Merge pull request #9548 from alalek:python_tests

CMakeLists.txt

@@ -853,6 +853,7 @@ if(ANDROID OR NOT UNIX)
 endif()
 
 if(COMMAND ocv_pylint_finalize)
+  ocv_pylint_add_directory(${CMAKE_CURRENT_LIST_DIR}/modules/python/test)
   ocv_pylint_add_directory(${CMAKE_CURRENT_LIST_DIR}/samples/python)
   ocv_pylint_add_directory(${CMAKE_CURRENT_LIST_DIR}/samples/dnn)
   ocv_pylint_add_directory_recurse(${CMAKE_CURRENT_LIST_DIR}/samples/python/tutorial_code)

modules/python/test/test.py

@@ -1,21 +1,9 @@
 #!/usr/bin/env python
 
 from __future__ import print_function
-import unittest
+
-import random
-import time
-import math
-import sys
-import array
-import tarfile
-import hashlib
 import os
-import getopt
+import unittest
-import operator
-import functools
-import numpy as np
-import cv2
-import argparse
 
 # Python 3 moved urlopen to urllib.requests
 try:
@@ -25,7 +13,6 @@ except ImportError:
 
 from tests_common import NewOpenCVTests
 
-# Tests to run first; check the handful of basic operations that the later tests rely on
 
 basedir = os.path.abspath(os.path.dirname(__file__))
 
@@ -33,184 +20,5 @@ def load_tests(loader, tests, pattern):
     tests.addTests(loader.discover(basedir, pattern='test_*.py'))
     return tests
 
