예제 #1
0
        public void Compare(Mat mat1, Mat mat2)
        {
            CvInvoke.Resize(mat2, mat2, mat1.Size);
            CvInvoke.CvtColor(mat1, mat1, ColorConversion.Bgr2Gray);
            CvInvoke.CvtColor(mat2, mat2, ColorConversion.Bgr2Gray);

            //直方图尺寸设置
            //一个灰度值可以设定一个bins,256个灰度值就可以设定256个bins
            //对应HSV格式,构建二维直方图
            //每个维度的直方图灰度值划分为256块进行统计,也可以使用其他值
            int hBins = 256, sBins = 256;

            int[] histSize = { hBins };
            //H:0~180, S:0~255,V:0~255
            //H色调取值范围
            float[] hRanges = { 0, 180 };
            //S饱和度取值范围
            float[]   sRanges = { 180 };
            float[][] ranges = { new float[] { 0, 40 }, new float[] { 40, 80 }, new float[] { 40, 255 } };
            int[]     channels = { 0 };//二维直方图
            Mat       hist1 = new Mat(), hist2 = new Mat();

            CvInvoke.CalcHist(mat1, channels, new Mat(), hist1, histSize, sRanges, false);
            CvInvoke.CalcHist(mat2, channels, new Mat(), hist2, histSize, sRanges, false);
            var result = CvInvoke.CompareHist(hist1, hist2, HistogramCompMethod.Correl);

            Console.WriteLine($"result:{result}");
        }
예제 #2
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파일: Form1.cs 프로젝트: xiaodelea/Emgucv
        private void calculateToolStripMenuItem_Click(object sender, EventArgs e)
        {
            try
            {
                if (pictureBox1.Image == null)
                {
                    return;
                }

                var img = new Bitmap(pictureBox1.Image)
                          .ToImage <Gray, byte>();
                Mat     hist     = new Mat();
                float[] ranges   = new float[] { 0, 256 };
                int[]   channel  = { 0 };
                int[]   histSize = { 256 };

                VectorOfMat ms = new VectorOfMat();
                ms.Push(img);
                CvInvoke.CalcHist(ms, channel, null, hist, histSize, ranges, false);

                HistogramViewer viewer = new HistogramViewer();
                viewer.Text     = "Image Histogram";
                viewer.ShowIcon = false;
                viewer.HistogramCtrl.AddHistogram("Image Histogram", Color.Blue, hist, 256, ranges);
                viewer.HistogramCtrl.Refresh();
                viewer.Show();


                //pictureBox1.Image = CreateGraph(hist).GetImage();
            }
            catch (Exception ex)
            {
                MessageBox.Show(ex.Message);
            }
        }
        /// <summary>
        /// Calculate histogram for given vector of matrices, allowing masking.
        /// </summary>
        /// <param name="vm">matrices</param>
        /// <param name="tempMask">mask</param>
        /// <returns>histogram as umat</returns>
        public static UMat CalculateHistogram(VectorOfMat vm, Image <Gray, byte> tempMask)
        {
            UMat hist = new UMat();

            int[]   channel  = new int[] { 0 };
            int[]   histSize = new int[] { 32 };
            float[] range    = new float[] { 0.0f, 256.0f };
            CvInvoke.CalcHist(vm, channel, tempMask, hist, histSize, range, false);
            return(hist);
        }
예제 #4
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        private void button2_Click(object sender, EventArgs e)
        {
            int[] ch = { 0, 0 };
            CvInvoke.MixChannels(hsvImage, hueImage, ch);
            //【2】直方图计算,并归一化
            float[] hue_range = new float[2] {
                0.00f, 180.00f
            };
            Mat hist = new Mat(hueImage.Size, Emgu.CV.CvEnum.DepthType.Cv8U, 1);

            int[] channels = new int[1] {
                0
            };
            int[] histSize = new int[1] {
                Math.Max(g_bins, 2)
            };
            float[] ranges = new float[2] {
                0, 180
            };
            CvInvoke.CalcHist(hueImage, channels, null, hist, histSize, ranges, true);
            CvInvoke.Normalize(hist, hist, 0, 255, NormType.MinMax, DepthType.Default, null);
            //【3】计算反向投影
            Mat backproj = new Mat();

