C++ Mat::row方法代码示例

void sharpen(const cv::Mat& image, cv::Mat& result){
    // allocate if necessary
    result.create(global::image.size(), global::image.type());
    std::cout << "Size: " << global::image.size() << std::endl;
    std::cout << "Cols: " << global::image.cols << "\n" << "Rows: " << global::image.rows << std::endl;

    for (int j=1; j<global::image.rows-1; j++){ // for all rows except first and last
        const uchar* previous = global::image.ptr<const uchar>(j-1); // previous row
        const uchar* current = global::image.ptr<const uchar>(j); // current row
        const uchar* next = global::image.ptr<const uchar>(j+1); // next row

        uchar* output = result.ptr<uchar>(j); // output row

        for (int i=1; i<global::image.cols-1; i++){
            *output = cv::saturate_cast<uchar>(5*current[i]-current[i-1]-current[i+1]-previous[i]-next[i]);
            output++;

        }
    }

    // set unprocessed pixels to 0
    result.row(0).setTo(cv::Scalar(0));
    result.row(result.rows-1).setTo(cv::Scalar(0));
    result.col(0).setTo(cv::Scalar(0));
    result.col(result.cols-1).setTo(cv::Scalar(0));
}

/* This function will rearrange dataset for training in a random order. This step is
* necessary to make training more accurate.
*/
void LetterClassifier::ShuffleDataset(cv::Mat &training_data, cv::Mat &label_mat, int numIter)
{
	/* initialize random seed: */
	srand(time(NULL));
	int x = 0, y = 0;

	assert(training_data.cols == label_mat.rows);

	int numData = training_data.cols;
	if (numIter <= 0)
		numIter = numData;

	if (training_data.type() != CV_32FC1)
		training_data.convertTo(training_data, CV_32FC1);
	cv::Mat temp_data_mat(training_data.rows, 1, CV_32FC1);
	cv::Mat temp_label_mat(1, 1, CV_32FC1);


	// Interate 'numIter' to rearrange dataset
	for (int n = 0; n < numIter; n++)
	{
		x = (rand() % numData);
		y = (rand() % numData);

		// swap data
		training_data.col(x).copyTo(temp_data_mat.col(0));
		training_data.col(y).copyTo(training_data.col(x));
		temp_data_mat.col(0).copyTo(training_data.col(y));

		// swap label
		label_mat.row(x).copyTo(temp_label_mat.row(0));
		label_mat.row(y).copyTo(label_mat.row(x));
		temp_label_mat.row(0).copyTo(label_mat.row(y));
	}
}

// Evaluates a Gaussian kernel with bandwidth SIGMA for all relative shifts between input images X and Y, which must both be MxN. They must    also be periodic (ie., pre-processed with a cosine window).
cv::Mat KCFTracker::gaussianCorrelation(cv::Mat x1, cv::Mat x2)
{
    using namespace FFTTools;
    cv::Mat c = cv::Mat( cv::Size(size_patch[1], size_patch[0]), CV_32F, cv::Scalar(0) );
    // HOG features
    if (_hogfeatures) {
        cv::Mat caux;
        cv::Mat x1aux;
        cv::Mat x2aux;
        for (int i = 0; i < size_patch[2]; i++) {
            x1aux = x1.row(i);   // Procedure do deal with cv::Mat multichannel bug
            x1aux = x1aux.reshape(1, size_patch[0]);
            x2aux = x2.row(i).reshape(1, size_patch[0]);
            cv::mulSpectrums(fftd(x1aux), fftd(x2aux), caux, 0, true); 
            caux = fftd(caux, true);
            rearrange(caux);
            caux.convertTo(caux,CV_32F);
            c = c + real(caux);
        }
    }
    // Gray features
    else {
        cv::mulSpectrums(fftd(x1), fftd(x2), c, 0, true);
        c = fftd(c, true);
        rearrange(c);
        c = real(c);
    }
    cv::Mat d; 
    cv::max(( (cv::sum(x1.mul(x1))[0] + cv::sum(x2.mul(x2))[0])- 2. * c) / (size_patch[0]*size_patch[1]*size_patch[2]) , 0, d);

