import numpy as np
import pytest
import scipy as sp

from skimage.morphology import (
    remove_small_objects,
    remove_small_holes,
    remove_objects_by_distance,
    local_maxima,
    label,
)

from skimage._shared import testing
from skimage._shared.testing import assert_array_equal, assert_equal
from skimage._shared._warnings import expected_warnings


test_image = np.array([[0, 0, 0, 1, 0], [1, 1, 1, 0, 0], [1, 1, 1, 0, 1]], bool)

# Dtypes supported by the `label_image` parameter in `remove_objects_by_distance`
supported_dtypes = [
    np.uint8,
    np.uint16,
    np.uint32,
    np.int8,
    np.int16,
    np.int32,
    np.int64,
]


def test_one_connectivity():
    expected = np.array([[0, 0, 0, 0, 0], [1, 1, 1, 0, 0], [1, 1, 1, 0, 0]], bool)
    observed = remove_small_objects(test_image, min_size=6)
    assert_array_equal(observed, expected)


def test_two_connectivity():
    expected = np.array([[0, 0, 0, 1, 0], [1, 1, 1, 0, 0], [1, 1, 1, 0, 0]], bool)
    observed = remove_small_objects(test_image, min_size=7, connectivity=2)
    assert_array_equal(observed, expected)


def test_in_place():
    image = test_image.copy()
    observed = remove_small_objects(image, min_size=6, out=image)
    assert_equal(
        observed is image, True, "remove_small_objects in_place argument failed."
    )


@pytest.mark.parametrize("in_dtype", [bool, int, np.int32])
@pytest.mark.parametrize("out_dtype", [bool, int, np.int32])
def test_out(in_dtype, out_dtype):
    image = test_image.astype(in_dtype, copy=True)
    expected_out = np.empty_like(test_image, dtype=out_dtype)

    if out_dtype != bool:
        # object with only 1 label will warn on non-bool output dtype
        exp_warn = ["Only one label was provided"]
    else:
        exp_warn = []

    with expected_warnings(exp_warn):
        out = remove_small_objects(image, min_size=6, out=expected_out)

    assert out is expected_out


def test_labeled_image():
    labeled_image = np.array(
        [[2, 2, 2, 0, 1], [2, 2, 2, 0, 1], [2, 0, 0, 0, 0], [0, 0, 3, 3, 3]], dtype=int
    )
    expected = np.array(
        [[2, 2, 2, 0, 0], [2, 2, 2, 0, 0], [2, 0, 0, 0, 0], [0, 0, 3, 3, 3]], dtype=int
    )
    observed = remove_small_objects(labeled_image, min_size=3)
    assert_array_equal(observed, expected)


def test_uint_image():
    labeled_image = np.array(
        [[2, 2, 2, 0, 1], [2, 2, 2, 0, 1], [2, 0, 0, 0, 0], [0, 0, 3, 3, 3]],
        dtype=np.uint8,
    )
    expected = np.array(
        [[2, 2, 2, 0, 0], [2, 2, 2, 0, 0], [2, 0, 0, 0, 0], [0, 0, 3, 3, 3]],
        dtype=np.uint8,
    )
    observed = remove_small_objects(labeled_image, min_size=3)
    assert_array_equal(observed, expected)


def test_single_label_warning():
    image = np.array([[0, 0, 0, 1, 0], [1, 1, 1, 0, 0], [1, 1, 1, 0, 0]], int)
    with expected_warnings(['use a boolean array?']):
        remove_small_objects(image, min_size=6)


def test_float_input():
    float_test = np.random.rand(5, 5)
    with testing.raises(TypeError):
        remove_small_objects(float_test)


def test_negative_input():
    negative_int = np.random.randint(-4, -1, size=(5, 5))
    with testing.raises(ValueError):
        remove_small_objects(negative_int)


test_holes_image = np.array(
    [
        [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
        [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
        [0, 1, 0, 0, 1, 1, 0, 0, 0, 0],
        [0, 1, 1, 1, 0, 1, 0, 0, 0, 0],
        [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
        [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
        [0, 0, 0, 0, 0, 0, 0, 1, 0, 1],
        [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
    ],
    bool,
)


def test_one_connectivity_holes():
    expected = np.array(
        [
            [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
            [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
            [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
            [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
            [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
            [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
            [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
            [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
        ],
        bool,
    )
    observed = remove_small_holes(test_holes_image, area_threshold=3)
    assert_array_equal(observed, expected)


