680 lines
22 KiB
Python
680 lines
22 KiB
Python
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import numpy as np
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from scipy.ndimage import map_coordinates
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from skimage.data import checkerboard, astronaut
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from skimage.util.dtype import img_as_float
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from skimage.color.colorconv import rgb2gray
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from skimage.draw.draw import circle_perimeter_aa
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from skimage.feature.peak import peak_local_max
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from skimage._shared import testing
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from skimage._shared.testing import (assert_almost_equal, assert_equal,
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test_parallel)
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from skimage._shared._warnings import expected_warnings
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from skimage.transform._warps import (_stackcopy,
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_linear_polar_mapping,
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_log_polar_mapping, warp,
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warp_coords, rotate, resize,
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rescale, warp_polar, swirl,
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downscale_local_mean)
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from skimage.transform._geometric import (AffineTransform,
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ProjectiveTransform,
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SimilarityTransform)
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np.random.seed(0)
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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_almost_equal(x[..., i], y)
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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_almost_equal(x90, np.rot90(x))
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x90 = warp(x, tform.inverse, order=1)
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assert_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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def shift(xy):
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return xy + 1
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outx = warp(x, shift, order=1)
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assert_almost_equal(outx, refx)
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@test_parallel()
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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_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_warp_nd():
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for dim in range(2, 8):
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shape = dim * (5,)
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x = np.zeros(shape, dtype=np.double)
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x_c = dim * (2,)
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x[x_c] = 1
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refx = np.zeros(shape, dtype=np.double)
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refx_c = dim * (1,)
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refx[refx_c] = 1
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coord_grid = dim * (slice(0, 5, 1),)
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coords = np.array(np.mgrid[coord_grid]) + 1
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outx = warp(x, coords, order=0, cval=0)
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assert_almost_equal(outx, refx)
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def test_warp_clip():
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x = np.zeros((5, 5), dtype=np.double)
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x[2, 2] = 1
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outx = rescale(x, 3, order=3, clip=False,
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multichannel=False, anti_aliasing=False, mode='constant')
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assert outx.min() < 0
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outx = rescale(x, 3, order=3, clip=True,
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multichannel=False, anti_aliasing=False, mode='constant')
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assert_almost_equal(outx.min(), 0)
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assert_almost_equal(outx.max(), 1)
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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_almost_equal(x90, np.rot90(x))
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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_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_rotate_center():
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x = np.zeros((10, 10), dtype=np.double)
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x[4, 4] = 1
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refx = np.zeros((10, 10), dtype=np.double)
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refx[2, 5] = 1
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x20 = rotate(x, 20, order=0, center=(0, 0))
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assert_almost_equal(x20, refx)
