Esempio n. 1
0
        /// <summary>
        /// filter滤波
        /// </summary>
        private void Filter()
        {
            //isWarningImage = false;
            Matrix <byte> BoxArray = this.AlignImage.Clone();           //滤波矩阵

            //Mat MedianMat = InitMiddleImage.Clone();
            //Matrix<byte> MedianMat = this.AlignImage.Clone();
            //Mat Outputtemp = new Mat();
            //BoxArray._Mul(MedianMat);

            //中值滤波
            CvInvoke.MedianBlur(BoxArray, BoxArray, 9);
            //Boxfilter
            CvInvoke.BoxFilter(BoxArray, BoxArray, DepthType.Cv8U, new Size(9, 9), new Point(-1, -1), true, BorderType.Reflect101);
            //CvInvoke.FastNlMeansDenoising(MedianMat,BoxArray);

            //背景归0
            //Matrix<byte> tempArray = new Matrix<byte>(new Size(BoxArray.Width, BoxArray.Height));
            //CvInvoke.Threshold(BoxArray, tempArray, 100, 1, ThresholdType.Binary);
            //BoxArray._Mul(tempArray);

            //ACE图像增强
            BoxArray = ACE(BoxArray);
            //CvInvoke.MedianBlur(BoxArray, BoxArray, 9);
            CvInvoke.BoxFilter(BoxArray, BoxArray, DepthType.Cv8U, new Size(9, 9), new Point(-1, -1), true, BorderType.Reflect101);

            //输出图像为滤波后图像转三通道
            //CvInvoke.Threshold(BoxArray, Outputtemp, Th>50?Th:50, 255, ThresholdType.ToZero);
            CvInvoke.CvtColor(BoxArray, this.FinalImage, ColorConversion.Gray2Bgr);//Outputtemp,BoxArray
            this.FilterImage = BoxArray;
        }
        //######################################################映美金#####################################################################
        /// <summary>
        /// 映美金粗略找合适的轮廓
        /// </summary>
        public static void getContoursForYMJ(Image <Gray, byte> garyImage, PictureBox ptbDisplay)
        {
            GLB.TitleStr = "";
            int AREA = TisCamera.width * TisCamera.height;                    //总面积
            Image <Gray, byte>    dnc      = new Image <Gray, byte>(TisCamera.width, TisCamera.height);
            VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint(2000); //区块集合

            CvInvoke.Threshold(garyImage, garyImage, 120, 255, ThresholdType.Otsu);
            //ptbDisplay.Image = garyImage.ToBitmap();//显示映美金图像

            CvInvoke.BoxFilter(garyImage, garyImage, Emgu.CV.CvEnum.DepthType.Cv8U, new Size(3, 3), new Point(-1, -1)); //方框滤波
            CvInvoke.FindContours(garyImage, contours, dnc, RetrType.Ccomp, ChainApproxMethod.ChainApproxSimple);       //轮廓集合
            List <VectorOfPoint> myContours = new List <VectorOfPoint>();                                               //序号,轮廓

            for (int k = 0; k < contours.Size; k++)
            {
                double area = CvInvoke.ContourArea(contours[k]); //获取各连通域的面积
                if (area > 0.15 * AREA && area < 0.75 * AREA)    //根据面积作筛选(指定最小面积,最大面积):
                {
                    myContours.Add(contours[k]);
                }
            }

            if (myContours.Count != 0)
            {
                try
                {
                    int maxSize = myContours.Max(t => t.Size);
                    //VectorOfPoint productContour = (VectorOfPoint)myContours.Where(t => t.Size.Equals(maxSize));
                    int           index          = myContours.FindIndex(t => t.Size.Equals(maxSize));//最大的轮廓
                    VectorOfPoint productContour = myContours[index];
                    getProduceInfoForYMJ(productContour);

