'Filter' 태그의 글 목록

Parameters:	src – The source image dst – The destination image kernel – Convolution kernel, a single-channel floating point matrix. If you want to apply different kernels to different channels, split the image into separate color planes using Split and process them individually anchor – The anchor of the kernel that indicates the relative position of a filtered point within the kernel. The anchor shoud lie within the kernel. The special default value (-1,-1) means that it is at the kernel center

src – The source image
dst – The destination image
kernel – Convolution kernel, a single-channel floating point matrix. If you want to apply different kernels to different channels, split the image into separate color planes using Split and process them individually
anchor – The anchor of the kernel that indicates the relative position of a filtered point within the kernel. The anchor shoud lie within the kernel. The special default value (-1,-1) means that it is at the kernel center

elt3470@naver: 사용자가 kernel에 원하는 행렬을 입력함으로써, LPF, HPF 등을 직접 디자인해서 사용할 수 있습니다.

=> 그러므로, DoG (Derivative of Gaussian) 필터도 만들어 넣을 수 있다.

예로, 5x5 Gaussian kernel을 만들어서 필터링하면 다음과 같이 영상을 smoothing하게 된다.

ref.
2010/04/03 - [Visual Information Processing Lab] - Image Filtering

/* Test: 2-D DOG (Derivative of Gaussian) filter using cvFilter2D() in OpenCV
ref.
1) http://opencv.willowgarage.com/documentation/image_filtering.html?highlight=cvsobel#cvFilter2D
2) Richard Szeliski, Computer Vision: Algorithms and Applications 115쪽
camera: Logitech QuickCam Pro 4000
*/

#include <OpenCV/OpenCV.h>
#include <iostream>
using namespace std;

int main()
{
    IplImage* iplInput = 0; // input image
    IplImage* iplGray = 0; // grey image converted from input image
   IplImage* iplGauss = 0; // filtered image by 5x5 Gaussian kernel

   // define a kernel
   double gaussian[] = { 1, 4, 6, 4, 1,
                        2, 8, 12, 8, 2,
                          6, 24, 36, 24, 6,
                          2, 8, 12, 8, 2,
                          1, 4, 6, 3, 1 };
   for( int k = 0; k < 25; k++ )
   {
        gaussian[k] = gaussian[k] / 256;
   }
   CvMat G = cvMat( 5, 5, CV_64FC1, gaussian );

   char title[200];
    sprintf(title, "filtered image by Gaussian kernel");

    cvNamedWindow("input");
   cvNamedWindow(title);

    // initialize capture from a camera
    CvCapture* capture = cvCaptureFromCAM(0); // capture from video device #0
    int count = 0; // number of grabbed frames

    while(1) {
        // get video frames from the camera
        if ( !cvGrabFrame(capture) ) {
            printf("Could not grab a frame\n\7");
            exit(0);
        }
        else {
            cvGrabFrame( capture ); // capture a frame
            iplInput = cvRetrieveFrame(capture); // retrieve the caputred frame

            if(iplInput) {
                if(0 == count) {
                    // create an image header and allocate the image data
                    iplGray = cvCreateImage(cvGetSize(iplInput), 8, 1);
                    iplGauss = cvCreateImage(cvGetSize(iplInput), 8, 1);
                }
                // convert the input color image to gray one
                cvCvtColor(iplInput, iplGray, CV_BGR2GRAY); // convert an image from one color space to another
                count++;
            }
            cvShowImage( "input", iplInput );

            // convolve an image with the kernel
            // void cvFilter2D(const CvArr* src, CvArr* dst, const CvMat* kernel, CvPoint anchor=cvPoint(-1, -1)
            cvFilter2D( iplGray, iplGauss, &G ); // convolve an image with 5x5 Gaussian kernel
            cvShowImage( title, iplGauss );

            // save images to files
            cvSaveImage("input.bmp", iplInput);
            cvSaveImage("gray.bmp", iplGray);
            cvSaveImage("DoG.bmp", iplGauss);

            if( cvWaitKey(10) >= 0 )
                break;
        }
    }

    cvReleaseCapture( &capture ); // release the capture source
    cvDestroyWindow("input");
    cvDestroyWindow(title);

    return 0;
}

입력 영상

흑백 영상

2차원 5x5 Gaussian convolution 영상 (smoothing)

저작자표시 비영리 동일조건

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OpenCV: CV_IMAGE_ELEM (0)	2010.04.02

posted by maetel

OpenCV: cvSobel() 연습 코드

2010. 4. 3. 22:27 Computer Vision

OpenCV 2.0 C++ reference - Image Filtering

void cvSobel(const CvArr* src, CvArr* dst, int xorder, int yorder, int apertureSize=3)¶

Calculates the first, second, third or mixed image derivatives using an extended Sobel operator.

