コード例 #1
0
ファイル: main.cpp プロジェクト: NTheo/GlassPainting
/* multiplication between sparse and classic matrix A=D*Q
	* hists: sparse matrix (typically matrix of database words)
	* nhist: matrix (typically matrix of query words)
	* return: hists*nhists (matrix)
*/
Mat mul(const SparseMat &hists, const Mat &nhist)
{
    Mat answer = Mat::zeros(hists.size(0),1,CV_32F);
    SparseMatConstIterator
    it = hists.begin(),
    it_end = hists.end();
    for (; it!=it_end;++it)
    {
		const SparseMat::Node* n = it.node();
		answer.at<float>(n->idx[0])+=nhist.at<float>(n->idx[1])*it.value<float>();
    }
    return answer;
}
コード例 #2
0
ファイル: sparsemat.C プロジェクト: shkeshavarz/OOF2
SparseMatRowConstIterator::SparseMatRowConstIterator(
				       const SparseMatConstIterator &smi)
  : mat(smi.mat),
    row_(smi.row()),
    coliter(smi.coliter)
{
}
コード例 #3
0
ファイル: CmShow.cpp プロジェクト: 20083017/CmCode
void CmShow::showTinySparseMat(CStr &title, const SparseMat &A1d)
{
    int N = A1d.nzcount();
    if (N > 1000)
        return;

    struct NodeSM {
        int r, c;
        double v;
        bool operator < (const NodeSM &n) {
            if (r < n.r)
                return true;
            else if (r > n.r)
                return false;
            else
                return c < n.c;
        }

        void print() {
            if (abs(v) > 1e-8) printf("(%d, %d) %-12g\t", r, c, v);
        }
    };

    vector<NodeSM> nodes(N);
    SparseMatConstIterator it = A1d.begin();

    for (int i = 0; i < N; i++, ++it) {
        const int* idx = it.node()->idx;
        nodes[i].r = idx[0] + 1;
        nodes[i].c = idx[1] + 1;
        nodes[i].v = it.value<double>();
    }
    sort(nodes.begin(), nodes.end());
    for (int i = 0; i < N; i++)
        nodes[i].print();
    printf("\n");

    Mat m;
    A1d.convertTo(m, CV_64F);
    showTinyMat(title, m);
}
コード例 #4
0
ファイル: MxArray.cpp プロジェクト: HiDiYANG/gPb
/**
 * Convert float cv::SparseMat to MxArray
 * @param mat cv::SparseMat object
 * @return MxArray object
 */
MxArray::MxArray(const cv::SparseMat& mat)
{
	if (mat.dims() != 2)
		mexErrMsgIdAndTxt("mexopencv:error","cv::Mat is not 2D");
	if (mat.type() != CV_32FC1)
		mexErrMsgIdAndTxt("mexopencv:error","cv::Mat is not float");
	
	// Create sparse array
	int m = mat.size(0), n = mat.size(1), nnz = mat.nzcount();
	p_ = mxCreateSparse(m, n, nnz, mxREAL);
	if (!p_)
		mexErrMsgIdAndTxt("mexopencv:error","Allocation error");
	mwIndex *ir = mxGetIr(p_);
	mwIndex *jc = mxGetJc(p_);
	if (ir==NULL || jc==NULL)
		mexErrMsgIdAndTxt("mexopencv:error","Unknown error");

	// Sort nodes before we put elems into mxArray
	vector<const SparseMat::Node*> nodes;
	nodes.reserve(nnz);
	for (SparseMatConstIterator it = mat.begin(); it != mat.end(); ++it)
		nodes.push_back(it.node());
	sort(nodes.begin(),nodes.end(),compareSparseMatNode);

	// Copy data
	double *pr = mxGetPr(p_);
	int i = 0;
	jc[0] = 0;
	for (vector<const SparseMat::Node*>::const_iterator it = nodes.begin();
		 it != nodes.end(); ++it)
	{
		mwIndex row = (*it)->idx[0], col = (*it)->idx[1];
		ir[i] = row;
		jc[col+1] = i+1;
		pr[i] = static_cast<double>(mat.value<float>(*it));
		++i;
	}
}