Merge branch '3.4' into merge-3.4

cmake/OpenCVDetectApacheAnt.cmake

+set(OPENCV_JAVA_SOURCE_VERSION "" CACHE STRING "Java source version (javac Ant target)")
+set(OPENCV_JAVA_TARGET_VERSION "" CACHE STRING "Java target version (javac Ant target)")
+
 file(TO_CMAKE_PATH "$ENV{ANT_DIR}" ANT_DIR_ENV_PATH)
 file(TO_CMAKE_PATH "$ENV{ProgramFiles}" ProgramFiles_ENV_PATH)
 

cmake/OpenCVDetectInferenceEngine.cmake

@@ -78,9 +78,9 @@ endif()
 
 if(INF_ENGINE_TARGET)
   if(NOT INF_ENGINE_RELEASE)
-    message(WARNING "InferenceEngine version have not been set, 2018R4 will be used by default. Set INF_ENGINE_RELEASE variable if you experience build errors.")
+    message(WARNING "InferenceEngine version have not been set, 2018R5 will be used by default. Set INF_ENGINE_RELEASE variable if you experience build errors.")
   endif()
-  set(INF_ENGINE_RELEASE "2018040000" CACHE STRING "Force IE version, should be in form YYYYAABBCC (e.g. 2018R2.0.2 -> 2018020002)")
+  set(INF_ENGINE_RELEASE "2018050000" CACHE STRING "Force IE version, should be in form YYYYAABBCC (e.g. 2018R2.0.2 -> 2018020002)")
   set_target_properties(${INF_ENGINE_TARGET} PROPERTIES
     INTERFACE_COMPILE_DEFINITIONS "HAVE_INF_ENGINE=1;INF_ENGINE_RELEASE=${INF_ENGINE_RELEASE}"
   )

doc/tutorials/dnn/dnn_android/dnn_android.markdown

@@ -12,7 +12,7 @@ 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-3.4.4-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-3.4.5-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`.
 

modules/core/include/opencv2/core/hal/intrin_avx.hpp

@@ -1278,6 +1278,16 @@ OPENCV_HAL_IMPL_AVX_CHECK_FLT(v_float64x4, 15)
 OPENCV_HAL_IMPL_AVX_MULADD(v_float32x8, ps)
 OPENCV_HAL_IMPL_AVX_MULADD(v_float64x4, pd)
 
+inline v_int32x8 v_fma(const v_int32x8& a, const v_int32x8& b, const v_int32x8& c)
+{
+    return a * b + c;
+}
+
+inline v_int32x8 v_muladd(const v_int32x8& a, const v_int32x8& b, const v_int32x8& c)
+{
+    return v_fma(a, b, c);
+}
+
 inline v_float32x8 v_invsqrt(const v_float32x8& x)
 {
     v_float32x8 half = x * v256_setall_f32(0.5);

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

@@ -750,6 +750,7 @@ CV__DNN_INLINE_NS_BEGIN
      *  @brief Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format.
      *  @param model    path to the file, dumped from Torch by using torch.save() function.
      *  @param isBinary specifies whether the network was serialized in ascii mode or binary.
+     *  @param evaluate specifies testing phase of network. If true, it's similar to evaluate() method in Torch.
      *  @returns Net object.
      *
      *  @note Ascii mode of Torch serializer is more preferable, because binary mode extensively use `long` type of C language,
@@ -771,7 +772,7 @@ CV__DNN_INLINE_NS_BEGIN
      *
      * Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.
      */
-     CV_EXPORTS_W Net readNetFromTorch(const String &model, bool isBinary = true);
+     CV_EXPORTS_W Net readNetFromTorch(const String &model, bool isBinary = true, bool evaluate = true);
 
      /**
       * @brief Read deep learning network represented in one of the supported formats.