    cv::Mat k;
    cv::exp((-d / (sigma * sigma)), k);
    return k;
}

// Eigenvectors as rows
void projectToEigenvectors( const RLearning::PCA &pcaPos, const cv::Mat &evecsRowsPos,
                            const RLearning::PCA &pcaNeg, const cv::Mat &evecsRowsNeg)
{
    cv::Mat selEvecsRowsPos( 2, evecsRowsPos.cols, evecsRowsPos.type());
    cv::Mat selEvecsRowsNeg( 2, evecsRowsNeg.cols, evecsRowsNeg.type());
    evecsRowsPos.row(0).copyTo( selEvecsRowsPos.row(0));
    evecsRowsNeg.row(0).copyTo( selEvecsRowsNeg.row(0));

    for ( int i = 0; i < 10; ++i)
    {
        evecsRowsPos.row(i+1).copyTo( selEvecsRowsPos.row(1));
        evecsRowsNeg.row(i+1).copyTo( selEvecsRowsNeg.row(1));

        std::ostringstream oss;
        oss << i+1;
        const string rowVals = oss.str();

        cv::Mat posProjColVecs = pcaPos.project( selEvecsRowsPos);
        const string posfname = string("pos_pts_") + rowVals + string(".txt");
        std::ofstream ofs1( posfname.c_str());
        RLearning::writePoints( ofs1, (cv::Mat_<double>)posProjColVecs, true);
        ofs1.close();

        cv::Mat negProjColVecs = pcaNeg.project( selEvecsRowsNeg);
        const string negfname = string("neg_pts_") + rowVals + string(".txt");
        std::ofstream ofs2( negfname.c_str());
        RLearning::writePoints( ofs2, (cv::Mat_<double>)negProjColVecs, true);
        ofs2.close();
    }   // end for
}   // end projectToEigenvectors

//*
void ext_pooling(cv::Mat pool_fea, cv::Mat &center, cv::Mat &range, int K)
{
    int num = pool_fea.rows;
    //cv::flann::KDTreeIndexParams indexParams;
    //cv::flann::Index fea_tree;
    //fea_tree.build(pool_fea, indexParams);
    
    cv::Mat labels;
    cv::kmeans(pool_fea, K, labels, cv::TermCriteria(CV_TERMCRIT_ITER|CV_TERMCRIT_EPS, 1000, 1e-6), 5, cv::KMEANS_PP_CENTERS, center);
        
    range = cv::Mat::zeros(center.rows, 1, CV_32FC1);
    std::vector<size_t> count(center.rows, 0);
    int *idx = (int *)labels.data;
    for( int i = 0 ; i < pool_fea.rows ; i++, idx++ )
    {
        float cur_dist = cv::norm(pool_fea.row(i), center.row(*idx), cv::NORM_L2);
        //if( range.at<float>(*idx, 0) < cur_dist )
            range.at<float>(*idx, 0) += cur_dist;
            count[*idx]++;
    }    
    
    float *ptr = (float *)range.data;
    for( int i = 0 ; i < range.rows ; i++, ptr++ )
        *ptr /= count[i];
    
}

void sharpen(const cv::Mat& image, cv::Mat& result)
{

    result.create(image.size(), image.type()); // allocate if necessary

    for (int j = 1; j < image.rows - 1; j++)
    { // for all rows (except first and last)

        const uchar* previous = image.ptr<const uchar>(j - 1); // previous row
        const uchar* current = image.ptr<const uchar>(j);      // current row
        const uchar* next = image.ptr<const uchar>(j + 1);     // next row

        uchar* output = result.ptr<uchar>(j); // output row

        for (int i = 1; i < image.cols - 1; i++)
        {

            *output++ = cv::saturate_cast<uchar>(5 * current[i] - current[i - 1] - current[i + 1] -
                                                 previous[i] - next[i]);
            //			output[i]=
            //cv::saturate_cast<uchar>(5*current[i]-current[i-1]-current[i+1]-previous[i]-next[i]);
        }
    }

