c# - OpenCVSharp findContours 返回错误数据

Question

我正在尝试实现一个在二进制图像中找到轮廓并过滤掉小轮廓的函数。

这是我的代码和示例图像。这是一个 super 简单的功能，可以去除小面积的 Blob 。但我不断得到“边缘轮廓”而不是区域轮廓。 :S

    private IplImage RemoveNoise( IplImage image, int minArea )
    {

        List<CvPoint[]> listOfPoints = new List<CvPoint[]>();

        CvSeq<CvPoint> contoursRaw;

        List<ContourData> contours = new List<ContourData>();
        using( CvMemStorage storage = new CvMemStorage() )
        {
            //find contoures
            //Cv.FindContours( image, storage, out contoursRaw );
            Cv.FindContours( image, storage, out contoursRaw, CvContour.SizeOf, ContourRetrieval.Tree, ContourChain.ApproxSimple );
            //contoursRaw = Cv.ApproxPoly( contoursRaw, CvContour.SizeOf, storage, ApproxPolyMethod.DP, 3, true );

            while( contoursRaw != null )
            {
                CvSeq<CvPoint> result = contoursRaw;
                double area = Cv.ContourArea( result );

                //filter out small regions
                if( area >= minArea )
                {                   
                    List<CvPoint> points = new List<CvPoint>();
                    int i = 0;
                    while( result[ i ] != null )
                    {
                        points.Add( new CvPoint( result[ i ].Value.X, result[ i ].Value.Y ) );
                        i++;
                    }
                    listOfPoints.Add( points.ToArray() );

                }
                contoursRaw = contoursRaw.HNext;
            }
        }

        // draw large regions
        IplImage output = new IplImage( image.Size, image.Depth, 1 );
        output.Set( CvColor.Black );
        CvPoint[][] ArrayOfPoints = listOfPoints.ToArray();
        output.FillPoly( ArrayOfPoints, CvColor.White );

        return output;
    }

为什么我不断得到“边缘轮廓”而不是区域轮廓？

结果如下:
enter image description here

Answer 1

试试这个。

IplImage input = new IplImage(@"C:\Users\20396600\Downloads\cont.jpg");
IplImage gray = new IplImage(input.Size, BitDepth.U8, 1);
IplImage invert = gray.Clone();
input.CvtColor(gray, ColorConversion.BgrToGray);
gray.Threshold(invert, 70, 255, ThresholdType.BinaryInv);
RemoveNoise(invert, 150);

private IplImage RemoveNoise(IplImage image, int minArea)
{
    IplImage output = new IplImage(image.Size, BitDepth.U8, 3);//image.Depth, 1);
    output.Set(CvColor.Black);
    CvSeq<CvPoint> contoursRaw;

    using (CvMemStorage storage = new CvMemStorage())
    {
        //find contours
        Cv.FindContours(image, storage, out contoursRaw, CvContour.SizeOf, ContourRetrieval.Tree, ContourChain.ApproxSimple);

        //Taken straight from one of the OpenCvSharp samples
        using (CvContourScanner scanner = new CvContourScanner(image, storage, CvContour.SizeOf, ContourRetrieval.Tree, ContourChain.ApproxSimple))
        {
            foreach (CvSeq<CvPoint> c in scanner)
            {
                //Some contours are negative so make them all positive for easy comparison
                double area = Math.Abs(c.ContourArea());
                //Uncomment below to see the area of each contour
                //Console.WriteLine(area.ToString());
                if (area >= minArea)
                {
                    List<CvPoint[]> points = new List<CvPoint[]>();
                    List<CvPoint> point = new List<CvPoint>();
                    foreach (CvPoint p in c.ToArray())
                        point.Add(p);

                    points.Add(point.ToArray());

                    //Use FillPoly instead of DrawContours as requested
                    output.FillPoly(points.ToArray(), CvColor.Red, LineType.AntiAlias);

                    //-1 means fill the polygon
                    //output.DrawContours(c, CvColor.White, CvColor.Green, 0, -1, LineType.AntiAlias);

                    //Uncomment two lines below to see contours being drawn gradually
                    //Cv.ShowImage("Window", output);
                    //Cv.WaitKey();
                }
            }
        } 
    }
    output.SaveImage("output.png");

    return output;
}

根据这里的解释请求是秘诀。

反转图像有助于正确找到轮廓。 FindContour 想要在黑色背景上查找白色对象。

ContourArea() 返回负值，因此 Math.Abs​​() 有助于过滤到您想要的内容。

如果 -1 作为厚度传递，DrawContour() 函数将填充轮廓。

其他一切都与 OpenCvSharp 下载中提供的示例非常相似。希望这可以帮助。

编辑:通过另一个 channel ，作者要求能够使用 FillPoly 而不是 DrawContours，因此示例代码已更新以反射(reflect)这一点。