from numpy.testing import (assert_almost_equal, run_module_suite,
                           assert_equal, assert_raises)
import numpy as np
from scipy.ndimage import map_coordinates

from skimage.transform._warps import _stackcopy
from skimage.transform import (warp, warp_coords, rotate, resize, rescale,
                               AffineTransform,
                               ProjectiveTransform,
                               SimilarityTransform,
                               downscale_local_mean)
from skimage import transform as tf, data, img_as_float
from skimage.color import rgb2gray
from skimage._shared._warnings import expected_warnings
from skimage._shared.testing import test_parallel


np.random.seed(0)


def test_stackcopy():
    layers = 4
    x = np.empty((3, 3, layers))
    y = np.eye(3, 3)
    _stackcopy(x, y)
    for i in range(layers):
        assert_almost_equal(x[..., i], y)


def test_warp_tform():
    x = np.zeros((5, 5), dtype=np.double)
    x[2, 2] = 1
    theta = - np.pi / 2
    tform = SimilarityTransform(scale=1, rotation=theta, translation=(0, 4))

    x90 = warp(x, tform, order=1)
    assert_almost_equal(x90, np.rot90(x))

    x90 = warp(x, tform.inverse, order=1)
    assert_almost_equal(x90, np.rot90(x))


def test_warp_callable():
    x = np.zeros((5, 5), dtype=np.double)
    x[2, 2] = 1
    refx = np.zeros((5, 5), dtype=np.double)
    refx[1, 1] = 1

    shift = lambda xy: xy + 1

    outx = warp(x, shift, order=1)
    assert_almost_equal(outx, refx)


@test_parallel()
def test_warp_matrix():
    x = np.zeros((5, 5), dtype=np.double)
    x[2, 2] = 1
    refx = np.zeros((5, 5), dtype=np.double)
    refx[1, 1] = 1

    matrix = np.array([[1, 0, 1], [0, 1, 1], [0, 0, 1]])

    # _warp_fast
    outx = warp(x, matrix, order=1)
    assert_almost_equal(outx, refx)
    # check for ndimage.map_coordinates
    outx = warp(x, matrix, order=5)


def test_warp_nd():
    for dim in range(2, 8):
        shape = dim * (5,)

        x = np.zeros(shape, dtype=np.double)
        x_c = dim * (2,)
        x[x_c] = 1
        refx = np.zeros(shape, dtype=np.double)
        refx_c = dim * (1,)
        refx[refx_c] = 1

        coord_grid = dim * (slice(0, 5, 1),)
        coords = np.array(np.mgrid[coord_grid]) + 1

        outx = warp(x, coords, order=0, cval=0)

        assert_almost_equal(outx, refx)


def test_warp_clip():
    x = np.zeros((5, 5), dtype=np.double)
    x[2, 2] = 1

    with expected_warnings(['The default mode']):
        outx = rescale(x, 3, order=3, clip=False)
    assert outx.min() < 0

    with expected_warnings(['The default mode']):
        outx = rescale(x, 3, order=3, clip=True)
    assert_almost_equal(outx.min(), 0)
    assert_almost_equal(outx.max(), 1)


def test_homography():
    x = np.zeros((5, 5), dtype=np.double)
    x[1, 1] = 1
    theta = -np.pi / 2
    M = np.array([[np.cos(theta), - np.sin(theta), 0],
                  [np.sin(theta),   np.cos(theta), 4],
                  [0,               0,             1]])

    x90 = warp(x,
               inverse_map=ProjectiveTransform(M).inverse,
               order=1)
    assert_almost_equal(x90, np.rot90(x))


def test_rotate():
    x = np.zeros((5, 5), dtype=np.double)
    x[1, 1] = 1
    x90 = rotate(x, 90)
    assert_almost_equal(x90, np.rot90(x))


def test_rotate_resize():
    x = np.zeros((10, 10), dtype=np.double)

    x45 = rotate(x, 45, resize=False)
    assert x45.shape == (10, 10)

    x45 = rotate(x, 45, resize=True)
    # new dimension should be d = sqrt(2 * (10/2)^2)
    assert x45.shape == (14, 14)


