Open Source Computer Vision Library
https://opencv.org/
744 lines
30 KiB
744 lines
30 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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// 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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// * 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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static inline 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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} // namespace |
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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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// Whenever predicted bounding boxes are respresented in YXHW instead of XYWH layout. |
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bool _locPredTransposed; |
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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_ErrorNoReturn(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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_locPredTransposed = getParameter<bool>(params, "loc_pred_transposed", 0, false, false); |
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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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CV_TRACE_FUNCTION(); |
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CV_TRACE_ARG_VALUE(name, "name", name.c_str()); |
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std::vector<LabelBBox> allDecodedBBoxes; |
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std::vector<std::vector<std::vector<float> > > allConfidenceScores; |
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int num = inputs[0]->size[0]; |
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// extract predictions from input layers |
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{ |
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int numPriors = inputs[2]->size[2] / 4; |
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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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// 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, _locPredTransposed, allLocationPredictions); |
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// Retrieve all confidences |
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GetConfidenceScores(confidenceData, num, numPriors, _numClasses, allConfidenceScores); |
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// Retrieve all prior bboxes |
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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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// Decode all loc predictions to bboxes |
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DecodeBBoxesAll(allLocationPredictions, priorBBoxes, priorVariances, num, |
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_shareLocation, _numLocClasses, _backgroundLabelId, |
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_codeType, _varianceEncodedInTarget, false, allDecodedBBoxes); |
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} |
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size_t 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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numKept += processDetections_(allDecodedBBoxes[i], allConfidenceScores[i], allIndices); |
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} |
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if (numKept == 0) |
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{ |
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return; |
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} |
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int outputShape[] = {1, 1, (int)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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size_t count = 0; |
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for (int i = 0; i < num; ++i) |
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{ |
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count += outputDetections_(i, &outputsData[count * 7], |
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allDecodedBBoxes[i], allConfidenceScores[i], |
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allIndices[i]); |
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} |
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CV_Assert(count == numKept); |
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} |
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size_t outputDetections_( |
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const int i, float* outputsData, |
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const LabelBBox& decodeBBoxes, const std::vector<std::vector<float> >& confidenceScores, |
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const std::map<int, std::vector<int> >& indicesMap |
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) |
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{ |
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size_t count = 0; |
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for (std::map<int, std::vector<int> >::const_iterator it = indicesMap.begin(); it != indicesMap.end(); ++it) |
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{ |
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int label = it->first; |
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if (confidenceScores.size() <= label) |
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CV_ErrorNoReturn_(cv::Error::StsError, ("Could not find confidence predictions for label %d", label)); |
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const std::vector<float>& scores = confidenceScores[label]; |
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int locLabel = _shareLocation ? -1 : label; |
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LabelBBox::const_iterator label_bboxes = decodeBBoxes.find(locLabel); |
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if (label_bboxes == decodeBBoxes.end()) |
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CV_ErrorNoReturn_(cv::Error::StsError, ("Could not find location predictions for label %d", locLabel)); |
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const std::vector<int>& indices = it->second; |
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for (size_t j = 0; j < indices.size(); ++j, ++count) |
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{ |
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int idx = indices[j]; |
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const caffe::NormalizedBBox& decode_bbox = label_bboxes->second[idx]; |
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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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outputsData[count * 7 + 3] = decode_bbox.xmin(); |
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outputsData[count * 7 + 4] = decode_bbox.ymin(); |
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outputsData[count * 7 + 5] = decode_bbox.xmax(); |
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outputsData[count * 7 + 6] = decode_bbox.ymax(); |
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} |
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} |
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return count; |
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} |
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size_t processDetections_( |
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const LabelBBox& decodeBBoxes, const std::vector<std::vector<float> >& confidenceScores, |
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std::vector<std::map<int, std::vector<int> > >& allIndices |
