C++ openCV中的小波变换
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时间：2020-08-27 22:54:16 来源:igfitidea点击:
Wavelet transform in openCV
提问by la lluvia
did someone tried to implement DWT in opencv or in C++? I saw older posts on this subject and i didn't find them useful for me, because I need a approximation coefficient and details as a result of wavelet transformation.
有人尝试在 opencv 或 C++ 中实现 DWT 吗？我看到了关于这个主题的旧帖子，但我没有发现它们对我有用，因为我需要一个近似系数和细节作为小波变换的结果。
I tried to add this (http://wavelet2d.sourceforge.net/) to my project but it's not working as well as planned.
我尝试将此（http://wavelet2d.sourceforge.net/）添加到我的项目中，但它没有按计划运行。
And this is to simple, because as a result parameters i need approximation coefficient and details:
这很简单，因为作为结果参数，我需要近似系数和细节：
void haar1(float *vec, int n, int w)
{
int i=0;
float *vecp = new float[n];
for(i=0;i<n;i++)
vecp[i] = 0;
w/=2;
for(i=0;i<w;i++)
{
vecp[i] = (vec[2*i] + vec[2*i+1])/sqrt(2.0);
vecp[i+w] = (vec[2*i] - vec[2*i+1])/sqrt(2.0);
}
for(i=0;i<(w*2);i++)
vec[i] = vecp[i];
delete [] vecp;
}
void haar2(float **matrix, int rows, int cols)
{
float *temp_row = new float[cols];
float *temp_col = new float[rows];
int i=0,j=0;
int w = cols, h=rows;
while(w>1 || h>1)
{
if(w>1)
{
for(i=0;i<h;i++)
{
for(j=0;j<cols;j++)
temp_row[j] = matrix[i][j];
haar1(temp_row,cols,w);
for(j=0;j<cols;j++)
matrix[i][j] = temp_row[j];
}
}
if(h>1)
{
for(i=0;i<w;i++)
{
for(j=0;j<rows;j++)
temp_col[j] = matrix[j][i];
haar1(temp_col, rows, h);
for(j=0;j<rows;j++)
matrix[j][i] = temp_col[j];
}
}
if(w>1)
w/=2;
if(h>1)
h/=2;
}
delete [] temp_row;
delete [] temp_col;
}
So can someone help me find dwt implemented in C++ or point me how to extract from above code coefficients. Thanks
那么有人可以帮我找到用 C++ 实现的 dwt 或者告诉我如何从上面的代码系数中提取。谢谢
回答by Andrey Smorodov
Here is direct and inverse Haar Wavelet transform (used for filtering):
这是直接和逆 Haar 小波变换（用于过滤）：
#include "opencv2/opencv.hpp"
#include <iostream>
#include <vector>
#include <stdio.h>
using namespace cv;
using namespace std;
// Filter type
#define NONE 0 // no filter
#define HARD 1 // hard shrinkage
#define SOFT 2 // soft shrinkage
#define GARROT 3 // garrot filter
//--------------------------------
// signum
//--------------------------------
float sgn(float x)
{
float res=0;
if(x==0)
{
res=0;
}
if(x>0)
{
res=1;
}
if(x<0)
{
res=-1;
}
return res;
}
//--------------------------------
// Soft shrinkage
//--------------------------------
float soft_shrink(float d,float T)
{
float res;
if(fabs(d)>T)
{
res=sgn(d)*(fabs(d)-T);
}
else
{
res=0;
}
return res;
}
//--------------------------------
// Hard shrinkage
//--------------------------------
float hard_shrink(float d,float T)
{
float res;
if(fabs(d)>T)
{
res=d;
}
else
{
res=0;
}
return res;
}
//--------------------------------
// Garrot shrinkage
//--------------------------------
