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798 lines
32 KiB
798 lines
32 KiB
/*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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// |
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// Copyright (C) 2013, OpenCV Foundation, 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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// * Redistribution's 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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// * Redistribution's 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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// * The name of the copyright holders may not be used to endorse or promote products |
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// derived from this software 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 the Intel Corporation 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 "layers_common.hpp" |
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#include <float.h> |
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#include <string> |
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#include <caffe.pb.h> |
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namespace cv |
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{ |
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namespace dnn |
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{ |
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namespace util |
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{ |
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template <typename T> |
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std::string to_string(T value) |
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{ |
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std::ostringstream stream; |
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stream << value; |
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return stream.str(); |
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} |
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template <typename T> |
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void make_error(const std::string& message1, const T& message2) |
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{ |
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std::string error(message1); |
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error += std::string(util::to_string<int>(message2)); |
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CV_Error(Error::StsBadArg, error.c_str()); |
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} |
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template <typename T> |
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bool SortScorePairDescend(const std::pair<float, T>& pair1, |
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const std::pair<float, T>& pair2) |
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{ |
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return pair1.first > pair2.first; |
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} |
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} |
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class DetectionOutputLayerImpl : public DetectionOutputLayer |
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{ |
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public: |
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unsigned _numClasses; |
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bool _shareLocation; |
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int _numLocClasses; |
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int _backgroundLabelId; |
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typedef caffe::PriorBoxParameter_CodeType CodeType; |
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CodeType _codeType; |
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bool _varianceEncodedInTarget; |
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int _keepTopK; |
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float _confidenceThreshold; |
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float _nmsThreshold; |
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int _topK; |
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enum { _numAxes = 4 }; |
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static const std::string _layerName; |
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typedef std::map<int, std::vector<caffe::NormalizedBBox> > LabelBBox; |
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bool getParameterDict(const LayerParams ¶ms, |
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const std::string ¶meterName, |
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DictValue& result) |
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{ |
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if (!params.has(parameterName)) |
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{ |
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return false; |
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} |
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result = params.get(parameterName); |
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return true; |
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} |
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template<typename T> |
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T getParameter(const LayerParams ¶ms, |
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const std::string ¶meterName, |
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const size_t &idx=0, |
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const bool required=true, |
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const T& defaultValue=T()) |
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{ |
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DictValue dictValue; |
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bool success = getParameterDict(params, parameterName, dictValue); |
