vpFernClassifier.cpp

/****************************************************************************
 *
 * $Id: vpFernClassifier.cpp 3730 2012-05-14 17:09:58Z fspindle $
 *
 * This file is part of the ViSP software.
 * Copyright (C) 2005 - 2012 by INRIA. All rights reserved.
 * 
 * This software is free software; you can redistribute it and/or
 * modify it under the terms of the GNU General Public License
 * ("GPL") version 2 as published by the Free Software Foundation.
 * See the file LICENSE.GPL at the root directory of this source
 * distribution for additional information about the GNU GPL.
 *
 * For using ViSP with software that can not be combined with the GNU
 * GPL, please contact INRIA about acquiring a ViSP Professional 
 * Edition License.
 *
 * See http://www.irisa.fr/lagadic/visp/visp.html for more information.
 * 
 * This software was developed at:
 * INRIA Rennes - Bretagne Atlantique
 * Campus Universitaire de Beaulieu
 * 35042 Rennes Cedex
 * France
 * http://www.irisa.fr/lagadic
 *
 * If you have questions regarding the use of this file, please contact
 * INRIA at visp@inria.fr
 * 
 * This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE
 * WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.
 *
 *
 * Description:
 * Class that implements the Fern classifier and the YAPE detector thanks
 * to the OpenCV library.
 *
 * Authors:
 * Romain Tallonneau
 *
 *****************************************************************************/

#include <visp/vpConfig.h>

#if (VISP_HAVE_OPENCV_VERSION >= 0x020000) // Require opencv >= 2.0.0

#include <visp/vpFernClassifier.h>
#include <visp/vpImageTools.h>
#include <visp/vpImageConvert.h>
#include <visp/vpColor.h>
#include <visp/vpDisplay.h>

vpFernClassifier::vpFernClassifier():vpBasicKeyPoint(), gen(0, 256, 5, true, 0.6, 1.5, -CV_PI/2, CV_PI/2, -CV_PI/2, CV_PI/2)
{
  init();
}

vpFernClassifier::vpFernClassifier(const std::string& _dataFile, const std::string& _objectName)
{
  init();
  this->load(_dataFile, _objectName);
}

vpFernClassifier::~vpFernClassifier()
{
  if(curIplImg != NULL){
    if(curIplImg->width % 8 == 0){
      curIplImg->imageData = NULL;
      cvReleaseImageHeader(&curIplImg);
    }else{
      cvReleaseImage(&curIplImg);
    }
    curIplImg = NULL;
  }  
}

void 
vpFernClassifier::init()
{
  hasLearn = false; 
  nbClassfier = 100;
  ClassifierSize = 11;
  nbPoints = 200;
  curIplImg = NULL;
  blurImage = true;
  radiusBlur = 7;
  sigmaBlur = 1;
  patchSize = 32;
  radius = 7;
  threshold = 20;
  nbOctave = 2;
  nbView = 2000;
  dist = 2;  
  nbMinPoint = 10;
}




void
vpFernClassifier::train()
{
    // initialise detector
  cv::LDetector d(radius, threshold, nbOctave, nbView, patchSize, dist);
  
    //blur 
  cv::Mat obj = (cv::Mat)curIplImg;
  
  if(this->getBlurSetting()){
    cv::GaussianBlur(obj, obj, cv::Size(getBlurSize(), getBlurSize()), getBlurSigma(), getBlurSigma());
  }
  
    // build pyramid 
  std::vector<cv::Mat> objpyr;
  cv::buildPyramid(obj, objpyr, d.nOctaves-1);
  
    // getPoints
  d.getMostStable2D(obj, objKeypoints, 100, gen);
  
  ldetector = d;
  
    // train classifier
  modelROI = cv::Rect(0, 0, objpyr[0].cols, objpyr[0].rows);
  ldetector.getMostStable2D(objpyr[0], modelPoints, 100, gen);
  
  fernClassifier.trainFromSingleView(objpyr[0], modelPoints,
                                     patchSize, (int)modelPoints.size(), 100, 11, 10000,
                                     cv::FernClassifier::COMPRESSION_NONE, gen);

  /* from OpenCV format to ViSP format */
  referenceImagePointsList.resize(0);
  for (unsigned int i = 0; i < modelPoints.size(); i += 1){
    vpImagePoint ip( 
        modelPoints[i].pt.y + modelROI_Ref.y, 
        modelPoints[i].pt.x + modelROI_Ref.x);
    referenceImagePointsList.push_back(ip);
  }
 
