Commit 0cd05e86 authored by Jay's avatar Jay Committed by jay
Browse files

Update for CR

parent 5a5b2dcf

autocnet/graph/network.py

+17 −3
Original line number Diff line number Diff line
@@ -203,9 +203,17 @@ class CandidateGraph(nx.Graph): 
                     The identifier for the source node

        destination_key : str

                          The identifier for the destination node



        outlier_algorithm : object

                            An openCV outlier detections algorithm, e.g. cv2.RANSAC

        Returns

        -------

         : tuple

        transformation_matrix : ndarray

                                The 3x3 transformation matrix



        mask : ndarray

               Boolean array of the outliers



           A tuple of the form (transformation matrix, bad entry mask)

           The returned tuple is empty if there is no edge between the source and destination nodes or

           if it exists, but has not been populated with a matches dataframe.
@@ -230,13 +238,19 @@ class CandidateGraph(nx.Graph): 


                    source_keypoints.append(src_keypoint)

                    destination_keypoints.append(dest_keypoint)

                return cv2.findHomography(np.array(source_keypoints), np.array(destination_keypoints),

                                          outlier_algorithm, 5.0)

                transformation_matrix, mask = cv2.findHomography(np.array(source_keypoints),

                                                                 np.array(destination_keypoints),

                                                                 outlier_algorithm,

                                                                 5.0)

                mask = mask.astype(bool)

                return transformation_matrix, mask

            else:

                return ('', '')

        else:

            return ('','')







    def to_cnet(self, clean_keys=[]):

        """

        Generate a control network (C) object from a graph

autocnet/matcher/matcher.py

+9 −5
Original line number Diff line number Diff line
@@ -13,6 +13,8 @@ DEFAULT_FLANN_PARAMETERS = dict(algorithm=FLANN_INDEX_KDTREE, 
def pattern_match(template, image, upsampling=10,

                  func=match_template):

    """

    Call an arbitrary pattern matcher



    Parameters

    ----------

    template : ndarray
@@ -38,7 +40,7 @@ def pattern_match(template, image, upsampling=10, 
        The y offset



    strength : float

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

               The strength of the correlation in the range [-1, 1].
@@ -63,13 +65,15 @@ def pattern_match(template, image, upsampling=10, 
    y /= upsampling



    # Offset from the UL origin to the image center

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

    x += (template.shape[1] / 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

    ideal_y = image.shape[0] / 2

    ideal_x = image.shape[1] / 2



    x = ideal_x - x

    y = ideal_y - y



    # Find the maximum correlation

    strength = np.max(match)

autocnet/matcher/outlier_detector.py

+2 −1
Original line number Diff line number Diff line
@@ -56,7 +56,7 @@ def distance_ratio(matches, ratio=0.8): 
        group_size = len(group)

        # If we can not perform the ratio check because all matches are symmetrical

        if len(group['destination_idx'].unique()) == 1:

            mask[counter:counter + group_size] = False

            mask[counter:counter + group_size] = True

            counter += group_size

        else:

            # Otherwise, we can perform the ratio test
@@ -111,3 +111,4 @@ def mirroring_test(matches): 
    duplicates = matches.duplicated(keep='first').values

    duplicates.astype(bool, copy=False)

    return duplicates

autocnet/matcher/tests/test_outlier_detector.py

+1 −2
Original line number Diff line number Diff line
@@ -39,7 +39,6 @@ class TestOutlierDetector(unittest.TestCase): 
        self.assertTrue(len(outlier_detector.distance_ratio(self.matches)), 13)



    def test_self_neighbors(self):

        print(self.matches[outlier_detector.self_neighbors(self.matches)])

        # returned mask should be same length as input df

        self.assertEquals(len(outlier_detector.self_neighbors(self.matches)), len(self.matches))

functional_tests/test_two_image.py

+1 −2
Original line number Diff line number Diff line
@@ -70,12 +70,11 @@ class TestTwoImageMatching(unittest.TestCase): 


            # Perform the ratio test

            ratio_mask = od.distance_ratio(matches, ratio=0.95)

            self.assertIn(ratio_mask.sum(), range(30,40))

            self.assertIn(ratio_mask.sum(), range(30,45))

            attributes['ratio'] = ratio_mask



            mask = np.array(ratio_mask * symmetry_mask)

            self.assertIn(len(matches.loc[mask]), range(4,10))



        # Step: And create a C object

        cnet = cg.to_cnet(clean_keys=['symmetry', 'ratio'])