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- /*
- * 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
- static 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));
- }
- }
- static Mat formatImagesForPCA(const vector<Mat> &data)
- {
- Mat dst( static_cast<int>(data.size()), data[0].rows * data[0].cols, CV_32F ); //static_cast:ǿ ת
- 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] << " <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, 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;
- }
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