    // Set the unprocess pixels to 0
    result.row(0).setTo(cv::Scalar(0));
    result.row(result.rows - 1).setTo(cv::Scalar(0));
    result.col(0).setTo(cv::Scalar(0));
    result.col(result.cols - 1).setTo(cv::Scalar(0));
}

void sharpen(const cv::Mat& image, cv::Mat& result)
{
    //allocate if necessary
    result.create(image.size(),image.type());

    for(int j= 1; j < image.rows-1; ++j) { // for all rows
                                // except the first and last row
        const uchar* previous =
                image.ptr<uchar>(j-1);
        const uchar* current =
                image.ptr<uchar>(j);
        const uchar* next =
                image.ptr<uchar>(j+1);

        uchar* output = result.ptr<uchar>(j); // output row

        for(int i= 1; i < (image.cols-1) * image.channels(); ++i) {
            // stature_cast: avoid the mathematical expression applied on the pixels leads to a
            // result that goes out of the range of the permited pixel value( 0 - 255)
            *output ++ = cv::saturate_cast<uchar>(5 * current[i] - current[i-1] - current[i+1] -
                         previous[i] - next[i]);
        }
    }


        //set the unprocess pixels to zero
        result.row(0).setTo(cv::Scalar(0));
        result.row(result.rows-1).setTo(cv::Scalar(0));
        result.col(0).setTo(cv::Scalar(0));
        result.col(result.cols-1).setTo(cv::Scalar(0));

}

void sharpen2(const cv::Mat& image, cv::Mat& result)
{

    result.create(image.size(), image.type()); // allocate if necessary

    int step = image.step1();
    const uchar* previous = image.data;        // ptr to previous row
    const uchar* current = image.data + step;  // ptr to current row
    const uchar* next = image.data + 2 * step; // ptr to next row
    uchar* output = result.data + step;        // ptr to output row

    for (int j = 1; j < image.rows - 1; j++)
    { // for each row (except first and last)
        for (int i = 1; i < image.cols - 1; i++)
        { // for each column (except first and last)

            output[i] = cv::saturate_cast<uchar>(5 * current[i] - current[i - 1] - current[i + 1] -
                                                 previous[i] - next[i]);
        }

        previous += step;
        current += step;
        next += step;
        output += step;
    }

    // Set the unprocess pixels to 0
    result.row(0).setTo(cv::Scalar(0));
    result.row(result.rows - 1).setTo(cv::Scalar(0));
    result.col(0).setTo(cv::Scalar(0));
    result.col(result.cols - 1).setTo(cv::Scalar(0));
}

void Processor::sharpen(const cv::Mat &image, cv::Mat &result) {

	startTimer();
// allocate if necessary
	result.create(image.rows, image.cols, image.type());
	for (int j = 1; j < image.rows - 1; j++) { // for all rows
// (except first and last)
		const uchar* previous = image.ptr<const uchar>(j - 1); // previous row
		const uchar* current = image.ptr<const uchar>(j); // current row
		const uchar* next = image.ptr<const uchar>(j + 1); // next row
		uchar* output = result.ptr<uchar>(j); // output row
		for (int i = 1; i < image.cols - 1; i++) {
			for (int k = 0; k < image.channels(); k++) {
				result.at<cv::Vec3b>(j, i)[k] = cv::saturate_cast<uchar>(
						5 * image.at<cv::Vec3b>(j, i)[k]
								- image.at<cv::Vec3b>(j, i - 1)[k]
								- image.at<cv::Vec3b>(j, i + 1)[k]
								- image.at<cv::Vec3b>(j - 1, i)[k]
								- image.at<cv::Vec3b>(j + 1, i)[k]);
			}
		}
	}
// Set the unprocess pixels to 0
	result.row(0).setTo(cv::Scalar(0));
	result.row(result.rows - 1).setTo(cv::Scalar(0));
	result.col(0).setTo(cv::Scalar(0));
	result.col(result.cols - 1).setTo(cv::Scalar(0));

