f4334277
 Hu Chunming
提交3rdparty
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#!/usr/bin/env python
'''
Robust line fitting.
==================
Example of using cv.fitLine function for fitting line
to points in presence of outliers.
Switch through different M-estimator functions and see,
how well the robust functions fit the line even
in case of ~50% of outliers.
'''
# Python 2/3 compatibility
from __future__ import print_function
import sys
PY3 = sys.version_info[0] == 3
import numpy as np
import cv2 as cv
from tests_common import NewOpenCVTests
w, h = 512, 256
def toint(p):
return tuple(map(int, p))
def sample_line(p1, p2, n, noise=0.0):
np.random.seed(10)
p1 = np.float32(p1)
t = np.random.rand(n,1)
return p1 + (p2-p1)*t + np.random.normal(size=(n, 2))*noise
dist_func_names = ['DIST_L2', 'DIST_L1', 'DIST_L12', 'DIST_FAIR', 'DIST_WELSCH', 'DIST_HUBER']
class fitline_test(NewOpenCVTests):
def test_fitline(self):
noise = 5
n = 200
r = 5 / 100.0
outn = int(n*r)
p0, p1 = (90, 80), (w-90, h-80)
line_points = sample_line(p0, p1, n-outn, noise)
outliers = np.random.rand(outn, 2) * (w, h)
points = np.vstack([line_points, outliers])
lines = []
for name in dist_func_names:
func = getattr(cv, name)
vx, vy, cx, cy = cv.fitLine(np.float32(points), func, 0, 0.01, 0.01)
line = [float(vx), float(vy), float(cx), float(cy)]
lines.append(line)
eps = 0.05
refVec = (np.float32(p1) - p0) / cv.norm(np.float32(p1) - p0)
for i in range(len(lines)):
self.assertLessEqual(cv.norm(refVec - lines[i][0:2], cv.NORM_L2), eps)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
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