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x0 = rotate(x20, -20, order=0, center=(0, 0))
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assert_almost_equal(x0, x)
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def test_rotate_resize_center():
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x = np.zeros((10, 10), dtype=np.double)
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x[0, 0] = 1
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ref_x45 = np.zeros((14, 14), dtype=np.double)
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ref_x45[6, 0] = 1
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ref_x45[7, 0] = 1
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x45 = rotate(x, 45, resize=True, center=(3, 3), order=0)
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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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assert_equal(x45, ref_x45)
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def test_rotate_resize_90():
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x90 = rotate(np.zeros((470, 230), dtype=np.double), 90, resize=True)
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assert x90.shape == (230, 470)
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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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multichannel=False, anti_aliasing=False, mode='constant')
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ref = np.zeros((10, 10))
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ref[2:4, 2:4] = 1
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assert_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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multichannel=False, anti_aliasing=False, mode='constant')
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ref = np.zeros((10, 5))
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ref[2:4, 1] = 1
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assert_almost_equal(scaled, ref)
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def test_rescale_invalid_scale():
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x = np.zeros((10, 10, 3))
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with testing.raises(ValueError):
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rescale(x, (2, 2),
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multichannel=False, anti_aliasing=False, mode='constant')
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with testing.raises(ValueError):
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rescale(x, (2, 2, 2),
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multichannel=True, anti_aliasing=False, mode='constant')
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def test_rescale_multichannel():
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# 1D + channels
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x = np.zeros((8, 3), dtype=np.double)
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scaled = rescale(x, 2, order=0, multichannel=True, anti_aliasing=False,
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mode='constant')
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assert_equal(scaled.shape, (16, 3))
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# 2D
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scaled = rescale(x, 2, order=0, multichannel=False, anti_aliasing=False,
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mode='constant')
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assert_equal(scaled.shape, (16, 6))
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# 2D + channels
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x = np.zeros((8, 8, 3), dtype=np.double)
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scaled = rescale(x, 2, order=0, multichannel=True, anti_aliasing=False,
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mode='constant')
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assert_equal(scaled.shape, (16, 16, 3))
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# 3D
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scaled = rescale(x, 2, order=0, multichannel=False, anti_aliasing=False,
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mode='constant')
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assert_equal(scaled.shape, (16, 16, 6))
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# 3D + channels
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x = np.zeros((8, 8, 8, 3), dtype=np.double)
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scaled = rescale(x, 2, order=0, multichannel=True, anti_aliasing=False,
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mode='constant')
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assert_equal(scaled.shape, (16, 16, 16, 3))
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# 4D
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scaled = rescale(x, 2, order=0, multichannel=False, anti_aliasing=False,
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mode='constant')
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assert_equal(scaled.shape, (16, 16, 16, 6))
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def test_rescale_multichannel_multiscale():
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x = np.zeros((5, 5, 3), dtype=np.double)
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scaled = rescale(x, (2, 1), order=0, multichannel=True,
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anti_aliasing=False, mode='constant')
