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

modules/core/include/opencv2/core/bindings_utils.hpp

@@ -7,6 +7,7 @@
 
 #include <opencv2/core/async.hpp>
 #include <opencv2/core/detail/async_promise.hpp>
+#include <opencv2/core/utils/logger.hpp>
 
 #include <stdexcept>
 
@@ -147,7 +148,27 @@ AsyncArray testAsyncException()
 namespace fs {
     CV_EXPORTS_W cv::String getCacheDirectoryForDownloads();
 } // namespace fs
-//! @}
-}} // namespaces cv /  utils
+
+//! @}  // core_utils
+}  // namespace cv::utils
+
+//! @cond IGNORED
+
+CV_WRAP static inline
+int setLogLevel(int level)
+{
+    // NB: Binding generators doesn't work with enums properly yet, so we define separate overload here
+    return cv::utils::logging::setLogLevel((cv::utils::logging::LogLevel)level);
+}
+
+CV_WRAP static inline
+int getLogLevel()
+{
+    return cv::utils::logging::getLogLevel();
+}
+
+//! @endcond IGNORED
+
+} // namespaces cv /  utils
 
 #endif // OPENCV_CORE_BINDINGS_UTILS_HPP

modules/dnn/include/opencv2/dnn/utils/inference_engine.hpp

@@ -49,6 +49,8 @@ CV_EXPORTS_W void resetMyriadDevice();
 #define CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2 "Myriad2"
 /// Intel(R) Neural Compute Stick 2, NCS2 (USB 03e7:2485), MyriadX (https://software.intel.com/ru-ru/neural-compute-stick)
 #define CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X "MyriadX"
+#define CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE "ARM_COMPUTE"
+#define CV_DNN_INFERENCE_ENGINE_CPU_TYPE_X86         "X86"
 
 
 /** @brief Returns Inference Engine VPU type.
@@ -57,6 +59,11 @@ CV_EXPORTS_W void resetMyriadDevice();
  */
 CV_EXPORTS_W cv::String getInferenceEngineVPUType();
 
+/** @brief Returns Inference Engine CPU type.
+ *
+ * Specify OpenVINO plugin: CPU or ARM.
+ */
+CV_EXPORTS_W cv::String getInferenceEngineCPUType();
 
 /** @brief Release a HDDL plugin.
  */

modules/dnn/src/dnn.cpp

@@ -1382,11 +1382,12 @@ struct Net::Impl : public detail::NetImplBase
         CV_Assert(preferableBackend != DNN_BACKEND_HALIDE ||
                   preferableTarget == DNN_TARGET_CPU ||
                   preferableTarget == DNN_TARGET_OPENCL);
+#ifdef HAVE_INF_ENGINE
         if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
             preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         {
             CV_Assert(
-                  preferableTarget == DNN_TARGET_CPU ||
+                  (preferableTarget == DNN_TARGET_CPU && (!isArmComputePlugin() || preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)) ||
                   preferableTarget == DNN_TARGET_OPENCL ||
                   preferableTarget == DNN_TARGET_OPENCL_FP16 ||
                   preferableTarget == DNN_TARGET_MYRIAD ||
@@ -1394,6 +1395,7 @@ struct Net::Impl : public detail::NetImplBase
                   preferableTarget == DNN_TARGET_FPGA
             );
         }
+#endif
         CV_Assert(preferableBackend != DNN_BACKEND_VKCOM ||
                   preferableTarget == DNN_TARGET_VULKAN);
         CV_Assert(preferableBackend != DNN_BACKEND_CUDA ||
@@ -2098,8 +2100,8 @@ struct Net::Impl : public detail::NetImplBase
             return;
         }
 
-        bool supportsCPUFallback = preferableTarget == DNN_TARGET_CPU ||
-                                   BackendRegistry::checkIETarget(DNN_TARGET_CPU);
+        bool supportsCPUFallback = !isArmComputePlugin() && (preferableTarget == DNN_TARGET_CPU ||
+                                   BackendRegistry::checkIETarget(DNN_TARGET_CPU));
 