def test_two_connectivity_holes():
    expected = np.array(
        [
            [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
            [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
            [0, 1, 0, 0, 1, 1, 0, 0, 0, 0],
            [0, 1, 1, 1, 0, 1, 0, 0, 0, 0],
            [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
            [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
            [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
            [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
        ],
        bool,
    )
    observed = remove_small_holes(test_holes_image, area_threshold=3, connectivity=2)
    assert_array_equal(observed, expected)


def test_in_place_holes():
    image = test_holes_image.copy()
    observed = remove_small_holes(image, area_threshold=3, out=image)
    assert_equal(
        observed is image, True, "remove_small_holes in_place argument failed."
    )


def test_out_remove_small_holes():
    image = test_holes_image.copy()
    expected_out = np.empty_like(image)
    out = remove_small_holes(image, area_threshold=3, out=expected_out)

    assert out is expected_out


def test_non_bool_out():
    image = test_holes_image.copy()
    expected_out = np.empty_like(image, dtype=int)
    with testing.raises(TypeError):
        remove_small_holes(image, area_threshold=3, out=expected_out)


def test_labeled_image_holes():
    labeled_holes_image = np.array(
        [
            [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
            [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
            [0, 1, 0, 0, 1, 1, 0, 0, 0, 0],
            [0, 1, 1, 1, 0, 1, 0, 0, 0, 0],
            [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
            [0, 0, 0, 0, 0, 0, 0, 2, 2, 2],
            [0, 0, 0, 0, 0, 0, 0, 2, 0, 2],
            [0, 0, 0, 0, 0, 0, 0, 2, 2, 2],
        ],
        dtype=int,
    )
    expected = np.array(
        [
            [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
            [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
            [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
            [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
            [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
            [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
            [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
            [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
        ],
        dtype=bool,
    )
    with expected_warnings(['returned as a boolean array']):
        observed = remove_small_holes(labeled_holes_image, area_threshold=3)
    assert_array_equal(observed, expected)


def test_uint_image_holes():
    labeled_holes_image = np.array(
        [
            [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
            [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
            [0, 1, 0, 0, 1, 1, 0, 0, 0, 0],
            [0, 1, 1, 1, 0, 1, 0, 0, 0, 0],
            [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
            [0, 0, 0, 0, 0, 0, 0, 2, 2, 2],
            [0, 0, 0, 0, 0, 0, 0, 2, 0, 2],
            [0, 0, 0, 0, 0, 0, 0, 2, 2, 2],
        ],
        dtype=np.uint8,
    )
    expected = np.array(
        [
            [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
            [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
            [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
            [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
            [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
            [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
            [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
            [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
        ],
        dtype=bool,
    )
    with expected_warnings(['returned as a boolean array']):
        observed = remove_small_holes(labeled_holes_image, area_threshold=3)
    assert_array_equal(observed, expected)


def test_label_warning_holes():
    labeled_holes_image = np.array(
        [
            [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
            [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
            [0, 1, 0, 0, 1, 1, 0, 0, 0, 0],
            [0, 1, 1, 1, 0, 1, 0, 0, 0, 0],
            [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
            [0, 0, 0, 0, 0, 0, 0, 2, 2, 2],
            [0, 0, 0, 0, 0, 0, 0, 2, 0, 2],
            [0, 0, 0, 0, 0, 0, 0, 2, 2, 2],
        ],
        dtype=int,
    )
    with expected_warnings(['use a boolean array?']):
        remove_small_holes(labeled_holes_image, area_threshold=3)
    remove_small_holes(labeled_holes_image.astype(bool), area_threshold=3)


def test_float_input_holes():
    float_test = np.random.rand(5, 5)
    with testing.raises(TypeError):
        remove_small_holes(float_test)


class Test_remove_near_objects:

    @pytest.mark.parametrize("min_distance", [2.1, 5, 30.99, 49])
    @pytest.mark.parametrize("dtype", supported_dtypes)
    def test_min_distance_1d(self, min_distance, dtype):
        # First 3 objects are only just to close, last one is just far enough
        d = int(np.floor(min_distance))
        labels = np.zeros(d * 3 + 2, dtype=dtype)
        labels[[0, d, 2 * d, 3 * d + 1]] = 1
        labels, _ = sp.ndimage.label(labels, output=dtype)
        desired = labels.copy()
        desired[d] = 0

        result = remove_objects_by_distance(labels, min_distance)
        assert result.dtype == desired.dtype
        assert_array_equal(result, desired)