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) |
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{ |
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std::map<int, std::vector<int> > indices; |
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size_t 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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continue; // Ignore background class. |
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if (c >= confidenceScores.size()) |
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CV_ErrorNoReturn_(cv::Error::StsError, ("Could not find confidence predictions for label %d", c)); |
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const std::vector<float>& scores = confidenceScores[c]; |
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int label = _shareLocation ? -1 : c; |
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LabelBBox::const_iterator label_bboxes = decodeBBoxes.find(label); |
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if (label_bboxes == decodeBBoxes.end()) |
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CV_ErrorNoReturn_(cv::Error::StsError, ("Could not find location predictions for label %d", label)); |
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ApplyNMSFast(label_bboxes->second, scores, _confidenceThreshold, _nmsThreshold, 1.0, _topK, indices[c]); |
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numDetections += indices[c].size(); |
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} |
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if (_keepTopK > -1 && numDetections > (size_t)_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 (label >= confidenceScores.size()) |
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CV_ErrorNoReturn_(cv::Error::StsError, ("Could not find location predictions for label %d", label)); |
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const std::vector<float>& scores = confidenceScores[label]; |
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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(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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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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return (size_t)_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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return numDetections; |
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} |
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} |
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// ************************************************************** |
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// Utility functions |
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// ************************************************************** |
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// Compute bbox size |
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template<bool normalized> |
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static float BBoxSize(const caffe::NormalizedBBox& bbox) |
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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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return 0; // If bbox is invalid (e.g. xmax < xmin or ymax < ymin), 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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// Decode a bbox according to a prior bbox |
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template<bool variance_encoded_in_target> |
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static 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, |
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const bool clip_bbox, const caffe::NormalizedBBox& bbox, |
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caffe::NormalizedBBox& decode_bbox) |
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{ |
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float bbox_xmin = variance_encoded_in_target ? bbox.xmin() : prior_variance[0] * bbox.xmin(); |
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float bbox_ymin = variance_encoded_in_target ? bbox.ymin() : prior_variance[1] * bbox.ymin(); |
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float bbox_xmax = variance_encoded_in_target ? bbox.xmax() : prior_variance[2] * bbox.xmax(); |
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float bbox_ymax = variance_encoded_in_target ? bbox.ymax() : prior_variance[3] * bbox.ymax(); |
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switch(code_type) |
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{ |
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case caffe::PriorBoxParameter_CodeType_CORNER: |
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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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break; |
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case caffe::PriorBoxParameter_CodeType_CENTER_SIZE: |
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{ |
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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()) * .5; |
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float prior_center_y = (prior_bbox.ymin() + prior_bbox.ymax()) * .5; |
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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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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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decode_bbox.set_xmin(decode_bbox_center_x - decode_bbox_width * .5); |
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decode_bbox.set_ymin(decode_bbox_center_y - decode_bbox_height * .5); |
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decode_bbox.set_xmax(decode_bbox_center_x + decode_bbox_width * .5); |
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decode_bbox.set_ymax(decode_bbox_center_y + decode_bbox_height * .5); |
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break; |
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} |
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default: |
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CV_ErrorNoReturn(Error::StsBadArg, "Unknown type."); |
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}; |
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if (clip_bbox) |
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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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decode_bbox.set_xmin(std::max(std::min(decode_bbox.xmin(), 1.f), 0.f)); |
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decode_bbox.set_ymin(std::max(std::min(decode_bbox.ymin(), 1.f), 0.f)); |
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decode_bbox.set_xmax(std::max(std::min(decode_bbox.xmax(), 1.f), 0.f)); |
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decode_bbox.set_ymax(std::max(std::min(decode_bbox.ymax(), 1.f), 0.f)); |
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} |
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decode_bbox.clear_size(); |
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decode_bbox.set_size(BBoxSize<true>(decode_bbox)); |
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} |
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// Decode a set of bboxes according to a set of prior bboxes |
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static 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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{ |
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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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size_t num_bboxes = prior_bboxes.size(); |
