C++ InputArray::getMat方法代码示例

void Regression::write(cv::InputArray array)
{
    write() << "kind" << array.kind();
    write() << "type" << array.type();
    if (isVector(array))
    {
        int total = (int)array.total();
        int idx = regRNG.uniform(0, total);
        write() << "len" << total;
        write() << "idx" << idx;

        cv::Mat m = array.getMat(idx);

        if (m.total() * m.channels() < 26) //5x5 or smaller
            write() << "val" << m;
        else
            write(m);
    }
    else
    {
        if (array.total() * array.channels() < 26) //5x5 or smaller
            write() << "val" << array.getMat();
        else
            write(array.getMat());
    }
}

void illuminationChange(cv::InputArray _src,
                        cv::InputArray _mask,
                        cv::OutputArray _dst,
                        float a,
                        float b)
{

    Mat src  = _src.getMat();
    Mat mask  = _mask.getMat();
    _dst.create(src.size(), src.type());
    Mat blend = _dst.getMat();
    float alpha = a;
    float beta = b;

    Mat gray = Mat::zeros(mask.size(), CV_8UC1);

    if(mask.channels() == 3)
        cvtColor(mask, gray, COLOR_BGR2GRAY);
    else
        gray = mask;

    Mat cs_mask = Mat::zeros(src.size(), CV_8UC3);

    src.copyTo(cs_mask, gray);

    Cloning obj;
    obj.illum_change(src, cs_mask, gray, blend, alpha, beta);

}

void stereo_disparity_normal(cv::InputArray left_image, cv::InputArray right_image, cv::OutputArray disp_, 
					  int max_dis_level, int scale, float sigma) {
	cv::Mat imL = left_image.getMat();
	cv::Mat imR = right_image.getMat();
	
	CV_Assert(imL.size() == imR.size());
	CV_Assert(imL.type() == CV_8UC3 && imR.type() == CV_8UC3);

	cv::Size imageSize = imL.size();

	disp_.create(imageSize, CV_8U);
	cv::Mat disp = disp_.getMat();
	
	CDisparityHelper dispHelper;
	
//step 1: cost initialization
	cv::Mat costVol = dispHelper.GetMatchingCost(imL, imR, max_dis_level);

//step 2: cost aggregation
	CSegmentTree stree;
	CColorWeight cWeight(imL);
	stree.BuildSegmentTree(imL.size(), sigma, TAU, cWeight);
	stree.Filter(costVol, max_dis_level);

//step 3: disparity computation
	cv::Mat disparity = dispHelper.GetDisparity_WTA((float*)costVol.data, 
		imageSize.width, imageSize.height, max_dis_level);

	MeanFilter(disparity, disparity, 3);

	disparity *= scale;
	disparity.copyTo(disp);
}

void colorChange(cv::InputArray _src,
                 cv::InputArray _mask,
                 cv::OutputArray _dst,
                 float r,
                 float g,
                 float b)
{
    Mat src  = _src.getMat();
    Mat mask  = _mask.getMat();
    _dst.create(src.size(), src.type());
    Mat blend = _dst.getMat();

    float red = r;
    float green = g;
    float blue = b;

    Mat gray = Mat::zeros(mask.size(), CV_8UC1);

    if(mask.channels() == 3)
        cvtColor(mask, gray, COLOR_BGR2GRAY);
    else
        gray = mask;

    Mat cs_mask = Mat::zeros(src.size(), CV_8UC3);

    src.copyTo(cs_mask, gray);

    Cloning obj;
    obj.local_color_change(src, cs_mask, gray, blend, red, green, blue);
}

void textureFlattening(cv::InputArray _src,
                       cv::InputArray _mask,
                       cv::OutputArray _dst,
                       double low_threshold,
                       double high_threshold,
                       int kernel_size)
{

    Mat src  = _src.getMat();
    Mat mask  = _mask.getMat();
    _dst.create(src.size(), src.type());
    Mat blend = _dst.getMat();

    Mat gray = Mat::zeros(mask.size(), CV_8UC1);

    if(mask.channels() == 3)
        cvtColor(mask, gray, COLOR_BGR2GRAY);
    else
        gray = mask;

    Mat cs_mask = Mat::zeros(src.size(), CV_8UC3);

    src.copyTo(cs_mask, gray);

    Cloning obj;
    obj.texture_flatten(src, cs_mask, gray, low_threshold, high_threshold, kernel_size, blend);
}

void globalMatting(cv::InputArray _image, cv::InputArray _trimap, cv::OutputArray _foreground, cv::OutputArray _alpha, cv::OutputArray _conf)
{
    cv::Mat image = _image.getMat();
    cv::Mat trimap = _trimap.getMat();

    if (image.empty())
        CV_Error(CV_StsBadArg, "image is empty");
    if (image.type() != CV_8UC3)
        CV_Error(CV_StsBadArg, "image mush have CV_8UC3 type");

    if (trimap.empty())
        CV_Error(CV_StsBadArg, "trimap is empty");
    if (trimap.type() != CV_8UC1)
        CV_Error(CV_StsBadArg, "trimap mush have CV_8UC1 type");

