Question

I have a problem with filling white holes inside a black coin so that I can have only 0-255 binary images with filled black coins. I have used a Median filter to accomplish it but in that case connection bridge between coins grows and it goes impossible to recognize them after several times of erosion... So I need a simple floodFill like method in opencv

这里,我的形象是:

“entergraph

<>strong>EDIT:floodliver, 如同功能一样,必须填充大部件中的空洞,而无需打X, Y坐标为种子......

EDIT:我尝试使用<代码>cvDrawContours功能,但用斜体填充较大体内的轮.。

我的守则是:

        CvMemStorage mem = cvCreateMemStorage(0);
        CvSeq contours = new CvSeq();
        CvSeq ptr = new CvSeq();
        int sizeofCvContour = Loader.sizeof(CvContour.class);
        
        cvThreshold(gray, gray, 150, 255, CV_THRESH_BINARY_INV);
        
        int numOfContours = cvFindContours(gray, mem, contours, sizeofCvContour, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);
        System.out.println("The num of contours: "+numOfContours); //prints 87, ok
        
        Random rand = new Random();
        for (ptr = contours; ptr != null; ptr = ptr.h_next()) {
            Color randomColor = new Color(rand.nextFloat(), rand.nextFloat(), rand.nextFloat());
            CvScalar color = CV_RGB( randomColor.getRed(), randomColor.getGreen(), randomColor.getBlue());
            cvDrawContours(gray, ptr, color, color, -1, CV_FILLED, 8);
        }
        CanvasFrame canvas6  = new CanvasFrame("drawContours");
        canvas6.showImage(gray);

结果:(你可以看到每个硬币中的黑洞)

enter image description here

Answer 1

这样做有两种方法:

<>strong>1 Contour Filling:

首先,歪曲图像,在图像中找到镜子,用黑体和反面填充。

des = cv2.bitwise_not(gray)
contour,hier = cv2.findContours(des,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_SIMPLE)

for cnt in contour:
    cv2.drawContours(des,[cnt],0,255,-1)

gray = cv2.bitwise_not(des)

塑造形象:

“entergraph

2

kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3)) res = cv2.morphologyEx(gray,cv2.MORPH_OPEN,kernel)

由此形成的形象如下:

你可以看到,这两种情况都没有什么不同。

<<>NB:gray - graypl图像所有法典都载于开放式公民委员会。

参考:

Answer 2

我想象,一个简单的分裂和侵蚀会很好地缩小差距。我认为,这或许是你所期待的。

更强有力的解决办法是对整个形象进行前沿检测,然后对圈子进行ough变。快速通道显示,有各种语言的密码样本,可用来对使用ough变的圈子进行不间断的探测,因此希望能让你去做一些事情。

使用“ough变”的好处是,算法实际上将给你一个对每个圈子大小和位置的估计,以便你能够根据这一模式重建理想形象。重叠也应当非常有力,特别是考虑到这里的投入形象的质量(即对不实正面的担忧较少),这样可以降低结果的门面。

Answer 3

您不妨查阅 Fillhole transformation,应用形态图像重建。

这一转变将填补贵表层的漏洞,尽管也弥补了相邻组之间的所有漏洞。其他海报建议的“粗略空间”或开放式解决办法可能给你更好的高级别承认结果。

Answer 4

Try using cvFindContours() 你们可以利用这一系统寻找相关部件。有了正确的参数,这一职能将每个相关组成部分的轮.列入清单。

Find the contours which represent a hole. Then use cvDrawContours() to fill up the selected contour by the foreground color thereby closing the holes.

Answer 5

如果有人正在寻求执行,

            std::vector<std::vector<cv::Point> > contours_vector;

            cv::findContours(input_image, contours_vector, CV_RETR_LIST, CV_CHAIN_APPROX_NONE);

            cv::Mat contourImage(input_image.size(), CV_8UC1, cv::Scalar(0));
            for ( ushort contour_index = 0; contour_index < contours_vector.size(); contour_index++) {
                cv::drawContours(contourImage, contours_vector, contour_index, cv::Scalar(255), -1);
            }

            cv::imshow("con", contourImage);
            cv::waitKey(0);

Answer 6

I think if the objects are touched or crowded, there will be some problems using the contours and the math morophology opening. Instead, the following simple solution is found and tested. It is working very well, and not only for this images, but also for any other images.

http://blogs.mathwork.com/steve/2008/08/05/meetinging-small-holes/“rel=“nofollow”>。

请注意: 投入图

1. filled_I = floodfill(I). // fill every hole in the image.
2. inverted_I = invert(I)`.   
3. holes_I = filled_I AND inverted_I. // finds all holes 
4. cc_list = connectedcomponent(holes_I) // list of all connected component in holes_I.
5. holes_I = remove(cc_list,holes_I, smallholes_threshold_size) // remove all holes from holes_I having size > smallholes_threshold_size.
6. out_I = I OR holes_I. // fill only the small holes.

简言之,算法只是找到所有的漏洞,然后将大的漏洞删除,然后只用原形象写小的。

Answer 7

我先在互联网上寻找适当的imlif功能(作为Matlab的功能),但在C与开放CV合作。在进行了一些回馈之后,我终于找到了解决办法:

IplImage* imfill(IplImage* src)
{
    CvScalar white = CV_RGB( 255, 255, 255 );

    IplImage* dst = cvCreateImage( cvGetSize(src), 8, 3);
    CvMemStorage* storage = cvCreateMemStorage(0);
    CvSeq* contour = 0;

    cvFindContours(src, storage, &contour, sizeof(CvContour), CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE );
    cvZero( dst );

    for( ; contour != 0; contour = contour->h_next )
    {
        cvDrawContours( dst, contour, white, white, 0, CV_FILLED);
    }

    IplImage* bin_imgFilled = cvCreateImage(cvGetSize(src), 8, 1);
    cvInRangeS(dst, white, white, bin_imgFilled);

    return bin_imgFilled;
}

http://img688.imageshack.us/img688/4715/originalbinaryimage.jpg”rel=“nofollow” 原文:

成果是:

The trick is in the parameters setting of the cvDrawContours function: cvDrawContours( dst, contour, white, white, 0, CV_FILLED);

dst = destination image
contour = pointer to the first contour
white = color used to fill the contour
0 = Maximal level for drawn contours. If 0, only contour is drawn
CV_FILLED = Thickness of lines the contours are drawn with. If it is negative (For example, =CV_FILLED), the contour interiors are drawn.

开放式中心文件的内容更多。

There is probably a way to get "dst" directly as a binary image but I couldn t find how to use the cvDrawContours function with binary values.

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