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https://github.com/wassname/scikit-image.git
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improve and restructure geomtric transformations
This commit is contained in:
Johannes Schönberger
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Johannes Schönberger
Johannes Schönberger
parent
94e290ce38
commit
84e18de02d
@@ -0,0 +1,146 @@
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import numpy as np
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from numpy.testing import assert_array_almost_equal
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from skimage.transform.geometric import _stackcopy
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from skimage.transform import make_tform
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from skimage.transform import homography, fast_homography
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from skimage import transform as tf, data, img_as_float
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from skimage.color import rgb2gray
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SRC = np.array([
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[-12.3705, -10.5075],
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[-10.7865, 15.4305],
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[8.6985, 10.8675],
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[11.4975, -9.5715],
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[7.8435, 7.4835],
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[-5.3325, 6.5025],
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[6.7905, -6.3765],
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[-6.1695, -0.8235],
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])
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DST = np.array([
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[0, 0],
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[0, 5800],
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[4900, 5800],
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[4900, 0],
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[4479, 4580],
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[1176, 3660],
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[3754, 790],
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[1024, 1931],
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])
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def test_stackcopy():
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layers = 4
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x = np.empty((3, 3, layers))
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y = np.eye(3, 3)
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_stackcopy(x, y)
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for i in range(layers):
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assert_array_almost_equal(x[...,i], y)
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def test_similarity():
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#: exact solution
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tform = make_tform('similarity', src=SRC[:2,:], dst=DST[:2,:])
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assert_array_almost_equal(tform.fwd(SRC[:2,:]), DST[:2,:])
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assert_array_almost_equal(tform.inv(tform.fwd(SRC)), SRC)
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#: over-determined
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tform = make_tform('similarity', src=SRC, dst=DST)
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ref = np.array(
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[[2.3632898110e+02, -5.5876792257e+00, 2.5331569391e+03],
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[5.5876792257e+00, 2.3632898110e+02, 2.4358232635e+03],
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[0.0000000000e+00, 0.0000000000e+00, 1.0000000000e+00]])
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assert_array_almost_equal(tform.matrix, ref)
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assert_array_almost_equal(tform.inv(tform.fwd(SRC)), SRC)
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def test_affine():
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#: exact solution
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tform = make_tform('affine', src=SRC[:3,:], dst=DST[:3,:])
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assert_array_almost_equal(tform.fwd(SRC[:3,:]), DST[:3,:])
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assert_array_almost_equal(tform.inv(tform.fwd(SRC)), SRC)
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#: over-determined
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tform = make_tform('affine', src=SRC, dst=DST)
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ref = np.array(
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[[2.2573930047e+02, 7.1588596765e+00, 2.5126622012e+03],
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[2.1234856855e+01, 2.4931019555e+02, 2.4143862183e+03],
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[0.0000000000e+00, 0.0000000000e+00, 1.0000000000e+00]])
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assert_array_almost_equal(tform.matrix, ref)
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assert_array_almost_equal(tform.inv(tform.fwd(SRC)), SRC)
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def test_projective():
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#: exact solution
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tform = make_tform('projective', src=SRC[:4,:], dst=DST[:4,:])
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ref = np.array(
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[[ 1.9466901291e+02, -1.1888183994e+01, 2.2832379309e+03],
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[ -8.6910077540e+00, 2.2162069773e+02, 2.2211673699e+03],
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[ -1.2695966735e-02, -9.6053624285e-03, 1.0000000000e+00]])
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assert_array_almost_equal(tform.matrix, ref, 6)
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assert_array_almost_equal(tform.inv(tform.fwd(SRC)), SRC)
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#: over-determined
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tform = make_tform('projective', src=SRC[:4,:], dst=DST[:4,:])
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ref = np.array(
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[[ 1.9466901291e+02, -1.1888183994e+01, 2.2832379309e+03],
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[ -8.6910077540e+00, 2.2162069773e+02, 2.2211673699e+03],
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[ -1.2695966735e-02, -9.6053624285e-03, 1.0000000000e+00]])
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assert_array_almost_equal(tform.matrix, ref, 6)
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assert_array_almost_equal(tform.inv(tform.fwd(SRC)), SRC)
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def test_polynomial():
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tform = make_tform('polynomial', src=SRC, dst=DST, order=10)
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assert_array_almost_equal(tform.fwd(SRC), DST, 6)
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def test_homography():
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x = img_as_float(np.arange(9, dtype=np.uint8).reshape((3, 3)) + 1)
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theta = -np.pi/2
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M = np.array([[np.cos(theta),-np.sin(theta),0],
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[np.sin(theta), np.cos(theta),2],
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[0, 0, 1]])
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x90 = homography(x, M, order=1)
