Merge remote-tracking branch 'upstream/3.4' into merge-3.4

3rdparty/carotene/hal/tegra_hal.hpp

@@ -1844,14 +1844,18 @@ TegraCvtColor_Invoker(bgrx2hsvf, bgrx2hsv, src_data + static_cast<size_t>(range.
 #define cv_hal_cvtBGRtoGray TEGRA_CVTBGRTOGRAY
 #undef cv_hal_cvtGraytoBGR
 #define cv_hal_cvtGraytoBGR TEGRA_CVTGRAYTOBGR
+#if 0  // bit-exact tests are failed
 #undef cv_hal_cvtBGRtoYUV
 #define cv_hal_cvtBGRtoYUV TEGRA_CVTBGRTOYUV
+#endif
 #undef cv_hal_cvtBGRtoHSV
 #define cv_hal_cvtBGRtoHSV TEGRA_CVTBGRTOHSV
+#if 0  // bit-exact tests are failed
 #undef cv_hal_cvtTwoPlaneYUVtoBGR
 #define cv_hal_cvtTwoPlaneYUVtoBGR TEGRA_CVT2PYUVTOBGR
 #undef cv_hal_cvtTwoPlaneYUVtoBGREx
 #define cv_hal_cvtTwoPlaneYUVtoBGREx TEGRA_CVT2PYUVTOBGR_EX
+#endif
 
 #endif // OPENCV_IMGPROC_HAL_INTERFACE_H
 

cmake/OpenCVDetectPython.cmake

@@ -177,7 +177,7 @@ if(NOT ${found})
 
     if(NOT ANDROID AND NOT IOS)
       if(CMAKE_HOST_UNIX)
-        execute_process(COMMAND ${_executable} -c "from distutils.sysconfig import *; print(get_python_lib())"
+        execute_process(COMMAND ${_executable} -c "from sysconfig import *; print(get_path('purelib'))"
                         RESULT_VARIABLE _cvpy_process
                         OUTPUT_VARIABLE _std_packages_path
                         OUTPUT_STRIP_TRAILING_WHITESPACE)

cmake/OpenCVFindLibsGUI.cmake

@@ -66,7 +66,6 @@ if(WITH_OPENGL)
     find_package (OpenGL QUIET)
     if(OPENGL_FOUND)
       set(HAVE_OPENGL TRUE)
-      list(APPEND OPENCV_LINKER_LIBS ${OPENGL_LIBRARIES})
       if(QT_QTOPENGL_FOUND)
         set(HAVE_QT_OPENGL TRUE)
       else()

doc/js_tutorials/js_video/js_lucas_kanade/js_lucas_kanade.markdown

@@ -133,9 +133,9 @@ Dense Optical Flow in OpenCV.js
 
 Lucas-Kanade method computes optical flow for a sparse feature set (in our example, corners detected
 using Shi-Tomasi algorithm). OpenCV.js provides another algorithm to find the dense optical flow. It
-computes the optical flow for all the points in the frame. It is based on Gunner Farneback's
+computes the optical flow for all the points in the frame. It is based on Gunnar Farneback's
 algorithm which is explained in "Two-Frame Motion Estimation Based on Polynomial Expansion" by
-Gunner Farneback in 2003.
+Gunnar Farneback in 2003.
 
 We use the function: **cv.calcOpticalFlowFarneback (prev, next, flow, pyrScale, levels, winsize,
 iterations, polyN, polySigma, flags)**

doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.markdown

@@ -78,7 +78,7 @@ if len(good)>MIN_MATCH_COUNT:
     M, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC,5.0)
     matchesMask = mask.ravel().tolist()
 
-    h,w,d = img1.shape
+    h,w = img1.shape
     pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
     dst = cv.perspectiveTransform(pts,M)
 

doc/tutorials/others/optical_flow.markdown

@@ -139,9 +139,9 @@ Dense Optical Flow in OpenCV
 
 Lucas-Kanade method computes optical flow for a sparse feature set (in our example, corners detected
 using Shi-Tomasi algorithm). OpenCV provides another algorithm to find the dense optical flow. It
-computes the optical flow for all the points in the frame. It is based on Gunner Farneback's
+computes the optical flow for all the points in the frame. It is based on Gunnar Farneback's
 algorithm which is explained in "Two-Frame Motion Estimation Based on Polynomial Expansion" by
-Gunner Farneback in 2003.
+Gunnar Farneback in 2003.
 
