release: OpenCV 3.4.12

backported commit: 7fe87d9a5b

3rdparty/ffmpeg/ffmpeg.cmake

@@ -1,8 +1,8 @@
-# Binaries branch name: ffmpeg/3.4_20200608
-# Binaries were created for OpenCV: 458f1d5ebe31e22789d9d781d0ca2ca936758fde
-ocv_update(FFMPEG_BINARIES_COMMIT "57064cd66d98994503b34aade3c8d8ff25007b46")
-ocv_update(FFMPEG_FILE_HASH_BIN32 "6fff20f5617bd1b7362058790db52caa")
-ocv_update(FFMPEG_FILE_HASH_BIN64 "15df55131471191b575668a424dff385")
+# Binaries branch name: ffmpeg/3.4_20200907
+# Binaries were created for OpenCV: 03bee14372f5537daa56c62e771ec16181ca1f98
+ocv_update(FFMPEG_BINARIES_COMMIT "2a96257b743695a47f8012aab1ffb995a1dee8b4")
+ocv_update(FFMPEG_FILE_HASH_BIN32 "5e68a3ff82f43ac6524e50e448a34c9c")
+ocv_update(FFMPEG_FILE_HASH_BIN64 "205db629d893e7d4865fd1459807ff47")
 ocv_update(FFMPEG_FILE_HASH_CMAKE "3b90f67f4b429e77d3da36698cef700c")
 
 function(download_win_ffmpeg script_var)

CMakeLists.txt

@@ -463,6 +463,7 @@ OCV_OPTION(BUILD_JAVA               "Enable Java support"
 # OpenCV installation options
 # ===================================================
 OCV_OPTION(INSTALL_CREATE_DISTRIB   "Change install rules to build the distribution package" OFF )
+OCV_OPTION(INSTALL_BIN_EXAMPLES     "Install prebuilt examples" WIN32 IF BUILD_EXAMPLES)
 OCV_OPTION(INSTALL_C_EXAMPLES       "Install C examples"        OFF )
 OCV_OPTION(INSTALL_PYTHON_EXAMPLES  "Install Python examples"   OFF )
 OCV_OPTION(INSTALL_ANDROID_EXAMPLES "Install Android examples"  OFF IF ANDROID )

cmake/OpenCVDetectCUDA.cmake

@@ -90,7 +90,7 @@ if(CUDA_FOUND)
   set(_arch_pascal  "6.0;6.1")
   set(_arch_volta   "7.0")
   set(_arch_turing  "7.5")
-  set(_arch_ampere  "8.0")
+  set(_arch_ampere  "8.0;8.6")
   if(NOT CMAKE_CROSSCOMPILING)
     list(APPEND _generations "Auto")
   endif()
@@ -198,7 +198,7 @@ if(CUDA_FOUND)
 
       if(${status} EQUAL 0)
         # cache detected values
-        set(OPENCV_CACHE_CUDA_ACTIVE_CC ${${result_list}} CACHE INTERNAL "")
+        set(OPENCV_CACHE_CUDA_ACTIVE_CC ${${output}} CACHE INTERNAL "")
         set(OPENCV_CACHE_CUDA_ACTIVE_CC_check "${__cache_key_check}" CACHE INTERNAL "")
       endif()
     endif()

cmake/OpenCVFindIPP.cmake

@@ -252,6 +252,7 @@ if(NOT DEFINED IPPROOT)
   else()
     ocv_install_3rdparty_licenses(ippicv "${ICV_PACKAGE_ROOT}/EULA.txt")
   endif()
+  ocv_install_3rdparty_licenses(ippicv "${ICV_PACKAGE_ROOT}/third-party-programs.txt")
 endif()
 
 file(TO_CMAKE_PATH "${IPPROOT}" __IPPROOT)

cmake/OpenCVModule.cmake

@@ -1337,8 +1337,8 @@ function(ocv_add_samples)
         endif()
         add_dependencies(${parent_target} ${the_target})
 
-        if(WIN32)
-          install(TARGETS ${the_target} RUNTIME DESTINATION "samples/${module_id}" COMPONENT samples)
+        if(INSTALL_BIN_EXAMPLES)
+          install(TARGETS ${the_target} RUNTIME DESTINATION "${OPENCV_SAMPLES_BIN_INSTALL_PATH}/${module_id}" COMPONENT samples)
         endif()
       endforeach()
     endif()

doc/py_tutorials/py_imgproc/py_contours/py_contour_properties/py_contour_properties.markdown

@@ -78,7 +78,7 @@ pixelpoints = np.transpose(np.nonzero(mask))
 Here, two methods, one using Numpy functions, next one using OpenCV function (last commented line)
 are given to do the same. Results are also same, but with a slight difference. Numpy gives
 coordinates in **(row, column)** format, while OpenCV gives coordinates in **(x,y)** format. So
-basically the answers will be interchanged. Note that, **row = x** and **column = y**.
+basically the answers will be interchanged. Note that, **row = y** and **column = x**.
 
 7. Maximum Value, Minimum Value and their locations
 ---------------------------------------------------

doc/stylesheet.css

@@ -6,12 +6,11 @@ body, table, div, p, dl {
 }
 
 code {
-    font: 12px Consolas, "Liberation Mono", Courier, monospace;
     font-size: 85%;
+    font-family: "SFMono-Regular",Consolas,"Liberation Mono",Menlo,Courier,monospace;
     white-space: pre-wrap;
     padding: 1px 5px;
-    padding: 0;
-    background-color: #ddd;
+    background-color: rgb(223, 229, 241);
     vertical-align: baseline;
 }
 
@@ -20,6 +19,16 @@ body {
     margin: 0 auto;
 }
 
+div.fragment {
+    padding: 3px;
+    padding-bottom: 0px;
+}
+
+div.line {
+    padding-bottom: 3px;
+    font-family: "SFMono-Regular",Consolas,"Liberation Mono",Menlo,Courier,monospace;
+}
+
 div.contents {
     width: 980px;
     margin: 0 auto;
@@ -35,3 +44,11 @@ span.arrow {
 div.image img{
     max-width: 900px;
 }
+
+#projectlogo
+{
+    text-align: center;
+    vertical-align: middle;
+    border-collapse: separate;
+    padding-left: 0.5em;
+}

doc/tutorials/dnn/dnn_android/dnn_android.markdown

@@ -15,7 +15,7 @@ Tutorial was written for the following versions of corresponding software:
 
 - Download and install Android Studio from https://developer.android.com/studio.
 
-- Get the latest pre-built OpenCV for Android release from https://github.com/opencv/opencv/releases and unpack it (for example, `opencv-3.4.11-android-sdk.zip`).
+- Get the latest pre-built OpenCV for Android release from https://github.com/opencv/opencv/releases and unpack it (for example, `opencv-3.X.Y-android-sdk.zip`).
 
 - Download MobileNet object detection model from https://github.com/chuanqi305/MobileNet-SSD. We need a configuration file `MobileNetSSD_deploy.prototxt` and weights `MobileNetSSD_deploy.caffemodel`.
 

doc/tutorials/introduction/cross_referencing/tutorial_cross_referencing.markdown

@@ -39,14 +39,14 @@ Open your Doxyfile using your favorite text editor and search for the key
 `TAGFILES`. Change it as follows:
 
 @code
-TAGFILES = ./docs/doxygen-tags/opencv.tag=http://docs.opencv.org/3.4.11
+TAGFILES = ./docs/doxygen-tags/opencv.tag=http://docs.opencv.org/3.4.12
 @endcode
 
 If you had other definitions already, you can append the line using a `\`:
 
 @code
 TAGFILES = ./docs/doxygen-tags/libstdc++.tag=https://gcc.gnu.org/onlinedocs/libstdc++/latest-doxygen \
-           ./docs/doxygen-tags/opencv.tag=http://docs.opencv.org/3.4.11
+           ./docs/doxygen-tags/opencv.tag=http://docs.opencv.org/3.4.12
 @endcode
 
 Doxygen can now use the information from the tag file to link to the OpenCV

modules/calib3d/include/opencv2/calib3d.hpp

@@ -2163,7 +2163,7 @@ final fundamental matrix. It can be set to something like 1-3, depending on the
 point localization, image resolution, and the image noise.
 @param confidence Parameter used for the RANSAC and LMedS methods only. It specifies a desirable level
 of confidence (probability) that the estimated matrix is correct.
-@param mask
+@param[out] mask optional output mask
 @param maxIters The maximum number of robust method iterations.
 
 The epipolar geometry is described by the following equation:

modules/calib3d/src/fundam.cpp

@@ -374,6 +374,9 @@ cv::Mat cv::findHomography( InputArray _points1, InputArray _points2,
                 return Mat();
             convertPointsFromHomogeneous(p, p);
         }
+        // Need at least 4 point correspondences to calculate Homography
+        if( npoints < 4 )
+            CV_Error(Error::StsVecLengthErr , "The input arrays should have at least 4 corresponding point sets to calculate Homography");
         p.reshape(2, npoints).convertTo(m, CV_32F);
     }
 

modules/calib3d/src/ippe.cpp

@@ -77,18 +77,18 @@ void PoseSolver::solveGeneric(InputArray _objectPoints, InputArray _normalizedIn
                               OutputArray _Ma, OutputArray _Mb)
 {
     //argument checking:
-    size_t n = static_cast<size_t>(_objectPoints.rows() * _objectPoints.cols()); //number of points
+    size_t n = static_cast<size_t>(_normalizedInputPoints.rows()) * static_cast<size_t>(_normalizedInputPoints.cols()); //number of points
     int objType = _objectPoints.type();
     int type_input = _normalizedInputPoints.type();
 
     CV_CheckType(objType, objType == CV_32FC3 || objType == CV_64FC3,
                  "Type of _objectPoints must be CV_32FC3 or CV_64FC3" );
     CV_CheckType(type_input, type_input == CV_32FC2 || type_input == CV_64FC2,
-                 "Type of _normalizedInputPoints must be CV_32FC3 or CV_64FC3" );
+                 "Type of _normalizedInputPoints must be CV_32FC2 or CV_64FC2" );
     CV_Assert(_objectPoints.rows() == 1 || _objectPoints.cols() == 1);
     CV_Assert(_objectPoints.rows() >= 4 || _objectPoints.cols() >= 4);
     CV_Assert(_normalizedInputPoints.rows() == 1 || _normalizedInputPoints.cols() == 1);
-    CV_Assert(static_cast<size_t>(_objectPoints.rows() * _objectPoints.cols()) == n);
+    CV_Assert(static_cast<size_t>(_objectPoints.rows()) * static_cast<size_t>(_objectPoints.cols()) == n);
 
     Mat normalizedInputPoints;
     if (type_input == CV_32FC2)
@@ -101,7 +101,7 @@ void PoseSolver::solveGeneric(InputArray _objectPoints, InputArray _normalizedIn
     }
 
     Mat objectInputPoints;
-    if (type_input == CV_32FC3)
+    if (objType == CV_32FC3)
     {
         _objectPoints.getMat().convertTo(objectInputPoints, CV_64F);
     }

modules/calib3d/test/test_homography.cpp

@@ -63,6 +63,7 @@ namespace opencv_test { namespace {
 #define MESSAGE_RANSAC_DIFF "Reprojection error for current pair of points more than required."
 
