Python scipy.ndimage.filters.gaussian_laplace() Examples

The following are 3 code examples of scipy.ndimage.filters.gaussian_laplace(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module scipy.ndimage.filters , or try the search function .
Example #1
Source File: sct_maths.py    From spinalcordtoolbox with MIT License 5 votes vote down vote up 
def laplacian(data, sigmas):
    """
    Apply Laplacian filter
    """
    assert len(data.shape) == len(sigmas)
    from scipy.ndimage.filters import gaussian_laplace
    return gaussian_laplace(data.astype(float), sigmas)
    # from scipy.ndimage.filters import laplace
    # return laplace(data.astype(float))
Example #2
Source File: test_kernel_class.py    From Carnets with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def test_scipy_filter_gaussian_laplace(self, width):
        """
        Test RickerWavelet kernels against SciPy ndimage gaussian laplace filters.
        """
        ricker_kernel_1D = RickerWavelet1DKernel(width)
        ricker_kernel_2D = RickerWavelet2DKernel(width)

        astropy_1D = convolve(delta_pulse_1D, ricker_kernel_1D, boundary='fill', normalize_kernel=False)
        astropy_2D = convolve(delta_pulse_2D, ricker_kernel_2D, boundary='fill', normalize_kernel=False)

        with pytest.raises(Exception) as exc:
            astropy_1D = convolve(delta_pulse_1D, ricker_kernel_1D, boundary='fill', normalize_kernel=True)
        assert 'sum is close to zero' in exc.value.args[0]

        with pytest.raises(Exception) as exc:
            astropy_2D = convolve(delta_pulse_2D, ricker_kernel_2D, boundary='fill', normalize_kernel=True)
        assert 'sum is close to zero' in exc.value.args[0]

        # The Laplace of Gaussian filter is an inverted Ricker Wavelet filter.
        scipy_1D = -filters.gaussian_laplace(delta_pulse_1D, width)
        scipy_2D = -filters.gaussian_laplace(delta_pulse_2D, width)

        # There is a slight deviation in the normalization. They differ by a
        # factor of ~1.0000284132604045. The reason is not known.
        assert_almost_equal(astropy_1D, scipy_1D, decimal=5)
        assert_almost_equal(astropy_2D, scipy_2D, decimal=5)
Example #3
Source File: descriptors.py    From pyImSegm with BSD 3-Clause "New" or "Revised" License 4 votes vote down vote up 
def create_filter_bank_lm_2d(radius=16, sigmas=DEFAULT_FILTERS_SIGMAS,
                             nb_orient=8):
    """ create filter bank with  rotation, Gaussian, Laplace-Gaussian, ...

    :param radius:
    :param sigmas:
    :param nb_orient:
    :return np.ndarray<nb_samples, nb_features>, list(str):

    >>> filters, names = create_filter_bank_lm_2d(6, SHORT_FILTERS_SIGMAS, 2)
    >>> [f.shape for f in filters]  # doctest: +NORMALIZE_WHITESPACE
    [(2, 13, 13), (2, 13, 13), (1, 13, 13), (1, 13, 13), (1, 13, 13),
     (2, 13, 13), (2, 13, 13), (1, 13, 13), (1, 13, 13), (1, 13, 13),
     (2, 13, 13), (2, 13, 13), (1, 13, 13), (1, 13, 13), (1, 13, 13)]
    >>> names  # doctest: +NORMALIZE_WHITESPACE
    ['sigma1.4-edge', 'sigma1.4-bar',
     'sigma1.4-Gauss', 'sigma1.4-GaussLap', 'sigma1.4-GaussLap2',
     'sigma2.0-edge', 'sigma2.0-bar',
     'sigma2.0-Gauss', 'sigma2.0-GaussLap', 'sigma2.0-GaussLap2',
     'sigma4.0-edge', 'sigma4.0-bar',
     'sigma4.0-Gauss', 'sigma4.0-GaussLap', 'sigma4.0-GaussLap2']
    """
    logging.debug('creating Leung-Malik filter bank')
    support = 2 * radius + 1
    x, y = np.mgrid[-radius:radius + 1, radius:-radius - 1:-1]
    org_pts = np.vstack([x.ravel(), y.ravel()])
    a = np.zeros((support, support))
    a[radius, radius] = 1

    filters, names = [], []
    for sigma in sigmas:
        orient_edge, orient_bar = [], []
        for orient in range(nb_orient):
            # Not 2pi as filters have symmetry
            angle = np.pi * orient / nb_orient
            c, s = np.cos(angle), np.sin(angle)
            rot_points = np.dot(np.array([[c, -s], [s, c]]), org_pts)
            orient_edge.append(make_edge_filter2d(sigma, 1, rot_points, support))
            orient_bar.append(make_edge_filter2d(sigma, 2, rot_points, support))
        filters.append(np.asarray(orient_edge))
        filters.append(np.asarray(orient_bar))

        filters.append(gaussian_filter(a, sigma)[np.newaxis, :, :])
        filters.append(gaussian_laplace(a, sigma)[np.newaxis, :, :])
        filters.append(gaussian_laplace(a, sigma ** 2)[np.newaxis, :, :])
        names += ['sigma%.1f-%s' % (sigma, n)
                  for n in ['edge', 'bar', 'Gauss', 'GaussLap', 'GaussLap2']]
    return filters, names