 Below sample shows how to find the dense optical flow using above algorithm. We get a 2-channel
 array with optical flow vectors, \f$(u,v)\f$. We find their magnitude and direction. We color code the

modules/core/CMakeLists.txt

@@ -144,6 +144,7 @@ ocv_create_module(${extra_libs})
 
 ocv_target_link_libraries(${the_module} PRIVATE
     "${ZLIB_LIBRARIES}" "${OPENCL_LIBRARIES}" "${VA_LIBRARIES}"
+    "${OPENGL_LIBRARIES}"
     "${LAPACK_LIBRARIES}" "${CPUFEATURES_LIBRARIES}" "${HALIDE_LIBRARIES}"
     "${ITT_LIBRARIES}"
     "${OPENCV_HAL_LINKER_LIBS}"

modules/core/include/opencv2/core/types.hpp

@@ -1895,13 +1895,33 @@ Rect_<_Tp>& operator -= ( Rect_<_Tp>& a, const Size_<_Tp>& b )
 template<typename _Tp> static inline
 Rect_<_Tp>& operator &= ( Rect_<_Tp>& a, const Rect_<_Tp>& b )
 {
-    _Tp x1 = std::max(a.x, b.x);
-    _Tp y1 = std::max(a.y, b.y);
-    a.width = std::min(a.x + a.width, b.x + b.width) - x1;
-    a.height = std::min(a.y + a.height, b.y + b.height) - y1;
-    a.x = x1;
-    a.y = y1;
-    if( a.width <= 0 || a.height <= 0 )
+    if (a.empty() || b.empty()) {
+        a = Rect();
+        return a;
+    }
+    const Rect_<_Tp>& Rx_min = (a.x < b.x) ? a : b;
+    const Rect_<_Tp>& Rx_max = (a.x < b.x) ? b : a;
+    const Rect_<_Tp>& Ry_min = (a.y < b.y) ? a : b;
+    const Rect_<_Tp>& Ry_max = (a.y < b.y) ? b : a;
+    // Looking at the formula below, we will compute Rx_min.width - (Rx_max.x - Rx_min.x)
+    // but we want to avoid overflows. Rx_min.width >= 0 and (Rx_max.x - Rx_min.x) >= 0
+    // by definition so the difference does not overflow. The only thing that can overflow
+    // is (Rx_max.x - Rx_min.x). And it can only overflow if Rx_min.x < 0.
+    // Let us first deal with the following case.
+    if ((Rx_min.x < 0 && Rx_min.x + Rx_min.width < Rx_max.x) ||
+        (Ry_min.y < 0 && Ry_min.y + Ry_min.height < Ry_max.y)) {
+        a = Rect();
+        return a;
+    }
+    // We now know that either Rx_min.x >= 0, or
+    // Rx_min.x < 0 && Rx_min.x + Rx_min.width >= Rx_max.x and therefore
+    // Rx_min.width >= (Rx_max.x - Rx_min.x) which means (Rx_max.x - Rx_min.x)
+    // is inferior to a valid int and therefore does not overflow.
+    a.width = std::min(Rx_min.width - (Rx_max.x - Rx_min.x), Rx_max.width);
+    a.height = std::min(Ry_min.height - (Ry_max.y - Ry_min.y), Ry_max.height);
+    a.x = Rx_max.x;
+    a.y = Ry_max.y;
+    if (a.empty())
         a = Rect();
     return a;
 }