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

@@ -6,7 +6,7 @@
 #define OPENCV_DNN_VERSION_HPP
 
 /// Use with major OpenCV version only.
-#define OPENCV_DNN_API_VERSION 20181205
+#define OPENCV_DNN_API_VERSION 20181221
 
 #if !defined CV_DOXYGEN && !defined CV_DNN_DONT_ADD_INLINE_NS
 #define CV__DNN_INLINE_NS __CV_CAT(dnn4_v, OPENCV_DNN_API_VERSION)

modules/dnn/src/layers/mvn_layer.cpp

@@ -116,9 +116,15 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
+#ifdef HAVE_INF_ENGINE
         if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
+#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
+            return !zeroDev && eps <= 1e-7f;
+#else
             return !zeroDev && (preferableTarget == DNN_TARGET_CPU || eps <= 1e-7f);
+#endif
         else
+#endif  // HAVE_INF_ENGINE
             return backendId == DNN_BACKEND_OPENCV;
     }
 

modules/dnn/src/onnx/onnx_importer.cpp

@@ -420,31 +420,30 @@ void ONNXImporter::populateNet(Net dstNet)
         }
         else if (layer_type == "Sub")
         {
-            Mat blob = (-1.0f) * getBlob(node_proto, constBlobs, 1);
-            blob = blob.reshape(1, 1);
+            Mat blob = getBlob(node_proto, constBlobs, 1);
             if (blob.total() == 1) {
                 layerParams.type = "Power";
-                layerParams.set("shift", blob.at<float>(0));
+                layerParams.set("shift", -blob.at<float>(0));
             }
             else {
                 layerParams.type = "Scale";
                 layerParams.set("has_bias", true);
-                layerParams.blobs.push_back(blob);
+                layerParams.blobs.push_back(-1.0f * blob.reshape(1, 1));
             }
         }
         else if (layer_type == "Div")
         {
             Mat blob = getBlob(node_proto, constBlobs, 1);
             CV_Assert_N(blob.type() == CV_32F, blob.total());
-            divide(1.0, blob, blob);
             if (blob.total() == 1)
             {
-                layerParams.set("scale", blob.at<float>(0));
+                layerParams.set("scale", 1.0f / blob.at<float>(0));
                 layerParams.type = "Power";
             }
             else
             {
                 layerParams.type = "Scale";
+                divide(1.0, blob, blob);
                 layerParams.blobs.push_back(blob);
                 layerParams.set("bias_term", false);
             }

modules/dnn/src/op_inf_engine.hpp

@@ -26,10 +26,11 @@
 #define INF_ENGINE_RELEASE_2018R2 2018020000
 #define INF_ENGINE_RELEASE_2018R3 2018030000
 #define INF_ENGINE_RELEASE_2018R4 2018040000
+#define INF_ENGINE_RELEASE_2018R5 2018050000
 
 #ifndef INF_ENGINE_RELEASE
-#warning("IE version have not been provided via command-line. Using 2018R4 by default")
-#define INF_ENGINE_RELEASE INF_ENGINE_RELEASE_2018R4
+#warning("IE version have not been provided via command-line. Using 2018R5 by default")
+#define INF_ENGINE_RELEASE INF_ENGINE_RELEASE_2018R5
 #endif
 
 #define INF_ENGINE_VER_MAJOR_GT(ver) (((INF_ENGINE_RELEASE) / 10000) > ((ver) / 10000))

modules/dnn/src/torch/torch_importer.cpp

@@ -129,13 +129,15 @@ struct TorchImporter
     Module *rootModule;
     Module *curModule;
     int moduleCounter;
+    bool testPhase;
 
-    TorchImporter(String filename, bool isBinary)
+    TorchImporter(String filename, bool isBinary, bool evaluate)
     {
         CV_TRACE_FUNCTION();
 
         rootModule = curModule = NULL;
         moduleCounter = 0;
+        testPhase = evaluate;
 
         file = cv::Ptr<THFile>(THDiskFile_new(filename, "r", 0), THFile_free);
         CV_Assert(file && THFile_isOpened(file));
@@ -680,7 +682,8 @@ struct TorchImporter
                     layerParams.blobs.push_back(tensorParams["bias"].second);
                 }
 
