Vocabulary.cpp

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/*
Copyright (c) 2011-2014, Mathieu Labbe - IntRoLab - Universite de Sherbrooke
All rights reserved.

Redistribution and use in source and binary forms, with or without
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      notice, this list of conditions and the following disclaimer.
    * Redistributions in binary form must reproduce the above copyright
      notice, this list of conditions and the following disclaimer in the
      documentation and/or other materials provided with the distribution.
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      names of its contributors may be used to endorse or promote products
      derived from this software without specific prior written permission.

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#include "find_object/Settings.h"

#include "find_object/utilite/ULogger.h"
#include "Vocabulary.h"
#include <QtCore/QVector>
#include <stdio.h>
#include <opencv2/gpu/gpu.hpp>

namespace find_object {

Vocabulary::Vocabulary()
{
}

Vocabulary::~Vocabulary()
{
}

void Vocabulary::clear()
{
        indexedDescriptors_ = cv::Mat();
        notIndexedDescriptors_ = cv::Mat();
        wordToObjects_.clear();
        notIndexedWordIds_.clear();
}

void Vocabulary::save(QDataStream & streamPtr) const
{
        if(!indexedDescriptors_.empty() && !wordToObjects_.empty())
        {
                UASSERT(notIndexedDescriptors_.empty() && notIndexedWordIds_.empty());

                // save index
                streamPtr << wordToObjects_;

                // save words
                qint64 dataSize = indexedDescriptors_.elemSize()*indexedDescriptors_.cols*indexedDescriptors_.rows;
                streamPtr << indexedDescriptors_.rows <<
                                indexedDescriptors_.cols <<
                                indexedDescriptors_.type() <<
                                dataSize;
                streamPtr << QByteArray((char*)indexedDescriptors_.data, dataSize);
        }
}

void Vocabulary::load(QDataStream & streamPtr)
{
        // load index
        streamPtr >> wordToObjects_;

        // load words
        int rows,cols,type;
        qint64 dataSize;
        streamPtr >> rows >> cols >> type >> dataSize;
        QByteArray data;
        streamPtr >> data;
        indexedDescriptors_ = cv::Mat(rows, cols, type, data.data()).clone();

        update();
}

QMultiMap<int, int> Vocabulary::addWords(const cv::Mat & descriptors, int objectId, bool incremental)
{
        QMultiMap<int, int> words;
        if (descriptors.empty())
        {
                return words;
        }

        if(incremental)
        {
                int k = 2;
                cv::Mat results;
                cv::Mat dists;

                bool globalSearch = false;
                if(!indexedDescriptors_.empty() && indexedDescriptors_.rows >= (int)k)
                {
                        UASSERT(indexedDescriptors_.type() == descriptors.type() && indexedDescriptors_.cols == descriptors.cols);
                        this->search(descriptors, results, dists, k);

                        if( dists.type() == CV_32S )
                        {
                                cv::Mat temp;
                                dists.convertTo(temp, CV_32F);
                                dists = temp;
                        }

                        globalSearch = true;
                }

                notIndexedWordIds_.reserve(notIndexedWordIds_.size() + descriptors.rows);
                notIndexedDescriptors_.reserve(notIndexedDescriptors_.rows + descriptors.rows);
                int matches = 0;
                for(int i = 0; i < descriptors.rows; ++i)
                {
                        QMultiMap<float, int> fullResults; // nearest descriptors sorted by distance
                        if(notIndexedDescriptors_.rows)
                        {
                                UASSERT(notIndexedDescriptors_.type() == descriptors.type() && notIndexedDescriptors_.cols == descriptors.cols);

                                // Check if this descriptor matches with a word not already added to the vocabulary
                                // Do linear search only
                                cv::Mat tmpResults;
                                cv::Mat tmpDists;
                                if(descriptors.type()==CV_8U)
                                {
                                        //normType – One of NORM_L1, NORM_L2, NORM_HAMMING, NORM_HAMMING2. L1 and L2 norms are
                                        //                       preferable choices for SIFT and SURF descriptors, NORM_HAMMING should be
                                        //                       used with ORB, BRISK and BRIEF, NORM_HAMMING2 should be used with ORB
                                        //                       when WTA_K==3 or 4 (see ORB::ORB constructor description).
                                        int normType = cv::NORM_HAMMING;
                                        if(Settings::currentDescriptorType().compare("ORB") &&
                                                (Settings::getFeature2D_ORB_WTA_K()==3 || Settings::getFeature2D_ORB_WTA_K()==4))
                                        {
                                                normType = cv::NORM_HAMMING2;
                                        }

                                        cv::batchDistance( descriptors.row(i),
                                                                        notIndexedDescriptors_,
                                                                        tmpDists,
                                                                        CV_32S,
                                                                        tmpResults,
                                                                        normType,
                                                                        notIndexedDescriptors_.rows>=k?k:1,
                                                                        cv::Mat(),
                                                                        0,
                                                                        false);
                                }
                                else
                                {
                                        cv::flann::Index tmpIndex;
                                        tmpIndex.build(notIndexedDescriptors_, cv::flann::LinearIndexParams(), Settings::getFlannDistanceType());
                                        tmpIndex.knnSearch(descriptors.row(i), tmpResults, tmpDists, notIndexedDescriptors_.rows>1?k:1, Settings::getFlannSearchParams());
                                }

                                if( tmpDists.type() == CV_32S )
                                {
                                        cv::Mat temp;
                                        tmpDists.convertTo(temp, CV_32F);
                                        tmpDists = temp;
                                }