         // Build Inference Engine networks from sets of layers that support this
         // backend. Split a whole model on several Inference Engine networks if

modules/dnn/src/layers/convolution_layer.cpp

@@ -324,10 +324,13 @@ public:
 #ifdef HAVE_INF_ENGINE
         if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         {
-            if (ksize == 1)
+            bool isArmTarget = preferableTarget == DNN_TARGET_CPU && isArmComputePlugin();
+            if (isArmTarget && blobs.empty())
                 return false;
+            if (ksize == 1)
+                return isArmTarget;
             if (ksize == 3)
-                return preferableTarget == DNN_TARGET_CPU;
+                return preferableTarget != DNN_TARGET_MYRIAD && !isArmTarget;
             bool isMyriad = preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL;
             if ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || !isMyriad) && blobs.empty())
                 return false;
@@ -805,7 +808,7 @@ public:
         CV_Assert_N(inputs.size() >= 1, nodes.size() >= 1);
         auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
         std::vector<size_t> dims = ieInpNode->get_shape();
-        CV_Assert(dims.size() == 4 || dims.size() == 5);
+        CV_Check(dims.size(), dims.size() >= 3 && dims.size() <= 5, "");
         std::shared_ptr<ngraph::Node> ieWeights = nodes.size() > 1 ? nodes[1].dynamicCast<InfEngineNgraphNode>()->node : nullptr;
         if (nodes.size() > 1)
             CV_Assert(ieWeights);  // dynamic_cast should not fail
@@ -843,7 +846,7 @@ public:
         else
         {
             auto shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
-                             ngraph::Shape{kernel_shape.size()}, kernel_shape.data());
+                             ngraph::Shape{kernel_shape.size()}, std::vector<int64_t>(kernel_shape.begin(), kernel_shape.end()));
             ieWeights  = std::make_shared<ngraph::op::v1::Reshape>(ieWeights, shape, true);
         }
 
@@ -878,7 +881,7 @@ public:
             if (nodes.size() == 3)
             {
                 auto bias_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
-                                    ngraph::Shape{shape.size()}, shape.data());
+                                    ngraph::Shape{shape.size()}, std::vector<int64_t>(shape.begin(), shape.end()));
                 bias = std::make_shared<ngraph::op::v1::Reshape>(nodes[2].dynamicCast<InfEngineNgraphNode>()->node, bias_shape, true);
             }
             else

modules/dnn/src/layers/elementwise_layers.cpp

@@ -1354,11 +1354,15 @@ struct PowerFunctor : public BaseFunctor
                                                                  ngraph::Shape{1}, &scale);
         auto shift_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
                                                                  ngraph::Shape{1}, &shift);
-        auto power_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
-                                                                 ngraph::Shape{1}, &power);
 
         auto mul = std::make_shared<ngraph::op::v1::Multiply>(scale_node, node, ngraph::op::AutoBroadcastType::NUMPY);
         auto scale_shift = std::make_shared<ngraph::op::v1::Add>(mul, shift_node, ngraph::op::AutoBroadcastType::NUMPY);
+
+        if (power == 1)
+            return scale_shift;
+
+        auto power_node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
+                                                                 ngraph::Shape{1}, &power);
         return std::make_shared<ngraph::op::v1::Power>(scale_shift, power_node, ngraph::op::AutoBroadcastType::NUMPY);
     }
 #endif  // HAVE_DNN_NGRAPH