-                if (nnName == "InstanceNormalization")
+                bool trainPhase = scalarParams.get<bool>("train", false);
+                if (nnName == "InstanceNormalization" || (trainPhase && !testPhase))
                 {
                     cv::Ptr<Module> mvnModule(new Module(nnName));
                     mvnModule->apiType = "MVN";
@@ -1243,18 +1246,18 @@ struct TorchImporter
 
 Mat readTorchBlob(const String &filename, bool isBinary)
 {
-    TorchImporter importer(filename, isBinary);
+    TorchImporter importer(filename, isBinary, true);
     importer.readObject();
     CV_Assert(importer.tensors.size() == 1);
 
     return importer.tensors.begin()->second;
 }
 
-Net readNetFromTorch(const String &model, bool isBinary)
+Net readNetFromTorch(const String &model, bool isBinary, bool evaluate)
 {
     CV_TRACE_FUNCTION();
 
-    TorchImporter importer(model, isBinary);
+    TorchImporter importer(model, isBinary, evaluate);
     Net net;
     importer.populateNet(net);
     return net;

modules/dnn/test/test_backends.cpp

@@ -226,9 +226,9 @@ TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
 TEST_P(DNNTestNetwork, OpenFace)
 {
 #if defined(INF_ENGINE_RELEASE)
-#if INF_ENGINE_RELEASE < 2018030000
+#if (INF_ENGINE_RELEASE < 2018030000 || INF_ENGINE_RELEASE == 2018050000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
-        throw SkipTestException("Test is enabled starts from OpenVINO 2018R3");
+        throw SkipTestException("");
 #elif INF_ENGINE_RELEASE < 2018040000
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
         throw SkipTestException("Test is enabled starts from OpenVINO 2018R4");

modules/dnn/test/test_ie_models.cpp

@@ -190,6 +190,14 @@ TEST_P(DNNTestOpenVINO, models)
                                          modelName == "landmarks-regression-retail-0009" ||
                                           modelName == "semantic-segmentation-adas-0001")))
         throw SkipTestException("");
+#elif INF_ENGINE_RELEASE == 2018050000
+    if (modelName == "single-image-super-resolution-0063" ||
+        modelName == "single-image-super-resolution-1011" ||
+        modelName == "single-image-super-resolution-1021" ||
+        (target == DNN_TARGET_OPENCL_FP16 && modelName == "face-reidentification-retail-0095") ||
+        (target == DNN_TARGET_MYRIAD && (modelName == "license-plate-recognition-barrier-0001" ||
+                                         modelName == "semantic-segmentation-adas-0001")))
+        throw SkipTestException("");
 #endif
 #endif
 

modules/dnn/test/test_layers.cpp

@@ -295,6 +295,10 @@ TEST_P(Test_Caffe_layers, Eltwise)
 {
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
         throw SkipTestException("");
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
+        throw SkipTestException("Test is disabled for OpenVINO 2018R5");
+#endif
     testLayerUsingCaffeModels("layer_eltwise");
 }
 

modules/dnn/test/test_onnx_importer.cpp

@@ -164,6 +164,8 @@ TEST_P(Test_ONNX_layers, MultyInputs)
 
 TEST_P(Test_ONNX_layers, DynamicReshape)
 {
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
+        throw SkipTestException("");
     testONNXModels("dynamic_reshape");
 }
 
@@ -249,6 +251,10 @@ TEST_P(Test_ONNX_nets, VGG16)
     else if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL) {
         lInf = 1.2e-4;
     }
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2018050000
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
+        l1 = 0.131;
+#endif
     testONNXModels("vgg16", pb, l1, lInf);
 }
 
@@ -327,7 +333,7 @@ TEST_P(Test_ONNX_nets, CNN_MNIST)
 TEST_P(Test_ONNX_nets, MobileNet_v2)
 {
     // output range: [-166; 317]
-    const double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.38 : 7e-5;
+    const double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.4 : 7e-5;
     const double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 2.87 : 5e-4;
     testONNXModels("mobilenetv2", pb, l1, lInf);
 }
@@ -350,7 +356,17 @@ TEST_P(Test_ONNX_nets, LResNet100E_IR)
 