Parameters:	src – Source image of type CvArr* dst – Destination image xorder – Order of the derivative x yorder – Order of the derivative y apertureSize – Size of the extended Sobel kernel, must be 1, 3, 5 or 7

간단한 예로, 다음과 같은 (1차 미분, 크기3의) 마스크를 적용하면,

x방향 Sobal mask:

$\vecthreethree {-1}{0}{1} {-2}{0}{2} {-1}{0}{1}$

y방향 Sobel mask:

$\vecthreethree {-1}{-2}{-1} {0}{0}{0} {1}{2}{1}$

...ing

/* Test: image filter using Sobel mask in OpenCV
ref. http://opencv.willowgarage.com/documentation/image_filtering.html?highlight=cvsobel#cvSobel
camera: Logitech QuickCam Pro 4000
*/

#include <OpenCV/OpenCV.h>
#include <iostream>
using namespace std;

int main()
{
    IplImage* iplInput = 0; // input image
    IplImage* iplGray = 0; // grey image converted from input image
    IplImage* iplSobel = 0; // filtered images by Sobel mask
    IplImage* iplSobelx = 0; // x-derivative image by sobel mask
    IplImage* iplSobely = 0; // y-derivative image by sobel mask

    char titlefx[200], titlefy[200], titlef[200];
    sprintf(titlefx, "x-derivative image by sobel mask");
    sprintf(titlefy, "y-derivative image by sobel mask");
    sprintf(titlef, "filtered image by sobel mask");

    cvNamedWindow("input");
    cvNamedWindow(titlefx);
    cvNamedWindow(titlefy);
    cvNamedWindow(titlef);

    // initialize capture from a camera
    CvCapture* capture = cvCaptureFromCAM(0); // capture from video device #0
    int count = 0; // number of grabbed frames

    while(1) {
        // get video frames from the camera
        if ( !cvGrabFrame(capture) ) {
            printf("Could not grab a frame\n\7");
            exit(0);
        }
        else {
            cvGrabFrame( capture ); // capture a frame
            iplInput = cvRetrieveFrame(capture); // retrieve the caputred frame

            if(iplInput) {
                if(0 == count) {
                    // create an image header and allocate the image data
                    iplGray = cvCreateImage(cvGetSize(iplInput), 8, 1);
                    iplSobel = cvCreateImage(cvGetSize(iplInput), 8, 1);
                    iplSobelx = cvCreateImage(cvGetSize(iplInput), 8, 1);
                    iplSobely = cvCreateImage(cvGetSize(iplInput), 8, 1);
                }
                // convert the input color image to gray one
                cvCvtColor(iplInput, iplGray, CV_BGR2GRAY); // convert an image from one color space to another
                count++;
            }
            cvShowImage( "input", iplInput );

            // calculate the first, second, third or mixed image derivatives using an extended Sobel operator
            //void cvSobel(const CvArr* src, CvArr* dst, int xorder, int yorder, int apertureSize=3)
            cvSobel( iplGray, iplSobelx, 1, 0, 3); // x-derivative image by sobel mask
            cvSobel( iplGray, iplSobely, 0, 1, 3); // y-derivative image by sobel mask
            cvShowImage( titlefx, iplSobelx );
            cvShowImage( titlefy, iplSobely );
            cvShowImage( titlef, iplSobel );

            // save images to files
            cvSaveImage("input.bmp", iplInput);
            cvSaveImage("gray.bmp", iplGray);
            cvSaveImage("sobelx.bmp", iplSobelx);
            cvSaveImage("sobely.bmp", iplSobely);
            cvSaveImage("sobel.bmp", iplSobel);

            if( cvWaitKey(10) >= 0 )
                break;
        }
    }

    cvReleaseCapture( &capture ); // release the capture source
    cvDestroyWindow("input");
    cvDestroyWindow(titlefx);
    cvDestroyWindow(titlefy);
    cvDestroyWindow(titlef);

    return 0;
}

입력 영상

흑백 영상

x방향 Sobel 마스크로 필터링한 영상

y방향 Sobel 마스크로 필터링한 영상

입력 영상

흑백 영상

x방향 Sobel 마스크로 필터링한 영상

y방향 Sobel 마스크로 필터링한 영상

저작자표시 비영리 동일조건

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Harris corner detector (0)	2010.03.31

posted by maetel

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