Esempio n. 1
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def test_wavelet_denoising_nd():
    rstate = np.random.RandomState(1234)
    for method in ['VisuShrink', 'BayesShrink']:
        for ndim in range(1, 5):
            # Generate a very simple test image
            if ndim < 3:
                img = 0.2*np.ones((128, )*ndim)
            else:
                img = 0.2*np.ones((16, )*ndim)
            img[(slice(5, 13), ) * ndim] = 0.8

            sigma = 0.1
            noisy = img + sigma * rstate.randn(*(img.shape))
            noisy = np.clip(noisy, 0, 1)

            # Mark H. 2018.08:
            #   The issue arises because when ndim in [1, 2]
            #   ``waverecn`` calls ``_match_coeff_dims``
            #   Which includes a numpy 1.15 deprecation.
            #   for larger number of dimensions _match_coeff_dims isn't called
            #   for some reason.
            anticipated_warnings = (PYWAVELET_ND_INDEXING_WARNING
                                    if ndim < 3 else None)
            with expected_warnings([anticipated_warnings]):
                # Verify that SNR is improved with internally estimated sigma
                denoised = restoration.denoise_wavelet(noisy, method=method)
            psnr_noisy = compare_psnr(img, noisy)
            psnr_denoised = compare_psnr(img, denoised)
            assert_(psnr_denoised > psnr_noisy)
Esempio n. 2
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def test_clear_border_non_binary_3d():
    image3d = np.array(
        [[[1, 2, 3, 1, 2],
          [3, 3, 3, 4, 2],
          [3, 4, 3, 4, 2],
          [3, 3, 2, 1, 2]],
         [[1, 2, 3, 1, 2],
          [3, 3, 5, 4, 2],
          [3, 4, 5, 4, 2],
          [3, 3, 2, 1, 2]],
         [[1, 2, 3, 1, 2],
          [3, 3, 3, 4, 2],
          [3, 4, 3, 4, 2],
          [3, 3, 2, 1, 2]],
         ])

    result = clear_border(image3d)
    expected = np.array(
        [[[0, 0, 0, 0, 0],
          [0, 0, 0, 0, 0],
          [0, 0, 0, 0, 0],
          [0, 0, 0, 0, 0]],
         [[0, 0, 0, 0, 0],
          [0, 0, 5, 0, 0],
          [0, 0, 5, 0, 0],
          [0, 0, 0, 0, 0]],
         [[0, 0, 0, 0, 0],
          [0, 0, 0, 0, 0],
          [0, 0, 0, 0, 0],
          [0, 0, 0, 0, 0]],
         ])

    assert_array_equal(result, expected)
    assert_(not np.all(image3d == result))
Esempio n. 3
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def test_wavelet_denoising_levels():
    rstate = np.random.RandomState(1234)
    ndim = 2
    N = 256
    wavelet = 'db1'
    # Generate a very simple test image
    img = 0.2*np.ones((N, )*ndim)
    img[[slice(5, 13), ] * ndim] = 0.8

    sigma = 0.1
    noisy = img + sigma * rstate.randn(*(img.shape))
    noisy = np.clip(noisy, 0, 1)

    denoised = restoration.denoise_wavelet(noisy, wavelet=wavelet)
    denoised_1 = restoration.denoise_wavelet(noisy, wavelet=wavelet,
                                             wavelet_levels=1)
    psnr_noisy = compare_psnr(img, noisy)
    psnr_denoised = compare_psnr(img, denoised)
    psnr_denoised_1 = compare_psnr(img, denoised_1)

    # multi-level case should outperform single level case
    assert_(psnr_denoised > psnr_denoised_1 > psnr_noisy)

    # invalid number of wavelet levels results in a ValueError
    max_level = pywt.dwt_max_level(np.min(img.shape),
                                   pywt.Wavelet(wavelet).dec_len)
    with testing.raises(ValueError):
        restoration.denoise_wavelet(
            noisy,
            wavelet=wavelet, wavelet_levels=max_level+1)
    with testing.raises(ValueError):
        restoration.denoise_wavelet(
            noisy,
            wavelet=wavelet, wavelet_levels=-1)
Esempio n. 4
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def test_mask():
    length = 100
    ramps = [np.linspace(0, 4 * np.pi, length),
             np.linspace(0, 8 * np.pi, length),
             np.linspace(0, 6 * np.pi, length)]
    image = np.vstack(ramps)
    mask_1d = np.ones((length,), dtype=np.bool)
    mask_1d[0] = mask_1d[-1] = False
    for i in range(len(ramps)):
        # mask all ramps but the i'th one
        mask = np.zeros(image.shape, dtype=np.bool)
        mask |= mask_1d.reshape(1, -1)
        mask[i, :] = False   # unmask i'th ramp
        image_wrapped = np.ma.array(np.angle(np.exp(1j * image)), mask=mask)
        image_unwrapped = unwrap_phase(image_wrapped)
        image_unwrapped -= image_unwrapped[0, 0]    # remove phase shift
        # The end of the unwrapped array should have value equal to the
        # endpoint of the unmasked ramp
        assert_array_almost_equal_nulp(image_unwrapped[:, -1], image[i, -1])
        assert_(np.ma.isMaskedArray(image_unwrapped))

        # Same tests, but forcing use of the 3D unwrapper by reshaping
        with expected_warnings(['length 1 dimension']):
            shape = (1,) + image_wrapped.shape
            image_wrapped_3d = image_wrapped.reshape(shape)
            image_unwrapped_3d = unwrap_phase(image_wrapped_3d)
            # remove phase shift
            image_unwrapped_3d -= image_unwrapped_3d[0, 0, 0]
        assert_array_almost_equal_nulp(image_unwrapped_3d[:, :, -1],
                                       image[i, -1])
Esempio n. 5
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def test_wavelet_threshold():
    rstate = np.random.RandomState(1234)

    img = astro_gray
    sigma = 0.1
    noisy = img + sigma * rstate.randn(*(img.shape))
    noisy = np.clip(noisy, 0, 1)

    # employ a single, user-specified threshold instead of BayesShrink sigmas
    with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
        denoised = _wavelet_threshold(noisy, wavelet='db1', method=None,
                                      threshold=sigma)
    psnr_noisy = compare_psnr(img, noisy)
    psnr_denoised = compare_psnr(img, denoised)
    assert_(psnr_denoised > psnr_noisy)

    # either method or threshold must be defined
    with testing.raises(ValueError):
        _wavelet_threshold(noisy, wavelet='db1', method=None, threshold=None)