 TEST_P(Test_ONNX_nets, Emotion_ferplus)
 {
-    testONNXModels("emotion_ferplus", pb);
+    double l1 = default_l1;
+    double lInf = default_lInf;
+    // Output values are in range [-2.01109, 2.11111]
+    if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
+        l1 = 0.007;
+    else if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
+    {
+        l1 = 0.021;
+        lInf = 0.034;
+    }
+    testONNXModels("emotion_ferplus", pb, l1, lInf);
 }
 
 TEST_P(Test_ONNX_nets, Inception_v2)
@@ -371,6 +387,10 @@ TEST_P(Test_ONNX_nets, DenseNet121)
 
 TEST_P(Test_ONNX_nets, Inception_v1)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
+        throw SkipTestException("");
+#endif
     testONNXModels("inception_v1", pb);
 }
 

modules/dnn/test/test_tf_importer.cpp

@@ -241,6 +241,10 @@ TEST_P(Test_TensorFlow_layers, unfused_flatten)
 
 TEST_P(Test_TensorFlow_layers, leaky_relu)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
+        throw SkipTestException("");
+#endif
     runTensorFlowNet("leaky_relu_order1");
     runTensorFlowNet("leaky_relu_order2");
     runTensorFlowNet("leaky_relu_order3");
@@ -383,6 +387,10 @@ TEST_P(Test_TensorFlow_nets, Faster_RCNN)
 
 TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD_PPN)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
+        throw SkipTestException("Unstable test case");
+#endif
     checkBackend();
     std::string proto = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pbtxt", false);
     std::string model = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pb", false);
@@ -560,6 +568,10 @@ TEST_P(Test_TensorFlow_layers, slice)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
         (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
         throw SkipTestException("");
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
+        throw SkipTestException("");
+#endif
     runTensorFlowNet("slice_4d");
 }
 

modules/dnn/test/test_torch_importer.cpp

@@ -73,7 +73,7 @@ class Test_Torch_layers : public DNNTestLayer
 {
 public:
     void runTorchNet(const String& prefix, String outLayerName = "",
-                     bool check2ndBlob = false, bool isBinary = false,
+                     bool check2ndBlob = false, bool isBinary = false, bool evaluate = true,
                      double l1 = 0.0, double lInf = 0.0)
     {
         String suffix = (isBinary) ? ".dat" : ".txt";
@@ -84,7 +84,7 @@ public:
 
         checkBackend(backend, target, &inp, &outRef);
 
-        Net net = readNetFromTorch(_tf(prefix + "_net" + suffix), isBinary);
+        Net net = readNetFromTorch(_tf(prefix + "_net" + suffix), isBinary, evaluate);
         ASSERT_FALSE(net.empty());
 
         net.setPreferableBackend(backend);
@@ -114,7 +114,7 @@ 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;
-    runTorchNet("net_conv", "", false, true, l1, lInf);
+    runTorchNet("net_conv", "", false, true, true, l1, lInf);
 }
 
 TEST_P(Test_Torch_layers, run_pool_max)
@@ -147,7 +147,7 @@ TEST_P(Test_Torch_layers, run_reshape)
 TEST_P(Test_Torch_layers, run_reshape_single_sample)
 {
     // Reference output values in range [14.4586, 18.4492].
-    runTorchNet("net_reshape_single_sample", "", false, false,
+    runTorchNet("net_reshape_single_sample", "", false, false, true,
                 (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 0.0073 : default_l1,
                 (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 0.025 : default_lInf);
 }
@@ -166,7 +166,7 @@ TEST_P(Test_Torch_layers, run_concat)
 
 TEST_P(Test_Torch_layers, run_depth_concat)
 {
-    runTorchNet("net_depth_concat", "", false, true, 0.0,
+    runTorchNet("net_depth_concat", "", false, true, true, 0.0,
                 target == DNN_TARGET_OPENCL_FP16 ? 0.021 : 0.0);
 }
 
