updated mutal_information interface

import numpy as np

from scipy.ndimage.measurements import center_of_mass

from skimage.transform import AffineTransform

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

def mutual_information(reference_roi, walking_roi, affine=AffineTransform(), **kwargs):

    """

    Computes the correlation coefficient between two images using a histogram

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

    Parameters

    ----------

    t1 : ndarray

    reference_roi : Roi

                    First image to use in the histogram comparison

    t2 : ndarray

    moving_roi : Roi

                   Second image to use in the histogram comparison

    Returns

    -------

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

    """

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

    reference_image = reference_roi.clip()

    walking_template = walking_roi.clip(affine)

    # walking_template = tf.warp(walking_template, affine, order=3)

    if reference_roi.ndv == None or walking_roi.ndv == None:

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

        return

    if t1.shape != t2.shape:

    # print(reference_roi.size_y, walking_roi.size_y)

    if reference_roi.size_y != walking_roi.size_y and reference_roi.size_x != walking_roi.size_x:

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

        return

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

    hgram, x_edges, y_edges = np.histogram2d(reference_image.ravel(), walking_template.ravel(), **kwargs)

    # Convert bins counts to probability values

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

import sys

import unittest

from unittest.mock import patch

from autocnet.transformation.roi import Roi

import pytest

import numpy as np

from .. import mutual_information

def test_good_mi():

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

    test_image1 = Roi(np.array([[i for i in range(50)] for j in range(50)]), 25, 25, 25, 25, ndv=22222222)

    corrilation = mutual_information.mutual_information(test_image1, test_image1)

    assert corrilation == pytest.approx(2.30258509299404)

    x_coords = [base_startx, base_startx, base_stopx, base_stopx, bcenter_x]

    y_coords = [base_starty, base_stopy, base_stopy, base_starty, bcenter_y]

    try:

        # Dispatch to the sensor to get the a priori pixel location in the input image

        lons, lats = isis.image_to_ground(reference_image.file_name, x_coords, y_coords, allowoutside=True)

        xs, ys = isis.ground_to_image(moving_image.file_name, lons, lats, allowoutside=True)

    except Exception as e:

        print(e.stderr)

        raise e

    # Check for any coords that do not project between images

    base_gcps = []

from math import modf, floor

import numpy as np

from autocnet.transformation import dtypes

import pvl

class Roi():