C++ Mat::dot方法代码示例

cv::Point3f
rayPlaneIntersection(cv::Point2f uv, const cv::Mat& centroid, const cv::Mat& normal, const cv::Mat_<float>& Kinv)
{
    cv::Matx33d dKinv(Kinv);
    cv::Vec3d dNormal(normal);
    return rayPlaneIntersection(cv::Vec3d(uv.x, uv.y, 1), centroid.dot(normal), dNormal, dKinv);
}

void ImageSegmentation::backProject(const cv::Mat &planeNormal, const double planeDistance, const cv::Point2d &pointImage, cv::Mat &pointWorld)
{
  pointWorld = cv::Mat(3, 1, CV_64F);

  pointWorld.at<double>(0) = pointImage.x;
  pointWorld.at<double>(1) = pointImage.y;
  pointWorld.at<double>(2) = 1;
  double t = -planeDistance / planeNormal.dot(pointWorld);
  pointWorld = pointWorld * t;
}

/**
 * Interpolate D19 pixel values
 */
void CalibrationFilter::interpolate()
{
    // Gaussian interpolation mask
    float coeff[9] = {
        .707,  1.0, .707,
         1.0,  0.0,  1.0,
        .707,  1.0, .707 };
    const cv::Mat mask(3, 3, CV_32F, coeff);

    for(int i=1; i<mFrame32F_big.rows-1; ++i)
    {
        for(int j=1; j<mFrame32F_big.cols-1; ++j)
        {
            // Check if pixel is dead
            if(mAlive_big.at<unsigned char>(i, j) == 0)
            {
                // Dead pixel: build interpolation matrix
                cv::Mat neighbor( 3, 3, CV_32F );
                mAlive_big( cv::Range(i-1, i+2), cv::Range(j-1, j+2) ).convertTo( neighbor, CV_32F );

                //QLOG_DEBUG() << TAG << "neighbor sum" << cv::sum( neighbor )[0];

                // Compute interpolated value
                // 'M' is the same as 'mask' but with 0-coefficient over dead pixels
                const cv::Mat M = mask.mul( neighbor, 1.0 );

                // 'roi' Region of interest is the 3x3 region centered on the dead pixel
                const cv::Mat roi = mFrame32F_big( cv::Range(i-1, i+2), cv::Range(j-1, j+2) );
                double sum = cv::sum( M )[0];
                if(sum >= 1.0)
                {
                    mFrame32F_big.at<float>(i, j) = M.dot( roi ) / sum;
                }
                else
                {
                    mFrame32F_big.at<float>(i, j) = 0;
                }
            }
        }
    }
}

//.........这里部分代码省略.........
					
					
						// displaying the first solution if it was found
						if (first_solution_found)
						{
							std::cout << std::endl << "first R: " << first_R << std::endl;
							std::cout << "first T: " << first_T << std::endl;
							std::cout << "first n: " << first_n << std::endl;
							for (double ii : first_solution)
							{
								std::cout << ii << " ";
							}
							std::cout << std::endl;
							
						}
						
						// displaying the second solution if it was found
						if (second_solution_found)
						{
							std::cout << std::endl << "second R: " << second_R << std::endl;
							std::cout << "second T: " << second_T << std::endl;
							std::cout << "second n: " << second_n << std::endl;
							for (double ii : second_solution)
							{
								std::cout << ii << " ";
							}
							std::cout << std::endl;
						}
						
						// because the reference is set to the exact value when when n should have only a z componenet, the correct
						// choice should be the one closest to n_ref = [0,0,1]^T which will be the one with the greatest dot product with n_ref
						if (first_solution_found && second_solution_found)
						{
							if (first_n.dot(n_ref) >= second_n.dot(n_ref))
							{
								R_fc = first_R;
								T_fc = first_T;
							}
							else
							{
								R_fc = second_R;
								T_fc = second_T;
							}
							fc_found = true;
						}
						else if(first_solution_found)
						{
							R_fc = first_R;
							T_fc = first_T;
							fc_found = true;
						}
						
						//if a solution was found will publish
						// need to convert to pose message so use
						if (fc_found)
						{
							// converting the rotation from a cv matrix to quaternion, first need it as a matrix3x3
							R_fc_tf[0][0] = R_fc.at<double>(0,0);
							R_fc_tf[0][1] = R_fc.at<double>(0,1);
							R_fc_tf[0][2] = R_fc.at<double>(0,2);
							R_fc_tf[1][0] = R_fc.at<double>(1,0);
							R_fc_tf[1][1] = R_fc.at<double>(1,1);
							R_fc_tf[1][2] = R_fc.at<double>(1,2);
							R_fc_tf[2][0] = R_fc.at<double>(2,0);
							R_fc_tf[2][1] = R_fc.at<double>(2,1);
							R_fc_tf[2][2] = R_fc.at<double>(2,2);