def test_rotate_center():
    x = np.zeros((10, 10), dtype=np.double)
    x[4, 4] = 1
    refx = np.zeros((10, 10), dtype=np.double)
    refx[2, 5] = 1
    x20 = rotate(x, 20, order=0, center=(0, 0))
    assert_almost_equal(x20, refx)
    x0 = rotate(x20, -20, order=0, center=(0, 0))
    assert_almost_equal(x0, x)


def test_rotate_resize_center():
    x = np.zeros((10, 10), dtype=np.double)
    x[0, 0] = 1

    ref_x45 = np.zeros((14, 14), dtype=np.double)
    ref_x45[6, 0] = 1
    ref_x45[7, 0] = 1

    x45 = rotate(x, 45, resize=True, center=(3, 3), order=0)
    # new dimension should be d = sqrt(2 * (10/2)^2)
    assert x45.shape == (14, 14)
    assert_equal(x45, ref_x45)


def test_rescale():
    # same scale factor
    x = np.zeros((5, 5), dtype=np.double)
    x[1, 1] = 1
    with expected_warnings(['The default mode']):
        scaled = rescale(x, 2, order=0)
    ref = np.zeros((10, 10))
    ref[2:4, 2:4] = 1
    assert_almost_equal(scaled, ref)

    # different scale factors
    x = np.zeros((5, 5), dtype=np.double)
    x[1, 1] = 1
    with expected_warnings(['The default mode']):
        scaled = rescale(x, (2, 1), order=0)
    ref = np.zeros((10, 5))
    ref[2:4, 1] = 1
    assert_almost_equal(scaled, ref)


def test_resize2d():
    x = np.zeros((5, 5), dtype=np.double)
    x[1, 1] = 1
    with expected_warnings(['The default mode']):
        resized = resize(x, (10, 10), order=0)
    ref = np.zeros((10, 10))
    ref[2:4, 2:4] = 1
    assert_almost_equal(resized, ref)


def test_resize3d_keep():
    # keep 3rd dimension
    x = np.zeros((5, 5, 3), dtype=np.double)
    x[1, 1, :] = 1
    with expected_warnings(['The default mode']):
        resized = resize(x, (10, 10), order=0)
    ref = np.zeros((10, 10, 3))
    ref[2:4, 2:4, :] = 1
    assert_almost_equal(resized, ref)
    with expected_warnings(['The default mode']):
        resized = resize(x, (10, 10, 3), order=0)
    assert_almost_equal(resized, ref)


def test_resize3d_resize():
    # resize 3rd dimension
    x = np.zeros((5, 5, 3), dtype=np.double)
    x[1, 1, :] = 1
    with expected_warnings(['The default mode']):
        resized = resize(x, (10, 10, 1), order=0)
    ref = np.zeros((10, 10, 1))
    ref[2:4, 2:4] = 1
    assert_almost_equal(resized, ref)


def test_resize3d_2din_3dout():
    # 3D output with 2D input
    x = np.zeros((5, 5), dtype=np.double)
    x[1, 1] = 1
    with expected_warnings(['The default mode']):
        resized = resize(x, (10, 10, 1), order=0)
    ref = np.zeros((10, 10, 1))
    ref[2:4, 2:4] = 1
    assert_almost_equal(resized, ref)


def test_resize3d_bilinear():
    # bilinear 3rd dimension
    x = np.zeros((5, 5, 2), dtype=np.double)
    x[1, 1, 0] = 0
    x[1, 1, 1] = 1
    resized = resize(x, (10, 10, 1), order=1, mode='constant')
    ref = np.zeros((10, 10, 1))
    ref[1:5, 1:5, :] = 0.03125
    ref[1:5, 2:4, :] = 0.09375
    ref[2:4, 1:5, :] = 0.09375
    ref[2:4, 2:4, :] = 0.28125
    assert_almost_equal(resized, ref)


def test_swirl():
    image = img_as_float(data.checkerboard())