modules/core/test/test_misc.cpp

@@ -821,4 +821,36 @@ TEST(Core_Types, trivially_copyable_extra)
 }
 #endif
 
+template <typename T> class Rect_Test : public testing::Test {};
+
+TYPED_TEST_CASE_P(Rect_Test);
+
+// Reimplement C++11 std::numeric_limits<>::lowest.
+template<typename T> T cv_numeric_limits_lowest();
+template<> int cv_numeric_limits_lowest<int>() { return INT_MIN; }
+template<> float cv_numeric_limits_lowest<float>() { return -FLT_MAX; }
+template<> double cv_numeric_limits_lowest<double>() { return -DBL_MAX; }
+
+TYPED_TEST_P(Rect_Test, Overflows) {
+  typedef Rect_<TypeParam> R;
+  TypeParam num_max = std::numeric_limits<TypeParam>::max();
+  TypeParam num_lowest = cv_numeric_limits_lowest<TypeParam>();
+  EXPECT_EQ(R(0, 0, 10, 10), R(0, 0, 10, 10) & R(0, 0, 10, 10));
+  EXPECT_EQ(R(5, 6, 4, 3), R(0, 0, 10, 10) & R(5, 6, 4, 3));
+  EXPECT_EQ(R(5, 6, 3, 2), R(0, 0, 8, 8) & R(5, 6, 4, 3));
+  // Test with overflowing dimenions.
+  EXPECT_EQ(R(5, 0, 5, 10), R(0, 0, 10, 10) & R(5, 0, num_max, num_max));
+  // Test with overflowing dimensions for floats/doubles.
+  EXPECT_EQ(R(num_max, 0, num_max / 4, 10), R(num_max, 0, num_max / 2, 10) & R(num_max, 0, num_max / 4, 10));
+  // Test with overflowing coordinates.
+  EXPECT_EQ(R(), R(20, 0, 10, 10) & R(num_lowest, 0, 10, 10));
+  EXPECT_EQ(R(), R(20, 0, 10, 10) & R(0, num_lowest, 10, 10));
+  EXPECT_EQ(R(), R(num_lowest, 0, 10, 10) & R(0, num_lowest, 10, 10));
+}
+REGISTER_TYPED_TEST_CASE_P(Rect_Test, Overflows);
+
+typedef ::testing::Types<int, float, double> RectTypes;
+INSTANTIATE_TYPED_TEST_CASE_P(Negative_Test, Rect_Test, RectTypes);
+
+
 }} // namespace

modules/imgproc/src/opencl/resize.cl

@@ -51,8 +51,6 @@
 #endif
 #endif
 
-#define INTER_RESIZE_COEF_SCALE (1 << INTER_RESIZE_COEF_BITS)
-#define CAST_BITS (INTER_RESIZE_COEF_BITS << 1)
 #define INC(x,l) min(x+1,l-1)
 
 #define noconvert
@@ -188,7 +186,9 @@ __kernel void resizeLN(__global const uchar * srcptr, int src_step, int src_offs
         int y_ = INC(y, src_rows);
         int x_ = INC(x, src_cols);
 
-#if depth <= 4
+#if depth <= 1  // 8U/8S only, 16U+ cause integer overflows
+#define INTER_RESIZE_COEF_SCALE (1 << INTER_RESIZE_COEF_BITS)
+#define CAST_BITS (INTER_RESIZE_COEF_BITS << 1)
         u = u * INTER_RESIZE_COEF_SCALE;
         v = v * INTER_RESIZE_COEF_SCALE;
 
@@ -214,7 +214,7 @@ __kernel void resizeLN(__global const uchar * srcptr, int src_step, int src_offs
         WT data2 = convertToWT(loadpix(srcptr + mad24(y_, src_step, mad24(x, TSIZE, src_offset))));
         WT data3 = convertToWT(loadpix(srcptr + mad24(y_, src_step, mad24(x_, TSIZE, src_offset))));
 
-        T uval = u1 * v1 * data0 + u * v1 * data1 + u1 * v *data2 + u * v *data3;
+        T uval = convertToDT((u1 * v1) * data0 + (u * v1) * data1 + (u1 * v) * data2 + (u * v) * data3);
 #endif
         storepix(uval, dstptr + mad24(dy, dst_step, mad24(dx, TSIZE, dst_offset)));
     }

modules/imgproc/src/resize.cpp

@@ -3376,7 +3376,8 @@ static bool ocl_resize( InputArray _src, OutputArray _dst, Size dsize,
         }
         else
         {
-            int wdepth = std::max(depth, CV_32S), wtype = CV_MAKETYPE(wdepth, cn);
+            int wdepth = depth <= CV_8S ? CV_32S : std::max(depth, CV_32F);
+            int wtype = CV_MAKETYPE(wdepth, cn);
             k.create("resizeLN", ocl::imgproc::resize_oclsrc,
                      format("-D INTER_LINEAR -D depth=%d -D T=%s -D T1=%s "
                             "-D WT=%s -D convertToWT=%s -D convertToDT=%s -D cn=%d "