            CvInvoke.CalcBackProject(hueImage, channels, hist, backproj, ranges, 1);
            //【4】显示反向投影
            imageBox2.Image = backproj;
            //【5】绘制直方图
            Image <Gray, float> img = new Image <Gray, float>(hist.Bitmap);

            float[] data = new float[img.Data.Length];
            for (int i = 0; i < img.Data.Length; i++)
            {
                data[i] = img.Data[i, 0, 0];
            }
            float max = data[0];

            //获取最大值
            for (int i = 1; i < data.Length; i++)
            {
                if (data[i] > max)
                {
                    max = data[i];
                }
            }
            Image <Bgr, byte> image = new Image <Bgr, byte>(300, 300, new Bgr(0, 0, 0));

            for (int i = 0; i < data.Length; i++)
            {
                data[i] = data[i] * 256 / max;
                image.Draw(new LineSegment2DF(new PointF(i + 20, 255), new PointF(i + 21, 255 - data[i])), new Bgr(255, 255, 255), 2);
            }
            imageBox3.Image = image;
        }
예제 #5
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        private void button_hist_Click(object sender, EventArgs e)
        {
            if (Image_Target == null || Image_Texture == null)
            {
                return;
            }
            double             temp_weight                  = (double)trackBar_hist.Value / 10.0;
            Image <Gray, Byte> target_gray                  = Image_Target.Clone().Convert <Gray, Byte>();
            Image <Gray, Byte> texture_gray                 = Image_Texture.Clone().Convert <Gray, Byte>();
            Image <Gray, Byte> target_hist_matched          = new Image <Gray, Byte>(Image_Target.Size);
            Image <Gray, Byte> target_hist_matched_weighted = new Image <Gray, Byte>(Image_Texture.Size);

            Matrix <byte> histLUT      = new Matrix <byte>(1, 256);
            Mat           hist_target  = new Mat();
            Mat           hist_texture = new Mat();

            VectorOfMat vm_target  = new VectorOfMat();
            VectorOfMat vm_texture = new VectorOfMat();

            vm_target.Push(target_gray);
            vm_texture.Push(texture_gray);

            CvInvoke.CalcHist(vm_target, new int[] { 0 }, null, hist_target, new int[] { 256 }, new float[] { 0, 255 }, false);
            CvInvoke.CalcHist(vm_texture, new int[] { 0 }, null, hist_texture, new int[] { 256 }, new float[] { 0, 255 }, false);

            float[] CDF_hist_target  = new float[256];
            float[] CDF_hist_texture = new float[256];
            Marshal.Copy(hist_target.DataPointer, CDF_hist_target, 0, 256);
            Marshal.Copy(hist_texture.DataPointer, CDF_hist_texture, 0, 256);

            for (int i = 1; i < 256; i++)
            {
                CDF_hist_target[i]  += CDF_hist_target[i - 1];
                CDF_hist_texture[i] += CDF_hist_texture[i - 1];
            }

            for (int i = 0; i < 256; i++)
            {
                histLUT.Data[0, i] = 0;
                for (int j = 0; j < 256; j++)
                {
                    if (CDF_hist_texture[j] >= CDF_hist_target[i])
                    {
                        histLUT.Data[0, i] = (byte)j;
                        break;
                    }
                }
            }
            CvInvoke.LUT(target_gray, histLUT, target_hist_matched);
            target_hist_matched_weighted = target_hist_matched * temp_weight + target_gray * (1.0 - temp_weight);
            imageBox_hist.Image          = target_hist_matched_weighted;
        }
예제 #6
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        private void StartNewTrack(Rectangle toTrack, Image <Gray, byte> imgTrackingImage, Image <Gray, byte> imgRoi, CamshiftOutput output)
        {
            _matBackProjectionMask = new Mat();
            _rectangleSearchWindow = toTrack;// GetIncreasedRectangle(toTrack, IncreaseRegion);
            _histogram             = new DenseHistogram(BinSize, new RangeF(0, BinSize));

            using (VectorOfMat vmTrackingImageRoi = new VectorOfMat(imgRoi.Mat))
            {
                CvInvoke.CalcHist(vmTrackingImageRoi, _channels, _matBackProjectionMask, _histogram, _histogramSize, _ranges, Accumulate);
                CvInvoke.Normalize(_histogram, _histogram, 0, 255, NormType.MinMax);
            }
            _trackStarted = true;
        }
예제 #7
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        public void applyHistogram(Mat image)
        {
            Mat hist = new Mat();
            int hbins = 10, sbins = 12;

            int[]   histSize = { hbins, sbins };
            float[] hrange   = { 0, 180 };
            float[] srange   = { 0, 256 };
            float[] range    = { 1, 255 };
            int[]   channel  = { 0, 1 };