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if(!success) |
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{ |
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if(required) |
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{ |
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std::string message = _layerName; |
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message += " layer parameter does not contain "; |
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message += parameterName; |
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message += " parameter."; |
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CV_Error(Error::StsBadArg, message); |
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} |
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else |
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{ |
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return defaultValue; |
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} |
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} |
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return dictValue.get<T>(idx); |
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} |
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void getCodeType(const LayerParams ¶ms) |
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{ |
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String codeTypeString = params.get<String>("code_type").toLowerCase(); |
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if (codeTypeString == "corner") |
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_codeType = caffe::PriorBoxParameter_CodeType_CORNER; |
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else if (codeTypeString == "center_size") |
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_codeType = caffe::PriorBoxParameter_CodeType_CENTER_SIZE; |
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else |
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_codeType = caffe::PriorBoxParameter_CodeType_CORNER; |
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} |
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DetectionOutputLayerImpl(const LayerParams ¶ms) |
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{ |
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_numClasses = getParameter<unsigned>(params, "num_classes"); |
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_shareLocation = getParameter<bool>(params, "share_location"); |
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_numLocClasses = _shareLocation ? 1 : _numClasses; |
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_backgroundLabelId = getParameter<int>(params, "background_label_id"); |
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_varianceEncodedInTarget = getParameter<bool>(params, "variance_encoded_in_target", 0, false, false); |
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_keepTopK = getParameter<int>(params, "keep_top_k"); |
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_confidenceThreshold = getParameter<float>(params, "confidence_threshold", 0, false, -FLT_MAX); |
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_topK = getParameter<int>(params, "top_k", 0, false, -1); |
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getCodeType(params); |
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// Parameters used in nms. |
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_nmsThreshold = getParameter<float>(params, "nms_threshold"); |
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CV_Assert(_nmsThreshold > 0.); |
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setParamsFrom(params); |
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} |
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void checkInputs(const std::vector<Mat*> &inputs) |
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{ |
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for (size_t i = 1; i < inputs.size(); i++) |
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{ |
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CV_Assert(inputs[i]->size == inputs[0]->size); |
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} |
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} |
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bool getMemoryShapes(const std::vector<MatShape> &inputs, |
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const int requiredOutputs, |
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std::vector<MatShape> &outputs, |
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std::vector<MatShape> &internals) const |
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{ |
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CV_Assert(inputs.size() > 0); |
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CV_Assert(inputs[0][0] == inputs[1][0]); |
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int numPriors = inputs[2][2] / 4; |
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CV_Assert((numPriors * _numLocClasses * 4) == inputs[0][1]); |
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CV_Assert(int(numPriors * _numClasses) == inputs[1][1]); |
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// num() and channels() are 1. |
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// Since the number of bboxes to be kept is unknown before nms, we manually |
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// set it to (fake) 1. |
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// Each row is a 7 dimension std::vector, which stores |
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// [image_id, label, confidence, xmin, ymin, xmax, ymax] |
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outputs.resize(1, shape(1, 1, 1, 7)); |
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return false; |
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} |
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void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals) |
