Emgu 学习(7)threshold ，图像过滤

Threshold

代码如下

        static void Main(String[] args)

        {

            Mat img = CvInvoke.Imread(@"C:\Users\dell\Pictures\facesGray.png", ImreadModes.Grayscale);

            Mat dst = new Mat();

            double thresholdValue = ;

            double max = ;

            CvInvoke.Threshold(img, dst, thresholdValue, max, ThresholdType.Binary);

            CvInvoke.Imshow("src", img);

            CvInvoke.Imshow("Binary", dst);

            CvInvoke.Threshold(img, dst, thresholdValue, max, ThresholdType.BinaryInv);

            CvInvoke.Imshow("BinaryInv", dst);

            CvInvoke.Threshold(img, dst, thresholdValue, max, ThresholdType.Otsu);

            CvInvoke.Imshow("Otsu", dst);

            CvInvoke.Threshold(img, dst, thresholdValue, max, ThresholdType.ToZero);

            CvInvoke.Imshow("ToZero", dst);

            CvInvoke.Threshold(img, dst, thresholdValue, max, ThresholdType.ToZeroInv);

            CvInvoke.Imshow("ToZeroInv", dst);

            CvInvoke.Threshold(img, dst, thresholdValue, max, ThresholdType.Trunc);

            CvInvoke.Imshow("Trunc", dst);

            CvInvoke.WaitKey();

        }

例子

    class Program

    {

        static void Main(String[] args)

        {

            Mat img = CvInvoke.Imread(@"C:\Users\dell\Pictures\faces.png");

            Mat dst = new Mat();

            Mat weighted = new Mat();

            weighted = sum_rgb(img, dst).Clone();

            CvInvoke.Imshow("src", img);

            CvInvoke.Imshow("weighted", weighted);

            CvInvoke.Imshow("dst", dst);

            CvInvoke.WaitKey();

        }

        static Mat sum_rgb(Mat src,Mat dst)

        {

            int ch = src.NumberOfChannels;

            VectorOfMat vMat = new VectorOfMat(ch);

            CvInvoke.Split(src, vMat);

            Mat b = vMat[];

            Mat g = vMat[];

            Mat r=  vMat[];

            Mat s = new Mat();

            CvInvoke.AddWeighted(r, 1.0 / , g, 1.0 / , 0.0,s);

            CvInvoke.AddWeighted(s, 1.0, b, 1.0 / , 0.0, s);

            CvInvoke.Threshold(s, dst, , , ThresholdType.Trunc);

            return s;

        }

    }

自适应Threshold

代码

        static void Main(String[] args)

        {

            Mat img = CvInvoke.Imread(@"C:\Users\dell\Pictures\faces.png",);

            Mat Gaussian = new Mat();

            Mat Mean = new Mat();

            CvInvoke.AdaptiveThreshold(img, Gaussian, , AdaptiveThresholdType.GaussianC, ThresholdType.Binary, , );

            CvInvoke.AdaptiveThreshold(img, Mean, , AdaptiveThresholdType.MeanC, ThresholdType.Binary, , );

            CvInvoke.Imshow("src", img);

            CvInvoke.Imshow("Gaussian", Gaussian);

            CvInvoke.Imshow("Mean", Mean);

            CvInvoke.WaitKey();

        }

代码

        static void Main(String[] args)

        {

            Mat img = CvInvoke.Imread(@"C:\Users\dell\Pictures\faces.png");

            Mat dst = new Mat();

            CvInvoke.BoxFilter(img, dst, DepthType.Default, new Size(, ), new Point(-, -));

            CvInvoke.Imshow("src", img);

            CvInvoke.Imshow("boxFilter", dst);

            CvInvoke.Blur(img, dst, new Size(, ), new Point(-, -));

            CvInvoke.Imshow("blur", dst);

            CvInvoke.GaussianBlur(img, dst, new Size(, ), );

            CvInvoke.Imshow("Gaussian", dst);

            CvInvoke.MedianBlur(img, dst, );

            CvInvoke.Imshow("median", dst);

            CvInvoke.BilateralFilter(img, dst, , 30.0, 2.0);

            CvInvoke.Imshow("Bilateral", dst);

            CvInvoke.WaitKey();

        }

效果如下