                    if (ProduceMacth() == true)//匹配成功
                    {
                        GLB.robot_device_point.X = GLB.MatYMJCam[0] * GLB.camera_device_point.X + GLB.MatYMJCam[1] * GLB.camera_device_point.Y + GLB.MatYMJCam[2];
                        GLB.robot_device_point.Y = GLB.MatYMJCam[3] * GLB.camera_device_point.X + GLB.MatYMJCam[4] * GLB.camera_device_point.Y + GLB.MatYMJCam[5];
                        GLB.robot_device_point.Z = 990;                                           //平台高度
                        GLB.device_angl         += -2f;                                           //映美金相机与机器人夹角2度
                        GLB.device_angl          = (float)(GLB.device_angl * Math.PI / 180f);
                        if (GLB.robot_device_point.X < 1500 || GLB.robot_device_point.X > 2500 || //限定范围
                            GLB.robot_device_point.Y < -600 || GLB.robot_device_point.Y > 600)
                        {
                            GLB.Match_success = false;
                            GLB.TitleStr     += ",但是超出范围";
                        }
                        else
                        {
                            GLB.Match_success = true;
                        }
                    }
                    else
                    {
                        GLB.Match_success = false;
                    }
                }
                catch { }
            }
            myContours.Clear();
        }
Esempio n. 3
0
        //输入图像为单通道,预估的透射率图 引导图像为单通道,原图像的灰度图 输出图像为单通道,导向滤波后的透射图
        private static Image <Gray, byte> GuidedFilter(Image <Gray, Byte> p, Image <Gray, Byte> I, int r, double e)
        {
            //int r,  r;
            //w = h = 2 * r + 1;

            Image <Gray, byte> mean_p = new Image <Gray, byte>(p.Width, p.Height);
            Image <Gray, byte> mean_I = new Image <Gray, byte>(I.Width, I.Height);

            Image <Gray, byte> II = new Image <Gray, byte>(I.Width, I.Height);
            Image <Gray, byte> Ip = new Image <Gray, byte>(I.Width, I.Height);

            Image <Gray, byte> corr_II = new Image <Gray, byte>(I.Width, I.Height);
            Image <Gray, byte> corr_Ip = new Image <Gray, byte>(I.Width, I.Height);

            Image <Gray, byte> var_II = new Image <Gray, byte>(I.Width, I.Height);
            Image <Gray, byte> cov_Ip = new Image <Gray, byte>(I.Width, I.Height);

            Image <Gray, byte> a = new Image <Gray, byte>(I.Width, I.Height);
            Image <Gray, byte> b = new Image <Gray, byte>(I.Width, I.Height);

            Image <Gray, byte> mean_a = new Image <Gray, byte>(I.Width, I.Height);
            Image <Gray, byte> mean_b = new Image <Gray, byte>(I.Width, I.Height);

            Image <Gray, byte> q = new Image <Gray, byte>(p.Width, p.Height);

            //利用 boxFilter 计算均值  原始均值 导向均值  自相关均值  互相关均值
            CvInvoke.BoxFilter(p, mean_p, DepthType.Cv8U, new Size(r, r), new Point(-1, -1), true, BorderType.Reflect101);
            CvInvoke.BoxFilter(I, mean_I, DepthType.Cv8U, new Size(r, r), new Point(-1, -1), true, BorderType.Reflect101);

            CvInvoke.Multiply(I, I, II);
            CvInvoke.Multiply(I, p, Ip);

            CvInvoke.BoxFilter(II, corr_II, DepthType.Cv8U, new Size(r, r), new Point(-1, -1), true, BorderType.Reflect101);
            CvInvoke.BoxFilter(Ip, corr_Ip, DepthType.Cv8U, new Size(r, r), new Point(-1, -1), true, BorderType.Reflect101);

            CvInvoke.Multiply(mean_I, mean_I, var_II);
            CvInvoke.Subtract(corr_II, var_II, var_II);

            CvInvoke.Multiply(mean_I, mean_p, cov_Ip);
            CvInvoke.Subtract(corr_Ip, cov_Ip, cov_Ip);

            CvInvoke.Divide(cov_Ip, var_II + e, a);
            CvInvoke.Multiply(a, mean_I, b);
            CvInvoke.Subtract(mean_p, b, b);

            CvInvoke.BoxFilter(a, mean_a, DepthType.Cv8U, new Size(r, r), new Point(-1, -1), true, BorderType.Reflect101);
            CvInvoke.BoxFilter(b, mean_b, DepthType.Cv8U, new Size(r, r), new Point(-1, -1), true, BorderType.Reflect101);

            CvInvoke.Multiply(mean_a, I, q);
            CvInvoke.Add(mean_b, q, q);

            return(q);
        }
Esempio n. 4
0
        private void button2_Click(object sender, EventArgs e)
        {
            Image <Bgr, byte> dst = src.CopyBlank();