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{ |
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const float* locationData = inputs[0]->ptr<float>(); |
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const float* confidenceData = inputs[1]->ptr<float>(); |
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const float* priorData = inputs[2]->ptr<float>(); |
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int num = inputs[0]->size[0]; |
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int numPriors = inputs[2]->size[2] / 4; |
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// Retrieve all location predictions. |
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std::vector<LabelBBox> allLocationPredictions; |
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GetLocPredictions(locationData, num, numPriors, _numLocClasses, |
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_shareLocation, &allLocationPredictions); |
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// Retrieve all confidences. |
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std::vector<std::map<int, std::vector<float> > > allConfidenceScores; |
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GetConfidenceScores(confidenceData, num, numPriors, _numClasses, |
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&allConfidenceScores); |
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// Retrieve all prior bboxes. It is same within a batch since we assume all |
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// images in a batch are of same dimension. |
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std::vector<caffe::NormalizedBBox> priorBBoxes; |
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std::vector<std::vector<float> > priorVariances; |
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GetPriorBBoxes(priorData, numPriors, &priorBBoxes, &priorVariances); |
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const bool clip_bbox = false; |
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// Decode all loc predictions to bboxes. |
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std::vector<LabelBBox> allDecodedBBoxes; |
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DecodeBBoxesAll(allLocationPredictions, priorBBoxes, priorVariances, num, |
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_shareLocation, _numLocClasses, _backgroundLabelId, |
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_codeType, _varianceEncodedInTarget, clip_bbox, &allDecodedBBoxes); |
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int numKept = 0; |
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std::vector<std::map<int, std::vector<int> > > allIndices; |
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for (int i = 0; i < num; ++i) |
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{ |
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const LabelBBox& decodeBBoxes = allDecodedBBoxes[i]; |
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const std::map<int, std::vector<float> >& confidenceScores = |
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allConfidenceScores[i]; |
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std::map<int, std::vector<int> > indices; |
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int numDetections = 0; |
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for (int c = 0; c < (int)_numClasses; ++c) |
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{ |
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if (c == _backgroundLabelId) |
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{ |
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// Ignore background class. |
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continue; |
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} |
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if (confidenceScores.find(c) == confidenceScores.end()) |
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{ |
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// Something bad happened if there are no predictions for current label. |
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util::make_error<int>("Could not find confidence predictions for label ", c); |
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} |
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const std::vector<float>& scores = confidenceScores.find(c)->second; |
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int label = _shareLocation ? -1 : c; |
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if (decodeBBoxes.find(label) == decodeBBoxes.end()) |
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{ |
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// Something bad happened if there are no predictions for current label. |
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util::make_error<int>("Could not find location predictions for label ", label); |
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continue; |
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} |
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const std::vector<caffe::NormalizedBBox>& bboxes = |
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decodeBBoxes.find(label)->second; |
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ApplyNMSFast(bboxes, scores, _confidenceThreshold, _nmsThreshold, 1.0, |
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_topK, &(indices[c])); |
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numDetections += indices[c].size(); |
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} |
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if (_keepTopK > -1 && numDetections > _keepTopK) |
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{ |
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std::vector<std::pair<float, std::pair<int, int> > > scoreIndexPairs; |
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for (std::map<int, std::vector<int> >::iterator it = indices.begin(); |
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it != indices.end(); ++it) |
