Commit 587292b5 authored by jlaura's avatar jlaura
Browse files

Merge pull request #124 from Kelvinrr/docs 

Docs
parents 62ad00a7 eae8d272

autocnet/graph/edge.py

+1 −2
Original line number Diff line number Diff line
@@ -4,12 +4,11 @@ from collections import MutableMapping 
import numpy as np

import pandas as pd



from autocnet.cg import cg

from autocnet.matcher import health

from autocnet.matcher import outlier_detector as od

from autocnet.matcher import suppression_funcs as spf

from autocnet.matcher import subpixel as sp

from autocnet.matcher.matcher import FlannMatcher

from autocnet.matcher.feature import FlannMatcher

from autocnet.transformation.transformations import FundamentalMatrix, Homography

from autocnet.vis.graph_view import plot_edge

from autocnet.cg import cg

autocnet/matcher/matcher.py→autocnet/matcher/feature.py

+1 −77
Original line number Diff line number Diff line
@@ -2,84 +2,9 @@ import warnings 


import cv2

import pandas as pd

import numpy as np

import math



from scipy.misc import imresize

from scipy.ndimage.interpolation import zoom





FLANN_INDEX_KDTREE = 1  # Algorithm to set centers,

DEFAULT_FLANN_PARAMETERS = dict(algorithm=FLANN_INDEX_KDTREE,

                                trees=3)





def pattern_match(template, image, upsampling=16, func=cv2.TM_CCOEFF_NORMED, error_check=False):

    """

    Call an arbitrary pattern matcher



    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

           Options: {cv2.TM_CCORR_NORMED, cv2.TM_CCOEFF_NORMED, cv2.TM_SQDIFF_NORMED}

           In testing the first two options perform significantly better with Apollo data.



    error_check : bool

                  If True, also apply a different matcher and test that the values

                  are not too divergent.  Default, False.



    Returns

    -------



    x : float

        The x offset



    y : float

        The y offset



    strength : float

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

    """



    different = {cv2.TM_SQDIFF_NORMED: cv2.TM_CCOEFF_NORMED,

                 cv2.TM_CCORR_NORMED: cv2.TM_SQDIFF_NORMED,

                 cv2.TM_CCOEFF_NORMED: cv2.TM_SQDIFF_NORMED}



    if upsampling < 1:

        raise ValueError



    u_template = zoom(template, upsampling, order=3)

    u_image = zoom(image, upsampling, order=3)



    result = cv2.matchTemplate(u_image, u_template, method=func)

    min_corr, max_corr, min_loc, max_loc = cv2.minMaxLoc(result)

    if func == cv2.TM_SQDIFF or func == cv2.TM_SQDIFF_NORMED:

        x, y = (min_loc[0], min_loc[1])

    else:

        x, y = (max_loc[0], max_loc[1])



    # Compute the idealized shift (image center)

    ideal_y = u_image.shape[0] / 2

    ideal_x = u_image.shape[1] / 2



    # Compute the shift from template upper left to template center

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

    x += (u_template.shape[1] / 2)



    x = (ideal_x - x) / upsampling

    y = (ideal_y - y) / upsampling

    return x, y, max_corr

DEFAULT_FLANN_PARAMETERS = dict(algorithm=FLANN_INDEX_KDTREE, trees=3)





class FlannMatcher(object):
@@ -182,4 +107,3 @@ class FlannMatcher(object): 
        return pd.DataFrame(matched, columns=['source_image', 'source_idx',

                                              'destination_image', 'destination_idx',

                                              'distance'])

autocnet/matcher/naive_template.py

0 → 100644
+71 −0
Original line number Diff line number Diff line 
import cv2

from scipy.ndimage.interpolation import zoom





def pattern_match(template, image, upsampling=16, func=cv2.TM_CCOEFF_NORMED, error_check=False):

    """

    Call an arbitrary pattern matcher



    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

           Options: {cv2.TM_CCORR_NORMED, cv2.TM_CCOEFF_NORMED, cv2.TM_SQDIFF_NORMED}

           In testing the first two options perform significantly better with Apollo data.



    error_check : bool

                  If True, also apply a different matcher and test that the values

                  are not too divergent.  Default, False.



    Returns

    -------



    x : float

        The x offset



    y : float

        The y offset



    strength : float

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

    """



    different = {cv2.TM_SQDIFF_NORMED: cv2.TM_CCOEFF_NORMED,

                 cv2.TM_CCORR_NORMED: cv2.TM_SQDIFF_NORMED,

                 cv2.TM_CCOEFF_NORMED: cv2.TM_SQDIFF_NORMED}



    if upsampling < 1:

        raise ValueError



    u_template = zoom(template, upsampling, order=3)

    u_image = zoom(image, upsampling, order=3)



    result = cv2.matchTemplate(u_image, u_template, method=func)

    min_corr, max_corr, min_loc, max_loc = cv2.minMaxLoc(result)

    if func == cv2.TM_SQDIFF or func == cv2.TM_SQDIFF_NORMED:

        x, y = (min_loc[0], min_loc[1])

    else:

        x, y = (max_loc[0], max_loc[1])



    # Compute the idealized shift (image center)

    ideal_y = u_image.shape[0] / 2

    ideal_x = u_image.shape[1] / 2



    # Compute the shift from template upper left to template center

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

    x += (u_template.shape[1] / 2)



    x = (ideal_x - x) / upsampling

    y = (ideal_y - y) / upsampling

    return x, y, max_corr

autocnet/matcher/subpixel.py

+2 −2
Original line number Diff line number Diff line 
import numpy as np



from autocnet.matcher import matcher

from autocnet.matcher import naive_template



# TODO: look into KeyPoint.size and perhaps use to determine an appropriately-sized search/template.
@@ -74,7 +74,7 @@ def subpixel_offset(template, search, **kwargs): 
               Strength of the correspondence in the range [-1, 1]

    """



    x_offset, y_offset, strength = matcher.pattern_match(template, search, **kwargs)

    x_offset, y_offset, strength = naive_template.pattern_match(template, search, **kwargs)

    return x_offset, y_offset, strength



'''

autocnet/matcher/tests/test_matcher.py

+2 −2
Original line number Diff line number Diff line
@@ -5,7 +5,7 @@ import warnings 


import cv2



from .. import matcher

from .. import feature

from autocnet.examples import get_path



sys.path.append(os.path.abspath('..'))
@@ -25,7 +25,7 @@ class TestMatcher(unittest.TestCase): 
        self.fd['AS15-M-0297_SML.png'] = sift.detectAndCompute(im2, None)



    def test_flann_match_k_eq_2(self):

        fmatcher = matcher.FlannMatcher()

        fmatcher = feature.FlannMatcher()

        source_image = self.fd['AS15-M-0296_SML.png']

        fmatcher.add(source_image[1], 0)