Emgu 学习(7)threshold ，图像过滤

Threshold

代码如下

        static void Main(String[] args)
        {
            Mat img = CvInvoke.Imread(@"C:UsersdellPicturesfacesGray.png", ImreadModes.Grayscale);
            Mat dst = new Mat();
            double thresholdValue = 122;
            double max = 255;
            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(0);
        }

例子

    class Program
    {
        static void Main(String[] args)
        {
            Mat img = CvInvoke.Imread(@"C:UsersdellPicturesfaces.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(0);
        }
        static Mat sum_rgb(Mat src,Mat dst)
        {
            int ch = src.NumberOfChannels;
            VectorOfMat vMat = new VectorOfMat(ch);
            CvInvoke.Split(src, vMat);
            Mat b = vMat[0];
            Mat g = vMat[1];
            Mat r=  vMat[2];
            Mat s = new Mat();
            CvInvoke.AddWeighted(r, 1.0 / 3, g, 1.0 / 3, 0.0,s);
            CvInvoke.AddWeighted(s, 1.0, b, 1.0 / 3, 0.0, s);
            CvInvoke.Threshold(s, dst, 100, 100, ThresholdType.Trunc);
            return s;
        }
    }

自适应Threshold

代码

        static void Main(String[] args)
        {
            Mat img = CvInvoke.Imread(@"C:UsersdellPicturesfaces.png",0);
            Mat Gaussian = new Mat();
            Mat Mean = new Mat();
            CvInvoke.AdaptiveThreshold(img, Gaussian, 255, AdaptiveThresholdType.GaussianC, ThresholdType.Binary, 11, 5);
            CvInvoke.AdaptiveThreshold(img, Mean, 255, AdaptiveThresholdType.MeanC, ThresholdType.Binary, 11, 5);
            CvInvoke.Imshow("src", img);
            CvInvoke.Imshow("Gaussian", Gaussian);
            CvInvoke.Imshow("Mean", Mean);
            CvInvoke.WaitKey(0);
        }

代码

        static void Main(String[] args)
        {
            Mat img = CvInvoke.Imread(@"C:UsersdellPicturesfaces.png");

            Mat dst = new Mat();
            CvInvoke.BoxFilter(img, dst, DepthType.Default, new Size(3, 3), new Point(-1, -1));
            CvInvoke.Imshow("src", img);
            CvInvoke.Imshow("boxFilter", dst);
            CvInvoke.Blur(img, dst, new Size(5, 5), new Point(-1, -1));
            CvInvoke.Imshow("blur", dst);
            CvInvoke.GaussianBlur(img, dst, new Size(5, 5), 1);
            CvInvoke.Imshow("Gaussian", dst);
            CvInvoke.MedianBlur(img, dst, 3);
            CvInvoke.Imshow("median", dst);
            CvInvoke.BilateralFilter(img, dst, 5, 30.0, 2.0);
            CvInvoke.Imshow("Bilateral", dst);
            CvInvoke.WaitKey(0);
        }

效果如下