modules/imgproc/test/ocl/test_warp.cpp

@@ -327,6 +327,20 @@ OCL_TEST_P(Resize, Mat)
     }
 }
 
+OCL_TEST(Resize, overflow_21198)
+{
+    Mat src(Size(600, 600), CV_16UC3, Scalar::all(32768));
+    UMat src_u;
+    src.copyTo(src_u);
+
+    Mat dst;
+    cv::resize(src, dst, Size(1024, 1024), 0, 0, INTER_LINEAR);
+    UMat dst_u;
+    cv::resize(src_u, dst_u, Size(1024, 1024), 0, 0, INTER_LINEAR);
+    EXPECT_LE(cv::norm(dst_u, dst, NORM_INF), 1.0f);
+}
+
+
 /////////////////////////////////////////////////////////////////////////////////////////////////
 // remap
 

modules/imgproc/test/test_color.cpp

@@ -2812,6 +2812,83 @@ TEST(Imgproc_ColorLuv_Full, bitExactness)
     }
 }
 
+
+static
+void runCvtColorBitExactCheck(ColorConversionCodes code, int inputType, uint32_t hash, Size sz = Size(263, 255), int rngSeed = 0)
+{
+    RNG rng(rngSeed);
+
+    Mat src(sz, inputType, Scalar::all(0));
+    Mat dst;
+    rng.fill(src, RNG::UNIFORM, 0, 255, true);
+
+    cv::cvtColor(src, dst, code);
+
+    uint32_t dst_hash = adler32(dst);
+
+    EXPECT_EQ(hash, dst_hash) << cv::format("0x%08llx", (long long int)dst_hash);
+
+    if (cvtest::debugLevel > 0)
+    {
+        const ::testing::TestInfo* const test_info = ::testing::UnitTest::GetInstance()->current_test_info();
+        CV_Assert(test_info);
+        std::string name = (std::string(test_info->test_case_name()) + "--" + test_info->name() + ".xml");
+        cv::FileStorage fs(name, cv::FileStorage::WRITE);
+        fs << "dst" << dst;
+    }
+}
+
+TEST(Imgproc_cvtColor_BE, COLOR_BGR2YUV) { runCvtColorBitExactCheck(COLOR_BGR2YUV, CV_8UC3, 0xc2cbcfda); }
+TEST(Imgproc_cvtColor_BE, COLOR_RGB2YUV) { runCvtColorBitExactCheck(COLOR_RGB2YUV, CV_8UC3, 0x4e98e757); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2BGR) { runCvtColorBitExactCheck(COLOR_YUV2BGR, CV_8UC3, 0xb2c62a3f); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2RGB) { runCvtColorBitExactCheck(COLOR_YUV2RGB, CV_8UC3, 0x6d242a3f); }
+
+// packed input
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2RGB_NV12) { runCvtColorBitExactCheck(COLOR_YUV2RGB_NV12, CV_8UC1, 0x46a1bb76, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2BGR_NV12) { runCvtColorBitExactCheck(COLOR_YUV2BGR_NV12, CV_8UC1, 0x3843bb76, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2RGB_NV21) { runCvtColorBitExactCheck(COLOR_YUV2RGB_NV21, CV_8UC1, 0xf3fdf2ea, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2BGR_NV21) { runCvtColorBitExactCheck(COLOR_YUV2BGR_NV21, CV_8UC1, 0x6e84f2ea, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2RGBA_NV12) { runCvtColorBitExactCheck(COLOR_YUV2RGBA_NV12, CV_8UC1, 0xb6a16bd3, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2BGRA_NV12) { runCvtColorBitExactCheck(COLOR_YUV2BGRA_NV12, CV_8UC1, 0xa8436bd3, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2RGBA_NV21) { runCvtColorBitExactCheck(COLOR_YUV2RGBA_NV21, CV_8UC1, 0x1c7fa347, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2BGRA_NV21) { runCvtColorBitExactCheck(COLOR_YUV2BGRA_NV21, CV_8UC1, 0x96f7a347, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2RGB_YV12) { runCvtColorBitExactCheck(COLOR_YUV2RGB_YV12, CV_8UC1, 0xc5da1651, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2BGR_YV12) { runCvtColorBitExactCheck(COLOR_YUV2BGR_YV12, CV_8UC1, 0x12161651, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2RGB_IYUV) { runCvtColorBitExactCheck(COLOR_YUV2RGB_IYUV, CV_8UC1, 0xb4e62ea5, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2BGR_IYUV) { runCvtColorBitExactCheck(COLOR_YUV2BGR_IYUV, CV_8UC1, 0xfa632ea5, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2RGBA_YV12) { runCvtColorBitExactCheck(COLOR_YUV2RGBA_YV12, CV_8UC1, 