/* * pca.cpp * * Author: * Kevin Hughes * * Special Thanks to: * Philipp Wagner * * This program demonstrates how to use OpenCV PCA with a * specified amount of variance to retain. The effect * is illustrated further by using a trackbar to * change the value for retained varaince. * * The program takes as input a text file with each line * begin the full path to an image. PCA will be performed * on this list of images. The author recommends using * the first 15 faces of the AT&T face data set: * http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html * * so for example your input text file would look like this: * * /orl_faces/s1/1.pgm * /orl_faces/s2/1.pgm * /orl_faces/s3/1.pgm * /orl_faces/s4/1.pgm * /orl_faces/s5/1.pgm * /orl_faces/s6/1.pgm * /orl_faces/s7/1.pgm * /orl_faces/s8/1.pgm * /orl_faces/s9/1.pgm * /orl_faces/s10/1.pgm * /orl_faces/s11/1.pgm * /orl_faces/s12/1.pgm * /orl_faces/s13/1.pgm * /orl_faces/s14/1.pgm * /orl_faces/s15/1.pgm * */ #include #include #include #include #include using namespace cv; using namespace std; /////////////////////// // Functions static void read_imgList(const string& filename, vector& images) { std::ifstream file(filename.c_str(), ifstream::in); if (!file) { string error_message = "No valid input file was given, please check the given filename."; CV_Error(CV_StsBadArg, error_message); } string line; while (getline(file, line)) { images.push_back(imread(line, 0)); } } static Mat formatImagesForPCA(const vector &data) { Mat dst(data.size(), data[0].rows*data[0].cols, CV_32F); for(unsigned int i = 0; i < data.size(); i++) { Mat image_row = data[i].clone().reshape(1,1); Mat row_i = dst.row(i); image_row.convertTo(row_i,CV_32F); } return dst; } static Mat toGrayscale(InputArray _src) { Mat src = _src.getMat(); // only allow one channel if(src.channels() != 1) { CV_Error(CV_StsBadArg, "Only Matrices with one channel are supported"); } // create and return normalized image Mat dst; cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1); return dst; } struct params { Mat data; int ch; int rows; PCA pca; string winName; }; static void onTrackbar(int pos, void* ptr) { cout << "Retained Variance = " << pos << "% "; cout << "re-calculating PCA..." << std::flush; double var = pos / 100.0; struct params *p = (struct params *)ptr; p->pca = PCA(p->data, cv::Mat(), CV_PCA_DATA_AS_ROW, var); Mat point = p->pca.project(p->data.row(0)); Mat reconstruction = p->pca.backProject(point); reconstruction = reconstruction.reshape(p->ch, p->rows); reconstruction = toGrayscale(reconstruction); imshow(p->winName, reconstruction); cout << "done! # of principal components: " << p->pca.eigenvectors.rows << endl; } /////////////////////// // Main int main(int argc, char** argv) { if (argc != 2) { cout << "usage: " << argv[0] << " " << endl; exit(1); } // Get the path to your CSV. string imgList = string(argv[1]); // vector to hold the images vector images; // Read in the data. This can fail if not valid try { read_imgList(imgList, images); } catch (cv::Exception& e) { cerr << "Error opening file \"" << imgList << "\". Reason: " << e.msg << endl; exit(1); } // Quit if there are not enough images for this demo. if(images.size() <= 1) { string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!"; CV_Error(CV_StsError, error_message); } // Reshape and stack images into a rowMatrix Mat data = formatImagesForPCA(images); // perform PCA PCA pca(data, cv::Mat(), CV_PCA_DATA_AS_ROW, 0.95); // trackbar is initially set here, also this is a common value for retainedVariance // Demonstration of the effect of retainedVariance on the first image Mat point = pca.project(data.row(0)); // project into the eigenspace, thus the image becomes a "point" Mat reconstruction = pca.backProject(point); // re-create the image from the "point" reconstruction = reconstruction.reshape(images[0].channels(), images[0].rows); // reshape from a row vector into image shape reconstruction = toGrayscale(reconstruction); // re-scale for displaying purposes // init highgui window string winName = "Reconstruction | press 'q' to quit"; namedWindow(winName, CV_WINDOW_NORMAL); // params struct to pass to the trackbar handler params p; p.data = data; p.ch = images[0].channels(); p.rows = images[0].rows; p.pca = pca; p.winName = winName; // create the tracbar int pos = 95; createTrackbar("Retained Variance (%)", winName, &pos, 100, onTrackbar, (void*)&p); // display until user presses q imshow(winName, reconstruction); int key = 0; while(key != 'q') key = waitKey(); return 0; }