def test_multichannel(): a = np.zeros((5, 5, 3)) a[1, 1] = np.arange(1, 4) gaussian_rgb_a = gaussian_filter(a, sigma=1, mode='reflect', multichannel=True) # Check that the mean value is conserved in each channel # (color channels are not mixed together) assert np.allclose([a[..., i].mean() for i in range(3)], [gaussian_rgb_a[..., i].mean() for i in range(3)]) # Test multichannel = None with expected_warnings(['multichannel']): gaussian_rgb_a = gaussian_filter(a, sigma=1, mode='reflect') # Check that the mean value is conserved in each channel # (color channels are not mixed together) assert np.allclose([a[..., i].mean() for i in range(3)], [gaussian_rgb_a[..., i].mean() for i in range(3)]) # Iterable sigma gaussian_rgb_a = gaussian_filter(a, sigma=[1, 2], mode='reflect', multichannel=True) assert np.allclose([a[..., i].mean() for i in range(3)], [gaussian_rgb_a[..., i].mean() for i in range(3)])
def test_multichannel(): a = np.zeros((5, 5, 3)) a[1, 1] = np.arange(1, 4) gaussian_rgb_a = gaussian_filter(a, sigma=1, mode='reflect', multichannel=True) # Check that the mean value is conserved in each channel # (color channels are not mixed together) assert np.allclose([a[..., i].mean() for i in range(3)], [gaussian_rgb_a[..., i].mean() for i in range(3)]) # Test multichannel = None gaussian_rgb_a = gaussian_filter(a, sigma=1, mode='reflect') # Check that the mean value is conserved in each channel # (color channels are not mixed together) assert np.allclose([a[..., i].mean() for i in range(3)], [gaussian_rgb_a[..., i].mean() for i in range(3)]) # Iterable sigma gaussian_rgb_a = gaussian_filter(a, sigma=[1, 2], mode='reflect', multichannel=True) assert np.allclose([a[..., i].mean() for i in range(3)], [gaussian_rgb_a[..., i].mean() for i in range(3)])
def test_energy_decrease(): a = np.zeros((3, 3)) a[1, 1] = 1. gaussian_a = gaussian_filter(a, sigma=1, mode='reflect') assert gaussian_a.std() < a.std()
def test_null_sigma(): a = np.zeros((3, 3)) a[1, 1] = 1. assert np.all(gaussian_filter(a, 0) == a)