    if (image.size() != trimap.size())
        CV_Error(CV_StsBadArg, "image and trimap mush have same size");

    cv::Mat &foreground = _foreground.getMatRef();
    cv::Mat &alpha = _alpha.getMatRef();
    cv::Mat tempConf;

    globalMattingHelper(image, trimap, foreground, alpha, tempConf);

    if(_conf.needed())
        tempConf.copyTo(_conf);
}

FilterCall::FilterCall(cv::InputArray in, cv::InputArray out,
                       impl::CallMetaData data, QString type,
                       QString description, QString requestedView)
    : Call{ data,                   std::move(type),
	    std::move(description), std::move(requestedView) },
      input_{ in.getMat().clone() }, output_{ out.getMat().clone() }
{
}

void sadTemplate(cv::InputArray tar, cv::InputArray tmp, cv::OutputArray res, int *minx, int *miny){
	//引数の入力をMatとして受け取る
	cv::Mat tarM = tar.getMat();	
	cv::Mat tmpM = tmp.getMat();
	cv::Mat resM = res.getMat();

	//sadが最小値のところがマッチングしたい箇所なので
	int minsad = std::numeric_limits<int>::max();	
	int sad = 0;  //各回のsadを格納
	int diff;	//sadに加算する前の作業変数
	int tarx,tary;	//目的のxy座標


	for(int y=0;y<tarM.rows - tmpM.rows;y++){
		for(int x=0;x<tarM.cols - tmpM.cols;x++){
			sad = 0;	//次の領域の計算の前に初期化
			//探索
			for(int yt = 0; yt < tmpM.rows; yt++){
				for(int xt = 0; xt < tmpM.cols; xt++){
					diff = (int)(tarM.at<uchar>(y+yt,x+xt) - tmpM.at<uchar>(yt,xt));
					if(diff < 0){  //負なら正に変換
						diff = -diff;
					}
					sad += diff;
					////残差逐次検定法
					if(sad > minsad){
						yt = tmpM.rows;	
						break;
					}
				}
			}

			//探索結果：sadが今までで最小なら
			if(sad < minsad){
				minsad = sad;	//最小値を更新
				//目的のxyを格納
				tarx = x;
				tary = y;
			}
		}
	}

	//outputに出力
	for(int y=0;y<resM.rows;y++){
		for(int x=0;x<resM.cols;x++){
			if(x==tarx && y==tary){
				resM.at<uchar>(y,x) = (uchar)0;
			}else{
				resM.at<uchar>(y,x) = (uchar)255;
			}
		}
	}
	std::cout << "最小値＝" << minsad << std::endl;
	std::cout << "最小点＝[" << tarx << ", " << tary << "]" <<  std::endl;
	*minx = tarx;
	*miny = tary;
}

void stereo::stereoMatching(cv::InputArray _recImage1, cv::InputArray _recIamge2, cv::OutputArray _disparityMap, int minDisparity, int numDisparities, int SADWindowSize, int P1, int P2)
{
    Mat img1 = _recImage1.getMat();
    Mat img2 = _recIamge2.getMat();
    _disparityMap.create(img1.size(), CV_16S);
    Mat dis = _disparityMap.getMat();
    StereoSGBM matcher(minDisparity, numDisparities, SADWindowSize, P1, P2);
    matcher(img1, img2, dis);
    dis = dis / 16.0;
}

void testFunction(cv::InputArray ip, cv::InputArray op) {
    cv::Mat img = ip.getMat();
    cv::Mat obj = op.getMat();

    printMatrix(img);
    printMatrix(obj);

//	std::cerr<<img.checkVector()<<std::endl;

}

void IPPE::PoseSolver::solveGeneric(cv::InputArray _objectPoints, cv::InputArray _normalizedInputPoints,
                                    cv::OutputArray _Ma, cv::OutputArray _Mb)
{

    //argument checking:
    size_t n = _objectPoints.rows() * _objectPoints.cols(); //number of points
    int objType = _objectPoints.type();
    int type_input = _normalizedInputPoints.type();
    assert((objType == CV_32FC3) | (objType == CV_64FC3));
    assert((type_input == CV_32FC2) | (type_input == CV_64FC2));
    assert((_objectPoints.rows() == 1) | (_objectPoints.cols() == 1));
    assert((_objectPoints.rows() >= 4) | (_objectPoints.cols() >= 4));
    assert((_normalizedInputPoints.rows() == 1) | (_normalizedInputPoints.cols() == 1));
    assert(static_cast<size_t>(_objectPoints.rows() * _objectPoints.cols()) == n);

    cv::Mat normalizedInputPoints;
    if (type_input == CV_32FC2) {
        _normalizedInputPoints.getMat().convertTo(normalizedInputPoints, CV_64FC2);
    }
    else {
        normalizedInputPoints = _normalizedInputPoints.getMat();
    }

    cv::Mat objectInputPoints;
    if (type_input == CV_32FC3) {
        _objectPoints.getMat().convertTo(objectInputPoints, CV_64FC3);
    }
    else {
        objectInputPoints = _objectPoints.getMat();
    }

    cv::Mat canonicalObjPoints;
    cv::Mat MmodelPoints2Canonical;