 #define MAX_COUNT_OF_POINTS 303
+#define MIN_COUNT_OF_POINTS 4
 #define COUNT_NORM_TYPES 3
 #define METHODS_COUNT 4
 
@@ -249,7 +250,7 @@ void CV_HomographyTest::print_information_8(int _method, int j, int N, int k, in
 
 void CV_HomographyTest::run(int)
 {
-    for (int N = 4; N <= MAX_COUNT_OF_POINTS; ++N)
+    for (int N = MIN_COUNT_OF_POINTS; N <= MAX_COUNT_OF_POINTS; ++N)
     {
         RNG& rng = ts->get_rng();
 
@@ -711,4 +712,27 @@ TEST(Calib3d_Homography, fromImages)
     ASSERT_GE(ninliers1, 80);
 }
 
+TEST(Calib3d_Homography, minPoints)
+{
+    float pt1data[] =
+    {
+        2.80073029e+002f, 2.39591217e+002f, 2.21912201e+002f, 2.59783997e+002f
+    };
+
+    float pt2data[] =
+    {
+        1.84072723e+002f, 1.43591202e+002f, 1.25912483e+002f, 1.63783859e+002f
+    };
+
+    int npoints = (int)(sizeof(pt1data)/sizeof(pt1data[0])/2);
+    printf("npoints = %d\n", npoints);  // npoints = 2
+
+    Mat p1(1, npoints, CV_32FC2, pt1data);
+    Mat p2(1, npoints, CV_32FC2, pt2data);
+    Mat mask;
+
+    // findHomography should raise an error since npoints < MIN_COUNT_OF_POINTS
+    EXPECT_THROW(findHomography(p1, p2, RANSAC, 0.01, mask), cv::Exception);
+}
+
 }} // namespace

modules/core/include/opencv2/core/check.hpp

@@ -63,7 +63,7 @@ struct CheckContext {
 #define CV__CHECK_LOCATION_VARNAME(id) CVAUX_CONCAT(CVAUX_CONCAT(__cv_check_, id), __LINE__)
 #define CV__DEFINE_CHECK_CONTEXT(id, message, testOp, p1_str, p2_str) \
     static const cv::detail::CheckContext CV__CHECK_LOCATION_VARNAME(id) = \
-            { CV__CHECK_FUNCTION, CV__CHECK_FILENAME, __LINE__, testOp, message, p1_str, p2_str }
+            { CV__CHECK_FUNCTION, CV__CHECK_FILENAME, __LINE__, testOp, "" message, "" p1_str, "" p2_str }
 
 CV_EXPORTS void CV_NORETURN check_failed_auto(const int v1, const int v2, const CheckContext& ctx);
 CV_EXPORTS void CV_NORETURN check_failed_auto(const size_t v1, const size_t v2, const CheckContext& ctx);

modules/core/include/opencv2/core/eigen.hpp

@@ -58,11 +58,13 @@
 #pragma warning( disable: 4244 ) //conversion from '__int64' to 'int', possible loss of data
 #endif
 
+#if !defined(OPENCV_DISABLE_EIGEN_TENSOR_SUPPORT)
 #if EIGEN_WORLD_VERSION == 3 && EIGEN_MAJOR_VERSION >= 3 \
     && defined(CV_CXX11) && defined(CV_CXX_STD_ARRAY)
 #include <unsupported/Eigen/CXX11/Tensor>
-#define OPENCV_EIGEN_TENSOR_SUPPORT
-#endif // EIGEN_WORLD_VERSION == 3 && EIGEN_MAJOR_VERSION >= 3
+#define OPENCV_EIGEN_TENSOR_SUPPORT 1
+#endif  // EIGEN_WORLD_VERSION == 3 && EIGEN_MAJOR_VERSION >= 3
+#endif  // !defined(OPENCV_DISABLE_EIGEN_TENSOR_SUPPORT)
 
 namespace cv
 {

modules/core/include/opencv2/core/version.hpp

@@ -7,8 +7,8 @@
 
 #define CV_VERSION_MAJOR    3
 #define CV_VERSION_MINOR    4
-#define CV_VERSION_REVISION 11
-#define CV_VERSION_STATUS   "-dev"
+#define CV_VERSION_REVISION 12
+#define CV_VERSION_STATUS   ""
 
 #define CVAUX_STR_EXP(__A)  #__A
 #define CVAUX_STR(__A)      CVAUX_STR_EXP(__A)

modules/core/src/matrix.cpp

@@ -237,12 +237,19 @@ void setSize( Mat& m, int _dims, const int* _sz, const size_t* _steps, bool auto
 
         if( _steps )
         {
-            if (_steps[i] % esz1 != 0)
+            if (i < _dims-1)
             {
-                CV_Error(Error::BadStep, "Step must be a multiple of esz1");
-            }
+                if (_steps[i] % esz1 != 0)
+                {
+                    CV_Error_(Error::BadStep, ("Step %zu for dimension %d must be a multiple of esz1 %zu", _steps[i], i, esz1));
+                }
 
-            m.step.p[i] = i < _dims-1 ? _steps[i] : esz;
+                m.step.p[i] = _steps[i];
+            }
+            else
+            {
+                m.step.p[i] = esz;
+            }
         }
         else if( autoSteps )
         {

modules/core/src/matrix_wrap.cpp

@@ -1247,6 +1247,7 @@ void _OutputArray::create(int d, const int* sizes, int mtype, int i,
     {
         CV_Assert( i < 0 );
         Mat& m = *(Mat*)obj;
+        CV_Assert(!(m.empty() && fixedType() && fixedSize()) && "Can't reallocate empty Mat with locked layout (probably due to misused 'const' modifier)");
         if (allowTransposed && !m.empty() &&
             d == 2 && m.dims == 2 &&
             m.type() == mtype && m.rows == sizes[1] && m.cols == sizes[0] &&
@@ -1260,13 +1261,13 @@ void _OutputArray::create(int d, const int* sizes, int mtype, int i,
             if(CV_MAT_CN(mtype) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0 )
                 mtype = m.type();
             else
-                CV_CheckTypeEQ(m.type(), CV_MAT_TYPE(mtype), "");
+                CV_CheckTypeEQ(m.type(), CV_MAT_TYPE(mtype), "Can't reallocate Mat with locked type (probably due to misused 'const' modifier)");
         }
         if(fixedSize())
         {
-            CV_CheckEQ(m.dims, d, "");
+            CV_CheckEQ(m.dims, d, "Can't reallocate Mat with locked size (probably due to misused 'const' modifier)");
             for(int j = 0; j < d; ++j)
-                CV_CheckEQ(m.size[j], sizes[j], "");
+                CV_CheckEQ(m.size[j], sizes[j], "Can't reallocate Mat with locked size (probably due to misused 'const' modifier)");
         }
         m.create(d, sizes, mtype);
         return;
@@ -1276,6 +1277,7 @@ void _OutputArray::create(int d, const int* sizes, int mtype, int i,
     {
         CV_Assert( i < 0 );
         UMat& m = *(UMat*)obj;
+        CV_Assert(!(m.empty() && fixedType() && fixedSize()) && "Can't reallocate empty UMat with locked layout (probably due to misused 'const' modifier)");
         if (allowTransposed && !m.empty() &&
             d == 2 && m.dims == 2 &&
             m.type() == mtype && m.rows == sizes[1] && m.cols == sizes[0] &&
@@ -1289,13 +1291,13 @@ void _OutputArray::create(int d, const int* sizes, int mtype, int i,
             if(CV_MAT_CN(mtype) == m.channels() && ((1 << CV_MAT_TYPE(flags)) & fixedDepthMask) != 0 )
                 mtype = m.type();
             else
-                CV_CheckTypeEQ(m.type(), CV_MAT_TYPE(mtype), "");
+                CV_CheckTypeEQ(m.type(), CV_MAT_TYPE(mtype), "Can't reallocate UMat with locked type (probably due to misused 'const' modifier)");
         }
         if(fixedSize())
         {
-            CV_CheckEQ(m.dims, d, "");
+            CV_CheckEQ(m.dims, d, "Can't reallocate UMat with locked size (probably due to misused 'const' modifier)");
             for(int j = 0; j < d; ++j)
-                CV_CheckEQ(m.size[j], sizes[j], "");
+                CV_CheckEQ(m.size[j], sizes[j], "Can't reallocate UMat with locked size (probably due to misused 'const' modifier)");
         }
         m.create(d, sizes, mtype);
         return;

modules/core/src/ocl.cpp

@@ -2755,7 +2755,7 @@ KernelArg KernelArg::Constant(const Mat& m)
 struct Kernel::Impl
 {
     Impl(const char* kname, const Program& prog) :
-        refcount(1), handle(NULL), isInProgress(false), nu(0)
+        refcount(1), handle(NULL), isInProgress(false), isAsyncRun(false), nu(0)
     {
         cl_program ph = (cl_program)prog.ptr();
         cl_int retval = 0;
@@ -2832,6 +2832,7 @@ struct Kernel::Impl
     enum { MAX_ARRS = 16 };
     UMatData* u[MAX_ARRS];
     bool isInProgress;
+    bool isAsyncRun;  // true if kernel was scheduled in async mode
     int nu;
     std::list<Image2D> images;
     bool haveTempDstUMats;
@@ -3111,13 +3112,45 @@ bool Kernel::run(int dims, size_t _globalsize[], size_t _localsize[],
 }
 
 
+static bool isRaiseErrorOnReuseAsyncKernel()
+{
+    static bool initialized = false;
+    static bool value = false;
+    if (!initialized)
+    {
+        value = cv::utils::getConfigurationParameterBool("OPENCV_OPENCL_RAISE_ERROR_REUSE_ASYNC_KERNEL", false);
+        initialized = true;
+    }
+    return value;
+}
+
 bool Kernel::Impl::run(int dims, size_t globalsize[], size_t localsize[],
         bool sync, int64* timeNS, const Queue& q)
 {
     CV_INSTRUMENT_REGION_OPENCL_RUN(name.c_str());
 
-    if (!handle || isInProgress)
+    if (!handle)
+    {
+        CV_LOG_ERROR(NULL, "OpenCL kernel has zero handle: " << name);
         return false;
+    }
+
+    if (isAsyncRun)
+    {
+        CV_LOG_ERROR(NULL, "OpenCL kernel can't be reused in async mode: " << name);
+        if (isRaiseErrorOnReuseAsyncKernel())
+            CV_Assert(0);
+        return false;  // OpenCV 5.0: raise error
+    }
+    isAsyncRun = !sync;
+
+    if (isInProgress)
+    {
+        CV_LOG_ERROR(NULL, "Previous OpenCL kernel launch is not finished: " << name);
+        if (isRaiseErrorOnReuseAsyncKernel())
+            CV_Assert(0);
+        return false;  // OpenCV 5.0: raise error
+    }
 
     cl_command_queue qq = getQueue(q);
     if (haveTempDstUMats)

modules/core/src/parallel.cpp

@@ -54,7 +54,8 @@
 #endif
 
 #if defined __linux__ || defined __APPLE__ || defined __GLIBC__ \
-    || defined __HAIKU__ || defined __EMSCRIPTEN__ || defined __FreeBSD__
+    || defined __HAIKU__ || defined __EMSCRIPTEN__ || defined __FreeBSD__ \
+    || defined __OpenBSD__
     #include <unistd.h>
     #include <stdio.h>
     #include <sys/types.h>

modules/core/test/test_mat.cpp

@@ -2185,4 +2185,32 @@ TEST(Mat, empty_iterator_16855)
     EXPECT_TRUE(m.begin<uchar>() == m.end<uchar>());
 }
 