modules/dnn/src/layers/normalize_bbox_layer.cpp

@@ -334,8 +334,8 @@ public:
         if (!acrossSpatial) {
             axes_data.push_back(1);
         } else {
-            axes_data.resize(ieInpNode->get_shape().size());
-            std::iota(axes_data.begin(), axes_data.end(), 0);
+            axes_data.resize(ieInpNode->get_shape().size() - 1);
+            std::iota(axes_data.begin(), axes_data.end(), 1);
         }
         auto axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{axes_data.size()}, axes_data);
         auto norm = std::make_shared<ngraph::op::NormalizeL2>(ieInpNode, axes, epsilon, ngraph::op::EpsMode::ADD);
@@ -344,23 +344,18 @@ public:
         std::vector<size_t> shape(ieInpNode->get_shape().size(), 1);
         shape[0] = blobs.empty() ? 1 : batch;
         shape[1] = numChannels;
-        std::shared_ptr<ngraph::op::Constant> weight;
-        if (blobs.empty())
+        if (!blobs.empty())
         {
-            std::vector<float> ones(numChannels, 1);
-            weight = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape(shape), ones.data());
-        }
-        else
-        {
-            weight = std::make_shared<ngraph::op::Constant>(
+            auto weight = std::make_shared<ngraph::op::Constant>(
                                       ngraph::element::f32, ngraph::Shape(shape), blobs[0].data);
-        }
 #if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2021_2)
-        auto mul = std::make_shared<ngraph::op::v1::Multiply>(norm, weight, ngraph::op::AutoBroadcastType::NUMPY);
+            auto mul = std::make_shared<ngraph::op::v1::Multiply>(norm, weight, ngraph::op::AutoBroadcastType::NUMPY);
 #else
-        auto mul = std::make_shared<ngraph::op::v0::Multiply>(norm, weight, ngraph::op::AutoBroadcastType::NUMPY);
+            auto mul = std::make_shared<ngraph::op::v0::Multiply>(norm, weight, ngraph::op::AutoBroadcastType::NUMPY);
 #endif
-        return Ptr<BackendNode>(new InfEngineNgraphNode(mul));
+            return Ptr<BackendNode>(new InfEngineNgraphNode(mul));
+        }
+        return Ptr<BackendNode>(new InfEngineNgraphNode(norm));
     }
 #endif  // HAVE_DNN_NGRAPH
 

modules/dnn/src/layers/padding_layer.cpp

@@ -105,9 +105,10 @@ public:
         if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         {
             bool isMyriad = preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL;
-            return INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) &&
-                   (!isMyriad ||
-                    (dstRanges.size() == 4 && paddings[0].first == 0 && paddings[0].second == 0));
+            if (INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) && isMyriad)
+                return dstRanges.size() == 4 && paddings[0].first == 0 && paddings[0].second == 0;
+
+            return (dstRanges.size() <= 4 || !isArmComputePlugin());
         }
 #endif
         return backendId == DNN_BACKEND_OPENCV ||

modules/dnn/src/layers/permute_layer.cpp

@@ -113,6 +113,10 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
+#ifdef HAVE_INF_ENGINE
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && preferableTarget == DNN_TARGET_CPU)
+            return _order.size() <= 4 || !isArmComputePlugin();
+#endif
         return backendId == DNN_BACKEND_OPENCV ||
                backendId == DNN_BACKEND_CUDA ||
                ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine()) ||

modules/dnn/src/layers/pooling_layer.cpp

@@ -220,7 +220,9 @@ public:
 #endif
         if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         {
-            return !computeMaxIdx && type != STOCHASTIC && kernel_size.size() > 1;
+#ifdef HAVE_DNN_NGRAPH
+            return !computeMaxIdx && type != STOCHASTIC && kernel_size.size() > 1 && (kernel_size.size() != 3 || !isArmComputePlugin());
+#endif
         }
         else if (backendId == DNN_BACKEND_OPENCV)
         {

modules/dnn/src/layers/region_layer.cpp

@@ -460,8 +460,10 @@ public:
             std::vector<int64_t> mask(anchors, 1);
             region = std::make_shared<ngraph::op::RegionYolo>(tr_input, coords, classes, anchors, useSoftmax, mask, 1, 3, anchors_vec);
 
+            auto tr_shape = tr_input->get_shape();
             auto shape_as_inp = std::make_shared<ngraph::op::Constant>(ngraph::element::i64,
-                                                                       ngraph::Shape{tr_input->get_shape().size()}, tr_input->get_shape().data());
+                                                                       ngraph::Shape{tr_shape.size()},
+                                                                       std::vector<int64_t>(tr_shape.begin(), tr_shape.end()));
 
