219 lines
7.1 KiB
Python
219 lines
7.1 KiB
Python
import os
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import numpy as np
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from skimage import data
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from skimage.metrics import structural_similarity
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from skimage._shared import testing
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from skimage._shared._warnings import expected_warnings
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from skimage._shared.testing import (assert_equal, assert_almost_equal,
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assert_array_almost_equal, fetch)
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np.random.seed(5)
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cam = data.camera()
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sigma = 20.0
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cam_noisy = np.clip(cam + sigma * np.random.randn(*cam.shape), 0, 255)
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cam_noisy = cam_noisy.astype(cam.dtype)
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np.random.seed(1234)
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def test_structural_similarity_patch_range():
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N = 51
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X = (np.random.rand(N, N) * 255).astype(np.uint8)
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Y = (np.random.rand(N, N) * 255).astype(np.uint8)
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assert(structural_similarity(X, Y, win_size=N) < 0.1)
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assert_equal(structural_similarity(X, X, win_size=N), 1)
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def test_structural_similarity_image():
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N = 100
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X = (np.random.rand(N, N) * 255).astype(np.uint8)
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Y = (np.random.rand(N, N) * 255).astype(np.uint8)
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S0 = structural_similarity(X, X, win_size=3)
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assert_equal(S0, 1)
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S1 = structural_similarity(X, Y, win_size=3)
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assert(S1 < 0.3)
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S2 = structural_similarity(X, Y, win_size=11, gaussian_weights=True)
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assert(S2 < 0.3)
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mssim0, S3 = structural_similarity(X, Y, full=True)
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assert_equal(S3.shape, X.shape)
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mssim = structural_similarity(X, Y)
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assert_equal(mssim0, mssim)
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# structural_similarity of image with itself should be 1.0
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assert_equal(structural_similarity(X, X), 1.0)
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# Because we are forcing a random seed state, it is probably good to test
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# against a few seeds in case on seed gives a particularly bad example
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@testing.parametrize('seed', [1, 2, 3, 5, 8, 13])
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def test_structural_similarity_grad(seed):
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N = 30
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# NOTE: This test is known to randomly fail on some systems (Mac OS X 10.6)
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# And when testing tests in parallel. Therefore, we choose a few
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# seeds that are known to work.
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# The likely cause of this failure is that we are setting a hard
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# threshold on the value of the gradient. Often the computed gradient
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# is only slightly larger than what was measured.
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# X = np.random.rand(N, N) * 255
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# Y = np.random.rand(N, N) * 255
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rnd = np.random.RandomState(seed)
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X = rnd.rand(N, N) * 255
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Y = rnd.rand(N, N) * 255
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f = structural_similarity(X, Y, data_range=255)
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g = structural_similarity(X, Y, data_range=255, gradient=True)
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assert f < 0.05
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assert g[0] < 0.05
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assert np.all(g[1] < 0.05)
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mssim, grad, s = structural_similarity(
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X, Y, data_range=255, gradient=True, full=True)
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assert np.all(grad < 0.05)
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def test_structural_similarity_dtype():
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N = 30
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X = np.random.rand(N, N)
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Y = np.random.rand(N, N)
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S1 = structural_similarity(X, Y)
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X = (X * 255).astype(np.uint8)
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Y = (X * 255).astype(np.uint8)
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S2 = structural_similarity(X, Y)
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assert S1 < 0.1
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assert S2 < 0.1
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def test_structural_similarity_multichannel():
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N = 100
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X = (np.random.rand(N, N) * 255).astype(np.uint8)
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Y = (np.random.rand(N, N) * 255).astype(np.uint8)
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S1 = structural_similarity(X, Y, win_size=3)
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# replicate across three channels. should get identical value
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Xc = np.tile(X[..., np.newaxis], (1, 1, 3))
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Yc = np.tile(Y[..., np.newaxis], (1, 1, 3))
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S2 = structural_similarity(Xc, Yc, multichannel=True, win_size=3)
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assert_almost_equal(S1, S2)
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# full case should return an image as well
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m, S3 = structural_similarity(Xc, Yc, multichannel=True, full=True)
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assert_equal(S3.shape, Xc.shape)