    # warns if a threshold is provided in a case where it would be ignored
    with expected_warnings(["Thresholding method ",
                            PYWAVELET_ND_INDEXING_WARNING]):
        _wavelet_threshold(noisy, wavelet='db1', method='BayesShrink',
                           threshold=sigma)
Esempio n. 6
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def check_wrap_around(ndim, axis):
    # create a ramp, but with the last pixel along axis equalling the first
    elements = 100
    ramp = np.linspace(0, 12 * np.pi, elements)
    ramp[-1] = ramp[0]
    image = ramp.reshape(tuple([elements if n == axis else 1
                                for n in range(ndim)]))
    image_wrapped = np.angle(np.exp(1j * image))

    index_first = tuple([0] * ndim)
    index_last = tuple([-1 if n == axis else 0 for n in range(ndim)])
    # unwrap the image without wrap around
    with warnings.catch_warnings():
        # We do not want warnings about length 1 dimensions
        warnings.simplefilter("ignore")
        image_unwrap_no_wrap_around = unwrap_phase(image_wrapped, seed=0)
    print('endpoints without wrap_around:',
          image_unwrap_no_wrap_around[index_first],
          image_unwrap_no_wrap_around[index_last])
    # without wrap around, the endpoints of the image should differ
    assert_(abs(image_unwrap_no_wrap_around[index_first] -
                image_unwrap_no_wrap_around[index_last]) > np.pi)
    # unwrap the image with wrap around
    wrap_around = [n == axis for n in range(ndim)]
    with warnings.catch_warnings():
        # We do not want warnings about length 1 dimensions
        warnings.simplefilter("ignore")
        image_unwrap_wrap_around = unwrap_phase(image_wrapped, wrap_around,
                                                seed=0)
    print('endpoints with wrap_around:',
          image_unwrap_wrap_around[index_first],
          image_unwrap_wrap_around[index_last])
    # with wrap around, the endpoints of the image should be equal
    assert_almost_equal(image_unwrap_wrap_around[index_first],
                        image_unwrap_wrap_around[index_last])
Esempio n. 7
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def test_denoise_tv_chambolle_1d():
    """Apply the TV denoising algorithm on a 1D sinusoid."""
    x = 125 + 100*np.sin(np.linspace(0, 8*np.pi, 1000))
    x += 20 * np.random.rand(x.size)
    x = np.clip(x, 0, 255)
    res = restoration.denoise_tv_chambolle(x.astype(np.uint8), weight=0.1)
    assert_(res.dtype == np.float)
    assert_(res.std() * 255 < x.std())
Esempio n. 8
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def test_no_denoising_for_small_h():
    img = np.zeros((40, 40))
    img[10:-10, 10:-10] = 1.
    img += 0.3*np.random.randn(*img.shape)
    # very small h should result in no averaging with other patches
    denoised = restoration.denoise_nl_means(img, 7, 5, 0.01, fast_mode=True,
                                            multichannel=True)
    assert_(np.allclose(denoised, img))
    denoised = restoration.denoise_nl_means(img, 7, 5, 0.01, fast_mode=False,
                                            multichannel=True)
    assert_(np.allclose(denoised, img))
Esempio n. 9
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def test_denoise_tv_chambolle_3d():
    """Apply the TV denoising algorithm on a 3D image representing a sphere."""
    x, y, z = np.ogrid[0:40, 0:40, 0:40]
    mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2
    mask = 100 * mask.astype(np.float)
    mask += 60
    mask += 20 * np.random.rand(*mask.shape)
    mask[mask < 0] = 0
    mask[mask > 255] = 255
    res = restoration.denoise_tv_chambolle(mask.astype(np.uint8), weight=0.1)
    assert_(res.dtype == np.float)
    assert_(res.std() * 255 < mask.std())
Esempio n. 10
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def test_denoise_tv_bregman_3d():
    img = checkerboard.copy()
    # add some random noise
    img += 0.5 * img.std() * np.random.rand(*img.shape)
    img = np.clip(img, 0, 1)

    out1 = restoration.denoise_tv_bregman(img, weight=10)
    out2 = restoration.denoise_tv_bregman(img, weight=5)

    # make sure noise is reduced in the checkerboard cells
    assert_(img[30:45, 5:15].std() > out1[30:45, 5:15].std())
    assert_(out1[30:45, 5:15].std() > out2[30:45, 5:15].std())
Esempio n. 11
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def test_denoise_nl_means_multichannel():
    # for true 3D data, 3D denoising is better than denoising as 2D+channels
    img = np.zeros((13, 10, 8))
    img[6, 4:6, 2:-2] = 1.
    sigma = 0.3
    imgn = img + sigma * np.random.randn(*img.shape)
    denoised_wrong_multichannel = restoration.denoise_nl_means(
        imgn, 3, 4, 0.6 * sigma, fast_mode=True, multichannel=True)
    denoised_ok_multichannel = restoration.denoise_nl_means(
        imgn, 3, 4, 0.6 * sigma, fast_mode=True, multichannel=False)
    psnr_wrong = compare_psnr(img, denoised_wrong_multichannel)
    psnr_ok = compare_psnr(img, denoised_ok_multichannel)
    assert_(psnr_ok > psnr_wrong)
Esempio n. 12
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def test_denoise_bilateral_color():
    img = checkerboard.copy()[:50, :50]
    # add some random noise
    img += 0.5 * img.std() * np.random.rand(*img.shape)
    img = np.clip(img, 0, 1)

    out1 = restoration.denoise_bilateral(img, sigma_color=0.1,
                                         sigma_spatial=10, multichannel=True)
    out2 = restoration.denoise_bilateral(img, sigma_color=0.2,
                                         sigma_spatial=20, multichannel=True)

    # make sure noise is reduced in the checkerboard cells
    assert_(img[30:45, 5:15].std() > out1[30:45, 5:15].std())
    assert_(out1[30:45, 5:15].std() > out2[30:45, 5:15].std())
Esempio n. 13
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def test_wavelet_denoising(img, multichannel, convert2ycbcr):
    rstate = np.random.RandomState(1234)
    sigma = 0.1
    noisy = img + sigma * rstate.randn(*(img.shape))
    noisy = np.clip(noisy, 0, 1)

    # Verify that SNR is improved when true sigma is used
    with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
        denoised = restoration.denoise_wavelet(noisy,
                                               sigma=sigma,
                                               multichannel=multichannel,
                                               convert2ycbcr=convert2ycbcr,
                                               rescale_sigma=True)
    psnr_noisy = peak_signal_noise_ratio(img, noisy)
    psnr_denoised = peak_signal_noise_ratio(img, denoised)
    assert_(psnr_denoised > psnr_noisy)

    # Verify that SNR is improved with internally estimated sigma
    with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
        denoised = restoration.denoise_wavelet(noisy,
                                               multichannel=multichannel,
                                               convert2ycbcr=convert2ycbcr,
                                               rescale_sigma=True)
    psnr_noisy = peak_signal_noise_ratio(img, noisy)
    psnr_denoised = peak_signal_noise_ratio(img, denoised)
    assert_(psnr_denoised > psnr_noisy)

    # SNR is improved less with 1 wavelet level than with the default.
    with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
        denoised_1 = restoration.denoise_wavelet(noisy,
                                                 multichannel=multichannel,
                                                 wavelet_levels=1,
                                                 convert2ycbcr=convert2ycbcr,
                                                 rescale_sigma=True)
    psnr_denoised_1 = peak_signal_noise_ratio(img, denoised_1)
    assert_(psnr_denoised > psnr_denoised_1)
    assert_(psnr_denoised_1 > psnr_noisy)

    # Test changing noise_std (higher threshold, so less energy in signal)
    with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
        res1 = restoration.denoise_wavelet(noisy,
                                           sigma=2 * sigma,
                                           multichannel=multichannel,
                                           rescale_sigma=True)
    with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
        res2 = restoration.denoise_wavelet(noisy,
                                           sigma=sigma,
                                           multichannel=multichannel,
                                           rescale_sigma=True)
    assert_(np.sum(res1**2) <= np.sum(res2**2))
Esempio n. 14
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def test_wavelet_denoising_levels(rescale_sigma):
    rstate = np.random.RandomState(1234)
    ndim = 2
    N = 256
    wavelet = 'db1'
    # Generate a very simple test image
    img = 0.2*np.ones((N, )*ndim)
    img[(slice(5, 13), ) * ndim] = 0.8

    sigma = 0.1
    noisy = img + sigma * rstate.randn(*(img.shape))
    noisy = np.clip(noisy, 0, 1)

    with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
        denoised = restoration.denoise_wavelet(noisy, wavelet=wavelet,
                                               rescale_sigma=rescale_sigma)
        denoised_1 = restoration.denoise_wavelet(noisy, wavelet=wavelet,
                                                 wavelet_levels=1,
                                                 rescale_sigma=rescale_sigma)
    psnr_noisy = peak_signal_noise_ratio(img, noisy)
    psnr_denoised = peak_signal_noise_ratio(img, denoised)
    psnr_denoised_1 = peak_signal_noise_ratio(img, denoised_1)