            CvInvoke.BoxFilter(src, dst, Emgu.CV.CvEnum.DepthType.Default, new Size(g_nBoxFilterValue, g_nBoxFilterValue), new Point(-1, -1));
            //第一个参数,InputArray类型的src,输入图像,即源图像。
            //第二个参数,OutputArray类型的dst,即目标图像,需要和源图片有一样的尺寸和类型。
            //第三个参数,int类型的ddepth,输出图像的深度,-1代表使用原图深度,即src.depth()。
            //第四个参数,Size类型的ksize,内核的大小。一般这样写Size(w, h)来表示内核的大小(其中,w 为像素宽度, h为像素高度)。Size(3,3)就表示3x3的核大小,Size(5,5)就表示5x5的核大小,也就是滤波器模板的大小。
            //第五个参数,Point类型的anchor,表示锚点(即被平滑的那个点),默认值为Point(-1, -1)。如果这个点坐标是负值的话,就表示取核的中心为锚点,所以默认值Point(-1, -1)表示这个锚点在核的中心。
            //第六个参数,bool类型的normalize,默认值为true,一个标识符,表示内核是否被其区域归一化(normalized)了。
            //第七个参数,int类型的borderType,用于推断图像外部像素的某种边界模式。有默认值BORDER_DEFAULT,我们一般不去管它。
            imageBox2.Image = dst;
        }
Esempio n. 5
0
        private void FrmBlurAverage_PassValuesEvent(object sender, FunctionArgs.BlurAverageArgs e)
        {
            Size  ksize  = new Size(e.KernelSize, e.KernelSize);
            Point anchor = new Point(-1, -1);
            //ToDo: 添加BorderType的选项(问题点:BoderType.Constant)
            BorderType borderType = BorderType.Default;

            switch (e.BlurType)
            {
            case FilterType.Average:
                CvInvoke.Blur(mCurrentImage, mTempImage, ksize, anchor, borderType);
                break;

            case FilterType.Box:
                CvInvoke.BoxFilter(mCurrentImage, mTempImage, DepthType.Default, ksize, anchor, e.Normalize, borderType);
                break;

            case FilterType.Gaussian:
                //ToDo: 添加SigmaX的选项
                CvInvoke.GaussianBlur(mCurrentImage, mTempImage, ksize, e.SigmaX, 0, borderType);
                break;

            case FilterType.Median:
                CvInvoke.MedianBlur(mCurrentImage, mTempImage, e.KernelSize);
                break;

            case FilterType.Bilateral:
                //ToDo: 双边滤波的选项
                //CvInvoke.BilateralFilter(mCurrentImage,mTempImage)
                break;

            default:
                break;
            }

            //没有启用预览,恢复当前的图
            if (!e.PreviewEnabled)
            {
                mFrmMainImage.SetImageSource(mCurrentImage);
            }
            else
            {
                mFrmMainImage.SetImageSource(mTempImage);
            }
        }
Esempio n. 6
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 public Image <Bgr, byte> BoxFilter(Image <Bgr, byte> image, int filterSize)
 {
     CvInvoke.BoxFilter(image, image, Emgu.CV.CvEnum.DepthType.Default, new System.Drawing.Size(filterSize, filterSize), new System.Drawing.Point(-1, -1));
     return(image);
 }
Esempio n. 7
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        public Form1()
        {
            InitializeComponent();

            this.imageBox1.Image = image;


            // Task 1
            ConvolutionKernelF Kernel = new ConvolutionKernelF(
                new float[, ] {
                { -0.1f, -0.2f, -0.1f,
                  -0.2f, 2.5f, -0.2f,
                  -0.1f, -0.2f, -0.1f,
                  -0.1f, -0.2f, -0.1f,
                  -0.1f, -0.2f, -0.1f,
                  -0.1f, -0.2f, -0.1f }
            }
                );
            var img1 = image.Copy();

            CvInvoke.Filter2D(image, img1, Kernel, new Point()
            {
                X = -1, Y = -1
            });
            this.imageBox2.Image = img1;


            // Task 2
            var img2 = image.Copy();

            CvInvoke.Blur(image, img2, new Size {
                Height = 10, Width = 10
            }, new Point()
            {
                X = 1, Y = 1
            });
            this.imageBox3.Image = img2;

            var img3 = image.Copy();

            CvInvoke.BoxFilter(image, img3, Emgu.CV.CvEnum.DepthType.Default, new Size {
                Height = 15, Width = 15
            }, new Point()
            {
                X = 1, Y = 1
            });
            this.imageBox4.Image = img3;

            var img4 = image.Copy();

