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 »
In this part, I trained an SVM over images of “face” or “not face” (36 × 36 pixels), using HOG features. I used VLFeat library for both HOG and the SVM. Example of face images: Example of nonface images: I divided the dataset into a training and a test set (80% and 20% respectively) and computed the HOG
Multi scale face detector using HOG features and support vector machine Read More »
This work is the second part of my master thesis (part I). In this part, I developed an algorithm for 2D pose estimation of the human body. To do this, I created a software with QT that could generate 2D contours representing human body. Then I send these contours for evaluation to CNS(Contrast Normalized Sobel) [1]
2D pose estimation of human body using CNS and PCA Read More »
This is the part I of the work that I did for my master thesis (part II). In this work first, I computed HOG (Histogram of oriented gradients) on my images and then sent the computed histogram to a linear SVM (support vector machine). The SVM was trained with human and non-human images. The output of the classifier was
Human detection on mobile camera using HOG and tracking them using Kalman filter Read More »
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#include <sensor_msgs/point_cloud_conversion.h> sensor_msgs::PointCloud2 point_cloud2; sensor_msgs::PointCloud point_cloud; sensor_msgs::convertPointCloudToPointCloud2(point_cloud, point_cloud2); sensor_msgs::convertPointCloud2ToPointCloud(point_cloud2, point_cloud); |
Converting sensor_msgs::PCLPointCloud2 to sensor_msgs::PointCloud and reverse Read More »
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#include <pcl/conversions.h> |
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pcl::PCLPointCloud2 point_cloud2; pcl::PointCloud<pcl::PointXYZ> point_cloud; pcl::fromPCLPointCloud2( point_cloud2, point_cloud); pcl::toPCLPointCloud2(point_cloud, point_cloud2); |
more about pcl point cloud and conversion
Converting pcl::PCLPointCloud2 to pcl::PointCloud and reverse Read More »
Have you ever wondered why the selfie that you are taking with your phone is so bad while when you taking a photo a decent Nikon or Canon DSLR you get a nice portrait? This happens due to the size of the sensor and focal length. Since the sensor in your cell-phone is very small
Effect of focal length on image Read More »
This Matlab tutorial I use SIFT, RANSAC, and homography to find corresponding points between two images. Here I have used vlfeat to find SIFT features. Full code is available at my GitHub repository Major steps are: 0.Adding vlfeat to your Matlab workspace:
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run('<path_to_vlfeat>/toolbox/vl_setup') |
1.Detect key points and extract descriptors. In the image below you can see some SIFT key
Stitching image using SIFT and Homography Read More »