@@ -182,6 +182,7 @@ TEST_P(Test_Torch_layers, run_deconv)
 TEST_P(Test_Torch_layers, run_batch_norm)
 {
     runTorchNet("net_batch_norm", "", false, true);
+    runTorchNet("net_batch_norm_train", "", false, true, false);
 }
 
 TEST_P(Test_Torch_layers, net_prelu)
@@ -216,7 +217,7 @@ TEST_P(Test_Torch_layers, net_conv_gemm_lrn)
 {
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
         throw SkipTestException("");
-    runTorchNet("net_conv_gemm_lrn", "", false, true,
+    runTorchNet("net_conv_gemm_lrn", "", false, true, true,
                 target == DNN_TARGET_OPENCL_FP16 ? 0.046 : 0.0,
                 target == DNN_TARGET_OPENCL_FP16 ? 0.023 : 0.0);
 }
@@ -266,9 +267,9 @@ class Test_Torch_nets : public DNNTestLayer {};
 
 TEST_P(Test_Torch_nets, OpenFace_accuracy)
 {
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
+#if defined(INF_ENGINE_RELEASE) && (INF_ENGINE_RELEASE < 2018030000 || INF_ENGINE_RELEASE == 2018050000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
-        throw SkipTestException("Test is enabled starts from OpenVINO 2018R3");
+        throw SkipTestException("");
 #endif
     checkBackend();
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
@@ -389,6 +390,10 @@ TEST_P(Test_Torch_nets, ENet_accuracy)
 //   -model models/instance_norm/feathers.t7
 TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
+        throw SkipTestException("");
+#endif
     checkBackend();
     std::string models[] = {"dnn/fast_neural_style_eccv16_starry_night.t7",
                             "dnn/fast_neural_style_instance_norm_feathers.t7"};

modules/features2d/src/keypoint.cpp

@@ -77,7 +77,7 @@ void KeyPointsFilter::retainBest(std::vector<KeyPoint>& keypoints, int n_points)
             return;
         }
         //first use nth element to partition the keypoints into the best and worst.
-        std::nth_element(keypoints.begin(), keypoints.begin() + n_points, keypoints.end(), KeypointResponseGreater());
+        std::nth_element(keypoints.begin(), keypoints.begin() + n_points - 1, keypoints.end(), KeypointResponseGreater());
         //this is the boundary response, and in the case of FAST may be ambiguous
         float ambiguous_response = keypoints[n_points - 1].response;
         //use std::partition to grab all of the keypoints with the boundary response.

modules/features2d/test/test_utils.cpp

+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+#include "test_precomp.hpp"
+
+namespace opencv_test { namespace {
+
+TEST(Features2D_KeypointUtils, retainBest_issue_12594)
+{
+    const size_t N = 9;
+
+    // Construct 4-way tie for 3rd highest - correct answer for "3 best" is 6
+    const float no_problem[] = { 5.0f, 4.0f, 1.0f, 2.0f, 0.0f, 3.0f, 3.0f, 3.0f, 3.0f };
+
+    // Same set, different order that exposes partial sort property of std::nth_element
+    // Note: the problem case may depend on your particular implementation of STL
+    const float problem[] = { 3.0f, 3.0f, 3.0f, 3.0f, 4.0f, 5.0f, 0.0f, 1.0f, 2.0f };
+
+    const size_t NBEST  = 3u;
+    const size_t ANSWER = 6u;
+
+    std::vector<cv::KeyPoint> sorted_cv(N);
+    std::vector<cv::KeyPoint> unsorted_cv(N);
+
+    for (size_t i = 0; i < N; ++i)
+    {
+        sorted_cv[i].response   = no_problem[i];
+        unsorted_cv[i].response = problem[i];
+    }
+
+    cv::KeyPointsFilter::retainBest(sorted_cv, NBEST);
+    cv::KeyPointsFilter::retainBest(unsorted_cv, NBEST);
+
+    EXPECT_EQ(ANSWER, sorted_cv.size());
+    EXPECT_EQ(ANSWER, unsorted_cv.size());
+}
+
+}} // namespace

