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扫描图片并检测线条

c++opencvfeature-detection
3
我有一个任务，需要使用合适的卷积核(cvFilter2D函数)扫描一张图片，然后只保留+-45度和+-60度的直线。有人能给我一些提示吗，特别是如何计算卷积核？
你需要《学习OpenCV》这本书，其中第6章涵盖了这个内容。 - QuentinUK
2个回答
1
谢谢！但是你说的“将它们归一化，使它们加起来等于1”是什么意思？ - Quang Chánh Nguyễn
你有一个内核，比如5x5的内核，里面填充了1和0（在没有抗锯齿的情况下）。如果你把内核中所有的数字加起来，它们可能会加起来等于13。这是不允许的，因为在将内核应用于图像后，亮度将增加13倍。所以当你制作内核时，确保获取内核中像素的总和，并将每个像素除以该数字，使内核被归一化。 - Boyko Perfanov
谢谢！看起来如果线条足够粗，那么该线条的结果将包括2个边缘线！我已经阅读了一些网页，它们说我们必须在应用内核之前进行“骨架化”！你能给我一个提示如何做到这一点吗？ - Quang Chánh Nguyễn
0
抱歉回复晚了！我已经完成了这个任务！首先，再次感谢Perfanoff！多亏了你提供的参考链接，我找到了解决问题的方法！以下是我的代码：
// Image Transforms.cpp : Defines the entry point for the console application.

/*The purpose of this program is to detect lines which are +-45 degree and +- 60 degree from a binary picture.*/
 #include "stdafx.h"
 #include "cv.h"
 #include "highgui.h" 

int _tmain(int argc, _TCHAR* argv[])
{
//IplImage* src = cvLoadImage("C:\\Users\\USER\\Desktop\\black white 1.jpg");
IplImage* src = cvLoadImage("C:\\Users\\USER\\Desktop\\line detection 4.png");

cvNamedWindow("src", CV_WINDOW_NORMAL);
cvShowImage("src", src);    

IplImage* DstSum = cvCreateImage(cvGetSize(src),src->depth, 3);
IplImage* Dst45 = cvCreateImage(cvGetSize(src),src->depth, 3);
IplImage* Dst135 = cvCreateImage(cvGetSize(src),src->depth, 3);
IplImage* Dst60 = cvCreateImage(cvGetSize(src),src->depth, 3);
IplImage* Dst120 = cvCreateImage(cvGetSize(src),src->depth, 3);

/*double Ker0 [] = { -0.1,-0.1,-0.1,-0.1,-0.1,
                0, 0, 0, 0, 0, 0,
                0.2,0.2,0.2,0.2,0.2,
                0, 0, 0, 0, 0, 0,
                -0.1,-0.1,-0.1,-0.1,-0.1
                };
double Ker90 [] = {-0.1,0,0.2,0,-0.1,
                -0.1,0,0.2,0,-0.1,
                -0.1,0,0.2,0,-0.1,
                -0.1,0,0.2,0,-0.1,
                -0.1,0,0.2,0,-0.1
                }; */

double Ker45[]={
                 0,-0.1,-0.1, 0, 0.2,
                -0.1,-0.1, 0, 0.2, 0,
                -0.1, 0, 0.2, 0,-0.1,
                 0, 0.2, 0,-0.1,-0.1,
                0.2, 0,-0.1,-0.1, 0
                };// 45 degree 

CvMat Kernel45=cvMat(5, 5, CV_64FC1,Ker45);

double Ker135[]={
        0.2, 0,-0.1,-0.1, 0,
        0, 0.2, 0,-0.1,-0.1,
        -0.1, 0, 0.2, 0,-0.1,
        -0.1,-0.1, 0, 0.2, 0,
        0,-0.1,-0.1, 0, 0.2
        };// 135 degree 

CvMat Kernel135=cvMat(5, 5, CV_64FC1,Ker135);

double Ker120[] = {0,0,0,0,0,0,0,
                  1/7,0.25/7,0,0,0,0,0,
                  0,0.75/7,0.75/7,0.25/7,0,0,0,
                  0,0,0,0.75/7,0.6/7,0.25/7,0,
                  0,0,0,0,0.4/7,0.75/7,1/7,
                  0,0,0,0,0,0,0,
                  0,0,0,0,0,0,0
                };//120 degree
CvMat Kernel120=cvMat(7, 7, CV_64FC1,Ker120);

double Ker60[] = {0,0,0,0,1/7,0,0,
                  0,0,0,0.25/7,0.75/7,0,0,
                  0,0,0,0.6/7,0.4/7,0,0,
                  0,0,0.25/7,0.75/7,0,0,0,
                  0,0,0.75/7,0.25/7,0,0,0,
                  0,0.25/7,0.75/7,0,0,0,0,
                  0,1/7,0,0,0,0,0
                };//60 degree

CvMat Kernel60=cvMat(7, 7, CV_64FC1,Ker60);

cvFilter2D(src,Dst60,&Kernel60,cvPoint(-1,-1));
cvThreshold(Dst60,Dst60,100,255,CV_THRESH_BINARY);

cvFilter2D(src,Dst120,&Kernel120,cvPoint(-1,-1));
cvThreshold(Dst120,Dst120,100,255,CV_THRESH_BINARY);

cvFilter2D(src,Dst45,&Kernel45,cvPoint(-1,-1));
cvThreshold(Dst45,Dst45,200,255,CV_THRESH_BINARY);

cvFilter2D(src,Dst135,&Kernel135,cvPoint(-1,-1));
cvThreshold(Dst135,Dst135,200,255,CV_THRESH_BINARY);

cvAdd(Dst45,Dst60,DstSum,NULL);
cvAdd(Dst135,DstSum,DstSum,NULL);
cvAdd(Dst120,DstSum,DstSum,NULL);

cvNamedWindow("dst", CV_WINDOW_NORMAL);
cvShowImage("dst", DstSum);

cvReleaseImage(&DstSum);

cvWaitKey(0);

cvReleaseImage(&src);

cvDestroyWindow("src");
cvDestroyWindow("dst"); 

return 0;

}

这是结果： 输入图像描述
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