C# open cv即emgu cv 定位车牌思路及代码

最近没什么事，了解图像处理，所以学习了以下！由于我不太会C++，只能试着用C#编写代码！但是网上关于emgu cv的资料少之又少，而且很多还是英文的，而且讲的不详细。所以慢慢琢磨。写了个c#定位车牌的代码，不过效果不是很理想。参考了c++高手的代码！

思路就是1.灰度化，竖向边缘检测

2.自适应二值化处理

3.形态学处理（膨胀和腐蚀）

4.轮廓查找与筛选

代码如下：

            Image<Bgr, Byte> simage = img;    //new Image<Bgr, byte>("license-plate.jpg");
            //Image<Bgr, Byte> simage = sizeimage.Resize(400, 300, Emgu.CV.CvEnum.INTER.CV_INTER_NN);
            Image<Gray, Byte> GrayImg = new Image<Gray, Byte>(simage.Width, simage.Height);
            IntPtr GrayImg1 = CvInvoke.cvCreateImage(simage.Size, Emgu.CV.CvEnum.IPL_DEPTH.IPL_DEPTH_8U, 1);
            //灰度化
            CvInvoke.cvCvtColor(simage.Ptr, GrayImg1, Emgu.CV.CvEnum.COLOR_CONVERSION.BGR2GRAY);
            //首先创建一张16深度有符号的图像区域
            IntPtr Sobel = CvInvoke.cvCreateImage(simage.Size, Emgu.CV.CvEnum.IPL_DEPTH.IPL_DEPTH_16S, 1);
            //X方向的Sobel算子检测
            CvInvoke.cvSobel(GrayImg1, Sobel, 2, 0, 3);
            IntPtr temp = CvInvoke.cvCreateImage(simage.Size, Emgu.CV.CvEnum.IPL_DEPTH.IPL_DEPTH_8U, 1);
            CvInvoke.cvConvertScale(Sobel, temp, 0.00390625, 0);
            ////int it = ComputeThresholdValue(GrayImg.ToBitmap());
            ////二值化处理
            ////Image<Gray, Byte> dest = GrayImg.ThresholdBinary(new Gray(it), new Gray(255));
            Image<Gray, Byte> dest = new Image<Gray, Byte>(simage.Width, simage.Height);
            //二值化处理
            CvInvoke.cvThreshold(temp, dest, 0, 255, Emgu.CV.CvEnum.THRESH.CV_THRESH_OTSU);
            IntPtr temp1 = CvInvoke.cvCreateImage(simage.Size, Emgu.CV.CvEnum.IPL_DEPTH.IPL_DEPTH_8U, 1);
            Image<Gray, Byte> dest1 = new Image<Gray, Byte>(simage.Width, simage.Height);
            CvInvoke.cvCreateStructuringElementEx(3, 1, 1, 0, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT, temp1);
            CvInvoke.cvDilate(dest, dest1, temp1, 6);
            CvInvoke.cvErode(dest1, dest1, temp1, 7);
            CvInvoke.cvDilate(dest1, dest1, temp1, 1);
            CvInvoke.cvCreateStructuringElementEx(1, 3, 0, 1, Emgu.CV.CvEnum.CV_ELEMENT_SHAPE.CV_SHAPE_RECT, temp1);
            CvInvoke.cvErode(dest1, dest1, temp1, 2);
            CvInvoke.cvDilate(dest1, dest1, temp1, 2);
            IntPtr dst = CvInvoke.cvCreateImage(simage.Size, Emgu.CV.CvEnum.IPL_DEPTH.IPL_DEPTH_8U, 3);
            CvInvoke.cvZero(dst);
            //dest.Dilate(10);
            //dest.Erode(5);
            using (MemStorage stor = new MemStorage())
            {
                Contour<Point> contours = dest1.FindContours(
                    Emgu.CV.CvEnum.CHAIN_APPROX_METHOD.CV_CHAIN_APPROX_SIMPLE,
                    Emgu.CV.CvEnum.RETR_TYPE.CV_RETR_CCOMP,
                    stor);
                for (; contours != null; contours = contours.HNext)
                {

                    Rectangle box = contours.BoundingRectangle;
                    Image<Bgr, Byte> test = simage.CopyBlank();
                    test.SetValue(255.0);
                    double whRatio = (double)box.Width / box.Height;
                    int area = (int)box.Width * box.Height;
                    if (area > 1000 && area<10000)
                    {
                        if ((3.0 < whRatio && whRatio < 6.0))
                        {
                            test.Draw(box, new Bgr(Color.Red), 2);
                            simage.Draw(box, new Bgr(Color.Red), 2);//CvInvoke.cvNamedWindow("dst");
                            //CvInvoke.cvShowImage("dst", dst);
                            imageBox1.Image = simage;
                        }
                    }
                }
            }

 

还是有一些细节没处理好啊，希望大家能指点我这个新手