-class Hackathon244Tests(NewOpenCVTests):
-
-    def test_int_array(self):
-        a = np.array([-1, 2, -3, 4, -5])
-        absa0 = np.abs(a)
-        self.assertTrue(cv2.norm(a, cv2.NORM_L1) == 15)
-        absa1 = cv2.absdiff(a, 0)
-        self.assertEqual(cv2.norm(absa1, absa0, cv2.NORM_INF), 0)
-
-    def test_imencode(self):
-        a = np.zeros((480, 640), dtype=np.uint8)
-        flag, ajpg = cv2.imencode("img_q90.jpg", a, [cv2.IMWRITE_JPEG_QUALITY, 90])
-        self.assertEqual(flag, True)
-        self.assertEqual(ajpg.dtype, np.uint8)
-        self.assertGreater(ajpg.shape[0], 1)
-        self.assertEqual(ajpg.shape[1], 1)
-
-    def test_projectPoints(self):
-        objpt = np.float64([[1,2,3]])
-        imgpt0, jac0 = cv2.projectPoints(objpt, np.zeros(3), np.zeros(3), np.eye(3), np.float64([]))
-        imgpt1, jac1 = cv2.projectPoints(objpt, np.zeros(3), np.zeros(3), np.eye(3), None)
-        self.assertEqual(imgpt0.shape, (objpt.shape[0], 1, 2))
-        self.assertEqual(imgpt1.shape, imgpt0.shape)
-        self.assertEqual(jac0.shape, jac1.shape)
-        self.assertEqual(jac0.shape[0], 2*objpt.shape[0])
-
-    def test_estimateAffine3D(self):
-        pattern_size = (11, 8)
-        pattern_points = np.zeros((np.prod(pattern_size), 3), np.float32)
-        pattern_points[:,:2] = np.indices(pattern_size).T.reshape(-1, 2)
-        pattern_points *= 10
-        (retval, out, inliers) = cv2.estimateAffine3D(pattern_points, pattern_points)
-        self.assertEqual(retval, 1)
-        if cv2.norm(out[2,:]) < 1e-3:
-            out[2,2]=1
-        self.assertLess(cv2.norm(out, np.float64([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]])), 1e-3)
-        self.assertEqual(cv2.countNonZero(inliers), pattern_size[0]*pattern_size[1])
-
-    def test_fast(self):
-        fd = cv2.FastFeatureDetector_create(30, True)
-        img = self.get_sample("samples/data/right02.jpg", 0)
-        img = cv2.medianBlur(img, 3)
-        imgc = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-        keypoints = fd.detect(img)
-        self.assertTrue(600 <= len(keypoints) <= 700)
-        for kpt in keypoints:
-            self.assertNotEqual(kpt.response, 0)
-
-    def check_close_angles(self, a, b, angle_delta):
-        self.assertTrue(abs(a - b) <= angle_delta or
-                        abs(360 - abs(a - b)) <= angle_delta)
-
-    def check_close_pairs(self, a, b, delta):
-        self.assertLessEqual(abs(a[0] - b[0]), delta)
-        self.assertLessEqual(abs(a[1] - b[1]), delta)
-
-    def check_close_boxes(self, a, b, delta, angle_delta):
-        self.check_close_pairs(a[0], b[0], delta)
-        self.check_close_pairs(a[1], b[1], delta)
-        self.check_close_angles(a[2], b[2], angle_delta)
-
-    def test_geometry(self):
-        npt = 100
-        np.random.seed(244)
-        a = np.random.randn(npt,2).astype('float32')*50 + 150
-
-        img = np.zeros((300, 300, 3), dtype='uint8')
-        be = cv2.fitEllipse(a)
-        br = cv2.minAreaRect(a)
-        mc, mr = cv2.minEnclosingCircle(a)
-
-        be0 = ((150.2511749267578, 150.77322387695312), (158.024658203125, 197.57696533203125), 37.57804489135742)
-        br0 = ((161.2974090576172, 154.41793823242188), (199.2301483154297, 207.7177734375), -9.164555549621582)
-        mc0, mr0 = (160.41790771484375, 144.55152893066406), 136.713500977
-
-        self.check_close_boxes(be, be0, 5, 15)
-        self.check_close_boxes(br, br0, 5, 15)
-        self.check_close_pairs(mc, mc0, 5)
-        self.assertLessEqual(abs(mr - mr0), 5)
-
-    def test_inheritance(self):
-        bm = cv2.StereoBM_create()
-        bm.getPreFilterCap() # from StereoBM
-        bm.getBlockSize() # from SteroMatcher
-
-        boost = cv2.ml.Boost_create()
-        boost.getBoostType() # from ml::Boost
-        boost.getMaxDepth() # from ml::DTrees
-        boost.isClassifier() # from ml::StatModel
-
-    def test_umat_construct(self):
-        data = np.random.random([512, 512])
-        # UMat constructors
-        data_um = cv2.UMat(data)  # from ndarray
-        data_sub_um = cv2.UMat(data_um, [128, 256], [128, 256])  # from UMat
-        data_dst_um = cv2.UMat(128, 128, cv2.CV_64F)  # from size/type
-        # test continuous and submatrix flags
-        assert data_um.isContinuous() and not data_um.isSubmatrix()
-        assert not data_sub_um.isContinuous() and data_sub_um.isSubmatrix()
-        # test operation on submatrix
-        cv2.multiply(data_sub_um, 2., dst=data_dst_um)
-        assert np.allclose(2. * data[128:256, 128:256], data_dst_um.get())
-
-    def test_umat_handle(self):
-        a_um = cv2.UMat(256, 256, cv2.CV_32F)
-        ctx_handle = cv2.UMat.context()  # obtain context handle
-        queue_handle = cv2.UMat.queue()  # obtain queue handle
-        a_handle = a_um.handle(cv2.ACCESS_READ)  # obtain buffer handle
-        offset = a_um.offset  # obtain buffer offset
-
-    def test_umat_matching(self):
-        img1 = self.get_sample("samples/data/right01.jpg")
-        img2 = self.get_sample("samples/data/right02.jpg")
-
-        orb = cv2.ORB_create()
-
-        img1, img2 = cv2.UMat(img1), cv2.UMat(img2)
-        ps1, descs_umat1 = orb.detectAndCompute(img1, None)
-        ps2, descs_umat2 = orb.detectAndCompute(img2, None)
-
-        self.assertIsInstance(descs_umat1, cv2.UMat)
-        self.assertIsInstance(descs_umat2, cv2.UMat)
-        self.assertGreater(len(ps1), 0)
-        self.assertGreater(len(ps2), 0)
-
-        bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
-
-        res_umats = bf.match(descs_umat1, descs_umat2)
-        res = bf.match(descs_umat1.get(), descs_umat2.get())
-
-        self.assertGreater(len(res), 0)
-        self.assertEqual(len(res_umats), len(res))
-
-    def test_umat_optical_flow(self):
-        img1 = self.get_sample("samples/data/right01.jpg", cv2.IMREAD_GRAYSCALE)
-        img2 = self.get_sample("samples/data/right02.jpg", cv2.IMREAD_GRAYSCALE)
-        # Note, that if you want to see performance boost by OCL implementation - you need enough data
-        # For example you can increase maxCorners param to 10000 and increase img1 and img2 in such way:
-        # img = np.hstack([np.vstack([img] * 6)] * 6)
-
-        feature_params = dict(maxCorners=239,
-                              qualityLevel=0.3,
-                              minDistance=7,
-                              blockSize=7)
-
-        p0 = cv2.goodFeaturesToTrack(img1, mask=None, **feature_params)
-        p0_umat = cv2.goodFeaturesToTrack(cv2.UMat(img1), mask=None, **feature_params)
-        self.assertEqual(p0_umat.get().shape, p0.shape)
-
-        p0 = np.array(sorted(p0, key=lambda p: tuple(p[0])))
-        p0_umat = cv2.UMat(np.array(sorted(p0_umat.get(), key=lambda p: tuple(p[0]))))
-        self.assertTrue(np.allclose(p0_umat.get(), p0))
-
-        p1_mask_err = cv2.calcOpticalFlowPyrLK(img1, img2, p0, None)
-
-        p1_mask_err_umat0 = map(cv2.UMat.get, cv2.calcOpticalFlowPyrLK(img1, img2, p0_umat, None))
-        p1_mask_err_umat1 = map(cv2.UMat.get, cv2.calcOpticalFlowPyrLK(cv2.UMat(img1), img2, p0_umat, None))
-        p1_mask_err_umat2 = map(cv2.UMat.get, cv2.calcOpticalFlowPyrLK(img1, cv2.UMat(img2), p0_umat, None))
-
-        # # results of OCL optical flow differs from CPU implementation, so result can not be easily compared
-        # for p1_mask_err_umat in [p1_mask_err_umat0, p1_mask_err_umat1, p1_mask_err_umat2]:
-        #     for data, data_umat in zip(p1_mask_err, p1_mask_err_umat):
-        #         self.assertTrue(np.allclose(data, data_umat))
-
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='run OpenCV python tests')
+    NewOpenCVTests.bootstrap()
-    parser.add_argument('--repo', help='use sample image files from local git repository (path to folder), '
-                                       'if not set, samples will be downloaded from github.com')
-    parser.add_argument('--data', help='<not used> use data files from local folder (path to folder), '
-                                        'if not set, data files will be downloaded from docs.opencv.org')
-    args, other = parser.parse_known_args()
-    print("Testing OpenCV", cv2.__version__)
-    print("Local repo path:", args.repo)
-    NewOpenCVTests.repoPath = args.repo
-    try:
-        NewOpenCVTests.extraTestDataPath = os.environ['OPENCV_TEST_DATA_PATH']
-    except KeyError:
-        print('Missing opencv extra repository. Some of tests may fail.')
-    random.seed(0)
-    unit_argv = [sys.argv[0]] + other;
-    unittest.main(argv=unit_argv)

modules/python/test/test_calibration.py

@@ -16,8 +16,6 @@ from tests_common import NewOpenCVTests
 class calibration_test(NewOpenCVTests):
 
     def test_calibration(self):
-
-        from glob import glob
         img_names = []
         for i in range(1, 15):
             if i < 10:
@@ -34,7 +32,6 @@ class calibration_test(NewOpenCVTests):
         obj_points = []
         img_points = []
         h, w = 0, 0
-        img_names_undistort = []
         for fn in img_names:
             img = self.get_sample(fn, 0)
             if img is None:
@@ -53,7 +50,7 @@ class calibration_test(NewOpenCVTests):
             obj_points.append(pattern_points)
 