    // set flag
  hasLearn = true;
}

unsigned int 
vpFernClassifier::buildReference(const vpImage<unsigned char> &_I)
{
  this->setImage(_I);
  
  train();

  _reference_computed = true;
  return objKeypoints.size();

}

unsigned int 
vpFernClassifier::buildReference(const vpImage<unsigned char> &_I, 
          const vpImagePoint &_iP,
          const unsigned int _height, const unsigned int _width)
{
  if((_iP.get_i()+_height) >= _I.getHeight()
     || (_iP.get_j()+_width) >= _I.getWidth()) {
    vpTRACE("Bad size for the subimage");
    throw(vpException(vpImageException::notInTheImage ,
              "Bad size for the subimage"));
  }
    
  vpImage<unsigned char> subImage;
  vpImageTools::createSubImage(_I,
                   (unsigned int)_iP.get_i(),
                   (unsigned int)_iP.get_j(),
                   _height, _width, subImage);
  this->setImage(subImage);
  
    /* initialise a structure containing the region of interest used in the 
    reference image */
  modelROI_Ref.x = (int)_iP.get_u();
  modelROI_Ref.y = (int)_iP.get_v();
  modelROI_Ref.width = (int)_width;
  modelROI_Ref.height = (int)_height;  
  
  train();

  return objKeypoints.size(); 
}

unsigned int 
vpFernClassifier::buildReference(const vpImage<unsigned char> &_I, 
          const vpRect& _rectangle)
{
  vpImagePoint iP;
  iP.set_i(_rectangle.getTop());
  iP.set_j(_rectangle.getLeft());
  return (this->buildReference(_I, iP,
                   (unsigned int)_rectangle.getHeight(),
                   (unsigned int)_rectangle.getWidth()));
}

unsigned int 
vpFernClassifier::matchPoint(const vpImage<unsigned char> &_I)
{
  if(!hasLearn){
    vpERROR_TRACE("The object has not been learned. ");
    throw vpException(vpException::notInitialized , "object is not learned, load database or build the reference ");
  }

  setImage(_I);
  // resize image  
//  cv::resize(_image, image, Size(), 1./imgscale, 1./imgscale, INTER_CUBIC);
  cv::Mat img = this->curIplImg;
  
  if(this->getBlurSetting()){
    cv::GaussianBlur(img, img, cv::Size(this->getBlurSize(), this->getBlurSize()), this->getBlurSigma(), this->getBlurSigma());
  }
  
  std::vector<cv::Mat> imgPyr;
  cv::buildPyramid(img, imgPyr, ldetector.nOctaves-1);
  
  
  ldetector(imgPyr, imgKeypoints, 500);

  unsigned int m = modelPoints.size();
  unsigned int n = imgKeypoints.size();
  std::vector<int> bestMatches(m, -1);
  std::vector<float> maxLogProb(m, -FLT_MAX);
  std::vector<float> signature;
  unsigned int totalMatch = 0;

  
  /* part of code from OpenCV planarObjectDetector */
  currentImagePointsList.resize(0);
  matchedReferencePoints.resize(0);

  for(unsigned int i = 0; i < n; i++ ){
    cv::KeyPoint kpt = imgKeypoints[i];
    kpt.pt.x /= (float)(1 << kpt.octave);
    kpt.pt.y /= (float)(1 << kpt.octave);
    int k = fernClassifier(imgPyr[(unsigned int)kpt.octave], kpt.pt, signature);
    if( k >= 0 && (bestMatches[(unsigned int)k] < 0 || signature[(unsigned int)k] > maxLogProb[(unsigned int)k]) ){
      maxLogProb[(unsigned int)k] = signature[(unsigned int)k];
      bestMatches[(unsigned int)k] = (int)i;
      totalMatch++;
      
      vpImagePoint ip_cur( imgKeypoints[i].pt.y, imgKeypoints[i].pt.x);
    
      currentImagePointsList.push_back(ip_cur);
      matchedReferencePoints.push_back((unsigned int)k);
      