    @pytest.mark.parametrize("dtype", supported_dtypes)
    @pytest.mark.parametrize("order", ["C", "F"])
    def test_handcrafted_2d(self, dtype, order):
        label = np.array(
            [
                [8, 0, 0, 0, 0, 0, 0, 0, 0, 9, 9],
                [8, 8, 8, 0, 0, 0, 0, 0, 0, 9, 9],
                [0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0],
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [0, 0, 3, 0, 0, 0, 5, 0, 0, 0, 0],
                [2, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],
                [0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0],
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7],
            ],
            dtype=dtype,
            order=order,
        )
        priority = np.arange(10)
        desired = np.array(
            [
                [8, 0, 0, 0, 0, 0, 0, 0, 0, 9, 9],
                [8, 8, 8, 0, 0, 0, 0, 0, 0, 9, 9],
                [0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0],
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7],
            ],
            dtype=dtype,
        )
        result = remove_objects_by_distance(label, 3, priority=priority)
        assert result.flags["C_CONTIGUOUS"]
        assert_array_equal(result, desired)

    @pytest.mark.parametrize("ndim", [1, 2, 3, 4, 5])
    def test_large_objects_nd(self, ndim):
        shape = (5,) * ndim
        a = np.ones(shape, dtype=np.uint8)
        a[-2, ...] = 0
        labels, _ = sp.ndimage.label(a)
        desired = labels.copy()
        desired[-2:, ...] = 0

        result = remove_objects_by_distance(labels, 2)
        assert_array_equal(result, desired)

    @pytest.mark.parametrize("distance", [5, 50, 100])
    @pytest.mark.parametrize("p_norm", [1, 2, np.inf])
    def test_random(self, distance, p_norm):
        rng = np.random.default_rng(1713648513)
        image = rng.random(size=(400, 400))
        maxima = local_maxima(image)
        objects = label(maxima)

        spaced_objects = remove_objects_by_distance(objects, distance, p_norm=p_norm)
        kdtree = sp.spatial.cKDTree(
            np.array(np.nonzero(spaced_objects), dtype=np.float64).transpose(),
        )

        # Compute distance between all objects that are equal or smaller `distance`
        distances = kdtree.sparse_distance_matrix(
            kdtree, max_distance=distance, p=p_norm
        )
        # There should be no objects left
        assert distances.count_nonzero() == 0

        # But increasing by 1 should reveal a few objects
        distances = kdtree.sparse_distance_matrix(
            kdtree, max_distance=distance + 1, p=p_norm
        )
        assert distances.count_nonzero() > 0

    @pytest.mark.parametrize("value", [0, 1])
    @pytest.mark.parametrize("dtype", supported_dtypes)
    def test_constant(self, value, dtype):
        labels = np.empty((10, 10), dtype=dtype)
        labels.fill(value)

        result = remove_objects_by_distance(labels, 3)
        assert_array_equal(labels, result)

    def test_empty(self):
        labels = np.empty((3, 3, 0), dtype=int)
        result = remove_objects_by_distance(labels, 3)
        assert_equal(labels, result)

    def test_priority(self):
        labels = np.array([0, 1, 4, 1])

        # Object with more samples takes precedence
        result = remove_objects_by_distance(labels, 3)
        desired = np.array([0, 1, 0, 1])
        assert_array_equal(result, desired)

        # Assigning priority with equal values, sorts by higher label ID second
        priority = np.array([0, 1, 1, 1, 1])
        result = remove_objects_by_distance(labels, 3, priority=priority)
        desired = np.array([0, 0, 4, 0])
        assert_array_equal(result, desired)

        # But given a different priority that order can be overruled
        priority = np.array([0, 1, 1, 1, -1])
        result = remove_objects_by_distance(labels, 3, priority=priority)
        desired = np.array([0, 1, 0, 1])
        assert_array_equal(result, desired)

    @pytest.mark.parametrize("order", ["C", "F"])
    def test_out(self, order):
        labels_original = np.array([[1, 0, 2], [1, 0, 2]], order=order)
        desired = np.array([[0, 0, 2], [0, 0, 2]], order=order)

        # By default, input image is not modified
        labels = labels_original.copy(order=order)
        remove_objects_by_distance(labels, 2)
        assert_array_equal(labels, labels_original)

        # But modified if passed to `out`
        remove_objects_by_distance(labels, 2, out=labels)
        assert labels.flags[f"{order}_CONTIGUOUS"]
        assert_array_equal(labels, desired)