         # calculate camera distortion
-        rms, camera_matrix, dist_coefs, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, (w, h), None, None, flags = 0)
+        rms, camera_matrix, dist_coefs, _rvecs, _tvecs = cv2.calibrateCamera(obj_points, img_points, (w, h), None, None, flags = 0)
 
         eps = 0.01
         normCamEps = 10.0
@@ -69,3 +66,8 @@ class calibration_test(NewOpenCVTests):
         self.assertLess(abs(rms - 0.196334638034), eps)
         self.assertLess(cv2.norm(camera_matrix - cameraMatrixTest, cv2.NORM_L1), normCamEps)
         self.assertLess(cv2.norm(dist_coefs - distCoeffsTest, cv2.NORM_L1), normDistEps)
+
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_camshift.py

@@ -73,7 +73,7 @@ class camshift_test(NewOpenCVTests):
                 prob = cv2.calcBackProject([hsv], [0], self.hist, [0, 180], 1)
                 prob &= mask
                 term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )
-                track_box, self.track_window = cv2.CamShift(prob, self.track_window, term_crit)
+                _track_box, self.track_window = cv2.CamShift(prob, self.track_window, term_crit)
 
             trackingRect = np.array(self.track_window)
             trackingRect[2] += trackingRect[0]
@@ -89,4 +89,8 @@ class camshift_test(NewOpenCVTests):
 
     def test_camshift(self):
         self.prepareRender()
-        self.runTracker()
+        self.runTracker()
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_dft.py

@@ -43,4 +43,8 @@ class dft_test(NewOpenCVTests):
         img_backTest = cv2.normalize(img_backTest, 0.0, 1.0, cv2.NORM_MINMAX)
         img_back = cv2.normalize(img_back, 0.0, 1.0, cv2.NORM_MINMAX)
 
-        self.assertLess(cv2.norm(img_back - img_backTest), eps)
+        self.assertLess(cv2.norm(img_back - img_backTest), eps)
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_digits.py

@@ -71,7 +71,7 @@ class KNearest(StatModel):
         self.model.train(samples, cv2.ml.ROW_SAMPLE, responses)
 
     def predict(self, samples):
-        retval, results, neigh_resp, dists = self.model.findNearest(samples, self.k)
+        _retval, results, _neigh_resp, _dists = self.model.findNearest(samples, self.k)
         return results.ravel()
 
 class SVM(StatModel):
@@ -147,7 +147,7 @@ class digits_test(NewOpenCVTests):
         samples = preprocess_hog(digits2)
 
         train_n = int(0.9*len(samples))
-        digits_train, digits_test = np.split(digits2, [train_n])
+        _digits_train, digits_test = np.split(digits2, [train_n])
         samples_train, samples_test = np.split(samples, [train_n])
         labels_train, labels_test = np.split(labels, [train_n])
         errors = list()
@@ -194,4 +194,8 @@ class digits_test(NewOpenCVTests):
         self.assertLess(cv2.norm(confusionMatrixes[1] - confusionSVM, cv2.NORM_L1), normEps)
 
         self.assertLess(errors[0] - 0.034, eps)
-        self.assertLess(errors[1] - 0.018, eps)
+        self.assertLess(errors[1] - 0.018, eps)
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_facedetect.py

@@ -23,8 +23,6 @@ from tests_common import NewOpenCVTests, intersectionRate
 class facedetect_test(NewOpenCVTests):
 
     def test_facedetect(self):
-        import sys, getopt
-
         cascade_fn = self.repoPath + '/data/haarcascades/haarcascade_frontalface_alt.xml'
         nested_fn  = self.repoPath + '/data/haarcascades/haarcascade_eye.xml'
 
@@ -87,4 +85,8 @@ class facedetect_test(NewOpenCVTests):
                             eyes_matches += 1
 
         self.assertEqual(faces_matches, 2)
-        self.assertEqual(eyes_matches, 2)
+        self.assertEqual(eyes_matches, 2)
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_feature_homography.py

@@ -28,7 +28,7 @@ def intersectionRate(s1, s2):
     x1, y1, x2, y2 = s1
     s1 = np.array([[x1, y1], [x2,y1], [x2, y2], [x1, y2]])
 
-    area, intersection = cv2.intersectConvexConvex(s1, np.array(s2))
+    area, _intersection = cv2.intersectConvexConvex(s1, np.array(s2))
     return 2 * area / (cv2.contourArea(s1) + cv2.contourArea(np.array(s2)))
 
 from tests_common import NewOpenCVTests
@@ -157,4 +157,8 @@ class PlaneTracker:
         keypoints, descrs = self.detector.detectAndCompute(frame, None)
         if descrs is None:  # detectAndCompute returns descs=None if no keypoints found
             descrs = []
-        return keypoints, descrs
+        return keypoints, descrs
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_fitline.py

@@ -63,4 +63,8 @@ class fitline_test(NewOpenCVTests):
         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)
+            self.assertLessEqual(cv2.norm(refVec - lines[i][0:2], cv2.NORM_L2), eps)
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_gaussian_mix.py

