added a pattern matching function

import cv2

import numpy as np

import pandas as pd

from autocnet.graph.network import CandidateGraph

import numpy as np

from skimage.feature import match_template

from scipy.misc import imresize

FLANN_INDEX_KDTREE = 1  # Algorithm to set centers,

DEFAULT_FLANN_PARAMETERS = dict(algorithm=FLANN_INDEX_KDTREE,

                                trees=3)

def pattern_match(template, image, upsampling=10,

                  func=match_template):

    """

    Parameters

    ----------

    template : ndarray

               The input search template used to 'query' the destination

               image

    image : ndarray

            The image or sub-image to be searched

    upsampling : int

                 The multiplier to upsample the template and image.

    func : object

           The function to be used to perform the template based matching

    Returns

    -------

    x : float

        The x offset

    y : float

        The y offset

    strength : float

               The strength of teh correlation in the range [-1, 1].

    """

    if upsampling < 1:

        raise ValueError

    u_template = imresize(template, (template.shape[0] * upsampling,

                                   template.shape[1] * upsampling),

                        interp='bicubic')

    u_image = imresize(image, (image.shape[0] * upsampling,

                             image.shape[1] * upsampling),

                     interp='bicubic')

    # Find the the upper left origin of the template in the image

    match = func(u_image, u_template)

    y, x = np.unravel_index(np.argmax(match), match.shape)

    # Resample the match back to the native image resolution

    x /= upsampling

    y /= upsampling

    # Offset from the UL origin to the image center

    x += (template.shape[0] / 2)

    y += (template.shape[0] / 2)

    # Compute the offset to adjust the image match point location

    ideal_center = image.shape[0] / 2

    x = ideal_center - x

    y = ideal_center - y

    # Find the maximum correlation

    strength = np.max(match)

    return x, y, strength

class FlannMatcher(object):

    """

    A wrapper to the OpenCV Flann based matcher class that adds