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{ |
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int label = it->first; |
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const std::vector<int>& labelIndices = it->second; |
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if (confidenceScores.find(label) == confidenceScores.end()) |
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{ |
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// Something bad happened for current label. |
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util::make_error<int>("Could not find location predictions for label ", label); |
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continue; |
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} |
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const std::vector<float>& scores = confidenceScores.find(label)->second; |
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for (size_t j = 0; j < labelIndices.size(); ++j) |
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{ |
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size_t idx = labelIndices[j]; |
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CV_Assert(idx < scores.size()); |
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scoreIndexPairs.push_back( |
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std::make_pair(scores[idx], std::make_pair(label, idx))); |
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} |
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} |
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// Keep outputs k results per image. |
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std::sort(scoreIndexPairs.begin(), scoreIndexPairs.end(), |
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util::SortScorePairDescend<std::pair<int, int> >); |
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scoreIndexPairs.resize(_keepTopK); |
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// Store the new indices. |
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std::map<int, std::vector<int> > newIndices; |
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for (size_t j = 0; j < scoreIndexPairs.size(); ++j) |
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{ |
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int label = scoreIndexPairs[j].second.first; |
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int idx = scoreIndexPairs[j].second.second; |
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newIndices[label].push_back(idx); |
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} |
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allIndices.push_back(newIndices); |
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numKept += _keepTopK; |
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} |
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else |
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{ |
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allIndices.push_back(indices); |
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numKept += numDetections; |
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} |
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} |
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if (numKept == 0) |
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{ |
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CV_ErrorNoReturn(Error::StsError, "Couldn't find any detections"); |
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return; |
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} |
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int outputShape[] = {1, 1, numKept, 7}; |
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outputs[0].create(4, outputShape, CV_32F); |
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float* outputsData = outputs[0].ptr<float>(); |
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int count = 0; |
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for (int i = 0; i < num; ++i) |
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{ |
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const std::map<int, std::vector<float> >& confidenceScores = |
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allConfidenceScores[i]; |
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const LabelBBox& decodeBBoxes = allDecodedBBoxes[i]; |
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for (std::map<int, std::vector<int> >::iterator it = allIndices[i].begin(); |
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it != allIndices[i].end(); ++it) |
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{ |
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int label = it->first; |
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if (confidenceScores.find(label) == confidenceScores.end()) |
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{ |
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// Something bad happened if there are no predictions for current label. |
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util::make_error<int>("Could not find confidence predictions for label ", label); |
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continue; |
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} |
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const std::vector<float>& scores = confidenceScores.find(label)->second; |
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int locLabel = _shareLocation ? -1 : label; |
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if (decodeBBoxes.find(locLabel) == decodeBBoxes.end()) |
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{ |
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// Something bad happened if there are no predictions for current label. |
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util::make_error<int>("Could not find location predictions for label ", locLabel); |
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continue; |
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} |