@@ -15,7 +15,7 @@ import cv2
 def make_gaussians(cluster_n, img_size):
     points = []
     ref_distrs = []
-    for i in xrange(cluster_n):
+    for _ in xrange(cluster_n):
         mean = (0.1 + 0.8*random.rand(2)) * img_size
         a = (random.rand(2, 2)-0.5)*img_size*0.1
         cov = np.dot(a.T, a) + img_size*0.05*np.eye(2)
@@ -44,7 +44,7 @@ class gaussian_mix_test(NewOpenCVTests):
         em.trainEM(points)
         means = em.getMeans()
         covs = em.getCovs()  # Known bug: https://github.com/opencv/opencv/pull/4232
-        found_distrs = zip(means, covs)
+        #found_distrs = zip(means, covs)
 
         matches_count = 0
 
@@ -57,4 +57,8 @@ class gaussian_mix_test(NewOpenCVTests):
                     cv2.norm(covs[i] - ref_distrs[j][1], cv2.NORM_L2) / cv2.norm(ref_distrs[j][1], cv2.NORM_L2) < covEps):
                     matches_count += 1
 
-        self.assertEqual(matches_count, cluster_n)
+        self.assertEqual(matches_count, cluster_n)
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_goodfeatures.py

@@ -27,10 +27,14 @@ class TestGoodFeaturesToTrack_test(NewOpenCVTests):
                     self.assertTrue(cv2.norm(results[t][i][0] - results2[t][i][0]) == 0)
 
         for t0,t1 in zip(threshes, threshes[1:]):
-             r0 = results[t0]
+            r0 = results[t0]
-             r1 = results[t1]
+            r1 = results[t1]
-             # Increasing thresh should make result list shorter
+            # Increasing thresh should make result list shorter
-             self.assertTrue(len(r0) > len(r1))
+            self.assertTrue(len(r0) > len(r1))
-             # Increasing thresh should monly truncate result list
+            # Increasing thresh should monly truncate result list
-             for i in range(len(r1)):
+            for i in range(len(r1)):
-                self.assertTrue(cv2.norm(r1[i][0] - r0[i][0])==0)
+                self.assertTrue(cv2.norm(r1[i][0] - r0[i][0])==0)
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_grabcut.py

@@ -64,4 +64,8 @@ class grabcut_test(NewOpenCVTests):
             exp_mask2 = self.scaleMask(mask)
             cv2.imwrite(self.extraTestDataPath + '/cv/grabcut/exp_mask2py.png', exp_mask2)
 
-        self.assertEqual(self.verify(self.scaleMask(mask), exp_mask2), True)
+        self.assertEqual(self.verify(self.scaleMask(mask), exp_mask2), True)
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_houghcircles.py

@@ -17,7 +17,6 @@ from tests_common import NewOpenCVTests
 def circleApproximation(circle):
 
     nPoints = 30
-    phi = 0
     dPhi = 2*pi / nPoints
     contour = []
     for i in range(nPoints):
@@ -78,4 +77,8 @@ class houghcircles_test(NewOpenCVTests):
                     matches_counter += 1
 
         self.assertGreater(float(matches_counter) / len(testCircles), .5)
-        self.assertLess(float(len(circles) - matches_counter) / len(circles), .75)
+        self.assertLess(float(len(circles) - matches_counter) / len(circles), .75)
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_houghlines.py

@@ -62,4 +62,8 @@ class houghlines_test(NewOpenCVTests):
                 if linesDiff(testLines[i], lines[j]) < eps:
                     matches_counter += 1
 
-        self.assertGreater(float(matches_counter) / len(testLines), .7)
+        self.assertGreater(float(matches_counter) / len(testLines), .7)
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_kmeans.py

@@ -21,7 +21,7 @@ def make_gaussians(cluster_n, img_size):
     points = []
     ref_distrs = []
     sizes = []
-    for i in xrange(cluster_n):
+    for _ in xrange(cluster_n):
         mean = (0.1 + 0.8*random.rand(2)) * img_size
         a = (random.rand(2, 2)-0.5)*img_size*0.1
         cov = np.dot(a.T, a) + img_size*0.05*np.eye(2)
@@ -59,7 +59,7 @@ class kmeans_test(NewOpenCVTests):
         points, _, clusterSizes = make_gaussians(cluster_n, img_size)
 
         term_crit = (cv2.TERM_CRITERIA_EPS, 30, 0.1)
-        ret, labels, centers = cv2.kmeans(points, cluster_n, None, term_crit, 10, 0)
+        _ret, labels, centers = cv2.kmeans(points, cluster_n, None, term_crit, 10, 0)
 
         self.assertEqual(len(centers), cluster_n)
 
@@ -67,4 +67,8 @@ class kmeans_test(NewOpenCVTests):
         for i in range(cluster_n):
             confidence = getMainLabelConfidence(labels[offset : (offset + clusterSizes[i])], cluster_n)
             offset += clusterSizes[i]
-            self.assertGreater(confidence, 0.9)
+            self.assertGreater(confidence, 0.9)
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_legacy.py

