1 /* 2 * pca.cpp 3 * 4 * Author: 5 * Kevin Hughes <kevinhughes27[at]gmail[dot]com> 6 * 7 * Special Thanks to: 8 * Philipp Wagner <bytefish[at]gmx[dot]de> 9 * 10 * This program demonstrates how to use OpenCV PCA with a 11 * specified amount of variance to retain. The effect 12 * is illustrated further by using a trackbar to 13 * change the value for retained varaince. 14 * 15 * The program takes as input a text file with each line 16 * begin the full path to an image. PCA will be performed 17 * on this list of images. The author recommends using 18 * the first 15 faces of the AT&T face data set: 19 * http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html 20 * 21 * so for example your input text file would look like this: 22 * 23 * <path_to_at&t_faces>/orl_faces/s1/1.pgm 24 * <path_to_at&t_faces>/orl_faces/s2/1.pgm 25 * <path_to_at&t_faces>/orl_faces/s3/1.pgm 26 * <path_to_at&t_faces>/orl_faces/s4/1.pgm 27 * <path_to_at&t_faces>/orl_faces/s5/1.pgm 28 * <path_to_at&t_faces>/orl_faces/s6/1.pgm 29 * <path_to_at&t_faces>/orl_faces/s7/1.pgm 30 * <path_to_at&t_faces>/orl_faces/s8/1.pgm 31 * <path_to_at&t_faces>/orl_faces/s9/1.pgm 32 * <path_to_at&t_faces>/orl_faces/s10/1.pgm 33 * <path_to_at&t_faces>/orl_faces/s11/1.pgm 34 * <path_to_at&t_faces>/orl_faces/s12/1.pgm 35 * <path_to_at&t_faces>/orl_faces/s13/1.pgm 36 * <path_to_at&t_faces>/orl_faces/s14/1.pgm 37 * <path_to_at&t_faces>/orl_faces/s15/1.pgm 38 * 39 */ 40 41 #include <iostream> 42 #include <fstream> 43 #include <sstream> 44 45 #include <opencv2/core/core.hpp> 46 #include "opencv2/imgcodecs.hpp" 47 #include <opencv2/highgui/highgui.hpp> 48 49 using namespace cv; 50 using namespace std; 51 52 /////////////////////// 53 // Functions 54 static void read_imgList(const string& filename, vector<Mat>& images) { 55 std::ifstream file(filename.c_str(), ifstream::in); 56 if (!file) { 57 string error_message = "No valid input file was given, please check the given filename."; 58 CV_Error(Error::StsBadArg, error_message); 59 } 60 string line; 61 while (getline(file, line)) { 62 images.push_back(imread(line, 0)); 63 } 64 } 65 66 static Mat formatImagesForPCA(const vector<Mat> &data) 67 { 68 Mat dst(static_cast<int>(data.size()), data[0].rows*data[0].cols, CV_32F); 69 for(unsigned int i = 0; i < data.size(); i++) 70 { 71 Mat image_row = data[i].clone().reshape(1,1); 72 Mat row_i = dst.row(i); 73 image_row.convertTo(row_i,CV_32F); 74 } 75 return dst; 76 } 77 78 static Mat toGrayscale(InputArray _src) { 79 Mat src = _src.getMat(); 80 // only allow one channel 81 if(src.channels() != 1) { 82 CV_Error(Error::StsBadArg, "Only Matrices with one channel are supported"); 83 } 84 // create and return normalized image 85 Mat dst; 86 cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1); 87 return dst; 88 } 89 90 struct params 91 { 92 Mat data; 93 int ch; 94 int rows; 95 PCA pca; 96 string winName; 97 }; 98 99 static void onTrackbar(int pos, void* ptr) 100 { 101 cout << "Retained Variance = " << pos << "% "; 102 cout << "re-calculating PCA..." << std::flush; 103 104 double var = pos / 100.0; 105 106 struct params *p = (struct params *)ptr; 107 108 p->pca = PCA(p->data, cv::Mat(), PCA::DATA_AS_ROW, var); 109 110 Mat point = p->pca.project(p->data.row(0)); 111 Mat reconstruction = p->pca.backProject(point); 112 reconstruction = reconstruction.reshape(p->ch, p->rows); 113 reconstruction = toGrayscale(reconstruction); 114 115 imshow(p->winName, reconstruction); 116 cout << "done! # of principal components: " << p->pca.eigenvectors.rows << endl; 117 } 118 119 120 /////////////////////// 121 // Main 122 int main(int argc, char** argv) 123 { 124 if (argc != 2) { 125 cout << "usage: " << argv[0] << " <image_list.txt>" << endl; 126 exit(1); 127 } 128 129 // Get the path to your CSV. 130 string imgList = string(argv[1]); 131 132 // vector to hold the images 133 vector<Mat> images; 134 135 // Read in the data. This can fail if not valid 136 try { 137 read_imgList(imgList, images); 138 } catch (cv::Exception& e) { 139 cerr << "Error opening file \"" << imgList << "\". Reason: " << e.msg << endl; 140 exit(1); 141 } 142 143 // Quit if there are not enough images for this demo. 144 if(images.size() <= 1) { 145 string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!"; 146 CV_Error(Error::StsError, error_message); 147 } 148 149 // Reshape and stack images into a rowMatrix 150 Mat data = formatImagesForPCA(images); 151 152 // perform PCA 153 PCA pca(data, cv::Mat(), PCA::DATA_AS_ROW, 0.95); // trackbar is initially set here, also this is a common value for retainedVariance 154 155 // Demonstration of the effect of retainedVariance on the first image 156 Mat point = pca.project(data.row(0)); // project into the eigenspace, thus the image becomes a "point" 157 Mat reconstruction = pca.backProject(point); // re-create the image from the "point" 158 reconstruction = reconstruction.reshape(images[0].channels(), images[0].rows); // reshape from a row vector into image shape 159 reconstruction = toGrayscale(reconstruction); // re-scale for displaying purposes 160 161 // init highgui window 162 string winName = "Reconstruction | press 'q' to quit"; 163 namedWindow(winName, WINDOW_NORMAL); 164 165 // params struct to pass to the trackbar handler 166 params p; 167 p.data = data; 168 p.ch = images[0].channels(); 169 p.rows = images[0].rows; 170 p.pca = pca; 171 p.winName = winName; 172 173 // create the tracbar 174 int pos = 95; 175 createTrackbar("Retained Variance (%)", winName, &pos, 100, onTrackbar, (void*)&p); 176 177 // display until user presses q 178 imshow(winName, reconstruction); 179 180 int key = 0; 181 while(key != 'q') 182 key = waitKey(); 183 184 return 0; 185 } 186
