+/*
+* pca.cpp
+*
+* Author:
+* Kevin Hughes <kevinhughes27[at]gmail[dot]com>
+*
+* Special Thanks to:
+* Philipp Wagner <bytefish[at]gmx[dot]de>
+*
+* 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:
+*
+* <path_to_at&t_faces>/orl_faces/s1/1.pgm
+* <path_to_at&t_faces>/orl_faces/s2/1.pgm
+* <path_to_at&t_faces>/orl_faces/s3/1.pgm
+* <path_to_at&t_faces>/orl_faces/s4/1.pgm
+* <path_to_at&t_faces>/orl_faces/s5/1.pgm
+* <path_to_at&t_faces>/orl_faces/s6/1.pgm
+* <path_to_at&t_faces>/orl_faces/s7/1.pgm
+* <path_to_at&t_faces>/orl_faces/s8/1.pgm
+* <path_to_at&t_faces>/orl_faces/s9/1.pgm
+* <path_to_at&t_faces>/orl_faces/s10/1.pgm
+* <path_to_at&t_faces>/orl_faces/s11/1.pgm
+* <path_to_at&t_faces>/orl_faces/s12/1.pgm
+* <path_to_at&t_faces>/orl_faces/s13/1.pgm
+* <path_to_at&t_faces>/orl_faces/s14/1.pgm
+* <path_to_at&t_faces>/orl_faces/s15/1.pgm
+*
+*/
+
+#include <iostream>
+#include <fstream>
+#include <sstream>
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+
+using namespace cv;
+using namespace std;
+
+///////////////////////
+// Functions
+void read_imgList(const string& filename, vector<Mat>& 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));
+ }
+}
+
+Mat formatImagesForPCA(const vector<Mat> &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;
+}
+
+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;
+};
+
+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] << " <image_list.txt>" << endl;
+ exit(1);
+ }
+
+ // Get the path to your CSV.
+ string imgList = string(argv[1]);
+
+ // vector to hold the images
+ vector<Mat> 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);
+
+ char key = 0;
+ while(key != 'q')
+ key = waitKey();
+
+ return 0;
+}