def test_03_02_adaptive_threshold_different(self):
r = np.random.RandomState()
r.seed(31)
block = r.uniform(size=(10,10))
i,j = np.mgrid[0:10:2,0:10:2]
block[i,j] *= .5
i,j = np.mgrid[0:50,0:50]
img = block[i%10, j%10] * .5
#
# Make the middle higher in intensity
#
img[20:30, 20:30] *= 2
global_threshold = T.get_global_threshold(T.TM_OTSU, block)
adaptive_threshold = T.get_adaptive_threshold(
T.TM_OTSU, img, global_threshold,
adaptive_window_size = 10)
#
# Check that the gradients are positive for i,j<15 and negative
# for i,j>=15
#
gradient = convolve1d(adaptive_threshold, [-1, 0, 1], 0)
self.assertTrue(np.all(gradient[20:25, 20:30] < 0))
self.assertTrue(np.all(gradient[25:30, 20:30] > 0))
gradient = convolve1d(adaptive_threshold, [-1, 0, 1], 1)
self.assertTrue(np.all(gradient[20:30, 20:25] < 0))
self.assertTrue(np.all(gradient[20:30, 25:30] > 0))
def test_03_02_adaptive_threshold_different(self):
r = np.random.RandomState()
r.seed(31)
block = r.uniform(size=(10,10))
i,j = np.mgrid[0:10:2,0:10:2]
block[i,j] *= .5
i,j = np.mgrid[0:50,0:50]
img = block[i%10, j%10] * .5
#
# Make the middle higher in intensity
#
img[20:30, 20:30] *= 2
global_threshold = T.get_global_threshold(T.TM_OTSU, block)
adaptive_threshold = T.get_adaptive_threshold(
T.TM_OTSU, img, global_threshold,
adaptive_window_size = 10)
#
# Check that the gradients are positive for i,j<15 and negative
# for i,j>=15
#
gradient = convolve1d(adaptive_threshold, [-1, 0, 1], 0)
self.assertTrue(np.all(gradient[20:25, 20:30] < 0))
self.assertTrue(np.all(gradient[25:30, 20:30] > 0))
gradient = convolve1d(adaptive_threshold, [-1, 0, 1], 1)
self.assertTrue(np.all(gradient[20:30, 20:25] < 0))
self.assertTrue(np.all(gradient[20:30, 25:30] > 0))
def test_03_01_adaptive_threshold_same(self):
r = np.random.RandomState()
r.seed(31)
block = r.uniform(size=(10, 10))
i, j = np.mgrid[0:10:2, 0:10:2]
block[i, j] *= 0.5
i, j = np.mgrid[0:50, 0:50]
img = block[i % 10, j % 10]
global_threshold = T.get_global_threshold(T.TM_OTSU, block)
adaptive_threshold = T.get_adaptive_threshold(T.TM_OTSU, img, global_threshold, adaptive_window_size=10)
np.testing.assert_almost_equal(adaptive_threshold, global_threshold)
def test_03_01_adaptive_threshold_same(self):
r = np.random.RandomState()
r.seed(31)
block = r.uniform(size=(10,10))
i,j = np.mgrid[0:10:2,0:10:2]
block[i,j] *= .5
i,j = np.mgrid[0:50,0:50]
img = block[i%10, j%10]
global_threshold = T.get_global_threshold(T.TM_OTSU, block)
adaptive_threshold = T.get_adaptive_threshold(
T.TM_OTSU, img, global_threshold,
adaptive_window_size = 10)
np.testing.assert_almost_equal(adaptive_threshold, global_threshold)
