参考博客：https://www.cnblogs.com/dengxiaojun/p/5278679.html，但是他的demo下载太贵了

可以下载这个https://download.csdn.net/download/dsq235612/10830805?utm_source=bbsseo，其实代码都差不多，目前只能识别简单的结构的图片

先添加引用，在nuget中添加OpenCVSharp类库和识别条码类库zxing

封装OpenCVSharp的调用代码：

 public class OpencvHelper

    {

        /// <summary>

        /// 灰度图

        /// </summary>

        /// <param name="srcImage">未处理的mat容器</param>

        /// <param name="grayImage">灰度图mat容器</param>

        public static void CvGrayImage(Mat srcImage, Mat grayImage)

        {

            if (srcImage.Channels() == )

            {

                Cv2.CvtColor(srcImage, grayImage, ColorConversionCodes.BGR2GRAY);

            }

            else

            {

                grayImage = srcImage.Clone();

            }

            //Imshow("灰度图", grayImage);

        }

        /// <summary>

        /// 图像的梯度幅值

        /// </summary>

        /// <param name="grayImage"></param>

        public static void CvConvertScaleAbs(Mat grayImage, Mat gradientImage)

        {

            //建立图像的梯度幅值

            Mat gradientXImage = new Mat();

            Mat gradientYImage = new Mat();

            Cv2.Sobel(grayImage, gradientXImage, MatType.CV_32F, xorder: , yorder: , ksize: -);

            Cv2.Sobel(grayImage, gradientYImage, MatType.CV_32F, xorder: , yorder: , ksize: -);

            //Cv2.Scharr(grayImage, gradientXImage, MatType.CV_32F, 1, 0);//CV_16S  CV_32F

            //Cv2.Scharr(grayImage, gradientYImage, MatType.CV_32F, 0, 1);

            //因为我们需要的条形码在需要X方向水平,所以更多的关注X方向的梯度幅值,而省略掉Y方向的梯度幅值

            Cv2.Subtract(gradientXImage, gradientYImage, gradientImage);

            //归一化为八位图像

            Cv2.ConvertScaleAbs(gradientImage, gradientImage);

            //看看得到的梯度图像是什么样子

            //Imshow("图像的梯度幅值", gradientImage);

        }

        /// <summary>

        /// 二值化图像

        /// </summary>

        public static void BlurImage(Mat gradientImage, Mat blurImage, Mat thresholdImage)

        {

            //对图片进行相应的模糊化,使一些噪点消除

            //new OpenCvSharp.Size(12, 12);   (9,9)

            Cv2.Blur(gradientImage, blurImage, new OpenCvSharp.Size(, ));

            //Cv2.GaussianBlur(gradientImage, blurImage, new OpenCvSharp.Size(7, 7), 0);//Size必须是奇数

            //模糊化以后进行阈值化,得到到对应的黑白二值化图像,二值化的阈值可以根据实际情况调整

            Cv2.Threshold(blurImage, thresholdImage, , , ThresholdTypes.Binary);

            //看看二值化图像

            //Imshow("二值化图像", thresholdImage);

        }

        /// <summary>

        /// 闭运算

        /// </summary>

        public static void MorphImage(Mat thresholdImage, Mat morphImage)

        {

            //二值化以后的图像,条形码之间的黑白没有连接起来,就要进行形态学运算,消除缝隙,相当于小型的黑洞,选择闭运算

            //因为是长条之间的缝隙,所以需要选择宽度大于长度

            Mat kernel = Cv2.GetStructuringElement(MorphShapes.Rect, new OpenCvSharp.Size(, ));

            Cv2.MorphologyEx(thresholdImage, morphImage, MorphTypes.Close, kernel);

            //看看形态学操作以后的图像

            //Imshow("闭运算", morphImage);

        }

        /// <summary>

        /// 膨胀腐蚀

        /// </summary>

        public static void DilationErosionImage(Mat morphImage)

        {

            //现在要让条形码区域连接在一起,所以选择膨胀腐蚀,而且为了保持图形大小基本不变,应该使用相同次数的膨胀腐蚀

            //先腐蚀,让其他区域的亮的地方变少最好是消除,然后膨胀回来,消除干扰,迭代次数根据实际情况选择

            OpenCvSharp.Size size = new OpenCvSharp.Size(, );

            OpenCvSharp.Point point = new OpenCvSharp.Point(-, -);

