这篇教程C++ CLOG函数代码示例写得很实用，希望能帮到您。
本文整理汇总了C++中CLOG函数的典型用法代码示例。如果您正苦于以下问题：C++ CLOG函数的具体用法？C++ CLOG怎么用？C++ CLOG使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。 在下文中一共展示了CLOG函数的16个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的C++代码示例。 示例1: CLOGvoid CvBayesClassifier::onClearDataset() { CLOG(LTRACE) << "CvBayesClassifier::onClearDataset/n"; training_dataset.clear(); training_responses.clear(); CLOG(LINFO) << "Training dataset cleared";}
开发者ID:maciek-slon，项目名称:DCL_CvBasic，代码行数:6，
示例2: LOGvoid SIFTNOMWriter::WriteNormals() { LOG(LTRACE) << "SIFTNOMWriter::WriteNormals"; // Try to save the model retrieved from the SOM data stream. ptree ptree_file; //if(in_cloud_xyzrgb_normals.empty()&&in_cloud_xyzsift.empty()&&in_mean_viewpoint_features_number.empty()){ // CLOG(LWARNING) << "There are no required datastreams enabling save of the SOM to file."; // return; //} if (!in_som.empty()) { LOG(LDEBUG) << "!in_som.empty()"; // Get SOM. SIFTObjectModel* som = in_som.read(); // Save point cloud. std::string name_cloud_xyzrgb_normals = std::string(dir) + std::string("/") + std::string(SOMname) + std::string("_xyzrgb_normals.pcd"); pcl::io::savePCDFileASCII (name_cloud_xyzrgb_normals, *(som->cloud_xyzrgb_normals)); CLOG(LTRACE) << "Write: saved " << som->cloud_xyzrgb_normals->points.size () << " cloud points to "<< name_cloud_xyzrgb_normals; // Save feature cloud. std::string name_cloud_xyzsift = std::string(dir) + std::string("/") + std::string(SOMname) + std::string("_xyzsift.pcd"); pcl::io::savePCDFileASCII (name_cloud_xyzsift, *(som->cloud_xyzsift)); CLOG(LTRACE) << "Write: saved " << som->cloud_xyzsift->points.size () << " feature points to "<< name_cloud_xyzsift; // Save JSON model description. ptree_file.put("name", SOMname); ptree_file.put("type", "SIFTObjectModel"); ptree_file.put("mean_viewpoint_features_number", som->mean_viewpoint_features_number); ptree_file.put("cloud_xyzsift", name_cloud_xyzsift); } // Try to save the model retrieved from the three separate data streams. if (!in_cloud_xyzsift.empty() && !in_mean_viewpoint_features_number.empty()) { LOG(LDEBUG) << "!in_cloud_xyzsift.empty() && !in_mean_viewpoint_features_number.empty()"; // Get model from datastreams. pcl::PointCloud<PointXYZSIFT>::Ptr cloud_xyzsift = in_cloud_xyzsift.read(); int mean_viewpoint_features_number = in_mean_viewpoint_features_number.read(); // Save feature cloud. std::string name_cloud_xyzsift = std::string(dir) + std::string("/") + std::string(SOMname) + std::string("_xyzsift.pcd"); pcl::io::savePCDFileASCII (name_cloud_xyzsift, *(cloud_xyzsift)); CLOG(LTRACE) << "Write: saved " << cloud_xyzsift->points.size () << " feature points to "<< name_cloud_xyzsift; // Save JSON model description. ptree_file.put("name", SOMname); ptree_file.put("type", "SIFTObjectModel"); ptree_file.put("mean_viewpoint_features_number", mean_viewpoint_features_number); ptree_file.put("cloud_xyzsift", name_cloud_xyzsift); } if (!in_cloud_xyzrgb_normals.empty()) { LOG(LDEBUG) << "!in_cloud_xyzrgb_normals.empty()"; // Get model from datastreams. pcl::PointCloud<pcl::PointXYZRGBNormal>::Ptr cloud_xyzrgb_normals = in_cloud_xyzrgb_normals.read(); // Save point cloud. std::string name_cloud_xyzrgb_normals = std::string(dir) + std::string("/") + std::string(SOMname) + std::string("_xyzrgb_normals.pcd"); pcl::io::savePCDFileASCII (name_cloud_xyzrgb_normals, *(cloud_xyzrgb_normals)); CLOG(LTRACE) << "Write: saved " << cloud_xyzrgb_normals->points.size () << " cloud points to "<< name_cloud_xyzrgb_normals; // Save JSON model description. //ptree ptree_file; ptree_file.put("name", SOMname); ptree_file.put("type", "SIFTObjectModel"); ptree_file.put("cloud_xyzrgb_normals", name_cloud_xyzrgb_normals); } write_json (std::string(dir) + std::string("/") + std::string(SOMname) + std::string(".json"), ptree_file);}
开发者ID:DisCODe，项目名称:SIFTObjectModel，代码行数:83，
示例3: CLOGvoid CalcStatistics::calculate() { CLOG(LDEBUG)<<"in calculate()"; Types::HomogMatrix homogMatrix; cv::Mat_<double> rvec; cv::Mat_<double> tvec; cv::Mat_<double> axis; cv::Mat_<double> rotMatrix; float fi; rotMatrix= cv::Mat_<double>::zeros(3,3); tvec = cv::Mat_<double>::zeros(3,1); axis = cv::Mat_<double>::zeros(3,1); // first homogMatrix on InputStream if(counter == 0) { homogMatrix = in_homogMatrix.read(); for (int i = 0; i < 3; ++i) { for (int j = 0; j < 3; ++j) { rotMatrix(i,j)=homogMatrix(i, j); } tvec(i, 0) = homogMatrix(i, 3); } Rodrigues(rotMatrix, rvec); cumulatedHomogMatrix = homogMatrix; cumulatedTvec = tvec; cumulatedRvec = rvec; fi = sqrt((pow(rvec(0,0), 2) + pow(rvec(1,0), 2)+pow(rvec(2,0),2))); cumulatedFi = fi; for(int k=0;k<3;k++) { axis(k,0)=rvec(k,0)/fi; } cumulatedAxis = axis; counter=1; return; } homogMatrix=in_homogMatrix.read(); for (int i = 0; i < 3; ++i) { for (int j = 0; j < 3; ++j) { rotMatrix(i,j)=homogMatrix(i, j); } tvec(i, 0) = homogMatrix(i, 3); } Rodrigues(rotMatrix, rvec); fi = sqrt((pow(rvec(0,0), 2) + pow(rvec(1,0), 2)+pow(rvec(2,0),2))); for(int k=0;k<3;k++) { axis(k,0)=rvec(k,0)/fi; } cumulatedFi += fi; cumulatedTvec += tvec; cumulatedRvec += rvec; cumulatedAxis += axis; counter++; avgFi = cumulatedFi/counter; avgAxis = cumulatedAxis/counter; avgRvec = avgAxis * avgFi; avgTvec = cumulatedTvec/counter; Types::HomogMatrix hm; cv::Mat_<double> rottMatrix; Rodrigues(avgRvec, rottMatrix); CLOG(LINFO)<<"U
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