My initial encounter with filters, convolution and correlation in OpenCV made me play around with the filters for Gaussian smooth, erosion and dilation operations on random image files. However I found the experience rather unsatisfactory and I wanted to get a real feel for the working of these operations. Suddenly a thought struck me. Could I restore an old family photograph of my parents? The photograph has areas of white patches that had to be removed.
So I started to dig a little more into the filters, convolution and correlation matrices to get a better understanding.
This is the original photograph.
My Mom & late Dad
As can be seen there are large patches in several places in the photograph. So I decided to use cvFloodFill to fill these areas.
a) cvFloodFill: Since I had to identify the spots where these patches occurred I took a dump of the cvMat of the image which I resized to about 29 x 42. By inspecting the data I could see that the patches typically corresponded to intensity values that were greater than 170. So I decided that the cvFloodFill should happen with the seed around these parts. So the code checks the intensity values > 170 and calls cvFloodFill. After much tweaking I could see that the white patches were now filled with gray (newval intensity= 150.0) So I was able to get rid of the white patches.
b) cvSmooth: The next step that I took was to perform a Gaussian smooth of the picture. This smoothed out the filled parts
c) cvErode & cvDilate: I followed this with cvErode to smooth out the dark areas and cvDilate to smooth out the bright areas.
d) cvFilter2D: I wanted to now sharpen the image. I did a lot of experiments with different kernel values but I found this to be extremely difficult to work with. After much trial and error I came with a kernel values of
double a[9]={-1,20,1,-1,20,1,-1,20,1};
The sharpening was reasonable but there are areas where there are white streaks. I still need to figure out a kernel that can sharpen images. For this also to understand what was happening I tried to dump the values of the image to get a feel of where the values lay.
e) cvSmooth: Finally I performed a cvSmooth of the filtered output.
While I have had fair success there is still a lot more left to be desired from the final version.
The complete process flow
The code is included below
#include “cv.h”
#include “highgui.h”
#include “stdio.h”
int main(int argc, char** argv)
{
IplImage* img = cvLoadImage(“dad_mom.jpg”,0);
IplImage* dst;
IplImage* dst1;
IplImage* dst2;
IplImage* dst3;
IplImage* dst4;
int i,j,k;
int height,width,step,channels;
uchar* data;
uchar* data1;
uchar* outdata;
CvScalar s;
CvScalar lodiff,highdiff,newval;
// get the image data
height = img->height;
width = img->width;
step = img->widthStep;
channels = img->nChannels;
data = (uchar *)img->imageData;
double a[9]={-1,20,1,-1,20,1,-1,20,1};
//double a[9]={1/16,1/8,1/16,1/8,1/4,1/8,1/16,1/8,1/16};
double values[9]={1/16,0,-1/16,2/16,0,-2/16,1/16,0,-1/16};
CvPoint seed;
CvMat kernel= cvMat(3,3,CV_32FC1,a);
printf(“Processing a %d x %d image with %d channels\n”,height,width,channels);
// Create windows
cvNamedWindow(“Original”,CV_WINDOW_AUTOSIZE);
cvNamedWindow(“Flood Fill”,CV_WINDOW_AUTOSIZE);
cvNamedWindow(“Smooth”,CV_WINDOW_AUTOSIZE);
cvNamedWindow(“Erode”,CV_WINDOW_AUTOSIZE);
cvNamedWindow(“Dilate”,CV_WINDOW_AUTOSIZE);
cvNamedWindow(“Filter”,CV_WINDOW_AUTOSIZE);
cvNamedWindow(“Smooth1”,CV_WINDOW_AUTOSIZE);
// Original image
cvShowImage(“Original”, img);
/* Flood fill in white patches intensity > 170.0 */
highdiff=cvRealScalar(5.0);
lodiff=cvRealScalar(5.0);
newval=cvRealScalar(150.0);
for(i=0;i<height;i++) {
for(j=0;j<width;j++) {
for(k=0;k<channels;k++) {
//printf(“Data = %d \n”,data[i*step+j*channels+k]);
if((data[i*step+j*channels+k]) > 170.0)
{
seed=cvPoint(j,i);
//printf(“data=%dFlood
seed=%d,%d\n”,data[i*step+j*channels+k],i,j);
cvFloodFill(img,seed,newval,lodiff,highdiff, NULL,CV_FLOODFILL_FIXED_RANGE,NULL); }
else
{
;
}
}
}
//printf(“\n”);
}
cvShowImage(“Flood Fill”,img);
// Gaussian smooth
dst = cvCloneImage(img);
cvSmooth( img, dst, CV_GAUSSIAN, 3, 3, 0, 0 );
cvShowImage(“Smooth”,dst);
// Erode the image
dst1 = cvCloneImage(img);
IplConvKernel* kern = cvCreateStructuringElementEx(3,3,1,1,CV_SHAPE_RECT,values);
cvErode(dst,dst1,kern,1);
cvShowImage(“Erode”,dst1);
// Perform dilation operation
dst2 = cvCloneImage(img);
cvDilate(dst1,dst2,kern,1);
cvShowImage(“Dilate”,dst2);
// Filter the image with convolution kernel. Sharpen the image
dst3 = cvCloneImage(img);
printf(“reached here\n”);
cvFilter2D(dst2,dst3,&kernel,cvPoint(-1,-1));
cvShowImage(“Filter”,dst3);
// Smoothen the image
dst4 = cvCloneImage(img);
cvSmooth( dst3, dst4, CV_MEDIAN, 3, 0, 0, 0 );
cvShowImage(“Smooth1”,dst4);
// Cleanup
cvWaitKey(0);
cvReleaseImage(&img);
cvReleaseImage(&dst);
cvDestroyWindow(“Original”);
vDestroyWindow(“Restore”);
}
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