             region = std::make_shared<ngraph::op::v1::Reshape>(region, shape_as_inp, true);
             new_axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{4}, std::vector<int64_t>{0, 2, 3, 1});
@@ -607,7 +609,7 @@ public:
         result = std::make_shared<ngraph::op::Transpose>(result, tr_axes);
         if (b > 1)
         {
-            std::vector<size_t> sizes = {(size_t)b, result->get_shape()[0] / b, result->get_shape()[1]};
+            std::vector<int64_t> sizes{b, static_cast<int64_t>(result->get_shape()[0]) / b, static_cast<int64_t>(result->get_shape()[1])};
             auto shape_node = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{sizes.size()}, sizes.data());
             result = std::make_shared<ngraph::op::v1::Reshape>(result, shape_node, true);
         }

modules/dnn/src/op_inf_engine.cpp

@@ -655,6 +655,22 @@ InferenceEngine::Core& getCore(const std::string& id)
 }
 #endif
 
+static bool detectArmPlugin_()
+{
+    InferenceEngine::Core& ie = getCore("CPU");
+    const std::vector<std::string> devices = ie.GetAvailableDevices();
+    for (std::vector<std::string>::const_iterator i = devices.begin(); i != devices.end(); ++i)
+    {
+        if (i->find("CPU") != std::string::npos)
+        {
+            const std::string name = ie.GetMetric(*i, METRIC_KEY(FULL_DEVICE_NAME)).as<std::string>();
+            CV_LOG_INFO(NULL, "CPU plugin: " << name);
+            return name.find("arm_compute::NEON") != std::string::npos;
+        }
+    }
+    return false;
+}
+
 #if !defined(OPENCV_DNN_IE_VPU_TYPE_DEFAULT)
 static bool detectMyriadX_(std::string device)
 {
@@ -1185,6 +1201,12 @@ bool isMyriadX()
     return myriadX;
 }
 
+bool isArmComputePlugin()
+{
+    static bool armPlugin = getInferenceEngineCPUType() == CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE;
+    return armPlugin;
+}
+
 static std::string getInferenceEngineVPUType_()
 {
     static std::string param_vpu_type = utils::getConfigurationParameterString("OPENCV_DNN_IE_VPU_TYPE", "");
@@ -1223,6 +1245,14 @@ cv::String getInferenceEngineVPUType()
     return vpu_type;
 }
 
+cv::String getInferenceEngineCPUType()
+{
+    static cv::String cpu_type = detectArmPlugin_() ?
+                                 CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE :
+                                 CV_DNN_INFERENCE_ENGINE_CPU_TYPE_X86;
+    return cpu_type;
+}
+
 #else  // HAVE_INF_ENGINE
 
 cv::String getInferenceEngineBackendType()
@@ -1238,6 +1268,11 @@ cv::String getInferenceEngineVPUType()
 {
     CV_Error(Error::StsNotImplemented, "This OpenCV build doesn't include InferenceEngine support");
 }
+
+cv::String getInferenceEngineCPUType()
+{
+    CV_Error(Error::StsNotImplemented, "This OpenCV build doesn't include InferenceEngine support");
+}
 #endif  // HAVE_INF_ENGINE
 
 

modules/dnn/src/op_inf_engine.hpp

@@ -255,8 +255,11 @@ CV__DNN_INLINE_NS_BEGIN
 
 bool isMyriadX();
 
+bool isArmComputePlugin();
+
 CV__DNN_INLINE_NS_END
 
+
 InferenceEngine::Core& getCore(const std::string& id);
 
 template<typename T = size_t>

modules/dnn/test/test_common.hpp

@@ -35,6 +35,7 @@
 #define CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2         "dnn_skip_ie_myriad2"
 #define CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X         "dnn_skip_ie_myriadx"
 #define CV_TEST_TAG_DNN_SKIP_IE_MYRIAD           CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2, CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X
+#define CV_TEST_TAG_DNN_SKIP_IE_ARM_CPU          "dnn_skip_ie_arm_cpu"
 
 #define CV_TEST_TAG_DNN_SKIP_VULKAN              "dnn_skip_vulkan"
 

modules/dnn/test/test_onnx_importer.cpp

@@ -156,6 +156,10 @@ TEST_P(Test_ONNX_layers, Convolution_variable_weight_bias)
     if (backend == DNN_BACKEND_VKCOM)
         applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); // not supported
 