float Garrot_shrink(float d,float T)
{
float res;
if(fabs(d)>T)
{
res=d-((T*T)/d);
}
else
{
res=0;
}
return res;
}
//--------------------------------
// Wavelet transform
//--------------------------------
static void cvHaarWavelet(Mat &src,Mat &dst,int NIter)
{
float c,dh,dv,dd;
assert( src.type() == CV_32FC1 );
assert( dst.type() == CV_32FC1 );
int width = src.cols;
int height = src.rows;
for (int k=0;k<NIter;k++)
{
for (int y=0;y<(height>>(k+1));y++)
{
for (int x=0; x<(width>>(k+1));x++)
{
c=(src.at<float>(2*y,2*x)+src.at<float>(2*y,2*x+1)+src.at<float>(2*y+1,2*x)+src.at<float>(2*y+1,2*x+1))*0.5;
dst.at<float>(y,x)=c;
dh=(src.at<float>(2*y,2*x)+src.at<float>(2*y+1,2*x)-src.at<float>(2*y,2*x+1)-src.at<float>(2*y+1,2*x+1))*0.5;
dst.at<float>(y,x+(width>>(k+1)))=dh;
dv=(src.at<float>(2*y,2*x)+src.at<float>(2*y,2*x+1)-src.at<float>(2*y+1,2*x)-src.at<float>(2*y+1,2*x+1))*0.5;
dst.at<float>(y+(height>>(k+1)),x)=dv;
dd=(src.at<float>(2*y,2*x)-src.at<float>(2*y,2*x+1)-src.at<float>(2*y+1,2*x)+src.at<float>(2*y+1,2*x+1))*0.5;
dst.at<float>(y+(height>>(k+1)),x+(width>>(k+1)))=dd;
}
}
dst.copyTo(src);
}
}
//--------------------------------
//Inverse wavelet transform
//--------------------------------
static void cvInvHaarWavelet(Mat &src,Mat &dst,int NIter, int SHRINKAGE_TYPE=0, float SHRINKAGE_T=50)
{
float c,dh,dv,dd;
assert( src.type() == CV_32FC1 );
assert( dst.type() == CV_32FC1 );
int width = src.cols;
int height = src.rows;
//--------------------------------
// NIter - number of iterations
//--------------------------------
for (int k=NIter;k>0;k--)
{
for (int y=0;y<(height>>k);y++)
{
for (int x=0; x<(width>>k);x++)
{
c=src.at<float>(y,x);
dh=src.at<float>(y,x+(width>>k));
dv=src.at<float>(y+(height>>k),x);
dd=src.at<float>(y+(height>>k),x+(width>>k));
// (shrinkage)
switch(SHRINKAGE_TYPE)
{
case HARD:
dh=hard_shrink(dh,SHRINKAGE_T);
dv=hard_shrink(dv,SHRINKAGE_T);
dd=hard_shrink(dd,SHRINKAGE_T);
break;
case SOFT:
dh=soft_shrink(dh,SHRINKAGE_T);
dv=soft_shrink(dv,SHRINKAGE_T);
dd=soft_shrink(dd,SHRINKAGE_T);
break;
case GARROT:
dh=Garrot_shrink(dh,SHRINKAGE_T);
dv=Garrot_shrink(dv,SHRINKAGE_T);
dd=Garrot_shrink(dd,SHRINKAGE_T);
break;
}
//-------------------
dst.at<float>(y*2,x*2)=0.5*(c+dh+dv+dd);
dst.at<float>(y*2,x*2+1)=0.5*(c-dh+dv-dd);
dst.at<float>(y*2+1,x*2)=0.5*(c+dh-dv-dd);
dst.at<float>(y*2+1,x*2+1)=0.5*(c-dh-dv+dd);
}
}
Mat C=src(Rect(0,0,width>>(k-1),height>>(k-1)));
Mat D=dst(Rect(0,0,width>>(k-1),height>>(k-1)));
D.copyTo(C);
}
}
//--------------------------------
//
//--------------------------------
int process(VideoCapture& capture)
{
int n = 0;
const int NIter=4;
char filename[200];
string window_name = "video | q or esc to quit";
cout << "press space to save a picture. q or esc to quit" << endl;
namedWindow(window_name, CV_WINDOW_KEEPRATIO); //resizable window;