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assert_equal(scaled.shape, (10, 5, 3))
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def test_rescale_multichannel_defaults():
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x = np.zeros((8, 3), dtype=np.double)
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scaled = rescale(x, 2, order=0, anti_aliasing=False, mode='constant')
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assert_equal(scaled.shape, (16, 6))
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x = np.zeros((8, 8, 3), dtype=np.double)
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scaled = rescale(x, 2, order=0, anti_aliasing=False, mode='constant')
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assert_equal(scaled.shape, (16, 16, 6))
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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, anti_aliasing=False,
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mode='constant')
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ref = np.zeros((10, 10))
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ref[2:4, 2:4] = 1
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assert_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, anti_aliasing=False,
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mode='constant')
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with testing.raises(ValueError):
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# output_shape too short
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resize(x, (10, ), order=0, anti_aliasing=False, mode='constant')
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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_almost_equal(resized, ref)
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resized = resize(x, (10, 10, 3), order=0, anti_aliasing=False,
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mode='constant')
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assert_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, anti_aliasing=False,
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mode='constant')
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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_almost_equal(resized, ref)
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def test_resize3d_2din_3dout():
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# 3D output with 2D input
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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, 1), order=0, anti_aliasing=False,
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mode='constant')
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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_almost_equal(resized, ref)
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def test_resize2d_4d():
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# resize with extra output dimensions
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x = np.zeros((5, 5), dtype=np.double)
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x[1, 1] = 1
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out_shape = (10, 10, 1, 1)
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resized = resize(x, out_shape, order=0, anti_aliasing=False,
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mode='constant')
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ref = np.zeros(out_shape)
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ref[2:4, 2:4, ...] = 1
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assert_almost_equal(resized, ref)
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def test_resize_nd():
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for dim in range(1, 6):
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shape = 2 + np.arange(dim) * 2
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x = np.ones(shape)
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out_shape = np.asarray(shape) * 1.5
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resized = resize(x, out_shape, order=0, mode='reflect',
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anti_aliasing=False)
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expected_shape = 1.5 * shape
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assert_equal(resized.shape, expected_shape)
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assert np.all(resized == 1)
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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, mode='constant',
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anti_aliasing=False)
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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_almost_equal(resized, ref)
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def test_resize_dtype():
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x = np.zeros((5, 5))
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x_f32 = x.astype(np.float32)
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x_u8 = x.astype(np.uint8)
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x_b = x.astype(bool)
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assert resize(x, (10, 10), preserve_range=False).dtype == x.dtype
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assert resize(x, (10, 10), preserve_range=True).dtype == x.dtype
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assert resize(x_u8, (10, 10), preserve_range=False).dtype == np.double
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assert resize(x_u8, (10, 10), preserve_range=True).dtype == np.double
