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

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This commit is contained in:
Alexander Alekhin 2021-03-22 12:05:23 +00:00
commit ca8c3dd9b5
21 changed files with 152 additions and 157 deletions
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25
modules/core/include/opencv2/core/bindings_utils.hpp
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@ -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

7
modules/dnn/include/opencv2/dnn/utils/inference_engine.hpp
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@ -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.
*/

8
modules/dnn/src/dnn.cpp
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@ -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

13
modules/dnn/src/layers/convolution_layer.cpp
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@ -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

8
modules/dnn/src/layers/elementwise_layers.cpp
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@ -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

23
modules/dnn/src/layers/normalize_bbox_layer.cpp
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@ -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

7
modules/dnn/src/layers/padding_layer.cpp
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@ -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 ||

4
modules/dnn/src/layers/permute_layer.cpp
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@ -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()) ||

4
modules/dnn/src/layers/pooling_layer.cpp
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@ -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)
{

6
modules/dnn/src/layers/region_layer.cpp
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@ -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);
}

35
modules/dnn/src/op_inf_engine.cpp
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@ -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

3
modules/dnn/src/op_inf_engine.hpp
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@ -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>

1
modules/dnn/test/test_common.hpp
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@ -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"

6
modules/dnn/test/test_onnx_importer.cpp
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@ -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"));

3
modules/features2d/include/opencv2/features2d.hpp
View File

@ -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
*/

2
modules/features2d/src/mser.cpp
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@ -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.
*/

4
modules/js/generator/CMakeLists.txt
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@ -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}")

61
modules/ml/src/lr.cpp
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@ -1,57 +1,12 @@
///////////////////////////////////////////////////////////////////////////////////////
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
// 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
// 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 is a implementation of the Logistic Regression algorithm
//
#include "precomp.hpp"

10
modules/ml/test/test_lr.cpp
View File

@ -1,11 +1,13 @@
// 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"

20
platforms/js/opencv_js.config.py
View File

@ -1,11 +1,17 @@
# 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',\

59
samples/cpp/logistic_regression.cpp
View File

@ -1,60 +1,5 @@
/*//////////////////////////////////////////////////////////////////////////////////////
// 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>