Commit 2822edbb authored by jay's avatar jay
Browse files

Added a error computation to the homography

parent 245da80a

autocnet/graph/network.py

+11 −1
Original line number Diff line number Diff line
@@ -237,7 +237,6 @@ class CandidateGraph(nx.Graph): 


            transformation_matrix, ransac_mask = od.compute_homography(s_coords,

                                                                       d_coords)



            ransac_mask = ransac_mask.ravel()

            # Convert the truncated RANSAC mask back into a full length mask

            if clean_keys:
@@ -245,6 +244,17 @@ class CandidateGraph(nx.Graph): 
            else:

                mask = ransac_mask



            # Compute the error in the homography

            s_trans = np.hstack((s_coords[::-1],np.ones(s_coords.shape[0]).reshape(-1,1))).T

            s_trans = np.dot(transformation_matrix, s_trans).T



            #Normalize the error

            for i in range(3):

                s_trans[i] /= s_trans[2]



            d_trans = np.hstack((d_coords[::-1], np.ones(d_coords.shape[0]).reshape(-1,1)))



            attributes['homography_error'] = np.sqrt(np.sum((d_trans - s_trans)**2, axis=1))

            attributes['homography'] = transformation_matrix

            attributes['ransac'] = mask

autocnet/matcher/outlier_detector.py

+33 −1
Original line number Diff line number Diff line
@@ -148,3 +148,35 @@ def compute_homography(kp1, kp2, outlier_algorithm=cv2.RANSAC, reproj_threshold= 
                                                     reproj_threshold)

    mask = mask.astype(bool)

    return transformation_matrix, mask





def homography_test(kp1, kp2, homography, threshold=3.0):

    """

    Utilize the transformation matrix (homography) to check whether

    keypoint one (`kp1`) to keypoint two (`kp2`).  If the point is within

    threshold units (where the unit is agnositic and a function of the

    image pixel size) return true.



    Parameters

    ----------

    kp1 : list

          of x, y coordinates

    kp2 : list

          of x, y coordinates



    homography : ndarray

                 3x3 transformation matrix



    threshold : float

                The threshold within which true is returned



    Returns

    -------

     : bool

       True if within the threshold, else False

    """

    kp1 = kp1[::-1]

    kp2 = kp2[::-1]

    kp1 = np.array([*kp1, 1])

    kp2 = np.array([*kp2, 1])

    print(kp1, np.dot(kp1, homography), kp2)

functional_tests/test_two_image.py

+1 −1
Original line number Diff line number Diff line
@@ -70,7 +70,7 @@ class TestTwoImageMatching(unittest.TestCase): 


            # Perform the ratio test

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

            self.assertIn(ratio_mask.sum(), range(400, 451))

            self.assertIn(ratio_mask.sum(), range(390, 451))

            attributes['ratio'] = ratio_mask



            mask = np.array(ratio_mask * symmetry_mask)