import numpy as np
import pytest
from numpy.testing import assert_almost_equal, assert_equal

from skimage import data
from skimage._shared.testing import run_in_parallel, xfail, arch32, is_wasm
from skimage.feature import ORB
from skimage.util.dtype import _convert


img = data.coins()


@run_in_parallel()
@pytest.mark.parametrize('dtype', ['float32', 'float64', 'uint8', 'uint16', 'int64'])
def test_keypoints_orb_desired_no_of_keypoints(dtype):
    _img = _convert(img, dtype)
    detector_extractor = ORB(n_keypoints=10, fast_n=12, fast_threshold=0.20)
    detector_extractor.detect(_img)

    exp_rows = np.array(
        [141.0, 108.0, 214.56, 131.0, 214.272, 67.0, 206.0, 177.0, 108.0, 141.0]
    )
    exp_cols = np.array(
        [323.0, 328.0, 282.24, 292.0, 281.664, 85.0, 260.0, 284.0, 328.8, 267.0]
    )

    exp_scales = np.array([1, 1, 1.44, 1, 1.728, 1, 1, 1, 1.2, 1])

    exp_orientations = np.array(
        [
            -53.97446153,
            59.5055285,
            -96.01885186,
            -149.70789506,
            -94.70171899,
            -45.76429535,
            -51.49752849,
            113.57081195,
            63.30428063,
            -79.56091118,
        ]
    )
    exp_response = np.array(
        [
            1.01168357,
            0.82934145,
            0.67784179,
            0.57176438,
            0.56637459,
            0.52248355,
            0.43696175,
            0.42992376,
            0.37700486,
            0.36126832,
        ]
    )

    if np.dtype(dtype) == np.float32:
        assert detector_extractor.scales.dtype == np.float32
        assert detector_extractor.responses.dtype == np.float32
        assert detector_extractor.orientations.dtype == np.float32
    else:
        assert detector_extractor.scales.dtype == np.float64
        assert detector_extractor.responses.dtype == np.float64
        assert detector_extractor.orientations.dtype == np.float64

    assert_almost_equal(exp_rows, detector_extractor.keypoints[:, 0])
    assert_almost_equal(exp_cols, detector_extractor.keypoints[:, 1])
    assert_almost_equal(exp_scales, detector_extractor.scales)
    assert_almost_equal(exp_response, detector_extractor.responses, 5)
    assert_almost_equal(
        exp_orientations, np.rad2deg(detector_extractor.orientations), 4
    )

    detector_extractor.detect_and_extract(img)
    assert_almost_equal(exp_rows, detector_extractor.keypoints[:, 0])
    assert_almost_equal(exp_cols, detector_extractor.keypoints[:, 1])


@pytest.mark.parametrize('dtype', ['float32', 'float64', 'uint8', 'uint16', 'int64'])
def test_keypoints_orb_less_than_desired_no_of_keypoints(dtype):
    _img = _convert(img, dtype)
    detector_extractor = ORB(
        n_keypoints=15, fast_n=12, fast_threshold=0.33, downscale=2, n_scales=2
    )
    detector_extractor.detect(_img)

    exp_rows = np.array([108.0, 203.0, 140.0, 65.0, 58.0])
    exp_cols = np.array([293.0, 267.0, 202.0, 130.0, 291.0])

    exp_scales = np.array([1.0, 1.0, 1.0, 1.0, 1.0])

    exp_orientations = np.array(
        [151.93906, -56.90052, -79.46341, -59.42996, -158.26941]
    )

    exp_response = np.array([-0.1764169, 0.2652126, -0.0324343, 0.0400902, 0.2667641])

    assert_almost_equal(exp_rows, detector_extractor.keypoints[:, 0])
    assert_almost_equal(exp_cols, detector_extractor.keypoints[:, 1])
    assert_almost_equal(exp_scales, detector_extractor.scales)
    assert_almost_equal(exp_response, detector_extractor.responses)
    assert_almost_equal(
        exp_orientations, np.rad2deg(detector_extractor.orientations), 3
    )

    detector_extractor.detect_and_extract(img)
    assert_almost_equal(exp_rows, detector_extractor.keypoints[:, 0])
    assert_almost_equal(exp_cols, detector_extractor.keypoints[:, 1])


# Passing on WASM
@xfail(
    condition=arch32 and not is_wasm,
    reason=(
        'Known test failure on 32-bit platforms. See links for '
        'details: '
        'https://github.com/scikit-image/scikit-image/issues/3091 '
        'https://github.com/scikit-image/scikit-image/issues/2529'
    ),
)
def test_descriptor_orb():
    detector_extractor = ORB(fast_n=12, fast_threshold=0.20)
    exp_descriptors = np.array(
        [
            [0, 0, 0, 1, 0, 0, 0, 1, 0, 1],
            [1, 1, 0, 1, 0, 0, 0, 1, 0, 1],
            [1, 1, 0, 0, 1, 0, 0, 0, 1, 1],
            [1, 1, 1, 0, 0, 0, 1, 1, 1, 0],
            [0, 0, 0, 1, 0, 1, 1, 1, 1, 1],
            [1, 0, 0, 1, 1, 0, 0, 0, 1, 0],
            [0, 1, 1, 1, 1, 1, 1, 1, 1, 0],
            [1, 1, 1, 0, 1, 1, 1, 1, 0, 0],
            [1, 1, 1, 1, 0, 0, 0, 1, 1, 1],
            [0, 1, 1, 0, 0, 1, 1, 0, 1, 1],
            [1, 1, 0, 0, 0, 0, 0, 0, 1, 1],
            [1, 0, 0, 0, 0, 1, 0, 1, 1, 1],
            [1, 0, 1, 1, 1, 0, 1, 0, 1, 0],
            [0, 0, 1, 1, 0, 0, 0, 0, 1, 1],
            [0, 1, 1, 0, 0, 0, 1, 0, 0, 1],
            [0, 1, 1, 0, 0, 0, 1, 1, 1, 1],
            [0, 1, 1, 1, 1, 1, 1, 1, 1, 1],
            [0, 0, 1, 1, 1, 1, 0, 1, 1, 0],
            [0, 0, 1, 1, 1, 0, 1, 0, 0, 1],
            [0, 1, 0, 0, 0, 0, 0, 0, 1, 0],
        ],
        dtype=bool,
    )

    detector_extractor.detect(img)
    detector_extractor.extract(
        img,
        detector_extractor.keypoints,
        detector_extractor.scales,
        detector_extractor.orientations,
    )

    assert_equal(exp_descriptors, detector_extractor.descriptors[100:120, 10:20])

    detector_extractor.detect_and_extract(img)
    assert_equal(exp_descriptors, detector_extractor.descriptors[100:120, 10:20])
    keypoints_count = detector_extractor.keypoints.shape[0]
    assert keypoints_count == detector_extractor.descriptors.shape[0]
    assert keypoints_count == detector_extractor.orientations.shape[0]
    assert keypoints_count == detector_extractor.responses.shape[0]
    assert keypoints_count == detector_extractor.scales.shape[0]


def test_no_descriptors_extracted_orb():
    img = np.ones((128, 128))
    detector_extractor = ORB()
    with pytest.raises(RuntimeError):
        detector_extractor.detect_and_extract(img)


def test_img_too_small_orb():
    img = data.brick()[:64, :64]
    detector_extractor = ORB(downscale=2, n_scales=8)
    detector_extractor.detect(img)
    detector_extractor.detect_and_extract(img)