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']
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']
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']
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']
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']
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']
