def test_gaussian_kernel(self): gaussian_kernel = sr_image_util.create_gaussian_kernel(2, 0.1) expected_size = (5, 5) self.assertEqual(expected_size, np.shape(gaussian_kernel)) normalized = (np.sum(gaussian_kernel) > 0.99 and np.sum(gaussian_kernel) <= 1.01) self.assertTrue(normalized)
def _create_gaussian_kernel(self, radius=2, sigma=1.0): """Create a gaussian kernel with the given radius and sigma. Only support radius=1, 2. @param radius: radius for gaussian kernel @type radius: int @param sigma: @type sigma: float @return: gaussian kernel @rtype: L{PIL.ImageFilter.Kernel} """ gaussian_kernel = sr_image_util.create_gaussian_kernel(radius, sigma) size = np.shape(gaussian_kernel) return Kernel(size, list(gaussian_kernel.flatten()))
def __init__(self): self._method_type = "iccv09" self._kernel = sr_image_util.create_gaussian_kernel()
def test_gaussian_kernel(self): gaussian_kernel = sr_image_util.create_gaussian_kernel(2, 0.1) expected_size = (5, 5) self.assertEqual(expected_size, np.shape(gaussian_kernel)) normalized = np.sum(gaussian_kernel) > 0.99 and np.sum(gaussian_kernel) <= 1.01 self.assertTrue(normalized)