import numpy as np

import skimage.data
from skimage.measure import compare_psnr, compare_nrmse, compare_mse

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


np.random.seed(5)
cam = skimage.data.camera()
sigma = 20.0
cam_noisy = np.clip(cam + sigma * np.random.randn(*cam.shape), 0, 255)
cam_noisy = cam_noisy.astype(cam.dtype)


def test_PSNR_vs_IPOL():
    # Tests vs. imdiff result from the following IPOL article and code:
    # http://www.ipol.im/pub/art/2011/g_lmii/
    p_IPOL = 22.4497
    p = compare_psnr(cam, cam_noisy)
    assert_almost_equal(p, p_IPOL, decimal=4)


def test_PSNR_float():
    p_uint8 = compare_psnr(cam, cam_noisy)
    p_float64 = compare_psnr(cam / 255., cam_noisy / 255.,
                             data_range=1)
    assert_almost_equal(p_uint8, p_float64, decimal=5)

    # mixed precision inputs
    p_mixed = compare_psnr(cam / 255., np.float32(cam_noisy / 255.),
                           data_range=1)
    assert_almost_equal(p_mixed, p_float64, decimal=5)

    # mismatched dtype results in a warning if data_range is unspecified
    with expected_warnings(['Inputs have mismatched dtype']):
        p_mixed = compare_psnr(cam / 255., np.float32(cam_noisy / 255.))
    assert_almost_equal(p_mixed, p_float64, decimal=5)


def test_PSNR_errors():
    # shape mismatch
    with testing.raises(ValueError):
        compare_psnr(cam, cam[:-1, :])


def test_NRMSE():
    x = np.ones(4)
    y = np.asarray([0., 2., 2., 2.])
    assert_equal(compare_nrmse(y, x, 'mean'), 1/np.mean(y))
    assert_equal(compare_nrmse(y, x, 'Euclidean'), 1/np.sqrt(3))
    assert_equal(compare_nrmse(y, x, 'min-max'), 1/(y.max()-y.min()))

    # mixed precision inputs are allowed
    assert_almost_equal(compare_nrmse(y, np.float32(x), 'min-max'),
                        1 / (y.max() - y.min()))


def test_NRMSE_no_int_overflow():
    camf = cam.astype(np.float32)
    cam_noisyf = cam_noisy.astype(np.float32)
    assert_almost_equal(compare_mse(cam, cam_noisy),
                        compare_mse(camf, cam_noisyf))
    assert_almost_equal(compare_nrmse(cam, cam_noisy),
                        compare_nrmse(camf, cam_noisyf))


def test_NRMSE_errors():
    x = np.ones(4)
    # shape mismatch
    with testing.raises(ValueError):
        compare_nrmse(x[:-1], x)
    # invalid normalization name
    with testing.raises(ValueError):
        compare_nrmse(x, x, 'foo')