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# gradient case
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m, grad = structural_similarity(Xc, Yc, multichannel=True, gradient=True)
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assert_equal(grad.shape, Xc.shape)
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# full and gradient case
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m, grad, S3 = structural_similarity(
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Xc, Yc, multichannel=True, full=True, gradient=True)
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assert_equal(grad.shape, Xc.shape)
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assert_equal(S3.shape, Xc.shape)
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# fail if win_size exceeds any non-channel dimension
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with testing.raises(ValueError):
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structural_similarity(Xc, Yc, win_size=7, multichannel=False)
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def test_structural_similarity_nD():
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# test 1D through 4D on small random arrays
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N = 10
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for ndim in range(1, 5):
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xsize = [N, ] * 5
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X = (np.random.rand(*xsize) * 255).astype(np.uint8)
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Y = (np.random.rand(*xsize) * 255).astype(np.uint8)
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mssim = structural_similarity(X, Y, win_size=3)
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assert mssim < 0.05
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def test_structural_similarity_multichannel_chelsea():
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# color image example
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Xc = data.chelsea()
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sigma = 15.0
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Yc = np.clip(Xc + sigma * np.random.randn(*Xc.shape), 0, 255)
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Yc = Yc.astype(Xc.dtype)
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# multichannel result should be mean of the individual channel results
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mssim = structural_similarity(Xc, Yc, multichannel=True)
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mssim_sep = [structural_similarity(
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Yc[..., c], Xc[..., c]) for c in range(Xc.shape[-1])]
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assert_almost_equal(mssim, np.mean(mssim_sep))
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# structural_similarity of image with itself should be 1.0
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assert_equal(structural_similarity(Xc, Xc, multichannel=True), 1.0)
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def test_gaussian_structural_similarity_vs_IPOL():
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""" Tests vs. imdiff result from the following IPOL article and code:
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https://www.ipol.im/pub/art/2011/g_lmii/.
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Notes
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-----
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To generate mssim_IPOL, we need a local copy of cam_noisy:
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>>> from skimage import io
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>>> io.imsave('/tmp/cam_noisy.png', cam_noisy)
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Then, we use the following command:
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$ ./imdiff -m mssim <path to camera.png>/camera.png /tmp/cam_noisy.png
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Values for current data.camera() calculated by Gregory Lee on Sep, 2020.
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Available at:
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https://github.com/scikit-image/scikit-image/pull/4913#issuecomment-700653165
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"""
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mssim_IPOL = 0.357959091663361
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mssim = structural_similarity(cam, cam_noisy, gaussian_weights=True,
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use_sample_covariance=False)
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assert_almost_equal(mssim, mssim_IPOL, decimal=3)
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def test_mssim_vs_legacy():
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# check that ssim with default options matches skimage 0.17 result
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mssim_skimage_0pt17 = 0.3674518327910367
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mssim = structural_similarity(cam, cam_noisy)
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assert_almost_equal(mssim, mssim_skimage_0pt17)
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def test_mssim_mixed_dtype():
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mssim = structural_similarity(cam, cam_noisy)
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with expected_warnings(['Inputs have mismatched dtype']):
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mssim_mixed = structural_similarity(cam, cam_noisy.astype(np.float32))
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assert_almost_equal(mssim, mssim_mixed)
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# no warning when user supplies data_range
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mssim_mixed = structural_similarity(
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cam, cam_noisy.astype(np.float32), data_range=255)
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assert_almost_equal(mssim, mssim_mixed)
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def test_invalid_input():
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# size mismatch
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X = np.zeros((9, 9), dtype=np.double)
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Y = np.zeros((8, 8), dtype=np.double)
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with testing.raises(ValueError):
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structural_similarity(X, Y)
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# win_size exceeds image extent
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with testing.raises(ValueError):
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structural_similarity(X, X, win_size=X.shape[0] + 1)
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# some kwarg inputs must be non-negative
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with testing.raises(ValueError):
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structural_similarity(X, X, K1=-0.1)
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with testing.raises(ValueError):
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structural_similarity(X, X, K2=-0.1)
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with testing.raises(ValueError):
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structural_similarity(X, X, sigma=-1.0)
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