0x0db4c69f, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2BGRA_YV12) { runCvtColorBitExactCheck(COLOR_YUV2BGRA_YV12, CV_8UC1, 0x59e1c69f, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2RGBA_IYUV) { runCvtColorBitExactCheck(COLOR_YUV2RGBA_IYUV, CV_8UC1, 0xfe09def3, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2BGRA_IYUV) { runCvtColorBitExactCheck(COLOR_YUV2BGRA_IYUV, CV_8UC1, 0x4395def3, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2GRAY_420) { runCvtColorBitExactCheck(COLOR_YUV2GRAY_420, CV_8UC1, 0xf672b440, Size(262, 510)); }
+
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2RGB_UYVY) { runCvtColorBitExactCheck(COLOR_YUV2RGB_UYVY, CV_8UC2, 0x69bea2c1, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2BGR_UYVY) { runCvtColorBitExactCheck(COLOR_YUV2BGR_UYVY, CV_8UC2, 0xdc51a2c1, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2RGBA_UYVY) { runCvtColorBitExactCheck(COLOR_YUV2RGBA_UYVY, CV_8UC2, 0x851eab45, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2BGRA_UYVY) { runCvtColorBitExactCheck(COLOR_YUV2BGRA_UYVY, CV_8UC2, 0xf7b1ab45, Size(262, 510)); }
+
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2RGB_YUY2) { runCvtColorBitExactCheck(COLOR_YUV2RGB_YUY2, CV_8UC2, 0x607e8889, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2BGR_YUY2) { runCvtColorBitExactCheck(COLOR_YUV2BGR_YUY2, CV_8UC2, 0xfb148889, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2RGB_YVYU) { runCvtColorBitExactCheck(COLOR_YUV2RGB_YVYU, CV_8UC2, 0x239b13d4, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2BGR_YVYU) { runCvtColorBitExactCheck(COLOR_YUV2BGR_YVYU, CV_8UC2, 0x402b13d4, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2RGBA_YUY2) { runCvtColorBitExactCheck(COLOR_YUV2RGBA_YUY2, CV_8UC2, 0xf6af910d, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2BGRA_YUY2) { runCvtColorBitExactCheck(COLOR_YUV2BGRA_YUY2, CV_8UC2, 0x9154910d, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2RGBA_YVYU) { runCvtColorBitExactCheck(COLOR_YUV2RGBA_YVYU, CV_8UC2, 0x14481c58, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2BGRA_YVYU) { runCvtColorBitExactCheck(COLOR_YUV2BGRA_YVYU, CV_8UC2, 0x30d81c58, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2GRAY_UYVY) { runCvtColorBitExactCheck(COLOR_YUV2GRAY_UYVY, CV_8UC2, 0x228e669c, Size(262, 510)); }
+TEST(Imgproc_cvtColor_BE, COLOR_YUV2GRAY_YUY2) { runCvtColorBitExactCheck(COLOR_YUV2GRAY_YUY2, CV_8UC2, 0x125c62fd, Size(262, 510)); }
+
+TEST(Imgproc_cvtColor_BE, COLOR_RGB2YUV_I420) { runCvtColorBitExactCheck(COLOR_RGB2YUV_I420, CV_8UC3, 0x44bb076a, Size(262, 254)); }
+TEST(Imgproc_cvtColor_BE, COLOR_BGR2YUV_I420) { runCvtColorBitExactCheck(COLOR_BGR2YUV_I420, CV_8UC3, 0xf908ff52, Size(262, 254)); }
+TEST(Imgproc_cvtColor_BE, COLOR_RGBA2YUV_I420) { runCvtColorBitExactCheck(COLOR_RGBA2YUV_I420, CV_8UC3, 0x44bb076a, Size(262, 254)); }
+TEST(Imgproc_cvtColor_BE, COLOR_BGRA2YUV_I420) { runCvtColorBitExactCheck(COLOR_BGRA2YUV_I420, CV_8UC3, 0xf908ff52, Size(262, 254)); }
+
+TEST(Imgproc_cvtColor_BE, COLOR_RGB2YUV_YV12) { runCvtColorBitExactCheck(COLOR_RGB2YUV_YV12, CV_8UC3, 0x1b0d076a, Size(262, 254)); }
+TEST(Imgproc_cvtColor_BE, COLOR_BGR2YUV_YV12) { runCvtColorBitExactCheck(COLOR_BGR2YUV_YV12, CV_8UC3, 0xda8aff52, Size(262, 254)); }
+TEST(Imgproc_cvtColor_BE, COLOR_RGBA2YUV_YV12) { runCvtColorBitExactCheck(COLOR_RGBA2YUV_YV12, CV_8UC3, 0x1b0d076a, Size(262, 254)); }
+TEST(Imgproc_cvtColor_BE, COLOR_BGRA2YUV_YV12) { runCvtColorBitExactCheck(COLOR_BGRA2YUV_YV12, CV_8UC3, 0xda8aff52, Size(262, 254)); }
+
+
 static void test_Bayer2RGB_EdgeAware_8u(const Mat& src, Mat& dst, int code)
 {
     if (dst.empty())