            CvInvoke.CalcHist(image, channel, new Mat(), hist, histSize, range, true);
            subtractImage.Image = hist.Bitmap;
        }
예제 #8
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        public void Apply(Image <Bgr, byte> org, out Image <Bgr, byte> dst)
        {
            int width  = org.Width;
            int height = org.Height;

            var gray = org.Convert <Gray, byte>();
            Image <Gray, byte> dstImage = new Image <Gray, byte>(gray.Size);

            dstImage.SetZero();


            float[] ranges = { 0.0f, 255.0f };

            using (Mat histogram = new Mat())
                using (VectorOfMat vm = new VectorOfMat())
                {
                    vm.Push(gray.Mat);
                    CvInvoke.CalcHist(vm, new int[] { 0 }, null, histogram, new int[] { 255 }, ranges, false);

                    double[] binVal = new double[histogram.Size.Height];
                    GCHandle handle = GCHandle.Alloc(binVal, GCHandleType.Pinned);

                    using (Matrix <double> m = new Matrix <double>(binVal.Length, 1, handle.AddrOfPinnedObject(), sizeof(double)))
                    {
                        histogram.ConvertTo(m, DepthType.Cv64F);
                    }

                    int histMax = 0;

                    for (int i = 0; i < 255; i++)
                    {
                        if (binVal[i] > histMax)
                        {
                            histMax = (int)binVal[i];
                        }
                    }

                    for (int x = 0; x < width; x++)
                    {
                        int x2  = (int)((double)x * 255 / (double)width);
                        int row = (int)((double)binVal[x2] * height / (double)histMax);
                        for (int y = height - 1; y >= height - row; y--)
                        {
                            dstImage[y, x] = new Gray(255);
                        }
                    }

                    dst = dstImage.Convert <Bgr, byte>();
                }
        }
예제 #9
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        static void RunMeanshiftDemo()
        {
            VideoCapture video = new VideoCapture("mleko.mp4");  // mleko.mp4
            //VideoCapture video = new VideoCapture("mouthwash.avi");  // mouthwash.avi
            var firstFrame = new Mat();

            video.Read(firstFrame);
            int x = 290, y = 230, width = 100, height = 15; // mleko.mp4
            //int x = 300, y = 305, width = 100, height = 115; // mouthwash.avi
            var roi = new Mat(firstFrame, new Rectangle(x, y, width, height));

            ShowHueEmphasizedImage(roi);

            CvInvoke.Imshow("Roi", roi);
            var roiHsv = new Mat();

            CvInvoke.CvtColor(roi, roiHsv, ColorConversion.Bgr2Hsv);
            var histogram = new Mat();

            CvInvoke.CalcHist(new VectorOfMat(new Mat[] { roiHsv }), new int[] { 0 }, null, histogram, new int[] { 180 }, new float[] { 0, 180 }, false);
            CvInvoke.Normalize(histogram, histogram, 0, 255, NormType.MinMax);

            show2DHueHistogram(histogram);

            var nextFrame      = new Mat();
            var nextFrameHsv   = new Mat();
            var mask           = new Mat();
            var trackingWindow = new Rectangle(x, y, width, height);

            while (true)
            {
                video.Read(nextFrame);
                if (nextFrame.IsEmpty)
                {
                    break;
                }
                CvInvoke.CvtColor(nextFrame, nextFrameHsv, ColorConversion.Bgr2Hsv);
                CvInvoke.CalcBackProject(new VectorOfMat(new Mat[] { nextFrameHsv }), new int[] { 0 }, histogram, mask, new float[] { 0, 180 }, 1);
                CvInvoke.Imshow("mask", mask);
                CvInvoke.MeanShift(mask, ref trackingWindow, new MCvTermCriteria(10, 1));
                CvInvoke.Rectangle(nextFrame, trackingWindow, new MCvScalar(0, 255, 0), 2);
                CvInvoke.Imshow("nextFrame", nextFrame);
                CvInvoke.WaitKey(60);
            }
            Console.WriteLine("Koniec filmu.");
            CvInvoke.WaitKey();
        }
예제 #10
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        Array GetHistogramValues()
        {
            Mat hist = new Mat();

