def test_gauss():
w = gauss(3, std=1e-3)
w /= w.max()
expected = np.zeros((3, 3))
expected[1, 1] = 1
assert_array_equal(w, expected)
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')
plt.subplot(1, 3, 2)
plt.imshow(fwd.reshape(np.array(HR.shape) / scale),
interpolation='nearest',
cmap=plt.cm.gray)
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')
plt.subplot(1, 3, 2)
plt.imshow(fwd.reshape(np.array(HR.shape) / scale),
interpolation='nearest', cmap=plt.cm.gray)
plt.subplot(1, 3, 3)