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const std::vector<caffe::NormalizedBBox>& bboxes = |
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decodeBBoxes.find(locLabel)->second; |
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std::vector<int>& indices = it->second; |
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for (size_t j = 0; j < indices.size(); ++j) |
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{ |
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int idx = indices[j]; |
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outputsData[count * 7] = i; |
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outputsData[count * 7 + 1] = label; |
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outputsData[count * 7 + 2] = scores[idx]; |
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caffe::NormalizedBBox clipBBox = bboxes[idx]; |
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outputsData[count * 7 + 3] = clipBBox.xmin(); |
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outputsData[count * 7 + 4] = clipBBox.ymin(); |
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outputsData[count * 7 + 5] = clipBBox.xmax(); |
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outputsData[count * 7 + 6] = clipBBox.ymax(); |
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++count; |
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} |
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} |
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} |
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} |
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// Compute bbox size. |
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float BBoxSize(const caffe::NormalizedBBox& bbox, |
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const bool normalized=true) |
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{ |
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if (bbox.xmax() < bbox.xmin() || bbox.ymax() < bbox.ymin()) |
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{ |
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// If bbox is invalid (e.g. xmax < xmin or ymax < ymin), return 0. |
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return 0; |
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} |
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else |
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{ |
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if (bbox.has_size()) |
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{ |
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return bbox.size(); |
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} |
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else |
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{ |
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float width = bbox.xmax() - bbox.xmin(); |
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float height = bbox.ymax() - bbox.ymin(); |
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if (normalized) |
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{ |
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return width * height; |
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} |
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else |
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{ |
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// If bbox is not within range [0, 1]. |
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return (width + 1) * (height + 1); |
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} |
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} |
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} |
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} |
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// Clip the caffe::NormalizedBBox such that the range for each corner is [0, 1]. |
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void ClipBBox(const caffe::NormalizedBBox& bbox, |
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caffe::NormalizedBBox* clipBBox) |
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{ |
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clipBBox->set_xmin(std::max(std::min(bbox.xmin(), 1.f), 0.f)); |
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clipBBox->set_ymin(std::max(std::min(bbox.ymin(), 1.f), 0.f)); |
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clipBBox->set_xmax(std::max(std::min(bbox.xmax(), 1.f), 0.f)); |
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clipBBox->set_ymax(std::max(std::min(bbox.ymax(), 1.f), 0.f)); |
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clipBBox->clear_size(); |
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clipBBox->set_size(BBoxSize(*clipBBox)); |
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clipBBox->set_difficult(bbox.difficult()); |
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} |
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// Decode a bbox according to a prior bbox. |
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void DecodeBBox( |
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const caffe::NormalizedBBox& prior_bbox, const std::vector<float>& prior_variance, |
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const CodeType code_type, const bool variance_encoded_in_target, |
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const bool clip_bbox, const caffe::NormalizedBBox& bbox, |
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caffe::NormalizedBBox* decode_bbox) { |
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if (code_type == caffe::PriorBoxParameter_CodeType_CORNER) { |
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if (variance_encoded_in_target) { |
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// variance is encoded in target, we simply need to add the offset |
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// predictions. |
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decode_bbox->set_xmin(prior_bbox.xmin() + bbox.xmin()); |
