[0, 0] = 2.0
img0[0, 1] = 4.0
ny, nx = img0.shape
row_shift = 3
col_shift = 5
img1 = zeros([30, 30])
img1[3, 3] = 2.0
img1[3, 4] = 4.0
v, y, x = mm.register(img0, img1)
self.assertAlmostEqual(v, 1, places=8)
v, y, x = mm.register(img0, 0.5*img1)
self.assertAlmostEqual(v, 1, places=8)
v, y, x = mm.register(img0, img1, upsample=4)
self.assertAlmostEqual(v, 1, places=8)
v, y, x = mm.register(img0, 0.5*img1, upsample=4)
self.assertAlmostEqual(v, 1, places=8)
def test_normalization(self):
img0 = zeros([30, 30])
img0[0, 0] = 2.0
img0[0, 1] = 4.0
ny, nx = img0.shape
row_shift = 3
col_shift = 5
img1 = zeros([30, 30])
img1[3, 3] = 2.0
img1[3, 4] = 4.0
v, y, x = mm.register(img0, img1)
self.assertAlmostEqual(v, 1, places=8)
v, y, x = mm.register(img0, 0.5 * img1)
self.assertAlmostEqual(v, 1, places=8)
v, y, x = mm.register(img0, img1, upsample=4)
self.assertAlmostEqual(v, 1, places=8)
v, y, x = mm.register(img0, 0.5 * img1, upsample=4)
self.assertAlmostEqual(v, 1, places=8)
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)