Commit 8abef1cb authored by Kelvin Rodriguez's avatar Kelvin Rodriguez
Browse files

removed ciratefi, added MI api change

parent 27705fe7

autocnet/matcher/ciratefi.py

deleted100644 → 0
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File deleted.

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autocnet/matcher/mutual_information.py

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Original line number Diff line number Diff line 
from math import floor

import logging



from autocnet.transformation.roi import Roi

import numpy as np



from scipy.ndimage.measurements import center_of_mass

import skimage.transform as tf



# setup logging file

log = logging.getLogger(__name__)



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

def mutual_information(reference_arr, moving_arr, **kwargs):

    """

    Computes the correlation coefficient between two images using a histogram

    comparison (Mutual information for joint histograms). The corr_map coefficient
@@ -17,12 +14,13 @@ def mutual_information(t1, t2, **kwargs): 
    Parameters

    ----------



    t1 : ndarray

    reference_arr : ndarray

                    First image to use in the histogram comparison

    

    t2 : ndarray

    moving_arr : ndarray

                   Second image to use in the histogram comparison

    

    

    Returns

    -------
@@ -35,15 +33,15 @@ def mutual_information(t1, t2, **kwargs): 
    numpy.histogram2d : for the kwargs that can be passed to the comparison

    """

   

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

        log.warning('Unable to process due to NaN values in the input data')

    if np.isnan(reference_arr).any() or np.isnan(moving_arr).any():

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

        return

    

    if t1.shape != t2.shape:

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

    if reference_arr.shape != moving_arr.shape:

        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_arr.ravel(),moving_arr.ravel(), **kwargs)



    # Convert bins counts to probability values

    pxy = hgram / float(np.sum(hgram))
@@ -53,93 +51,3 @@ def mutual_information(t1, t2, **kwargs): 
    # 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]))
 


def mutual_information_match(d_template, s_image, subpixel_size=3,

                             func=None, **kwargs):

    """

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

    defined template





    Parameters

    ----------

    d_template : ndarray

                 The input search template used to 'query' the destination

                 image



    s_image : ndarray

              The image or sub-image to be searched



    subpixel_size : int

                    Subpixel area size to search for the center of mass

                    calculation



    func : function

           Function object to be used to compute the histogram comparison



    Returns

    -------

    x : float

        The x offset



    y : float

        The y offset



    max_corr : float

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



    corr_map : ndarray

               Map of corrilation coefficients when comparing the template to

               locations within the search area

    """



    if func == None:

        func = mutual_information



    image_size = s_image.shape

    template_size = d_template.shape



    y_diff = image_size[0] - template_size[0]

    x_diff = image_size[1] - template_size[1]



    max_corr = -np.inf

    corr_map = np.zeros((y_diff+1, x_diff+1))

    max_i = -1  # y

    max_j = -1  # x

    for i in range(y_diff+1):

        for j in range(x_diff+1):

            sub_image = s_image[i:i+template_size[1],  # y

                                j:j+template_size[0]]  # x

            corr = func(sub_image, d_template, **kwargs)

            if corr > max_corr:

                max_corr = corr

                max_i = i

                max_j = j

            corr_map[i, j] = corr



    y, x = np.unravel_index(np.argmax(corr_map, axis=None), corr_map.shape)



    upper = int(2 + floor(subpixel_size / 2))

    lower = upper - 1

    # x, y are the location of the upper left hand corner of the template in the image

    area = corr_map[y-lower:y+upper,

                    x-lower:x+upper]



    # Compute the y, x shift (subpixel) using scipys center_of_mass function

    cmass  = center_of_mass(area)



    if area.shape != (subpixel_size+2, subpixel_size+2):

        log.warning("Max correlation is too close to the boundary.")

        return None, None, 0, None



    subpixel_y_shift = subpixel_size - 1 - cmass[0]

    subpixel_x_shift = subpixel_size - 1 - cmass[1]



    y += subpixel_y_shift

    x += subpixel_x_shift



    # Compute the idealized shift (image center)

    y -= (s_image.shape[0] / 2) - (d_template.shape[0] / 2)

    x -= (s_image.shape[1] / 2) - (d_template.shape[1] / 2)



    return float(x), float(y), float(max_corr), corr_map

autocnet/matcher/tests/test_ciratefi.py

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Original line number Diff line number Diff line 
import math

import unittest

import logging

import re



import numpy as np

from imageio import imread

from scipy.ndimage.interpolation import rotate



from autocnet.examples import get_path

from autocnet.transformation import roi

from .. import ciratefi



import pytest



# Can be parameterized for more exhaustive tests

upsampling = 10

alpha = math.pi/2

cifi_thresh = 90

rafi_thresh = 90

tefi_thresh = 100

use_percentile = True

radii = list(range(1, 3))