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decode_bbox->set_ymin(prior_bbox.ymin() + bbox.ymin()); |
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decode_bbox->set_xmax(prior_bbox.xmax() + bbox.xmax()); |
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decode_bbox->set_ymax(prior_bbox.ymax() + bbox.ymax()); |
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} else { |
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// variance is encoded in bbox, we need to scale the offset accordingly. |
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decode_bbox->set_xmin( |
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prior_bbox.xmin() + prior_variance[0] * bbox.xmin()); |
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decode_bbox->set_ymin( |
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prior_bbox.ymin() + prior_variance[1] * bbox.ymin()); |
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decode_bbox->set_xmax( |
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prior_bbox.xmax() + prior_variance[2] * bbox.xmax()); |
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decode_bbox->set_ymax( |
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prior_bbox.ymax() + prior_variance[3] * bbox.ymax()); |
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} |
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} else if (code_type == caffe::PriorBoxParameter_CodeType_CENTER_SIZE) { |
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float prior_width = prior_bbox.xmax() - prior_bbox.xmin(); |
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CV_Assert(prior_width > 0); |
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float prior_height = prior_bbox.ymax() - prior_bbox.ymin(); |
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CV_Assert(prior_height > 0); |
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float prior_center_x = (prior_bbox.xmin() + prior_bbox.xmax()) / 2.; |
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float prior_center_y = (prior_bbox.ymin() + prior_bbox.ymax()) / 2.; |
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|
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float decode_bbox_center_x, decode_bbox_center_y; |
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float decode_bbox_width, decode_bbox_height; |
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if (variance_encoded_in_target) { |
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// variance is encoded in target, we simply need to retore the offset |
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// predictions. |
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decode_bbox_center_x = bbox.xmin() * prior_width + prior_center_x; |
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decode_bbox_center_y = bbox.ymin() * prior_height + prior_center_y; |
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decode_bbox_width = exp(bbox.xmax()) * prior_width; |
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decode_bbox_height = exp(bbox.ymax()) * prior_height; |
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} else { |
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// variance is encoded in bbox, we need to scale the offset accordingly. |
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decode_bbox_center_x = |
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prior_variance[0] * bbox.xmin() * prior_width + prior_center_x; |
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decode_bbox_center_y = |
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prior_variance[1] * bbox.ymin() * prior_height + prior_center_y; |
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decode_bbox_width = |
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exp(prior_variance[2] * bbox.xmax()) * prior_width; |
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decode_bbox_height = |
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exp(prior_variance[3] * bbox.ymax()) * prior_height; |
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} |
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decode_bbox->set_xmin(decode_bbox_center_x - decode_bbox_width / 2.); |
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decode_bbox->set_ymin(decode_bbox_center_y - decode_bbox_height / 2.); |
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decode_bbox->set_xmax(decode_bbox_center_x + decode_bbox_width / 2.); |
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decode_bbox->set_ymax(decode_bbox_center_y + decode_bbox_height / 2.); |
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} else { |
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CV_Error(Error::StsBadArg, "Unknown LocLossType."); |
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} |
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float bbox_size = BBoxSize(*decode_bbox); |
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decode_bbox->set_size(bbox_size); |
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if (clip_bbox) { |
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ClipBBox(*decode_bbox, decode_bbox); |
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} |
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} |
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|
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// Decode a set of bboxes according to a set of prior bboxes. |
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void DecodeBBoxes( |
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const std::vector<caffe::NormalizedBBox>& prior_bboxes, |
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const std::vector<std::vector<float> >& prior_variances, |
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const CodeType code_type, const bool variance_encoded_in_target, |
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const bool clip_bbox, const std::vector<caffe::NormalizedBBox>& bboxes, |
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std::vector<caffe::NormalizedBBox>* decode_bboxes) { |
