85 lines
2.9 KiB
Python
85 lines
2.9 KiB
Python
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import numpy as np
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import skimage.data
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from skimage.measure import compare_nrmse, compare_psnr, compare_mse
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from skimage._shared import testing
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from skimage._shared.testing import assert_equal, assert_almost_equal
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from skimage._shared._warnings import expected_warnings
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np.random.seed(5)
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cam = skimage.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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def test_PSNR_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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p_IPOL = 22.4497
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with expected_warnings(['DEPRECATED']):
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p = compare_psnr(cam, cam_noisy)
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assert_almost_equal(p, p_IPOL, decimal=4)
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def test_PSNR_float():
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with expected_warnings(['DEPRECATED']):
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p_uint8 = compare_psnr(cam, cam_noisy)
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p_float64 = compare_psnr(cam / 255., cam_noisy / 255.,
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data_range=1)
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assert_almost_equal(p_uint8, p_float64, decimal=5)
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# mixed precision inputs
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with expected_warnings(['DEPRECATED']):
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p_mixed = compare_psnr(cam / 255., np.float32(cam_noisy / 255.),
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data_range=1)
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assert_almost_equal(p_mixed, p_float64, decimal=5)
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# mismatched dtype results in a warning if data_range is unspecified
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with expected_warnings(['Inputs have mismatched dtype', 'DEPRECATED']):
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p_mixed = compare_psnr(cam / 255., np.float32(cam_noisy / 255.))
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assert_almost_equal(p_mixed, p_float64, decimal=5)
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def test_PSNR_errors():
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with expected_warnings(['DEPRECATED']):
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# shape mismatch
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with testing.raises(ValueError):
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compare_psnr(cam, cam[:-1, :])
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def test_NRMSE():
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x = np.ones(4)
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y = np.asarray([0., 2., 2., 2.])
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with expected_warnings(['DEPRECATED']):
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assert_equal(compare_nrmse(y, x, 'mean'), 1 / np.mean(y))
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assert_equal(compare_nrmse(y, x, 'Euclidean'), 1 / np.sqrt(3))
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assert_equal(compare_nrmse(y, x, 'min-max'), 1 / (y.max() - y.min()))
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# mixed precision inputs are allowed
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assert_almost_equal(compare_nrmse(y, np.float32(x), 'min-max'),
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1 / (y.max() - y.min()))
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def test_NRMSE_no_int_overflow():
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camf = cam.astype(np.float32)
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cam_noisyf = cam_noisy.astype(np.float32)
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with expected_warnings(['DEPRECATED']):
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assert_almost_equal(compare_mse(cam, cam_noisy),
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compare_mse(camf, cam_noisyf))
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assert_almost_equal(compare_nrmse(cam, cam_noisy),
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compare_nrmse(camf, cam_noisyf))
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def test_NRMSE_errors():
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x = np.ones(4)
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with expected_warnings(['DEPRECATED']):
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# shape mismatch
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with testing.raises(ValueError):
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compare_nrmse(x[:-1], x)
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# invalid normalization name
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with testing.raises(ValueError):
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compare_nrmse(x, x, norm_type='foo')
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