    # multi-level case should outperform single level case
    assert_(psnr_denoised > psnr_denoised_1 > psnr_noisy)

    # invalid number of wavelet levels results in a ValueError or UserWarning
    max_level = pywt.dwt_max_level(np.min(img.shape),
                                   pywt.Wavelet(wavelet).dec_len)
    if Version(pywt.__version__) < '1.0.0':
        # exceeding max_level raises a ValueError in PyWavelets 0.4-0.5.2
        with testing.raises(ValueError):
            with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
                restoration.denoise_wavelet(
                    noisy, wavelet=wavelet, wavelet_levels=max_level + 1,
                    rescale_sigma=rescale_sigma)
    else:
        # exceeding max_level raises a UserWarning in PyWavelets >= 1.0.0
        with expected_warnings([
                'all coefficients will experience boundary effects']):
            restoration.denoise_wavelet(
                noisy, wavelet=wavelet, wavelet_levels=max_level + 1,
                rescale_sigma=rescale_sigma)

    with testing.raises(ValueError):
        with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
            restoration.denoise_wavelet(
                noisy,
                wavelet=wavelet, wavelet_levels=-1,
                rescale_sigma=rescale_sigma)
Esempio n. 15
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def test_no_denoising_for_small_h(fast_mode, dtype):
    img = np.zeros((40, 40))
    img[10:-10, 10:-10] = 1.
    img += 0.3*np.random.randn(*img.shape)
    img = img.astype(dtype)
    # very small h should result in no averaging with other patches
    denoised = restoration.denoise_nl_means(img, 7, 5, 0.01,
                                            fast_mode=fast_mode,
                                            multichannel=True)
    assert_(np.allclose(denoised, img))
    denoised = restoration.denoise_nl_means(img, 7, 5, 0.01,
                                            fast_mode=fast_mode,
                                            multichannel=True)
    assert_(np.allclose(denoised, img))
Esempio n. 16
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def test_wavelet_denoising():
    rstate = np.random.RandomState(1234)

    # version with one odd-sized dimension
    astro_gray_odd = astro_gray[:, :-1]
    astro_odd = astro[:, :-1]

    for img, multichannel, convert2ycbcr in [(astro_gray, False, False),
                                             (astro_gray_odd, False, False),
                                             (astro_odd, True, False),
                                             (astro_odd, True, True)]:
        sigma = 0.1
        noisy = img + sigma * rstate.randn(*(img.shape))
        noisy = np.clip(noisy, 0, 1)

        # Verify that SNR is improved when true sigma is used
        with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
            denoised = restoration.denoise_wavelet(noisy, sigma=sigma,
                                                   multichannel=multichannel,
                                                   convert2ycbcr=convert2ycbcr)
        psnr_noisy = compare_psnr(img, noisy)
        psnr_denoised = compare_psnr(img, denoised)
        assert_(psnr_denoised > psnr_noisy)

        # Verify that SNR is improved with internally estimated sigma
        with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
            denoised = restoration.denoise_wavelet(noisy,
                                                   multichannel=multichannel,
                                                   convert2ycbcr=convert2ycbcr)
        psnr_noisy = compare_psnr(img, noisy)
        psnr_denoised = compare_psnr(img, denoised)
        assert_(psnr_denoised > psnr_noisy)

        # SNR is improved less with 1 wavelet level than with the default.
        denoised_1 = restoration.denoise_wavelet(noisy,
                                                 multichannel=multichannel,
                                                 wavelet_levels=1,
                                                 convert2ycbcr=convert2ycbcr)
        psnr_denoised_1 = compare_psnr(img, denoised_1)
        assert_(psnr_denoised > psnr_denoised_1)
        assert_(psnr_denoised_1 > psnr_noisy)

        # Test changing noise_std (higher threshold, so less energy in signal)
        with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
            res1 = restoration.denoise_wavelet(noisy, sigma=2 * sigma,
                                               multichannel=multichannel)
        with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
            res2 = restoration.denoise_wavelet(noisy, sigma=sigma,
                                               multichannel=multichannel)
        assert_(np.sum(res1**2) <= np.sum(res2**2))
Esempio n. 17
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def test_denoise_nl_means_multichannel(fast_mode, dtype):
    # for true 3D data, 3D denoising is better than denoising as 2D+channels
    img = np.zeros((13, 10, 8), dtype=dtype)
    img[6, 4:6, 2:-2] = 1.
    sigma = 0.3
    imgn = img + sigma * np.random.randn(*img.shape)
    imgn = imgn.astype(dtype)
    denoised_wrong_multichannel = restoration.denoise_nl_means(
        imgn, 3, 4, 0.6 * sigma, fast_mode=fast_mode, multichannel=True)
    denoised_ok_multichannel = restoration.denoise_nl_means(
        imgn, 3, 4, 0.6 * sigma, fast_mode=fast_mode, multichannel=False)
    psnr_wrong = peak_signal_noise_ratio(img, denoised_wrong_multichannel)
    psnr_ok = peak_signal_noise_ratio(img, denoised_ok_multichannel)
    assert_(psnr_ok > psnr_wrong)
Esempio n. 18
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def test_range():
    """Output of edge detection should be in [0, 1]"""
    image = np.random.random((100, 100))
    for detector in (filters.sobel, filters.scharr,
                     filters.prewitt, filters.roberts):
        out = detector(image)
        assert_(out.min() >= 0,
                "Minimum of `{0}` is smaller than zero".format(
                    detector.__name__)
                )
        assert_(out.max() <= 1,
                "Maximum of `{0}` is larger than 1".format(
                    detector.__name__)
                )
Esempio n. 19
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def test_wavelet_denoising():
    rstate = np.random.RandomState(1234)

    # version with one odd-sized dimension
    astro_gray_odd = astro_gray[:, :-1]
    astro_odd = astro[:, :-1]

    for img, multichannel, convert2ycbcr in [(astro_gray, False, False),
                                             (astro_gray_odd, False, False),
                                             (astro_odd, True, False),
                                             (astro_odd, True, True)]:
        sigma = 0.1
        noisy = img + sigma * rstate.randn(*(img.shape))
        noisy = np.clip(noisy, 0, 1)

        # Verify that SNR is improved when true sigma is used
        with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
            denoised = restoration.denoise_wavelet(noisy, sigma=sigma,
                                                   multichannel=multichannel,
                                                   convert2ycbcr=convert2ycbcr)
        psnr_noisy = compare_psnr(img, noisy)
        psnr_denoised = compare_psnr(img, denoised)
        assert_(psnr_denoised > psnr_noisy)

        # Verify that SNR is improved with internally estimated sigma
        with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
            denoised = restoration.denoise_wavelet(noisy,
                                                   multichannel=multichannel,
                                                   convert2ycbcr=convert2ycbcr)
        psnr_noisy = compare_psnr(img, noisy)
        psnr_denoised = compare_psnr(img, denoised)
        assert_(psnr_denoised > psnr_noisy)