    @pytest.mark.parametrize("min_distance", [-10, -0.1])
    def test_negative_min_distance(self, min_distance):
        labels = np.array([1, 0, 2])
        with pytest.raises(ValueError, match="must be >= 0"):
            remove_objects_by_distance(labels, min_distance)

    def test_p_norm(self):
        labels = np.array([[2, 0], [0, 1]])
        removed = np.array([[2, 0], [0, 0]])

        # p_norm=2, default (Euclidean distance)
        result = remove_objects_by_distance(labels, 1.4)
        assert_array_equal(result, labels)
        result = remove_objects_by_distance(labels, np.sqrt(2))
        assert_array_equal(result, removed)

        # p_norm=1 (Manhatten distance)
        result = remove_objects_by_distance(
            labels,
            min_distance=1.9,
            p_norm=1,
        )
        assert_array_equal(result, labels)
        result = remove_objects_by_distance(labels, 2, p_norm=1)
        assert_array_equal(result, removed)

        # p_norm=np.inf (Chebyshev distance)
        result = remove_objects_by_distance(labels, 0.9, p_norm=np.inf)
        assert_array_equal(result, labels)
        result = remove_objects_by_distance(labels, 1, p_norm=np.inf)
        assert_array_equal(result, removed)

    @pytest.mark.parametrize(
        "shape",
        [
            (0,),
        ],
    )
    def test_priority_shape(self, shape):
        remove_objects_by_distance(np.array([0, 0, 0]), 3, priority=np.ones((0,)))
        remove_objects_by_distance(np.array([0, 0, 0]), 3, priority=np.ones((1,)))

        error_msg = r"shape of `priority` must be \(np\.amax\(label_image\) \+ 1,\)"
        with pytest.raises(ValueError, match=error_msg):
            remove_objects_by_distance(np.array([1, 0, 0]), 3, priority=np.ones((0,)))
        with pytest.raises(ValueError, match=error_msg):
            remove_objects_by_distance(np.array([1, 0, 0]), 3, priority=np.ones((1,)))
        with pytest.raises(ValueError, match=error_msg):
            remove_objects_by_distance(np.array([1, 0, 0]), 3, priority=np.ones((1,)))

    def test_negative_label_ids(self):
        labels = np.array(
            [
                [1, 1, -1, 2, 2, 2],
                [1, 1, 3, 2, 2, 2],
                [1, 1, 1, 2, 2, 2],
                [3, 3, 3, 3, 3, 3],
            ]
        )
        with pytest.raises(ValueError, match=".*object with negative ID"):
            remove_objects_by_distance(labels, 1, priority=np.ones(4))

    def test_objects_with_inside(self):
        labels = np.array(
            [
                [1, 1, 1, 2, 2, 2],
                [1, 1, 1, 2, 2, 2],
                [1, 1, 1, 2, 2, 2],
                [3, 3, 3, 3, 3, 3],
            ]
        )
        desired = np.array(
            [
                [0, 0, 0, 0, 0, 0],
                [0, 0, 0, 0, 0, 0],
                [0, 0, 0, 0, 0, 0],
                [3, 3, 3, 3, 3, 3],
            ]
        )
        result = remove_objects_by_distance(labels, 1, priority=np.arange(4))
        assert_array_equal(result, desired)

    def test_spacing(self):
        labels = np.array(
            [[1, 0, 0, 2], [0, 0, 0, 0], [0, 0, 0, 0], [3, 0, 0, 4]], dtype=int
        )

        # Stretch second dimension
        result = remove_objects_by_distance(labels, 3, spacing=(1, 3))
        expected = np.array(
            [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [3, 0, 0, 4]], dtype=int
        )
        np.testing.assert_array_equal(result, expected)

        # Compress second dimension
        result = remove_objects_by_distance(labels, 1, spacing=(1, 1 / 3))
        expected = np.array(
            [[0, 0, 0, 2], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 4]], dtype=int
        )
        np.testing.assert_array_equal(result, expected)

    @pytest.mark.parametrize("spacing", [(-1, -1), (1,), (1, 1, 1), [[1, 1]], 1])
    def test_spacing_raises(self, spacing):
        labels = np.array(
            [[1, 0, 0, 2], [0, 0, 0, 0], [0, 0, 0, 0], [3, 0, 0, 4]], dtype=int
        )
        regex = ".*must contain exactly one positive factor for each dimension"
        with pytest.raises(ValueError, match=regex):
            remove_objects_by_distance(labels, 3, spacing=spacing)