|
||
|
|
assert resize(x_b, (10, 10), preserve_range=False).dtype == np.double
|
||
|
|
assert resize(x_b, (10, 10), preserve_range=True).dtype == np.double
|
||
|
|
assert resize(x_f32, (10, 10), preserve_range=False).dtype == x_f32.dtype
|
||
|
|
assert resize(x_f32, (10, 10), preserve_range=True).dtype == x_f32.dtype
|
||
|
|
|
||
|
|
|
||
|
|
def test_swirl():
|
||
|
|
image = img_as_float(checkerboard())
|
||
|
|
|
||
|
|
swirl_params = {'radius': 80, 'rotation': 0, 'order': 2, 'mode': 'reflect'}
|
||
|
|
|
||
|
|
with expected_warnings(['Bi-quadratic.*bug']):
|
||
|
|
swirled = swirl(image, strength=10, **swirl_params)
|
||
|
|
unswirled = swirl(swirled, strength=-10, **swirl_params)
|
||
|
|
|
||
|
|
assert np.mean(np.abs(image - unswirled)) < 0.01
|
||
|
|
|
||
|
|
swirl_params.pop('mode')
|
||
|
|
|
||
|
|
with expected_warnings(['Bi-quadratic.*bug']):
|
||
|
|
swirled = swirl(image, strength=10, **swirl_params)
|
||
|
|
unswirled = swirl(swirled, strength=-10, **swirl_params)
|
||
|
|
|
||
|
|
assert np.mean(np.abs(image[1:-1, 1:-1] - unswirled[1:-1, 1:-1])) < 0.01
|
||
|
|
|
||
|
|
|
||
|
|
def test_const_cval_out_of_range():
|
||
|
|
img = np.random.randn(100, 100)
|
||
|
|
cval = - 10
|
||
|
|
warped = warp(img, AffineTransform(translation=(10, 10)), cval=cval)
|
||
|
|
assert np.sum(warped == cval) == (2 * 100 * 10 - 10 * 10)
|
||
|
|
|
||
|
|
|
||
|
|
def test_warp_identity():
|
||
|
|
img = img_as_float(rgb2gray(astronaut()))
|
||
|
|
assert len(img.shape) == 2
|
||
|
|
assert np.allclose(img, warp(img, AffineTransform(rotation=0)))
|
||
|
|
assert not np.allclose(img, warp(img, AffineTransform(rotation=0.1)))
|
||
|
|
rgb_img = np.transpose(np.asarray([img, np.zeros_like(img), img]),
|
||
|
|
(1, 2, 0))
|
||
|
|
warped_rgb_img = warp(rgb_img, AffineTransform(rotation=0.1))
|
||
|
|
assert np.allclose(rgb_img, warp(rgb_img, AffineTransform(rotation=0)))
|
||
|
|
assert not np.allclose(rgb_img, warped_rgb_img)
|
||
|
|
# assert no cross-talk between bands
|
||
|
|
assert np.all(0 == warped_rgb_img[:, :, 1])
|
||
|
|
|
||
|
|
|
||
|
|
def test_warp_coords_example():
|
||
|
|
image = astronaut().astype(np.float32)
|
||
|
|
assert 3 == image.shape[2]
|
||
|
|
tform = SimilarityTransform(translation=(0, -10))
|
||
|
|
coords = warp_coords(tform, (30, 30, 3))
|
||
|
|
map_coordinates(image[:, :, 0], coords[:2])
|
||
|
|
|
||
|
|
|
||
|
|
def test_downsize():
|
||
|
|
x = np.zeros((10, 10), dtype=np.double)
|
||
|
|
x[2:4, 2:4] = 1
|
||
|
|
scaled = resize(x, (5, 5), order=0, anti_aliasing=False, mode='constant')
|
||
|
|
assert_equal(scaled.shape, (5, 5))
|
||
|
|
assert_equal(scaled[1, 1], 1)
|
||
|
|
assert_equal(scaled[2:, :].sum(), 0)
|
||
|
|
assert_equal(scaled[:, 2:].sum(), 0)
|
||
|
|
|
||
|
|
|
||
|
|
def test_downsize_anti_aliasing():
|
||
|
|
x = np.zeros((10, 10), dtype=np.double)
|
||
|
|
x[2, 2] = 1
|
||
|
|
scaled = resize(x, (5, 5), order=1, anti_aliasing=True, mode='constant')
|
||
|
|
assert_equal(scaled.shape, (5, 5))
|
||
|
|
assert np.all(scaled[:3, :3] > 0)
|
||
|
|
assert_equal(scaled[3:, :].sum(), 0)
|
||
|
|
assert_equal(scaled[:, 3:].sum(), 0)
|
||
|
|
|
||
|
|
sigma = 0.125
|
||
|
|
out_size = (5, 5)
|
||
|
|
resize(x, out_size, order=1, mode='constant',
|
||
|
|
anti_aliasing=True, anti_aliasing_sigma=sigma)
|
||
|
|
resize(x, out_size, order=1, mode='edge',
|
||
|
|
anti_aliasing=True, anti_aliasing_sigma=sigma)
|
||
|
|
resize(x, out_size, order=1, mode='symmetric',
|
||
|
|
anti_aliasing=True, anti_aliasing_sigma=sigma)
|
||
|
|
resize(x, out_size, order=1, mode='reflect',
|
||
|
|
anti_aliasing=True, anti_aliasing_sigma=sigma)
|
||
|
|
resize(x, out_size, order=1, mode='wrap',
|
||
|
|
anti_aliasing=True, anti_aliasing_sigma=sigma)
|
||
|
|
|
||
|
|
with testing.raises(ValueError): # Unknown mode, or cannot translate mode
|
||
|
|
resize(x, out_size, order=1, mode='non-existent',
|
||
|
|
anti_aliasing=True, anti_aliasing_sigma=sigma)
|
||
|
|
|
||
|
|
|
||
|
|
def test_downsize_anti_aliasing_invalid_stddev():
|
||
|
|
x = np.zeros((10, 10), dtype=np.double)
|
||
|
|
with testing.raises(ValueError):
|
||
|
|
resize(x, (5, 5), order=0, anti_aliasing=True, anti_aliasing_sigma=-1,
|
||
|
|
mode='constant')
|
||
|
|
with expected_warnings(["Anti-aliasing standard deviation greater"]):
|
||
|
|
resize(x, (5, 15), order=0, anti_aliasing=True,
|
||
|
|
anti_aliasing_sigma=(1, 1), mode="reflect")
|
||
|
|
resize(x, (5, 15), order=0, anti_aliasing=True,
|
||
|
|
anti_aliasing_sigma=(0, 1), mode="reflect")
|
||
|
|
|
||
|
|
|
||
|
|
def test_downscale():
|
||
|
|
x = np.zeros((10, 10), dtype=np.double)
|
||
|
|
x[2:4, 2:4] = 1
|
||
|
|
scaled = rescale(x, 0.5, order=0, anti_aliasing=False,
|
||
|
|
multichannel=False, mode='constant')
|
||
|
|
assert_equal(scaled.shape, (5, 5))
|
||
|
|
assert_equal(scaled[1, 1], 1)
|
||
|
|
assert_equal(scaled[2:, :].sum(), 0)
|
||
|
|
assert_equal(scaled[:, 2:].sum(), 0)
|
||
|
|
|
||
|
|
|
||
|
|
def test_downscale_anti_aliasing():
|
||
|
|
x = np.zeros((10, 10), dtype=np.double)
|
||
|
|
x[2, 2] = 1
|
||
|
|
scaled = rescale(x, 0.5, order=1, anti_aliasing=True,
|
||
|
|
multichannel=False, mode='constant')
|
||
|
|
assert_equal(scaled.shape, (5, 5))
|
||
|
|
assert np.all(scaled[:3, :3] > 0)
|
||
|
|
assert_equal(scaled[3:, :].sum(), 0)
|
||
|
|
assert_equal(scaled[:, 3:].sum(), 0)
|
||
|
|
|
||
|
|
|
||
|
|
def test_downscale_local_mean():
|
||
|
|
image1 = np.arange(4 * 6).reshape(4, 6)
|
||
|
|
out1 = downscale_local_mean(image1, (2, 3))
|
||
|
|
expected1 = np.array([[4., 7.],
|
||
|
|
[16., 19.]])
