C++ Mat::col方法代码示例

// 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

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 optimizeEmat (cv::Mat p1, cv::Mat p2, cv::Mat K, cv::Mat *E)
	// input: p1, p2, normalized image points correspondences
{
	int n = p1.cols;
	double* measurement = new double[n];	
	for (int i=0; i<n; ++i) 
		measurement[i] = 0;	
	double opts[LM_OPTS_SZ], info[LM_INFO_SZ];
	opts[0]=LM_INIT_MU*0.5; //
	opts[1]=1E-50;
	opts[2]=1E-100; // original 1e-50
	opts[3]=1E-20;
	opts[4]= -LM_DIFF_DELTA;
	int matIter = 100;
	
	cv::Mat R1, R2, t;
	
	decEssential (E, &R1, &R2, &t);
	
	cv::Mat Rt = // find true R and t
		findTrueRt(R1,R2,t,mat2cvpt(p1.col(0)),mat2cvpt(p2.col(0)));
	
	if(Rt.cols < 3) return;

	cv::Mat R =  Rt.colRange(0,3);
	t = Rt.col(3);
	Matrix3d Rx;
	Rx << R.at<double>(0,0), R.at<double>(0,1), R.at<double>(0,2),
		R.at<double>(1,0), R.at<double>(1,1), R.at<double>(1,2),
		R.at<double>(2,0), R.at<double>(2,1), R.at<double>(2,2);
	Quaterniond q(Rx);

	double para[7] = {q.w(), q.x(), q.y(), q.z(), t.at<double>(0),
		t.at<double>(1), t.at<double>(2)};

	data_optimizeEmat data;
	data.p1 = p1;
	data.p2 = p2;
	data.K  = K;
	
	int ret = dlevmar_dif(costfun_optimizeEmat, para, measurement, 7, n,
		matIter, opts, info, NULL, NULL, (void*)&data);
	delete[] measurement;
	q.w() = para[0];
	q.x() = para[1];
	q.y() = para[2];
	q.z() = para[3];
	q.normalize();
	
	for (int i=0; i<3; ++i)
		for (int j=0; j<3; ++j)
			R.at<double>(i,j) = q.matrix()(i,j);

	cv::Mat tx = (cv::Mat_<double>(3,3)<< 0, -para[6], para[5],
		para[6], 0 ,  -para[4],
		-para[5], para[4], 0 );
	tx = tx/sqrt(para[4]*para[4]+para[5]*para[5]+para[6]*para[6]);
	*E = tx * R;
//	cout<<" "<<R<<endl<<para[4]<<"\t"<<para[5]<<"\t"<<para[6]<<endl;
}

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));
}

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");
}

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));

}

// M - step
// Improves the mean, covariance, and weight of each gaussian using
// the responsibilites computed in the E step
void GMM::_M(const cv::Mat &gamma, const cv::Mat &samples)
{
    for(int k = 0; k < _Gaussians.size(); k++)
    {
        // Get the kth gaussian responsibilities
        cv::Mat gammak = gamma.col(k);
        
        // Compute Nk, the sum of all responsibilties for this gaussian
        double Nk = cv::sum(gammak)[0];
        
        // Update the mean
        cv::Mat uNew = cv::Mat::zeros(gammak.cols, 1, CV_64F);
        for(int n = 0; n < gammak.rows; n++)
        {
            uNew += gammak.at<double>(n, 1) * samples.col(n);
        }
        
        uNew /= Nk;
        _Gaussians[k].Mean() = uNew;
        
        // Update the covariance
        cv::Mat sigmaNew = cv::Mat::zeros(gammak.rows, gammak.rows, CV_64F);
        for(int n = 0; n < gammak.rows; n++)
        {
            cv::Mat meanDistance = samples.col(n) - uNew;
            sigmaNew += gammak.at<double>(n, 1) * (meanDistance * meanDistance.t());
        }
        
        sigmaNew /= Nk;
        _Gaussians[k].Covariance() = sigmaNew;
        
        // Udpate weight
        _Gaussians[k].Weight() = Nk / samples.cols;
    }
}

  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));
      
      
    
    
    
  }

vector<pair<int, double>> OMP(cv::Mat x, cv::Mat base, int coeff_count)
{
	vector<pair<int, double>> result;
	cv::Mat residual = x.clone();
	for (int i = 0; i < coeff_count; ++i)
	{
		int max_index = 0;
		double max_value = 0;
		for (int j = 0; j < base.cols; ++j)
		{
			double current_value = abs(
				static_cast<cv::Mat>(residual.t() * base.col(j)).at<double>(0));
			if (current_value > max_value)
			{
				max_value = current_value;
				max_index = j;
			}
		}

		result.push_back(make_pair(max_index, 0));
		cv::Mat sparse_base(base.rows, result.size(), CV_64FC1);
		for (int j = 0; j < result.size(); ++j)
			base.col(result[j].first).copyTo(sparse_base.col(j));

		cv::Mat beta;
		cv::solve(sparse_base.t() * sparse_base, 
			sparse_base.t() * x, beta, cv::DECOMP_SVD);
		for (int j = 0; j < result.size(); ++j)
			result[j].second = beta.at<double>(j);
		residual -= sparse_base * beta;
	}

	return result;
}

/* 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));
	}
}

// Update stage
void ParticleFilter::update(cv::Mat measurement)
{
	// Propose indicators
	std::vector<int> indicators = resample(gmm.weight, gmm.nParticles);
	std::vector<double> weights;
	wsum = 0;
	int i;
	std::vector<state_params> temp;
	
	for (int j = 0; j < gmm.nParticles; j++) //update KF for each track using indicator samples
	{
		i = indicators[j];
		gmm.KFtracker[i].predict(gmm.tracks[j].state,gmm.tracks[j].cov);
		weights.push_back(mvnpdf(measurement.col(j),gmm.KFtracker[i].H*gmm.tracks[j].state+gmm.KFtracker[i].BH,gmm.KFtracker[i].H*gmm.tracks[j].cov*gmm.KFtracker[i].H.t()+gmm.KFtracker[i].R));
		wsum = wsum + weights[j];
		gmm.KFtracker[i].update(measurement.col(j),gmm.tracks[j].state,gmm.tracks[j].cov);
		temp.push_back(gmm.tracks[j]);
	}
	
		
	for (int i = 0; i < (int)gmm.tracks.size(); i++)
	{
		weights[i] = weights[i]/wsum;
	}
	
	// Re-sample tracks
	indicators.clear();
	indicators = resample(weights, gmm.nParticles);
	for (int j = 0; j < gmm.nParticles; j++) //update KF for each track using indicator samples
	{
		gmm.tracks[j] = temp[indicators[j]];
	}
	wsum = 1.0;
}

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 AlphaBlender::performBlendX(const cv::Mat& image1,const cv::Mat& image2,cv::Mat& outputImage){
	double alpha=1,beta=0;
	for(int i=0;i<image1.cols;i++){

		beta=(double)i/(image1.cols-1);
		alpha=1-beta;
		cv::addWeighted(image1.col(i),alpha,image2.col(i),beta,0,outputImage.col(i));
	}
}

inline
cv::Mat
opt_feat::Shift_Image( cv::Mat src_in, int num_pixels_x, int num_pixels_y)
{


    cv::Mat img_out;


    cv::Mat rot_mat = (cv::Mat_<double>(2,3) << 1, 0, num_pixels_x, 0, 1, num_pixels_y);
    warpAffine( src_in, img_out, rot_mat, src_in.size() );

    if (num_pixels_x>0) //Move right
    {

        cv::Mat col = src_in.col(0);
        cv::Mat row = src_in.row(0);


        for (int i=0; i<abs(num_pixels_x); ++i)
        {
            col.col(0).copyTo(img_out.col(i));

        }

        for (int i=0; i<abs(num_pixels_y); ++i)
        {
            row.row(0).copyTo(img_out.row(i));
            //src_in.copyTo(img_out,crop);
        }
    }

    if (num_pixels_x<0) //Move left
    {

        int w = src_in.size().width;
        int h = src_in.size().height;
        cv::Mat col = src_in.col(w-1);
        cv::Mat row = src_in.row(h-1);

        for (int i=w-abs(num_pixels_x) ; i<w; ++i)
        {
            col.col(0).copyTo(img_out.col(i));
            //row.row(0).copyTo(img_out.row(i));
        }

        for (int i=h-abs(num_pixels_y) ; i<h; ++i)
        {
            //col.col(0).copyTo(img_out.col(i));
            row.row(0).copyTo(img_out.row(i));
        }
    }


    return img_out;
}

void drawBgImage(cv::Mat & render, cv::Mat & bgImage, float yaw, float fov, int nBeams) {

	int bgImageStart = bgImage.cols - yaw*nBeams/fov;
	for(int c = 0; c<render.cols; c++) {
		int cBgImage = bgImageStart + c;
		if(cBgImage >= bgImage.cols) cBgImage -= bgImage.cols;
		bgImage.col(cBgImage).copyTo(render.col(c));
	}

}