modules/python/common.cmake

@@ -73,7 +73,7 @@ else()
   if("${${PYTHON}_VERSION_MAJOR}" STREQUAL "2")
     set(__python_ext_suffix_var "SO")
   endif()
-  execute_process(COMMAND ${${PYTHON}_EXECUTABLE} -c "import distutils.sysconfig; print(distutils.sysconfig.get_config_var('${__python_ext_suffix_var}'))"
+  execute_process(COMMAND ${${PYTHON}_EXECUTABLE} -c "import sysconfig; print(sysconfig.get_config_var('${__python_ext_suffix_var}'))"
                   RESULT_VARIABLE PYTHON_CVPY_PROCESS
                   OUTPUT_VARIABLE CVPY_SUFFIX
                   OUTPUT_STRIP_TRAILING_WHITESPACE)

samples/cpp/CMakeLists.txt

@@ -56,6 +56,7 @@ foreach(sample_filename ${cpp_samples})
   endif()
   if(HAVE_OPENGL AND sample_filename MATCHES "detect_mser")
     target_compile_definitions(${tgt} PRIVATE HAVE_OPENGL)
+    ocv_target_link_libraries(${tgt} PRIVATE "${OPENGL_LIBRARIES}")
   endif()
   if(sample_filename MATCHES "simd_")
     # disabled intentionally - demonstration purposes only

samples/opengl/CMakeLists.txt

@@ -23,7 +23,7 @@ if(BUILD_EXAMPLES AND OCV_DEPENDENCIES_FOUND)
   endif()
   foreach(sample_filename ${all_samples})
     ocv_define_sample(tgt ${sample_filename} opengl)
-    ocv_target_link_libraries(${tgt} PRIVATE ${OPENCV_LINKER_LIBS} ${OPENCV_OPENGL_SAMPLES_REQUIRED_DEPS})
+    ocv_target_link_libraries(${tgt} PRIVATE "${OPENGL_LIBRARIES}" "${OPENCV_OPENGL_SAMPLES_REQUIRED_DEPS}")
     if(sample_filename STREQUAL "opengl_interop.cpp")
       ocv_target_link_libraries(${tgt} PRIVATE ${X11_LIBRARIES})
       ocv_target_include_directories(${tgt} ${X11_INCLUDE_DIR})

samples/python/tutorial_code/video/optical_flow/optical_flow.py

@@ -17,12 +17,12 @@ feature_params = dict( maxCorners = 100,
                        blockSize = 7 )
 
 # Parameters for lucas kanade optical flow
-lk_params = dict( winSize  = (15,15),
+lk_params = dict( winSize  = (15, 15),
                   maxLevel = 2,
                   criteria = (cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 0.03))
 