    swirl_params = {'radius': 80, 'rotation': 0, 'order': 2, 'mode': 'reflect'}

    with expected_warnings(['Bi-quadratic.*bug']):
        swirled = tf.swirl(image, strength=10, **swirl_params)
        unswirled = tf.swirl(swirled, strength=-10, **swirl_params)

    assert np.mean(np.abs(image - unswirled)) < 0.01

    swirl_params.pop('mode')

    with expected_warnings(['Bi-quadratic.*bug', 'default']):
        swirled = tf.swirl(image, strength=10, **swirl_params)
        unswirled = tf.swirl(swirled, strength=-10, **swirl_params)

    assert np.mean(np.abs(image[1:-1,1:-1] - unswirled[1:-1,1:-1])) < 0.01


def test_const_cval_out_of_range():
    img = np.random.randn(100, 100)
    cval = - 10
    warped = warp(img, AffineTransform(translation=(10, 10)), cval=cval)
    assert np.sum(warped == cval) == (2 * 100 * 10 - 10 * 10)


def test_warp_identity():
    img = img_as_float(rgb2gray(data.astronaut()))
    assert len(img.shape) == 2
    assert np.allclose(img, warp(img, AffineTransform(rotation=0)))
    assert not np.allclose(img, warp(img, AffineTransform(rotation=0.1)))
    rgb_img = np.transpose(np.asarray([img, np.zeros_like(img), img]),
                            (1, 2, 0))
    warped_rgb_img = warp(rgb_img, AffineTransform(rotation=0.1))
    assert np.allclose(rgb_img, warp(rgb_img, AffineTransform(rotation=0)))
    assert not np.allclose(rgb_img, warped_rgb_img)
    # assert no cross-talk between bands
    assert np.all(0 == warped_rgb_img[:, :, 1])


def test_warp_coords_example():
    image = data.astronaut().astype(np.float32)
    assert 3 == image.shape[2]
    tform = SimilarityTransform(translation=(0, -10))
    coords = warp_coords(tform, (30, 30, 3))
    map_coordinates(image[:, :, 0], coords[:2])


def test_downscale_local_mean():
    image1 = np.arange(4 * 6).reshape(4, 6)
    out1 = downscale_local_mean(image1, (2, 3))
    expected1 = np.array([[  4.,   7.],
                          [ 16.,  19.]])
    assert_equal(expected1, out1)

    image2 = np.arange(5 * 8).reshape(5, 8)
    out2 = downscale_local_mean(image2, (4, 5))
    expected2 = np.array([[ 14. ,  10.8],
                          [  8.5,   5.7]])
    assert_equal(expected2, out2)


def test_invalid():
    assert_raises(ValueError, warp, np.ones((4, 3, 3, 3)),
                  SimilarityTransform())


def test_inverse():
    tform = SimilarityTransform(scale=0.5, rotation=0.1)
    inverse_tform = SimilarityTransform(matrix=np.linalg.inv(tform.params))
    image = np.arange(10 * 10).reshape(10, 10).astype(np.double)
    assert_equal(warp(image, inverse_tform), warp(image, tform.inverse))


def test_slow_warp_nonint_oshape():
    image = np.random.rand(5, 5)

    assert_raises(ValueError, warp, image, lambda xy: xy,
                  output_shape=(13.1, 19.5))

    warp(image, lambda xy: xy, output_shape=(13.0001, 19.9999))


def test_keep_range():
    image = np.linspace(0, 2, 25).reshape(5, 5)

    with expected_warnings(['The default mode']):
        out = rescale(image, 2, preserve_range=False, clip=True, order=0)
    assert out.min() == 0
    assert out.max() == 2

    with expected_warnings(['The default mode']):
        out = rescale(image, 2, preserve_range=True, clip=True, order=0)
    assert out.min() == 0
    assert out.max() == 2

    with expected_warnings(['The default mode']):
        out = rescale(image.astype(np.uint8), 2, preserve_range=False,
                      clip=True, order=0)
    assert out.min() == 0
    assert out.max() == 2 / 255.0



if __name__ == "__main__":
    run_module_suite()