+
+TEST(Mat, regression_18473)
+{
+    std::vector<int> sizes(3);
+    sizes[0] = 20;
+    sizes[1] = 50;
+    sizes[2] = 100;
+#if 1  // with the fix
+    std::vector<size_t> steps(2);
+    steps[0] = 50*100*2;
+    steps[1] = 100*2;
+#else  // without the fix
+    std::vector<size_t> steps(3);
+    steps[0] = 50*100*2;
+    steps[1] = 100*2;
+    steps[2] = 2;
+#endif
+    std::vector<short> data(20*50*100, 0);  // 1Mb
+    data[data.size() - 1] = 5;
+
+    // param steps Array of ndims-1 steps
+    Mat m(sizes, CV_16SC1, (void*)data.data(), (const size_t*)steps.data());
+
+    ASSERT_FALSE(m.empty());
+    EXPECT_EQ((int)5, (int)m.at<short>(19, 49, 99));
+}
+
+
 }} // namespace

modules/cudaarithm/src/core.cpp

@@ -163,8 +163,12 @@ void cv::cuda::flip(InputArray _src, OutputArray _dst, int flipCode, Stream& str
 
     _dst.create(src.size(), src.type());
     GpuMat dst = getOutputMat(_dst, src.size(), src.type(), stream);
+    bool isInplace = (src.data == dst.data) || (src.refcount == dst.refcount);
+    bool isSizeOdd = (src.cols & 1) == 1 || (src.rows & 1) == 1;
+    if (isInplace && isSizeOdd)
+        CV_Error(Error::BadROISize, "In-place version of flip only accepts even width/height");
 
-    if (src.refcount != dst.refcount)
+    if (isInplace == false)
         funcs[src.depth()][src.channels() - 1](src, dst, flipCode, StreamAccessor::getStream(stream));
     else // in-place
         ifuncs[src.depth()][src.channels() - 1](src, flipCode, StreamAccessor::getStream(stream));

modules/cudaarithm/test/test_core.cpp

@@ -282,8 +282,13 @@ CUDA_TEST_P(Flip, Accuracy)
 CUDA_TEST_P(Flip, AccuracyInplace)
 {
     cv::Mat src = randomMat(size, type);
-
+    bool isSizeOdd = ((size.width & 1) == 1) || ((size.height & 1) == 1);
     cv::cuda::GpuMat srcDst = loadMat(src, useRoi);
+    if(isSizeOdd)
+    {
+        EXPECT_THROW(cv::cuda::flip(srcDst, srcDst, flip_code), cv::Exception);
+        return;
+    }
     cv::cuda::flip(srcDst, srcDst, flip_code);
 
     cv::Mat dst_gold;

modules/dnn/include/opencv2/dnn/dnn.hpp

@@ -47,9 +47,9 @@
 #include "opencv2/core/async.hpp"
 
 #if !defined CV_DOXYGEN && !defined CV_STATIC_ANALYSIS && !defined CV_DNN_DONT_ADD_EXPERIMENTAL_NS
-#define CV__DNN_EXPERIMENTAL_NS_BEGIN namespace experimental_dnn_34_v18 {
+#define CV__DNN_EXPERIMENTAL_NS_BEGIN namespace experimental_dnn_34_v19 {
 #define CV__DNN_EXPERIMENTAL_NS_END }
-namespace cv { namespace dnn { namespace experimental_dnn_34_v18 { } using namespace experimental_dnn_34_v18; }}
+namespace cv { namespace dnn { namespace experimental_dnn_34_v19 { } using namespace experimental_dnn_34_v19; }}
 #else
 #define CV__DNN_EXPERIMENTAL_NS_BEGIN
 #define CV__DNN_EXPERIMENTAL_NS_END

modules/dnn/perf/perf_net.cpp

@@ -111,6 +111,10 @@ PERF_TEST_P_(DNNTestNetwork, ENet)
     if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU) ||
         (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
         throw SkipTestException("");
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2021010000)
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+        throw SkipTestException("");
+#endif
     processNet("dnn/Enet-model-best.net", "", "enet.yml",
             Mat(cv::Size(512, 256), CV_32FC3));
 }
@@ -202,6 +206,10 @@ PERF_TEST_P_(DNNTestNetwork, YOLOv3)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
         throw SkipTestException("Test is disabled in OpenVINO 2020.4");
 #endif
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021010000)  // nGraph compilation failure
+    if (target == DNN_TARGET_MYRIAD)
+        throw SkipTestException("");
+#endif
 
     Mat sample = imread(findDataFile("dnn/dog416.png"));
     cvtColor(sample, sample, COLOR_BGR2RGB);
@@ -214,7 +222,7 @@ PERF_TEST_P_(DNNTestNetwork, YOLOv4)
 {
     if (backend == DNN_BACKEND_HALIDE)
         throw SkipTestException("");
-    if (target == DNN_TARGET_MYRIAD)
+    if (target == DNN_TARGET_MYRIAD)  // not enough resources
         throw SkipTestException("");
 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000)  // nGraph compilation failure
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
@@ -233,6 +241,10 @@ PERF_TEST_P_(DNNTestNetwork, YOLOv4_tiny)
 {
     if (backend == DNN_BACKEND_HALIDE)
         throw SkipTestException("");
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021010000)  // nGraph compilation failure
+    if (target == DNN_TARGET_MYRIAD)
+        throw SkipTestException("");
+#endif
     Mat sample = imread(findDataFile("dnn/dog416.png"));
     cvtColor(sample, sample, COLOR_BGR2RGB);
     Mat inp;
@@ -263,6 +275,10 @@ PERF_TEST_P_(DNNTestNetwork, Inception_v2_Faster_RCNN)
 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
         throw SkipTestException("Test is disabled in OpenVINO 2019R2");
+#endif
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021010000)
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
+        throw SkipTestException("Test is disabled in OpenVINO 2021.1 / MYRIAD");
 #endif
     if (backend == DNN_BACKEND_HALIDE ||
         (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU) ||

modules/dnn/src/darknet/darknet_io.cpp

@@ -984,8 +984,8 @@ namespace cv {
                     }
 
                     std::string activation = getParam<std::string>(layer_params, "activation", "linear");
-                    if(activation == "leaky" || activation == "swish" || activation == "mish" || activation == "logistic")
-                        ++cv_layers_counter;  // For ReLU, Swish, Mish, Sigmoid
+                    if (activation != "linear")
+                        ++cv_layers_counter;  // For ReLU, Swish, Mish, Sigmoid, etc
 
                     if(!darknet_layers_counter)
                         tensor_shape.resize(1);

modules/dnn/src/dnn.cpp

@@ -2113,7 +2113,9 @@ struct Net::Impl : public detail::NetImplBase
 
                     auto ieInpNode = inputNodes[i].dynamicCast<InfEngineNgraphNode>();
                     CV_Assert(oid < ieInpNode->node->get_output_size());
-#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_3)
+#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4)
+                    inputNodes[i] = Ptr<BackendNode>(new InfEngineNgraphNode(ieInpNode->node));
+#elif INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_3)
                     inputNodes[i] = Ptr<BackendNode>(new InfEngineNgraphNode(ieInpNode->node->get_output_as_single_output_node(oid)));
 #else
                     inputNodes[i] = Ptr<BackendNode>(new InfEngineNgraphNode(ieInpNode->node->get_output_as_single_output_node(oid, false)));
@@ -2411,14 +2413,42 @@ struct Net::Impl : public detail::NetImplBase
                 }
 
                 // fuse convolution layer followed by eltwise + relu
-                if ( IS_DNN_OPENCL_TARGET(preferableTarget) && ld.layerInstance->type == "Convolution" )
+                while (nextData && IS_DNN_OPENCL_TARGET(preferableTarget) && ld.layerInstance->type == "Convolution")  // semantic of 'if'
                 {
-                    Ptr<EltwiseLayer> nextEltwiseLayer;
-                    if( nextData )
-                        nextEltwiseLayer = nextData->layerInstance.dynamicCast<EltwiseLayer>();
+                    Ptr<EltwiseLayer> nextEltwiseLayer = nextData->layerInstance.dynamicCast<EltwiseLayer>();
+                    if (nextEltwiseLayer.empty())
+                        break;
+
+                    if (pinsToKeep.count(lpNext) != 0)
+                        break;
+                    if (nextData->inputBlobsId.size() != 2)
+                        break;
+
+                    if (!nextData->params.has("operation") || nextData->params.get<String>("operation").toLowerCase() == "sum")
+                    {
+                        if (nextData->params.has("coeff"))
+                        {
+                            DictValue paramCoeff = nextData->params.get("coeff");
+                            int n = paramCoeff.size();
+                            bool isCoeffOneOne = (n == 2);
+                            for (int i = 0; isCoeffOneOne && i < n; i++)
+                            {
+                                float c = paramCoeff.get<float>(i);
+                                isCoeffOneOne &= (c == 1.0f);
+                            }
+                            if (!isCoeffOneOne)
+                            {
+                                CV_LOG_DEBUG(NULL, "DNN/OpenCL: fusion of 'Sum' without coeffs (or {1.0, 1.0}) is supported only");
+                                break;
+                            }
+                        }
+                    }
+                    else
+                    {
+                        CV_LOG_DEBUG(NULL, "DNN/OpenCL: fusion with eltwise operation is not supported: " << nextData->params.get<String>("operation"));
+                        break;
+                    }
 
-                    if( !nextEltwiseLayer.empty() && pinsToKeep.count(lpNext) == 0 &&
-                        nextData && nextData->inputBlobsId.size() == 2 )
                     {
                         LayerData *eltwiseData = nextData;
 
@@ -2458,10 +2488,12 @@ struct Net::Impl : public detail::NetImplBase
                                     if( nextData )
                                         nextActivLayer = nextData->layerInstance.dynamicCast<ActivationLayer>();
 
+                                    Ptr<PowerLayer> activ_power;
                                     if( !nextActivLayer.empty() &&
                                             (!nextData->type.compare("ReLU") ||
                                              !nextData->type.compare("ChannelsPReLU") ||
-                                             !nextData->type.compare("Power")) &&
+                                             (!nextData->type.compare("Power") && (activ_power = nextActivLayer.dynamicCast<PowerLayer>()) && activ_power->scale == 1.0f)
+                                            ) &&
                                             currLayer->setActivation(nextActivLayer) )
                                     {
                                         CV_Assert_N(biasLayerData->outputBlobsWrappers.size() == 1, ld.inputBlobsWrappers.size() == 1);
@@ -2513,6 +2545,8 @@ struct Net::Impl : public detail::NetImplBase
                             }
                         }
                     }
+
+                    break;
                 }
             }
 