Mat frame;
capture >> frame;
Mat GrayFrame=Mat(frame.rows, frame.cols, CV_8UC1);
Mat Src=Mat(frame.rows, frame.cols, CV_32FC1);
Mat Dst=Mat(frame.rows, frame.cols, CV_32FC1);
Mat Temp=Mat(frame.rows, frame.cols, CV_32FC1);
Mat Filtered=Mat(frame.rows, frame.cols, CV_32FC1);
for (;;)
{
Dst=0;
capture >> frame;
if (frame.empty()) continue;
cvtColor(frame, GrayFrame, CV_BGR2GRAY);
GrayFrame.convertTo(Src,CV_32FC1);
cvHaarWavelet(Src,Dst,NIter);
Dst.copyTo(Temp);
cvInvHaarWavelet(Temp,Filtered,NIter,GARROT,30);
imshow(window_name, frame);
double M=0,m=0;
//----------------------------------------------------
// Normalization to 0-1 range (for visualization)
//----------------------------------------------------
minMaxLoc(Dst,&m,&M);
if((M-m)>0) {Dst=Dst*(1.0/(M-m))-m/(M-m);}
imshow("Coeff", Dst);
minMaxLoc(Filtered,&m,&M);
if((M-m)>0) {Filtered=Filtered*(1.0/(M-m))-m/(M-m);}
imshow("Filtered", Filtered);
char key = (char)waitKey(5);
switch (key)
{
case 'q':
case 'Q':
case 27: //escape key
return 0;
case ' ': //Save an image
sprintf(filename,"filename%.3d.jpg",n++);
imwrite(filename,frame);
cout << "Saved " << filename << endl;
break;
default:
break;
}
}
return 0;
}
int main(int ac, char** av)
{
VideoCapture capture(0);
if (!capture.isOpened())
{
return 1;
}
return process(capture);
}
回答by la lluvia
Hereis another implementation of Wavelet transform in OpenCV from Mahavir:
这是Mahavir在 OpenCV 中的另一种小波变换实现：
#include <opencv2\highgui\highgui.hpp>
#include <opencv2\core\core.hpp>
#include <opencv2\core\mat.hpp>
#include <opencv2\imgproc\imgproc.hpp>
#include<iostream>
#include<math.h>
#include<conio.h>
using namespace std;
using namespace cv;
class image
{
public:
Mat im,im1,im2,im3,im4,im5,im6,temp,im11,im12,im13,im14,imi,imd,imr;
float a,b,c,d;
int getim();
};
int image::getim()
{
im=imread("lena.jpg",0); //Load image in Gray Scale
imi=Mat::zeros(im.rows,im.cols,CV_8U);
im.copyTo(imi);
im.convertTo(im,CV_32F,1.0,0.0);
im1=Mat::zeros(im.rows/2,im.cols,CV_32F);
im2=Mat::zeros(im.rows/2,im.cols,CV_32F);
im3=Mat::zeros(im.rows/2,im.cols/2,CV_32F);
im4=Mat::zeros(im.rows/2,im.cols/2,CV_32F);
im5=Mat::zeros(im.rows/2,im.cols/2,CV_32F);
im6=Mat::zeros(im.rows/2,im.cols/2,CV_32F);
//--------------Decomposition-------------------
for(int rcnt=0;rcnt<im.rows;rcnt+=2)
{
for(int ccnt=0;ccnt<im.cols;ccnt++)
{
a=im.at<float>(rcnt,ccnt);
b=im.at<float>(rcnt+1,ccnt);
c=(a+b)*0.707;
d=(a-b)*0.707;
int _rcnt=rcnt/2;
im1.at<float>(_rcnt,ccnt)=c;
im2.at<float>(_rcnt,ccnt)=d;
}
}
for(int rcnt=0;rcnt<im.rows/2;rcnt++)
{
for(int ccnt=0;ccnt<im.cols;ccnt+=2)
{
a=im1.at<float>(rcnt,ccnt);
b=im1.at<float>(rcnt,ccnt+1);
c=(a+b)*0.707;
d=(a-b)*0.707;
int _ccnt=ccnt/2;
im3.at<float>(rcnt,_ccnt)=c;
im4.at<float>(rcnt,_ccnt)=d;
}
}
for(int rcnt=0;rcnt<im.rows/2;rcnt++)
{
