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Usage of imread(): magic number 0, unchecked result * docs: rewrite 0/1 to IMREAD_GRAYSCALE/IMREAD_COLOR in imread() * samples, apps: rewrite 0/1 to IMREAD_GRAYSCALE/IMREAD_COLOR in imread() * tests: rewrite 0/1 to IMREAD_GRAYSCALE/IMREAD_COLOR in imread() * doc/py_tutorials: check imread() result
192 lines
5.7 KiB
C++
192 lines
5.7 KiB
C++
/*
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* pca.cpp
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*
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* Author:
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* Kevin Hughes <kevinhughes27[at]gmail[dot]com>
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*
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* Special Thanks to:
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* Philipp Wagner <bytefish[at]gmx[dot]de>
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*
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* This program demonstrates how to use OpenCV PCA with a
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* specified amount of variance to retain. The effect
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* is illustrated further by using a trackbar to
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* change the value for retained variance.
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*
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* The program takes as input a text file with each line
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* begin the full path to an image. PCA will be performed
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* on this list of images. The author recommends using
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* the first 15 faces of the AT&T face data set:
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* http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
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*
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* so for example your input text file would look like this:
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*
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* <path_to_at&t_faces>/orl_faces/s1/1.pgm
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* <path_to_at&t_faces>/orl_faces/s2/1.pgm
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* <path_to_at&t_faces>/orl_faces/s3/1.pgm
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* <path_to_at&t_faces>/orl_faces/s4/1.pgm
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* <path_to_at&t_faces>/orl_faces/s5/1.pgm
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* <path_to_at&t_faces>/orl_faces/s6/1.pgm
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* <path_to_at&t_faces>/orl_faces/s7/1.pgm
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* <path_to_at&t_faces>/orl_faces/s8/1.pgm
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* <path_to_at&t_faces>/orl_faces/s9/1.pgm
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* <path_to_at&t_faces>/orl_faces/s10/1.pgm
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* <path_to_at&t_faces>/orl_faces/s11/1.pgm
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* <path_to_at&t_faces>/orl_faces/s12/1.pgm
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* <path_to_at&t_faces>/orl_faces/s13/1.pgm
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* <path_to_at&t_faces>/orl_faces/s14/1.pgm
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* <path_to_at&t_faces>/orl_faces/s15/1.pgm
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*
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*/
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#include <iostream>
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#include <fstream>
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#include <sstream>
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#include <opencv2/core.hpp>
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#include "opencv2/imgcodecs.hpp"
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#include <opencv2/highgui.hpp>
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using namespace cv;
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using namespace std;
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///////////////////////
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// Functions
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static void read_imgList(const string& filename, vector<Mat>& images) {
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std::ifstream file(filename.c_str(), ifstream::in);
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if (!file) {
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string error_message = "No valid input file was given, please check the given filename.";
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CV_Error(Error::StsBadArg, error_message);
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}
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string line;
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while (getline(file, line)) {
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images.push_back(imread(line, IMREAD_GRAYSCALE));
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}
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}
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static Mat formatImagesForPCA(const vector<Mat> &data)
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{
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Mat dst(static_cast<int>(data.size()), data[0].rows*data[0].cols, CV_32F);
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for(unsigned int i = 0; i < data.size(); i++)
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{
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Mat image_row = data[i].clone().reshape(1,1);
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Mat row_i = dst.row(i);
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image_row.convertTo(row_i,CV_32F);
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}
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return dst;
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}
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static Mat toGrayscale(InputArray _src) {
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Mat src = _src.getMat();
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// only allow one channel
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if(src.channels() != 1) {
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CV_Error(Error::StsBadArg, "Only Matrices with one channel are supported");
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}
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// create and return normalized image
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Mat dst;
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cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
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return dst;
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}
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struct params
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{
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Mat data;
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int ch;
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int rows;
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PCA pca;
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string winName;
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};
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static void onTrackbar(int pos, void* ptr)
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{
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cout << "Retained Variance = " << pos << "% ";
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cout << "re-calculating PCA..." << std::flush;
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double var = pos / 100.0;
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struct params *p = (struct params *)ptr;
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p->pca = PCA(p->data, cv::Mat(), PCA::DATA_AS_ROW, var);
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Mat point = p->pca.project(p->data.row(0));
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Mat reconstruction = p->pca.backProject(point);
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reconstruction = reconstruction.reshape(p->ch, p->rows);
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reconstruction = toGrayscale(reconstruction);
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imshow(p->winName, reconstruction);
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cout << "done! # of principal components: " << p->pca.eigenvectors.rows << endl;
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}
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///////////////////////
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// Main
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int main(int argc, char** argv)
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{
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cv::CommandLineParser parser(argc, argv, "{@input||image list}{help h||show help message}");
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if (parser.has("help"))
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{
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parser.printMessage();
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exit(0);
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}
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// Get the path to your CSV.
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string imgList = parser.get<string>("@input");
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if (imgList.empty())
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{
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parser.printMessage();
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exit(1);
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}
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// vector to hold the images
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vector<Mat> images;
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// Read in the data. This can fail if not valid
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try {
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read_imgList(imgList, images);
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} catch (const cv::Exception& e) {
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cerr << "Error opening file \"" << imgList << "\". Reason: " << e.msg << endl;
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exit(1);
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}
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// Quit if there are not enough images for this demo.
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if(images.size() <= 1) {
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string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
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CV_Error(Error::StsError, error_message);
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}
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// Reshape and stack images into a rowMatrix
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Mat data = formatImagesForPCA(images);
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// perform PCA
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PCA pca(data, cv::Mat(), PCA::DATA_AS_ROW, 0.95); // trackbar is initially set here, also this is a common value for retainedVariance
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// Demonstration of the effect of retainedVariance on the first image
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Mat point = pca.project(data.row(0)); // project into the eigenspace, thus the image becomes a "point"
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Mat reconstruction = pca.backProject(point); // re-create the image from the "point"
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reconstruction = reconstruction.reshape(images[0].channels(), images[0].rows); // reshape from a row vector into image shape
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reconstruction = toGrayscale(reconstruction); // re-scale for displaying purposes
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// init highgui window
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string winName = "Reconstruction | press 'q' to quit";
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namedWindow(winName, WINDOW_NORMAL);
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// params struct to pass to the trackbar handler
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params p;
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p.data = data;
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p.ch = images[0].channels();
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p.rows = images[0].rows;
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p.pca = pca;
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p.winName = winName;
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// create the tracbar
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int pos = 95;
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createTrackbar("Retained Variance (%)", winName, &pos, 100, onTrackbar, (void*)&p);
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// display until user presses q
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imshow(winName, reconstruction);
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char key = 0;
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while(key != 'q')
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key = (char)waitKey();
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return 0;
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}
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