        # SNR is improved less with 1 wavelet level than with the default.
        denoised_1 = restoration.denoise_wavelet(noisy,
                                                 multichannel=multichannel,
                                                 wavelet_levels=1,
                                                 convert2ycbcr=convert2ycbcr)
        psnr_denoised_1 = compare_psnr(img, denoised_1)
        assert_(psnr_denoised > psnr_denoised_1)
        assert_(psnr_denoised_1 > psnr_noisy)

        # Test changing noise_std (higher threshold, so less energy in signal)
        with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
            res1 = restoration.denoise_wavelet(noisy, sigma=2 * sigma,
                                               multichannel=multichannel)
        with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
            res2 = restoration.denoise_wavelet(noisy, sigma=sigma,
                                               multichannel=multichannel)
        assert_(np.sum(res1**2) <= np.sum(res2**2))
Esempio n. 20
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def test_denoise_bilateral_color():
    img = checkerboard.copy()[:50, :50]
    # add some random noise
    img += 0.5 * img.std() * np.random.rand(*img.shape)
    img = np.clip(img, 0, 1)

    out1 = restoration.denoise_bilateral(img, sigma_color=0.1,
                                         sigma_spatial=10, multichannel=True)
    out2 = restoration.denoise_bilateral(img, sigma_color=0.2,
                                         sigma_spatial=20, multichannel=True)

    # make sure noise is reduced in the checkerboard cells
    assert_(img[30:45, 5:15].std() > out1[30:45, 5:15].std())
    assert_(out1[30:45, 5:15].std() > out2[30:45, 5:15].std())
Esempio n. 21
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def test_denoise_tv_chambolle_weighting():
    # make sure a specified weight gives consistent results regardless of
    # the number of input image dimensions
    rstate = np.random.RandomState(1234)
    img2d = astro_gray.copy()
    img2d += 0.15 * rstate.standard_normal(img2d.shape)
    img2d = np.clip(img2d, 0, 1)

    # generate 4D image by tiling
    img4d = np.tile(img2d[..., None, None], (1, 1, 2, 2))

    w = 0.2
    denoised_2d = restoration.denoise_tv_chambolle(img2d, weight=w)
    denoised_4d = restoration.denoise_tv_chambolle(img4d, weight=w)
    assert_(structural_similarity(denoised_2d, denoised_4d[:, :, 0, 0]) > 0.99)
Esempio n. 22
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def test_denoise_nl_means_2d():
    img = np.zeros((40, 40))
    img[10:-10, 10:-10] = 1.
    sigma = 0.3
    img += sigma * np.random.randn(*img.shape)
    for s in [sigma, 0]:
        denoised = restoration.denoise_nl_means(img, 7, 5, 0.2, fast_mode=True,
                                                multichannel=True, sigma=s)
        # make sure noise is reduced
        assert_(img.std() > denoised.std())
        denoised = restoration.denoise_nl_means(img, 7, 5, 0.2,
                                                fast_mode=False,
                                                multichannel=True, sigma=s)
        # make sure noise is reduced
        assert_(img.std() > denoised.std())
Esempio n. 23
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def test_range():
    """Output of edge detection should be in [0, 1]"""
    image = np.random.random((100, 100))
    for detector in (filters.sobel, filters.scharr,
                     filters.prewitt, filters.roberts,
                     filters.farid):
        out = detector(image)
        assert_(out.min() >= 0,
                "Minimum of `{0}` is smaller than zero".format(
                    detector.__name__)
                )
        assert_(out.max() <= 1,
                "Maximum of `{0}` is larger than 1".format(
                    detector.__name__)
                )
Esempio n. 24
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def test_denoise_nl_means_2d():
    img = np.zeros((40, 40))
    img[10:-10, 10:-10] = 1.
    sigma = 0.3
    img += sigma * np.random.randn(*img.shape)
    for s in [sigma, 0]:
        denoised = restoration.denoise_nl_means(img, 7, 5, 0.2, fast_mode=True,
                                                multichannel=True, sigma=s)
        # make sure noise is reduced
        assert_(img.std() > denoised.std())
        denoised = restoration.denoise_nl_means(img, 7, 5, 0.2,
                                                fast_mode=False,
                                                multichannel=True, sigma=s)
        # make sure noise is reduced
        assert_(img.std() > denoised.std())
Esempio n. 25
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def test_denoise_nl_means_3d():
    img = np.zeros((20, 20, 10))
    img[5:-5, 5:-5, 3:-3] = 1.
    sigma = 0.3
    img += sigma * np.random.randn(*img.shape)
    for s in [sigma, 0]:
        denoised = restoration.denoise_nl_means(img, 5, 4, 0.2, fast_mode=True,
                                                multichannel=False, sigma=s)
        # make sure noise is reduced
        assert_(img.std() > denoised.std())
        denoised = restoration.denoise_nl_means(img, 5, 4, 0.2,
                                                fast_mode=False,
                                                multichannel=False, sigma=s)
        # make sure noise is reduced
        assert_(img.std() > denoised.std())
Esempio n. 26
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def test_denoise_nl_means_multichannel():
    img = np.zeros((21, 20, 10))
    img[10, 9:11, 2:-2] = 1.
    img += 0.3 * np.random.randn(*img.shape)
    denoised_wrong_multichannel = restoration.denoise_nl_means(
        img, 5, 4, 0.1, fast_mode=True, multichannel=True)
    denoised_ok_multichannel = restoration.denoise_nl_means(img,
                                                            5,
                                                            4,
                                                            0.1,
                                                            fast_mode=True,
                                                            multichannel=False)
    snr_wrong = 10 * np.log10(1. /
                              ((denoised_wrong_multichannel - img)**2).mean())
    snr_ok = 10 * np.log10(1. / ((denoised_ok_multichannel - img)**2).mean())
    assert_(snr_ok > snr_wrong)
Esempio n. 27
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def test_laplacian_pyramid_max_layers():
    for downscale in [2, 3, 5, 7]:
        img = np.random.randn(32, 8)
        pyramid = pyramids.pyramid_laplacian(img, downscale=downscale,
                                             multichannel=False)
        max_layer = int(np.ceil(math.log(np.max(img.shape), downscale)))
        for layer, out in enumerate(pyramid):
            if layer < max_layer:
                # should not reach all axes as size 1 prior to final level
                assert_(np.max(out.shape) > 1)

        # total number of images is max_layer + 1
        assert_equal(max_layer, layer)

        # final layer should be size 1 on all axes
        assert_array_equal((out.shape), (1, 1))
Esempio n. 28
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def test_clear_border_non_binary_3d():
    image3d = np.array([
        [[1, 2, 3, 1, 2], [3, 3, 3, 4, 2], [3, 4, 3, 4, 2], [3, 3, 2, 1, 2]],
        [[1, 2, 3, 1, 2], [3, 3, 5, 4, 2], [3, 4, 5, 4, 2], [3, 3, 2, 1, 2]],
        [[1, 2, 3, 1, 2], [3, 3, 3, 4, 2], [3, 4, 3, 4, 2], [3, 3, 2, 1, 2]],
    ])

    result = clear_border(image3d)
    expected = np.array([
        [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]],
        [[0, 0, 0, 0, 0], [0, 0, 5, 0, 0], [0, 0, 5, 0, 0], [0, 0, 0, 0, 0]],
        [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]],
    ])

    assert_array_equal(result, expected)
    assert_(not np.all(image3d == result))
Esempio n. 29
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def test_unwrap_3d_all_masked():
    # all elements masked
    image = np.ma.zeros((10, 10, 10))
    image[:] = np.ma.masked
    unwrap = unwrap_phase(image)
    assert_(np.ma.isMaskedArray(unwrap))
    assert_(np.all(unwrap.mask))

