利用OpenCV库函数实现PCA（主成成分分析）算法，该算法是...

admin · 发表于 2024-1-6 20:27:32

/*
* 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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利用OpenCV库函数实现PCA（主成成分分析）算法，该算法是...

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