In the below example I implemented a “Naive Bayes classifier” in python and in the following I used “sklearn” package to solve it again: and the output is:
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male posterior is: 1.54428667821e-07 female posterior is: 0.999999845571 Then our data must belong to the female class Then our data must belong to the class number: [2] |
Naive Bayes Classifier Example with Python Code Read More »
In this tutorial, I explain the math and theory of robot localization and I will solve an example of Markov localization.
Markov Localization Explained Read More »
In this tutorial, I will explain the maths behind the Kalman Filter and I will drive the equations and their parameters.
Kalman Filter Explained Read More »
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a data clustering algorithm It is a density-based clustering algorithm because it finds a number of clusters starting from the estimated density distribution of corresponding nodes. It starts with an arbitrary starting point that has not been visited. This point’s epsilon-neighborhood is retrieved, and if it
Density-Based Spatial Clustering (DBSCAN) with Python Code Read More »
In this tutorial I explain the bayes filter from scratch:
Bayes Filter Explained Read More »
The snippet below shows how to computer PCA eigenvectors and eigenvalues (in this case could be interpreted as the moment of inertia).
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#include <pcl/features/moment_of_inertia_estimation.h> #include <pcl/point_cloud.h> #include <pcl/io/pcd_io.h> #include <pcl/visualization/cloud_viewer.h> #include <pcl/io/ply_io.h> template <typename PointT> void computeMomentOfInertia(boost::shared_ptr<pcl::PointCloud<PointT> > cloud_ptr) { pcl::MomentOfInertiaEstimation <PointT> feature_extractor; feature_extractor.setInputCloud (cloud_ptr); feature_extractor.compute (); std::vector <float> moment_of_inertia; std::vector <float> eccentricity; pcl::PointXYZRGB min_point_AABB; pcl::PointXYZRGB max_point_AABB; pcl::PointXYZRGB min_point_OBB; pcl::PointXYZRGB max_point_OBB; pcl::PointXYZRGB position_OBB; Eigen::Matrix3f rotational_matrix_OBB; float major_value, middle_value, minor_value; Eigen::Vector3f major_vector, middle_vector, minor_vector; Eigen::Vector3f mass_center; feature_extractor.getMomentOfInertia (moment_of_inertia); feature_extractor.getEccentricity (eccentricity); feature_extractor.getAABB (min_point_AABB, max_point_AABB); feature_extractor.getOBB (min_point_OBB, max_point_OBB, position_OBB, rotational_matrix_OBB); feature_extractor.getEigenValues (major_value, middle_value, minor_value); feature_extractor.getEigenVectors (major_vector, middle_vector, minor_vector); feature_extractor.getMassCenter (mass_center); boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer (new pcl::visualization::PCLVisualizer ("3D Viewer")); viewer->setBackgroundColor (0, 0, 0); viewer->addCoordinateSystem (0.1); viewer->initCameraParameters (); pcl::PointXYZ center (mass_center (0), mass_center (1), mass_center (2)); pcl::PointXYZ x_axis (major_vector (0) + mass_center (0), major_vector (1) + mass_center (1), major_vector (2) + mass_center (2)); pcl::PointXYZ y_axis (middle_vector (0) + mass_center (0), middle_vector (1) + mass_center (1), middle_vector (2) + mass_center (2)); pcl::PointXYZ z_axis (minor_vector (0) + mass_center (0), minor_vector (1) + mass_center (1), minor_vector (2) + mass_center (2)); viewer->addLine (center, x_axis, 1.0f, 0.0f, 0.0f, "major eigen vector"); viewer->addLine (center, y_axis, 0.0f, 1.0f, 0.0f, "middle eigen vector"); viewer->addLine (center, z_axis, 0.0f, 0.0f, 1.0f, "minor eigen vector"); viewer->addPointCloud<pcl::PointXYZRGB> (cloud_ptr, "Moment Of Inertia (PCA Eigen Vector Axes)"); while (!viewer->wasStopped ()) { viewer->spinOnce (); } } int main(int argc, char **argv) { pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloudrgb_ptr(new pcl::PointCloud<pcl::PointXYZRGB>); pcl::PLYReader PLYFileReader; const int offset=0; PLYFileReader.read< pcl::PointXYZRGB >(argv[1],*cloudrgb_ptr,offset); //pcl::io::loadPCDFile(argv[1],*cloudrgb_ptr); computeMomentOfInertia(cloudrgb_ptr); return 0; } |
Eigenvectors of PCL pointcloud (moment of inertia) Read More »
This ROS package enables you to crop the scene from Kinect (input topic type: PCL pointcloud). You can even enable fitting a plane to remove the ground from the scene and by adjusting correct parameter you can get the desired object from the scene. code available on my Github.
A GUI ROS-package for cropping pcl pointcloud with dynamic reconfigure Read More »
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/* C1 C2 C3 C4 CV_8U 0 8 16 24 CV_8S 1 9 17 25 CV_16U 2 10 18 26 CV_16S 3 11 19 27 CV_32S 4 12 20 28 CV_32F 5 13 21 29 CV_64F 6 14 22 30 Unsigned 8bits uchar 0~255 IplImage: IPL_DEPTH_8U Mat: CV_8UC1, CV_8UC2, CV_8UC3, CV_8UC4 Signed 8bits char -128~127 IplImage: IPL_DEPTH_8S Mat: CV_8SC1，CV_8SC2，CV_8SC3，CV_8SC4 Unsigned 16bits ushort 0~65535 IplImage: IPL_DEPTH_16U Mat: CV_16UC1，CV_16UC2，CV_16UC3，CV_16UC4 Signed 16bits short -32768~32767 IplImage: IPL_DEPTH_16S Mat: CV_16SC1，CV_16SC2，CV_16SC3，CV_16SC4 Signed 32bits int -2147483648~2147483647 IplImage: IPL_DEPTH_32S Mat: CV_32SC1，CV_32SC2，CV_32SC3，CV_32SC4 Float 32bits float -1.18*10-38~3.40*10-38 IplImage: IPL_DEPTH_32F Mat: CV_32FC1，CV_32FC2，CV_32FC3，CV_32FC4 Double 64bits double Mat: CV_64FC1，CV_64FC2，CV_64FC3，CV_64FC4 Unsigned 1bit bool IplImage: IPL_DEPTH_1U */ //example: int number_of_rows, number_of_columns; number_of_rows=3; number_of_columns=4; cv::Mat mat_CV_8U(number_of_rows, number_of_columns,CV_8UC1); std::cout<<"mat_CV_8U.type(): " <<mat_CV_8U.type() <<std::endl; cv::Mat mat_CV_64FC1(number_of_rows, number_of_columns,CV_64FC1); std::cout<<"mat_CV_64FC1.type(): " <<mat_CV_64FC1.type() <<std::endl; |
ref: Special thanks to this post.
List of OpenCv matrix types and mapping numbers Read More »
There are several approaches for estimating the probability distribution function of a given data: 1)Parametric 2)Semi-parametric 3)Non-parametric A parametric one is GMM via algorithm such as expectation maximization. Here is my other post for expectation maximization. Example of Non-parametric is the histogram, where data are assigned to only one bin and depending on the number bins that fall within
Kernel Density Estimation (KDE) for estimating probability distribution function Read More »
Silhouette coefficient is another method to determine the optimal number of clusters. Here I introduced c-index earlier. The silhouette coefficient of a data measures how well data are assigned to its own cluster and how far they are from other clusters. A silhouette close to 1 means the data points are in an appropriate cluster and a silhouette
Silhouette coefficient for finding optimal number of clusters Read More »