@@ -0,0 +1,89 @@
+#!/usr/bin/env python
+from __future__ import print_function
+
+import numpy as np
+import cv2
+
+from tests_common import NewOpenCVTests
+
+class Hackathon244Tests(NewOpenCVTests):
+
+    def test_int_array(self):
+        a = np.array([-1, 2, -3, 4, -5])
+        absa0 = np.abs(a)
+        self.assertTrue(cv2.norm(a, cv2.NORM_L1) == 15)
+        absa1 = cv2.absdiff(a, 0)
+        self.assertEqual(cv2.norm(absa1, absa0, cv2.NORM_INF), 0)
+
+    def test_imencode(self):
+        a = np.zeros((480, 640), dtype=np.uint8)
+        flag, ajpg = cv2.imencode("img_q90.jpg", a, [cv2.IMWRITE_JPEG_QUALITY, 90])
+        self.assertEqual(flag, True)
+        self.assertEqual(ajpg.dtype, np.uint8)
+        self.assertGreater(ajpg.shape[0], 1)
+        self.assertEqual(ajpg.shape[1], 1)
+
+    def test_projectPoints(self):
+        objpt = np.float64([[1,2,3]])
+        imgpt0, jac0 = cv2.projectPoints(objpt, np.zeros(3), np.zeros(3), np.eye(3), np.float64([]))
+        imgpt1, jac1 = cv2.projectPoints(objpt, np.zeros(3), np.zeros(3), np.eye(3), None)
+        self.assertEqual(imgpt0.shape, (objpt.shape[0], 1, 2))
+        self.assertEqual(imgpt1.shape, imgpt0.shape)
+        self.assertEqual(jac0.shape, jac1.shape)
+        self.assertEqual(jac0.shape[0], 2*objpt.shape[0])
+
+    def test_estimateAffine3D(self):
+        pattern_size = (11, 8)
+        pattern_points = np.zeros((np.prod(pattern_size), 3), np.float32)
+        pattern_points[:,:2] = np.indices(pattern_size).T.reshape(-1, 2)
+        pattern_points *= 10
+        (retval, out, inliers) = cv2.estimateAffine3D(pattern_points, pattern_points)
+        self.assertEqual(retval, 1)
+        if cv2.norm(out[2,:]) < 1e-3:
+            out[2,2]=1
+        self.assertLess(cv2.norm(out, np.float64([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]])), 1e-3)
+        self.assertEqual(cv2.countNonZero(inliers), pattern_size[0]*pattern_size[1])
+
+    def test_fast(self):
+        fd = cv2.FastFeatureDetector_create(30, True)
+        img = self.get_sample("samples/data/right02.jpg", 0)
+        img = cv2.medianBlur(img, 3)
+        keypoints = fd.detect(img)
+        self.assertTrue(600 <= len(keypoints) <= 700)
+        for kpt in keypoints:
+            self.assertNotEqual(kpt.response, 0)
+
+    def check_close_angles(self, a, b, angle_delta):
+        self.assertTrue(abs(a - b) <= angle_delta or
+                        abs(360 - abs(a - b)) <= angle_delta)
+
+    def check_close_pairs(self, a, b, delta):
+        self.assertLessEqual(abs(a[0] - b[0]), delta)
+        self.assertLessEqual(abs(a[1] - b[1]), delta)
+
+    def check_close_boxes(self, a, b, delta, angle_delta):
+        self.check_close_pairs(a[0], b[0], delta)
+        self.check_close_pairs(a[1], b[1], delta)
+        self.check_close_angles(a[2], b[2], angle_delta)
+
+    def test_geometry(self):
+        npt = 100
+        np.random.seed(244)
+        a = np.random.randn(npt,2).astype('float32')*50 + 150
+
+        be = cv2.fitEllipse(a)
+        br = cv2.minAreaRect(a)
+        mc, mr = cv2.minEnclosingCircle(a)
+
+        be0 = ((150.2511749267578, 150.77322387695312), (158.024658203125, 197.57696533203125), 37.57804489135742)
+        br0 = ((161.2974090576172, 154.41793823242188), (199.2301483154297, 207.7177734375), -9.164555549621582)
+        mc0, mr0 = (160.41790771484375, 144.55152893066406), 136.713500977
+
+        self.check_close_boxes(be, be0, 5, 15)
+        self.check_close_boxes(br, br0, 5, 15)
+        self.check_close_pairs(mc, mc0, 5)
+        self.assertLessEqual(abs(mr - mr0), 5)
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_letter_recog.py

@@ -59,12 +59,12 @@ class RTrees(LetterStatModel):
         self.model = cv2.ml.RTrees_create()
 
     def train(self, samples, responses):
-        sample_n, var_n = samples.shape
+        #sample_n, var_n = samples.shape
         self.model.setMaxDepth(20)
         self.model.train(samples, cv2.ml.ROW_SAMPLE, responses.astype(int))
 
     def predict(self, samples):
-        ret, resp = self.model.predict(samples)
+        _ret, resp = self.model.predict(samples)
         return resp.ravel()
 
 
@@ -76,7 +76,7 @@ class KNearest(LetterStatModel):
         self.model.train(samples, cv2.ml.ROW_SAMPLE, responses)
 
     def predict(self, samples):
-        retval, results, neigh_resp, dists = self.model.findNearest(samples, k = 10)
+        _retval, results, _neigh_resp, _dists = self.model.findNearest(samples, k = 10)
         return results.ravel()
 
 
@@ -85,7 +85,7 @@ class Boost(LetterStatModel):
         self.model = cv2.ml.Boost_create()
 
     def train(self, samples, responses):
-        sample_n, var_n = samples.shape
+        _sample_n, var_n = samples.shape
         new_samples = self.unroll_samples(samples)
         new_responses = self.unroll_responses(responses)
         var_types = np.array([cv2.ml.VAR_NUMERICAL] * var_n + [cv2.ml.VAR_CATEGORICAL, cv2.ml.VAR_CATEGORICAL], np.uint8)
@@ -96,7 +96,7 @@ class Boost(LetterStatModel):
 
     def predict(self, samples):
         new_samples = self.unroll_samples(samples)
-        ret, resp = self.model.predict(new_samples)
+        _ret, resp = self.model.predict(new_samples)
 
         return resp.ravel().reshape(-1, self.class_n).argmax(1)
 