for(int ccnt=0;ccnt<im.cols;ccnt+=2)
{
a=im2.at<float>(rcnt,ccnt);
b=im2.at<float>(rcnt,ccnt+1);
c=(a+b)*0.707;
d=(a-b)*0.707;
int _ccnt=ccnt/2;
im5.at<float>(rcnt,_ccnt)=c;
im6.at<float>(rcnt,_ccnt)=d;
}
}
imr=Mat::zeros(256,256,CV_32F);
imd=Mat::zeros(256,256,CV_32F);
im3.copyTo(imd(Rect(0,0,128,128)));
im4.copyTo(imd(Rect(0,127,128,128)));
im5.copyTo(imd(Rect(127,0,128,128)));
im6.copyTo(imd(Rect(127,127,128,128)));
//---------------------------------Reconstruction-------------------------------------
im11=Mat::zeros(im.rows/2,im.cols,CV_32F);
im12=Mat::zeros(im.rows/2,im.cols,CV_32F);
im13=Mat::zeros(im.rows/2,im.cols,CV_32F);
im14=Mat::zeros(im.rows/2,im.cols,CV_32F);
for(int rcnt=0;rcnt<im.rows/2;rcnt++)
{
for(int ccnt=0;ccnt<im.cols/2;ccnt++)
{
int _ccnt=ccnt*2;
im11.at<float>(rcnt,_ccnt)=im3.at<float>(rcnt,ccnt); //Upsampling of stage I
im12.at<float>(rcnt,_ccnt)=im4.at<float>(rcnt,ccnt);
im13.at<float>(rcnt,_ccnt)=im5.at<float>(rcnt,ccnt);
im14.at<float>(rcnt,_ccnt)=im6.at<float>(rcnt,ccnt);
}
}
for(int rcnt=0;rcnt<im.rows/2;rcnt++)
{
for(int ccnt=0;ccnt<im.cols;ccnt+=2)
{
a=im11.at<float>(rcnt,ccnt);
b=im12.at<float>(rcnt,ccnt);
c=(a+b)*0.707;
im11.at<float>(rcnt,ccnt)=c;
d=(a-b)*0.707; //Filtering at Stage I
im11.at<float>(rcnt,ccnt+1)=d;
a=im13.at<float>(rcnt,ccnt);
b=im14.at<float>(rcnt,ccnt);
c=(a+b)*0.707;
im13.at<float>(rcnt,ccnt)=c;
d=(a-b)*0.707;
im13.at<float>(rcnt,ccnt+1)=d;
}
}
temp=Mat::zeros(im.rows,im.cols,CV_32F);
for(int rcnt=0;rcnt<im.rows/2;rcnt++)
{
for(int ccnt=0;ccnt<im.cols;ccnt++)
{
int _rcnt=rcnt*2;
imr.at<float>(_rcnt,ccnt)=im11.at<float>(rcnt,ccnt); //Upsampling at stage II
temp.at<float>(_rcnt,ccnt)=im13.at<float>(rcnt,ccnt);
}
}
for(int rcnt=0;rcnt<im.rows;rcnt+=2)
{
for(int ccnt=0;ccnt<im.cols;ccnt++)
{
a=imr.at<float>(rcnt,ccnt);
b=temp.at<float>(rcnt,ccnt);
c=(a+b)*0.707;
imr.at<float>(rcnt,ccnt)=c; //Filtering at Stage II
d=(a-b)*0.707;
imr.at<float>(rcnt+1,ccnt)=d;
}
}
imd.convertTo(imd,CV_8U);
namedWindow("Input Image",1);
imshow("Input Image",imi);
namedWindow("Wavelet Decomposition",1);
imshow("Wavelet Decomposition",imd);
imr.convertTo(imr,CV_8U);
namedWindow("Wavelet Reconstruction",1);
imshow("Wavelet Reconstruction",imr);
waitKey(0);
return 0;
}
int main()
{
image my;
my.getim();
return 0;
}
Hope someone finds it useful!
希望有人觉得它有用！
回答by RochaRF
I see that there's very few code examples for wavelet in java, especially if you're using openCV. I had to use wavelet in java with openCV and I used the C code from @la luvia and converted to java.
我看到 java 中的小波代码示例很少，特别是如果您使用的是 openCV。我不得不在带有 openCV 的 java 中使用小波，我使用了来自@la luvia 的 C 代码并转换为 java。
There was a lot of trouble while translating the code, because it had a lot of diferences in the openCV methods and ways of using it. This book helped me a lot in the process too.