|
||
|
|
assert_equal(expected1, out1)
|
||
|
|
|
||
|
|
image2 = np.arange(5 * 8).reshape(5, 8)
|
||
|
|
out2 = downscale_local_mean(image2, (4, 5))
|
||
|
|
expected2 = np.array([[14., 10.8],
|
||
|
|
[8.5, 5.7]])
|
||
|
|
assert_equal(expected2, out2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_invalid():
|
||
|
|
with testing.raises(ValueError):
|
||
|
|
warp(np.ones((4, 3, 3, 3)),
|
||
|
|
SimilarityTransform())
|
||
|
|
|
||
|
|
|
||
|
|
def test_inverse():
|
||
|
|
tform = SimilarityTransform(scale=0.5, rotation=0.1)
|
||
|
|
inverse_tform = SimilarityTransform(matrix=np.linalg.inv(tform.params))
|
||
|
|
image = np.arange(10 * 10).reshape(10, 10).astype(np.double)
|
||
|
|
assert_equal(warp(image, inverse_tform), warp(image, tform.inverse))
|
||
|
|
|
||
|
|
|
||
|
|
def test_slow_warp_nonint_oshape():
|
||
|
|
image = np.random.rand(5, 5)
|
||
|
|
|
||
|
|
with testing.raises(ValueError):
|
||
|
|
warp(image, lambda xy: xy,
|
||
|
|
output_shape=(13.1, 19.5))
|
||
|
|
|
||
|
|
warp(image, lambda xy: xy, output_shape=(13.0001, 19.9999))
|
||
|
|
|
||
|
|
|
||
|
|
def test_keep_range():
|
||
|
|
image = np.linspace(0, 2, 25).reshape(5, 5)
|
||
|
|
out = rescale(image, 2, preserve_range=False, clip=True, order=0,
|
||
|
|
mode='constant', multichannel=False, anti_aliasing=False)
|
||
|
|
assert out.min() == 0
|
||
|
|
assert out.max() == 2
|
||
|
|
|
||
|
|
out = rescale(image, 2, preserve_range=True, clip=True, order=0,
|
||
|
|
mode='constant', multichannel=False, anti_aliasing=False)
|
||
|
|
assert out.min() == 0
|
||
|
|
assert out.max() == 2
|
||
|
|
|
||
|
|
out = rescale(image.astype(np.uint8), 2, preserve_range=False,
|
||
|
|
mode='constant', multichannel=False, anti_aliasing=False,
|
||
|
|
clip=True, order=0)
|
||
|
|
assert out.min() == 0
|
||
|
|
assert out.max() == 2 / 255.0
|
||
|
|
|
||
|
|
|
||
|
|
def test_zero_image_size():
|
||
|
|
with testing.raises(ValueError):
|
||
|
|
warp(np.zeros(0),
|
||
|
|
SimilarityTransform())
|
||
|
|
with testing.raises(ValueError):
|
||
|
|
warp(np.zeros((0, 10)),
|
||
|
|
SimilarityTransform())
|
||
|
|
with testing.raises(ValueError):
|
||
|
|
warp(np.zeros((10, 0)),
|
||
|
|
SimilarityTransform())
|
||
|
|
with testing.raises(ValueError):
|
||
|
|
warp(np.zeros((10, 10, 0)),
|
||
|
|
SimilarityTransform())
|
||
|
|
|
||
|
|
|
||
|
|
def test_linear_polar_mapping():
|
||
|
|
output_coords = np.array([[0, 0],
|
||
|
|
[0, 90],
|
||
|
|
[0, 180],
|
||
|
|
[0, 270],
|
||
|
|
[99, 0],
|
||
|
|
[99, 180],
|
||
|
|
[99, 270],
|
||
|
|
[99, 45]])
|
||
|
|
ground_truth = np.array([[100, 100],
|
||
|
|
[100, 100],
|
||
|
|
[100, 100],
|
||
|
|
[100, 100],
|
||
|
|
[199, 100],
|
||
|
|
[1, 100],
|
||
|
|
[100, 1],
|
||
|
|
[170.00357134, 170.00357134]])
|
||
|
|
k_angle = 360 / (2 * np.pi)
|
||
|
|
k_radius = 1
|
||
|
|
center = (100, 100)
|
||
|
|
coords = _linear_polar_mapping(output_coords, k_angle, k_radius, center)
|
||
|
|
assert np.allclose(coords, ground_truth)
|
||
|
|
|
||
|
|
|
||
|
|
def test_log_polar_mapping():
|
||
|
|
output_coords = np.array([[0, 0],
|
||
|
|
[0, 90],
|
||
|
|
[0, 180],
|
||
|
|
[0, 270],
|
||
|
|
[99, 0],
|
||
|
|
[99, 180],
|
||
|
|
[99, 270],
|
||
|
|
[99, 45]])
|
||
|
|
ground_truth = np.array([[101, 100],
|
||
|
|
[100, 101],
|
||
|
|
[99, 100],
|
||
|
|
[100, 99],
|
||
|
|
[195.4992586, 100],