    # 1 unmasked element, still zero edges
    image = np.ma.zeros((10, 10, 10))
    image[:] = np.ma.masked
    image[0, 0, 0] = 0
    unwrap = unwrap_phase(image)
    assert_(np.ma.isMaskedArray(unwrap))
    assert_(np.sum(unwrap.mask) == 999)  # all but one masked
    assert_(unwrap[0, 0, 0] == 0)
Esempio n. 30
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def test_denoise_tv_chambolle_weighting():
    # make sure a specified weight gives consistent results regardless of
    # the number of input image dimensions
    rstate = np.random.RandomState(1234)
    img2d = astro_gray.copy()
    img2d += 0.15 * rstate.standard_normal(img2d.shape)
    img2d = np.clip(img2d, 0, 1)

    # generate 4D image by tiling
    img4d = np.tile(img2d[..., None, None], (1, 1, 2, 2))

    w = 0.2
    denoised_2d = restoration.denoise_tv_chambolle(img2d, weight=w)
    denoised_4d = restoration.denoise_tv_chambolle(img4d, weight=w)
    assert_(measure.compare_ssim(denoised_2d,
                                 denoised_4d[:, :, 0, 0]) > 0.99)
Esempio n. 31
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def test_unwrap_3d_all_masked():
    # all elements masked
    image = np.ma.zeros((10, 10, 10))
    image[:] = np.ma.masked
    unwrap = unwrap_phase(image)
    assert_(np.ma.isMaskedArray(unwrap))
    assert_(np.all(unwrap.mask))

    # 1 unmasked element, still zero edges
    image = np.ma.zeros((10, 10, 10))
    image[:] = np.ma.masked
    image[0, 0, 0] = 0
    unwrap = unwrap_phase(image)
    assert_(np.ma.isMaskedArray(unwrap))
    assert_(np.sum(unwrap.mask) == 999)   # all but one masked
    assert_(unwrap[0, 0, 0] == 0)
Esempio n. 32
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def test_wavelet_denoising(img, multichannel, convert2ycbcr):
    rstate = np.random.RandomState(1234)
    sigma = 0.1
    noisy = img + sigma * rstate.randn(*(img.shape))
    noisy = np.clip(noisy, 0, 1)

    channel_axis = -1 if multichannel else None

    # Verify that SNR is improved when true sigma is used
    denoised = restoration.denoise_wavelet(noisy,
                                           sigma=sigma,
                                           channel_axis=channel_axis,
                                           convert2ycbcr=convert2ycbcr,
                                           rescale_sigma=True)
    psnr_noisy = peak_signal_noise_ratio(img, noisy)
    psnr_denoised = peak_signal_noise_ratio(img, denoised)
    assert_(psnr_denoised > psnr_noisy)

    # Verify that SNR is improved with internally estimated sigma
    denoised = restoration.denoise_wavelet(noisy,
                                           channel_axis=channel_axis,
                                           convert2ycbcr=convert2ycbcr,
                                           rescale_sigma=True)
    psnr_noisy = peak_signal_noise_ratio(img, noisy)
    psnr_denoised = peak_signal_noise_ratio(img, denoised)
    assert_(psnr_denoised > psnr_noisy)

    # SNR is improved less with 1 wavelet level than with the default.
    denoised_1 = restoration.denoise_wavelet(noisy,
                                             channel_axis=channel_axis,
                                             wavelet_levels=1,
                                             convert2ycbcr=convert2ycbcr,
                                             rescale_sigma=True)
    psnr_denoised_1 = peak_signal_noise_ratio(img, denoised_1)
    assert_(psnr_denoised > psnr_denoised_1)
    assert_(psnr_denoised_1 > psnr_noisy)

    # Test changing noise_std (higher threshold, so less energy in signal)
    res1 = restoration.denoise_wavelet(noisy,
                                       sigma=2 * sigma,
                                       channel_axis=channel_axis,
                                       rescale_sigma=True)
    res2 = restoration.denoise_wavelet(noisy,
                                       sigma=sigma,
                                       channel_axis=channel_axis,
                                       rescale_sigma=True)
    assert_(np.sum(res1**2) <= np.sum(res2**2))
Esempio n. 33
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def test_denoise_nl_means_3d():
    img = np.zeros((12, 12, 8))
    img[5:-5, 5:-5, 2:-2] = 1.
    sigma = 0.3
    imgn = img + sigma * np.random.randn(*img.shape)
    psnr_noisy = compare_psnr(img, imgn)
    for s in [sigma, 0]:
        denoised = restoration.denoise_nl_means(imgn, 3, 4, h=0.75 * sigma,
                                                fast_mode=True,
                                                multichannel=False, sigma=s)
        # make sure noise is reduced
        assert_(compare_psnr(img, denoised) > psnr_noisy)
        denoised = restoration.denoise_nl_means(imgn, 3, 4, h=0.75 * sigma,
                                                fast_mode=False,
                                                multichannel=False, sigma=s)
        # make sure noise is reduced
        assert_(compare_psnr(img, denoised) > psnr_noisy)
Esempio n. 34
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def test_calibrate_denoiser_extra_output():
    parameter_ranges = {'sigma': np.linspace(0.1, 1, 5) / 2}
    _, (parameters_tested,
        losses) = calibrate_denoiser(noisy_img,
                                     _denoise_wavelet,
                                     denoise_parameters=parameter_ranges,
                                     extra_output=True)

    all_denoised = [
        _invariant_denoise(noisy_img,
                           _denoise_wavelet,
                           denoiser_kwargs=denoiser_kwargs)
        for denoiser_kwargs in parameters_tested
    ]

    ground_truth_losses = [mse(img, test_img) for img in all_denoised]
    assert_(np.argmin(losses) == np.argmin(ground_truth_losses))
Esempio n. 35
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def test_denoise_nl_means_3d():
    img = np.zeros((12, 12, 8))
    img[5:-5, 5:-5, 2:-2] = 1.
    sigma = 0.3
    imgn = img + sigma * np.random.randn(*img.shape)
    psnr_noisy = compare_psnr(img, imgn)
    for s in [sigma, 0]:
        denoised = restoration.denoise_nl_means(imgn, 3, 4, h=0.75 * sigma,
                                                fast_mode=True,
                                                multichannel=False, sigma=s)
        # make sure noise is reduced
        assert_(compare_psnr(img, denoised) > psnr_noisy)
        denoised = restoration.denoise_nl_means(imgn, 3, 4, h=0.75 * sigma,
                                                fast_mode=False,
                                                multichannel=False, sigma=s)
        # make sure noise is reduced
        assert_(compare_psnr(img, denoised) > psnr_noisy)
Esempio n. 36
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def test_denoise_nl_means_3d(fast_mode, dtype):
    img = np.zeros((12, 12, 8), dtype=dtype)
    img[5:-5, 5:-5, 2:-2] = 1.
    sigma = 0.3
    imgn = img + sigma * np.random.randn(*img.shape)
    imgn = imgn.astype(dtype)
    psnr_noisy = peak_signal_noise_ratio(img, imgn)
    for s in [sigma, 0]:
        denoised = restoration.denoise_nl_means(imgn,
                                                3,
                                                4,
                                                h=0.75 * sigma,
                                                fast_mode=fast_mode,
                                                multichannel=False,
                                                sigma=s)
        # make sure noise is reduced
        assert_(peak_signal_noise_ratio(img, denoised) > psnr_noisy)
Esempio n. 37
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def test_denoise_tv_bregman_float_result_range():
    # astronaut image
    img = astro_gray.copy()
    int_astro = np.multiply(img, 255).astype(np.uint8)
    assert_(np.max(int_astro) > 1)
    denoised_int_astro = restoration.denoise_tv_bregman(int_astro, weight=60.0)
    # test if the value range of output float data is within [0.0:1.0]
    assert_(denoised_int_astro.dtype == np.float)
    assert_(np.max(denoised_int_astro) <= 1.0)
    assert_(np.min(denoised_int_astro) >= 0.0)
Esempio n. 38
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def test_denoise_tv_bregman_float_result_range():
    # astronaut image
    img = astro_gray.copy()
    int_astro = np.multiply(img, 255).astype(np.uint8)
    assert_(np.max(int_astro) > 1)
    denoised_int_astro = restoration.denoise_tv_bregman(int_astro, weight=60.0)
    # test if the value range of output float data is within [0.0:1.0]
    assert_(denoised_int_astro.dtype == np.float)
    assert_(np.max(denoised_int_astro) <= 1.0)
    assert_(np.min(denoised_int_astro) >= 0.0)
Esempio n. 39
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def test_denoise_tv_chambolle_float_result_range():
    # astronaut image
    img = astro_grayT
    int_astroT = torch.mul(img, 255).type(torch.uint8)
    assert_(torch.max(int_astroT) > 1)
    denoised_int_astroT = denoise_tv_chambolle_torch(int_astroT, weight=0.1)
    # test if the value range of output float data is within [0.0:1.0]
    assert_((denoised_int_astroT.numpy()).dtype == np.float)
    assert_(torch.max(denoised_int_astroT) <= 1.0)
    assert_(torch.min(denoised_int_astroT) >= 0.0)
Esempio n. 40
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def test_wavelet_denoising_levels():
    rstate = np.random.RandomState(1234)
    ndim = 2
    N = 256
    wavelet = 'db1'
    # Generate a very simple test image
    img = 0.2*np.ones((N, )*ndim)
    img[(slice(5, 13), ) * ndim] = 0.8