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CV_Assert(prior_bboxes.size() == prior_variances.size()); |
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CV_Assert(prior_bboxes.size() == bboxes.size()); |
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int num_bboxes = prior_bboxes.size(); |
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if (num_bboxes >= 1) { |
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CV_Assert(prior_variances[0].size() == 4); |
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} |
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decode_bboxes->clear(); |
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for (int i = 0; i < num_bboxes; ++i) { |
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caffe::NormalizedBBox decode_bbox; |
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DecodeBBox(prior_bboxes[i], prior_variances[i], code_type, |
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variance_encoded_in_target, clip_bbox, bboxes[i], &decode_bbox); |
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decode_bboxes->push_back(decode_bbox); |
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} |
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} |
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|
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// Decode all bboxes in a batch. |
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void DecodeBBoxesAll(const std::vector<LabelBBox>& all_loc_preds, |
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const std::vector<caffe::NormalizedBBox>& prior_bboxes, |
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const std::vector<std::vector<float> >& prior_variances, |
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const int num, const bool share_location, |
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const int num_loc_classes, const int background_label_id, |
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const CodeType code_type, const bool variance_encoded_in_target, |
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const bool clip, std::vector<LabelBBox>* all_decode_bboxes) { |
|
CV_Assert(all_loc_preds.size() == num); |
|
all_decode_bboxes->clear(); |
|
all_decode_bboxes->resize(num); |
|
for (int i = 0; i < num; ++i) { |
|
// Decode predictions into bboxes. |
|
LabelBBox& decode_bboxes = (*all_decode_bboxes)[i]; |
|
for (int c = 0; c < num_loc_classes; ++c) { |
|
int label = share_location ? -1 : c; |
|
if (label == background_label_id) { |
|
// Ignore background class. |
|
continue; |
|
} |
|
if (all_loc_preds[i].find(label) == all_loc_preds[i].end()) { |
|
// Something bad happened if there are no predictions for current label. |
|
util::make_error<int>("Could not find location predictions for label ", label); |
|
} |
|
const std::vector<caffe::NormalizedBBox>& label_loc_preds = |
|
all_loc_preds[i].find(label)->second; |
|
DecodeBBoxes(prior_bboxes, prior_variances, |
|
code_type, variance_encoded_in_target, clip, |
|
label_loc_preds, &(decode_bboxes[label])); |
|
} |
|
} |
|
} |
|
|
|
// Get prior bounding boxes from prior_data. |
|
// prior_data: 1 x 2 x num_priors * 4 x 1 blob. |
|
// num_priors: number of priors. |
|
// prior_bboxes: stores all the prior bboxes in the format of caffe::NormalizedBBox. |
|
// prior_variances: stores all the variances needed by prior bboxes. |
|
void GetPriorBBoxes(const float* priorData, const int& numPriors, |
|
std::vector<caffe::NormalizedBBox>* priorBBoxes, |
|
std::vector<std::vector<float> >* priorVariances) |
|
{ |
|
priorBBoxes->clear(); |
|
priorVariances->clear(); |
|
for (int i = 0; i < numPriors; ++i) |
|
{ |
|
int startIdx = i * 4; |
|
caffe::NormalizedBBox bbox; |
|
bbox.set_xmin(priorData[startIdx]); |
|
bbox.set_ymin(priorData[startIdx + 1]); |
|
bbox.set_xmax(priorData[startIdx + 2]); |
|
bbox.set_ymax(priorData[startIdx + 3]); |
|
float bboxSize = BBoxSize(bbox); |
|
bbox.set_size(bboxSize); |
|
priorBBoxes->push_back(bbox); |
|
} |
|
|
|
for (int i = 0; i < numPriors; ++i) |
|
{ |
|
int startIdx = (numPriors + i) * 4; |
|
std::vector<float> var; |
|
for (int j = 0; j < 4; ++j) |
|
{ |
|
var.push_back(priorData[startIdx + j]); |
|
} |
|
priorVariances->push_back(var); |
|
} |
|
} |
|
|
|
// Scale the caffe::NormalizedBBox w.r.t. height and width. |
|
void ScaleBBox(const caffe::NormalizedBBox& bbox, |
|
const int height, const int width, |
|
caffe::NormalizedBBox* scaleBBox) |
|
{ |
|
scaleBBox->set_xmin(bbox.xmin() * width); |
|
scaleBBox->set_ymin(bbox.ymin() * height); |
|
scaleBBox->set_xmax(bbox.xmax() * width); |
|
scaleBBox->set_ymax(bbox.ymax() * height); |
|
scaleBBox->clear_size(); |
|
bool normalized = !(width > 1 || height > 1); |
|
scaleBBox->set_size(BBoxSize(*scaleBBox, normalized)); |
|
scaleBBox->set_difficult(bbox.difficult()); |
|
} |
|
|
|
// Get location predictions from loc_data. |
|
// loc_data: num x num_preds_per_class * num_loc_classes * 4 blob. |
|
// num: the number of images. |
|
// num_preds_per_class: number of predictions per class. |
|
// num_loc_classes: number of location classes. It is 1 if share_location is |
|
// true; and is equal to number of classes needed to predict otherwise. |
|
// share_location: if true, all classes share the same location prediction. |
|
// loc_preds: stores the location prediction, where each item contains |
|
// location prediction for an image. |
|
void GetLocPredictions(const float* locData, const int num, |
|
const int numPredsPerClass, const int numLocClasses, |
|
const bool shareLocation, std::vector<LabelBBox>* locPreds) |
|
{ |
|
locPreds->clear(); |
|
if (shareLocation) |
|
{ |
|
CV_Assert(numLocClasses == 1); |
|
} |
|
locPreds->resize(num); |
|
for (int i = 0; i < num; ++i) |
|
{ |
|
LabelBBox& labelBBox = (*locPreds)[i]; |
|
for (int p = 0; p < numPredsPerClass; ++p) |
|
{ |
|
int startIdx = p * numLocClasses * 4; |
|
for (int c = 0; c < numLocClasses; ++c) |
|
{ |
|
int label = shareLocation ? -1 : c; |
|
if (labelBBox.find(label) == labelBBox.end()) |
|
{ |
|
