def test_denoise_tv_bregman_3d():
img = lena
# add some random noise
img += 0.5 * img.std() * np.random.random(img.shape)
img = np.clip(img, 0, 1)
out1 = filter.denoise_tv_bregman(img, weight=10)
out2 = filter.denoise_tv_bregman(img, weight=5)
# make sure noise is reduced
assert img.std() > out1.std()
assert out1.std() > out2.std()
def test_denoise_tv_bregman_3d():
img = lena
# add some random noise
img += 0.5 * img.std() * np.random.random(img.shape)
img = np.clip(img, 0, 1)
out1 = filter.denoise_tv_bregman(img, weight=10)
out2 = filter.denoise_tv_bregman(img, weight=5)
# make sure noise is reduced
assert img.std() > out1.std()
assert out1.std() > out2.std()
def test_denoise_tv_bregman_float_result_range():
# lena image
img = lena_gray
int_lena = np.multiply(img, 255).astype(np.uint8)
assert np.max(int_lena) > 1
denoised_int_lena = filter.denoise_tv_bregman(int_lena, weight=60.0)
# test if the value range of output float data is within [0.0:1.0]
assert denoised_int_lena.dtype == np.float
assert np.max(denoised_int_lena) <= 1.0
assert np.min(denoised_int_lena) >= 0.0
def test_denoise_tv_bregman_float_result_range():
# lena image
img = lena_gray
int_lena = np.multiply(img, 255).astype(np.uint8)
assert np.max(int_lena) > 1
denoised_int_lena = filter.denoise_tv_bregman(int_lena, weight=60.0)
# test if the value range of output float data is within [0.0:1.0]
assert denoised_int_lena.dtype == np.float
assert np.max(denoised_int_lena) <= 1.0
assert np.min(denoised_int_lena) >= 0.0
def _denoise(self, weight):
return denoise_tv_bregman(numpy.array(self.image), weight)