cv::Mat SGDStep::predictImpl(const bool debugMode,
                             const cv::Mat &input) const
{
    cv::Ptr<SGDConfig> config;
    try {
        config = config_cast<SGDConfig>(this->mConfig);
    } catch(std::bad_cast) {
        std::stringstream s;
        s << "Wrong config type: " << this->mConfig->identifier();
        throw MLError(s.str(), currentMethod, currentLine);
    }

    std::vector<std::string> classifiers = config->classifierFiles();
    if(debugMode) { debug(classifiers.size(), "classifier(s)"); }
    cv::Mat1d results(1, classifiers.size());

    for(size_t idx = 0; idx < classifiers.size(); ++idx) {
        std::string classifierFile = classifiers[idx];
        if(debugMode) { debug("Loading classifier", classifierFile); }
        std::tuple<cv::Mat1d, double, vl_size> classifierData;
        std::tuple<cv::Mat1d, double, vl_size, double, double> plattClassifierData;
        try {
            if(config->plattScale()) {
                plattClassifierData = this->loadWithPlatt(classifierFile);
            } else {
                classifierData = this->load(classifierFile);
            }
            if(input.cols != std::get<0>(classifierData).cols) {
                std::stringstream s;
                s << "Data doesn't fit trained model." << std::endl;
                throw MLError(s.str(), currentMethod, currentLine);
            } else {
                if(input.type() != CV_64F) {
                    cv::Mat tmp;
                    input.convertTo(tmp, CV_64F);
                    if(config->plattScale()) {
                        double score = tmp.dot(std::get<0>(classifierData)) + std::get<1>(classifierData);
                        results.at<double>(idx) = Platt::sigmoid_predict(score,
                                                                         std::get<3>(plattClassifierData),
                                                                         std::get<4>(plattClassifierData));
                    } else {
                        double score = tmp.dot(std::get<0>(classifierData)) + std::get<1>(classifierData);
                        results.at<double>(idx) = score;
                    }
                } else {
                    if(config->plattScale()) {
                        double score = input.dot(std::get<0>(classifierData)) + std::get<1>(classifierData);
                        results.at<double>(idx) = Platt::sigmoid_predict(score,
                                                                         std::get<3>(plattClassifierData),
                                                                         std::get<4>(plattClassifierData));
                    } else {
                        double score = input.dot(std::get<0>(classifierData)) + std::get<1>(classifierData);
                        results.at<double>(idx) = score;
                    }
                }
            }
        } catch(MLError) {
            throw;
        }
    }

    if(config->binary()) {
        if(config->plattScale()) {
            double min, max;
            cv::Point minIdx, maxIdx;
            cv::minMaxLoc(results, &min, &max, &minIdx, &maxIdx);
            int32_t best = maxIdx.x;
            results.setTo(0);
            results.at<double>(best) = 1;
        } else {
            results.setTo(1, results > 0);
            results.setTo(-1, results < 0);
        }
    }

    return results;
}

 bool Frame::isInFrustum(MapPoint *pMP, float viewingCosLimit)
 {
     pMP->mbTrackInView = false;
     
     // 3D in absolute coordinates
     cv::Mat P = pMP->GetWorldPos();
     
     // 3D in camera coordinates
     const cv::Mat Pc = mRcw*P+mtcw;
     const float PcX = Pc.at<float>(0);
     const float PcY= Pc.at<float>(1);
     const float PcZ = Pc.at<float>(2);
     
     // Check positive depth
     if(PcZ<0.0)
         return false;
     
     // Project in image and check it is not outside
     const float invz = 1.0/PcZ;
     const float u=fx*PcX*invz+cx;
     const float v=fy*PcY*invz+cy;
     
     if(u<mnMinX || u>mnMaxX)
         return false;
     if(v<mnMinY || v>mnMaxY)
         return false;
     
     // Check distance is in the scale invariance region of the MapPoint
     const float maxDistance = pMP->GetMaxDistanceInvariance();
     const float minDistance = pMP->GetMinDistanceInvariance();
     const cv::Mat PO = P-mOw;
     const float dist = cv::norm(PO);
     
     if(dist<minDistance || dist>maxDistance)
         return false;
     
     // Check viewing angle
     cv::Mat Pn = pMP->GetNormal();
     
     float viewCos = PO.dot(Pn)/dist;
     
     if(viewCos<viewingCosLimit)
         return false;
     
     // Predict scale level acording to the distance
     float ratio = dist/minDistance;
     
     vector<float>::iterator it = lower_bound(mvScaleFactors.begin(), mvScaleFactors.end(), ratio);
     int nPredictedLevel = it-mvScaleFactors.begin();
     
     if(nPredictedLevel>=mnScaleLevels)
         nPredictedLevel=mnScaleLevels-1;
     
     // Data used by the tracking
     pMP->mbTrackInView = true;
     pMP->mTrackProjX = u;
     pMP->mTrackProjY = v;
     pMP->mnTrackScaleLevel= nPredictedLevel;
     pMP->mTrackViewCos = viewCos;
     
     return true;
 }