翻译代码时遇到了很多麻烦，因为它在openCV方法和使用方式上有很多差异。这本书在这个过程中也给了我很大的帮助。
I hope this code and the book give some perspective of how to use the lib and few diferences between the C and Java.
我希望这段代码和这本书对如何使用 lib 以及 C 和 Java 之间的一些区别给出了一些看法。
Here's the code:
这是代码：
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
public class Wavelet {
//Imperative in java
static{
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
}
String pathname = "C:/Users/user/img096";
public static void main(String[] args) {
Wavelet wavelet = new Wavelet();
wavelet.applyHaarFoward();
wavelet.applyHaarReverse();
Imgcodecs.imwrite(wavelet.pathname+"imi.jpg", wavelet.imi);
Imgcodecs.imwrite(wavelet.pathname+"imd.jpg", wavelet.imd);
Imgcodecs.imwrite(wavelet.pathname+"imr.jpg", wavelet.imr);
}
Mat im,im1,im2,im3,im4,im5,im6,temp,im11,im12,im13,im14,imi,imd,imr;
float a,b,c,d;
private void applyHaarFoward(){
try{
im = Imgcodecs.imread(pathname+".jpg", 0);
imi = new Mat(im.rows(), im.cols(), CvType.CV_8U);
im.copyTo(imi);
//in CvType. If the number of channels is omitted, it evaluates to 1.
im.convertTo(im, CvType.CV_32F, 1.0, 0.0);
im1 = new Mat(im.rows()/2, im.cols(), CvType.CV_32F);
im2 = new Mat(im.rows()/2, im.cols(), CvType.CV_32F);
im3 = new Mat(im.rows()/2, im.cols()/2, CvType.CV_32F);
im4 = new Mat(im.rows()/2, im.cols()/2, CvType.CV_32F);
im5 = new Mat(im.rows()/2, im.cols()/2, CvType.CV_32F);
im6 = new Mat(im.rows()/2, im.cols()/2, CvType.CV_32F);
// ------------------- Decomposition -------------------
for (int rcnt = 0; rcnt < im.rows(); rcnt+=2) {
for (int ccnt = 0; ccnt < im.cols(); ccnt++) {
//even though the CvType is float with only one channel
//the method Mat.get() return a double array
//with only one position, [0].
a = (float) im.get(rcnt, ccnt)[0];
b = (float) im.get(rcnt+1, ccnt)[0];
c = (float) ((a+b)*0.707);
d = (float) ((a-b)*0.707);
int _rcnt= rcnt/2;
im1.put(_rcnt, ccnt, c);
im2.put(_rcnt, ccnt, d);
}
}
for (int rcnt = 0; rcnt < im.rows()/2; rcnt++) {
for (int ccnt = 0; ccnt < im.cols() - 2; ccnt+=2) {
a = (float) im1.get(rcnt, ccnt)[0];
b = (float) im1.get(rcnt, ccnt+1)[0];
c = (float) ((a+b)*0.707);
d = (float) ((a-b)*0.707);
int _ccnt = ccnt/2;
im3.put(rcnt, _ccnt, c);
im4.put(rcnt, _ccnt, d);
}
}
for (int rcnt = 0; rcnt < im.rows()/2; rcnt++) {
for (int ccnt = 0; ccnt < im.cols() - 2; ccnt+=2) {
a = (float) im2.get(rcnt, ccnt)[0];
b = (float) im2.get(rcnt, ccnt+1)[0];
c = (float) ((a+b)*0.707);
d = (float) ((a-b)*0.707);
int _ccnt = ccnt/2;
im5.put(rcnt, _ccnt, c);
im6.put(rcnt, _ccnt, d);
}
}
imr = Mat.zeros(im.rows(), im.cols(), CvType.CV_32F);//imr = Mat.zeros(512, 512, CvType.CV_32F);
imd = Mat.zeros(512, 512, CvType.CV_32F);
im3.copyTo(imd.adjustROI(0, 0, 256, 256));
im4.copyTo(imd.adjustROI(0, 255, 256, 256));
im5.copyTo(imd.adjustROI(255, 0, 256, 256));
im6.copyTo(imd.adjustROI(255, 255, 256, 256));