	stopTimer("Sharpen");
}

// How to do sharpening without explicitly using a convolution filter and cv::filter2D
void RFeatures::sharpen_OLD( const cv::Mat &img, cv::Mat &out)
{
    out.create( img.size(), img.type());    // Allocate if necessary

    int channels = img.channels();
    int nc = img.cols * channels;

    for ( int j = 1; j < img.rows-1; ++j) // All rows except first and last
    {
        const uchar* previous = img.ptr<const uchar>(j-1); // Previous row
        const uchar* current = img.ptr<const uchar>(j); // Current row
        const uchar* next = img.ptr<const uchar>(j+1);  // Next row
        uchar* output = out.ptr<uchar>(j);  // Output row

        for ( int i = channels; i < nc - channels; ++i)   // All columns except first and last
        {
            uchar v = 5*current[i] - current[i-channels] - current[i+channels] - previous[i] - next[i];
            *output++ = cv::saturate_cast<uchar>(v);
        }   // end for
    }   // end for

    // Set the unprocesses pixels to 0
    cv::Scalar s(0);
    if (img.channels() == 3)
        s = cv::Scalar(0,0,0);
    out.row(0).setTo( s);
    out.row(out.rows-1).setTo( s);
    out.col(0).setTo( s);
    out.col(out.cols-1).setTo( s);
}   // end sharpen_OLD

/*
 *	Function to convert the training data to be used by the SVM classifier
 */
void convert_to_ml(const std::vector<cv::Mat> &train_samples, cv::Mat& trainData )
{
    const int rows = (int)train_samples.size();
    const int cols = (int)std::max( train_samples[0].cols, train_samples[0].rows );

    cv::Mat tmp(1, cols,CV_32FC1);
    trainData = cv::Mat(rows,cols,CV_32FC1 );
    std::vector<cv::Mat>::const_iterator itr = train_samples.begin();
    std::vector<cv::Mat>::const_iterator end = train_samples.end();

    for( int i = 0 ; itr != end ; ++itr, ++i ) {

        CV_Assert( itr->cols == 1 || itr->rows == 1 );

        if( itr->cols == 1 ) {

            transpose( *(itr), tmp );
            tmp.copyTo( trainData.row( i ) );
        }
        else if( itr->rows == 1 ) {

            itr->copyTo( trainData.row( i ) );
        }
    }
}

  void sharpen(cv::Mat &image, cv::Mat &out)
  {
      out.create(image.size(), image.type());
      for(int j=1; j < image.rows-1;j++)
      {
	  const uchar* previous = image.ptr<const uchar>(j-1);
	  const uchar* current  = image.ptr<const uchar>(j);
	  const uchar* next     = image.ptr<const uchar>(j+1);
	  uchar* output = out.ptr<uchar>(j);
	  
	  for(int i=1; i<image.cols-1; i++)
	  {
	      *output++=cv::saturate_cast<uchar>(
			  5*current[i]-current[i-1]
			  -current[i+1]-previous[1]-next[1]);
		  
	      
	  }
      }
      out.row(0).setTo(cv::Scalar(0));
      out.row(out.rows-1).setTo(cv::Scalar(0));
      out.col(0).setTo(cv::Scalar(0));
      out.col(out.cols-1).setTo(cv::Scalar(0));
      
      
    
    
    
  }

void _hammingmatch_lowetest_slow(const cv::Mat &_des1, const cv::Mat &_des2, MatchList &_good, const float ratio){
	// compute and copmare norms row by row
	int _n1 = _des1.rows;
	int _n2 = _des2.rows;
	int _idx, _mindist1, _mindist2;
	cv::DMatch _mtch;
	for ( int _i = 0; _i < _n1; ++_i ){
		_idx=-1;	_mindist1 = 10000;	_mindist2 = 1000000;	// some arbirtrary large value for initializing
		// compute best idx for row _i of _des1
		for ( int _j = 0; _j < _n2; ++_j ){
			// calculate hamming distance
			int _val = cv::normHamming(_des1.row(_i).data, _des2.row(_j).data, 4);
			if ( _val > 8) {continue;}	// skip to make it fast
			_val = cv::normHamming(_des1.row(_i).data, _des2.row(_j).data, _des2.cols);
			if ( _val < _mindist2 ){
			if ( _val < _mindist1 ){
				_mindist2 = _mindist1;
				_mindist1 = _val;
				_idx = _j;
			} else _mindist2 = _val;
			}
		}
		// ratio test
		if ( (_idx != -1) && (_mindist1 < (_mindist2 * ratio ))){
			// all is okay
			printf("distance %d: %d\n", _i, _mindist1);
			_mtch.distance = _mindist1;
			_mtch.queryIdx = _i;		// the first
			_mtch.trainIdx = _idx;		// the second
			_good.push_back(_mtch);
		}
	}
}