    sigma = 0.1
    noisy = img + sigma * rstate.randn(*(img.shape))
    noisy = np.clip(noisy, 0, 1)

    with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
        denoised = restoration.denoise_wavelet(noisy, wavelet=wavelet)
    denoised_1 = restoration.denoise_wavelet(noisy, wavelet=wavelet,
                                             wavelet_levels=1)
    psnr_noisy = compare_psnr(img, noisy)
    psnr_denoised = compare_psnr(img, denoised)
    psnr_denoised_1 = compare_psnr(img, denoised_1)

    # multi-level case should outperform single level case
    assert_(psnr_denoised > psnr_denoised_1 > psnr_noisy)

    # invalid number of wavelet levels results in a ValueError or UserWarning
    max_level = pywt.dwt_max_level(np.min(img.shape),
                                   pywt.Wavelet(wavelet).dec_len)
    if Version(pywt.__version__) < '1.0.0':
        # exceeding max_level raises a ValueError in PyWavelets 0.4-0.5.2
        with testing.raises(ValueError):
            with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
                restoration.denoise_wavelet(
                    noisy, wavelet=wavelet, wavelet_levels=max_level + 1)
    else:
        # exceeding max_level raises a UserWarning in PyWavelets >= 1.0.0
        with expected_warnings([
                'all coefficients will experience boundary effects']):
            restoration.denoise_wavelet(
                noisy, wavelet=wavelet, wavelet_levels=max_level + 1)

    with testing.raises(ValueError):
        with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
            restoration.denoise_wavelet(
                noisy,
                wavelet=wavelet, wavelet_levels=-1)
Esempio n. 41
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def test_unwrap_2d_all_masked():
    # Segmentation fault when image is masked array with a all elements masked
    # GitHub issue #1347
    # all elements masked
    image = np.ma.zeros((10, 10))
    image[:] = np.ma.masked
    unwrap = unwrap_phase(image)
    assert_(np.ma.isMaskedArray(unwrap))
    assert_(np.all(unwrap.mask))

    # 1 unmasked element, still zero edges
    image = np.ma.zeros((10, 10))
    image[:] = np.ma.masked
    image[0, 0] = 0
    unwrap = unwrap_phase(image)
    assert_(np.ma.isMaskedArray(unwrap))
    assert_(np.sum(unwrap.mask) == 99)    # all but one masked
    assert_(unwrap[0, 0] == 0)
Esempio n. 42
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def test_unwrap_2d_all_masked():
    # Segmentation fault when image is masked array with a all elements masked
    # GitHub issue #1347
    # all elements masked
    image = np.ma.zeros((10, 10))
    image[:] = np.ma.masked
    unwrap = unwrap_phase(image)
    assert_(np.ma.isMaskedArray(unwrap))
    assert_(np.all(unwrap.mask))

    # 1 unmasked element, still zero edges
    image = np.ma.zeros((10, 10))
    image[:] = np.ma.masked
    image[0, 0] = 0
    unwrap = unwrap_phase(image)
    assert_(np.ma.isMaskedArray(unwrap))
    assert_(np.sum(unwrap.mask) == 99)  # all but one masked
    assert_(unwrap[0, 0] == 0)
Esempio n. 43
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def test_denoise_nl_means_2drgb():
    # reduce image size because nl means is very slow
    img = np.copy(astro[:50, :50])
    # add some random noise
    sigma = 0.5
    img += sigma * img.std() * np.random.random(img.shape)
    img = np.clip(img, 0, 1)
    for s in [sigma, 0]:
        denoised = restoration.denoise_nl_means(img, 7, 9, 0.3,
                                                fast_mode=True,
                                                multichannel=True, sigma=s)
        # make sure noise is reduced
        assert_(img.std() > denoised.std())
        denoised = restoration.denoise_nl_means(img, 7, 9, 0.3,
                                                fast_mode=False,
                                                multichannel=True, sigma=s)
        # make sure noise is reduced
        assert_(img.std() > denoised.std())
Esempio n. 44
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def test_denoise_tv_chambolle_weighting():
    # make sure a specified weight gives consistent results regardless of
    # the number of input image dimensions
    rstate = np.random.RandomState(1234)
    img2d = astro_gray.copy()
    img2d += 0.15 * rstate.standard_normal(img2d.shape)
    img2d = np.clip(img2d, 0, 1)

    # generate 4D image by tiling
    img4d = np.tile(img2d[..., None, None], (1, 1, 2, 2))
    img2dT = torch.tensor(img2d)
    img4dT = torch.tensor(img4d)
    w = 0.2
    denoised_2d = denoise_tv_chambolle_torch(img2dT, weight=w)
    denoised_4d = denoise_tv_chambolle_torch(img4dT, weight=w)
    assert_(
        measure.compare_ssim(denoised_2d.numpy(),
                             denoised_4d[:, :, 0, 0].numpy()) > 0.99)
Esempio n. 45
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def test_wavelet_denoising_channel_axis(channel_axis, convert2ycbcr):
    rstate = np.random.RandomState(1234)
    sigma = 0.1
    img = astro_odd
    noisy = img + sigma * rstate.randn(*(img.shape))
    noisy = np.clip(noisy, 0, 1)

    img = np.moveaxis(img, -1, channel_axis)
    noisy = np.moveaxis(noisy, -1, channel_axis)

