mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-01 01:44:02 +08:00
Johannes Schönberger
0ca7933a7d
Make bilinear_interpolation function callable by other cython code and change fast_homography to use this function. Also, improve performance of some functions by making them inline and fix broken test of fast_homography.
79 lines
2.2 KiB
Python
79 lines
2.2 KiB
Python
from numpy.testing import assert_array_almost_equal, run_module_suite
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import numpy as np
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from skimage.transform import (warp, homography, fast_homography,
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SimilarityTransform, ProjectiveTransform)
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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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def test_warp():
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x = np.zeros((5, 5), dtype=np.uint8)
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x[2, 2] = 255
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x = img_as_float(x)
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theta = - np.pi / 2
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tform = SimilarityTransform(scale=1, rotation=theta, translation=(0, 4))
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x90 = warp(x, tform, order=1)
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assert_array_almost_equal(x90, np.rot90(x))
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x90 = warp(x, tform.inverse, order=1)
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assert_array_almost_equal(x90, np.rot90(x))
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def test_homography():
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x = np.zeros((5, 5), dtype=np.uint8)
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x[1, 1] = 255
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x = img_as_float(x)
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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),4],
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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())
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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 = warp(img, ProjectiveTransform(H).inverse, mode=mode, order=1)
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p1 = fast_homography(img, H, mode=mode)
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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 < 0.001
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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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run_module_suite()
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