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU &&
+        getInferenceEngineCPUType() == CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_ARM_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
+
     String basename = "conv_variable_wb";
     Net net = readNetFromONNX(_tf("models/" + basename + ".onnx"));
     ASSERT_FALSE(net.empty());
@@ -766,6 +770,8 @@ TEST_P(Test_ONNX_layers, Conv1d_variable_weight_bias)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
     {
         if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
+        if (target == DNN_TARGET_CPU && getInferenceEngineCPUType() == CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE)
+            applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_ARM_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
     }
     String basename = "conv1d_variable_wb";
     Net net = readNetFromONNX(_tf("models/" + basename + ".onnx"));

modules/features2d/include/opencv2/features2d.hpp

@@ -481,8 +481,7 @@ article](http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions)).
 than union-find method; it actually get 1.5~2m/s on my centrino L7200 1.2GHz laptop.
 
 - the color image algorithm is taken from: @cite forssen2007maximally ; it should be much slower
-than grey image method ( 3~4 times ); the chi_table.h file is taken directly from paper's source
-code which is distributed under GPL.
+than grey image method ( 3~4 times )
 
 - (Python) A complete example showing the use of the %MSER detector can be found at samples/python/mser.py
 */

modules/features2d/src/mser.cpp

@@ -35,7 +35,7 @@
  *    it actually get 1.5~2m/s on my centrino L7200 1.2GHz laptop.
  * 3. the color image algorithm is taken from: Maximally Stable Colour Regions for Recognition and Match;
  *    it should be much slower than gray image method ( 3~4 times );
- *    the chi_table.h file is taken directly from paper's source code which is distributed under GPL.
+ *    the chi_table.h file is taken directly from paper's source code which is distributed under permissive BSD-like license: http://users.isy.liu.se/cvl/perfo/software/chi_table.h
  * 4. though the name is *contours*, the result actually is a list of point set.
  */
 

modules/js/generator/CMakeLists.txt

@@ -26,7 +26,9 @@ ocv_list_filterout(opencv_hdrs "modules/cuda.*")
 ocv_list_filterout(opencv_hdrs "modules/cudev")
 ocv_list_filterout(opencv_hdrs "modules/core/.*/hal/")
 ocv_list_filterout(opencv_hdrs "modules/.*/detection_based_tracker.hpp") # Conditional compilation
-ocv_list_filterout(opencv_hdrs "modules/core/include/opencv2/core/utils/.*")
+ocv_list_filterout(opencv_hdrs "modules/core/include/opencv2/core/utils/*.private.*")
+ocv_list_filterout(opencv_hdrs "modules/core/include/opencv2/core/utils/instrumentation.hpp")
+ocv_list_filterout(opencv_hdrs "modules/core/include/opencv2/core/utils/trace*")
 
 ocv_update_file("${CMAKE_CURRENT_BINARY_DIR}/headers.txt" "${opencv_hdrs}")
 

modules/ml/src/lr.cpp

-///////////////////////////////////////////////////////////////////////////////////////
-// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-
-// This is a implementation of the Logistic Regression algorithm in C++ in OpenCV.
-
-// AUTHOR:
-// Rahul Kavi rahulkavi[at]live[at]com
-
-// # You are free to use, change, or redistribute the code in any way you wish for
-// # non-commercial purposes, but please maintain the name of the original author.
-// # This code comes with no warranty of any kind.
-
-// #
-// # You are free to use, change, or redistribute the code in any way you wish for
-// # non-commercial purposes, but please maintain the name of the original author.
-// # This code comes with no warranty of any kind.
-
-// # Logistic Regression ALGORITHM
-
-
-//                           License Agreement
-//                For Open Source Computer Vision Library
-
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-
-//   * Redistributions of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-
-//   * Redistributions in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
+// 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.
+//
+// AUTHOR: Rahul Kavi rahulkavi[at]live[at]com
+
+//
+// This is a implementation of the Logistic Regression algorithm
+//
 
 #include "precomp.hpp"
 

modules/ml/test/test_lr.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.
+//
+// AUTHOR: Rahul Kavi rahulkavi[at]live[at]com
 