    //transform object points to the canonical position (zero centred and on the plane z=0):
    makeCanonicalObjectPoints(objectInputPoints, canonicalObjPoints, MmodelPoints2Canonical);

    //compute the homography mapping the model's points to normalizedInputPoints
    cv::Mat H;
    HomographyHO::homographyHO(canonicalObjPoints, _normalizedInputPoints, H);

    //now solve
    cv::Mat MaCanon, MbCanon;
    solveCanonicalForm(canonicalObjPoints, normalizedInputPoints, H, MaCanon, MbCanon);

    //transform computed poses to account for canonical transform:
    cv::Mat Ma = MaCanon * MmodelPoints2Canonical;
    cv::Mat Mb = MbCanon * MmodelPoints2Canonical;

    //output poses:
    Ma.copyTo(_Ma);
    Mb.copyTo(_Mb);
}

void stereo::stereoRectify(cv::InputArray _K1, cv::InputArray _K2, cv::InputArray _R, cv::InputArray _T,
                           cv::OutputArray _R1, cv::OutputArray _R2, cv::OutputArray _P1, cv::OutputArray _P2)
{
    Mat K1 = _K1.getMat(), K2 = _K2.getMat(), R = _R.getMat(), T = _T.getMat();
    _R1.create(3, 3, CV_32F);
    _R2.create(3, 3, CV_32F);
    Mat R1 = _R1.getMat();
    Mat R2 = _R2.getMat();
    _P1.create(3, 4, CV_32F);
    _P2.create(3, 4, CV_32F);
    Mat P1 = _P1.getMat();
    Mat P2 = _P2.getMat();

    if(K1.type()!=CV_32F)
        K1.convertTo(K1,CV_32F);
    if(K2.type()!=CV_32F)
        K2.convertTo(K2,CV_32F);
    if(R.type()!=CV_32F)
        R.convertTo(R,CV_32F);
    if(T.type()!=CV_32F)
        T.convertTo(T,CV_32F);
    if(T.rows != 3)
        T = T.t();

    // R and T is the transformation from the first to the second camera
    // Get the transformation from the second to the first camera
    Mat R_inv = R.t();
    Mat T_inv = -R.t()*T;

    Mat e1, e2, e3;
    e1 = T_inv.t() / norm(T_inv);
    /*Mat z = (Mat_<float>(1, 3) << 0.0,0.0,-1.0);
    e2 = e1.cross(z);
    e2 = e2 / norm(e2);*/
    e2 = (Mat_<float>(1,3) << T_inv.at<float>(1)*-1, T_inv.at<float>(0), 0.0 );
    e2 = e2 / (sqrt(e2.at<float>(0)*e2.at<float>(0) + e2.at<float>(1)*e2.at<float>(1)));
    e3 = e1.cross(e2);
    e3 = e3 / norm(e3);
    e1.copyTo(R1.row(0));
    e2.copyTo(R1.row(1));
    e3.copyTo(R1.row(2));
    R2 = R_inv * R1;

    P1.setTo(Scalar(0));
    R1.copyTo(P1.colRange(0, 3));
    P1 = K1 * P1;

    P2.setTo(Scalar(0));
    R2.copyTo(P2.colRange(0, 3));
    P2 = K2 * P2;

}

cv::Mat flutter::estimate_rigid_transform(cv::InputArray src1, cv::InputArray src2,
	double ransac_good_ratio, double ransac_threshold)
{
	Mat M(2, 3, CV_64F), A = src1.getMat(), B = src2.getMat();
	CvMat matA = A, matB = B, matM = M;
	int err = estimate_rigid_transform_detail(&matA, &matB, &matM,
		ransac_good_ratio, ransac_threshold);
	if (err == 1) {
		return M;
	} else {
		return Mat();
	}
}

void StereoMatch::StereoMatching(cv::InputArray rec_image1, cv::InputArray rec_image2,
    cv::OutputArray disparity_map, int min_disparity, int num_disparities, int SAD_window_size,
    int P1, int P2)
{
    cv::Mat img1 = rec_image1.getMat();
    cv::Mat img2 = rec_image2.getMat();
    disparity_map.create(img1.size(), CV_16S);
    cv::Mat dis = disparity_map.getMat();
    
    cv::StereoSGBM matcher(min_disparity, num_disparities, SAD_window_size, P1, P2);
    matcher(img1, img2, dis);
    dis = dis / 16.0;
}

void Picture::setAnimation(const AnimationEnum &animationEnum, 
	const cv::InputArray &startImg, const cv::InputArray &endImg)
{
	delete animation;

	Mat startImage = startImg.getMat();
	Mat endImage = endImg.getMat();
	if (!startImg.empty() && startImg.size() != this->size())
		cv::resize(startImg, startImage, this->size());
	if (!endImg.empty() && endImg.size() != this->size())
		cv::resize(endImg, endImage, this->size());
	
	animation = AnimationFactory::createAnimation(animationEnum, startImage, endImage);
}