            Cv2.Erode(morphImage, morphImage, Cv2.GetStructuringElement(MorphShapes.Rect, size), point, );

            Cv2.Dilate(morphImage, morphImage, Cv2.GetStructuringElement(MorphShapes.Rect, size), point, );

            //看看形态学操作以后的图像

            //Imshow("膨胀腐蚀", morphImage);

        }

        /// <summary>

        /// 显示处理后的图片

        /// </summary>

        /// <param name="name">处理过程名称</param>

        /// <param name="srcImage">图片盒子</param>

        public static void Imshow(string name, Mat srcImage)

        {

            using (var window = new Window(name, image: srcImage, flags: WindowMode.AutoSize))

            {

                Cv2.WaitKey();

            }

            //Cv2.ImShow(name, srcImage);

            //Cv2.WaitKey(0);

        }

        /// <summary>

        /// 旋转图片

        /// </summary>

        public static void RotateImage(Mat src, Mat dst, double angle, double scale)

        {

            var imageCenter = new Point2f(src.Cols / 2f, src.Rows / 2f);

            var rotationMat = Cv2.GetRotationMatrix2D(imageCenter, angle, scale);

            Cv2.WarpAffine(src, dst, rotationMat, src.Size());

        }

    }

调用封装的OpenCVSharp类的方法

 /// <summary>

        /// 读取图片

        /// </summary>

        private void DiscernImage()

        {

            string filename = FileHelper.OpenImageFile();

            if (string.IsNullOrEmpty(filename)) return;

            Image image = Image.FromFile(filename);

            picImage.Image = image;

            _imageFilePath = filename;

        }

        private void OpenCV()

        {

            if (string.IsNullOrEmpty(_imageFilePath)) return;

            Mat srcImage = new Mat(_imageFilePath, ImreadModes.Color);

            if (srcImage.Empty()) { return; }

            //图像转换为灰度图像

            Mat grayImage = new Mat();

            OpencvHelper.CvGrayImage(srcImage, grayImage);

            ShowImage("灰度图像", grayImage);

            //OpencvHelper.RotateImage(grayImage, grayImage, 50, 1);

            //OpencvHelper.Imshow("旋转", grayImage);

            //建立图像的梯度幅值

            Mat gradientImage = new Mat();

            OpencvHelper.CvConvertScaleAbs(grayImage, gradientImage);

            ShowImage("梯度幅值", gradientImage);

            //对图片进行相应的模糊化,使一些噪点消除

            Mat blurImage = new Mat();

            Mat thresholdImage = new Mat();

            OpencvHelper.BlurImage(gradientImage, blurImage, thresholdImage);

            ShowImage("二值化", blurImage);

            //二值化以后的图像,条形码之间的黑白没有连接起来,就要进行形态学运算,消除缝隙,相当于小型的黑洞,选择闭运算

            //因为是长条之间的缝隙,所以需要选择宽度大于长度

            Mat morphImage = new Mat();

            OpencvHelper.MorphImage(thresholdImage, morphImage);

            ShowImage("闭运算", morphImage);

            //现在要让条形码区域连接在一起,所以选择膨胀腐蚀,而且为了保持图形大小基本不变,应该使用相同次数的膨胀腐蚀

            //先腐蚀,让其他区域的亮的地方变少最好是消除,然后膨胀回来,消除干扰,迭代次数根据实际情况选择

            OpencvHelper.DilationErosionImage(morphImage);

            ShowImage("膨胀腐蚀", morphImage);

            Mat[] contours = new Mat[];

            List<double> OutArray = new List<double>();

            //接下来对目标轮廓进行查找,目标是为了计算图像面积

            Cv2.FindContours(morphImage, out contours, OutputArray.Create(OutArray), RetrievalModes.External, ContourApproximationModes.ApproxSimple);

            //看看轮廓图像

            //Cv2.DrawContours(srcImage, contours, -1, Scalar.Yellow);

            //OpencvHelper.Imshow("目标轮廓", srcImage);

            //计算轮廓的面积并且存放

            for (int i = ; i < OutArray.Count; i++)

            {

                OutArray[i] = contours[i].ContourArea(false);

            }

            List<string> codes = new List<string>();

            int num = ;

            while (num < ) //找出10个面积最大的矩形

            {

                //找出面积最大的轮廓

                double minValue, maxValue;

                OpenCvSharp.Point minLoc, maxLoc;

                Cv2.MinMaxLoc(InputArray.Create(OutArray), out minValue, out maxValue, out minLoc, out maxLoc);

