Python cv2 模块，FM_RANSAC 实例源码

def compute_fundamental(x1, x2, method=cv2.FM_RANSAC): 
    """
    Computes the fundamental matrix from corresponding points x1, x2 using
    the 8 point algorithm.

    Options: 
    CV_FM_7POINT for a 7-point algorithm.  N = 7
    CV_FM_8POINT for an 8-point algorithm.  N >= 8
    CV_FM_RANSAC for the RANSAC algorithm.  N >= 8
    CV_FM_LMEDS for the LMedS algorithm.  N >= 8"
    """
    assert(x1.shape == x2.shape)
    if len(x1) < 8:
        raise RuntimeError('Fundamental matrix requires N >= 8 pts')

    F, mask = cv2.findFundamentalMat(x1, x2, method)
    return F, mask

def calculate_fundamental_matrix(self, previous_pts, current_pts):
        fundamental_matrix, mask = cv2.findFundamentalMat(
            previous_pts,
            current_pts,
            cv2.FM_RANSAC
        )

        if fundamental_matrix is None or fundamental_matrix.shape == (1, 1):
            # dang, no fundamental matrix found
            raise Exception('No fundamental matrix found')
        elif fundamental_matrix.shape[0] > 3:
            # more than one matrix found, just pick the first
            fundamental_matrix = fundamental_matrix[0:3, 0:3]

        return np.matrix(fundamental_matrix)

def old_get_fundamental_mat_normalized(p1,p2,use_ransac=False):
    """
    A small wrapper around cv2.getFundamentalMat, with normalization of
    point locations.

    """
    mu1 = p1.mean(axis=0)
    std1 = p1.std(axis=0)
    mu2 = p2.mean(axis=0)
    std2 = p2.std(axis=0)

    p1_ = (p1 - mu1) / std1
    p2_ = (p2 - mu2) / std2

    if use_ransac:
        F_, inliers = cv2.findFundamentalMat(p1_,p2_,method=cv2.FM_RANSAC,param1=4 * 2.0/(std1+std2).mean())
    else:
        F_,inliers = cv2.findFundamentalMat(p1_,p2_,method=cv2.FM_LMEDS)
    A1 = np.array([[1.0/std1[0], 0.0, -mu1[0]/std1[0]],
                   [0, 1.0/std1[1], -mu1[1]/std1[1]],
                   [0,0,1.0]])
    A2 = np.array([[1.0/std2[0], 0.0, -mu2[0]/std2[0]],
                   [0, 1.0/std2[1], -mu2[1]/std2[1]],
                   [0,0,1.0]])
    F = A2.T.dot(F_).dot(A1)
    #F = A2inv.dot(H_).dot(A1)
    return F,inliers


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# Normalization functions
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