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 __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)