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assert_array_almost_equal(x90, np.rot90(x))
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def test_fast_homography():
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img = rgb2gray(data.lena()).astype(np.uint8)
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img = img[:, :100]
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theta = np.deg2rad(30)
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scale = 0.5
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tx, ty = 50, 50
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H = np.eye(3)
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S = scale * np.sin(theta)
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C = scale * np.cos(theta)
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H[:2, :2] = [[C, -S], [S, C]]
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H[:2, 2] = [tx, ty]
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for mode in ('constant', 'mirror', 'wrap'):
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p0 = homography(img, H, mode=mode, order=1)
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p1 = fast_homography(img, H, mode=mode)
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p1 = np.round(p1)
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## import matplotlib.pyplot as plt
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## f, (ax0, ax1, ax2, ax3) = plt.subplots(1, 4)
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## ax0.imshow(img)
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## ax1.imshow(p0, cmap=plt.cm.gray)
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## ax2.imshow(p1, cmap=plt.cm.gray)
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## ax3.imshow(np.abs(p0 - p1), cmap=plt.cm.gray)
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## plt.show()
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d = np.mean(np.abs(p0 - p1))
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assert d < 0.2
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def test_swirl():
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image = img_as_float(data.checkerboard())
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swirl_params = {'radius': 80, 'rotation': 0, 'order': 2, 'mode': 'reflect'}
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swirled = tf.swirl(image, strength=10, **swirl_params)
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unswirled = tf.swirl(swirled, strength=-10, **swirl_params)
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assert np.mean(np.abs(image - unswirled)) < 0.01
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if __name__ == "__main__":
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from numpy.testing import run_module_suite
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run_module_suite()
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@@ -1,63 +0,0 @@
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import numpy as np
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from numpy.testing import assert_array_almost_equal
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from skimage.transform._warp import _stackcopy
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from skimage.transform import homography, fast_homography
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from skimage import data
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from skimage.color import rgb2gray
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def test_stackcopy():
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layers = 4
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x = np.empty((3, 3, layers))
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y = np.eye(3, 3)
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_stackcopy(x, y)
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for i in range(layers):
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assert_array_almost_equal(x[..., i], y)
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def test_homography():
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x = np.arange(9, dtype=np.uint8).reshape((3, 3)) + 1
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theta = -np.pi / 2
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M = np.array([[np.cos(theta), -np.sin(theta), 0],
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[np.sin(theta), np.cos(theta), 2],
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[0, 0, 1]])
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x90 = homography(x, M, order=1)
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assert_array_almost_equal(x90, np.rot90(x))
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def test_fast_homography():
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img = rgb2gray(data.lena()).astype(np.uint8)
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img = img[:, :100]
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theta = np.deg2rad(30)
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scale = 0.5
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tx, ty = 50, 50
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H = np.eye(3)
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S = scale * np.sin(theta)
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C = scale * np.cos(theta)
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H[:2, :2] = [[C, -S], [S, C]]
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H[:2, 2] = [tx, ty]
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for mode in ('constant', 'mirror', 'wrap'):
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p0 = homography(img, H, mode=mode, order=1)
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p1 = fast_homography(img, H, mode=mode)
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p1 = np.round(p1)
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## import matplotlib.pyplot as plt
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## f, (ax0, ax1, ax2, ax3) = plt.subplots(1, 4)
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## ax0.imshow(img)
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## ax1.imshow(p0, cmap=plt.cm.gray)
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## ax2.imshow(p1, cmap=plt.cm.gray)
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## ax3.imshow(np.abs(p0 - p1), cmap=plt.cm.gray)
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## plt.show()
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d = np.mean(np.abs(p0 - p1))
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assert d < 0.2
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if __name__ == "__main__":
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from numpy.testing import run_module_suite
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run_module_suite()
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@@ -1,17 +0,0 @@
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import numpy as np
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from numpy.testing import assert_array_almost_equal
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from skimage import transform as tf, data, img_as_float
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def test_roundtrip():
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image = img_as_float(data.checkerboard())
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swirl_params = {'radius': 80, 'rotation': 0, 'order': 2, 'mode': 'reflect'}
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swirled = tf.swirl(image, strength=10, **swirl_params)
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unswirled = tf.swirl(swirled, strength=-10, **swirl_params)
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assert np.mean(np.abs(image - unswirled)) < 0.01
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if __name__ == "__main__":
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np.testing.run_module_suite()
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