import itertools
import warnings

import numpy as np
import pytest
from numpy.testing import assert_allclose
from scipy.ndimage import fourier_shift
import scipy.fft as fft

from skimage import img_as_float
from skimage._shared._warnings import expected_warnings
from skimage._shared.utils import _supported_float_type
from skimage.data import camera, binary_blobs, eagle
from skimage.registration._phase_cross_correlation import (
    phase_cross_correlation, _upsampled_dft
)


@pytest.mark.parametrize('normalization', [None, 'phase'])
def test_correlation(normalization):
    reference_image = fft.fftn(camera())
    shift = (-7, 12)
    shifted_image = fourier_shift(reference_image, shift)

    # pixel precision
    result, _, _ = phase_cross_correlation(reference_image,
                                           shifted_image,
                                           space="fourier",
                                           normalization=normalization)
    assert_allclose(result[:2], -np.array(shift))


@pytest.mark.parametrize('normalization', ['nonexisting'])
def test_correlation_invalid_normalization(normalization):
    reference_image = fft.fftn(camera())
    shift = (-7, 12)
    shifted_image = fourier_shift(reference_image, shift)

    # pixel precision
    with pytest.raises(ValueError):
        phase_cross_correlation(reference_image,
                                shifted_image,
                                space="fourier",
                                normalization=normalization)


@pytest.mark.parametrize('normalization', [None, 'phase'])
def test_subpixel_precision(normalization):
    reference_image = fft.fftn(camera())
    subpixel_shift = (-2.4, 1.32)
    shifted_image = fourier_shift(reference_image, subpixel_shift)

    # subpixel precision
    result, _, _ = phase_cross_correlation(reference_image,
                                           shifted_image,
                                           upsample_factor=100,
                                           space="fourier",
                                           normalization=normalization)
    assert_allclose(result[:2], -np.array(subpixel_shift), atol=0.05)


@pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64])
def test_real_input(dtype):
    reference_image = camera().astype(dtype, copy=False)
    subpixel_shift = (-2.4, 1.32)
    shifted_image = fourier_shift(fft.fftn(reference_image), subpixel_shift)
    shifted_image = fft.ifftn(shifted_image).real.astype(dtype, copy=False)

    # subpixel precision
    result, error, diffphase = phase_cross_correlation(reference_image,
                                                       shifted_image,
                                                       upsample_factor=100)
    assert result.dtype == _supported_float_type(dtype)
    assert_allclose(result[:2], -np.array(subpixel_shift), atol=0.05)


def test_size_one_dimension_input():
    # take a strip of the input image
    reference_image = fft.fftn(camera()[:, 15]).reshape((-1, 1))
    subpixel_shift = (-2.4, 4)
    shifted_image = fourier_shift(reference_image, subpixel_shift)

    # subpixel precision
    result, error, diffphase = phase_cross_correlation(reference_image,
                                                       shifted_image,
                                                       upsample_factor=20,
                                                       space="fourier")
    assert_allclose(result[:2], -np.array((-2.4, 0)), atol=0.05)


def test_3d_input():
    phantom = img_as_float(binary_blobs(length=32, n_dim=3))
    reference_image = fft.fftn(phantom)
    shift = (-2., 1., 5.)
    shifted_image = fourier_shift(reference_image, shift)

    result, error, diffphase = phase_cross_correlation(reference_image,
                                                       shifted_image,
                                                       space="fourier")
    assert_allclose(result, -np.array(shift), atol=0.05)

    # subpixel precision now available for 3-D data

    subpixel_shift = (-2.3, 1.7, 5.4)
    shifted_image = fourier_shift(reference_image, subpixel_shift)
    result, error, diffphase = phase_cross_correlation(reference_image,
                                                       shifted_image,
                                                       upsample_factor=100,
                                                       space="fourier")
    assert_allclose(result, -np.array(subpixel_shift), atol=0.05)


def test_unknown_space_input():
    image = np.ones((5, 5))
    with pytest.raises(ValueError):
        phase_cross_correlation(
            image, image,
            space="frank")


def test_wrong_input():
    # Dimensionality mismatch
    image = np.ones((5, 5, 1))
    template = np.ones((5, 5))
    with pytest.raises(ValueError):
        phase_cross_correlation(template, image)

