def test_fast_homography():
img = rgb2gray(data.lena()).astype(np.uint8)
img = img[:, :100]
theta = np.deg2rad(30)
scale = 0.5
tx, ty = 50, 50
H = np.eye(3)
S = scale * np.sin(theta)
C = scale * np.cos(theta)
H[:2, :2] = [[C, -S], [S, C]]
H[:2, 2] = [tx, ty]
for mode in ('constant', 'mirror', 'wrap'):
print 'Transform mode:', mode
p0 = homography(img, H, mode=mode, order=1)
p1 = fast_homography(img, H, mode=mode)
p1 = np.round(p1)
## import matplotlib.pyplot as plt
## f, (ax0, ax1, ax2, ax3) = plt.subplots(1, 4)
## ax0.imshow(img)
## ax1.imshow(p0, cmap=plt.cm.gray)
## ax2.imshow(p1, cmap=plt.cm.gray)
## ax3.imshow(np.abs(p0 - p1), cmap=plt.cm.gray)
## plt.show()
d = np.mean(np.abs(p0 - p1))
print "delta=", d
assert d < 0.2
def test_homography():
x = np.arange(9).reshape((3, 3)) + 1
theta = -np.pi/2
M = np.array([[np.cos(theta),-np.sin(theta),0],
[np.sin(theta), np.cos(theta),2],
[0, 0, 1]])
x90 = homography(x, M, order=1)
assert_array_almost_equal(x90, np.rot90(x))
# Source coordinates
sc = np.array(plt.ginput(n=4))
screen_coords = [(0, 0),
(1023, 0),
(1023, 767),
(0, 767)]
# Homography: source to camera
H_SC = estimate_homography(screen_coords, sc)
tc = np.array(plt.ginput(n=4))
print tc
tc_in_screen = \
np.dot(np.linalg.inv(H_SC),
np.hstack((tc, np.ones((4,1)))).T).T
tc_in_screen /= tc_in_screen[:, 2, np.newaxis]
# Screen to screen homography
H_SS = estimate_homography(screen_coords,
tc_in_screen)
grid = sio.imread('fatgrid.jpg')
grid_warp = tf.homography(grid, H_SS,
output_shape=grid.shape,
order=2)
np.save('/tmp/H_SS.npy', H_SS)
sio.imsave('/tmp/grid_warp.png', grid_warp)
from scikits.image import io, transform
s = 0.7
img = io.imread('scikits_image_logo.png')
h, w, c = img.shape
print "\nScaling down logo by %.1fx..." % s
img = transform.homography(img, [[s, 0, 0],
[0, s, 0],
[0, 0, 1]],
output_shape=(int(h*s), int(w*s), 4),
order=3)
io.imsave('scikits_image_logo_small.png', img)