labelBBox[label].resize(numPredsPerClass); |
|
} |
|
labelBBox[label][p].set_xmin(locData[startIdx + c * 4]); |
|
labelBBox[label][p].set_ymin(locData[startIdx + c * 4 + 1]); |
|
labelBBox[label][p].set_xmax(locData[startIdx + c * 4 + 2]); |
|
labelBBox[label][p].set_ymax(locData[startIdx + c * 4 + 3]); |
|
} |
|
} |
|
locData += numPredsPerClass * numLocClasses * 4; |
|
} |
|
} |
|
|
|
// Get confidence predictions from conf_data. |
|
// conf_data: num x num_preds_per_class * num_classes blob. |
|
// num: the number of images. |
|
// num_preds_per_class: number of predictions per class. |
|
// num_classes: number of classes. |
|
// conf_preds: stores the confidence prediction, where each item contains |
|
// confidence prediction for an image. |
|
void GetConfidenceScores(const float* confData, const int num, |
|
const int numPredsPerClass, const int numClasses, |
|
std::vector<std::map<int, std::vector<float> > >* confPreds) |
|
{ |
|
confPreds->clear(); |
|
confPreds->resize(num); |
|
for (int i = 0; i < num; ++i) |
|
{ |
|
std::map<int, std::vector<float> >& labelScores = (*confPreds)[i]; |
|
for (int p = 0; p < numPredsPerClass; ++p) |
|
{ |
|
int startIdx = p * numClasses; |
|
for (int c = 0; c < numClasses; ++c) |
|
{ |
|
labelScores[c].push_back(confData[startIdx + c]); |
|
} |
|
} |
|
confData += numPredsPerClass * numClasses; |
|
} |
|
} |
|
|
|
// Do non maximum suppression given bboxes and scores. |
|
// Inspired by Piotr Dollar's NMS implementation in EdgeBox. |
|
// https://goo.gl/jV3JYS |
|
// bboxes: a set of bounding boxes. |
|
// scores: a set of corresponding confidences. |
|
// score_threshold: a threshold used to filter detection results. |
|
// nms_threshold: a threshold used in non maximum suppression. |
|
// top_k: if not -1, keep at most top_k picked indices. |
|
// indices: the kept indices of bboxes after nms. |
|
void ApplyNMSFast(const std::vector<caffe::NormalizedBBox>& bboxes, |
|
const std::vector<float>& scores, const float score_threshold, |
|
const float nms_threshold, const float eta, const int top_k, |
|
std::vector<int>* indices) { |
|
// Sanity check. |
|
CV_Assert(bboxes.size() == scores.size()); |
|
|
|
// Get top_k scores (with corresponding indices). |
|
std::vector<std::pair<float, int> > score_index_vec; |
|
GetMaxScoreIndex(scores, score_threshold, top_k, &score_index_vec); |
|
|
|
// Do nms. |
|
float adaptive_threshold = nms_threshold; |
|
indices->clear(); |
|
while (score_index_vec.size() != 0) { |
|
const int idx = score_index_vec.front().second; |
|
bool keep = true; |
|
for (int k = 0; k < indices->size(); ++k) { |
|
if (keep) { |
|
const int kept_idx = (*indices)[k]; |
|
float overlap = JaccardOverlap(bboxes[idx], bboxes[kept_idx]); |
|
keep = overlap <= adaptive_threshold; |
|
} else { |
|
break; |
|
} |
|
} |
|
if (keep) { |
|
indices->push_back(idx); |
|
} |
|
score_index_vec.erase(score_index_vec.begin()); |
|
if (keep && eta < 1 && adaptive_threshold > 0.5) { |
|
adaptive_threshold *= eta; |
|
} |
|
} |
|
} |
|
|
|
// Get max scores with corresponding indices. |
|
// scores: a set of scores. |
|
// threshold: only consider scores higher than the threshold. |
|
// top_k: if -1, keep all; otherwise, keep at most top_k. |
|
// score_index_vec: store the sorted (score, index) pair. |
|
void GetMaxScoreIndex(const std::vector<float>& scores, const float threshold,const int top_k, |
|
std::vector<std::pair<float, int> >* score_index_vec) |
|
{ |
|
// Generate index score pairs. |
|
for (size_t i = 0; i < scores.size(); ++i) |
|
{ |
|
if (scores[i] > threshold) |
|
{ |
|
score_index_vec->push_back(std::make_pair(scores[i], i)); |
|
} |
|
} |
|
|
|
// Sort the score pair according to the scores in descending order |
|
std::stable_sort(score_index_vec->begin(), score_index_vec->end(), |
|
util::SortScorePairDescend<int>); |
|
|
|
// Keep top_k scores if needed. |
|
if (top_k > -1 && top_k < (int)score_index_vec->size()) |
|
{ |
|
score_index_vec->resize(top_k); |
|
} |
|
} |
|
|
|
// Compute the intersection between two bboxes. |
|
void IntersectBBox(const caffe::NormalizedBBox& bbox1, |
|
const caffe::NormalizedBBox& bbox2, |
|
caffe::NormalizedBBox* intersect_bbox) { |
|
if (bbox2.xmin() > bbox1.xmax() || bbox2.xmax() < bbox1.xmin() || |
|
bbox2.ymin() > bbox1.ymax() || bbox2.ymax() < bbox1.ymin()) |
|
{ |
|
// Return [0, 0, 0, 0] if there is no intersection. |
|
intersect_bbox->set_xmin(0); |
|
intersect_bbox->set_ymin(0); |
|
intersect_bbox->set_xmax(0); |
|
intersect_bbox->set_ymax(0); |
|
} |
|
else |
|
{ |
|
intersect_bbox->set_xmin(std::max(bbox1.xmin(), bbox2.xmin())); |
|
intersect_bbox->set_ymin(std::max(bbox1.ymin(), bbox2.ymin())); |
|
intersect_bbox->set_xmax(std::min(bbox1.xmax(), bbox2.xmax())); |
|
intersect_bbox->set_ymax(std::min(bbox1.ymax(), bbox2.ymax())); |
|
} |
|
} |
|
|
|
// Compute the jaccard (intersection over union IoU) overlap between two bboxes. |
|
float JaccardOverlap(const caffe::NormalizedBBox& bbox1, |
|
const caffe::NormalizedBBox& bbox2, |
|
const bool normalized=true) |
|
{ |
|
caffe::NormalizedBBox intersect_bbox; |
|
IntersectBBox(bbox1, bbox2, &intersect_bbox); |
|
float intersect_width, intersect_height; |
|
if (normalized) |
|
{ |
|
intersect_width = intersect_bbox.xmax() - intersect_bbox.xmin(); |
|
intersect_height = intersect_bbox.ymax() - intersect_bbox.ymin(); |
|
} |
|
else |
|
{ |
|
intersect_width = intersect_bbox.xmax() - intersect_bbox.xmin() + 1; |
|
intersect_height = intersect_bbox.ymax() - intersect_bbox.ymin() + 1; |
|
} |
|
if (intersect_width > 0 && intersect_height > 0) |
|
{ |
|
float intersect_size = intersect_width * intersect_height; |
|
float bbox1_size = BBoxSize(bbox1); |
|
float bbox2_size = BBoxSize(bbox2); |
|
return intersect_size / (bbox1_size + bbox2_size - intersect_size); |
|
} |
|
else |
|
{ |
|
return 0.; |
|
} |
|
} |
|
}; |
|
|
|
const std::string DetectionOutputLayerImpl::_layerName = std::string("DetectionOutput"); |
|
|
|
Ptr<DetectionOutputLayer> DetectionOutputLayer::create(const LayerParams ¶ms) |
|
{ |
|
return Ptr<DetectionOutputLayer>(new DetectionOutputLayerImpl(params)); |
|
} |
|
|
|
} |
|
}
|
|
|