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Added DNN Darknet Yolo v2 for object detection
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‎modules/dnn/include/opencv2/dnn/all_layers.hpp

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@@ -473,6 +473,18 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
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static Ptr<PriorBoxLayer> create(const LayerParams& params);
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};
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class CV_EXPORTS ReorgLayer : public Layer
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{
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public:
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static Ptr<ReorgLayer> create(const LayerParams& params);
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};
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class CV_EXPORTS RegionLayer : public Layer
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{
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public:
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static Ptr<RegionLayer> create(const LayerParams& params);
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};
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class CV_EXPORTS DetectionOutputLayer : public Layer
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{
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public:

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

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virtual ~Importer();
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};
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/** @brief Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.
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* @param cfgFile path to the .cfg file with text description of the network architecture.
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* @param darknetModel path to the .weights file with learned network.
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* @returns Network object that ready to do forward, throw an exception in failure cases.
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* @details This is shortcut consisting from DarknetImporter and Net::populateNet calls.
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*/
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CV_EXPORTS_W Net readNetFromDarknet(const String &cfgFile, const String &darknetModel = String());
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/**
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* @deprecated Use @ref readNetFromCaffe instead.
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* @brief Creates the importer of <a href="http://caffe.berkeleyvision.org">Caffe</a> framework network.

‎modules/dnn/src/darknet/darknet_importer.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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// (3-clause BSD License)
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//
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// Copyright (C) 2017, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * Neither the names of the copyright holders nor the names of the contributors
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// may be used to endorse or promote products derived from this software
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// without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall copyright holders or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "../precomp.hpp"
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#include <iostream>
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#include <algorithm>
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#include <vector>
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#include <map>
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#include "darknet_io.hpp"
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namespace cv {
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namespace dnn {
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CV__DNN_EXPERIMENTAL_NS_BEGIN
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namespace
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{
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class DarknetImporter : public Importer
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{
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darknet::NetParameter net;
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public:
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DarknetImporter() {}
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DarknetImporter(const char *cfgFile, const char *darknetModel)
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{
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CV_TRACE_FUNCTION();
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ReadNetParamsFromCfgFileOrDie(cfgFile, &net);
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if (darknetModel && darknetModel[0])
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ReadNetParamsFromBinaryFileOrDie(darknetModel, &net);
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}
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struct BlobNote
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{
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BlobNote(const std::string &_name, int _layerId, int _outNum) :
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name(_name), layerId(_layerId), outNum(_outNum) {}
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std::string name;
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int layerId, outNum;
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};
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std::vector<BlobNote> addedBlobs;
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std::map<String, int> layerCounter;
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void populateNet(Net dstNet)
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{
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CV_TRACE_FUNCTION();
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int layersSize = net.layer_size();
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layerCounter.clear();
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addedBlobs.clear();
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addedBlobs.reserve(layersSize + 1);
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//setup input layer names
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{
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std::vector<String> netInputs(net.input_size());
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for (int inNum = 0; inNum < net.input_size(); inNum++)
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{
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addedBlobs.push_back(BlobNote(net.input(inNum), 0, inNum));
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netInputs[inNum] = net.input(inNum);
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}
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dstNet.setInputsNames(netInputs);
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}
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for (int li = 0; li < layersSize; li++)
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{
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const darknet::LayerParameter &layer = net.layer(li);
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String name = layer.name();
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String type = layer.type();
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LayerParams layerParams = layer.getLayerParams();
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int repetitions = layerCounter[name]++;
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if (repetitions)
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name += cv::format("_%d", repetitions);
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int id = dstNet.addLayer(name, type, layerParams);
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// iterate many bottoms layers (for example for: route -1, -4)
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for (int inNum = 0; inNum < layer.bottom_size(); inNum++)
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addInput(layer.bottom(inNum), id, inNum, dstNet, layer.name());
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for (int outNum = 0; outNum < layer.top_size(); outNum++)
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addOutput(layer, id, outNum);
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}
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addedBlobs.clear();
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}
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void addOutput(const darknet::LayerParameter &layer, int layerId, int outNum)
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{
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const std::string &name = layer.top(outNum);
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bool haveDups = false;
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for (int idx = (int)addedBlobs.size() - 1; idx >= 0; idx--)
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{
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if (addedBlobs[idx].name == name)
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{
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haveDups = true;
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break;
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}
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}
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if (haveDups)
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{
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bool isInplace = layer.bottom_size() > outNum && layer.bottom(outNum) == name;
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if (!isInplace)
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CV_Error(Error::StsBadArg, "Duplicate blobs produced by multiple sources");
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}
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addedBlobs.push_back(BlobNote(name, layerId, outNum));
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}
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void addInput(const std::string &name, int layerId, int inNum, Net &dstNet, std::string nn)
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{
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int idx;
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for (idx = (int)addedBlobs.size() - 1; idx >= 0; idx--)
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{
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if (addedBlobs[idx].name == name)
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break;
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}
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if (idx < 0)
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{
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CV_Error(Error::StsObjectNotFound, "Can't find output blob \"" + name + "\"");
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return;
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}
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dstNet.connect(addedBlobs[idx].layerId, addedBlobs[idx].outNum, layerId, inNum);
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}
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~DarknetImporter()
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{
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}
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};
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}
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Net readNetFromDarknet(const String &cfgFile, const String &darknetModel /*= String()*/)
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{
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DarknetImporter darknetImporter(cfgFile.c_str(), darknetModel.c_str());
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Net net;
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darknetImporter.populateNet(net);
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return net;
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}
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CV__DNN_EXPERIMENTAL_NS_END
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}} // namespace

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