@pytest.fixture

def img():

    return imread(get_path('AS15-M-0298_SML.png'), as_gray=True)



@pytest.fixture

def img_coord():

    return 482.09783936, 652.40679932



@pytest.fixture

def template(img, img_coord):

    coord_x, coord_y = img_coord

    template= roi.Roi(img, coord_x, coord_y, 5, 5).clip()

    template = rotate(template, 90)

    return template



@pytest.fixture

def search():

    coord_x, coord_y = (482.09783936, 652.40679932)

    img = imread(get_path('AS15-M-0298_SML.png'), as_gray=True)

    search = roi.Roi(img, coord_x, coord_y, 21, 21).clip()

    return search



@pytest.fixture

def offset_template(img, img_coord):

    coord_x, coord_y = img_coord

    coord_x += 1

    coord_y += 1

    offset_template = roi.Roi(img, coord_x, coord_y, 5, 5).clip()

    return offset_template



def test_cifi_radii_too_large(template, search, caplog):

    # check all logs

    ciratefi.cifi(template, search, 1.0, radii=[100], use_percentile=False)

    num_pattern = '\d+'

    captured_log = caplog.records[0].getMessage()

    match = re.findall(num_pattern, captured_log)

    assert (f'Max Radii is larger than original template, '\

        f'this may produce sub-par results.Max radii: {match[0]} max template dimension: {match[1]}'\

             == caplog.records[0].getMessage())





def test_cifi_bounds_error(template, search):

    with pytest.raises(ValueError), pytest.warns(UserWarning):

        ciratefi.cifi(template, search, -1.1, use_percentile=False)



def test_cifi_radii_none_error(template, search):

    with pytest.raises(ValueError):

        ciratefi.cifi(template, search, 90, radii=None)



def test_cifi_scales_none_error(template, search):

    with pytest.raises(ValueError):

        ciratefi.cifi(template, search, 90, scales=None)



def test_cifi_template_too_large_error(template, search):

    with pytest.raises(ValueError):

        ciratefi.cifi(search,template, 90, scales=None)



@pytest.mark.parametrize('cifi_thresh, radii', [(90,list(range(1, 3)))])

@pytest.mark.filterwarnings('ignore::UserWarning')  # skimage deprecation warnings to move to new defaults

def test_cifi(template, search, cifi_thresh, radii):

    pixels, scales = ciratefi.cifi(template, search, thresh=cifi_thresh,

                                   radii=radii, use_percentile=True)



    assert search.shape == scales.shape

    assert (np.floor(search.shape[0]/2), np.floor(search.shape[1]/2)) in pixels

    assert pixels.size in range(0,search.size)



@pytest.mark.filterwarnings('ignore::UserWarning')  # skimage deprecation warnings to move to new defaults

def test_rafi_warning(template, search,caplog):

    rafi_pixels = [(10, 10)]

    rafi_scales = np.ones(search.shape, dtype=float)

    ciratefi.rafi(template, search, rafi_pixels,

            rafi_scales, thresh=1, radii=[100],

            use_percentile=False)



def test_rafi_bounds_error(template, search):

    rafi_pixels = [(10, 10)]

    rafi_scales = np.ones(search.shape, dtype=float)

    with pytest.raises(ValueError) as f, pytest.warns(UserWarning) as g:

        ciratefi.rafi(template, search, rafi_pixels, rafi_scales, -1.1, use_percentile=False)



def test_rafi_radii_list_none_error(template, search):

    rafi_pixels = [(10, 10)]

    with pytest.raises(ValueError):

        ciratefi.rafi(search, template, rafi_pixels, -1.1, radii=None)



def test_rafi_pixel_list_error(template, search):

    rafi_pixels = []

    rafi_scales = np.ones(search.shape, dtype=float)

    with pytest.raises(ValueError):

        ciratefi.rafi(template, search, rafi_pixels, rafi_scales)