-// This is a implementation of the Logistic Regression algorithm in C++ in OpenCV.
-
-// AUTHOR:
-// Rahul Kavi rahulkavi[at]live[at]com
+//
+// Test data uses subset of data from the popular Iris Dataset (1936):
+// - http://archive.ics.uci.edu/ml/datasets/Iris
+// - https://en.wikipedia.org/wiki/Iris_flower_data_set
 //
 
 #include "test_precomp.hpp"

platforms/js/opencv_js.config.py

 # Classes and methods whitelist
-core = {'': ['absdiff', 'add', 'addWeighted', 'bitwise_and', 'bitwise_not', 'bitwise_or', 'bitwise_xor', 'cartToPolar',\
-             'compare', 'convertScaleAbs', 'copyMakeBorder', 'countNonZero', 'determinant', 'dft', 'divide', 'eigen', \
-             'exp', 'flip', 'getOptimalDFTSize','gemm', 'hconcat', 'inRange', 'invert', 'kmeans', 'log', 'magnitude', \
-             'max', 'mean', 'meanStdDev', 'merge', 'min', 'minMaxLoc', 'mixChannels', 'multiply', 'norm', 'normalize', \
-             'perspectiveTransform', 'polarToCart', 'pow', 'randn', 'randu', 'reduce', 'repeat', 'rotate', 'setIdentity', 'setRNGSeed', \
-             'solve', 'solvePoly', 'split', 'sqrt', 'subtract', 'trace', 'transform', 'transpose', 'vconcat'],
-        'Algorithm': []}
+
+core = {
+    '': [
+        'absdiff', 'add', 'addWeighted', 'bitwise_and', 'bitwise_not', 'bitwise_or', 'bitwise_xor', 'cartToPolar',
+        'compare', 'convertScaleAbs', 'copyMakeBorder', 'countNonZero', 'determinant', 'dft', 'divide', 'eigen',
+        'exp', 'flip', 'getOptimalDFTSize','gemm', 'hconcat', 'inRange', 'invert', 'kmeans', 'log', 'magnitude',
+        'max', 'mean', 'meanStdDev', 'merge', 'min', 'minMaxLoc', 'mixChannels', 'multiply', 'norm', 'normalize',
+        'perspectiveTransform', 'polarToCart', 'pow', 'randn', 'randu', 'reduce', 'repeat', 'rotate', 'setIdentity', 'setRNGSeed',
+        'solve', 'solvePoly', 'split', 'sqrt', 'subtract', 'trace', 'transform', 'transpose', 'vconcat',
+        'setLogLevel', 'getLogLevel',
+    ],
+    'Algorithm': [],
+}
 
 imgproc = {'': ['Canny', 'GaussianBlur', 'Laplacian', 'HoughLines', 'HoughLinesP', 'HoughCircles', 'Scharr','Sobel', \
                 'adaptiveThreshold','approxPolyDP','arcLength','bilateralFilter','blur','boundingRect','boxFilter',\

samples/cpp/logistic_regression.cpp

-/*//////////////////////////////////////////////////////////////////////////////////////
-// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-
-// This is a implementation of the Logistic Regression algorithm in C++ in OpenCV.
-
-// AUTHOR:
-// Rahul Kavi rahulkavi[at]live[at]com
-//
-
-// contains a subset of data from the popular Iris Dataset (taken from
-// "http://archive.ics.uci.edu/ml/datasets/Iris")
-
-// # You are free to use, change, or redistribute the code in any way you wish for
-// # non-commercial purposes, but please maintain the name of the original author.
-// # This code comes with no warranty of any kind.
-
-// #
-// # You are free to use, change, or redistribute the code in any way you wish for
-// # non-commercial purposes, but please maintain the name of the original author.
-// # This code comes with no warranty of any kind.
-
-// # Logistic Regression ALGORITHM
-
-//                           License Agreement
-//                For Open Source Computer Vision Library
-
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-
-//   * Redistributions of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-
-//   * Redistributions in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.*/
+// Logistic Regression sample
+// AUTHOR: Rahul Kavi rahulkavi[at]live[at]com
 
 #include <iostream>