modules/imgproc/src/median_blur.cpp

@@ -282,10 +282,10 @@ medianBlur_8u_O1( const Mat& _src, Mat& _dst, int ksize )
                         for ( ; luc[c][k] < j+r+1; ++luc[c][k] )
                         {
 #if CV_SIMD256
-                            v_fine += v256_load(px + 16 * MIN(luc[c][k], n - 1)) - v256_load(px + 16 * MAX(luc[c][k] - 2 * r - 1, 0));
+                            v_fine = v_fine + v256_load(px + 16 * MIN(luc[c][k], n - 1)) - v256_load(px + 16 * MAX(luc[c][k] - 2 * r - 1, 0));
 #elif CV_SIMD128
-                            v_finel += v_load(px + 16 * MIN(luc[c][k], n - 1)    ) - v_load(px + 16 * MAX(luc[c][k] - 2 * r - 1, 0));
-                            v_fineh += v_load(px + 16 * MIN(luc[c][k], n - 1) + 8) - v_load(px + 16 * MAX(luc[c][k] - 2 * r - 1, 0) + 8);
+                            v_finel = v_finel + v_load(px + 16 * MIN(luc[c][k], n - 1)    ) - v_load(px + 16 * MAX(luc[c][k] - 2 * r - 1, 0));
+                            v_fineh = v_fineh + v_load(px + 16 * MIN(luc[c][k], n - 1) + 8) - v_load(px + 16 * MAX(luc[c][k] - 2 * r - 1, 0) + 8);
 #else
                             for (int ind = 0; ind < 16; ++ind)
                                 H[c].fine[k][ind] += px[16 * MIN(luc[c][k], n - 1) + ind] - px[16 * MAX(luc[c][k] - 2 * r - 1, 0) + ind];
@@ -321,10 +321,10 @@ medianBlur_8u_O1( const Mat& _src, Mat& _dst, int ksize )
                     CV_Assert( b < 16 );
                 }
             }
+        }
 #if CV_SIMD
-            vx_cleanup();
+        vx_cleanup();
 #endif
-        }
     }
 
 #undef HOP

modules/imgproc/test/test_filter.cpp

@@ -2200,4 +2200,15 @@ TEST(Imgproc_Filter2D, dftFilter2d_regression_10683)
 
     EXPECT_LE(cvtest::norm(dst, expected, NORM_INF), 2);
 }
+
+TEST(Imgproc_MedianBlur, hires_regression_13409)
+{
+    Mat src(2048, 2048, CV_8UC1), dst_hires, dst_ref;
+    randu(src, 0, 256);
+
+    medianBlur(src, dst_hires, 9);
+    medianBlur(src(Rect(512, 512, 1024, 1024)), dst_ref, 9);
+
+    ASSERT_EQ(0.0, cvtest::norm(dst_hires(Rect(516, 516, 1016, 1016)), dst_ref(Rect(4, 4, 1016, 1016)), NORM_INF));
+}
 }} // namespace

modules/java/jar/CMakeLists.txt

@@ -18,6 +18,13 @@ set(depends gen_opencv_java_source "${OPENCV_DEPHELPER}/gen_opencv_java_source")
 ocv_copyfiles_add_target(${the_module}_jar_source_copy JAVA_SRC_COPY "Copy Java(JAR) source files" ${depends})
 set(depends ${the_module}_jar_source_copy "${OPENCV_DEPHELPER}/${the_module}_jar_source_copy")
 
+if(OPENCV_JAVA_SOURCE_VERSION)
+  set(OPENCV_ANT_JAVAC_EXTRA_ATTRS "${OPENCV_ANT_JAVAC_EXTRA_ATTRS} source=\"${OPENCV_JAVA_SOURCE_VERSION}\"")
+endif()
+if(OPENCV_JAVA_TARGET_VERSION)
+  set(OPENCV_ANT_JAVAC_EXTRA_ATTRS "${OPENCV_ANT_JAVAC_EXTRA_ATTRS} target=\"${OPENCV_JAVA_TARGET_VERSION}\"")
+endif()
+
 configure_file("${CMAKE_CURRENT_SOURCE_DIR}/build.xml.in" "${OPENCV_JAVA_DIR}/build.xml" @ONLY)
 list(APPEND depends "${OPENCV_JAVA_DIR}/build.xml")
 