    # Verify that SNR is improved when true sigma is used
    denoised = restoration.denoise_wavelet(noisy,
                                           sigma=sigma,
                                           channel_axis=channel_axis,
                                           convert2ycbcr=convert2ycbcr,
                                           rescale_sigma=True)
    psnr_noisy = peak_signal_noise_ratio(img, noisy)
    psnr_denoised = peak_signal_noise_ratio(img, denoised)
    assert_(psnr_denoised > psnr_noisy)
Esempio n. 46
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def assert_phase_almost_equal(a, b, *args, **kwargs):
    """An assert_almost_equal insensitive to phase shifts of n*2*pi."""
    shift = 2 * np.pi * np.round((b.mean() - a.mean()) / (2 * np.pi))
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        print('assert_phase_allclose, abs', np.max(np.abs(a - (b - shift))))
        print('assert_phase_allclose, rel',
              np.max(np.abs((a - (b - shift)) / a)))
    if np.ma.isMaskedArray(a):
        assert_(np.ma.isMaskedArray(b))
        assert_array_equal(a.mask, b.mask)
        au = np.asarray(a)
        bu = np.asarray(b)
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            print('assert_phase_allclose, no mask, abs',
                  np.max(np.abs(au - (bu - shift))))
            print('assert_phase_allclose, no mask, rel',
                  np.max(np.abs((au - (bu - shift)) / au)))
    assert_array_almost_equal_nulp(a + shift, b, *args, **kwargs)
Esempio n. 47
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def test_denoise_bilateral_multichannel_deprecation():
    img = checkerboard.copy()[:50, :50]
    # add some random noise
    img += 0.5 * img.std() * np.random.rand(*img.shape)
    img = np.clip(img, 0, 1)

    with expected_warnings(["`multichannel` is a deprecated argument"]):
        out1 = restoration.denoise_bilateral(img,
                                             sigma_color=0.1,
                                             sigma_spatial=10,
                                             multichannel=True)
    with expected_warnings(["`multichannel` is a deprecated argument"]):
        out2 = restoration.denoise_bilateral(img,
                                             sigma_color=0.2,
                                             sigma_spatial=20,
                                             multichannel=True)

    # make sure noise is reduced in the checkerboard cells
    assert_(img[30:45, 5:15].std() > out1[30:45, 5:15].std())
    assert_(out1[30:45, 5:15].std() > out2[30:45, 5:15].std())
Esempio n. 48
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def test_wavelet_denoising_nd():
    rstate = np.random.RandomState(1234)
    for method in ['VisuShrink', 'BayesShrink']:
        for ndim in range(1, 5):
            # Generate a very simple test image
            if ndim < 3:
                img = 0.2*np.ones((128, )*ndim)
            else:
                img = 0.2*np.ones((16, )*ndim)
            img[[slice(5, 13), ] * ndim] = 0.8

            sigma = 0.1
            noisy = img + sigma * rstate.randn(*(img.shape))
            noisy = np.clip(noisy, 0, 1)

            # Verify that SNR is improved with internally estimated sigma
            denoised = restoration.denoise_wavelet(noisy, method=method)
            psnr_noisy = compare_psnr(img, noisy)
            psnr_denoised = compare_psnr(img, denoised)
            assert_(psnr_denoised > psnr_noisy)
Esempio n. 49
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def assert_phase_almost_equal(a, b, *args, **kwargs):
    """An assert_almost_equal insensitive to phase shifts of n*2*pi."""
    shift = 2 * np.pi * np.round((b.mean() - a.mean()) / (2 * np.pi))
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        print('assert_phase_allclose, abs', np.max(np.abs(a - (b - shift))))
        print('assert_phase_allclose, rel',
              np.max(np.abs((a - (b - shift)) / a)))
    if np.ma.isMaskedArray(a):
        assert_(np.ma.isMaskedArray(b))
        assert_array_equal(a.mask, b.mask)
        au = np.asarray(a)
        bu = np.asarray(b)
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            print('assert_phase_allclose, no mask, abs',
                  np.max(np.abs(au - (bu - shift))))
            print('assert_phase_allclose, no mask, rel',
                  np.max(np.abs((au - (bu - shift)) / au)))
    assert_array_almost_equal_nulp(a + shift, b, *args, **kwargs)
Esempio n. 50
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def test_wavelet_denoising_nd():
    rstate = np.random.RandomState(1234)
    for method in ['VisuShrink', 'BayesShrink']:
        for ndim in range(1, 5):
            # Generate a very simple test image
            if ndim < 3:
                img = 0.2*np.ones((128, )*ndim)
            else:
                img = 0.2*np.ones((16, )*ndim)
            img[[slice(5, 13), ] * ndim] = 0.8

            sigma = 0.1
            noisy = img + sigma * rstate.randn(*(img.shape))
            noisy = np.clip(noisy, 0, 1)

            # Verify that SNR is improved with internally estimated sigma
            denoised = restoration.denoise_wavelet(noisy, method=method)
            psnr_noisy = compare_psnr(img, noisy)
            psnr_denoised = compare_psnr(img, denoised)
            assert_(psnr_denoised > psnr_noisy)
Esempio n. 51
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def assert_phase_almost_equal(a, b, *args, **kwargs):
    """An assert_almost_equal insensitive to phase shifts of n*2*pi."""
    shift = 2 * np.pi * np.round((b.mean() - a.mean()) / (2 * np.pi))
    with expected_warnings([r'invalid value encountered|\A\Z',
                            r'divide by zero encountered|\A\Z']):
        print('assert_phase_allclose, abs', np.max(np.abs(a - (b - shift))))
        print('assert_phase_allclose, rel',
              np.max(np.abs((a - (b - shift)) / a)))
    if np.ma.isMaskedArray(a):
        assert_(np.ma.isMaskedArray(b))
        assert_array_equal(a.mask, b.mask)
        assert_(a.fill_value == b.fill_value)
        au = np.asarray(a)
        bu = np.asarray(b)
        with expected_warnings([r'invalid value encountered|\A\Z',
                                r'divide by zero encountered|\A\Z']):
            print('assert_phase_allclose, no mask, abs',
                  np.max(np.abs(au - (bu - shift))))
            print('assert_phase_allclose, no mask, rel',
                  np.max(np.abs((au - (bu - shift)) / au)))
    assert_array_almost_equal_nulp(a + shift, b, *args, **kwargs)
Esempio n. 52
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def test_denoise_nl_means_2d_multichannel():
    # reduce image size because nl means is slow
    img = np.copy(astro[:50, :50])
    img = np.concatenate((img, ) * 2, )  # 6 channels

    # add some random noise
    sigma = 0.1
    imgn = img + sigma * np.random.standard_normal(img.shape)
    imgn = np.clip(imgn, 0, 1)
    for fast_mode in [True, False]:
        for s in [sigma, 0]:
            for n_channels in [2, 3, 6]:
                psnr_noisy = compare_psnr(img[..., :n_channels],
                                          imgn[..., :n_channels])
                denoised = restoration.denoise_nl_means(imgn[..., :n_channels],
                                                        3, 5, h=0.75 * sigma,
                                                        fast_mode=fast_mode,
                                                        multichannel=True,
                                                        sigma=s)
                psnr_denoised = compare_psnr(denoised[..., :n_channels],
                                             img[..., :n_channels])
                # make sure noise is reduced
                assert_(psnr_denoised > psnr_noisy)
Esempio n. 53
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def test_denoise_nl_means_2d_multichannel():
    # reduce image size because nl means is slow
    img = np.copy(astro[:50, :50])
    img = np.concatenate((img, ) * 2, )  # 6 channels

