samples/cpp/pca.cpp

An example using PCA for dimensionality reduction while maintaining an amount of variance

/*
* 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 variance.
*
* 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.hpp>
#include "opencv2/imgcodecs.hpp"
#include <opencv2/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(Error::StsBadArg, error_message);
    }
    string line;
    while (getline(file, line)) {
        images.push_back(imread(line, IMREAD_GRAYSCALE));
    }
}

static  Mat formatImagesForPCA(const vector<Mat> &data)
{
    Mat dst(static_cast<int>(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(Error::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(), 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)
{
    cv::CommandLineParser parser(argc, argv, "{@input||image list}{help h||show help message}");
    if (parser.has("help"))
    {
        parser.printMessage();
        exit(0);
    }
    // Get the path to your CSV.
    string imgList = parser.get<string>("@input");
    if (imgList.empty())
    {
        parser.printMessage();
        exit(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 (const 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(Error::StsError, error_message);
    }

    // Reshape and stack images into a rowMatrix
    Mat data = formatImagesForPCA(images);

    // perform PCA
    PCA pca(data, cv::Mat(), 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);

    char key = 0;
    while(key != 'q')
        key = (char)waitKey();

   return 0;
}