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CV_Assert(num_bboxes == 0 || prior_variances[0].size() == 4); |
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decode_bboxes.clear(); decode_bboxes.resize(num_bboxes); |
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if(variance_encoded_in_target) |
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{ |
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for (int i = 0; i < num_bboxes; ++i) |
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DecodeBBox<true>(prior_bboxes[i], prior_variances[i], code_type, |
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clip_bbox, bboxes[i], decode_bboxes[i]); |
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} |
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else |
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{ |
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for (int i = 0; i < num_bboxes; ++i) |
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DecodeBBox<false>(prior_bboxes[i], prior_variances[i], code_type, |
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clip_bbox, bboxes[i], decode_bboxes[i]); |
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} |
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} |
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// Decode all bboxes in a batch |
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static 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) |
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{ |
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CV_Assert(all_loc_preds.size() == num); |
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all_decode_bboxes.clear(); |
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all_decode_bboxes.resize(num); |
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for (int i = 0; i < num; ++i) |
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{ |
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// Decode predictions into bboxes. |
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const LabelBBox& loc_preds = all_loc_preds[i]; |
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LabelBBox& decode_bboxes = all_decode_bboxes[i]; |
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for (int c = 0; c < num_loc_classes; ++c) |
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{ |
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int label = share_location ? -1 : c; |
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if (label == background_label_id) |
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continue; // Ignore background class. |
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LabelBBox::const_iterator label_loc_preds = loc_preds.find(label); |
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if (label_loc_preds == loc_preds.end()) |
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CV_ErrorNoReturn_(cv::Error::StsError, ("Could not find location predictions for label %d", label)); |
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DecodeBBoxes(prior_bboxes, prior_variances, |
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code_type, variance_encoded_in_target, clip, |
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label_loc_preds->second, decode_bboxes[label]); |
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} |
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} |
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} |
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// Get prior bounding boxes from prior_data |
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// prior_data: 1 x 2 x num_priors * 4 x 1 blob. |
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// num_priors: number of priors. |
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// prior_bboxes: stores all the prior bboxes in the format of caffe::NormalizedBBox. |
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// prior_variances: stores all the variances needed by prior bboxes. |
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static void GetPriorBBoxes(const float* priorData, const int& numPriors, |
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std::vector<caffe::NormalizedBBox>& priorBBoxes, |
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std::vector<std::vector<float> >& priorVariances) |
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{ |
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priorBBoxes.clear(); priorBBoxes.resize(numPriors); |
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priorVariances.clear(); priorVariances.resize(numPriors); |
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for (int i = 0; i < numPriors; ++i) |
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{ |
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int startIdx = i * 4; |
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caffe::NormalizedBBox& bbox = priorBBoxes[i]; |
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bbox.set_xmin(priorData[startIdx]); |
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bbox.set_ymin(priorData[startIdx + 1]); |
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bbox.set_xmax(priorData[startIdx + 2]); |
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bbox.set_ymax(priorData[startIdx + 3]); |
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bbox.set_size(BBoxSize<true>(bbox)); |
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} |
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|
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for (int i = 0; i < numPriors; ++i) |
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{ |
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int startIdx = (numPriors + i) * 4; |
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// not needed here: priorVariances[i].clear(); |
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for (int j = 0; j < 4; ++j) |
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{ |
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priorVariances[i].push_back(priorData[startIdx + j]); |
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} |
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} |
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} |
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|
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// Get location predictions from loc_data. |
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// loc_data: num x num_preds_per_class * num_loc_classes * 4 blob. |
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// num: the number of images. |
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// num_preds_per_class: number of predictions per class. |
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// num_loc_classes: number of location classes. It is 1 if share_location is |
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// true; and is equal to number of classes needed to predict otherwise. |
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// share_location: if true, all classes share the same location prediction. |
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// loc_pred_transposed: if true, represent four bounding box values as |
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// [y,x,height,width] or [x,y,width,height] otherwise. |
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// loc_preds: stores the location prediction, where each item contains |
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// location prediction for an image. |
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static void GetLocPredictions(const float* locData, const int num, |
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const int numPredsPerClass, const int numLocClasses, |
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const bool shareLocation, const bool locPredTransposed, |
