def test_bilinear():
H = np.array([[1/2., 0, 0],
[0, 1/2., 0],
[0, 0, 1]])
A = bilinear(HR.shape[0], HR.shape[1],
H,
HR.shape[0] / 2, HR.shape[1]/2)
p = np.prod(HR.shape)
assert_equal(A.shape, (p/4, np.prod(HR.shape)))
HR_small = (A * HR.flat).reshape(np.array(HR.shape) / 2)
err_norm = np.linalg.norm(ndi.zoom(HR, 0.5) - HR_small)
err_norm /= np.prod(HR_small.shape)
assert err_norm < 2
Y = scipy.linalg.block_diag(X, X, X)
bd = block_diag(X.shape[0], X.shape[1],
X.shape[0] * 3, X.shape[1] * 3)
assert_array_equal((bd * X.flat).reshape(np.array(X.shape) * 3), Y)
if __name__ == "__main__":
scale = 3
theta = 5 / 180. * np.pi
C = np.cos(theta)
S = np.sin(theta)
tx, ty = 0, 0
A = bilinear(HR.shape[0], HR.shape[1],
np.array([[C/scale, -S, tx],
[S, C/scale, ty],
[0, 0, 1.]]),
HR.shape[0] / scale, HR.shape[1] / scale)
C = convolve(HR.shape[0], HR.shape[1], gauss(5, std=1))
import matplotlib.pyplot as plt
plt.spy((A * C).todense())
plt.figure()
fwd = (A * C * HR.flat)
rev = C.T * A.T * fwd
plt.subplot(1, 3, 1)
plt.imshow(HR, cmap=plt.cm.gray, interpolation='nearest')