    # Size mismatch
    image = np.ones((5, 5))
    template = np.ones((4, 4))
    with pytest.raises(ValueError):
        phase_cross_correlation(template, image)

    # NaN values in data
    image = np.ones((5, 5))
    image[0][0] = np.nan
    template = np.ones((5, 5))
    with expected_warnings(
        [
            r"invalid value encountered in true_divide"
            + r"|"
            + r"invalid value encountered in divide"
            + r"|\A\Z"
        ]
    ):
        with pytest.raises(ValueError):
            phase_cross_correlation(template, image)


def test_4d_input_pixel():
    phantom = img_as_float(binary_blobs(length=32, n_dim=4))
    reference_image = fft.fftn(phantom)
    shift = (-2., 1., 5., -3)
    shifted_image = fourier_shift(reference_image, shift)
    result, error, diffphase = phase_cross_correlation(reference_image,
                                                       shifted_image,
                                                       space="fourier")
    assert_allclose(result, -np.array(shift), atol=0.05)


def test_4d_input_subpixel():
    phantom = img_as_float(binary_blobs(length=32, n_dim=4))
    reference_image = fft.fftn(phantom)
    subpixel_shift = (-2.3, 1.7, 5.4, -3.2)
    shifted_image = fourier_shift(reference_image, subpixel_shift)
    result, error, diffphase = phase_cross_correlation(reference_image,
                                                       shifted_image,
                                                       upsample_factor=10,
                                                       space="fourier")
    assert_allclose(result, -np.array(subpixel_shift), atol=0.05)


def test_mismatch_upsampled_region_size():
    with pytest.raises(ValueError):
        _upsampled_dft(
            np.ones((4, 4)),
            upsampled_region_size=[3, 2, 1, 4])


def test_mismatch_offsets_size():
    with pytest.raises(ValueError):
        _upsampled_dft(np.ones((4, 4)), 3,
                       axis_offsets=[3, 2, 1, 4])


@pytest.mark.parametrize(
        ('shift0', 'shift1'),
        itertools.product((100, -100, 350, -350), (100, -100, 350, -350)),
        )
def test_disambiguate_2d(shift0, shift1):
    image = eagle()[500:, 900:]  # use a highly textured part of image
    shift = (shift0, shift1)
    origin0 = []
    for s in shift:
        if s > 0:
            origin0.append(0)
        else:
            origin0.append(-s)
    origin1 = np.array(origin0) + shift
    slice0 = tuple(slice(o, o+450) for o in origin0)
    slice1 = tuple(slice(o, o+450) for o in origin1)
    reference = image[slice0]
    moving = image[slice1]
    computed_shift, _, _ = phase_cross_correlation(
            reference, moving, disambiguate=True,
            )
    np.testing.assert_equal(shift, computed_shift)


def test_invalid_value_in_division_warnings():
    """Regression test for https://github.com/scikit-image/scikit-image/issues/7146."""
    im1 = np.zeros((100, 100))
    im1[50, 50] = 1
    im2 = np.zeros((100, 100))
    im2[60, 60] = 1
    with warnings.catch_warnings():
        warnings.simplefilter("error")
        phase_cross_correlation(im1, im2, disambiguate=True)


@pytest.mark.parametrize('disambiguate', [True, False])
def test_disambiguate_zero_shift(disambiguate):
    """When the shift is 0, disambiguation becomes degenerate.

    Some quadrants become size 0, which prevents computation of
    cross-correlation. This test ensures that nothing bad happens in that
    scenario.
    """
    image = camera()
    computed_shift, _, _ = phase_cross_correlation(
        image, image, disambiguate=disambiguate,
    )
    assert isinstance(computed_shift, np.ndarray)
    np.testing.assert_array_equal(computed_shift, np.array((0., 0.)))


@pytest.mark.parametrize(
    "return_error", [False, True, "always"]
)
def test_deprecated_return_error(return_error):
    img = np.ones((10, 10))
    with pytest.warns() as record:
        phase_cross_correlation(img, img, return_error=return_error)
    assert len(record) == 1
    assert "return_error argument is deprecated" in record[0].message.args[0]
    assert record[0].filename == __file__
