def test_04_03_robust_background_upper_outliers(self):
img = self.make_mog_image(.1, .05, .5, .2, .9, (45, 35))
t0 = T.get_global_threshold(T.TM_ROBUST_BACKGROUND, img,
upper_outlier_fraction=0)
t05 = T.get_global_threshold(T.TM_ROBUST_BACKGROUND, img,
upper_outlier_fraction=0.05)
self.assertNotEqual(t0, t05)
def test_04_04_robust_background_sd(self):
img = self.make_mog_image(.5, .1, .8, .01, .99, (45, 35))
t2 = T.get_global_threshold(T.TM_ROBUST_BACKGROUND, img,
lower_outlier_fraction = 0,
upper_outlier_fraction = 0)
self.assertLess(abs(t2 - .7), .02)
t3 = T.get_global_threshold(T.TM_ROBUST_BACKGROUND, img,
lower_outlier_fraction = 0,
upper_outlier_fraction = 0,
deviations_above_average = 2.5)
self.assertLess(abs(t3 - .75), .02)
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_04_05_robust_background_median(self):
img = self.make_mog_image(.3, .05, .5, .2, .9, (45, 35))
t = T.get_global_threshold(T.TM_ROBUST_BACKGROUND, img,
average_fn = np.median,
deviations_above_average = 0,
lower_outlier_fraction = 0,
upper_outlier_fraction = 0)
self.assertLess(abs(t - .3), .01)
def test_04_06_robust_background_mode(self):
img = self.make_mog_image(.3, .05, .5, .2, .9, (45, 35))
img[(img > .25) & (img < .35)] = .304
t = T.get_global_threshold(T.TM_ROBUST_BACKGROUND, img,
average_fn = T.binned_mode,
deviations_above_average = 0,
lower_outlier_fraction = 0,
upper_outlier_fraction = 0)
self.assertAlmostEqual(t, .304)
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_04_08_mad(self):
img = self.make_mog_image(.3, .05, .5, .2, .95, (45, 35))
t = T.get_global_threshold(T.TM_ROBUST_BACKGROUND, img,
variance_fn = T.mad,
deviations_above_average = 2,
lower_outlier_fraction = 0,
upper_outlier_fraction = 0)
norm = scipy.stats.norm(0, .05)
# the MAD should be the expected value at the 75th percentile
expected = .3 + 2 * norm.ppf(.75)
self.assertLess(np.abs(t - expected), .02)
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)
def test_04_01_robust_background(self):
img = self.make_mog_image(.1, .05, .5, .2, .975, (45, 35))
t = T.get_global_threshold(T.TM_ROBUST_BACKGROUND, img)
self.assertLess(abs(t-.2), .025)