@@ -113,7 +113,7 @@ class SVM(LetterStatModel):
         self.model.train(samples, cv2.ml.ROW_SAMPLE, responses.astype(int))
 
     def predict(self, samples):
-        ret, resp = self.model.predict(samples)
+        _ret, resp = self.model.predict(samples)
         return resp.ravel()
 
 
@@ -122,7 +122,7 @@ class MLP(LetterStatModel):
         self.model = cv2.ml.ANN_MLP_create()
 
     def train(self, samples, responses):
-        sample_n, var_n = samples.shape
+        _sample_n, var_n = samples.shape
         new_responses = self.unroll_responses(responses).reshape(-1, self.class_n)
         layer_sizes = np.int32([var_n, 100, 100, self.class_n])
 
@@ -136,7 +136,7 @@ class MLP(LetterStatModel):
         self.model.train(samples, cv2.ml.ROW_SAMPLE, np.float32(new_responses))
 
     def predict(self, samples):
-        ret, resp = self.model.predict(samples)
+        _ret, resp = self.model.predict(samples)
         return resp.argmax(-1)
 
 from tests_common import NewOpenCVTests
@@ -164,4 +164,8 @@ class letter_recog_test(NewOpenCVTests):
             test_rate  = np.mean(classifier.predict(samples[train_n:]) == responses[train_n:].astype(int))
 
             self.assertLess(train_rate - testErrors[Model][0], eps)
-            self.assertLess(test_rate - testErrors[Model][1], eps)
+            self.assertLess(test_rate - testErrors[Model][1], eps)
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_lk_homography.py

@@ -27,8 +27,8 @@ feature_params = dict( maxCorners = 1000,
                        blockSize = 19 )
 
 def checkedTrace(img0, img1, p0, back_threshold = 1.0):
-    p1, st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
+    p1, _st, _err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
-    p0r, st, err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
+    p0r, _st, _err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
     d = abs(p0-p0r).reshape(-1, 2).max(-1)
     status = d < back_threshold
     return p1, status
@@ -77,11 +77,11 @@ class lk_homography_test(NewOpenCVTests):
                 if len(self.p0) < 4:
                     self.p0 = None
                     continue
-                H, status = cv2.findHomography(self.p0, self.p1, cv2.RANSAC, 5.0)
+                _H, status = cv2.findHomography(self.p0, self.p1, cv2.RANSAC, 5.0)
 
                 goodPointsInRect = 0
                 goodPointsOutsideRect = 0
-                for (x0, y0), (x1, y1), good in zip(self.p0[:,0], self.p1[:,0], status[:,0]):
+                for (_x0, _y0), (x1, y1), good in zip(self.p0[:,0], self.p1[:,0], status[:,0]):
                     if good:
                         if isPointInRect((x1,y1), self.render.getCurrentRect()):
                             goodPointsInRect += 1
@@ -91,6 +91,10 @@ class lk_homography_test(NewOpenCVTests):
                     isForegroundHomographyFound = True
                     self.assertGreater(float(goodPointsInRect) / (self.numFeaturesInRectOnStart + 1), 0.6)
             else:
-                p = cv2.goodFeaturesToTrack(frame_gray, **feature_params)
+                self.p0 = cv2.goodFeaturesToTrack(frame_gray, **feature_params)
 
-        self.assertEqual(isForegroundHomographyFound, True)
+        self.assertEqual(isForegroundHomographyFound, True)
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_lk_track.py

@@ -63,8 +63,8 @@ class lk_track_test(NewOpenCVTests):
             if len(self.tracks) > 0:
                 img0, img1 = self.prev_gray, frame_gray
                 p0 = np.float32([tr[-1][0] for tr in self.tracks]).reshape(-1, 1, 2)
-                p1, st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
+                p1, _st, _err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
-                p0r, st, err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
+                p0r, _st, _err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
                 d = abs(p0-p0r).reshape(-1, 2).max(-1)
                 good = d < 1
                 new_tracks = []
@@ -108,4 +108,8 @@ class lk_track_test(NewOpenCVTests):
             self.prev_gray = frame_gray
 
             if self.frame_idx > 300:
-                break
+                break
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_misc.py

@@ -0,0 +1,22 @@
+#!/usr/bin/env python
+from __future__ import print_function
+
+import numpy as np
+import cv2
+
+from tests_common import NewOpenCVTests
+
+class Bindings(NewOpenCVTests):
+
+    def test_inheritance(self):
+        bm = cv2.StereoBM_create()
+        bm.getPreFilterCap() # from StereoBM
+        bm.getBlockSize() # from SteroMatcher
+
+        boost = cv2.ml.Boost_create()
+        boost.getBoostType() # from ml::Boost
+        boost.getMaxDepth() # from ml::DTrees
+        boost.isClassifier() # from ml::StatModel
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_morphology.py

@@ -48,4 +48,7 @@ class morphology_test(NewOpenCVTests):
 
         for mode in modes:
             res = update(mode)
-            self.assertEqual(self.hashimg(res), referenceHashes[mode])
+            self.assertEqual(self.hashimg(res), referenceHashes[mode])
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_mser.py

@@ -37,7 +37,7 @@ class mser_test(NewOpenCVTests):
         mserExtractor.setDelta(kDelta)
         np.random.seed(10)
 
-        for i in range(100):
+        for _i in range(100):
 
             use_big_image = int(np.random.rand(1,1)*7) != 0
             invert = int(np.random.rand(1,1)*2) != 0
@@ -67,3 +67,6 @@ class mser_test(NewOpenCVTests):
             self.assertEqual(nmsers, len(boxes))
             self.assertLessEqual(minRegs, nmsers)
             self.assertGreaterEqual(maxRegs, nmsers)
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_peopledetect.py

