Backport of new python tests from master branch(PR https://github.com/Itseez/opencv/pull/6025).

At the moment tests requre samples/data copied to source location from master branch.

modules/python/test/test_fitline.py

@@ -0,0 +1,66 @@
+#!/usr/bin/env python
+
+'''
+Robust line fitting.
+==================
+
+Example of using cv2.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
+
+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 = ['CV_DIST_L2', 'CV_DIST_L1', 'CV_DIST_L12', 'CV_DIST_FAIR', 'CV_DIST_WELSCH', 'CV_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(cv2.cv, name)
+            vx, vy, cx, cy = cv2.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) / cv2.norm(np.float32(p1) - p0)
+
+        for i in range(len(lines)):
+            self.assertLessEqual(cv2.norm(refVec - lines[i][0:2], cv2.NORM_L2), eps)