T-API python support implemented:

- cv2.UMat implemented - python thin wrapper for UMat
 - no implicit copy from GPU to Host done, resulting UMat can be passed to next function without overhead
 - cv2.UMat.get() - to fetch data to Host
 - new tests covers: ORB, BFMatcher, goodFeaturesToTrack, calcOpticalFlowPyrLK

modules/python/test/test.py

@@ -155,6 +155,60 @@ class Hackathon244Tests(NewOpenCVTests):
         boost.getMaxDepth() # from ml::DTrees
         boost.isClassifier() # from ml::StatModel
 
+    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')
     parser.add_argument('--repo', help='use sample image files from local git repository (path to folder), '