Is there any function there in OpenCV to do it?
If there is no function in OpenCV how can I implement it? any idea would be great..
The input will be an image and the output should be the blurriness and sharpness of the image.
In general, sum of magnitude of image shows focus fluctuations (smoothness).In special case, you can check the zero crossing in specific points of image. For more accurate value for smoothness compute FFT for image then you can survey the high pass frequency of image.
C:
double calcGradients(const IplImage*src,intaperture_size=7)
{
CvSize sz=cvGetSize(src);
IplImage*img16_x=cvCreateImage(sz,IPL_DEPTH_16S,1);
IplImage*img16_y=cvCreateImage(sz,IPL_DEPTH_16S,1);
cvSobel(src,img16_x,1,0,aperture_size);
cvSobel(src,img16_y,0,1,aperture_size);
IplImage*imgF_x=cvCreateImage(sz,IPL_DEPTH_32F,1);
IplImage*imgF_y=cvCreateImage(sz,IPL_DEPTH_32F,1);
cvScale(img16_x,imgF_x);
cvScale(img16_y,imgF_y);
IplImage*magnitude=cvCreateImage(sz,IPL_DEPTH_32F,1);
cvCartToPolar(imgF_x,imgF_y,magnitude);
double res=cvSum(magnitude).val[0];
cvReleaseImage(&magnitude);
cvReleaseImage(&imgF_x);
cvReleaseImage(&imgF_y);
cvReleaseImage(&img16_x);
cvReleaseImage(&img16_y);
return
res;
}
C++:
double contrast_measure( const Mat&img )
{
Mat dx, dy;
Sobel( img, dx, CV_32F, 1, 0, 3 );
Sobel( img, dy, CV_32F, 0, 1, 3 );
magnitude( dx, dy, dx );
return sum(dx)[0];
}
test code for the function calcBlurriness which is an undocumented OpenCV function
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"
#include "iostream"
using namespace cv;
float calcBlurriness( const Mat &src )
{
Mat Gx, Gy;
Sobel( src, Gx, CV_32F, 1, 0 );
Sobel( src, Gy, CV_32F, 0, 1 );
double normGx = norm( Gx );
double normGy = norm( Gy );
double sumSq = normGx * normGx + normGy * normGy;
return static_cast<float>( 1. / ( sumSq / src.size().area() + 1e-6 ));
}
int main( int argc, char** argv )
{
char* filename = argc >= 2 ? argv[1] : (char*)"lena.jpg";
Mat src = imread( filename );
if(src.data)
{
std::cout << "original image : " << calcBlurriness( src ) << std::endl;
for( int i =3; i < 80; i+=2 )
{
Mat blurred;
GaussianBlur( src, blurred, Size(i,i), 0);
imshow( "blurred image", blurred );
waitKey(200);
std::cout << "blurred image : " << calcBlurriness( blurred ) << std::endl;
}
}
return 0;
}
resuts : ( the lesser value means more sharpness )
original image : 4.97583e-005
blurred image : 7.70266e-005
blurred image : 9.97529e-005
blurred image : 0.000134977
blurred image : 0.000161698
blurred image : 0.00020089
blurred image : 0.000226519
blurred image : 0.000275304
blurred image : 0.00030933
blurred image : 0.000356515
blurred image : 0.000399192
blurred image : 0.000450388
blurred image : 0.000483238
blurred image : 0.00054965
blurred image : 0.000603829
blurred image : 0.000645158
blurred image : 0.00071919
blurred image : 0.000767566
blurred image : 0.000847372
blurred image : 0.000895128
blurred image : 0.000917833
blurred image : 0.00102065
blurred image : 0.00113219
.
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Asked: 2012-12-21 06:13:36 -0600
Seen: 30,080 times
Last updated: Dec 15 '15
