def test_varconvolve_disk(self):
        n = 4
        shape = (n, n)
        image = np.zeros(shape, dtype=cfg.PRECISION.np_float)
        image[n / 2, n / 2] = 1
        radii = np.ones_like(image) * 1e-3
        result = ip.varconvolve_disk(image, (radii, radii), normalized=False, smooth=False).get()
        # At least one pixel in the midle must exist (just copies the original image)
        self.assertEqual(1, np.sum(result))

        radii = np.ones_like(image) * 2
        norm_result = ip.varconvolve_disk(image, (radii, radii), normalized=True,
                                          smooth=False).get()
        self.assertAlmostEqual(1, np.sum(norm_result))
Пример #2
0
def main():
    args = parse_args()
    syris.init(device_index=0)
    m = 20

    if args.input == 'grid':
        image = make_grid(args.n, m * q.m).thickness.get()
    elif args.input == 'lena':
        from scipy.misc import lena
        image = lena().astype(cfg.PRECISION.np_float)
        if args.n != image.shape[0]:
            image = gutil.get_host(ip.rescale(image, (args.n, args.n)))

    n = image.shape[0]
    crop_n = n - 2 * m - 2
    y, x = np.mgrid[-n / 2:n / 2, -n / 2:n / 2]
    # Compute a such that the disk diameter is exactly the period when distance from the middle is n
    # / 2
    a = m / (2 * (crop_n / 2.)**2)
    radii = (a * np.sqrt(x**2 + y**2)**2 + 1e-3).astype(cfg.PRECISION.np_float)
    x_param = radii
    y_param = radii

    result = ip.varconvolve_disk(image, (y_param, x_param)).get()
    result = ip.crop(result, (m - 1, m - 1, crop_n, crop_n)).get()
    radii = ip.crop(radii, (m - 1, m - 1, crop_n, crop_n)).get()
    image = ip.crop(image, (m - 1, m - 1, crop_n, crop_n)).get()

    if args.output:
        save_image(args.output, result)

    show(image, title='Original Image')
    show(2 * radii, title='Blurring Disk Diameters')
    show(result, title='Blurred Image')
    plt.show()
Пример #3
0
def main():
    args = parse_args()
    syris.init(device_index=0)
    m = 20

    if args.input == 'grid':
        image = make_grid(args.n, m * q.m).thickness.get()
    elif args.input == 'lena':
        from scipy.misc import lena
        image = lena().astype(cfg.PRECISION.np_float)
        if args.n != image.shape[0]:
            image = gutil.get_host(ip.rescale(image, (args.n, args.n)))

    n = image.shape[0]
    crop_n = n - 2 * m - 2
    y, x = np.mgrid[-n / 2:n / 2, -n / 2:n / 2]
    # Compute a such that the disk diameter is exactly the period when distance from the middle is n
    # / 2
    a = m / (2 * (crop_n / 2.) ** 2)
    radii = (a * np.sqrt(x ** 2 + y ** 2) ** 2 + 1e-3).astype(cfg.PRECISION.np_float)
    x_param = radii
    y_param = radii

    result = ip.varconvolve_disk(image, (y_param, x_param)).get()
    result = ip.crop(result, (m - 1, m - 1, crop_n, crop_n)).get()
    radii = ip.crop(radii, (m - 1, m - 1, crop_n, crop_n)).get()
    image = ip.crop(image, (m - 1, m - 1, crop_n, crop_n)).get()

    if args.output:
        save_image(args.output, result)

    show(image, title='Original Image')
    show(2 * radii, title='Blurring Disk Diameters')
    show(result, title='Blurred Image')
    plt.show()