Python ransac Examples

Example #1

File: test_sfm.py Project: ziyeshanwai/pcv_practice

def F_from_ransac(x1, x2, model, maxiter=5000, match_threshold=1e-3):
    """ Robust estimation of a fundamental matrix F from point
    correspondences using RANSAC (ransac.py from
    http://www.scipy.org/Cookbook/RANSAC).

    input: x1, x2 (3*n arrays) points in hom. coordinates. """

    import PCV.tools.ransac as ransac
    data = np.vstack((x1, x2))
    d = 20 # 20 is the original
    F, ransac_data = ransac.ransac(data.T, model,
                                   18, maxiter, match_threshold, d, return_all=True)
    return F, ransac_data['inliers']

Example #2

File: BasicMatrix.py Project: wjh776a68/MLEXP-Team-Result

def F_from_ransac(x1, x2, model, maxiter=5000, match_threshold=1e-6):
    """
    使用RANSAC从点对应中稳健估计基本矩阵F.
  （来自http://www.scipy.org/Cookbook/RANSAC的ransac.py）。
    input: x1, x2 (3*n arrays) points in hom. coordinates. """
 
    from PCV.tools import ransac
    data = np.vstack((x1, x2))
    d = 10 # 20 is the original
    # 计算F并返回inlier索引
    F, ransac_data = ransac.ransac(data.T, model,
                                   8, maxiter, match_threshold, d, return_all=True)
    return F, ransac_data['inliers']

Example #3

File: sfm.py Project: onaries/Python-Exercise

def F_from_ransac(x1,x2,model,maxiter=5000,match_theshold=1e-6):
    """ Robust estimation of a fundamental matrix F from point 
        correspondences using RANSAC (ransac.py from
        http://www.scipy.org/Cookbook/RANSAC).

        input: x1,x2 (3*n arrays) points in hom. coordinates. """

    from PCV.tools import ransac

    data = vstack((x1,x2))
    
    # compute F and return with inlier index
    F,ransac_data = ransac.ransac(data.T,model,8,maxiter,match_theshold,20,return_all=True)
    return F, ransac_data['inliers']

Example #4

def F_from_ransac(x1,x2,model,maxiter=5000,match_theshold=1e-6):
    """ Robust estimation of a fundamental matrix F from point 
        correspondences using RANSAC (ransac.py from
        http://www.scipy.org/Cookbook/RANSAC).

        input: x1,x2 (3*n arrays) points in hom. coordinates. """

    from PCV.tools import ransac

    data = vstack((x1,x2))
    
    # compute F and return with inlier index
    F,ransac_data = ransac.ransac(data.T,model,8,maxiter,match_theshold,20,return_all=True)
    return F, ransac_data['inliers']

Example #5

File: homography.py Project: Adon-m/PCV

def H_from_ransac(fp,tp,model,maxiter=1000,match_theshold=10):
    """ Robust estimation of homography H from point 
        correspondences using RANSAC (ransac.py from
        http://www.scipy.org/Cookbook/RANSAC).
        
        input: fp,tp (3*n arrays) points in hom. coordinates. """
    
    from PCV.tools import ransac
    
    # group corresponding points
    data = vstack((fp,tp))
    
    # compute H and return
    H,ransac_data = ransac.ransac(data.T,model,4,maxiter,match_theshold,10,return_all=True)
    return H,ransac_data['inliers']

Example #6

File: homography.py Project: Nailim/shuttler

def H_from_ransac(fp, tp, model, maxiter=1000, match_theshold=10):
    """ Robust estimation of homography H from point
correspondences using RANSAC (ransac.py from
http://www.scipy.org/Cookbook/RANSAC).
input: fp,tp (3*n arrays) points in hom. coordinates. """

    from PCV.tools import ransac

    # group corresponding points
    data = vstack((fp, tp))

    # compute H and return
    H, ransac_data = ransac.ransac(data.T,
                                   model,
                                   4,
                                   maxiter,
                                   match_theshold,
                                   10,
                                   return_all=True)
    return H, ransac_data['inliers']