            CvInvoke.GaussianBlur(image, img4, new Size {
                Height = 201, Width = 201
            }, 4);
            this.imageBox5.Image = img4;

            var img5 = image.Copy();

            CvInvoke.MedianBlur(image, img5, 7);
            this.imageBox6.Image = img5;


            // Task 3
            var img6 = image.Copy();

            CvInvoke.Erode(image, img6, new Mat(), new Point()
            {
                X = -1, Y = -1
            }, 1, Emgu.CV.CvEnum.BorderType.Default, new MCvScalar(1));
            this.imageBox7.Image = img6;

            var img7 = image.Copy();

            CvInvoke.Dilate(image, img7, new Mat(), new Point()
            {
                X = -1, Y = -1
            }, 1, Emgu.CV.CvEnum.BorderType.Default, new MCvScalar(1));
            this.imageBox8.Image = img7;


            // Task 4
            var img_canny = new Image <Gray, byte>("C:/Disk D/Studying/Course_4/Multimedia/laba2/laba2/images/image.jpg");
            var img8      = img_canny.Copy();

            CvInvoke.Canny(image, img8, 10, 100);
            this.imageBox9.Image = img8;


            // Task 5
            var img  = new Image <Gray, byte>("C:/Disk D/Studying/Course_4/Multimedia/laba2/laba2/images/image.jpg");
            var img9 = img.Copy();

            CvInvoke.EqualizeHist(img, img9);
            this.imageBox10.Image = img9;
        }
Esempio n. 8
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        FileOperation fileOperation = new FileOperation();//实例化处理文本的类

        /// <summary>
        /// 获取各区块的轮廓
        /// </summary>
        public void getContours(TextBox txtTypeName, PictureBox ptb) //找最近的轮廓
        {
            GLB.Match_success = false;                               //重新检测赋值
            Image <Gray, byte> dnc         = new Image <Gray, byte>(GLB.BUFW, GLB.BUFH);
            Image <Gray, byte> threshImage = new Image <Gray, byte>(GLB.BUFW, GLB.BUFH);

            CvInvoke.CvtColor(GLB.frame, threshImage, ColorConversion.Bgra2Gray);//灰度化
            //CvInvoke.BilateralFilter(threshImage, threshImage, 10, 10, 4);//双边滤波
            //CvInvoke.GaussianBlur(threshImage, threshImage, new Size(3, 3), 4);//高斯滤波
            CvInvoke.BoxFilter(threshImage, threshImage, Emgu.CV.CvEnum.DepthType.Cv8U, new Size(3, 3), new Point(-1, -1));//方框滤波
            #region
            //var kernal1 = CvInvoke.GetStructuringElement(ElementShape.Rectangle, new Size(3, 3), new Point(-1, -1));
            //CvInvoke.Dilate(threshImage, threshImage, kernal1, new Point(-1, -1), 2, BorderType.Default, new MCvScalar());//膨胀
            //var kernal1 = CvInvoke.GetStructuringElement(ElementShape.Rectangle, new Size(3, 3), new Point(-1, -1));
            //CvInvoke.Erode(threshImage, threshImage, kernal1, new Point(-1, -1), 2, BorderType.Default, new MCvScalar());//腐蚀

            //方式1
            //CvInvoke.Threshold(threshImage, threshImage, 100, 255, ThresholdType.BinaryInv | ThresholdType.Otsu);//二值化
            //if (Mainform.runMode == 6)//匹配托盘
            //{
            //    var kernal1 = CvInvoke.GetStructuringElement(ElementShape.Rectangle, new Size(9, 9), new Point(-1, -1));
            //    CvInvoke.Erode(threshImage, threshImage, kernal1, new Point(-1, -1), 1, BorderType.Default, new MCvScalar());//腐蚀

            //}
            //else//匹配箱子
            //{
            //    var kernal1 = CvInvoke.GetStructuringElement(ElementShape.Rectangle, new Size(3, 3), new Point(-1, -1));
            //    CvInvoke.Erode(threshImage, threshImage, kernal1, new Point(-1, -1), 2, BorderType.Default, new MCvScalar());//腐蚀
            //}