modules/java/jar/build.xml.in

@@ -11,7 +11,7 @@
     <!-- This is to make a jar with a source attachment, for e.g. easy -->
     <!-- navigation in Eclipse. See this question: -->
     <!-- http://stackoverflow.com/questions/3584968/ant-how-to-compile-jar-that-includes-source-attachment -->
-    <javac sourcepath="" srcdir="java" destdir="build/classes" debug="on" includeantruntime="false" >
+    <javac sourcepath="" srcdir="java" destdir="build/classes" debug="on" includeantruntime="false" @OPENCV_ANT_JAVAC_EXTRA_ATTRS@ >
       <include name="**/*.java"/>
       <compilerarg line="-encoding utf-8"/>
     </javac>

modules/objdetect/src/qrcode.cpp

@@ -782,6 +782,9 @@ bool QRCodeDetector::detect(InputArray in, OutputArray points) const
     Mat inarr = in.getMat();
     CV_Assert(!inarr.empty());
     CV_Assert(inarr.depth() == CV_8U);
+    if (inarr.cols <= 20 || inarr.rows <= 20)
+        return false;  // image data is not enough for providing reliable results
+
     int incn = inarr.channels();
     if( incn == 3 || incn == 4 )
     {
@@ -1054,6 +1057,8 @@ std::string QRCodeDetector::decode(InputArray in, InputArray points,
     Mat inarr = in.getMat();
     CV_Assert(!inarr.empty());
     CV_Assert(inarr.depth() == CV_8U);
+    if (inarr.cols <= 20 || inarr.rows <= 20)
+        return cv::String();  // image data is not enough for providing reliable results
 
     int incn = inarr.channels();
     if( incn == 3 || incn == 4 )
@@ -1092,6 +1097,8 @@ std::string QRCodeDetector::detectAndDecode(InputArray in,
     Mat inarr = in.getMat();
     CV_Assert(!inarr.empty());
     CV_Assert(inarr.depth() == CV_8U);
+    if (inarr.cols <= 20 || inarr.rows <= 20)
+        return cv::String();  // image data is not enough for providing reliable results
 
     int incn = inarr.channels();
     if( incn == 3 || incn == 4 )

modules/videoio/src/cap_v4l.cpp

@@ -237,6 +237,11 @@ make & enjoy!
 #include <sys/videoio.h>
 #endif
 
+// https://github.com/opencv/opencv/issues/13335
+#ifndef V4L2_CID_ISO_SENSITIVITY
+#define V4L2_CID_ISO_SENSITIVITY (V4L2_CID_CAMERA_CLASS_BASE+23)
+#endif
+
 /* Defaults - If your board can do better, set it here.  Set for the most common type inputs. */
 #define DEFAULT_V4L_WIDTH  640
 #define DEFAULT_V4L_HEIGHT 480
@@ -1757,7 +1762,7 @@ bool CvCaptureCAM_V4L::icvSetFrameSize(int _width, int _height)
     if (_width > 0)
         width_set = _width;
 
-    if (height > 0)
+    if (_height > 0)
         height_set = _height;
 
     /* two subsequent calls setting WIDTH and HEIGHT will change

modules/videoio/test/test_camera.cpp

@@ -11,7 +11,7 @@
 
 namespace opencv_test { namespace {
 
-static void test_readFrames(/*const*/ VideoCapture& capture, const int N = 100)
+static void test_readFrames(/*const*/ VideoCapture& capture, const int N = 100, Mat* lastFrame = NULL)
 {
     Mat frame;
     int64 time0 = cv::getTickCount();
@@ -26,6 +26,7 @@ static void test_readFrames(/*const*/ VideoCapture& capture, const int N = 100)
     }
     int64 time1 = cv::getTickCount();
     printf("Processed %d frames on %.2f FPS\n", N, (N * cv::getTickFrequency()) / (time1 - time0 + 1));
+    if (lastFrame) *lastFrame = frame.clone();
 }
 
