from os.path import abspath, dirname, join as pjoin

import numpy as np
from scipy.signal import convolve2d
from scipy import ndimage as nd

import skimage
from skimage.data import camera
from skimage import restoration
from skimage.restoration import uft

test_img = skimage.img_as_float(camera())


def test_wiener():
    psf = np.ones((5, 5)) / 25
    data = convolve2d(test_img, psf, 'same')
    np.random.seed(0)
    data += 0.1 * data.std() * np.random.standard_normal(data.shape)
    deconvolved = restoration.wiener(data, psf, 0.05)

    path = pjoin(dirname(abspath(__file__)), 'camera_wiener.npy')
    np.testing.assert_allclose(deconvolved, np.load(path), rtol=1e-3)

    _, laplacian = uft.laplacian(2, data.shape)
    otf = uft.ir2tf(psf, data.shape, is_real=False)
    deconvolved = restoration.wiener(data, otf, 0.05,
                                     reg=laplacian,
                                     is_real=False)
    np.testing.assert_allclose(np.real(deconvolved),
                               np.load(path),
                               rtol=1e-3)


def test_unsupervised_wiener():
    psf = np.ones((5, 5)) / 25
    data = convolve2d(test_img, psf, 'same')
    np.random.seed(0)
    data += 0.1 * data.std() * np.random.standard_normal(data.shape)
    deconvolved, _ = restoration.unsupervised_wiener(data, psf)

    path = pjoin(dirname(abspath(__file__)), 'camera_unsup.npy')
    np.testing.assert_allclose(deconvolved, np.load(path), rtol=1e-3)

    _, laplacian = uft.laplacian(2, data.shape)
    otf = uft.ir2tf(psf, data.shape, is_real=False)
    np.random.seed(0)
    deconvolved = restoration.unsupervised_wiener(
        data, otf, reg=laplacian, is_real=False,
        user_params={"callback": lambda x: None})[0]
    path = pjoin(dirname(abspath(__file__)), 'camera_unsup2.npy')
    np.testing.assert_allclose(np.real(deconvolved),
                               np.load(path),
                               rtol=1e-3)


def test_image_shape():
    """Test that shape of output image in deconvolution is same as input.

    This addresses issue #1172.
    """
    point = np.zeros((5, 5), np.float)
    point[2, 2] = 1.
    psf = nd.gaussian_filter(point, sigma=1.)
    # image shape: (45, 45), as reported in #1172
    image = skimage.img_as_float(camera()[110:155, 225:270]) # just the face
    image_conv = nd.convolve(image, psf)
    deconv_sup = restoration.wiener(image_conv, psf, 1)
    deconv_un = restoration.unsupervised_wiener(image_conv, psf)[0]
    # test the shape
    np.testing.assert_equal(image.shape, deconv_sup.shape)
    np.testing.assert_equal(image.shape, deconv_un.shape)
    # test the reconstruction error
    sup_relative_error = np.abs(deconv_sup - image) / image
    un_relative_error = np.abs(deconv_un - image) / image
    np.testing.assert_array_less(np.median(sup_relative_error), 0.1)
    np.testing.assert_array_less(np.median(un_relative_error), 0.1)


def test_richardson_lucy():
    psf = np.ones((5, 5)) / 25
    data = convolve2d(test_img, psf, 'same')
    np.random.seed(0)
    data += 0.1 * data.std() * np.random.standard_normal(data.shape)
    deconvolved = restoration.richardson_lucy(data, psf, 5)

    path = pjoin(dirname(abspath(__file__)), 'camera_rl.npy')
    np.testing.assert_allclose(deconvolved, np.load(path), rtol=1e-3)


if __name__ == '__main__':
    from numpy import testing
    testing.run_module_suite()