@@ -2694,11 +2728,11 @@ struct Net::Impl : public detail::NetImplBase
 
         Ptr<Layer> layer = ld.layerInstance;
 
-        TickMeter tm;
-        tm.start();
-
         if( !ld.skip )
         {
+            TickMeter tm;
+            tm.start();
+
             std::map<int, Ptr<BackendNode> >::iterator it = ld.backendNodes.find(preferableBackend);
             if (preferableBackend == DNN_BACKEND_OPENCV || it == ld.backendNodes.end() || it->second.empty())
             {
@@ -2877,12 +2911,15 @@ struct Net::Impl : public detail::NetImplBase
                     CV_Error(Error::StsNotImplemented, "Unknown backend identifier");
                 }
             }
+
+            tm.stop();
+            int64 t = tm.getTimeTicks();
+            layersTimings[ld.id] = (t > 0) ? t : t + 1;  // zero for skipped layers only
         }
         else
-            tm.reset();
-
-        tm.stop();
-        layersTimings[ld.id] = tm.getTimeTicks();
+        {
+            layersTimings[ld.id] = 0;
+        }
 
         ld.flag = 1;
     }
@@ -3486,11 +3523,16 @@ void Net::connect(String _outPin, String _inPin)
 Mat Net::forward(const String& outputName)
 {
     CV_TRACE_FUNCTION();
+    CV_Assert(!empty());
 
     String layerName = outputName;
 
     if (layerName.empty())
-        layerName = getLayerNames().back();
+    {
+        std::vector<String> layerNames = getLayerNames();
+        CV_Assert(!layerNames.empty());
+        layerName = layerNames.back();
+    }
 
     std::vector<LayerPin> pins(1, impl->getPinByAlias(layerName));
     impl->setUpNet(pins);
@@ -3502,11 +3544,17 @@ Mat Net::forward(const String& outputName)
 AsyncArray Net::forwardAsync(const String& outputName)
 {
     CV_TRACE_FUNCTION();
+    CV_Assert(!empty());
+
 #ifdef CV_CXX11
     String layerName = outputName;
 
     if (layerName.empty())
-        layerName = getLayerNames().back();
+    {
+        std::vector<String> layerNames = getLayerNames();
+        CV_Assert(!layerNames.empty());
+        layerName = layerNames.back();
+    }
 
     std::vector<LayerPin> pins(1, impl->getPinByAlias(layerName));
     impl->setUpNet(pins);
@@ -3527,11 +3575,16 @@ AsyncArray Net::forwardAsync(const String& outputName)
 void Net::forward(OutputArrayOfArrays outputBlobs, const String& outputName)
 {
     CV_TRACE_FUNCTION();
+    CV_Assert(!empty());
 
     String layerName = outputName;
 
     if (layerName.empty())
-        layerName = getLayerNames().back();
+    {
+        std::vector<String> layerNames = getLayerNames();
+        CV_Assert(!layerNames.empty());
+        layerName = layerNames.back();
+    }
 
     std::vector<LayerPin> pins(1, impl->getPinByAlias(layerName));
     impl->setUpNet(pins);
@@ -4118,6 +4171,8 @@ std::vector<Ptr<Layer> > Net::getLayerInputs(LayerId layerId)
 
 std::vector<String> Net::getLayerNames() const
 {
+    CV_TRACE_FUNCTION();
+
     std::vector<String> res;
     res.reserve(impl->layers.size());
 

modules/dnn/src/ie_ngraph.cpp

@@ -684,7 +684,11 @@ void InfEngineNgraphNet::initPlugin(InferenceEngine::CNNNetwork& net)
         }
         std::map<std::string, std::string> config;
         if (device_name == "MYRIAD") {
+#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4)
+            config.emplace("MYRIAD_DETECT_NETWORK_BATCH", CONFIG_VALUE(NO));
+#else
             config.emplace("VPU_DETECT_NETWORK_BATCH", CONFIG_VALUE(NO));
+#endif
         }
 
         bool isHetero = device_name == "FPGA";

modules/dnn/src/layers/convolution_layer.cpp

@@ -46,6 +46,8 @@
 #include "../op_inf_engine.hpp"
 #include "../ie_ngraph.hpp"
 
+#include <opencv2/core/utils/logger.hpp>
+
 #include "opencv2/core/hal/hal.hpp"
 #include "opencv2/core/hal/intrin.hpp"
 #include <iostream>
@@ -371,6 +373,14 @@ public:
             Ptr<PowerLayer> activ_power = activ.dynamicCast<PowerLayer>();
             if (!activ_power.empty())
             {
+                if (activ_power->scale != 1.0f)  // not supported well by implementation, #17964
+                {
+                    // FIXIT no way to check number of blobs (like, eltwise input)
+                    CV_LOG_DEBUG(NULL, "DNN/OpenCL: can't configure Power activation (scale != 1.0f)");
+                    activ.release();
+                    newActiv = false;
+                    return false;
+                }
                 if (activ_power->scale != 1.f || activ_power->shift != 0.f)
                 {
                     const int outCh = blobs[0].size[0];

modules/dnn/src/layers/resize_layer.cpp

@@ -39,7 +39,7 @@ public:
             CV_Assert(params.has("zoom_factor_x") && params.has("zoom_factor_y"));
         }
         interpolation = params.get<String>("interpolation");
-        CV_Assert(interpolation == "nearest" || interpolation == "opencv_linear" || interpolation == "bilinear");
+        CV_Check(interpolation, interpolation == "nearest" || interpolation == "opencv_linear" || interpolation == "bilinear", "");
 
         alignCorners = params.get<bool>("align_corners", false);
     }

modules/dnn/src/ocl4dnn/src/math_functions.cpp

@@ -46,6 +46,8 @@
 #include <vector>
 #include "opencl_kernels_dnn.hpp"
 
+#include "opencv2/core/utils/logger.hpp"
+
 namespace cv { namespace dnn { namespace ocl4dnn {
 
 enum gemm_data_type_t
@@ -238,10 +240,6 @@ static bool ocl4dnnFastImageGEMM(const CBLAS_TRANSPOSE TransA,
         kernel_name += "_float";
     }
 
-    ocl::Kernel oclk_gemm_float(kernel_name.c_str(), ocl::dnn::gemm_image_oclsrc, opts);
-    if (oclk_gemm_float.empty())
-        return false;
-
     while (C_start_y < M)
     {
         blockC_width = std::min(static_cast<int>(N) - C_start_x, blocksize);
@@ -348,6 +346,10 @@ static bool ocl4dnnFastImageGEMM(const CBLAS_TRANSPOSE TransA,
             }
             local[1] = 1;
 
+            ocl::Kernel oclk_gemm_float(kernel_name.c_str(), ocl::dnn::gemm_image_oclsrc, opts);
+            if (oclk_gemm_float.empty())
+                return false;
+
             cl_uint arg_idx = 0;
             if (is_image_a)
                 oclk_gemm_float.set(arg_idx++, ocl::KernelArg::PtrReadOnly(A));
@@ -378,7 +380,10 @@ static bool ocl4dnnFastImageGEMM(const CBLAS_TRANSPOSE TransA,
             oclk_gemm_float.set(arg_idx++, isFirstColBlock);
 
             if (!oclk_gemm_float.run(2, global, local, false))
+            {
+                CV_LOG_WARNING(NULL, "OpenCL kernel enqueue failed: " << kernel_name);
                 return false;
+            }
 
             if (TransA == CblasNoTrans)
                 A_start_x += blockA_width;

modules/dnn/src/ocl4dnn/src/ocl4dnn_conv_spatial.cpp

@@ -607,6 +607,7 @@ void OCL4DNNConvSpatial<Dtype>::calculateBenchmark(const UMat &bottom, UMat &ver
 {
     options_.str(""); options_.clear(); // clear contents and state flags
     createBasicKernel(1, 1, 1);
+    CV_Assert(!kernelQueue.empty());  // basic kernel must be available
     kernel_index_ = kernelQueue.size() - 1;
     convolve(bottom, verifyTop, weight, bias, numImages, kernelQueue[kernel_index_]);
     CV_Assert(phash.find(kernelQueue[kernel_index_]->kernelName) != phash.end());
@@ -1713,6 +1714,7 @@ void OCL4DNNConvSpatial<float>::useFirstAvailable(const UMat &bottom,
                                     tunerItems[i]->blockHeight,
                                     tunerItems[i]->blockDepth))
         {
+            CV_Assert(!kernelQueue.empty());  // basic kernel must be available
             int kernelIdx = kernelQueue.size() - 1;
             kernelConfig* config = kernelQueue[kernelIdx].get();
             bool failed = false;
@@ -1883,6 +1885,7 @@ void OCL4DNNConvSpatial<float>::setupConvolution(const UMat &bottom,
         CV_LOG_INFO(NULL, "fallback to basic kernel");
         options_.str(""); options_.clear(); // clear contents and state flags
         createBasicKernel(1, 1, 1);
+        CV_Assert(!kernelQueue.empty());  // basic kernel must be available
         kernel_index_ = kernelQueue.size() - 1;
     }
     this->bestKernelConfig = kernelQueue[kernel_index_];

modules/dnn/src/opencl/conv_layer_spatial.cl

@@ -205,7 +205,7 @@ __kernel void ConvolveBasic(
 #if APPLY_BIAS
                 ACTIVATION_FUNCTION(convolved_image, offset, sum[kern] + bias[biasIndex + kern], biasIndex + kern);
 #else
-                ACTIVATION_FUNCTION(convolved_image, offset, sum[kern], biasIndex + kern);
+                ACTIVATION_FUNCTION(convolved_image, offset, sum[kern], kernelNum + kern);
 #endif
             }
         }

modules/dnn/src/opencl/ocl4dnn_lrn.cl

@@ -83,7 +83,7 @@ __kernel void TEMPLATE(lrn_full_no_scale,Dtype)(const int nthreads, __global con
             * in_off[(head - size) * step];
       }
       scale_val = k + accum_scale * alpha_over_size;
-      out_off[(head - post_pad) * step] = in_off[(head - post_pad) * step] * (Dtype)native_powr((Dtype)scale_val, (Dtype)negative_beta);
+      out_off[(head - post_pad) * step] = in_off[(head - post_pad) * step] * (Dtype)native_powr(scale_val, negative_beta);
       ++head;
     }
     // subtract only
@@ -93,7 +93,7 @@ __kernel void TEMPLATE(lrn_full_no_scale,Dtype)(const int nthreads, __global con
             * in_off[(head - size) * step];
       }
       scale_val = k + accum_scale * alpha_over_size;
-      out_off[(head - post_pad) * step] = in_off[(head - post_pad) * step] * (Dtype)native_powr((Dtype)scale_val, (Dtype)negative_beta);
+      out_off[(head - post_pad) * step] = in_off[(head - post_pad) * step] * (Dtype)native_powr(scale_val, negative_beta);
       ++head;
     }
   }