            //方式2
            //if (Mainform.runMode == 6)//匹配托盘
            //{
            //    var kernal1 = CvInvoke.GetStructuringElement(ElementShape.Rectangle, new Size(9, 9), new Point(-1, -1));
            //    CvInvoke.Dilate(threshImage, threshImage, kernal1, new Point(-1, -1), 1, BorderType.Default, new MCvScalar());//膨胀
            //}
            //else //加了膨胀跳动更大
            //{
            //    var kernal1 = CvInvoke.GetStructuringElement(ElementShape.Rectangle, new Size(5, 5), new Point(-1, -1));
            //    CvInvoke.Dilate(threshImage, threshImage, kernal1, new Point(-1, -1), 1, BorderType.Default, new MCvScalar());//膨胀
            //}
            //ptb.Image = threshImage.ToBitmap();
            #endregion
            //检测连通域,每一个连通域以一系列的点表示,FindContours方法只能得到第一个域:
            try
            {
                VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint(2000);                                       //区块集合
                CvInvoke.FindContours(threshImage, contours, dnc, RetrType.Ccomp, ChainApproxMethod.ChainApproxSimple); //轮廓集合
                GLB.block_num = 0;

                Dictionary <int, VectorOfPoint> mycontours = new Dictionary <int, VectorOfPoint>(100);//序号,轮廓
                mycontours.Clear();
                for (int k = 0; k < contours.Size; k++)
                {
                    double area = CvInvoke.ContourArea(contours[k]); //获取各连通域的面积
                    if (area > 100000 && area < 800000)              //根据面积作筛选(指定最小面积,最大面积):
                    {
                        if (!mycontours.ContainsKey(k))
                        {
                            mycontours.Add(k, contours[k]);
                        }
                    }
                }
                float my_depth_temp = GLB.myp3d[(GLB.BUFH / 2 * GLB.BUFW + GLB.BUFW / 2) * 3 + 2];
                if (mycontours.Count == 0 && Mainform.ProduceArrive == true && Mainform.CarryMode == 0 && Mainform.runMode == 1 && (my_depth_temp > 1400 || double.IsNaN(my_depth_temp)))//空车来,小车自动离开
                {
                    Mainform.ProduceArrive = false;
                    Mainform.SetCarryArrive(0);                                                                                                                                                                           //修改产品没送到
                    ArrayList array = new ArrayList();                                                                                                                                                                    //多条SQL语句数组
                    string    sql   = "update Agv_list set isworking =0,stowerid ='',pronum =0 where agvid in(select agvid from Agvmission_list where fstatus =7 and messionType =1 and stowerid='" + GLB.RobotId + "')"; //修改小车状态
                    string    sql1  = "update Agvmission_list set fstatus =6 ,actionenddate=getdate() where fstatus =7 and messionType =1  and stowerid='" + GLB.RobotId + "'";                                           //修改任务 等待状态为完成状态
                    array.Add(sql);
                    array.Add(sql1);
                    bool isok = MyDataLib.transactionOp_list(array);
                    Mainform.SetRobotStatus(2, "等待送货");//修改码垛机器人状态
                }
                //按面积最大排序 生成新的字典
                Dictionary <int, VectorOfPoint> mycontours_SortedByKey = new Dictionary <int, VectorOfPoint>(100);//序号,轮廓;
                mycontours_SortedByKey.Clear();
                mycontours_SortedByKey = mycontours.OrderByDescending(o => CvInvoke.ContourArea(o.Value)).ToDictionary(p => p.Key, o => o.Value);
                GLB.obj.Clear();
                foreach (int k in mycontours_SortedByKey.Keys)
                {
                    OBJ obj = new OBJ();
                    {
                        if (!GLB.obj.ContainsKey(GLB.block_num))
                        {
                            GLB.obj.Add(GLB.block_num, obj);                                  //不含这个,就添加
                        }
                        GLB.obj[GLB.block_num].typName = txtTypeName.Text.Replace(" ", "");   // 对象名称

                        if (getMinAreaRect(mycontours_SortedByKey[k], GLB.block_num) == true) //获取最小外接矩形并处理相关参数
                        {
                            if (GLB.img_mode == 0)                                            //匹配模式
                            {
                                if (Device_Macth(GLB.block_num) == true)                      //与库对比,生成工件位置,法向量,旋转角
                                {
                                    Thread.Sleep(400);
                                    break;
                                }
                            }
                        }

                        GLB.TitleStr += "block_num=" + GLB.block_num;
                        GLB.block_num++;//区块计数器
                    }
                }
            }
            catch (Exception ex)
            {
                Console.WriteLine("发生错误: " + ex.Message);
                throw;
            }
        }