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'):
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))
assert d < 0.2
def stitchRL(image2, image3, points23):
"""
Stitch image1 to image2, with points points
"""
s = image2.shape[1]
image2b = np.zeros((image2.shape[0], image2.shape[1] + 500))
image2b[:, :image2.shape[1]] = image2
image2 = image2b
image3b = np.zeros((image3.shape[0], image3.shape[1] + 500))
image3b[:, :image3.shape[1]] = image3
image3 = image3b
#points23[:, 0] += 500
#points23[:, 2] += 500
H2 = calculate_homography(points23)
Hr = H2.copy()
# Hr[0, 2] = 0
# Hr[1, 2] = 0
image3H = transform.homography(image3, H2)
# image2Hr = np.zeros((image2.shape[0], 1500))
# image2Hr[:image2.shape[0], :image2.shape[1]] = image2
# image2Hr = transform.homography(image2Hr, Ht)
# em = image2Hr.copy()
# em[:image1.shape[0],:image1.shape[1]] = image1H
em = image2 + image3H
em[:,:s] = image2[:, :s]
return em
def test_homography():
x = np.arange(9, dtype=np.uint8).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))
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'):
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))
assert d < 0.2
def stitchLR(image1, image2, points):
"""
Stitch image1 to image2, with points points
"""
# image 2 is base image. ie, we need to translate image1
image1b = np.zeros((image1.shape[0], image1.shape[1] + 500))
image1b[:, 500:] = image1
image1 = image1b
image2b = np.zeros((image2.shape[0], image2.shape[1] + 500))
image2b[:, 500:] = image2
image2 = image2b
points[:, 1] += 500
points[:, 3] += 500
H2 = calculate_homography(points)
Hr = H2.copy()
# Hr[0, 2] = 0
# Hr[1, 2] = 0
image1H = transform.homography(image1, H2)
# image2Hr = np.zeros((image2.shape[0], 1500))
# image2Hr[:image2.shape[0], :image2.shape[1]] = image2
# image2Hr = transform.homography(image2Hr, Ht)
# em = image2Hr.copy()
# em[:image1.shape[0],:image1.shape[1]] = image1H
em = image1H + image2
em[:, 500:] = image2[:, 500:]
return em
def test_homography():
x = np.arange(9, dtype=np.uint8).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))
def test_homography():
x = np.zeros((5, 5), dtype=np.uint8)
x[1, 1] = 255
x = img_as_float(x)
theta = -np.pi/2
M = np.array([[np.cos(theta),-np.sin(theta),0],
[np.sin(theta), np.cos(theta),4],
[0, 0, 1]])
x90 = homography(x, M, order=1)
assert_array_almost_equal(x90, np.rot90(x))
def random_rotate(image3D):
theta = np.deg2rad(10)
tx = 0
ty = 0
S, C = np.sin(theta), np.cos(theta)
# Rotation matrix, angle theta, translation tx, ty
H = np.array([[C, -S, tx], [S, C, ty], [0, 0, 1]])
# Translation matrix to shift the image center to the origin
r, c = img.shape
T = np.array([[1, 0, -c / 2.], [0, 1, -r / 2.], [0, 0, 1]])
# Skew, for perspective
S = np.array([[1, 0, 0], [0, 1.3, 0], [0, 1e-3, 1]])
img_rot = transform.homography(img, H)
img_rot_center_skew = transform.homography(
img, S.dot(np.linalg.inv(T).dot(H).dot(T)))
return image3D
from skimage 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)
def test_homography_basic():
homography(np.random.random((25, 25)), np.eye(3))
import skimage
from skimage import io as sio
from skimage import transform as tf
from skimage import data, color
img = data.camera()
theta = np.deg2rad(30)
c = np.cos(theta)
s = np.sin(theta)
a = 0.4
b = 0.4
H = np.array([[a * c, -b * s, 130], [a * s, b * c, 20], [1e-4, -5e-4, 1]])
img_tf = tf.homography(img, H)
plt.subplot(121)
plt.grid()
plt.axis('image')
plt.xlim(0, X_OUT)
plt.ylim(0, Y_OUT)
ax = plt.gca()
ax.invert_yaxis()
plt.subplot(122)
plt.imshow(img_tf, cmap=plt.cm.gray, interpolation='nearest')
plt.suptitle(
'Dewarping\nSelect 4 points on the left grid, then 4 in the right '
'image.')
from skimage 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)
from skimage import data, color
img = data.camera()
theta = np.deg2rad(30)
c = np.cos(theta)
s = np.sin(theta)
a = 0.4
b = 0.4
H = np.array([[a*c, -b*s, 130],
[a*s, b*c, 20],
[1e-4, -5e-4, 1]])
img_tf = tf.homography(img, H)
plt.subplot(121)
plt.grid()
plt.axis('image')
plt.xlim(0, X_OUT)
plt.ylim(0, Y_OUT)
ax = plt.gca()
ax.invert_yaxis()
plt.subplot(122)
plt.imshow(img_tf, cmap=plt.cm.gray, interpolation='nearest')
plt.suptitle('Dewarping\nSelect 4 points on the left grid, then 4 in the right '
'image.')
theta = np.deg2rad(10)
tx = 0
ty = 0
S, C = np.sin(theta), np.cos(theta)
# Rotation matrix, angle theta, translation tx, ty
H = np.array([[C, -S, tx],
[S, C, ty],
[0, 0, 1]])
# Translation matrix to shift the image center to the origin
r, c = img.shape
T = np.array([[1, 0, -c / 2.],
[0, 1, -r / 2.],
[0, 0, 1]])
# Skew, for perspective
S = np.array([[1, 0, 0],
[0, 1.3, 0],
[0, 1e-3, 1]])
img_rot = transform.homography(img, H)
img_rot_center_skew = transform.homography(img, S.dot(np.linalg.inv(T).dot(H).dot(T)))
f, (ax0, ax1, ax2) = plt.subplots(1, 3)
ax0.imshow(img, cmap=plt.cm.gray, interpolation='nearest')
ax1.imshow(img_rot, cmap=plt.cm.gray, interpolation='nearest')
ax2.imshow(img_rot_center_skew, cmap=plt.cm.gray, interpolation='nearest')
plt.show()
