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66 lines
1.6 KiB
Python
66 lines
1.6 KiB
Python
#!/usr/bin/env python
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'''
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Robust line fitting.
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==================
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Example of using cv2.fitLine function for fitting line
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to points in presence of outliers.
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Switch through different M-estimator functions and see,
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how well the robust functions fit the line even
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in case of ~50% of outliers.
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'''
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# Python 2/3 compatibility
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from __future__ import print_function
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import sys
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PY3 = sys.version_info[0] == 3
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import numpy as np
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import cv2
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from tests_common import NewOpenCVTests
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w, h = 512, 256
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def toint(p):
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return tuple(map(int, p))
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def sample_line(p1, p2, n, noise=0.0):
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np.random.seed(10)
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p1 = np.float32(p1)
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t = np.random.rand(n,1)
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return p1 + (p2-p1)*t + np.random.normal(size=(n, 2))*noise
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dist_func_names = ['CV_DIST_L2', 'CV_DIST_L1', 'CV_DIST_L12', 'CV_DIST_FAIR', 'CV_DIST_WELSCH', 'CV_DIST_HUBER']
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class fitline_test(NewOpenCVTests):
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def test_fitline(self):
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noise = 5
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n = 200
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r = 5 / 100.0
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outn = int(n*r)
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p0, p1 = (90, 80), (w-90, h-80)
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line_points = sample_line(p0, p1, n-outn, noise)
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outliers = np.random.rand(outn, 2) * (w, h)
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points = np.vstack([line_points, outliers])
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lines = []
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for name in dist_func_names:
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func = getattr(cv2.cv, name)
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vx, vy, cx, cy = cv2.fitLine(np.float32(points), func, 0, 0.01, 0.01)
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line = [float(vx), float(vy), float(cx), float(cy)]
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lines.append(line)
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eps = 0.05
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refVec = (np.float32(p1) - p0) / cv2.norm(np.float32(p1) - p0)
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for i in range(len(lines)):
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self.assertLessEqual(cv2.norm(refVec - lines[i][0:2], cv2.NORM_L2), eps) |