@@ -38,7 +38,7 @@ class peopledetect_test(NewOpenCVTests):
 
             img = self.get_sample(dirPath + sample, 0)
 
-            found, w = hog.detectMultiScale(img, winStride=(8,8), padding=(32,32), scale=1.05)
+            found, _w = hog.detectMultiScale(img, winStride=(8,8), padding=(32,32), scale=1.05)
             found_filtered = []
             for ri, r in enumerate(found):
                 for qi, q in enumerate(found):
@@ -59,4 +59,7 @@ class peopledetect_test(NewOpenCVTests):
                     if intersectionRate(found_rect, testPeople[j][0]) > eps or intersectionRate(found_rect, testPeople[j][1]) > eps:
                         matches += 1
 
-            self.assertGreater(matches, 0)
+            self.assertGreater(matches, 0)
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_shape.py

@@ -7,8 +7,8 @@ class shape_test(NewOpenCVTests):
 
     def test_computeDistance(self):
 
-        a = self.get_sample('samples/data/shape_sample/1.png', cv2.IMREAD_GRAYSCALE);
+        a = self.get_sample('samples/data/shape_sample/1.png', cv2.IMREAD_GRAYSCALE)
-        b = self.get_sample('samples/data/shape_sample/2.png', cv2.IMREAD_GRAYSCALE);
+        b = self.get_sample('samples/data/shape_sample/2.png', cv2.IMREAD_GRAYSCALE)
 
         _, ca, _ = cv2.findContours(a, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_KCOS)
         _, cb, _ = cv2.findContours(b, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_KCOS)
@@ -21,3 +21,6 @@ class shape_test(NewOpenCVTests):
 
         self.assertAlmostEqual(d1, 26.4196891785, 3, "HausdorffDistanceExtractor")
         self.assertAlmostEqual(d2, 0.25804194808, 3, "ShapeContextDistanceExtractor")
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_squares.py

@@ -30,8 +30,8 @@ def find_squares(img):
                 bin = cv2.Canny(gray, 0, 50, apertureSize=5)
                 bin = cv2.dilate(bin, None)
             else:
-                retval, bin = cv2.threshold(gray, thrs, 255, cv2.THRESH_BINARY)
+                _retval, bin = cv2.threshold(gray, thrs, 255, cv2.THRESH_BINARY)
-            bin, contours, hierarchy = cv2.findContours(bin, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
+            bin, contours, _hierarchy = cv2.findContours(bin, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
             for cnt in contours:
                 cnt_len = cv2.arcLength(cnt, True)
                 cnt = cv2.approxPolyDP(cnt, 0.02*cnt_len, True)
@@ -44,7 +44,7 @@ def find_squares(img):
     return squares
 
 def intersectionRate(s1, s2):
-    area, intersection = cv2.intersectConvexConvex(np.array(s1), np.array(s2))
+    area, _intersection = cv2.intersectConvexConvex(np.array(s1), np.array(s2))
     return 2 * area / (cv2.contourArea(np.array(s1)) + cv2.contourArea(np.array(s2)))
 
 def filterSquares(squares, square):
@@ -93,4 +93,7 @@ class squares_test(NewOpenCVTests):
                     matches_counter += 1
 
         self.assertGreater(matches_counter / len(testSquares), 0.9)
-        self.assertLess( (len(squares) - matches_counter) / len(squares), 0.2)
+        self.assertLess( (len(squares) - matches_counter) / len(squares), 0.2)
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_stitching.py

@@ -11,10 +11,13 @@ class stitching_test(NewOpenCVTests):
         img2 = self.get_sample('stitching/a2.png')
 
         stitcher = cv2.createStitcher(False)
-        (result, pano) = stitcher.stitch((img1, img2))
+        (_result, pano) = stitcher.stitch((img1, img2))
 
         #cv2.imshow("pano", pano)
         #cv2.waitKey()
 
         self.assertAlmostEqual(pano.shape[0], 685, delta=100, msg="rows: %r" % list(pano.shape))
         self.assertAlmostEqual(pano.shape[1], 1025, delta=100, msg="cols: %r" % list(pano.shape))
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_texture_flow.py

@@ -42,3 +42,6 @@ class texture_flow_test(NewOpenCVTests):
         for i in range(len(textureVectors)):
             self.assertTrue(cv2.norm(textureVectors[i], cv2.NORM_L2) < eps
             or abs(cv2.norm(textureVectors[i], cv2.NORM_L2) - d) < eps)
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_umat.py

