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
49using namespace cv;
50using namespace std;
51
52///////////////////////
53// Functions
54static 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
66static  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
78static 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
90struct params
91{
92    Mat data;
93    int ch;
94    int rows;
95    PCA pca;
96    string winName;
97};
98
99static 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
122int 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