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test_structural_similarity.py
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test_structural_similarity.py
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import os
import numpy as np
from skimage import data
from skimage.metrics import structural_similarity
from skimage._shared import testing
from skimage._shared._warnings import expected_warnings
from skimage._shared.testing import (assert_equal, assert_almost_equal,
assert_array_almost_equal, fetch)
np.random.seed(5)
cam = data.camera()
sigma = 20.0
cam_noisy = np.clip(cam + sigma * np.random.randn(*cam.shape), 0, 255)
cam_noisy = cam_noisy.astype(cam.dtype)
np.random.seed(1234)
def test_structural_similarity_patch_range():
N = 51
X = (np.random.rand(N, N) * 255).astype(np.uint8)
Y = (np.random.rand(N, N) * 255).astype(np.uint8)
assert(structural_similarity(X, Y, win_size=N) < 0.1)
assert_equal(structural_similarity(X, X, win_size=N), 1)
def test_structural_similarity_image():
N = 100
X = (np.random.rand(N, N) * 255).astype(np.uint8)
Y = (np.random.rand(N, N) * 255).astype(np.uint8)
S0 = structural_similarity(X, X, win_size=3)
assert_equal(S0, 1)
S1 = structural_similarity(X, Y, win_size=3)
assert(S1 < 0.3)
S2 = structural_similarity(X, Y, win_size=11, gaussian_weights=True)
assert(S2 < 0.3)
mssim0, S3 = structural_similarity(X, Y, full=True)
assert_equal(S3.shape, X.shape)
mssim = structural_similarity(X, Y)
assert_equal(mssim0, mssim)
# structural_similarity of image with itself should be 1.0
assert_equal(structural_similarity(X, X), 1.0)
# Because we are forcing a random seed state, it is probably good to test
# against a few seeds in case on seed gives a particularly bad example
@testing.parametrize('seed', [1, 2, 3, 5, 8, 13])
def test_structural_similarity_grad(seed):
N = 30
# NOTE: This test is known to randomly fail on some systems (Mac OS X 10.6)
# And when testing tests in parallel. Therefore, we choose a few
# seeds that are known to work.
# The likely cause of this failure is that we are setting a hard
# threshold on the value of the gradient. Often the computed gradient
# is only slightly larger than what was measured.
# X = np.random.rand(N, N) * 255
# Y = np.random.rand(N, N) * 255
rnd = np.random.RandomState(seed)
X = rnd.rand(N, N) * 255
Y = rnd.rand(N, N) * 255
f = structural_similarity(X, Y, data_range=255)
g = structural_similarity(X, Y, data_range=255, gradient=True)
assert f < 0.05
assert g[0] < 0.05
assert np.all(g[1] < 0.05)
mssim, grad, s = structural_similarity(
X, Y, data_range=255, gradient=True, full=True)
assert np.all(grad < 0.05)
def test_structural_similarity_dtype():
N = 30
X = np.random.rand(N, N)
Y = np.random.rand(N, N)
S1 = structural_similarity(X, Y)
X = (X * 255).astype(np.uint8)
Y = (X * 255).astype(np.uint8)
S2 = structural_similarity(X, Y)
assert S1 < 0.1
assert S2 < 0.1
def test_structural_similarity_multichannel():
N = 100
X = (np.random.rand(N, N) * 255).astype(np.uint8)
Y = (np.random.rand(N, N) * 255).astype(np.uint8)
S1 = structural_similarity(X, Y, win_size=3)
# replicate across three channels. should get identical value
Xc = np.tile(X[..., np.newaxis], (1, 1, 3))
Yc = np.tile(Y[..., np.newaxis], (1, 1, 3))
S2 = structural_similarity(Xc, Yc, multichannel=True, win_size=3)
assert_almost_equal(S1, S2)
# full case should return an image as well
m, S3 = structural_similarity(Xc, Yc, multichannel=True, full=True)
assert_equal(S3.shape, Xc.shape)
# gradient case
m, grad = structural_similarity(Xc, Yc, multichannel=True, gradient=True)
assert_equal(grad.shape, Xc.shape)
# full and gradient case
m, grad, S3 = structural_similarity(
Xc, Yc, multichannel=True, full=True, gradient=True)
assert_equal(grad.shape, Xc.shape)
assert_equal(S3.shape, Xc.shape)
# fail if win_size exceeds any non-channel dimension
with testing.raises(ValueError):
structural_similarity(Xc, Yc, win_size=7, multichannel=False)
def test_structural_similarity_nD():
# test 1D through 4D on small random arrays
N = 10
for ndim in range(1, 5):
xsize = [N, ] * 5
X = (np.random.rand(*xsize) * 255).astype(np.uint8)
Y = (np.random.rand(*xsize) * 255).astype(np.uint8)
mssim = structural_similarity(X, Y, win_size=3)
assert mssim < 0.05
def test_structural_similarity_multichannel_chelsea():
# color image example
Xc = data.chelsea()
sigma = 15.0
Yc = np.clip(Xc + sigma * np.random.randn(*Xc.shape), 0, 255)
Yc = Yc.astype(Xc.dtype)
# multichannel result should be mean of the individual channel results
mssim = structural_similarity(Xc, Yc, multichannel=True)
mssim_sep = [structural_similarity(
Yc[..., c], Xc[..., c]) for c in range(Xc.shape[-1])]
assert_almost_equal(mssim, np.mean(mssim_sep))
# structural_similarity of image with itself should be 1.0
assert_equal(structural_similarity(Xc, Xc, multichannel=True), 1.0)
def test_gaussian_structural_similarity_vs_IPOL():
""" Tests vs. imdiff result from the following IPOL article and code:
https://www.ipol.im/pub/art/2011/g_lmii/.
Notes
-----
To generate mssim_IPOL, we need a local copy of cam_noisy:
>>> from skimage import io
>>> io.imsave('/tmp/cam_noisy.png', cam_noisy)
Then, we use the following command:
$ ./imdiff -m mssim <path to camera.png>/camera.png /tmp/cam_noisy.png
"""
mssim_IPOL = 0.350132882595062
mssim = structural_similarity(cam, cam_noisy, gaussian_weights=True,
use_sample_covariance=False)
assert_almost_equal(mssim, mssim_IPOL, decimal=3)
def test_mssim_vs_legacy():
# check that ssim with default options matches skimage 0.11 result
mssim_skimage_0pt17 = 0.3561711557677345
mssim = structural_similarity(cam, cam_noisy)
assert_almost_equal(mssim, mssim_skimage_0pt17)
def test_mssim_mixed_dtype():
mssim = structural_similarity(cam, cam_noisy)
with expected_warnings(['Inputs have mismatched dtype']):
mssim_mixed = structural_similarity(cam, cam_noisy.astype(np.float32))
assert_almost_equal(mssim, mssim_mixed)
# no warning when user supplies data_range
mssim_mixed = structural_similarity(
cam, cam_noisy.astype(np.float32), data_range=255)
assert_almost_equal(mssim, mssim_mixed)
def test_invalid_input():
# size mismatch
X = np.zeros((9, 9), dtype=np.double)
Y = np.zeros((8, 8), dtype=np.double)
with testing.raises(ValueError):
structural_similarity(X, Y)
# win_size exceeds image extent
with testing.raises(ValueError):
structural_similarity(X, X, win_size=X.shape[0] + 1)
# some kwarg inputs must be non-negative
with testing.raises(ValueError):
structural_similarity(X, X, K1=-0.1)
with testing.raises(ValueError):
structural_similarity(X, X, K2=-0.1)
with testing.raises(ValueError):
structural_similarity(X, X, sigma=-1.0)