modules/dnn/src/opencl/prior_box.cl

@@ -114,6 +114,6 @@ __kernel void clip(const int nthreads,
     for (int index = get_global_id(0); index < nthreads; index += get_global_size(0))
     {
         Dtype4 vec = vload4(index, dst);
-        vstore4(clamp(vec, 0.0f, 1.0f), index, dst);
+        vstore4(clamp(vec, (Dtype)0.0f, (Dtype)1.0f), index, dst);
     }
 }

modules/dnn/test/test_common.impl.hpp

@@ -63,10 +63,10 @@ void normAssert(
         double l1 /*= 0.00001*/, double lInf /*= 0.0001*/)
 {
     double normL1 = cvtest::norm(ref, test, cv::NORM_L1) / ref.getMat().total();
-    EXPECT_LE(normL1, l1) << comment;
+    EXPECT_LE(normL1, l1) << comment << "  |ref| = " << cvtest::norm(ref, cv::NORM_INF);
 
     double normInf = cvtest::norm(ref, test, cv::NORM_INF);
-    EXPECT_LE(normInf, lInf) << comment;
+    EXPECT_LE(normInf, lInf) << comment << "  |ref| = " << cvtest::norm(ref, cv::NORM_INF);
 }
 
 std::vector<cv::Rect2d> matToBoxes(const cv::Mat& m)

modules/dnn/test/test_darknet_importer.cpp

@@ -625,6 +625,11 @@ TEST_P(Test_Darknet_nets, YOLOv4_tiny)
         target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB
     );
 
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021010000)  // nGraph compilation failure
+    if (target == DNN_TARGET_MYRIAD)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+#endif
+
     const double confThreshold = 0.5;
     // batchId, classId, confidence, left, top, right, bottom
     const int N0 = 2;

modules/dnn/test/test_layers.cpp

@@ -2219,17 +2219,6 @@ TEST_P(ConvolutionActivationFusion, Accuracy)
     Backend backendId = get<0>(get<2>(GetParam()));
     Target targetId = get<1>(get<2>(GetParam()));
 
-    // bug: https://github.com/opencv/opencv/issues/17964
-    if (actType == "Power" && backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
-        applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
-
-    // bug: https://github.com/opencv/opencv/issues/17953
-    if (actType == "ChannelsPReLU" && bias_term == false &&
-        backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
-    {
-        applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
-    }
-
     Net net;
     int convId = net.addLayer(convParams.name, convParams.type, convParams);
     int activId = net.addLayerToPrev(activationParams.name, activationParams.type, activationParams);
@@ -2242,7 +2231,7 @@ TEST_P(ConvolutionActivationFusion, Accuracy)
             expectedFusedLayers.push_back(activId); // all activations are fused
         else if (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16)
         {
-            if (actType == "ReLU" || actType == "ChannelsPReLU" || actType == "ReLU6" || actType == "TanH" || actType == "Power")
+            if (actType == "ReLU" || actType == "ChannelsPReLU" || actType == "ReLU6" || actType == "TanH" /*|| actType == "Power"*/)
                 expectedFusedLayers.push_back(activId);
         }
     }
@@ -2352,21 +2341,6 @@ TEST_P(ConvolutionEltwiseActivationFusion, Accuracy)
     Backend backendId = get<0>(get<4>(GetParam()));
     Target targetId = get<1>(get<4>(GetParam()));
 
-    // bug: https://github.com/opencv/opencv/issues/17945
-    if ((eltwiseOp != "sum" || weightedEltwise) && backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
-        applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
-
-    // bug: https://github.com/opencv/opencv/issues/17953
-    if (eltwiseOp == "sum" && actType == "ChannelsPReLU" && bias_term == false &&
-        backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
-    {
-        applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
-    }
-
-    // bug: https://github.com/opencv/opencv/issues/17964
-    if (actType == "Power" && backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
-        applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
-
     Net net;
     int convId = net.addLayer(convParams.name, convParams.type, convParams);
     int eltwiseId = net.addLayer(eltwiseParams.name, eltwiseParams.type, eltwiseParams);
@@ -2383,7 +2357,9 @@ TEST_P(ConvolutionEltwiseActivationFusion, Accuracy)
             expectedFusedLayers.push_back(activId); // activation is fused with eltwise layer
         else if (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16)
         {
-            if (actType == "ReLU" || actType == "ChannelsPReLU" || actType == "Power")
+            if (eltwiseOp == "sum" && !weightedEltwise &&
+                (actType == "ReLU" || actType == "ChannelsPReLU" /*|| actType == "Power"*/)
+            )
             {
                 expectedFusedLayers.push_back(eltwiseId);
                 expectedFusedLayers.push_back(activId);
@@ -2445,17 +2421,6 @@ TEST_P(ConvolutionActivationEltwiseFusion, Accuracy)
     Backend backendId = get<0>(get<4>(GetParam()));
     Target targetId = get<1>(get<4>(GetParam()));
 
-    // bug: https://github.com/opencv/opencv/issues/17964
-    if (actType == "Power" && backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
-        applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
-
-    // bug: https://github.com/opencv/opencv/issues/17953
-    if (actType == "ChannelsPReLU" && bias_term == false &&
-        backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
-    {
-        applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
-    }
-
     Net net;
     int convId = net.addLayer(convParams.name, convParams.type, convParams);
     int activId = net.addLayer(activationParams.name, activationParams.type, activationParams);
@@ -2472,7 +2437,7 @@ TEST_P(ConvolutionActivationEltwiseFusion, Accuracy)
             expectedFusedLayers.push_back(activId); // activation fused with convolution
         else if (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16)
         {
-            if (actType == "ReLU" || actType == "ChannelsPReLU" || actType == "ReLU6" || actType == "TanH" || actType == "Power")
+            if (actType == "ReLU" || actType == "ChannelsPReLU" || actType == "ReLU6" || actType == "TanH" /*|| actType == "Power"*/)
                 expectedFusedLayers.push_back(activId); // activation fused with convolution
         }
     }

modules/dnn/test/test_misc.cpp

@@ -99,6 +99,15 @@ TEST(readNet, do_not_call_setInput)  // https://github.com/opencv/opencv/issues/
     EXPECT_TRUE(res.empty()) << res.size;
 }
 
+TEST(Net, empty_forward_18392)
+{
+    cv::dnn::Net net;
+    Mat image(Size(512, 512), CV_8UC3, Scalar::all(0));
+    Mat inputBlob = cv::dnn::blobFromImage(image, 1.0, Size(512, 512), Scalar(0,0,0), true, false);
+    net.setInput(inputBlob);
+    EXPECT_ANY_THROW(Mat output = net.forward());
+}
+
 #ifdef HAVE_INF_ENGINE
 static
 void test_readNet_IE_do_not_call_setInput(Backend backendId)

modules/dnn/test/test_onnx_importer.cpp

@@ -262,6 +262,18 @@ TEST_P(Test_ONNX_layers, ReduceSum)
     testONNXModels("reduce_sum");
 }
 
+TEST_P(Test_ONNX_layers, ReduceMaxGlobal)
+{
+    testONNXModels("reduce_max");
+}
+
+TEST_P(Test_ONNX_layers, Scale)
+{
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
+    testONNXModels("scale");
+}
+
 TEST_P(Test_ONNX_layers, ReduceMean3D)
 {
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
@@ -631,6 +643,11 @@ TEST_P(Test_ONNX_layers, MatmulWithTwoInputs)
     testONNXModels("matmul_with_two_inputs");
 }
 
+TEST_P(Test_ONNX_layers, ResizeOpset11_Torch1_6)
+{
+    testONNXModels("resize_opset11_torch1.6");
+}
+
 INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_ONNX_layers, dnnBackendsAndTargets());
 
 class Test_ONNX_nets : public Test_ONNX_layers

modules/dnn/test/test_torch_importer.cpp

@@ -113,7 +113,7 @@ TEST_P(Test_Torch_layers, run_convolution)
 {
     // Output reference values are in range [23.4018, 72.0181]
     double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.08 : default_l1;
-    double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.42 : default_lInf;
+    double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.43 : default_lInf;
     runTorchNet("net_conv", "", false, true, true, l1, lInf);
 }
 
@@ -165,7 +165,7 @@ TEST_P(Test_Torch_layers, run_concat)
 TEST_P(Test_Torch_layers, run_depth_concat)
 {
     runTorchNet("net_depth_concat", "", false, true, true, 0.0,
-                target == DNN_TARGET_OPENCL_FP16 ? 0.021 : 0.0);
+                target == DNN_TARGET_OPENCL_FP16 ? 0.032 : 0.0);
 }
 
 TEST_P(Test_Torch_layers, run_deconv)
@@ -359,6 +359,10 @@ TEST_P(Test_Torch_nets, ENet_accuracy)
         if (target == DNN_TARGET_MYRIAD)      applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
         throw SkipTestException("");
     }
+#endif
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2021010000)
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
 #endif
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
     {

modules/highgui/src/window_w32.cpp

@@ -250,13 +250,13 @@ CV_IMPL int cvInitSystem( int, char** )
         wndc.lpszMenuName = highGUIclassName;
         wndc.hIcon = LoadIcon(0, IDI_APPLICATION);
         wndc.hCursor = (HCURSOR)LoadCursor(0, (LPSTR)(size_t)IDC_CROSS );
-        wndc.hbrBackground = (HBRUSH)GetStockObject(GRAY_BRUSH);
+        wndc.hbrBackground = (HBRUSH)GetStockObject(DKGRAY_BRUSH);
 
         RegisterClass(&wndc);
 
         wndc.lpszClassName = mainHighGUIclassName;
         wndc.lpszMenuName = mainHighGUIclassName;
-        wndc.hbrBackground = (HBRUSH)GetStockObject(GRAY_BRUSH);
+        wndc.hbrBackground = (HBRUSH)GetStockObject(DKGRAY_BRUSH);
         wndc.lpfnWndProc = MainWindowProc;
 
         RegisterClass(&wndc);

modules/imgcodecs/src/loadsave.cpp

@@ -189,7 +189,19 @@ struct ImageCodecInitializer
     std::vector<ImageEncoder> encoders;
 };
 
-static ImageCodecInitializer codecs;
+static
+ImageCodecInitializer& getCodecs()
+{
+#ifdef CV_CXX11
+    static ImageCodecInitializer g_codecs;
+    return g_codecs;
+#else
+    // C++98 doesn't guarantee correctness of multi-threaded initialization of static global variables
+    // (memory leak here is not critical, use C++11 to avoid that)
+    static ImageCodecInitializer* g_codecs = new ImageCodecInitializer();
+    return *g_codecs;
+#endif
+}
 
 /**
  * Find the decoders
@@ -203,6 +215,7 @@ static ImageDecoder findDecoder( const String& filename ) {
     size_t i, maxlen = 0;
 
