scipy.ndimage.gaussian_filter

All Samples(273) | Call(233) | Derive(0) | Import(40)

src/n/i/NiPy-OLD-HEAD/examples/neurospin/neurospy/FSL_pre_processing.py NiPy-OLD(Download)

    sigma = fwhm/(voxel_width*2*sqrt(2*log(2)))
    for i in fmri.data:
        sn.gaussian_filter(i, sigma, order=0, output=None,
                           mode='reflect', cval=0.0)
    fmri.save(outputFile)

src/s/c/scikits-image-0.7.1/doc/source/auto_examples/plot_random_walker_segmentation.py scikits-image(Download)

    points = l * generator.rand(2, n ** 2)
    mask[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
    mask = ndimage.gaussian_filter(mask, sigma=l / (4. * n))
    return (mask > mask.mean()).astype(np.float)

src/s/c/scikits-image-0.7.1/doc/source/auto_examples/plot_medial_transform.py scikits-image(Download)

    points = l * generator.rand(2, n**2)
    mask[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
    mask = ndimage.gaussian_filter(mask, sigma=l/(4.*n))
    return mask > mask.mean()

src/s/c/scikits-image-0.7.1/doc/examples/plot_random_walker_segmentation.py scikits-image(Download)

    points = l * generator.rand(2, n ** 2)
    mask[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
    mask = ndimage.gaussian_filter(mask, sigma=l / (4. * n))
    return (mask > mask.mean()).astype(np.float)

src/s/c/scikits-image-0.7.1/doc/examples/plot_medial_transform.py scikits-image(Download)

    points = l * generator.rand(2, n**2)
    mask[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
    mask = ndimage.gaussian_filter(mask, sigma=l/(4.*n))
    return mask > mask.mean()

src/s/c/scikit-image-0.8.2/doc/examples/plot_random_walker_segmentation.py scikit-image(Download)

    points = l * generator.rand(2, n ** 2)
    mask[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
    mask = ndimage.gaussian_filter(mask, sigma=l / (4. * n))
    return (mask > mask.mean()).astype(np.float)

src/s/c/scikit-image-0.8.2/doc/examples/plot_medial_transform.py scikit-image(Download)

    points = l * generator.rand(2, n**2)
    mask[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
    mask = ndimage.gaussian_filter(mask, sigma=l/(4.*n))
    return mask > mask.mean()

src/s/c/scikit-learn-0.13.1/examples/applications/plot_tomography_l1_reconstruction.py scikit-learn(Download)

    points = l * rs.rand(2, n_pts)
    mask[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
    mask = ndimage.gaussian_filter(mask, sigma=l / n_pts)
    res = np.logical_and(mask > mask.mean(), mask_outer)
    return res - ndimage.binary_erosion(res)

src/s/c/scikits-image-0.7.1/doc/source/auto_examples/plot_canny.py scikits-image(Download)

 
im = ndimage.rotate(im, 15, mode='constant')
im = ndimage.gaussian_filter(im, 4)
im += 0.2 * np.random.random(im.shape)

src/s/c/scikits-image-0.7.1/doc/examples/plot_canny.py scikits-image(Download)

 
im = ndimage.rotate(im, 15, mode='constant')
im = ndimage.gaussian_filter(im, 4)
im += 0.2 * np.random.random(im.shape)

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