def test_affine_transformation():
x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
# list of transformations to be tested
T = np.eye(3)
I = np.eye(3)
S = np.eye(3)
A = np.eye(3)
translations = []
isometries = []
similarities = []
affinities = []
for i in xrange(100):
translations.append(T)
isometries.append(I)
similarities.append(S)
affinities.append(A)
T[0:2, 2] = np.random.random(2)
I = rotation_matrix(2 * np.pi * np.random.random_sample())
I[0:2, 2] = np.random.random(2)
S = similarity_matrix(2 * np.pi * np.random.random_sample(),
np.random.random_sample())
S[0:2, 2] = 100 * np.random.random(2)
A[0:2, :] = np.random.random((2, 3))
for B in translations + isometries + similarities + affinities:
xx = common.points_apply_homography(B, x)
E = estimation.affine_transformation(x, xx)
assert_array_almost_equal(E, B)
def test_affine_transformation():
x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
# list of transformations to be tested
T = np.eye(3)
I = np.eye(3)
S = np.eye(3)
A = np.eye(3)
translations = []
isometries = []
similarities = []
affinities = []
for i in xrange(100):
translations.append(T)
isometries.append(I)
similarities.append(S)
affinities.append(A)
T[0:2, 2] = np.random.random(2)
I = rotation_matrix(2*np.pi * np.random.random_sample())
I[0:2, 2] = np.random.random(2)
S = similarity_matrix(2*np.pi * np.random.random_sample(),
np.random.random_sample())
S[0:2, 2] = 100 * np.random.random(2)
A[0:2, :] = np.random.random((2, 3))
for B in translations + isometries + similarities + affinities:
xx = common.points_apply_homography(B, x)
E = estimation.affine_transformation(x, xx)
assert_array_almost_equal(E, B)
def global_from_local(tiles):
"""
Computes the pointing correction of a full roi using local corrections on
tiles.
Args:
tiles: list of paths to folders associated to each tile
Returns:
the estimated pointing correction for the specified tile
In each folder we expect to find the files pointing.txt and center.txt. The
file pointing.txt contains the local correction (a projective transform
given in homogeneous coordinates), and the file center.txt contains the
coordinates of the mean of the keypoints of the secondary image.
"""
# lists of matching points
x = []
xx = []
# loop over all the tiles
for f in tiles:
center = os.path.join(f, 'center_keypts_sec.txt')
pointing = os.path.join(f, 'pointing.txt')
if os.path.isfile(center) and os.path.isfile(pointing):
A = np.loadtxt(pointing)
p = np.loadtxt(center)
if A.shape == (3, 3) and p.shape == (2,):
q = np.dot(A, np.array([p[0], p[1], 1]))
x.append(p)
xx.append(q[0:2])
if not x:
return np.eye(3)
elif len(x) == 1:
return A
elif len(x) == 2:
#TODO: replace translation with similarity
return estimation.translation(np.array(x), np.array(xx))
else:
# estimate an affine transformation transforming x in xx
return estimation.affine_transformation(np.array(x), np.array(xx))