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std::vector<LabelBBox>& locPreds) |
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{ |
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locPreds.clear(); |
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if (shareLocation) |
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{ |
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CV_Assert(numLocClasses == 1); |
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} |
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locPreds.resize(num); |
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for (int i = 0; i < num; ++i, locData += numPredsPerClass * numLocClasses * 4) |
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{ |
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LabelBBox& labelBBox = locPreds[i]; |
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for (int p = 0; p < numPredsPerClass; ++p) |
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{ |
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int startIdx = p * numLocClasses * 4; |
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for (int c = 0; c < numLocClasses; ++c) |
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{ |
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int label = shareLocation ? -1 : c; |
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if (labelBBox.find(label) == labelBBox.end()) |
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{ |
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labelBBox[label].resize(numPredsPerClass); |
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} |
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caffe::NormalizedBBox& bbox = labelBBox[label][p]; |
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if (locPredTransposed) |
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{ |
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bbox.set_ymin(locData[startIdx + c * 4]); |
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bbox.set_xmin(locData[startIdx + c * 4 + 1]); |
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bbox.set_ymax(locData[startIdx + c * 4 + 2]); |
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bbox.set_xmax(locData[startIdx + c * 4 + 3]); |
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} |
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else |
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{ |
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bbox.set_xmin(locData[startIdx + c * 4]); |
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bbox.set_ymin(locData[startIdx + c * 4 + 1]); |
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bbox.set_xmax(locData[startIdx + c * 4 + 2]); |
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bbox.set_ymax(locData[startIdx + c * 4 + 3]); |
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} |
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} |
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} |
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} |
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} |
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|
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// Get confidence predictions from conf_data. |
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// conf_data: num x num_preds_per_class * num_classes blob. |
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// num: the number of images. |
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// num_preds_per_class: number of predictions per class. |
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// num_classes: number of classes. |
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// conf_preds: stores the confidence prediction, where each item contains |
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// confidence prediction for an image. |
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static void GetConfidenceScores(const float* confData, const int num, |
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const int numPredsPerClass, const int numClasses, |
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std::vector<std::vector<std::vector<float> > >& confPreds) |
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{ |
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confPreds.clear(); confPreds.resize(num); |
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for (int i = 0; i < num; ++i, confData += numPredsPerClass * numClasses) |
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{ |
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std::vector<std::vector<float> >& labelScores = confPreds[i]; |
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labelScores.resize(numClasses); |
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for (int c = 0; c < numClasses; ++c) |
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{ |
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std::vector<float>& classLabelScores = labelScores[c]; |
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classLabelScores.resize(numPredsPerClass); |
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for (int p = 0; p < numPredsPerClass; ++p) |
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{ |
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classLabelScores[p] = confData[p * numClasses + c]; |
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} |
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} |
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} |
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} |
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|
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// Do non maximum suppression given bboxes and scores. |
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// Inspired by Piotr Dollar's NMS implementation in EdgeBox. |
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// https://goo.gl/jV3JYS |
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// bboxes: a set of bounding boxes. |
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// scores: a set of corresponding confidences. |
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// score_threshold: a threshold used to filter detection results. |
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// nms_threshold: a threshold used in non maximum suppression. |
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// top_k: if not -1, keep at most top_k picked indices. |
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// indices: the kept indices of bboxes after nms. |
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static void ApplyNMSFast(const std::vector<caffe::NormalizedBBox>& bboxes, |
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const std::vector<float>& scores, const float score_threshold, |
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const float nms_threshold, const float eta, const int top_k, |
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std::vector<int>& indices) |
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{ |
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CV_Assert(bboxes.size() == scores.size()); |
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|
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// Get top_k scores (with corresponding indices). |
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std::vector<std::pair<float, int> > score_index_vec; |
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GetMaxScoreIndex(scores, score_threshold, top_k, score_index_vec); |
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|
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// Do nms. |
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float adaptive_threshold = nms_threshold; |
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indices.clear(); |