    }
  }

  refPt.resize(0);
  curPt.resize(0);
  for(unsigned int i = 0; i < m; i++ ){
    if( bestMatches[i] >= 0 ){
      refPt.push_back(modelPoints[i].pt);
      curPt.push_back(imgKeypoints[(unsigned int)bestMatches[i]].pt);
    }
  }

  return totalMatch;
}



unsigned int 
vpFernClassifier::matchPoint(const vpImage<unsigned char> &_I, 
      const vpImagePoint &_iP,
      const unsigned int _height, const unsigned int _width)
{
  if((_iP.get_i()+_height) >= _I.getHeight()
     || (_iP.get_j()+_width) >= _I.getWidth()) {
    vpTRACE("Bad size for the subimage");
    throw(vpException(vpImageException::notInTheImage ,
              "Bad size for the subimage"));
  }

  vpImage<unsigned char> subImage;

  vpImageTools::createSubImage(_I,
                   (unsigned int)_iP.get_i(),
                   (unsigned int)_iP.get_j(),
                   _height, _width, subImage);

  return this->matchPoint(subImage);
}

unsigned int 
vpFernClassifier::matchPoint(const vpImage<unsigned char> &_I, 
      const vpRect& _rectangle)
{
  vpImagePoint iP;
  iP.set_i(_rectangle.getTop());
  iP.set_j(_rectangle.getLeft());
  return (this->matchPoint(_I, iP,
               (unsigned int)_rectangle.getHeight(),
               (unsigned int)_rectangle.getWidth()));
}


void 
vpFernClassifier::display(const vpImage<unsigned char> &_Iref, 
                          const vpImage<unsigned char> &_Icurrent, unsigned int size)
{
  for (unsigned int i = 0; i < matchedReferencePoints.size(); i++){
      vpDisplay::displayCross (_Iref, referenceImagePointsList[matchedReferencePoints[i]], size, vpColor::red);
      vpDisplay::displayCross (_Icurrent, currentImagePointsList[i], size, vpColor::green);
  }

}


void 
vpFernClassifier::display(const vpImage<unsigned char> &_Icurrent, unsigned int size,
                          const vpColor &color)
{    
  for (unsigned int i = 0; i < matchedReferencePoints.size(); i++){
      vpDisplay::displayCross (_Icurrent, currentImagePointsList[i], size, color);
  }
}

/*             IO METHODS            */

void 
vpFernClassifier::load(const std::string& _dataFile, const std::string& /*_objectName*/)
{
  std::cout << " > Load data for the planar object detector..." << std::endl;
  
  /* part of code from OpenCV planarObjectDetector */
  cv::FileStorage fs(_dataFile, cv::FileStorage::READ);
  cv::FileNode node = fs.getFirstTopLevelNode();
    
  cv::FileNodeIterator it = node["model-roi"].begin(), it_end;
  it >> modelROI.x >> modelROI.y >> modelROI.width >> modelROI.height;
  
  ldetector.read(node["detector"]);
  fernClassifier.read(node["fern-classifier"]);
  
  const cv::FileNode node_ = node["model-points"];
  cv::read(node_, modelPoints);
  
  cv::LDetector d(radius, threshold, nbOctave, nbView, patchSize, dist);
  ldetector = d;
  hasLearn = true;
}


void
vpFernClassifier::record(const std::string& _objectName, const std::string& _dataFile )
{
  /* part of code from OpenCV planarObjectDetector */
  cv::FileStorage fs(_dataFile, cv::FileStorage::WRITE);
  
  cv::WriteStructContext ws(fs, _objectName, CV_NODE_MAP);
  
  {
    cv::WriteStructContext wsroi(fs, "model-roi", CV_NODE_SEQ + CV_NODE_FLOW);
    cv::write(fs, modelROI_Ref.x);
    cv::write(fs, modelROI_Ref.y);
    cv::write(fs, modelROI_Ref.width);
    cv::write(fs, modelROI_Ref.height);
  }
  
  ldetector.write(fs, "detector");
  cv::write(fs, "model-points", modelPoints);
  fernClassifier.write(fs, "fern-classifier");
}



void 
vpFernClassifier::setImage(const vpImage<unsigned char>& _I)
{  
  if(curIplImg != NULL){
    cvResetImageROI(curIplImg);
    if((curIplImg->width % 8) == 0){
      curIplImg->imageData = NULL;
      cvReleaseImageHeader(&curIplImg);
    }else{
      cvReleaseImage(&curIplImg);
    }
    curIplImg = NULL;
  }
  
  if((_I.getWidth()%8) == 0){
    curIplImg = cvCreateImageHeader(cvSize((int)_I.getWidth(), (int)_I.getHeight()), IPL_DEPTH_8U, 1);
    if(curIplImg != NULL){
      curIplImg->imageData = (char*)_I.bitmap;
    }else{
      throw vpException(vpException::memoryAllocationError, "Could not create the image in the OpenCV format.");
    }
  }else{
    vpImageConvert::convert(_I, curIplImg);
  }
  if(curIplImg == NULL){
    std::cout << "!> conversion failed" << std::endl;
    throw vpException(vpException::notInitialized , "conversion failed");
  }
}

#endif