cmp.h

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/*
 * Author: Steven Ludtke, 04/10/2003 (sludtke@bcm.edu)
 * Copyright (c) 2000-2006 Baylor College of Medicine
 *
 * This software is issued under a joint BSD/GNU license. You may use the
 * source code in this file under either license. However, note that the
 * complete EMAN2 and SPARX software packages have some GPL dependencies,
 * so you are responsible for compliance with the licenses of these packages
 * if you opt to use BSD licensing. The warranty disclaimer below holds
 * in either instance.
 *
 * This complete copyright notice must be included in any revised version of the
 * source code. Additional authorship citations may be added, but existing
 * author citations must be preserved.
 *
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 2 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program; if not, write to the Free Software
 * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
 *
 * */

#ifndef eman_cmp__h__
#define eman_cmp__h__ 1


#include "emobject.h"

namespace EMAN
{

        class EMData;
        class Cmp
        {
          public:
                virtual ~ Cmp()
                {
                }

                virtual float cmp(EMData * image, EMData * with) const = 0;

                virtual string get_name() const = 0;

                virtual string get_desc() const = 0;

                virtual Dict get_params() const
                {
                        return params;
                }

                virtual void set_params(const Dict & new_params)
                {
                        params = new_params;
                }

                virtual TypeDict get_param_types() const = 0;

        protected:
                void validate_input_args(const EMData * image, const EMData *with) const;

                mutable Dict params;
        };

        class CccCmp:public Cmp
        {
          public:
                float cmp(EMData * image, EMData * with) const;

                string get_name() const
                {
                        return "ccc";
                }

                string get_desc() const
                {
                        return "Cross-correlation coefficient (default -1 * ccc)";
                }

                static Cmp *NEW()
                {
                        return new CccCmp();
                }

                //param mask Image mask
                TypeDict get_param_types() const
                {
                        TypeDict d;
                        d.put("negative", EMObject::INT, "If set, returns -1 * ccc product. Set by default so smaller is better");
                        d.put("mask", EMObject::EMDATA, "image mask");
                        return d;
                }

        };

        //  I corrected this as there is no such thing as "variance between two images"
        //  I corrected naive coding to avoid square
        //  Also, the equation in return statement was incorrect, grrrr!!!
        //  Finally, I added mask option  PAP 04/23/06
        class SqEuclideanCmp:public Cmp
        {
          public:
                SqEuclideanCmp() {}

                float cmp(EMData * image, EMData * with) const;

                string get_name() const
                {
                        return "sqeuclidean";
                }

                string get_desc() const
                {
                        return "Squared Euclidean distance (sum(a - b)^2)/n.";
                }

                static Cmp *NEW()
                {
                        return new SqEuclideanCmp();
                }

                TypeDict get_param_types() const
                {
                        TypeDict d;
                        d.put("mask", EMObject::EMDATA, "image mask");
                        d.put("keepzero", EMObject::INT, "If set, zero pixels will not be adjusted in the linear density optimization");
                        return d;
                }

        };


        class DotCmp:public Cmp
        {
          public:
                float cmp(EMData * image, EMData * with) const;

                string get_name() const
                {
                        return "dot";
                }

                string get_desc() const
                {
                        return "Dot product (default -1 * dot product)";
                }

                static Cmp *NEW()
                {
                        return new DotCmp();
                }

                TypeDict get_param_types() const
                {
                        TypeDict d;
                        d.put("negative", EMObject::INT, "If set, returns -1 * dot product. Set by default so smaller is better");
                        d.put("normalize", EMObject::INT, "If set, returns normalized dot product (cosine of the angle) -1.0 - 1.0.");
                        d.put("mask", EMObject::EMDATA, "image mask");
                        return d;
                }
        };

        class TomoDotCmp:public Cmp
        {
          public:
                virtual float cmp(EMData * image, EMData * with) const;

                virtual string get_name() const
                {
                        return "dot.tomo";
                }

                virtual string get_desc() const
                {
                        return "straight dot product with consideration given for the missing wedge - normalization is applied by detecting significantly large Fourier amplitudes in the cross correlation image";
                }

                static Cmp *NEW()
                {
                        return new TomoDotCmp();
                }