 TEST(DISABLED_VideoIO_Camera, basic)
@@ -69,4 +70,39 @@ TEST(DISABLED_VideoIO_Camera, dshow_avermedia_capture)
     capture.release();
 }
 
+TEST(DISABLED_VideoIO_Camera, validate_V4L2_FrameSize)
+{
+    VideoCapture capture(CAP_V4L2);
+    ASSERT_TRUE(capture.isOpened());
+    std::cout << "Camera 0 via " << capture.getBackendName() << " backend" << std::endl;
+    std::cout << "Frame width: " << capture.get(CAP_PROP_FRAME_WIDTH) << std::endl;
+    std::cout << "     height: " << capture.get(CAP_PROP_FRAME_HEIGHT) << std::endl;
+    std::cout << "Capturing FPS: " << capture.get(CAP_PROP_FPS) << std::endl;
+    int fourcc = (int)capture.get(CAP_PROP_FOURCC);
+    std::cout << "FOURCC code: " << cv::format("0x%8x", fourcc) << std::endl;
+    test_readFrames(capture, 30);
+
+    EXPECT_TRUE(capture.set(CAP_PROP_FRAME_WIDTH, 640));
+    EXPECT_TRUE(capture.set(CAP_PROP_FRAME_HEIGHT, 480));
+    std::cout << "Frame width: " << capture.get(CAP_PROP_FRAME_WIDTH) << std::endl;
+    std::cout << "     height: " << capture.get(CAP_PROP_FRAME_HEIGHT) << std::endl;
+    std::cout << "Capturing FPS: " << capture.get(CAP_PROP_FPS) << std::endl;
+    Mat frame640x480;
+    test_readFrames(capture, 30, &frame640x480);
+    EXPECT_EQ(640, frame640x480.cols);
+    EXPECT_EQ(480, frame640x480.rows);
+
+    EXPECT_TRUE(capture.set(CAP_PROP_FRAME_WIDTH, 1280));
+    EXPECT_TRUE(capture.set(CAP_PROP_FRAME_HEIGHT, 720));
+    std::cout << "Frame width: " << capture.get(CAP_PROP_FRAME_WIDTH) << std::endl;
+    std::cout << "     height: " << capture.get(CAP_PROP_FRAME_HEIGHT) << std::endl;
+    std::cout << "Capturing FPS: " << capture.get(CAP_PROP_FPS) << std::endl;
+    Mat frame1280x720;
+    test_readFrames(capture, 30, &frame1280x720);
+    EXPECT_EQ(1280, frame1280x720.cols);
+    EXPECT_EQ(720, frame1280x720.rows);
+
+    capture.release();
+}
+
 }} // namespace

platforms/android/service/engine/AndroidManifest.xml

 <?xml version="1.0" encoding="utf-8"?>
 <manifest xmlns:android="http://schemas.android.com/apk/res/android"
     package="org.opencv.engine"
-    android:versionCode="344@ANDROID_PLATFORM_ID@"
-    android:versionName="3.44">
+    android:versionCode="345@ANDROID_PLATFORM_ID@"
+    android:versionName="3.45">
 
     <uses-sdk android:minSdkVersion="@ANDROID_NATIVE_API_LEVEL@" android:targetSdkVersion="22"/>
     <uses-feature android:name="android.hardware.touchscreen" android:required="false"/>

platforms/android/service/engine/src/org/opencv/engine/OpenCVEngineService.java

@@ -137,7 +137,7 @@ public class OpenCVEngineService extends Service {
 
             @Override
             public int getEngineVersion() throws RemoteException {
-                int version = 3440;
+                int version = 3450;
                 try {
                     version = getPackageManager().getPackageInfo(getPackageName(), 0).versionCode;
                 } catch (NameNotFoundException e) {

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_3.4.4-dev_Manager_3.44_armeabi-v7a.apk
+Example: OpenCV_3.4.5-dev_Manager_3.45_armeabi-v7a.apk
 
 Use the list of platforms below to determine proper OpenCV Manager package for your device: