C++ Mat_::type方法代码示例

    void serialize(Archive & ar, ::cv::Mat_<T>& m, const unsigned int version)
    {
      if(Archive::is_loading::value == true)
      {
        int cols, rows;
        size_t elem_size, elem_type;

        ar & cols;
        ar & rows;
        ar & elem_size;
        ar & elem_type;

        m.create(rows, cols);

        size_t data_size = m.cols * m.rows * elem_size;
        ar & boost::serialization::make_array(m.ptr(), data_size);
      }
      else
      {
        size_t elem_size = m.elemSize();
        size_t elem_type = m.type();

        ar & m.cols;
        ar & m.rows;
        ar & elem_size;
        ar & elem_type;

        const size_t data_size = m.cols * m.rows * elem_size;
        ar & boost::serialization::make_array(m.ptr(), data_size);
      }
    }

void writeMat(cv::Mat_<T>& mat, char* file){
	ofstream* ofile = new ofstream(file,ofstream::out|ofstream::binary);
	(*ofile)<<mat.rows<<" ";
	(*ofile)<<mat.cols<<" ";
	(*ofile)<<mat.type()<<" ";
	(*ofile)<<mat.total()*mat.elemSize();
	
	ofile->write((char*) mat.data,mat.total()*mat.elemSize());
	ofile->close();
}

void
extendMat(cv::Mat_<int> &m, unsigned int rows, unsigned int cols, int value = 0) {
    cv::Size2i interSize;
    interSize.height = std::min<int>(rows, m.rows);
    interSize.width = std::min<int>(cols, m.cols);
    cv::Mat tm(rows, cols, m.type());
    tm.setTo(cv::Scalar(value));
    if(interSize.width!=0 && interSize.height!=0)
        m(cv::Rect(cv::Point(0,0), interSize)).copyTo(tm(cv::Rect(cv::Point(0,0), interSize)));
    m = tm;
}

void ColorEdge::detectColorEdge(const cv::Mat_<cv::Vec3b> &image, cv::Mat_<uchar> &edge)
{
    cv::Mat_<double> edge_map(image.size());
    const int filter_half = static_cast<int>(filter_size_ / 2);

    for(int y = filter_half; y < (edge_map.rows - filter_half); ++y)
    {
        for(int x = filter_half; x < (edge_map.cols - filter_half); ++x)
        {
            cv::Mat_<cv::Vec3b> roi(image, cv::Rect(x - filter_half, y - filter_half, filter_size_, filter_size_));
            edge_map(y, x) = calculateMVD(roi);
        }
    }
    
    edge_map.convertTo(edge, edge.type());
}

//===========================================================================
// Calculate the PDM's Jacobian over rigid parameters (rotation, translation and scaling), the additional input W represents trust for each of the landmarks and is part of Non-Uniform RLMS 
void PDM::ComputeRigidJacobian(const cv::Mat_<float>& p_local, const cv::Vec6d& params_global, cv::Mat_<float> &Jacob, const cv::Mat_<float> W, cv::Mat_<float> &Jacob_t_w)
{
  	
	// number of verts
	int n = this->NumberOfPoints();
  
	Jacob.create(n * 2, 6);

	float X,Y,Z;

	float s = (float)params_global[0];
  	
	cv::Mat_<double> shape_3D_d;
	cv::Mat_<double> p_local_d;
	p_local.convertTo(p_local_d, CV_64F);
	this->CalcShape3D(shape_3D_d, p_local_d);
	
	cv::Mat_<float> shape_3D;
	shape_3D_d.convertTo(shape_3D, CV_32F);

	 // Get the rotation matrix
	cv::Vec3d euler(params_global[1], params_global[2], params_global[3]);
	cv::Matx33d currRot = Euler2RotationMatrix(euler);
	
	float r11 = (float) currRot(0,0);
	float r12 = (float) currRot(0,1);
	float r13 = (float) currRot(0,2);
	float r21 = (float) currRot(1,0);
	float r22 = (float) currRot(1,1);
	float r23 = (float) currRot(1,2);
	float r31 = (float) currRot(2,0);
	float r32 = (float) currRot(2,1);
	float r33 = (float) currRot(2,2);

	cv::MatIterator_<float> Jx = Jacob.begin();
	cv::MatIterator_<float> Jy = Jx + n * 6;

	for(int i = 0; i < n; i++)
	{
    
		X = shape_3D.at<float>(i,0);
		Y = shape_3D.at<float>(i+n,0);
		Z = shape_3D.at<float>(i+n*2,0);    
		
		// The rigid jacobian from the axis angle rotation matrix approximation using small angle assumption (R * R')
		// where R' = [1, -wz, wy
		//             wz, 1, -wx
		//             -wy, wx, 1]
		// And this is derived using the small angle assumption on the axis angle rotation matrix parametrisation

		// scaling term
		*Jx++ =  (X  * r11 + Y * r12 + Z * r13);
		*Jy++ =  (X  * r21 + Y * r22 + Z * r23);
		
		// rotation terms
		*Jx++ = (s * (Y * r13 - Z * r12) );
		*Jy++ = (s * (Y * r23 - Z * r22) );
		*Jx++ = (-s * (X * r13 - Z * r11));
		*Jy++ = (-s * (X * r23 - Z * r21));
		*Jx++ = (s * (X * r12 - Y * r11) );
		*Jy++ = (s * (X * r22 - Y * r21) );
		
		// translation terms
		*Jx++ = 1.0f;
		*Jy++ = 0.0f;
		*Jx++ = 0.0f;
		*Jy++ = 1.0f;

	}

	cv::Mat Jacob_w = cv::Mat::zeros(Jacob.rows, Jacob.cols, Jacob.type());
	
	Jx =  Jacob.begin();
	Jy =  Jx + n*6;

	cv::MatIterator_<float> Jx_w =  Jacob_w.begin<float>();
	cv::MatIterator_<float> Jy_w =  Jx_w + n*6;

	// Iterate over all Jacobian values and multiply them by the weight in diagonal of W
	for(int i = 0; i < n; i++)
	{
		float w_x = W.at<float>(i, i);
		float w_y = W.at<float>(i+n, i+n);

		for(int j = 0; j < Jacob.cols; ++j)
		{
			*Jx_w++ = *Jx++ * w_x;
			*Jy_w++ = *Jy++ * w_y;
		}		
	}

	Jacob_t_w = Jacob_w.t();
}