@@ -0,0 +1,86 @@
+#!/usr/bin/env python
+from __future__ import print_function
+
+import numpy as np
+import cv2
+
+from tests_common import NewOpenCVTests
+
+class UMat(NewOpenCVTests):
+
+    def test_umat_construct(self):
+        data = np.random.random([512, 512])
+        # UMat constructors
+        data_um = cv2.UMat(data)  # from ndarray
+        data_sub_um = cv2.UMat(data_um, [128, 256], [128, 256])  # from UMat
+        data_dst_um = cv2.UMat(128, 128, cv2.CV_64F)  # from size/type
+        # test continuous and submatrix flags
+        assert data_um.isContinuous() and not data_um.isSubmatrix()
+        assert not data_sub_um.isContinuous() and data_sub_um.isSubmatrix()
+        # test operation on submatrix
+        cv2.multiply(data_sub_um, 2., dst=data_dst_um)
+        assert np.allclose(2. * data[128:256, 128:256], data_dst_um.get())
+
+    def test_umat_handle(self):
+        a_um = cv2.UMat(256, 256, cv2.CV_32F)
+        _ctx_handle = cv2.UMat.context()  # obtain context handle
+        _queue_handle = cv2.UMat.queue()  # obtain queue handle
+        _a_handle = a_um.handle(cv2.ACCESS_READ)  # obtain buffer handle
+        _offset = a_um.offset  # obtain buffer offset
+
+    def test_umat_matching(self):
+        img1 = self.get_sample("samples/data/right01.jpg")
+        img2 = self.get_sample("samples/data/right02.jpg")
+
+        orb = cv2.ORB_create()
+
+        img1, img2 = cv2.UMat(img1), cv2.UMat(img2)
+        ps1, descs_umat1 = orb.detectAndCompute(img1, None)
+        ps2, descs_umat2 = orb.detectAndCompute(img2, None)
+
+        self.assertIsInstance(descs_umat1, cv2.UMat)
+        self.assertIsInstance(descs_umat2, cv2.UMat)
+        self.assertGreater(len(ps1), 0)
+        self.assertGreater(len(ps2), 0)
+
+        bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
+
+        res_umats = bf.match(descs_umat1, descs_umat2)
+        res = bf.match(descs_umat1.get(), descs_umat2.get())
+
+        self.assertGreater(len(res), 0)
+        self.assertEqual(len(res_umats), len(res))
+
+    def test_umat_optical_flow(self):
+        img1 = self.get_sample("samples/data/right01.jpg", cv2.IMREAD_GRAYSCALE)
+        img2 = self.get_sample("samples/data/right02.jpg", cv2.IMREAD_GRAYSCALE)
+        # Note, that if you want to see performance boost by OCL implementation - you need enough data
+        # For example you can increase maxCorners param to 10000 and increase img1 and img2 in such way:
+        # img = np.hstack([np.vstack([img] * 6)] * 6)
+
+        feature_params = dict(maxCorners=239,
+                              qualityLevel=0.3,
+                              minDistance=7,
+                              blockSize=7)
+
+        p0 = cv2.goodFeaturesToTrack(img1, mask=None, **feature_params)
+        p0_umat = cv2.goodFeaturesToTrack(cv2.UMat(img1), mask=None, **feature_params)
+        self.assertEqual(p0_umat.get().shape, p0.shape)
+
+        p0 = np.array(sorted(p0, key=lambda p: tuple(p[0])))
+        p0_umat = cv2.UMat(np.array(sorted(p0_umat.get(), key=lambda p: tuple(p[0]))))
+        self.assertTrue(np.allclose(p0_umat.get(), p0))
+
+        _p1_mask_err = cv2.calcOpticalFlowPyrLK(img1, img2, p0, None)
+
+        _p1_mask_err_umat0 = map(cv2.UMat.get, cv2.calcOpticalFlowPyrLK(img1, img2, p0_umat, None))
+        _p1_mask_err_umat1 = map(cv2.UMat.get, cv2.calcOpticalFlowPyrLK(cv2.UMat(img1), img2, p0_umat, None))
+        _p1_mask_err_umat2 = map(cv2.UMat.get, cv2.calcOpticalFlowPyrLK(img1, cv2.UMat(img2), p0_umat, None))
+
+        # # results of OCL optical flow differs from CPU implementation, so result can not be easily compared
+        # for p1_mask_err_umat in [p1_mask_err_umat0, p1_mask_err_umat1, p1_mask_err_umat2]:
+        #     for data, data_umat in zip(p1_mask_err, p1_mask_err_umat):
+        #         self.assertTrue(np.allclose(data, data_umat))
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/test_watershed.py

@@ -30,4 +30,7 @@ class watershed_test(NewOpenCVTests):
             refSegments = segments.copy()
             cv2.imwrite(self.extraTestDataPath + '/cv/watershed/wshed_segments.png', refSegments)
 
-        self.assertLess(cv2.norm(segments - refSegments, cv2.NORM_L1) / 255.0, 50)
+        self.assertLess(cv2.norm(segments - refSegments, cv2.NORM_L1) / 255.0, 50)
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

modules/python/test/tests_common.py

@@ -2,10 +2,13 @@
 
 from __future__ import print_function
 
-import unittest
-import sys
-import hashlib
 import os
+import sys
+import unittest
+import hashlib
+import random
+import argparse
+
 import numpy as np
 import cv2
 
@@ -62,6 +65,26 @@ class NewOpenCVTests(unittest.TestCase):
             if not a > b:
                 self.fail('%s not greater than %s' % (repr(a), repr(b)))
 
+    @staticmethod
+    def bootstrap():
+        parser = argparse.ArgumentParser(description='run OpenCV python tests')
+        parser.add_argument('--repo', help='use sample image files from local git repository (path to folder), '
+                                           'if not set, samples will be downloaded from github.com')
+        parser.add_argument('--data', help='<not used> use data files from local folder (path to folder), '
+                                            'if not set, data files will be downloaded from docs.opencv.org')
+        args, other = parser.parse_known_args()
+        print("Testing OpenCV", cv2.__version__)
+        print("Local repo path:", args.repo)
+        NewOpenCVTests.repoPath = args.repo
+        try:
+            NewOpenCVTests.extraTestDataPath = os.environ['OPENCV_TEST_DATA_PATH']
+        except KeyError:
+            print('Missing opencv extra repository. Some of tests may fail.')
+        random.seed(0)
+        unit_argv = [sys.argv[0]] + other
+        unittest.main(argv=unit_argv)
+
+
 def intersectionRate(s1, s2):
 
     x1, y1, x2, y2 = s1
@@ -70,7 +93,7 @@ def intersectionRate(s1, s2):
     x1, y1, x2, y2 = s2
     s2 = np.array([[x1, y1], [x2,y1], [x2, y2], [x1, y2]])
 
-    area, intersection = cv2.intersectConvexConvex(s1, s2)
+    area, _intersection = cv2.intersectConvexConvex(s1, s2)
     return 2 * area / (cv2.contourArea(s1) + cv2.contourArea(s2))
 
 def isPointInRect(p, rect):