ProgClassifyKmeans2D Class Reference

#include <classify_kmeans_2d.h>

Inheritance diagram for ProgClassifyKmeans2D:

Collaboration diagram for ProgClassifyKmeans2D:

Public Member Functions
void	readParams ()
	Read argument. More...
void	show ()
	Show. More...
void	defineParams ()
	Define parameters. More...
void	run ()
	Main routine. More...
Public Member Functions inherited from XmippProgram
const char *	getParam (const char *param, int arg=0)
const char *	getParam (const char *param, const char *subparam, int arg=0)
int	getIntParam (const char *param, int arg=0)
int	getIntParam (const char *param, const char *subparam, int arg=0)
double	getDoubleParam (const char *param, int arg=0)
double	getDoubleParam (const char *param, const char *subparam, int arg=0)
float	getFloatParam (const char *param, int arg=0)
float	getFloatParam (const char *param, const char *subparam, int arg=0)
void	getListParam (const char *param, StringVector &list)
int	getCountParam (const char *param)
bool	checkParam (const char *param)
bool	existsParam (const char *param)
void	addParamsLine (const String &line)
void	addParamsLine (const char *line)
ParamDef *	getParamDef (const char *param) const
virtual void	quit (int exit_code=0) const
virtual int	tryRun ()
void	initProgress (size_t total, size_t stepBin=60)
void	setProgress (size_t value=0)
void	endProgress ()
void	processDefaultComment (const char *param, const char *left)
void	setDefaultComment (const char *param, const char *comment)
virtual void	initComments ()
void	setProgramName (const char *name)
void	addUsageLine (const char *line, bool verbatim=false)
void	clearUsage ()
void	addExampleLine (const char *example, bool verbatim=true)
void	addSeeAlsoLine (const char *seeAlso)
void	addKeywords (const char *keywords)
const char *	name () const
virtual void	usage (int verb=0) const
virtual void	usage (const String &param, int verb=2)
int	version () const
virtual void	show () const
virtual void	read (int argc, const char **argv, bool reportErrors=true)
virtual void	read (int argc, char **argv, bool reportErrors=true)
void	read (const String &argumentsLine)
	XmippProgram ()
	XmippProgram (int argc, const char **argv)
virtual	~XmippProgram ()

Public Attributes
FileName	fnSel
FileName	fnOut
FileName	fnClusters
FileName	fnPoints
int	K
int	maxObjects
Public Attributes inherited from XmippProgram
bool	doRun
bool	runWithoutArgs
int	verbose
	Verbosity level. More...
int	debug

Additional Inherited Members
Protected Member Functions inherited from XmippProgram
void	defineCommons ()
Protected Attributes inherited from XmippProgram
int	errorCode
ProgramDef *	progDef
	Program definition and arguments parser. More...
std::map< String, CommentList >	defaultComments
int	argc
	Original command line arguments. More...
const char **	argv

Detailed Description

Definition at line 34 of file classify_kmeans_2d.h.

Member Function Documentation

defineParams()

void ProgClassifyKmeans2D::defineParams ( )

virtual

Define parameters.

Reimplemented from XmippProgram.

Definition at line 62 of file classify_kmeans_2d.cpp.

{
    addUsageLine("Clusters a set of images");
    addParamsLine("  -i <selfile>                  : Selfile containing images to be clustered");
    addParamsLine("  [-o <image=\"output.xmd\">]   : Output selfile");
    addParamsLine("  -k <int>                      : Number of clusters");
    addParamsLine("  [-c <image=\"clusters.xmd\">] : Filename with clusters");
    addParamsLine("  [-p <image=\"points.xmd\">]   : Filename with points");
    addParamsLine("  [-m <int=\"-1\">]             : Threshold for number of particles after which the position of clusters will be fixed");
}

readParams()

void ProgClassifyKmeans2D::readParams ( )

virtual

Read argument.

Reimplemented from XmippProgram.

Definition at line 36 of file classify_kmeans_2d.cpp.

{
    fnSel = getParam("-i");
    fnOut = getParam("-o");
    K = getIntParam("-k");
    fnClusters = getParam("-c");
    fnPoints = getParam("-p");
    maxObjects = getIntParam("-m");
}

run()

void ProgClassifyKmeans2D::run ( )

virtual

Main routine.

Reimplemented from XmippProgram.

Definition at line 417 of file classify_kmeans_2d.cpp.

{
    MetaDataVec SF, MDsummary, MDclass, MDallDone;
    FileName fnImg, fnClass, fnallDone;
    Image<double> I, Imasked;
    MultidimArray< std::complex<double> > Icomplex;
    CorrelationAux aux;
    std::vector<std::vector<double> > fvs;
    std::vector<double> fv, fv_temp;
    std::vector<KPoint> points;
    std::vector<Cluster> clusters;
    ProgExtractFeatures ef;
    srand (time(nullptr));