                TypeDict get_param_types() const
                {
                        TypeDict d;
                        d.put("threshold", EMObject::FLOAT,"Threshold applied to the Fourier amplitudes of the ccf image - helps to correct for the missing wedge.");
                        d.put("norm", EMObject::BOOL,"Whether the cross correlation image should be normalized. Default is false.");
                        d.put("ccf", EMObject::EMDATA,"The ccf image, can be provided if it already exists to avoid recalculating it");
                        d.put("tx", EMObject::INT, "The x location of the maximum in the ccf image. May be negative. Useful thing to supply if you know the maximum is not at the phase origin");
                        d.put("ty", EMObject::INT, "The y location of the maximum in the ccf image. May be negative. Useful thing to supply if you know the maximum is not at the phase origin");
                        d.put("tz", EMObject::INT, "The z location of the maximum in the ccf image. May be negative. Useful thing to supply if you know the maximum is not at the phase origin");

                        return d;
                }

        };

        class QuadMinDotCmp:public Cmp
        {
          public:
                float cmp(EMData * image, EMData * with) const;

                string get_name() const
                {
                        return "quadmindot";
                }

                string get_desc() const
                {
                        return "Caclultes dot product for each quadrant and returns worst value (default -1 * dot product)";
                }

                static Cmp *NEW()
                {
                        return new QuadMinDotCmp();
                }

                TypeDict get_param_types() const
                {
                        TypeDict d;
                        d.put("negative", EMObject::INT, "If set, returns -1 * dot product. Default = true (smaller is better)");
                        d.put("normalize", EMObject::INT, "If set, returns normalized dot product -1.0 - 1.0.");
                        return d;
                }

        };


        class OptVarianceCmp:public Cmp
        {
          public:
                OptVarianceCmp() : scale(0), shift(0) {}

                float cmp(EMData * image, EMData * with) const;

                string get_name() const
                {
                        return "optvariance";
                }

                string get_desc() const
                {
                        return "Real-space variance after density optimization, self should be noisy and target less noisy. Linear transform applied to density to minimize variance.";
                }

                static Cmp *NEW()
                {
                        return new OptVarianceCmp();
                }

                TypeDict get_param_types() const
                {
                        TypeDict d;
                        d.put("invert", EMObject::INT, "If set, 'with' is rescaled rather than 'this'. 'this' should still be the noisier image. (default=0)");
                        d.put("keepzero", EMObject::INT, "If set, zero pixels will not be adjusted in the linear density optimization. (default=1)");
                        d.put("matchfilt", EMObject::INT, "If set, with will be filtered so its radial power spectrum matches 'this' before density optimization of this. (default=1)");
                        d.put("matchamp", EMObject::INT, "Takes per-pixel Fourier amplitudes from self and imposes them on the target, but leaves the phases alone. (default=0)");
                        d.put("radweight", EMObject::INT, "Upweight variances closer to the edge of the image. (default=0)");
                        d.put("debug", EMObject::INT, "Performs various debugging actions if set.");
                        return d;
                }

                float get_scale() const
                {
                        return scale;
                }

                float get_shift() const
                {
                        return shift;
                }

        private:
                mutable float scale;
                mutable float shift;
        };
        class PhaseCmp:public Cmp
        {
          public:
                float cmp(EMData * image, EMData * with) const;

                string get_name() const
                {
                        return "phase";
                }

                string get_desc() const
                {
                        return "Mean phase difference";
                }

                static Cmp *NEW()
                {
                        return new PhaseCmp();
                }

                TypeDict get_param_types() const
                {
                        TypeDict d;
                        return d;
                }

#ifdef EMAN2_USING_CUDA
                 float cuda_cmp(EMData * image, EMData *with) const;
#endif //EMAN2_USING_CUDA
        };

        class FRCCmp:public Cmp
        {
          public:
                float cmp(EMData * image, EMData * with) const;

                string get_name() const
                {
                        return "frc";
                }

                string get_desc() const
                {
                        return "Computes the mean Fourier Ring Correlation between the image and reference (with optional weighting factors).";
                }

                static Cmp *NEW()
                {
                        return new FRCCmp();
                }

                TypeDict get_param_types() const
                {
                        TypeDict d;
                        d.put("snrweight", EMObject::INT, "If set, the SNR of 'this' will be used to weight the result. If 'this' lacks CTF info, it will check 'with'. (default=0)");
                        d.put("ampweight", EMObject::INT, "If set, the amplitude of 'this' will be used to weight the result (default=0)");
                        d.put("sweight", EMObject::INT, "If set, weight the (1-D) average by the number of pixels in each ring (default=1)");
                        d.put("nweight", EMObject::INT, "Downweight similarity based on number of particles in reference (default=0)");
                        d.put("zeromask", EMObject::INT, "Treat regions in either image that are zero as a mask");
                        return d;
                }
        };

        template <> Factory < Cmp >::Factory();

        void dump_cmps();
        map<string, vector<string> > dump_cmps_list();
}


#endif

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