wavelet1d/2DSWTdemo.md

2D Stationary Wavelet Transform using swt_2d

Sample Code

 #include <iostream>
#include <fstream>
#include <vector>
#include <string>
#include <complex>
#include <cmath>
#include <algorithm>
#include "wavelet.h"
#include "cv.h"
#include "highgui.h"
#include "cxcore.h"

using namespace std;
using namespace cv;

void* maxval(vector<vector<double> > &arr, double &max){
        max = 0;
        for (unsigned int i =0; i < arr.size(); i++) {
                for (unsigned int j =0; j < arr[0].size(); j++) {
                        if (max <= arr[i][j]){
                                max = arr[i][j];
                        }
                }
        }
        return 0;
}

int main() {
        IplImage* img = cvLoadImage("cameraman.bmp");
        if (!img){
                cout << " Can't read Image. Try Different Format." << endl;
                exit(1);
        }
        int height, width;
        height = img->height;
    width = img->width;
    int nc = img->nChannels;
 //   uchar* ptr2 =(uchar*) img->imageData;
    int pix_depth = img->depth;
    CvSize size;
    size.width =width;
    size.height=height;
    cout << "depth" << pix_depth <<  "Channels" << nc << endl;


    cvNamedWindow("Original Image", CV_WINDOW_AUTOSIZE);
        cvShowImage("Original Image", img);
        cvWaitKey();
        cvDestroyWindow("Original Image");
        cvSaveImage("orig.bmp",img);


   int rows =(int) height;
   int cols =(int) width;
   Mat matimg(img);

   vector<vector<double> > vec1(rows, vector<double>(cols));


    int k =1;
   for (int i=0; i < rows; i++) {
           for (int j =0; j < cols; j++){
                   unsigned char temp;
     temp = ((uchar*) matimg.data + i * matimg.step)[j  * matimg.elemSize() + k ];
     vec1[i][j] = (double) temp;
           }

   }

   string nm = "db2";
//   vector<double> l1,h1,l2,h2;
//   filtcoef(nm,l1,h1,l2,h2);


   vector<double> output;
   int J =3;
   swt_2d(vec1,J,nm,output);
   cout << "OUTPUT size" << output.size() << endl;
   cout << "LOOP OK" << endl;

   int row,col;
   dwt_output_dim(vec1, row, col );

   // Extract and Display Low Pass Image at the Jth stage

   vector<vector<double> > blur(row,vector<double>(col));

   for (int i=0;i < row; i++){
           for (int j=0; j < col;j++){
        double temp = output[i*col + j];
        blur[i][j]= temp;
           }
   }

  double max;
  maxval(blur,max);

        // Creating Image in OPENCV
          IplImage *cvImg; // image used for output
        CvSize imgSize; // size of output image

        imgSize.width = col;
        imgSize.height = row;

        cvImg = cvCreateImage( imgSize, 8, 1 );

        for (int i = 0; i < imgSize.height; i++ ) {
        for (int j = 0; j < imgSize.width; j++ ){
                  if ( blur[i][j] <= 0.0){
                          blur[i][j] = 0.0;
                  }

        ((uchar*)(cvImg->imageData + cvImg->widthStep*i))[j] =
         (char) ( (blur[i][j] / max) * 255.0);

          }
          }

          cvNamedWindow( "Low Pass Image", 1 ); // creation of a visualisation window
         cvShowImage( "Low Pass Image", cvImg ); // image visualisation
           cvWaitKey();
           cvDestroyWindow("Low Pass Image");

           // Displaying BandPass Images

    vector<vector<double> >  detail(3*row,vector<double>(J * col));

    for (int k=0; k < J; k++) {
    for (int i=0; i < 3*row; i++) {
        for(int j=0+ k*col; j < (k+1)*col; j++) {
                double temp = output[(3*k+1)*row*col+ i * col +j - k*col];
                detail[i][j]= temp;
        }
    }
    }
          IplImage *dvImg; // image used for output
      CvSize imgSz; // size of output image

      imgSz.width = J*col;
      imgSz.height = 3*row;

      dvImg = cvCreateImage( imgSz, 8, 1 );

      for (int i = 0; i < imgSz.height; i++ ) {
      for (int j = 0; j < imgSz.width; j++ ){
                  if ( detail[i][j] <= 0.0){
                          detail[i][j] = 0.0;
                  }

      ((uchar*)(dvImg->imageData + dvImg->widthStep*i))[j] =
       (char) detail[i][j];

          }
          }

          cvNamedWindow( "Band Pass Image", 1 ); // creation of a visualisation window
       cvShowImage( "Band Pass Image", dvImg ); // image visualisation
           cvWaitKey();
           cvDestroyWindow("Band Pass Image");
           cvSaveImage("detail.bmp",dvImg);


                return 0;
   }

Stationary Wavelet Transform is a redundant transform as will be seen below. The image used is 256X256 grayscale "cameraman" image.

Three level Stationary Wavelet Transform is computed using db2 wavelet. Approximation coefficients are stored only for the final (J=3) stage while the three detail coefficients( Horizontal, Vertical and Diagonal) are stored for each value. All 10 sets of coefficients are 256X256.

Approximation Coefficient

Detail Coefficients at Levels 3,2,1. Coefficients are arranged Horizontal,Vertical and Diagonal from top to bottom.