    // reading new images from input file
    SF.read(fnSel);
    for (size_t objId : SF.ids())
    {
        SF.getValue(MDL_IMAGE, fnImg, objId);
        I.read(fnImg);
        I().setXmippOrigin();
        centerImageTranslationally(I(), aux);
        fv.clear();
        ef.extractEntropy(I(), Imasked(), fv);
        //ef.extractZernike(I(), fv);
        //ef.extractLBP(I(), fv);
        //ef.extractVariance(I(), fv);
        //ef.extractGranulo(I(), fv);
        //ef.extractRamp(I(), fv);
        //ef.extractHistDist(I(), fv);
        fvs.push_back(fv);
    }

    double min_item, max_item;
    for(int i = 0; i < fv.size(); i++)
    {
        fv_temp.clear();
        for (int j = 0; j < fvs.size(); j++)
            fv_temp.push_back(fvs[j][i]);

        max_item = *std::max_element(fv_temp.begin(), fv_temp.end());
        min_item = *std::min_element(fv_temp.begin(), fv_temp.end());
        for (int j = 0; j < fvs.size(); j++)
            fvs[j][i] = (fvs[j][i] - min_item) / (max_item - min_item);
    }

    int allItems = 0;
    for (size_t objId : SF.ids())
    {
        allItems++;
        KPoint p(allItems, fvs.front());
        points.push_back(p);
        fvs.erase(fvs.begin());
    }

    // preparing all the paths to external files
    std::size_t extraPath = fnSel.find_last_of("/");
    fnOut = fnSel.substr(0, extraPath+1) + fnOut.c_str();
    fnClusters = fnSel.substr(0, extraPath+1) + fnClusters.c_str();
    fnPoints = fnSel.substr(0, extraPath+1) + fnPoints.c_str();
    fnallDone = fnSel.substr(0, extraPath+1) + "allDone.xmd";

    // loading all the stored points from file (their count is unknown here)
    std::vector<double> fv_load;
    std::fstream savedPoints(fnPoints.c_str());
    std::string line;
    while (savedPoints.good())
    {
        std::getline(savedPoints, line);
        if (line.size() < 2) break;
        allItems++;
        std::stringstream ss(line);
        fv_load.clear();
        double point_value;
        for (int j = 0; j < fv.size(); j++)
        {
            ss >> point_value;
            fv_load.push_back(point_value);
        }
        KPoint p(allItems, fv_load);
        points.push_back(p);
    }

    // performing k-means clustering
    KMeans kmeans(K, allItems, fv.size(), allItems, maxObjects);
    clusters = kmeans.run(points, fnClusters, fnPoints);

    // for cycle writing output file
    for (int i = 0; i < clusters.size(); i++)
    {
        size_t total_points_cluster = clusters[i].getTotalPoints();

        size_t ii = MDsummary.addObject();
        MDsummary.setValue(MDL_REF, i+1, ii);
        MDsummary.setValue(MDL_CLASS_COUNT, total_points_cluster, ii);

        std::ostringstream clusterValues;
        clusterValues << "[";
        for (int j = 0; j < fv.size()-1; j++)
            clusterValues << clusters[i].getCentralValue(j) << ", ";
        clusterValues << clusters[i].getCentralValue(fv.size()-1) << "]";

        MDsummary.setValue(MDL_KMEANS2D_CENTROID, clusterValues.str(), ii);
        MDsummary.write(formatString("classes@%s", fnOut.c_str()), MD_APPEND);
        MDclass.clear();

        std::ifstream f(fnallDone.c_str());
        if (f.good()) MDallDone.read(fnallDone);

        for (int j = 0; j < total_points_cluster; j++)
        {
            MDRowVec row;
            MDallDone.getRow(row, clusters[i].getPoint(j).getID());
            size_t recId = MDclass.addRow(row);
            MDclass.setValue(MDL_REF, i+1, recId);
        }
        MDclass.write(formatString("class%06d_images@%s", i+1,
                      fnOut.c_str()), MD_APPEND);
    }
}

show()

void ProgClassifyKmeans2D::show

(

)

Show.

Definition at line 47 of file classify_kmeans_2d.cpp.

{
    if (verbose==0)
        return;
    std::cerr
    << "Input selfile:                          " << fnSel      << std::endl
    << "Output selfile:                         " << fnOut      << std::endl
    << "Number of clusters:                     " << K          << std::endl
    << "Filename with clusters:                 " << fnClusters << std::endl
    << "Filename with points:                   " << fnPoints   << std::endl
    << "Threshold for number of particles:      " << maxObjects << std::endl
    ;
}

Member Data Documentation

fnClusters

FileName ProgClassifyKmeans2D::fnClusters

Filename clusters

Definition at line 44 of file classify_kmeans_2d.h.

fnOut

FileName ProgClassifyKmeans2D::fnOut

Filename output root

Definition at line 41 of file classify_kmeans_2d.h.

fnPoints

FileName ProgClassifyKmeans2D::fnPoints

Filename points

Definition at line 47 of file classify_kmeans_2d.h.

fnSel

FileName ProgClassifyKmeans2D::fnSel

Filename selection file containing the images

Definition at line 38 of file classify_kmeans_2d.h.

K

int ProgClassifyKmeans2D::K

Number of clusters

Definition at line 50 of file classify_kmeans_2d.h.

maxObjects

int ProgClassifyKmeans2D::maxObjects

Maximum number of objects used for clustering

Definition at line 53 of file classify_kmeans_2d.h.

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The documentation for this class was generated from the following files:

• xmipp/legacy/libraries/reconstruction/classify_kmeans_2d.h
• xmipp/legacy/libraries/reconstruction/classify_kmeans_2d.cpp