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2016-08-02 08:35:38 -0600	received badge	● Editor (source)
2016-08-02 08:23:57 -0600	commented question	PCA returns NaN In addition, transposing data_training_mat tends to appearance of some small numbers inside a bunch of NaNs and Infs in eigenvalues, if it clarifies some points!
2016-08-02 08:13:38 -0600	commented question	PCA returns NaN I check eigenvalues after that. All values are -NaN: `Mat average = Mat(); PCA pca(training_data_mat, average, CV_PCA_DATA_AS_ROW); cout << "checking pca... " << endl ; cout << pca.eigenvalues << endl ;`
2016-08-02 08:03:42 -0600	commented question	PCA returns NaN hog_ders is right now the vector of image histogram not HOG descriptors. However, converting the type to CV_32F does not change anything.
2016-08-02 07:55:51 -0600	asked a question	PCA returns NaN Hi everybody, I'm attempting to classify 10 classes of hand pictures using a specific feature space. Training data is converted to a Mat object containing 240 rows (number of training pictures) and 480 cols (number of features) normalized between 0 and 1. The type of training data mat is CV_64FC1. I'm going to pass this matrix to PCA in order to extract useful features. After that I want to pass PCA result to SVM in order to train a classifier. The PCA object returns all -NaN and Inf after creating the object: `PCA pca(training_data_mat, Mat(), CV_PCA_DATA_AS_ROW);` `training_data_mat` is the matrix of the training data with 240 rows and 450 cols for 240 training images and 450 features. It is created in the following line: `training_data_mat.push_back(extract_features_mat(descriptor,hog_ders)) ;` the `extract_features_mat` function is written such that returns a 1 row Mat of type CV_64FC1 containing features of the image. The following line is return value of the `extract_features_mat` function: `return Mat (1,feature_size, CV_64FC1, feature_value) ;` and `feature_value` is a double array defined and filled in this function. I'm totally confused what is wrong! This is my `extract_features_mat` function: Mat extract_features_mat(Mat descriptor, std::vector<float> hog_ders) { int feature_size = descriptor.cols + hog_ders.size() ; double feature_value[feature_size] ; double gamma = 0.05 ; for(int j = 0 ; j < descriptor.cols ; j++) { feature_value[j] = 0 ; for(int h = 0 ; h < descriptor.rows ; h++) { double to_be_added_value = (descriptor.at<double>(h,j)/pow(10,15) ) ; feature_value[j] += to_be_added_value; if(feature_value[j] > 1) feature_value[j] = 1 ; } } float max = 0 ; for(int j = 0 ; j < descriptor.cols ; j++) { if(feature_value[j] < 0.1 ) feature_value[j] = 0.1 ; feature_value[j] = 1 * pow(feature_value[j],gamma) ; if(max < feature_value[j]) max = feature_value[j] ; } for(int j =0 ; j < descriptor.cols ; j++) feature_value[j] = feature_value[j] / max ; max = 0 ; for(int j = descriptor.cols ; j < descriptor.cols + hog_ders.size() ; j++) { feature_value[j] = hog_ders.at(j - descriptor.cols) ; if(max < feature_value[j]) max = feature_value[j] ; } for(int j = descriptor.cols ; j < descriptor.cols + hog_ders.size() ; j++) feature_value[j] = feature_value[j] / max ; return Mat(1,feature_size, CV_64FC1, feature_value) ; }
2016-07-22 04:10:59 -0600	commented question	Segmentation fault on cv::imshow function I have changed that and everything is working good! Thanks a lot berak!
