import os import numpy as np from skimage import data, data_dir from skimage.measure import compare_ssim as ssim 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) 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_ssim_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(ssim(X, Y, win_size=N) < 0.1) assert_equal(ssim(X, X, win_size=N), 1) def test_ssim_image(): N = 100 X = (np.random.rand(N, N) * 255).astype(np.uint8) Y = (np.random.rand(N, N) * 255).astype(np.uint8) S0 = ssim(X, X, win_size=3) assert_equal(S0, 1) S1 = ssim(X, Y, win_size=3) assert(S1 < 0.3) S2 = ssim(X, Y, win_size=11, gaussian_weights=True) assert(S2 < 0.3) mssim0, S3 = ssim(X, Y, full=True) assert_equal(S3.shape, X.shape) mssim = ssim(X, Y) assert_equal(mssim0, mssim) # ssim of image with itself should be 1.0 assert_equal(ssim(X, X), 1.0) # NOTE: This test is known to randomly fail on some systems (Mac OS X 10.6) def test_ssim_grad(): N = 30 X = np.random.rand(N, N) * 255 Y = np.random.rand(N, N) * 255 f = ssim(X, Y, data_range=255) g = ssim(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 = ssim(X, Y, data_range=255, gradient=True, full=True) assert np.all(grad < 0.05) def test_ssim_dtype(): N = 30 X = np.random.rand(N, N) Y = np.random.rand(N, N) S1 = ssim(X, Y) X = (X * 255).astype(np.uint8) Y = (X * 255).astype(np.uint8) S2 = ssim(X, Y) assert S1 < 0.1 assert S2 < 0.1 def test_ssim_multichannel(): N = 100 X = (np.random.rand(N, N) * 255).astype(np.uint8) Y = (np.random.rand(N, N) * 255).astype(np.uint8) S1 = ssim(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 = ssim(Xc, Yc, multichannel=True, win_size=3) assert_almost_equal(S1, S2) # full case should return an image as well m, S3 = ssim(Xc, Yc, multichannel=True, full=True) assert_equal(S3.shape, Xc.shape) # gradient case m, grad = ssim(Xc, Yc, multichannel=True, gradient=True) assert_equal(grad.shape, Xc.shape) # full and gradient case m, grad, S3 = ssim(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): ssim(Xc, Yc, win_size=7, multichannel=False) def test_ssim_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 = ssim(X, Y, win_size=3) assert mssim < 0.05 def test_ssim_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 = ssim(Xc, Yc, multichannel=True) mssim_sep = [ssim(Yc[..., c], Xc[..., c]) for c in range(Xc.shape[-1])] assert_almost_equal(mssim, np.mean(mssim_sep)) # ssim of image with itself should be 1.0 assert_equal(ssim(Xc, Xc, multichannel=True), 1.0) def test_gaussian_mssim_vs_IPOL(): # Tests vs. imdiff result from the following IPOL article and code: # http://www.ipol.im/pub/art/2011/g_lmii/ mssim_IPOL = 0.327309966087341 mssim = ssim(cam, cam_noisy, gaussian_weights=True, use_sample_covariance=False) assert_almost_equal(mssim, mssim_IPOL, decimal=3) def test_gaussian_mssim_vs_author_ref(): """ test vs. result from original author's Matlab implementation available at https://ece.uwaterloo.ca/~z70wang/research/ssim/ Matlab test code: img1 = imread('camera.png') img2 = imread('camera_noisy.png') mssim = ssim_index(img1, img2) """ mssim_matlab = 0.327314295673357 mssim = ssim(cam, cam_noisy, gaussian_weights=True, use_sample_covariance=False) assert_almost_equal(mssim, mssim_matlab, decimal=3) def test_gaussian_mssim_and_gradient_vs_Matlab(): # comparison to Matlab implementation of N. Avanaki: # https://ece.uwaterloo.ca/~nnikvand/Coderep/SHINE%20TOOLBOX/SHINEtoolbox/ # Note: final line of ssim_sens.m was modified to discard image borders ref = np.load(os.path.join(data_dir, 'mssim_matlab_output.npz')) grad_matlab = ref['grad_matlab'] mssim_matlab = float(ref['mssim_matlab']) mssim, grad = ssim(cam, cam_noisy, gaussian_weights=True, gradient=True, use_sample_covariance=False) assert_almost_equal(mssim, mssim_matlab, decimal=3) # check almost equal aside from object borders assert_array_almost_equal(grad_matlab[5:-5], grad[5:-5]) def test_mssim_vs_legacy(): # check that ssim with default options matches skimage 0.11 result mssim_skimage_0pt11 = 0.34192589699605191 mssim = ssim(cam, cam_noisy) assert_almost_equal(mssim, mssim_skimage_0pt11) def test_mssim_mixed_dtype(): mssim = ssim(cam, cam_noisy) with expected_warnings(['Inputs have mismatched dtype']): mssim_mixed = ssim(cam, cam_noisy.astype(np.float32)) assert_almost_equal(mssim, mssim_mixed) # no warning when user supplies data_range mssim_mixed = ssim(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): ssim(X, Y) # win_size exceeds image extent with testing.raises(ValueError): ssim(X, X, win_size=X.shape[0] + 1) # some kwarg inputs must be non-negative with testing.raises(ValueError): ssim(X, X, K1=-0.1) with testing.raises(ValueError): ssim(X, X, K2=-0.1) with testing.raises(ValueError): ssim(X, X, sigma=-1.0)