mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 03:54:28 +08:00
Johannes Schönberger
239 lines
6.9 KiB
Python
239 lines
6.9 KiB
Python
from numpy.testing import assert_array_almost_equal, run_module_suite, assert_array_equal
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import numpy as np
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from scipy.ndimage import map_coordinates
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from skimage.transform import (warp, warp_coords, rotate, resize, rescale,
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AffineTransform,
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ProjectiveTransform,
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SimilarityTransform,
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downscale_local_mean)
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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_tform():
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x = np.zeros((5, 5), dtype=np.double)
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x[2, 2] = 1
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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_warp_callable():
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x = np.zeros((5, 5), dtype=np.double)
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x[2, 2] = 1
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refx = np.zeros((5, 5), dtype=np.double)
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refx[1, 1] = 1
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shift = lambda xy: xy + 1
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outx = warp(x, shift, order=1)
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assert_array_almost_equal(outx, refx)
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def test_warp_matrix():
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x = np.zeros((5, 5), dtype=np.double)
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x[2, 2] = 1
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refx = np.zeros((5, 5), dtype=np.double)
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refx[1, 1] = 1
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matrix = np.array([[1, 0, 1], [0, 1, 1], [0, 0, 1]])
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# _warp_fast
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outx = warp(x, matrix, order=1)
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assert_array_almost_equal(outx, refx)
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# check for ndimage.map_coordinates
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outx = warp(x, matrix, order=5)
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def test_homography():
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x = np.zeros((5, 5), dtype=np.double)
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x[1, 1] = 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), 4],
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[0, 0, 1]])
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x90 = warp(x,
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inverse_map=ProjectiveTransform(M).inverse,
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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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tform = ProjectiveTransform(H)
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coords = warp_coords(tform.inverse, (img.shape[0], img.shape[1]))
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for order in range(4):
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for mode in ('constant', 'reflect', 'wrap', 'nearest'):
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p0 = map_coordinates(img, coords, mode=mode, order=order)
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p1 = warp(img, tform, mode=mode, order=order)
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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_rotate():
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x = np.zeros((5, 5), dtype=np.double)
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x[1, 1] = 1
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x90 = rotate(x, 90)
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assert_array_almost_equal(x90, np.rot90(x))
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def test_rotate_resize():
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x = np.zeros((10, 10), dtype=np.double)
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x45 = rotate(x, 45, resize=False)
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assert x45.shape == (10, 10)
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x45 = rotate(x, 45, resize=True)
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# new dimension should be d = sqrt(2 * (10/2)^2)
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assert x45.shape == (14, 14)
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def test_rescale():
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# same scale factor
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x = np.zeros((5, 5), dtype=np.double)
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x[1, 1] = 1
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scaled = rescale(x, 2, order=0)
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ref = np.zeros((10, 10))
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ref[2:4, 2:4] = 1
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assert_array_almost_equal(scaled, ref)
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# different scale factors
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x = np.zeros((5, 5), dtype=np.double)
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x[1, 1] = 1
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scaled = rescale(x, (2, 1), order=0)
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ref = np.zeros((10, 5))
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ref[2:4, 1] = 1
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assert_array_almost_equal(scaled, ref)
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def test_resize2d():
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x = np.zeros((5, 5), dtype=np.double)
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x[1, 1] = 1
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resized = resize(x, (10, 10), order=0)
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ref = np.zeros((10, 10))
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ref[2:4, 2:4] = 1
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assert_array_almost_equal(resized, ref)
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def test_resize3d_keep():
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# keep 3rd dimension
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x = np.zeros((5, 5, 3), dtype=np.double)
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x[1, 1, :] = 1
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resized = resize(x, (10, 10), order=0)
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ref = np.zeros((10, 10, 3))
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ref[2:4, 2:4, :] = 1
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assert_array_almost_equal(resized, ref)
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resized = resize(x, (10, 10, 3), order=0)
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assert_array_almost_equal(resized, ref)
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def test_resize3d_resize():
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# resize 3rd dimension
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x = np.zeros((5, 5, 3), dtype=np.double)
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x[1, 1, :] = 1
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resized = resize(x, (10, 10, 1), order=0)
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ref = np.zeros((10, 10, 1))
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ref[2:4, 2:4] = 1
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assert_array_almost_equal(resized, ref)
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def test_resize3d_bilinear():
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# bilinear 3rd dimension
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x = np.zeros((5, 5, 2), dtype=np.double)
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x[1, 1, 0] = 0
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x[1, 1, 1] = 1
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resized = resize(x, (10, 10, 1), order=1)
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ref = np.zeros((10, 10, 1))
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ref[1:5, 1:5, :] = 0.03125
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ref[1:5, 2:4, :] = 0.09375
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ref[2:4, 1:5, :] = 0.09375
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ref[2:4, 2:4, :] = 0.28125
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assert_array_almost_equal(resized, ref)
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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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def test_const_cval_out_of_range():
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img = np.random.randn(100, 100)
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cval = - 10
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warped = warp(img, AffineTransform(translation=(10, 10)), cval=cval)
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assert np.sum(warped == cval) == (2 * 100 * 10 - 10 * 10)
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def test_warp_identity():
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lena = img_as_float(rgb2gray(data.lena()))
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assert len(lena.shape) == 2
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assert np.allclose(lena, warp(lena, AffineTransform(rotation=0)))
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assert not np.allclose(lena, warp(lena, AffineTransform(rotation=0.1)))
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rgb_lena = np.transpose(np.asarray([lena, np.zeros_like(lena), lena]),
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(1, 2, 0))
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warped_rgb_lena = warp(rgb_lena, AffineTransform(rotation=0.1))
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assert np.allclose(rgb_lena, warp(rgb_lena, AffineTransform(rotation=0)))
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assert not np.allclose(rgb_lena, warped_rgb_lena)
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# assert no cross-talk between bands
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assert np.all(0 == warped_rgb_lena[:, :, 1])
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def test_warp_coords_example():
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image = data.lena().astype(np.float32)
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assert 3 == image.shape[2]
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tform = SimilarityTransform(translation=(0, -10))
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coords = warp_coords(tform, (30, 30, 3))
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map_coordinates(image[:, :, 0], coords[:2])
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def test_downscale_local_mean():
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image1 = np.arange(4 * 6).reshape(4, 6)
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out1 = downscale_local_mean(image1, (2, 3))
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expected1 = np.array([[ 4., 7.],
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[ 16., 19.]])
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assert_array_equal(expected1, out1)
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image2 = np.arange(5 * 8).reshape(5, 8)
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out2 = downscale_local_mean(image2, (4, 5))
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expected2 = np.array([[ 14. , 10.8],
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[ 8.5, 5.7]])
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assert_array_equal(expected2, out2)
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if __name__ == "__main__":
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run_module_suite()
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