}catch(Exception ex){
System.err.println(ex.getLocalizedMessage());
ex.printStackTrace();
}
}
private void applyHaarReverse(){
// ------------------- Reconstruction -------------------
im11 = Mat.zeros(im.rows()/2, im.cols(), CvType.CV_32F);
im12 = Mat.zeros(im.rows()/2, im.cols(), CvType.CV_32F);
im13 = Mat.zeros(im.rows()/2, im.cols(), CvType.CV_32F);
im14 = Mat.zeros(im.rows()/2, im.cols(), CvType.CV_32F);
for (int rcnt = 0; rcnt < im.rows()/2; rcnt++) {
for (int ccnt = 0; ccnt < im.cols()/2; ccnt++) {
int _ccnt = ccnt*2;
im11.put(rcnt, _ccnt, im3.get(rcnt, ccnt));
im12.put(rcnt, _ccnt, im4.get(rcnt, ccnt));
im13.put(rcnt, _ccnt, im5.get(rcnt, ccnt));
im14.put(rcnt, _ccnt, im6.get(rcnt, ccnt));
}
}
for (int rcnt = 0; rcnt < im.rows()/2; rcnt++) {
for (int ccnt = 0; ccnt < im.cols() - 2; ccnt+=2) {
a = (float) im11.get(rcnt, ccnt)[0];
b = (float) im12.get(rcnt, ccnt)[0];
c = (float) ((a+b)*0.707);
im11.put(rcnt, ccnt, c);
d = (float) ((a-b)*0.707);
im11.put(rcnt, ccnt+1, d);
a = (float) im13.get(rcnt, ccnt)[0];
b = (float) im14.get(rcnt, ccnt)[0];
c = (float) ((a+b)*0.707);
im13.put(rcnt, ccnt, c);
d = (float) ((a-b)*0.707);
im13.put(rcnt, ccnt+1, d);
}
}
temp = Mat.zeros(im.rows(), im.cols(), CvType.CV_32F);
for (int rcnt = 0; rcnt < im.rows()/2; rcnt++) {
for (int ccnt = 0; ccnt < im.cols(); ccnt++) {
int _rcnt = rcnt*2;
imr.put(_rcnt, ccnt, im11.get(rcnt, ccnt));
temp.put(_rcnt, ccnt, im13.get(rcnt, ccnt));
}
}
for (int rcnt = 0; rcnt < im.rows()-2; rcnt+=2) {
for (int ccnt = 0; ccnt < im.cols(); ccnt++) {
a = (float) imr.get(rcnt, ccnt)[0];
b = (float) temp.get(rcnt, ccnt)[0];
c = (float) ((a+b)*0.707);
imr.put(rcnt, ccnt, c);
d = (float) ((a-b)*0.707);
imr.put(rcnt+1, ccnt, d);
}
}
}
}
Hope it's useful.
希望它有用。
回答by vacing
Suggestion: I don't suggest you to implement dwt from scratch, it is very hard to meet your demands. If you really need it's cpp version in your job, I recommend you try PyWavelets, whose basic functions of dwt are implementd in C, so you can migrate it to your program easily. Otherwise, you can also verify your thoughts with python first rather than take a risk of paying useless efforts.
建议：我不建议你从头开始实现dwt，很难满足你的要求。如果您在工作中确实需要它的 cpp 版本，我建议您尝试PyWavelets，其 dwt 的基本功能是用 C 实现的，因此您可以轻松地将其迁移到您的程序中。否则，你也可以先用 python 验证你的想法，而不是冒着付出无用努力的风险。
Here is one of my implementation of dwtwhich support many kinds of wavelet filter, it works but dosen't work well. With the increasing of level, the reconstrusion being more and more blurred.
这是我的 dwt 实现之一，它支持多种小波滤波器，它可以工作但效果不佳。随着级别的增加，重建越来越模糊。
If you want to change the wavelet filter, you can use matlab (wfilters(wname)) or pick from PyWavelets's source code, and it also gives the rule to get totall 4 filterfrom these coefficients.
如果你想改变小波滤波器，你可以使用matlab（wfilters(wname)）或从PyWavelets的源代码中选择，并且它还给出了从这些系数中获得总共4个滤波器的规则。