|
||
|
|
[4.5007414, 100],
|
||
|
|
[100, 4.5007414],
|
||
|
|
[167.52817336, 167.52817336]])
|
||
|
|
k_angle = 360 / (2 * np.pi)
|
||
|
|
k_radius = 100 / np.log(100)
|
||
|
|
center = (100, 100)
|
||
|
|
coords = _log_polar_mapping(output_coords, k_angle, k_radius, center)
|
||
|
|
assert np.allclose(coords, ground_truth)
|
||
|
|
|
||
|
|
|
||
|
|
def test_linear_warp_polar():
|
||
|
|
radii = [5, 10, 15, 20]
|
||
|
|
image = np.zeros([51, 51])
|
||
|
|
for rad in radii:
|
||
|
|
rr, cc, val = circle_perimeter_aa(25, 25, rad)
|
||
|
|
image[rr, cc] = val
|
||
|
|
warped = warp_polar(image, radius=25)
|
||
|
|
profile = warped.mean(axis=0)
|
||
|
|
peaks = peak_local_max(profile)
|
||
|
|
assert np.alltrue([peak in radii for peak in peaks])
|
||
|
|
|
||
|
|
|
||
|
|
def test_log_warp_polar():
|
||
|
|
radii = [np.exp(2), np.exp(3), np.exp(4), np.exp(5),
|
||
|
|
np.exp(5)-1, np.exp(5)+1]
|
||
|
|
radii = [int(x) for x in radii]
|
||
|
|
image = np.zeros([301, 301])
|
||
|
|
for rad in radii:
|
||
|
|
rr, cc, val = circle_perimeter_aa(150, 150, rad)
|
||
|
|
image[rr, cc] = val
|
||
|
|
warped = warp_polar(image, radius=200, scaling='log')
|
||
|
|
profile = warped.mean(axis=0)
|
||
|
|
peaks_coord = peak_local_max(profile)
|
||
|
|
peaks_coord.sort(axis=0)
|
||
|
|
gaps = peaks_coord[1:] - peaks_coord[:-1]
|
||
|
|
assert np.alltrue([x >= 38 and x <= 40 for x in gaps])
|
||
|
|
|
||
|
|
|
||
|
|
def test_invalid_scaling_polar():
|
||
|
|
with testing.raises(ValueError):
|
||
|
|
warp_polar(np.zeros((10, 10)), (5, 5), scaling='invalid')
|
||
|
|
with testing.raises(ValueError):
|
||
|
|
warp_polar(np.zeros((10, 10)), (5, 5), scaling=None)
|
||
|
|
|
||
|
|
|
||
|
|
def test_invalid_dimensions_polar():
|
||
|
|
with testing.raises(ValueError):
|
||
|
|
warp_polar(np.zeros((10, 10, 3)), (5, 5))
|
||
|
|
with testing.raises(ValueError):
|
||
|
|
warp_polar(np.zeros((10, 10)), (5, 5), multichannel=True)
|
||
|
|
with testing.raises(ValueError):
|
||
|
|
warp_polar(np.zeros((10, 10, 10, 3)), (5, 5), multichannel=True)
|
||
|
|
|
||
|
|
|
||
|
|
def test_bool_img_rescale():
|
||
|
|
img = np.ones((12, 18), dtype=bool)
|
||
|
|
img[2:-2, 4:-4] = False
|
||
|
|
res = rescale(img, 0.5)
|
||
|
|
|
||
|
|
expected = np.ones((6, 9))
|
||
|
|
expected[1:-1, 2:-2] = False
|
||
|
|
|
||
|
|
assert_equal(res, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_bool_img_resize():
|
||
|
|
img = np.ones((12, 18), dtype=bool)
|
||
|
|
img[2:-2, 4:-4] = False
|
||
|
|
res = resize(img, (6, 9))
|
||
|
|
|
||
|
|
expected = np.ones((6, 9))
|
||
|
|
expected[1:-1, 2:-2] = False
|
||
|
|
|
||
|
|
assert_equal(res, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_boll_array_warnings():
|
||
|
|
img = np.zeros((10, 10), dtype=bool)
|
||
|
|
|
||
|
|
with expected_warnings(['Input image dtype is bool']):
|
||
|
|
rescale(img, 0.5, anti_aliasing=True)
|
||
|
|
|
||
|
|
with expected_warnings(['Input image dtype is bool']):
|
||
|
|
resize(img, (5, 5), anti_aliasing=True)
|
||
|
|
|
||
|
|
with expected_warnings(['Input image dtype is bool']):
|
||
|
|
rescale(img, 0.5, order=1)
|
||
|
|
|
||
|
|
with expected_warnings(['Input image dtype is bool']):
|
||
|
|
resize(img, (5, 5), order=1)
|
||
|
|
|
||
|
|
with expected_warnings(['Input image dtype is bool']):
|
||
|
|
warp(img, np.eye(3), order=1)
|