                //计算面积最大的轮廓的最小的外包矩形

                RotatedRect minRect = Cv2.MinAreaRect(contours[maxLoc.Y]);

                //找到了矩形的角度,但是这是一个旋转矩形,所以还要重新获得一个外包最小矩形

                Rect myRect = Cv2.BoundingRect(contours[maxLoc.Y]);

                //将扫描的图像裁剪下来,并保存为相应的结果,保留一些X方向的边界,所以对rect进行一定的扩张

                myRect.X = myRect.X - (myRect.Width / );

                myRect.Width = (int)(myRect.Width * 1.1);

                //TermCriteria termc = new TermCriteria(CriteriaType.MaxIter, 1, 1);

                //Cv2.CamShift(srcImage, myRect, termc);

                //一次最大面积的

                var a = contours.ToList();

                a.Remove(contours[maxLoc.Y]);

                contours = a.ToArray();

                OutArray.Remove(OutArray[maxLoc.Y]);

                string code = DiscernBarCode(srcImage, myRect);

                if(!string.IsNullOrEmpty(code))

                {

                    //Cv2.Rectangle(srcImage, myRect, new Scalar(0, 255, 255), 3, LineTypes.AntiAlias);

                    codes.Add(code);

                }

                Cv2.Rectangle(srcImage, myRect, new Scalar(, , ), , LineTypes.AntiAlias);

                num++;

                if (contours.Count() <= )

                    break;

            }

            Image img2 = CreateImage(srcImage);

            picFindContours.Image = img2;

            txtcodess.Text = string.Join("\r\n", codes);

            ////找出面积最大的轮廓

            //double minValue, maxValue;

            //OpenCvSharp.Point minLoc, maxLoc;

            //Cv2.MinMaxLoc(InputArray.Create(OutArray), out minValue, out maxValue, out minLoc, out maxLoc);

            ////计算面积最大的轮廓的最小的外包矩形

            //RotatedRect minRect = Cv2.MinAreaRect(contours[maxLoc.Y]);

            ////为了防止找错,要检查这个矩形的偏斜角度不能超标

            ////如果超标,那就是没找到

            //if (minRect.Angle < 2.0)

            //{

            //    //找到了矩形的角度,但是这是一个旋转矩形,所以还要重新获得一个外包最小矩形

            //    Rect myRect = Cv2.BoundingRect(contours[maxLoc.Y]);

            //    //把这个矩形在源图像中画出来

            //    //Cv2.Rectangle(srcImage, myRect, new Scalar(0, 255, 255), 3, LineTypes.AntiAlias);

            //    //看看显示效果,找的对不对

            //    //Imshow("裁剪图片", srcImage);

            //    //将扫描的图像裁剪下来,并保存为相应的结果,保留一些X方向的边界,所以对rect进行一定的扩张

            //    myRect.X = myRect.X - (myRect.Width / 20);

            //    myRect.Width = (int)(myRect.Width * 1.1);

            //    Mat resultImage = new Mat(srcImage, myRect);

            //    //OpencvHelper.Imshow("结果图片", resultImage);

            //    Image img = CreateImage(resultImage);

            //    picCode.Image = img;

            //    DiscernBarcode(img);

            //    //看看轮廓图像

            //    Cv2.DrawContours(srcImage, contours, -1, Scalar.Red);

            //    //把这个矩形在源图像中画出来

            //    Cv2.Rectangle(srcImage, myRect, new Scalar(0, 255, 255), 3, LineTypes.AntiAlias);

            //    Image img2 = CreateImage(srcImage);

            //    picFindContours.Image = img2;

            //    //string path = Path.GetDirectoryName(@g_sFilePath) + "\\Ok.png";

            //    //if (File.Exists(@path)) File.Delete(@path);//如果文件存在 则删除

            //    //if (!Cv2.ImWrite(@path, resultImage))

            //}

            srcImage.Dispose();

        }

        private void HandelCode(Mat srcImage, Rect myRect, Mat[] contours)

        {

            Mat resultImage = new Mat(srcImage, myRect);

            Image img = CreateImage(resultImage);

            picCode.Image = img;

            DiscernBarcode(img);

            //看看轮廓图像

            Cv2.DrawContours(srcImage, contours, -1, Scalar.Red);

            //把这个矩形在源图像中画出来

            Cv2.Rectangle(srcImage, myRect, new Scalar(0, 255, 255), 3, LineTypes.AntiAlias);