 # Create some random colors
-color = np.random.randint(0,255,(100,3))
+color = np.random.randint(0, 255, (100, 3))
 
 # Take first frame and find corners in it
 ret, old_frame = cap.read()
@@ -33,7 +33,11 @@ p0 = cv.goodFeaturesToTrack(old_gray, mask = None, **feature_params)
 mask = np.zeros_like(old_frame)
 
 while(1):
-    ret,frame = cap.read()
+    ret, frame = cap.read()
+    if not ret:
+        print('No frames grabbed!')
+        break
+
     frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
 
     # calculate optical flow
@@ -45,18 +49,20 @@ while(1):
         good_old = p0[st==1]
 
     # draw the tracks
-    for i,(new,old) in enumerate(zip(good_new, good_old)):
-        a,b = new.ravel()
-        c,d = old.ravel()
-        mask = cv.line(mask, (int(a),int(b)),(int(c),int(d)), color[i].tolist(), 2)
-        frame = cv.circle(frame,(int(a),int(b)),5,color[i].tolist(),-1)
-    img = cv.add(frame,mask)
+    for i, (new, old) in enumerate(zip(good_new, good_old)):
+        a, b = new.ravel()
+        c, d = old.ravel()
+        mask = cv.line(mask, (int(a), int(b)), (int(c), int(d)), color[i].tolist(), 2)
+        frame = cv.circle(frame, (int(a), int(b)), 5, color[i].tolist(), -1)
+    img = cv.add(frame, mask)
 
-    cv.imshow('frame',img)
+    cv.imshow('frame', img)
     k = cv.waitKey(30) & 0xff
     if k == 27:
         break
 
     # Now update the previous frame and previous points
     old_gray = frame_gray.copy()
-    p0 = good_new.reshape(-1,1,2)
+    p0 = good_new.reshape(-1, 1, 2)
+
+cv.destroyAllWindows()

samples/python/tutorial_code/video/optical_flow/optical_flow_dense.py

@@ -2,22 +2,28 @@ import numpy as np
 import cv2 as cv
 cap = cv.VideoCapture(cv.samples.findFile("vtest.avi"))
 ret, frame1 = cap.read()
-prvs = cv.cvtColor(frame1,cv.COLOR_BGR2GRAY)
+prvs = cv.cvtColor(frame1, cv.COLOR_BGR2GRAY)
 hsv = np.zeros_like(frame1)
-hsv[...,1] = 255
+hsv[..., 1] = 255
 while(1):
     ret, frame2 = cap.read()
-    next = cv.cvtColor(frame2,cv.COLOR_BGR2GRAY)
-    flow = cv.calcOpticalFlowFarneback(prvs,next, None, 0.5, 3, 15, 3, 5, 1.2, 0)
-    mag, ang = cv.cartToPolar(flow[...,0], flow[...,1])
-    hsv[...,0] = ang*180/np.pi/2
-    hsv[...,2] = cv.normalize(mag,None,0,255,cv.NORM_MINMAX)
-    bgr = cv.cvtColor(hsv,cv.COLOR_HSV2BGR)
-    cv.imshow('frame2',bgr)
+    if not ret:
+        print('No frames grabbed!')
+        break
+
+    next = cv.cvtColor(frame2, cv.COLOR_BGR2GRAY)
+    flow = cv.calcOpticalFlowFarneback(prvs, next, None, 0.5, 3, 15, 3, 5, 1.2, 0)
+    mag, ang = cv.cartToPolar(flow[..., 0], flow[..., 1])
+    hsv[..., 0] = ang*180/np.pi/2
+    hsv[..., 2] = cv.normalize(mag, None, 0, 255, cv.NORM_MINMAX)
+    bgr = cv.cvtColor(hsv, cv.COLOR_HSV2BGR)
+    cv.imshow('frame2', bgr)
     k = cv.waitKey(30) & 0xff
     if k == 27:
         break
     elif k == ord('s'):
-        cv.imwrite('opticalfb.png',frame2)
-        cv.imwrite('opticalhsv.png',bgr)
+        cv.imwrite('opticalfb.png', frame2)
+        cv.imwrite('opticalhsv.png', bgr)
     prvs = next
+
+cv.destroyAllWindows()