     /// iterate through list of registered codecs
+    ImageCodecInitializer& codecs = getCodecs();
     for( i = 0; i < codecs.decoders.size(); i++ )
     {
         size_t len = codecs.decoders[i]->signatureLength();
@@ -240,6 +253,7 @@ static ImageDecoder findDecoder( const Mat& buf )
     if( buf.rows*buf.cols < 1 || !buf.isContinuous() )
         return ImageDecoder();
 
+    ImageCodecInitializer& codecs = getCodecs();
     for( i = 0; i < codecs.decoders.size(); i++ )
     {
         size_t len = codecs.decoders[i]->signatureLength();
@@ -272,6 +286,7 @@ static ImageEncoder findEncoder( const String& _ext )
     for( ext++; len < 128 && isalnum(ext[len]); len++ )
         ;
 
+    ImageCodecInitializer& codecs = getCodecs();
     for( size_t i = 0; i < codecs.encoders.size(); i++ )
     {
         String description = codecs.encoders[i]->getDescription();

modules/imgproc/src/connectedcomponents.cpp

@@ -145,14 +145,21 @@ namespace cv{
         void finish(){
             for (int l = 0; l < statsv.rows; ++l){
                 int *row =& statsv.at<int>(l, 0);
-                row[CC_STAT_WIDTH] = row[CC_STAT_WIDTH] - row[CC_STAT_LEFT] + 1;
-                row[CC_STAT_HEIGHT] = row[CC_STAT_HEIGHT] - row[CC_STAT_TOP] + 1;
-
-                Point2ui64& integral = integrals[l];
-                double *centroid = &centroidsv.at<double>(l, 0);
                 double area = ((unsigned*)row)[CC_STAT_AREA];
-                centroid[0] = double(integral.x) / area;
-                centroid[1] = double(integral.y) / area;
+                double *centroid = &centroidsv.at<double>(l, 0);
+                if (area > 0){
+                    row[CC_STAT_WIDTH] = row[CC_STAT_WIDTH] - row[CC_STAT_LEFT] + 1;
+                    row[CC_STAT_HEIGHT] = row[CC_STAT_HEIGHT] - row[CC_STAT_TOP] + 1;
+                    Point2ui64& integral = integrals[l];
+                    centroid[0] = double(integral.x) / area;
+                    centroid[1] = double(integral.y) / area;
+                } else {
+                    row[CC_STAT_WIDTH] = 0;
+                    row[CC_STAT_HEIGHT] = 0;
+                    row[CC_STAT_LEFT] = -1;
+                    centroid[0] = std::numeric_limits<double>::quiet_NaN();
+                    centroid[1] = std::numeric_limits<double>::quiet_NaN();
+                }
             }
         }
 

modules/imgproc/src/gcgraph.hpp

@@ -152,6 +152,8 @@ void GCGraph<TWeight>::addTermWeights( int i, TWeight sourceW, TWeight sinkW )
 template <class TWeight>
 TWeight GCGraph<TWeight>::maxFlow()
 {
+    CV_Assert(!vtcs.empty());
+    CV_Assert(!edges.empty());
     const int TERMINAL = -1, ORPHAN = -2;
     Vtx stub, *nilNode = &stub, *first = nilNode, *last = nilNode;
     int curr_ts = 0;

modules/imgproc/src/subdivision2d.cpp

@@ -758,7 +758,6 @@ void Subdiv2D::getTriangleList(std::vector<Vec6f>& triangleList) const
     triangleList.clear();
     int i, total = (int)(qedges.size()*4);
     std::vector<bool> edgemask(total, false);
-    const bool filterPoints = true;
     Rect2f rect(topLeft.x, topLeft.y, bottomRight.x - topLeft.x, bottomRight.y - topLeft.y);
 
     for( i = 4; i < total; i += 2 )
@@ -768,15 +767,15 @@ void Subdiv2D::getTriangleList(std::vector<Vec6f>& triangleList) const
         Point2f a, b, c;
         int edge_a = i;
         edgeOrg(edge_a, &a);
-        if (filterPoints && !rect.contains(a))
+        if ( !rect.contains(a) )
             continue;
         int edge_b = getEdge(edge_a, NEXT_AROUND_LEFT);
         edgeOrg(edge_b, &b);
-        if (filterPoints && !rect.contains(b))
+        if ( !rect.contains(b) )
             continue;
         int edge_c = getEdge(edge_b, NEXT_AROUND_LEFT);
         edgeOrg(edge_c, &c);
-        if (filterPoints && !rect.contains(c))
+        if ( !rect.contains(c) )
             continue;
         edgemask[edge_a] = true;
         edgemask[edge_b] = true;

modules/imgproc/test/test_connectedcomponents.cpp

@@ -225,5 +225,19 @@ TEST(Imgproc_ConnectedComponents, parallel_wu_labels)
     EXPECT_EQ(nbPixels, area);
 }
 
+TEST(Imgproc_ConnectedComponents, missing_background_pixels)
+{
+    cv::Mat m = Mat::ones(10, 10, CV_8U);
+    cv::Mat labels;
+    cv::Mat stats;
+    cv::Mat centroids;
+    EXPECT_NO_THROW(cv::connectedComponentsWithStats(m, labels, stats, centroids, 8, CV_32S, cv::CCL_WU) );
+    EXPECT_EQ(stats.at<int32_t>(0, cv::CC_STAT_WIDTH), 0);
+    EXPECT_EQ(stats.at<int32_t>(0, cv::CC_STAT_HEIGHT), 0);
+    EXPECT_EQ(stats.at<int32_t>(0, cv::CC_STAT_LEFT), -1);
+    EXPECT_TRUE(std::isnan(centroids.at<double>(0, 0)));
+    EXPECT_TRUE(std::isnan(centroids.at<double>(0, 1)));
+}
+
 
 }} // namespace

modules/java/generator/gen_java.py

@@ -769,6 +769,7 @@ class JavaWrapperGenerator(object):
                             inCode = True
                         if "</code>" in line:
                             inCode = False
+                        line = line.replace('@result ', '@return ')  # @result is valid in Doxygen, but invalid in Javadoc
                         if "@return " in line:
                             returnTag = True
 

modules/ml/misc/python/test/test_goodfeatures.py

@@ -11,7 +11,7 @@ from tests_common import NewOpenCVTests
 class TestGoodFeaturesToTrack_test(NewOpenCVTests):
     def test_goodFeaturesToTrack(self):
         arr = self.get_sample('samples/data/lena.jpg', 0)
-        original = arr.copy(True)
+        original = arr.copy()
         threshes = [ x / 100. for x in range(1,10) ]
         numPoints = 20000
 

modules/python/package/setup.py

@@ -9,7 +9,7 @@ def main():
     os.chdir(SCRIPT_DIR)
 
     package_name = 'opencv'
-    package_version = os.environ.get('OPENCV_VERSION', '3.4.11')  # TODO
+    package_version = os.environ.get('OPENCV_VERSION', '3.4.12')  # TODO
 
     long_description = 'Open Source Computer Vision Library Python bindings'  # TODO
 

modules/videoio/doc/videoio_overview.markdown

@@ -77,7 +77,7 @@ for the operating system. Thus you can't use  VideoCapture or VideoWriter with t
 To get access to their devices, manufactures provide their own C++ API and library that you have to
 include and link with your OpenCV application.
 
-Is common case that this libraries read/write images from/to a memory buffer. If it so, it is
+It is a common case that these libraries read/write images from/to a memory buffer. If it so, it is
 possible to make a `Mat` header for memory buffer (user-allocated data) and process it
 in-place using OpenCV functions. See cv::Mat::Mat() for more details.
 
@@ -85,9 +85,9 @@ The FFmpeg library
 ------------------
 
 OpenCV can use the FFmpeg library (http://ffmpeg.org/) as backend to record, convert and stream audio and video.
-FFMpeg is a complete, cross-reference solution. If you enable FFmpeg while configuring OpenCV than
+FFmpeg is a complete, cross-reference solution. If you enable FFmpeg while configuring OpenCV than
 CMake will download and install the binaries in `OPENCV_SOURCE_CODE/3rdparty/ffmpeg/`. To use
-FFMpeg at runtime, you must deploy the FFMepg binaries with your application.
+FFmpeg at runtime, you must deploy the FFMepg binaries with your application.
 
 @note FFmpeg is licensed under the GNU Lesser General Public License (LGPL) version 2.1 or later.
 See `OPENCV_SOURCE_CODE/3rdparty/ffmpeg/readme.txt` and http://ffmpeg.org/legal.html for details and

modules/videoio/src/cap_dc1394_v2.cpp

@@ -221,60 +221,57 @@ bool CvCaptureCAM_DC1394_v2_CPP::startCapture()
                                           DC1394_ISO_SPEED_3200);
     }
 
-    // should a specific mode be used
-    if (userMode >= 0)
+    dc1394video_modes_t videoModes;
+    dc1394_video_get_supported_modes(dcCam, &videoModes);
 
+    // should a specific mode be used
+    while (userMode >= 0)  // 'if' semantic, no real loop here
     {
         dc1394video_mode_t wantedMode;
-        dc1394video_modes_t videoModes;
-        dc1394_video_get_supported_modes(dcCam, &videoModes);
-
-        //set mode from number, for example the second supported mode, i.e userMode = 1
 
         if (userMode < (int)videoModes.num)
         {
+            // set mode from number, for example the second supported mode, i.e userMode = 1
             wantedMode = videoModes.modes[userMode];
         }
-
-        //set modes directly from DC134 constants (from dc1394video_mode_t)
-        else if ((userMode >= DC1394_VIDEO_MODE_MIN) && (userMode <= DC1394_VIDEO_MODE_MAX ))
+        else if ((userMode >= DC1394_VIDEO_MODE_MIN) && (userMode <= DC1394_VIDEO_MODE_MAX))
         {
+            // set modes directly from DC134 constants (from dc1394video_mode_t)
+
             //search for wanted mode, to check if camera supports it
             int j = 0;
-            while ((j< (int)videoModes.num) && videoModes.modes[j]!=userMode)
+            while ((j < (int)videoModes.num) && videoModes.modes[j] != userMode)
             {
                 j++;
             }
-
-            if ((int)videoModes.modes[j]==userMode)
-            {
-                wantedMode = videoModes.modes[j];
-            }
-            else
+            if (!(j < (int)videoModes.num))
             {
                 userMode = -1;  // wanted mode not supported, search for best mode
+                break;
             }
+
+            wantedMode = videoModes.modes[j];
         }
         else
         {
-            userMode = -1;      // wanted mode not supported, search for best mode
+            userMode = -1;  // wanted mode not supported, search for best mode
+            break;
         }
+
         //if userMode is available: set it and update size
-        if (userMode != -1)
         {
             code = dc1394_video_set_mode(dcCam, wantedMode);
-            uint32_t width, height;
+            uint32_t width = 0, height = 0;
             dc1394_get_image_size_from_video_mode(dcCam, wantedMode, &width, &height);
             frameWidth  = (int)width;
             frameHeight = (int)height;
         }
+        break;
     }
 
     if (userMode == -1 && (frameWidth > 0 || frameHeight > 0))
     {
-        dc1394video_mode_t bestMode = (dc1394video_mode_t) - 1;
-        dc1394video_modes_t videoModes;
-        dc1394_video_get_supported_modes(dcCam, &videoModes);
+        dc1394video_mode_t bestMode = (dc1394video_mode_t)(-1);
         for (i = 0; i < (int)videoModes.num; i++)
         {
             dc1394video_mode_t mode = videoModes.modes[i];

platforms/android/build_sdk.py

@@ -269,7 +269,7 @@ class Builder:
         # Add extra data
         apkxmldest = check_dir(os.path.join(apkdest, "res", "xml"), create=True)
         apklibdest = check_dir(os.path.join(apkdest, "libs", abi.name), create=True)
-        for ver, d in self.extra_packs + [("3.4.11", os.path.join(self.libdest, "lib"))]:
+        for ver, d in self.extra_packs + [("3.4.12", os.path.join(self.libdest, "lib"))]:
             r = ET.Element("library", attrib={"version": ver})
             log.info("Adding libraries from %s", d)
 

platforms/android/service/readme.txt

@@ -12,7 +12,7 @@ manually using adb tool:
 
     adb install <path-to-OpenCV-sdk>/apk/OpenCV_<version>_Manager_<app_version>_<platform>.apk
 