            //Image img2 = CreateImage(srcImage);

            //picFindContours.Image = img2;

        }

        private Image CreateImage(Mat resultImage)

        {

            byte[] bytes = resultImage.ToBytes();

            MemoryStream ms = new MemoryStream(bytes);

            return Bitmap.FromStream(ms, true);

        }

        private void ShowImage(string name, Mat resultImage)

        {

            //Image img = CreateImage(resultImage);

            //frmShowImage frm = new frmShowImage(name, img);

            //frm.ShowDialog();

        }

        /// <summary>

        /// 解析条形码图片

        /// </summary>

        private string DiscernBarCode(Mat srcImage, Rect myRect)

        {

            try

            {

                Mat resultImage = new Mat(srcImage, myRect);

                Image img = CreateImage(resultImage);

                Bitmap pImg = MakeGrayscale3((Bitmap)img);

                BarcodeReader reader = new BarcodeReader();

                reader.Options.CharacterSet = "UTF-8";

                Result result = reader.Decode(new Bitmap(pImg));

                Console.Write(result);

                if (result != null)

                    return result.ToString();

                else

                    return "";

            }

            catch (Exception ex)

            {

                Console.Write(ex);

                return "";

            }

        }

        /// <summary>

        /// 解析条形码图片

        /// </summary>

        private void DiscernBarcode(Image primaryImage)

        {

            //Bitmap pImg = MakeGrayscale3((Bitmap)primaryImage);

            picHandel.Image = primaryImage;

            BarcodeReader reader = new BarcodeReader();

            reader.Options.CharacterSet = "UTF-8";

            Result result = reader.Decode(new Bitmap(primaryImage));//Image.FromFile(path)

            Console.Write(result);

            if (result != null)

                txtBarCode.Text = result.ToString();

            else

                txtBarCode.Text = "";

            //watch.Start();

            //watch.Stop();

            //TimeSpan timeSpan = watch.Elapsed;

            //MessageBox.Show("扫描执行时间：" + timeSpan.TotalMilliseconds.ToString());

            //using (ZBar.ImageScanner scanner = new ZBar.ImageScanner())

            //{

            //    scanner.SetConfiguration(ZBar.SymbolType.None, ZBar.Config.Enable, 0);

            //    scanner.SetConfiguration(ZBar.SymbolType.CODE39, ZBar.Config.Enable, 1);

            //    scanner.SetConfiguration(ZBar.SymbolType.CODE128, ZBar.Config.Enable, 1);

            //    List<ZBar.Symbol> symbols = new List<ZBar.Symbol>();

            //    symbols = scanner.Scan((Image)pImg);

            //    if (symbols != null && symbols.Count > 0)

            //    {

            //        //string result = string.Empty;

            //        //symbols.ForEach(s => result += "条码内容:" + s.Data + " 条码质量:" + s.Type + Environment.NewLine);

            //        txtBarCode.Text = symbols.FirstOrDefault().Data;

            //    }

            //    else

            //    {

            //        txtBarCode.Text = "";

            //    }

            //}

        }

截图出来的条形码进行灰度处理

        /// <summary>

        /// 处理图片灰度

        /// </summary>

        /// <param name="original"></param>

        /// <returns></returns>

        public static Bitmap MakeGrayscale3(Bitmap original)

        {

            //create a blank bitmap the same size as original

            Bitmap newBitmap = new Bitmap(original.Width, original.Height);

            //get a graphics object from the new image

            Graphics g = Graphics.FromImage(newBitmap);

            //create the grayscale ColorMatrix

            System.Drawing.Imaging.ColorMatrix colorMatrix = new System.Drawing.Imaging.ColorMatrix(

               new float[][]

              {

                 new float[] {.3f, .3f, .3f, , },

                 new float[] {.59f, .59f, .59f, , },

                 new float[] {.11f, .11f, .11f, , },

                 new float[] {, , , , },

                 new float[] {, , , , }

              });

            //create some image attributes

            ImageAttributes attributes = new ImageAttributes();

            //set the color matrix attribute

            attributes.SetColorMatrix(colorMatrix);

            //draw the original image on the new image

            //using the grayscale color matrix

            g.DrawImage(original, new Rectangle(, , original.Width, original.Height),

               , , original.Width, original.Height, GraphicsUnit.Pixel, attributes);

            //dispose the Graphics object

            g.Dispose();

            return newBitmap;

        }

效果图：

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