VisionUtils.h

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/****************************************************************
*
* Copyright (c) 2010
*
* Fraunhofer Institute for Manufacturing Engineering
* and Automation (IPA)
*
* +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
*
* Project name: care-o-bot
* ROS stack name: cob_perception_common
* ROS package name: cob_vision_utils
* Description: Utility functions for Fraunhofer IPA vision library.
*
* +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
*
* Author: Jan Fischer, email:jan.fischer@ipa.fhg.de
* Supervised by: Jan Fischer, email:jan.fischer@ipa.fhg.de
*
* Date of creation: July 2008
* ToDo:
*
* +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* * Redistributions of source code must retain the above copyright
* 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.
* * Neither the name of the Fraunhofer Institute for Manufacturing
* Engineering and Automation (IPA) nor the names of its
* contributors may be used to endorse or promote products derived from
* this software without specific prior written permission.
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License LGPL as
* published by the Free Software Foundation, either version 3 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 Lesser General Public License LGPL for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License LGPL along with this program.
* If not, see <http://www.gnu.org/licenses/>.
*
****************************************************************/
 

#ifndef __IPA_VISIONUTILS_H__
#define __IPA_VISIONUTILS_H__

#ifdef __LINUX__
        #include "cob_vision_utils/GlobalDefines.h"
#else
        #include "cob_perception_common/cob_vision_utils/common/include/cob_vision_utils/GlobalDefines.h"
#endif

#include <opencv/cv.h>
#include <iostream>

namespace ipa_Utils {


cv::Mat vstack(const std::vector<cv::Mat> &mats);

void InitUndistortMap( const cv::Mat& A, const cv::Mat& dist_coeffs,
                    cv::Mat& mapxarr, cv::Mat& mapyarr );

unsigned long ConvertToShowImage(const cv::Mat& source, cv::Mat& dest, int channel = 1, double min = -1, double max = -1);

unsigned long MaskImage(const cv::Mat& source, cv::Mat& dest, const cv::Mat& mask, cv::Mat& destMask, float minMaskThresh = 0,
                                                float maxMaskTresh = 20000, int sourceChannel = 1, double sourceMin = -1, double sourceMax = -1);

unsigned long EvaluatePolynomial(double x, int degree, double* coefficients, double* y);

unsigned long FilterByAmplitude(cv::Mat& xyzImage, const cv::Mat& greyImage, cv::Mat* mask, cv::Mat* maskColor, float minMaskThresh, float maxMaskThresh);

unsigned long FilterTearOffEdges(cv::Mat& xyzImage, cv::Mat* mask, float piHalfFraction = 6);

unsigned long FilterSpeckles( cv::Mat& img, int maxSpeckleSize, double _maxDiff, cv::Mat& _buf );

cv::Vec3b GrayColorMap(double value, double min, double max);

cv::Mat GetColorcoded(const cv::Mat& img_32F);

cv::Mat GetColorcoded(const cv::Mat& img_32F, double min, double max);

unsigned long SaveMat(cv::Mat& mat, std::string filename, int type=CV_32F);

unsigned long LoadMat(cv::Mat& mat, std::string filename, int type=CV_32F);


template <typename Distance>
void ClusteringKMeanspp(int k, cv::Mat& dataset, int* indices, int indices_length, int* centers, int& centers_length, int numLocalTries=1)
{
        typedef typename Distance::ElementType ElementType;
    typedef typename Distance::ResultType DistanceType;

        Distance distance = Distance();

    int n = indices_length;

    double currentPot = 0;
    DistanceType* closestDistSq = new DistanceType[n];

    // Choose one random center and set the closestDistSq values
    int index = cvflann::rand_int(n);
    assert(index >=0 && index < n);
    centers[0] = indices[index];

    for (int i = 0; i < n; i++) {
        closestDistSq[i] = distance(dataset.ptr(indices[i]), 
                        dataset.ptr(indices[index]), dataset.cols);
        currentPot += closestDistSq[i];
    }

    // Choose each center
    int centerCount;
    for (centerCount = 1; centerCount < k; centerCount++) {

        // Repeat several trials
        double bestNewPot = -1;
        int bestNewIndex = 0;
        for (int localTrial = 0; localTrial < numLocalTries; localTrial++) {

            // Choose our center - have to be slightly careful to return a valid answer even accounting
            // for possible rounding errors
            double randVal = cvflann::rand_double(currentPot);
            for (index = 0; index < n-1; index++) {
                if (randVal <= closestDistSq[index]) break;
                else randVal -= closestDistSq[index];
            }

            // Compute the new potential
            double newPot = 0;
            for (int i = 0; i < n; i++) 
                                newPot += std::min( distance(dataset.ptr(indices[i]), 
                                        dataset.ptr(indices[index]), dataset.cols), closestDistSq[i] );

            // Store the best result
            if ((bestNewPot < 0)||(newPot < bestNewPot)) {
                bestNewPot = newPot;
                bestNewIndex = index;
            }
        }

        // Add the appropriate center
        centers[centerCount] = indices[bestNewIndex];
        currentPot = bestNewPot;
        for (int i = 0; i < n; i++) 
                        closestDistSq[i] = std::min( distance(dataset.ptr(indices[i]),
                                dataset.ptr(indices[bestNewIndex]), dataset.cols), closestDistSq[i] );
    }

    centers_length = centerCount;

    delete[] closestDistSq;
}

// Generator that yields an increasing sequence of integers
class UniqueNumber {
public:
  int current;
  UniqueNumber();
  int operator()();
};

// Returns random subset of input vector. Will not create duplicates.
// @param v Input vector
// @param n Number of items to return
// @return Vector of same type as input vector (length n)
template<class T> std::vector<T> takeRandomN(const std::vector<T> &v, int n)
{
    int current = 0;
    std::vector<int> indices(v.size());
    std::generate(indices.begin(), indices.end(), UniqueNumber());
    std::random_shuffle(indices.begin(), indices.end());
    
    std::vector<T> ret;
    for (int i = 0; i < n; i++)
    {
        ret.push_back(v[indices[i]]);
    }

    return ret;
}

} // end namespace __IPA_VISIONUTILS_H__


#endif