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while (score_index_vec.size() != 0) { |
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const int idx = score_index_vec.front().second; |
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bool keep = true; |
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for (int k = 0; k < (int)indices.size() && keep; ++k) { |
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const int kept_idx = indices[k]; |
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float overlap = JaccardOverlap<true>(bboxes[idx], bboxes[kept_idx]); |
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keep = overlap <= adaptive_threshold; |
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} |
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if (keep) |
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indices.push_back(idx); |
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score_index_vec.erase(score_index_vec.begin()); |
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if (keep && eta < 1 && adaptive_threshold > 0.5) { |
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adaptive_threshold *= eta; |
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} |
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} |
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} |
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|
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// Get max scores with corresponding indices. |
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// scores: a set of scores. |
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// threshold: only consider scores higher than the threshold. |
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// top_k: if -1, keep all; otherwise, keep at most top_k. |
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// score_index_vec: store the sorted (score, index) pair. |
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static void GetMaxScoreIndex(const std::vector<float>& scores, const float threshold, const int top_k, |
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std::vector<std::pair<float, int> >& score_index_vec) |
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{ |
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CV_DbgAssert(score_index_vec.empty()); |
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// Generate index score pairs. |
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for (size_t i = 0; i < scores.size(); ++i) |
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{ |
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if (scores[i] > threshold) |
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{ |
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score_index_vec.push_back(std::make_pair(scores[i], i)); |
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} |
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} |
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|
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// Sort the score pair according to the scores in descending order |
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std::stable_sort(score_index_vec.begin(), score_index_vec.end(), |
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util::SortScorePairDescend<int>); |
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|
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// Keep top_k scores if needed. |
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if (top_k > -1 && top_k < (int)score_index_vec.size()) |
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{ |
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score_index_vec.resize(top_k); |
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} |
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} |
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|
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// Compute the jaccard (intersection over union IoU) overlap between two bboxes. |
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template<bool normalized> |
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static float JaccardOverlap(const caffe::NormalizedBBox& bbox1, |
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const caffe::NormalizedBBox& bbox2) |
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{ |
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caffe::NormalizedBBox intersect_bbox; |
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if (bbox2.xmin() > bbox1.xmax() || bbox2.xmax() < bbox1.xmin() || |
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bbox2.ymin() > bbox1.ymax() || bbox2.ymax() < bbox1.ymin()) |
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{ |
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// Return [0, 0, 0, 0] if there is no intersection. |
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intersect_bbox.set_xmin(0); |
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intersect_bbox.set_ymin(0); |
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intersect_bbox.set_xmax(0); |
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intersect_bbox.set_ymax(0); |
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} |
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else |
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{ |
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intersect_bbox.set_xmin(std::max(bbox1.xmin(), bbox2.xmin())); |
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intersect_bbox.set_ymin(std::max(bbox1.ymin(), bbox2.ymin())); |
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intersect_bbox.set_xmax(std::min(bbox1.xmax(), bbox2.xmax())); |
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intersect_bbox.set_ymax(std::min(bbox1.ymax(), bbox2.ymax())); |
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} |
|
|
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float intersect_width, intersect_height; |
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intersect_width = intersect_bbox.xmax() - intersect_bbox.xmin(); |
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intersect_height = intersect_bbox.ymax() - intersect_bbox.ymin(); |
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if (intersect_width > 0 && intersect_height > 0) |
|
{ |
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if (!normalized) |
|
{ |
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intersect_width++; |
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intersect_height++; |
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} |
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float intersect_size = intersect_width * intersect_height; |
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float bbox1_size = BBoxSize<true>(bbox1); |
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float bbox2_size = BBoxSize<true>(bbox2); |
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return intersect_size / (bbox1_size + bbox2_size - intersect_size); |
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} |
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else |
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{ |
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return 0.; |
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} |
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} |
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}; |
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|
|
const std::string DetectionOutputLayerImpl::_layerName = std::string("DetectionOutput"); |
|
|
|
Ptr<DetectionOutputLayer> DetectionOutputLayer::create(const LayerParams ¶ms) |
|
{ |
|
return Ptr<DetectionOutputLayer>(new DetectionOutputLayerImpl(params)); |
|
} |
|
|
|
} |
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}
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