/*
 *calculate Affine Matrixes that transform image1 to 
 *image2 and image2 to image1.
 */
void ImageGraph::calAffineMatrix(ImageNode img1, ImageNode img2, cv::Mat &one2two, cv::Mat &two2one) {
    cv::BruteForceMatcher< cv::L2<float> > matcher;
    std::vector< cv::DMatch > matches;
    matcher.match( img1.descriptors, img2.descriptors, matches );
    
    double max_dist = 0;
    for (int i=0; i<matches.size(); i++) {
        if (matches[i].distance > max_dist) {
            max_dist = matches[i].distance;
        }
    }
    
    std::vector<cv::DMatch> goodmatches;
    std::vector<cv::Point2f> goodpoints1;
    std::vector<cv::Point2f> goodpoints2;
    for (int i=0; i<matches.size(); i++) {
        if (matches[i].distance <= max_dist) {
            goodmatches.push_back(matches[i]);
            goodpoints1.push_back(img1.keypoints[matches[i].queryIdx].pt);
            goodpoints2.push_back(img2.keypoints[matches[i].trainIdx].pt);
        }
    }
  //  cv::Mat outp;
  //  cv::drawMatches(img1.img, img1.keypoints, img2.img, img2.keypoints, goodmatches, outp);
  ////  cv::namedWindow("aaa");
//cv::imshow("aaa", outp);
  //  cvWaitKey(0);
    cv::Mat t2o = cv::estimateRigidTransform(goodpoints1, goodpoints2, true);
   // std::cout << t2o.rows << std::endl;
    assert(t2o.rows == 3);
    one2two = cv::Mat::zeros(3, 3, CV_64F);
   // std::cout << goodpoints1.size() << " " << goodpoints2.size() << std::endl;
   // std::cout << "t2o:  " << std::endl;
   // std::cout << t2o << std::endl;
    cv::Mat l = (cv::Mat_<double>(1,3)<< 0,0,1);
    t2o.row(0).copyTo(one2two.row(0));
    t2o.row(1).copyTo(one2two.row(1));
    l.copyTo(one2two.row(2));
    
    cv::Mat o2t = cv::estimateRigidTransform(goodpoints2, goodpoints1, true);
    assert(o2t.rows == 3);
    two2one = cv::Mat::zeros(3, 3, CV_64F);
    o2t.row(0).copyTo(two2one.row(0));
    o2t.row(1).copyTo(two2one.row(1));
    l.copyTo(two2one.row(2));
    std::cout << "two2one:" << std::endl;
    std::cout << two2one << std::endl;
    std::cout << "one2two" << std::endl;
    std::cout << one2two << std::endl;
    //std::cout << "asdf  " << two2one << std::endl;
 //   std::cout << "row 0: " << t2o.row(0) << std::endl;
 //   std::cout << "row 1: " << t2o.row(1) << std::endl;
  //  std::cout << "row 2: " << l << std::endl;
  //  std::cout << "xxx " << t2o << std::endl;
  //  std::cout << "yyy " << one2two << std::endl;

}

void AlphaBlender::performBlendY(const cv::Mat& image1,const cv::Mat& image2,cv::Mat& outputImage){

	double alpha=1,beta=0;
	for(int i=0;i<image1.rows;i++){
		beta=(double)i/(image1.rows-1);
		alpha=1-beta;
		cv::addWeighted(image1.row(i),alpha,image2.row(i),beta,0,outputImage.row(i));
	}
}