-Example: OpenCV_3.4.11-dev_Manager_3.49_armeabi-v7a.apk
+Example: OpenCV_3.4.12-dev_Manager_3.49_armeabi-v7a.apk
 
 Use the list of platforms below to determine proper OpenCV Manager package for your device:
 

platforms/ios/build_framework.py

@@ -150,7 +150,6 @@ class Builder:
         ] if self.debug_info else [])
 
         if len(self.exclude) > 0:
-            args += ["-DBUILD_opencv_world=OFF"] if not self.dynamic else []
             args += ["-DBUILD_opencv_%s=OFF" % m for m in self.exclude]
 
         if len(self.disable) > 0:

platforms/maven/opencv-it/pom.xml

@@ -4,7 +4,7 @@
     <parent>
         <groupId>org.opencv</groupId>
         <artifactId>opencv-parent</artifactId>
-        <version>3.4.11</version>
+        <version>3.4.12</version>
     </parent>
     <groupId>org.opencv</groupId>
     <artifactId>opencv-it</artifactId>

platforms/maven/opencv/pom.xml

@@ -4,7 +4,7 @@
     <parent>
         <groupId>org.opencv</groupId>
         <artifactId>opencv-parent</artifactId>
-        <version>3.4.11</version>
+        <version>3.4.12</version>
     </parent>
     <groupId>org.opencv</groupId>
     <artifactId>opencv</artifactId>

platforms/maven/pom.xml

@@ -3,7 +3,7 @@
     <modelVersion>4.0.0</modelVersion>
     <groupId>org.opencv</groupId>
     <artifactId>opencv-parent</artifactId>
-    <version>3.4.11</version>
+    <version>3.4.12</version>
     <packaging>pom</packaging>
     <name>OpenCV Parent POM</name>
     <licenses>

platforms/winpack_dldt/2020.4/20201005-dldt-fix-cldnn-compilation.patch

@@ -0,0 +1,12 @@
+diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/common/tensor_type.h b/inference-engine/thirdparty/clDNN/kernel_selector/common/tensor_type.h
+index 3dbdfd0b..6b04b910 100644
+--- a/inference-engine/thirdparty/clDNN/kernel_selector/common/tensor_type.h
++++ b/inference-engine/thirdparty/clDNN/kernel_selector/common/tensor_type.h
+@@ -15,6 +15,7 @@
+ 
+ #pragma once
+ 
++#include <stdexcept>
+ #include "common_types.h"
+ #include "common_tools.h"
+ #include <vector>

platforms/winpack_dldt/2020.4/patch.config.py

@@ -1,3 +1,4 @@
 applyPatch('20200701-dldt-disable-unused-targets.patch')
 applyPatch('20200413-dldt-pdb.patch')
 applyPatch('20200604-dldt-disable-multidevice.patch')
+applyPatch('20201005-dldt-fix-cldnn-compilation.patch')

platforms/winpack_dldt/2021.1/20200413-dldt-pdb.patch

@@ -0,0 +1,14 @@
+diff --git a/CMakeLists.txt b/CMakeLists.txt
+index 1f981ed2..90eb500a 100644
+--- a/CMakeLists.txt
++++ b/CMakeLists.txt
+@@ -22,6 +22,9 @@ endif()
+ 
+ project(OpenVINO)
+ 
++set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /Zi /FS")
++set(CMAKE_SHARED_LINKER_FLAGS_RELEASE "${CMAKE_SHARED_LINKER_FLAGS_RELEASE} /DEBUG /OPT:REF /OPT:ICF")
++
+ set(OpenVINO_MAIN_SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR})
+ set(IE_MAIN_SOURCE_DIR ${OpenVINO_MAIN_SOURCE_DIR}/inference-engine)
+ list(APPEND CMAKE_MODULE_PATH "${OpenVINO_MAIN_SOURCE_DIR}/cmake")

platforms/winpack_dldt/2021.1/20200604-dldt-disable-multidevice.patch

@@ -0,0 +1,13 @@
+diff --git a/inference-engine/src/CMakeLists.txt b/inference-engine/src/CMakeLists.txt
+index 0ba0dd78..7d34e7cb 100644
+--- a/inference-engine/src/CMakeLists.txt
++++ b/inference-engine/src/CMakeLists.txt
+@@ -26,7 +26,7 @@ endif()
+ 
+ add_subdirectory(hetero_plugin)
+ 
+-add_subdirectory(multi_device)
++#add_subdirectory(multi_device)
+ 
+ add_subdirectory(transformations)
+ 

platforms/winpack_dldt/2021.1/20201005-dldt-disable-unused-targets.patch

@@ -0,0 +1,178 @@
+diff --git a/inference-engine/CMakeLists.txt b/inference-engine/CMakeLists.txt
+index 7f45ab02..a7bac7e9 100644
+--- a/inference-engine/CMakeLists.txt
++++ b/inference-engine/CMakeLists.txt
+@@ -70,7 +70,7 @@ if(ENABLE_TESTS)
+     add_subdirectory(tests)
+ endif()
+ 
+-add_subdirectory(tools)
++#add_subdirectory(tools)
+ 
+ function(ie_build_samples)
+     # samples should be build with the same flags as from OpenVINO package,
+@@ -89,7 +89,7 @@ endfunction()
+ 
+ # gflags and format_reader targets are kept inside of samples directory and
+ # they must be built even if samples build is disabled (required for tests and tools).
+-ie_build_samples()
++#ie_build_samples()
+ 
+ file(GLOB_RECURSE SAMPLES_SOURCES samples/*.cpp samples/*.hpp samples/*.h)
+ add_cpplint_target(sample_cpplint
+@@ -180,7 +180,7 @@ endif()
+ # Developer package
+ #
+ 
+-ie_developer_export_targets(format_reader)
++#ie_developer_export_targets(format_reader)
+ ie_developer_export_targets(${NGRAPH_LIBRARIES})
+ 
+ # for Template plugin
+@@ -188,7 +188,7 @@ if(NGRAPH_INTERPRETER_ENABLE)
+     ie_developer_export_targets(ngraph_backend interpreter_backend)
+ endif()
+ 
+-ie_developer_export()
++#ie_developer_export()
+ 
+ configure_file(
+     "${IE_MAIN_SOURCE_DIR}/cmake/developer_package_config.cmake.in"
+diff --git a/inference-engine/src/inference_engine/CMakeLists.txt b/inference-engine/src/inference_engine/CMakeLists.txt
+index 9ab88898..8badb591 100644
+--- a/inference-engine/src/inference_engine/CMakeLists.txt
++++ b/inference-engine/src/inference_engine/CMakeLists.txt
+@@ -118,7 +118,7 @@ add_cpplint_target(${TARGET_NAME}_plugin_api_cpplint FOR_SOURCES ${plugin_api_sr
+ 
+ # Create common base object library
+ 
+-add_library(${TARGET_NAME}_common_obj OBJECT
++add_library(${TARGET_NAME}_common_obj OBJECT EXCLUDE_FROM_ALL
+             ${IE_BASE_SOURCE_FILES})
+ 
+ target_compile_definitions(${TARGET_NAME}_common_obj PRIVATE IMPLEMENT_INFERENCE_ENGINE_API)
+@@ -132,7 +132,7 @@ target_include_directories(${TARGET_NAME}_common_obj SYSTEM PRIVATE
+ 
+ # Create object library
+ 
+-add_library(${TARGET_NAME}_obj OBJECT
++add_library(${TARGET_NAME}_obj OBJECT EXCLUDE_FROM_ALL
+             ${LIBRARY_SRC}
+             ${LIBRARY_HEADERS}
+             ${PUBLIC_HEADERS})
+@@ -183,7 +183,7 @@ ie_register_plugins(MAIN_TARGET ${TARGET_NAME}
+ 
+ # Static library used for unit tests which are always built
+ 
+-add_library(${TARGET_NAME}_s STATIC
++add_library(${TARGET_NAME}_s STATIC EXCLUDE_FROM_ALL
+             $<TARGET_OBJECTS:${TARGET_NAME}_obj>
+             $<TARGET_OBJECTS:${TARGET_NAME}_common_obj>
+             $<TARGET_OBJECTS:${TARGET_NAME}_legacy_obj>
+diff --git a/inference-engine/src/legacy_api/CMakeLists.txt b/inference-engine/src/legacy_api/CMakeLists.txt
+index ed87a073..b30e6671 100644
+--- a/inference-engine/src/legacy_api/CMakeLists.txt
++++ b/inference-engine/src/legacy_api/CMakeLists.txt
+@@ -26,7 +26,7 @@ endif()
+ 
+ # Create object library
+ 
+-add_library(${TARGET_NAME}_obj OBJECT
++add_library(${TARGET_NAME}_obj OBJECT EXCLUDE_FROM_ALL
+             ${LIBRARY_SRC}
+             ${PUBLIC_HEADERS})
+ 
+diff --git a/inference-engine/src/mkldnn_plugin/CMakeLists.txt b/inference-engine/src/mkldnn_plugin/CMakeLists.txt
+index 166818cd..6c1e8e36 100644
+--- a/inference-engine/src/mkldnn_plugin/CMakeLists.txt
++++ b/inference-engine/src/mkldnn_plugin/CMakeLists.txt
+@@ -193,7 +193,7 @@ cross_compiled_file(${TARGET_NAME}
+ 
+ #  add test object library
+ 
+-add_library(${TARGET_NAME}_obj OBJECT ${SOURCES} ${HEADERS})
++add_library(${TARGET_NAME}_obj OBJECT EXCLUDE_FROM_ALL ${SOURCES} ${HEADERS})
+ 
+ target_include_directories(${TARGET_NAME}_obj PRIVATE $<TARGET_PROPERTY:inference_engine_preproc_s,INTERFACE_INCLUDE_DIRECTORIES>
+                                                       $<TARGET_PROPERTY:inference_engine_lp_transformations,INTERFACE_INCLUDE_DIRECTORIES>
+diff --git a/inference-engine/src/preprocessing/CMakeLists.txt b/inference-engine/src/preprocessing/CMakeLists.txt
+index f4fed72a..9cedd6b5 100644
+--- a/inference-engine/src/preprocessing/CMakeLists.txt
++++ b/inference-engine/src/preprocessing/CMakeLists.txt
+@@ -124,7 +124,7 @@ endif()
+ 
+ # Create object library
+ 
+-add_library(${TARGET_NAME}_obj OBJECT
++add_library(${TARGET_NAME}_obj OBJECT EXCLUDE_FROM_ALL
+             ${LIBRARY_SRC}
+             ${LIBRARY_HEADERS})
+ 
+@@ -175,7 +175,7 @@ add_cpplint_target(${TARGET_NAME}_cpplint FOR_TARGETS ${TARGET_NAME}
+ 
+ # Static library used for unit tests which are always built
+ 
+-add_library(${TARGET_NAME}_s STATIC
++add_library(${TARGET_NAME}_s STATIC EXCLUDE_FROM_ALL
+             $<TARGET_OBJECTS:${TARGET_NAME}_obj>)
+ 
+ set_ie_threading_interface_for(${TARGET_NAME}_s)
+diff --git a/inference-engine/src/vpu/common/CMakeLists.txt b/inference-engine/src/vpu/common/CMakeLists.txt
+index b291d5b4..74ab8287 100644
+--- a/inference-engine/src/vpu/common/CMakeLists.txt
++++ b/inference-engine/src/vpu/common/CMakeLists.txt
+@@ -57,7 +57,7 @@ add_common_target("vpu_common_lib" FALSE)
+ 
+ # Unit tests support for graph transformer
+ if(WIN32)
+-    add_common_target("vpu_common_lib_test_static" TRUE)
++    #add_common_target("vpu_common_lib_test_static" TRUE)
+ else()
+     add_library("vpu_common_lib_test_static" ALIAS "vpu_common_lib")
+ endif()
+diff --git a/inference-engine/src/vpu/graph_transformer/CMakeLists.txt b/inference-engine/src/vpu/graph_transformer/CMakeLists.txt
+index a4543745..807b8e36 100644
+--- a/inference-engine/src/vpu/graph_transformer/CMakeLists.txt
++++ b/inference-engine/src/vpu/graph_transformer/CMakeLists.txt
+@@ -65,7 +65,7 @@ add_graph_transformer_target("vpu_graph_transformer" FALSE)
+ 
+ # Unit tests support for graph transformer
+ if(WIN32)
+-    add_graph_transformer_target("vpu_graph_transformer_test_static" TRUE)
++    #add_graph_transformer_target("vpu_graph_transformer_test_static" TRUE)
+ else()
+     add_library("vpu_graph_transformer_test_static" ALIAS "vpu_graph_transformer")
+ endif()
+diff --git a/inference-engine/thirdparty/CMakeLists.txt b/inference-engine/thirdparty/CMakeLists.txt
+index a2550bfa..10ce316f 100644
+--- a/inference-engine/thirdparty/CMakeLists.txt
++++ b/inference-engine/thirdparty/CMakeLists.txt
+@@ -56,13 +56,13 @@ function(build_with_lto)
+     endfunction()
+ 
+     ie_build_pugixml()
+-    add_subdirectory(stb_lib)
++    #add_subdirectory(stb_lib)
+     add_subdirectory(ade)
+     add_subdirectory(fluid/modules/gapi)
+ 
+     target_include_directories(pugixml INTERFACE "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/pugixml/src>")
+ 
+-    set_target_properties(pugixml ade fluid stb_image
++    set_target_properties(pugixml ade fluid
+                           PROPERTIES FOLDER thirdparty)
+ 
+     # developer package
+diff --git a/inference-engine/thirdparty/pugixml/CMakeLists.txt b/inference-engine/thirdparty/pugixml/CMakeLists.txt
+index 8bcb2801..380fb468 100644
+--- a/inference-engine/thirdparty/pugixml/CMakeLists.txt
++++ b/inference-engine/thirdparty/pugixml/CMakeLists.txt
+@@ -41,7 +41,7 @@ if(BUILD_SHARED_LIBS)
+ else()
+ 	add_library(pugixml STATIC ${SOURCES})
+ 	if (MSVC)
+-		add_library(pugixml_mt STATIC ${SOURCES})
++               #add_library(pugixml_mt STATIC ${SOURCES})
+ 		#if (WIN32)
+ 		#	set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /MT")
+ 		#	set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} /MTd")

