Update to use scikit-image 0.19 when it is released

import numpy as np

from scipy.ndimage.measurements import center_of_mass

def mutual_information(t1, t2, **kwargs):

    """

    Computes the correlation coefficient between two images using a histogram

    comparison (Mutual information for joint histograms). The corr_map coefficient

    will be between 0 and 4

    Parameters

    ----------

    t1 : ndarray

         First image to use in the histogram comparison

    t2 : ndarray

         Second image to use in the histogram comparison

    Returns

    -------

    : float

      Correlation coefficient computed between the two images being compared

      between 0 and 4

    See Also

    --------

    numpy.histogram2d : for the kwargs that can be passed to the comparison

    """

    if np.isnan(t1).any() or np.isnan(t2).any():

        print('Unable to process due to NaN values in the input data')

        return

    if t1.shape != t2.shape:

        print('Unable compute MI. Image sizes are not identical.')

        return

    hgram, x_edges, y_edges = np.histogram2d(t1.ravel(),t2.ravel(), **kwargs)

    # Convert bins counts to probability values

    pxy = hgram / float(np.sum(hgram))

    px = np.sum(pxy, axis=1) # marginal for x over y

    py = np.sum(pxy, axis=0) # marginal for y over x

    px_py = px[:, None] * py[None, :] # Broadcast to multiply marginals

    # Now we can do the calculation using the pxy, px_py 2D arrays

    nzs = pxy > 0 # Only non-zero pxy values contribute to the sum

    return np.sum(pxy[nzs] * np.log(pxy[nzs] / px_py[nzs]))

from skimage.metrics import normalized_mutual_information

def mutual_information_match(d_template, s_image, subpixel_size=3,

                             func=None, **kwargs):

                             func=normalized_mutual_information, **kwargs):

    """

    Applys the mutual information matcher function over a search image using a

    defined template

        The y offset

    max_corr : float

               The strength of the correlation in the range [0, 4].

               The strength of the correlation

    corr_map : ndarray

               Map of corrilation coefficients when comparing the template to

               locations within the search area

    """

    if func == None:

        func = mutual_information

    See Also

    --------

    skimage.metrics.normalized_mutual_information : for the kwargs that can be passed to the matcher

    """

    image_size = s_image.shape

    template_size = d_template.shape

from .. import mutual_information

def test_good_mi():

    test_image1 = np.array([[i for i in range(50)] for j in range(50)])

    corrilation = mutual_information.mutual_information(test_image1, test_image1)

    assert corrilation == pytest.approx(2.30258509299404)

def test_bad_mi():

    test_image1 = np.array([[i for i in range(50)] for j in range(50)])

    test_image2 = np.ones((50, 50))

    corrilation = mutual_information.mutual_information(test_image1, test_image2)

    assert corrilation == pytest.approx(0)

def test_mutual_information():

    d_template = np.array([[i for i in range(50, 100)] for j in range(50)])

    s_image = np.ones((100, 100))

    s_image[25:75, 25:75] = d_template

    x_offset, y_offset, max_corr, corr_map = mutual_information.mutual_information_match(d_template, s_image, bins=20)

    assert x_offset == 0.01711861257171421

    assert x_offset == 0.010530473741837909

    assert y_offset == 0.0

    assert max_corr == 2.9755967600033015

    assert max_corr == 2.0

    assert corr_map.shape == (51, 51)

    assert np.min(corr_map) >= 0.0

  - pytest-cov

  - pytest-mock

  - richdem

  - scikit-image>=0.17

  - scikit-image>=0.19

  - scikit-learn

  - scipy<=1.2.1

  - shapely