def test_rafi_scales_list_error(template, search):

    rafi_pixels = [(10, 10)]

    with pytest.raises(ValueError):

        ciratefi.rafi(template, search, rafi_pixels, None)



def test_rafi_template_bigger_error(template, search):

    rafi_pixels = [(10, 10)]

    rafi_scales = np.ones(search.shape, dtype=float)

    with pytest.raises(ValueError):

        ciratefi.rafi(search, template, rafi_pixels,rafi_scales)



def test_rafi_shape_mismatch(template, search):

        rafi_pixels = [(10, 10)]

        rafi_scales = np.ones(search.shape, dtype=float)[:10]

        with pytest.raises(ValueError):

            ciratefi.rafi(template, search, rafi_pixels, rafi_scales)



@pytest.mark.parametrize("rafi_thresh, radii, alpha", [(90, list(range(1, 3)),math.pi/2)])

@pytest.mark.filterwarnings('ignore::UserWarning')  # skimage deprecation warnings to move to new defaults

def test_rafi(template, search, rafi_thresh, radii, alpha):

    rafi_pixels = [(10, 10)]

    rafi_scales = np.ones(search.shape, dtype=float)

    pixels, scales = ciratefi.rafi(template, search, rafi_pixels, rafi_scales,

                                   thresh=rafi_thresh, radii=radii, use_percentile=True,

                                   alpha=alpha)



    assert (np.floor(search.shape[0]/4), np.floor(search.shape[1]/4)) in pixels

    assert pixels.size in range(0, search.size)



# Alternate approach to the more verbose tests above - this tests all combinations

@pytest.mark.parametrize("tefi_pixels", [[(10,10)], None])

@pytest.mark.parametrize("tefi_scales", [np.ones((42,42), dtype=float),

                                         None,

                                         np.ones((42,42), dtype=float)[:10]])

@pytest.mark.parametrize("tefi_angles", [[3.14159265], None])

@pytest.mark.parametrize("thresh", [-1.1, 90])

@pytest.mark.parametrize("reverse", [False, True])

def test_tefi_errors(template, search, tefi_pixels, tefi_scales, tefi_angles, thresh, reverse):

    with pytest.raises(ValueError):

        if reverse:

            template, search = search, template

        ciratefi.tefi(template, search, tefi_pixels, tefi_scales,

                  tefi_angles, thresh=-1.1, use_percentile=False, alpha=math.pi/2)



@pytest.mark.filterwarnings('ignore::UserWarning')  # skimage deprecation warnings to move to new defaults

def test_tefi(template, search):

    tefi_pixels = [(10, 10)]

    tefi_scales = np.ones(search.shape, dtype=float)

    tefi_angles = [3.14159265]



    pixel = ciratefi.tefi(template, search, tefi_pixels, tefi_scales, tefi_angles,

                                   thresh=tefi_thresh, use_percentile=True, alpha=math.pi/2,

                                   upsampling=10)



    assert np.equal((11.5, 11.5), (pixel[1], pixel[0])).all()



@pytest.mark.filterwarnings('ignore::UserWarning')  # skimage deprecation warnings to move to new defaults

@pytest.mark.parametrize("cifi_thresh, rafi_thresh, tefi_thresh, alpha, radii",[

                         (90,90,100,math.pi/2,list(range(1, 3)))])

def test_ciratefi(template, search, cifi_thresh, rafi_thresh, tefi_thresh, alpha, radii):

    results = ciratefi.ciratefi(template, search, upsampling=10, cifi_thresh=cifi_thresh,

                                rafi_thresh=rafi_thresh, tefi_thresh=tefi_thresh,

                                use_percentile=True, alpha=alpha, radii=radii)



    assert len(results) == 3

    assert (np.array(results[1], results[0]) < 1).all()

docs/library/matcher/ciratefi.rst

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:mod:`matcher.ciratefi` --- RST-BC Invariant template match algorithm

=====================================================================



The :mod:`matcher.ciratefi` module



.. versionadded:: 0.1.0



.. automodule:: autocnet.matcher.ciratefi

   :synopsis:

   :members:

docs/library/matcher/index.rst

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@@ -22,7 +22,6 @@ Subpixel Refinement 
-------------------

.. toctree::

   subpixel

   ciratefi

   naive_template



High-Level Matchiing Examples