platforms/winpack_dldt/2021.1/patch.config.py

@@ -0,0 +1,3 @@
+applyPatch('20201005-dldt-disable-unused-targets.patch')
+applyPatch('20200413-dldt-pdb.patch')
+applyPatch('20200604-dldt-disable-multidevice.patch')

platforms/winpack_dldt/2021.1/sysroot.config.py

@@ -0,0 +1,56 @@
+sysroot_bin_dir = prepare_dir(self.sysrootdir / 'bin')
+copytree(self.build_dir / 'install', self.sysrootdir / 'ngraph')
+#rm_one(self.sysrootdir / 'ngraph' / 'lib' / 'ngraph.dll')
+
+build_config = 'Release' if not self.config.build_debug else 'Debug'
+build_bin_dir = self.build_dir / 'bin' / 'intel64' / build_config
+
+def copy_bin(name):
+    global build_bin_dir, sysroot_bin_dir
+    copytree(build_bin_dir / name, sysroot_bin_dir / name)
+
+dll_suffix = 'd' if self.config.build_debug else ''
+def copy_dll(name):
+    global copy_bin, dll_suffix
+    copy_bin(name + dll_suffix + '.dll')
+    copy_bin(name + dll_suffix + '.pdb')
+
+copy_bin('cache.json')
+copy_dll('clDNNPlugin')
+copy_dll('HeteroPlugin')
+copy_dll('inference_engine')
+copy_dll('inference_engine_ir_reader')
+copy_dll('inference_engine_legacy')
+copy_dll('inference_engine_transformations')  # runtime
+copy_dll('inference_engine_lp_transformations')  # runtime
+copy_dll('MKLDNNPlugin')  # runtime
+copy_dll('myriadPlugin')  # runtime
+#copy_dll('MultiDevicePlugin')  # runtime, not used
+copy_dll('ngraph')
+copy_bin('plugins.xml')
+copytree(self.build_dir / 'bin' / 'intel64' / 'pcie-ma248x.elf', sysroot_bin_dir / 'pcie-ma248x.elf')
+copytree(self.build_dir / 'bin' / 'intel64' / 'usb-ma2x8x.mvcmd', sysroot_bin_dir / 'usb-ma2x8x.mvcmd')
+copytree(self.build_dir / 'bin' / 'intel64' / 'usb-ma2450.mvcmd', sysroot_bin_dir / 'usb-ma2450.mvcmd')
+
+copytree(self.srcdir / 'inference-engine' / 'temp' / 'tbb' / 'bin', sysroot_bin_dir)
+copytree(self.srcdir / 'inference-engine' / 'temp' / 'tbb', self.sysrootdir / 'tbb')
+
+sysroot_ie_dir = prepare_dir(self.sysrootdir / 'deployment_tools' / 'inference_engine')
+sysroot_ie_lib_dir = prepare_dir(sysroot_ie_dir / 'lib' / 'intel64')
+
+copytree(self.srcdir / 'inference-engine' / 'include', sysroot_ie_dir / 'include')
+if not self.config.build_debug:
+    copytree(self.build_dir / 'install' / 'lib' / 'ngraph.lib', sysroot_ie_lib_dir / 'ngraph.lib')
+    copytree(build_bin_dir / 'inference_engine.lib', sysroot_ie_lib_dir / 'inference_engine.lib')
+    copytree(build_bin_dir / 'inference_engine_ir_reader.lib', sysroot_ie_lib_dir / 'inference_engine_ir_reader.lib')
+    copytree(build_bin_dir / 'inference_engine_legacy.lib', sysroot_ie_lib_dir / 'inference_engine_legacy.lib')
+else:
+    copytree(self.build_dir / 'install' / 'lib' / 'ngraphd.lib', sysroot_ie_lib_dir / 'ngraphd.lib')
+    copytree(build_bin_dir / 'inference_engined.lib', sysroot_ie_lib_dir / 'inference_engined.lib')
+    copytree(build_bin_dir / 'inference_engine_ir_readerd.lib', sysroot_ie_lib_dir / 'inference_engine_ir_readerd.lib')
+    copytree(build_bin_dir / 'inference_engine_legacyd.lib', sysroot_ie_lib_dir / 'inference_engine_legacyd.lib')
+
+sysroot_license_dir = prepare_dir(self.sysrootdir / 'etc' / 'licenses')
+copytree(self.srcdir / 'LICENSE', sysroot_license_dir / 'dldt-LICENSE')
+copytree(self.srcdir / 'ngraph/LICENSE', sysroot_license_dir / 'ngraph-LICENSE')
+copytree(self.sysrootdir / 'tbb/LICENSE', sysroot_license_dir / 'tbb-LICENSE')

platforms/winpack_dldt/build_package.py

@@ -443,8 +443,8 @@ class Builder:
 def main():
 
     dldt_src_url = 'https://github.com/openvinotoolkit/openvino'
-    dldt_src_commit = '2020.4'
-    dldt_release = '2020040000'
+    dldt_src_commit = '2021.1'
+    dldt_release = '2021010000'
 
     build_cache_dir_default = os.environ.get('BUILD_CACHE_DIR', '.build_cache')
     build_subst_drive = os.environ.get('BUILD_SUBST_DRIVE', None)

samples/samples_utils.cmake

@@ -15,8 +15,12 @@ function(ocv_define_sample out_target source sub)
   if(WIN32 AND MSVC AND NOT BUILD_SHARED_LIBS)
     set_target_properties(${the_target} PROPERTIES LINK_FLAGS "/NODEFAULTLIB:atlthunk.lib /NODEFAULTLIB:atlsd.lib /DEBUG")
   endif()
-  if(WIN32)
-    install(TARGETS ${the_target} RUNTIME DESTINATION "samples/${sub}" COMPONENT samples)
+  # Should be usable in stand-alone build scenario
+  if((NOT DEFINED INSTALL_BIN_EXAMPLES AND WIN32) OR INSTALL_BIN_EXAMPLES)
+    if(NOT DEFINED OPENCV_SAMPLES_BIN_INSTALL_PATH)
+      set(OPENCV_SAMPLES_BIN_INSTALL_PATH "samples")
+    endif()
+    install(TARGETS ${the_target} RUNTIME DESTINATION "${OPENCV_SAMPLES_BIN_INSTALL_PATH}/${sub}" COMPONENT samples)
   endif()
   # Add single target to build all samples in the group: 'make opencv_samples_cpp'
   set(parent_target opencv_samples_${sub})