                                for(int j = 0; j < tmpResults.cols; ++j)
                                {
                                        if(tmpResults.at<int>(0,j) >= 0)
                                        {
                                                //printf("local i=%d, j=%d, tmpDist=%f tmpResult=%d\n", i ,j, tmpDists.at<float>(0,j), tmpResults.at<int>(0,j));
                                                fullResults.insert(tmpDists.at<float>(0,j), notIndexedWordIds_.at(tmpResults.at<int>(0,j)));
                                        }
                                }
                        }

                        if(globalSearch)
                        {
                                for(int j=0; j<k; ++j)
                                {
                                        if(results.at<int>(i,j) >= 0)
                                        {
                                                //printf("global i=%d, j=%d, dist=%f\n", i ,j, dists.at<float>(i,j));
                                                fullResults.insert(dists.at<float>(i,j), results.at<int>(i,j));
                                        }
                                }
                        }

                        bool match = false;
                        // Apply NNDR
                        if(fullResults.size() >= 2 &&
                           fullResults.begin().key() <= Settings::getNearestNeighbor_4nndrRatio() * (++fullResults.begin()).key())
                        {
                                match = true;
                        }

                        if(match)
                        {
                                words.insert(fullResults.begin().value(), i);
                                wordToObjects_.insert(fullResults.begin().value(), objectId);
                                ++matches;
                        }
                        else
                        {
                                //concatenate new words
                                notIndexedWordIds_.push_back(indexedDescriptors_.rows + notIndexedDescriptors_.rows);
                                notIndexedDescriptors_.push_back(descriptors.row(i));
                                words.insert(notIndexedWordIds_.back(), i);
                                wordToObjects_.insert(notIndexedWordIds_.back(), objectId);
                        }
                }
        }
        else
        {
                for(int i = 0; i < descriptors.rows; ++i)
                {
                        wordToObjects_.insert(indexedDescriptors_.rows + notIndexedDescriptors_.rows+i, objectId);
                        words.insert(indexedDescriptors_.rows + notIndexedDescriptors_.rows+i, i);
                        notIndexedWordIds_.push_back(indexedDescriptors_.rows + notIndexedDescriptors_.rows+i);
                }

                //just concatenate descriptors
                notIndexedDescriptors_.push_back(descriptors);
        }

        return words;
}

void Vocabulary::update()
{
        if(!notIndexedDescriptors_.empty())
        {
                if(!indexedDescriptors_.empty())
                {
                        UASSERT(indexedDescriptors_.cols == notIndexedDescriptors_.cols &&
                                        indexedDescriptors_.type() == notIndexedDescriptors_.type() );
                }
                
                //concatenate descriptors
                indexedDescriptors_.push_back(notIndexedDescriptors_);

                notIndexedDescriptors_ = cv::Mat();
                notIndexedWordIds_.clear();
        }

        if(!indexedDescriptors_.empty() && !Settings::isBruteForceNearestNeighbor())
        {
                cv::flann::IndexParams * params = Settings::createFlannIndexParams();
                flannIndex_.build(indexedDescriptors_, *params, Settings::getFlannDistanceType());
                delete params;
        }
}

void Vocabulary::search(const cv::Mat & descriptors, cv::Mat & results, cv::Mat & dists, int k)
{
        if(!indexedDescriptors_.empty())
        {
                UASSERT(descriptors.type() == indexedDescriptors_.type() && descriptors.cols == indexedDescriptors_.cols);

                if(Settings::isBruteForceNearestNeighbor())
                {
                        std::vector<std::vector<cv::DMatch> > matches;
                        if(Settings::getNearestNeighbor_BruteForce_gpu() && cv::gpu::getCudaEnabledDeviceCount())
                        {
                                cv::gpu::GpuMat newDescriptorsGpu(descriptors);
                                cv::gpu::GpuMat lastDescriptorsGpu(indexedDescriptors_);
                                if(indexedDescriptors_.type()==CV_8U)
                                {
                                        cv::gpu::BruteForceMatcher_GPU<cv::Hamming> gpuMatcher;
                                        gpuMatcher.knnMatch(newDescriptorsGpu, lastDescriptorsGpu, matches, k);
                                }
                                else
                                {
                                        cv::gpu::BruteForceMatcher_GPU<cv::L2<float> > gpuMatcher;
                                        gpuMatcher.knnMatch(newDescriptorsGpu, lastDescriptorsGpu, matches, k);
                                }
                        }
                        else
                        {
                                cv::BFMatcher matcher(indexedDescriptors_.type()==CV_8U?cv::NORM_HAMMING:cv::NORM_L2);
                                matcher.knnMatch(descriptors, indexedDescriptors_, matches, k);
                        }

                        //convert back to matrix style
                        results = cv::Mat(matches.size(), k, CV_32SC1);
                        dists = cv::Mat(matches.size(), k, CV_32FC1);
                        for(unsigned int i=0; i<matches.size(); ++i)
                        {
                                for(int j=0; j<k; ++j)
                                {
                                        results.at<int>(i, j) = matches[i].at(j).trainIdx;
                                        dists.at<float>(i, j) = matches[i].at(j).distance;
                                }
                        }
                }
                else
                {
                        flannIndex_.knnSearch(descriptors, results, dists, k, Settings::getFlannSearchParams());
                }

                if( dists.type() == CV_32S )
                {
                        cv::Mat temp;
                        dists.convertTo(temp, CV_32F);
                        dists = temp;
                }
        }
}

} // namespace find_object