2016-07-22 03:44:43 -0600	asked a question	Segmentation fault on cv::imshow function Hello every one, here is my function "`optical_flow_using_schunck`". on line "`imshow("frame1", frame1)`" it throws "Segmentation fault (Core dumped)" error. Any idea?! int optical_flow_using_schunck(char * video_url) { int iteration_count = 20 ; double a = 0.001 ; namedWindow("frame1" , CV_WINDOW_AUTOSIZE) ; namedWindow("frame2" , CV_WINDOW_AUTOSIZE) ; cout << "Analyzing motion using Schunck approach for approximating optical flow..." << endl ; cout << "Reading video from " << video_url << endl ; VideoCapture vid (video_url); if(!vid.isOpened()) { cout << "An error has been occured. The video can not be opened!" << endl ; return -1 ; } Mat frame1, frame2 ; vid >> frame1 ; if(!frame1.empty()) vid >> frame2 ; cvtColor(frame1 , frame1 , CV_BGR2GRAY) ; cvtColor(frame2 , frame2 , CV_BGR2GRAY) ; pyrDown(frame1, frame1, Size(frame1.cols/2, frame1.rows/2)) ; pyrDown(frame2, frame2, Size(frame2.cols/2, frame2.rows/2)) ; cout <<"initializing velocity to zero" << endl ; int sz[] = {frame1.rows, frame1.cols}; Mat u(2, sz, CV_32FC1, Scalar::all(0)); Mat v(2, sz, CV_32FC1, Scalar::all(0)); Mat u_avg(2, sz, CV_32FC1, Scalar::all(0)); Mat v_avg(2, sz, CV_32FC1, Scalar::all(0)); Mat Ex(2, sz, CV_32FC1, Scalar::all(0)); Mat Ey(2, sz, CV_32FC1, Scalar::all(0)); Mat Et(2, sz, CV_32FC1, Scalar::all(0)); while(!frame2.empty()) { cout << "calculating Ex derivative..." << endl ; calculate_EX(frame1, frame2, &Ex) ; cout << "calculating Ey derivative..." << endl ; calculate_EY(frame1, frame2, &Ey) ; cout << "calculating Et derivative..." << endl ; calculate_ET(frame1, frame2, &Et) ; if(frame1.empty()) cout << "WOW!" << endl ; imshow("frame1" , frame1) ; imshow("frame2" , frame2) ; for(int iter = 0 ; iter < iteration_count ; iter ++) { cout << "iteration: " << iter << endl ; for(int i = 0 ; i < u.rows ; i ++) { for(int j = 0 ; j < u.cols ; j++) { // cout << "i: " << i << ", j:" << j << endl; double common_part = (Ex.at<double>(i,j)u_avg.at<double>(i,j) + Ey.at<double>(i,j) v_avg.at<double>(i,j) + Et.at<double>(i,j) ) / (pow(a,2) + pow(Ex.at<double>(i,j),2) + pow(Ey.at<double>(i,j),2) ) ; double delta_u = ( Ex.at<double>(i,j) * common_part) ; double delta_v = ( Ey.at<double>(i,j) * common_part) ; u.at<double>(i,j) = u_avg.at<double>(i,j) - delta_u ; v.at<double>(i,j) = u_avg.at<double>(i,j) - delta_v ; } } // cout << "u_avg is being updated"<< endl ; update_veloc_avg(u, &u_avg) ; // cout << "u_avg has been updated"<< endl ; update_veloc_avg(v, &v_avg) ; // cout << "v_avg has been updated"<< endl ; // imshow("U_veloc" , u) ; // imshow("V_veloc" , v) ; // waitKey(0) ; } cout << "6" << endl ; frame2.copyTo(frame1(Rect(0, 0, frame2.cols, frame2.rows))); cout << "7" << endl ; vid >> frame2 ; cout << "8" << endl ; cvtColor(frame2 , frame2 , CV_BGR2GRAY) ; cout << "9" << endl ; pyrDown(frame2, frame2, Size(frame2.cols/2, frame2.rows/2)) ; cout << "10" << endl ; waitKey(0) ; } cout << "************************************************\nSchunk ego motion estimation.\n\n\tTHE VIDEO FINISHED\n***********************************************\n" << endl ; } here are my other functions "`calculate_Ex`", "`calculate_Ey`" , "`calculate_Et`" and "`update_veloc_avg`" . `void calculate_EX(Mat frame1, Mat frame2, Mat E) { for(int i = 0 ; i < E->rows ; i++) { for(int j = 0 ; j < E->cols ; j++) { double res = 0 - (frame1.at<double>(i,j) + frame2.at<double>(i,j)) ; if(j < frame1.cols - 1) res += frame1.at<double>(i,j+1) ; if(j < frame1.cols - 1 && i < frame1.rows - 1) res += frame1.at<double ...` (more)
2016-04-18 13:03:03 -0600	received badge	● Student (source)
2016-04-06 10:52:25 -0600	asked a question	histogram smoothing I have an image in RGB, along with its planes' histograms. How I can smooth these histograms? (histograms are generated using opencv calcHist function)