    # add some random noise
    sigma = 0.1
    imgn = img + sigma * np.random.standard_normal(img.shape)
    imgn = np.clip(imgn, 0, 1)
    for fast_mode in [True, False]:
        for s in [sigma, 0]:
            for n_channels in [2, 3, 6]:
                psnr_noisy = compare_psnr(img[..., :n_channels],
                                          imgn[..., :n_channels])
                denoised = restoration.denoise_nl_means(imgn[..., :n_channels],
                                                        3, 5, h=0.75 * sigma,
                                                        fast_mode=fast_mode,
                                                        multichannel=True,
                                                        sigma=s)
                psnr_denoised = compare_psnr(denoised[..., :n_channels],
                                             img[..., :n_channels])
                # make sure noise is reduced
                assert_(psnr_denoised > psnr_noisy)
Esempio n. 54
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def test_wavelet_threshold():
    rstate = np.random.RandomState(1234)

    img = astro_gray
    sigma = 0.1
    noisy = img + sigma * rstate.randn(*(img.shape))
    noisy = np.clip(noisy, 0, 1)

    # employ a single, user-specified threshold instead of BayesShrink sigmas
    denoised = _wavelet_threshold(noisy, wavelet='db1', method=None,
                                  threshold=sigma)
    psnr_noisy = compare_psnr(img, noisy)
    psnr_denoised = compare_psnr(img, denoised)
    assert_(psnr_denoised > psnr_noisy)

    # either method or threshold must be defined
    with testing.raises(ValueError):
        _wavelet_threshold(noisy, wavelet='db1', method=None, threshold=None)

    # warns if a threshold is provided in a case where it would be ignored
    with expected_warnings(["Thresholding method "]):
        _wavelet_threshold(noisy, wavelet='db1', method='BayesShrink',
                           threshold=sigma)
Esempio n. 55
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def test_denoise_nl_means_2d(fast_mode):
    img = np.zeros((40, 40))
    img[10:-10, 10:-10] = 1.
    sigma = 0.3
    img += sigma * np.random.randn(*img.shape)
    img_f32 = img.astype('float32')
    for s in [sigma, 0]:
        denoised = restoration.denoise_nl_means(img, 7, 5, 0.2,
                                                fast_mode=fast_mode,
                                                multichannel=False,
                                                sigma=s)
        # make sure noise is reduced
        assert_(img.std() > denoised.std())

        denoised_f32 = restoration.denoise_nl_means(img_f32, 7, 5, 0.2,
                                                    fast_mode=fast_mode,
                                                    multichannel=False,
                                                    sigma=s)
        # make sure noise is reduced
        assert_(img.std() > denoised_f32.std())

        # Sheck single precision result
        assert np.allclose(denoised_f32, denoised, atol=1e-2)
Esempio n. 56
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def test_denoise_tv_chambolle_2d():
    # astronaut image
    img = astro_gray.copy()
    # add noise to astronaut
    img += 0.5 * img.std() * np.random.rand(*img.shape)
    # clip noise so that it does not exceed allowed range for float images.
    img = np.clip(img, 0, 1)
    # denoise
    denoised_astro = restoration.denoise_tv_chambolle(img, weight=0.1)
    # which dtype?
    assert_(denoised_astro.dtype in [np.float, np.float32, np.float64])
    from scipy import ndimage as ndi
    grad = ndi.morphological_gradient(img, size=((3, 3)))
    grad_denoised = ndi.morphological_gradient(denoised_astro, size=((3, 3)))
    # test if the total variation has decreased
    assert_(grad_denoised.dtype == np.float)
    assert_(np.sqrt((grad_denoised**2).sum()) < np.sqrt((grad**2).sum()))
Esempio n. 57
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def test_unwrap_3d_middle_wrap_around():
    # Segmentation fault in 3D unwrap phase with middle dimension connected
    # GitHub issue #1171
    image = np.zeros((20, 30, 40), dtype=np.float32)
    unwrap = unwrap_phase(image, wrap_around=[False, True, False])
    assert_(np.all(unwrap == 0))
Esempio n. 58
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def test_unwrap_2d_compressed_mask():
    # ValueError when image is masked array with a compressed mask (no masked
    # elments).  GitHub issue #1346
    image = np.ma.zeros((10, 10))
    unwrap = unwrap_phase(image)
    assert_(np.all(unwrap == 0))
Esempio n. 59
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def test_denoise_tv_chambolle_4d():
    """ TV denoising for a 4D input."""
    im = 255 * np.random.rand(8, 8, 8, 8)
    res = restoration.denoise_tv_chambolle(im.astype(np.uint8), weight=0.1)
    assert_(res.dtype == np.float)
    assert_(res.std() * 255 < im.std())
Esempio n. 60
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def test_cycle_spinning_multichannel():
    sigma = 0.1
    rstate = np.random.RandomState(1234)

    for multichannel in True, False:
        if multichannel:
            img = astro
            # can either omit or be 0 along the channels axis
            valid_shifts = [1, (0, 1), (1, 0), (1, 1), (1, 1, 0)]
            # can either omit or be 1 on channels axis.
            valid_steps = [1, 2, (1, 2), (1, 2, 1)]
            # too few or too many shifts or non-zero shift on channels
            invalid_shifts = [(1, 1, 2), (1, ), (1, 1, 0, 1)]
            # too few or too many shifts or any shifts <= 0
            invalid_steps = [(1, ), (1, 1, 1, 1), (0, 1), (-1, -1)]
        else:
            img = astro_gray
            valid_shifts = [1, (0, 1), (1, 0), (1, 1)]
            valid_steps = [1, 2, (1, 2)]
            invalid_shifts = [(1, 1, 2), (1, )]
            invalid_steps = [(1, ), (1, 1, 1), (0, 1), (-1, -1)]

        noisy = img.copy() + 0.1 * rstate.randn(*(img.shape))

        denoise_func = restoration.denoise_wavelet
        func_kw = dict(sigma=sigma, multichannel=multichannel)

        # max_shifts=0 is equivalent to just calling denoise_func
        with expected_warnings([PYWAVELET_ND_INDEXING_WARNING,
                                DASK_NOT_INSTALLED_WARNING]):
            dn_cc = restoration.cycle_spin(noisy, denoise_func, max_shifts=0,
                                           func_kw=func_kw,
                                           multichannel=multichannel)
            dn = denoise_func(noisy, **func_kw)
        assert_equal(dn, dn_cc)

        # denoising with cycle spinning will give better PSNR than without
        for max_shifts in valid_shifts:
            with expected_warnings([PYWAVELET_ND_INDEXING_WARNING,
                                    DASK_NOT_INSTALLED_WARNING]):
                dn_cc = restoration.cycle_spin(noisy, denoise_func,
                                               max_shifts=max_shifts,
                                               func_kw=func_kw,
                                               multichannel=multichannel)
            assert_(compare_psnr(img, dn_cc) > compare_psnr(img, dn))

        for shift_steps in valid_steps:
            with expected_warnings([PYWAVELET_ND_INDEXING_WARNING,
                                    DASK_NOT_INSTALLED_WARNING]):
                dn_cc = restoration.cycle_spin(noisy, denoise_func,
                                               max_shifts=2,
                                               shift_steps=shift_steps,
                                               func_kw=func_kw,
                                               multichannel=multichannel)
            assert_(compare_psnr(img, dn_cc) > compare_psnr(img, dn))

        for max_shifts in invalid_shifts:
            with testing.raises(ValueError):
                dn_cc = restoration.cycle_spin(noisy, denoise_func,
                                               max_shifts=max_shifts,
                                               func_kw=func_kw,
                                               multichannel=multichannel)
        for shift_steps in invalid_steps:
            with testing.raises(ValueError):
                dn_cc = restoration.cycle_spin(noisy, denoise_func,
                                               max_shifts=2,
                                               shift_steps=shift_steps,
                                               func_kw=func_kw,
                                               multichannel=multichannel)