            using (Emgu.CV.Util.VectorOfMat vm = new Emgu.CV.Util.VectorOfMat())
            {
                int[]   channels = { 0, 1, 2 };
                int[]   histSize = { 32, 32, 32 };
                float[] ranges   = { 0.0f, 256.0f, 0.0f, 256.0f, 0.0f, 256.0f };


                vm.Push(srcImage_Gray);
                CvInvoke.CalcHist(vm, new int[] { 0 }, null, hist, new int[] { 256 }, new float[] { 0.0f, 256.0f }, false);

                CvInvoke.Normalize(hist, hist, 0, srcImage_Gray.Rows, NormType.MinMax);
            }
            return(hist.GetData());
        }
예제 #11
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        private void button5_Click(object sender, EventArgs e)
        {
            //在比较直方图时,最佳操作是在HSV空间中操作,所以需要将BGR空间转换为HSV空间
            Mat srcHsvImage     = new Mat(CvInvoke.cvGetSize(src1), DepthType.Cv8U, 3);
            Mat compareHsvImage = new Mat(CvInvoke.cvGetSize(src2), DepthType.Cv8U, 3);

            CvInvoke.CvtColor(src1, srcHsvImage, ColorConversion.Bgr2Hsv);
            CvInvoke.CvtColor(src2, compareHsvImage, ColorConversion.Bgr2Hsv);

            //采用H-S直方图进行处理
            //首先得配置直方图的参数
            Mat srcHist  = new Mat(CvInvoke.cvGetSize(src1), DepthType.Cv8U, 3);
            Mat compHist = new Mat(CvInvoke.cvGetSize(src2), DepthType.Cv8U, 3);

            //H、S通道
            int[] channels = new int[2] {
                0, 1
            };
            int[] histSize = new int[2] {
                30, 32
            };
            float[] Ranges = new float[2] {
                0, 180
            };

            //进行原图直方图的计算
            CvInvoke.CalcHist(srcHsvImage, channels, null, srcHist, histSize, Ranges, true);
            //对需要比较的图进行直方图的计算
            CvInvoke.CalcHist(compareHsvImage, channels, null, compHist, histSize, Ranges, true);

            //注意:这里需要对两个直方图进行归一化操作
            CvInvoke.Normalize(srcHist, srcHist, 1, 0, NormType.MinMax);
            CvInvoke.Normalize(compHist, compHist, 1, 0, NormType.MinMax);

            //对得到的直方图对比
            //相关:CV_COMP_CORREL
            //卡方:CV_COMP_CHISQR
            //直方图相交:CV_COMP_INTERSECT
            //Bhattacharyya距离:CV_COMP_BHATTACHARYYA
            double g_dCompareRecult = CvInvoke.CompareHist(srcHist, compHist, his[comboBox1.SelectedIndex]);

            richTextBox1.Text = "方法 " + comboBox1.SelectedIndex + ":两幅图像比较的结果为:" + g_dCompareRecult + "\r\n";
        }
예제 #12
0
        public Mat initialtracker(Image <Bgr, Byte> SELECTION, out UMat model, out Mat Model, Mat selection, int value1, int value2, int value3, out Mat Mask)
        {
            Mat hsv = new Mat();

            model = SELECTION.Copy().ToUMat();
            Model = selection;
            S     = SELECTION;
            s     = selection;
            Image <Hsv, Byte> HSV = SELECTION.Convert <Hsv, Byte>();
            // CvInvoke.CvtColor(selection, hsv, ColorConversion.Bgr2Hsv);
            //  CvInvoke.CvtColor(skin_sample, skin_sample, ColorConversion.Bgr2Hsv);

            Mat mask = new Mat();

            CvInvoke.Threshold(mask, mask, 60, 255, ThresholdType.Binary);
            CvInvoke.InRange(HSV, new ScalarArray(new MCvScalar(0, value2 - 30, value3 - 45)), new ScalarArray(new MCvScalar(value1 + 30, value2 + 30, value3 + 30)), mask);
            //CvInvoke.InRange(HSV, new ScalarArray(new MCvScalar(0, value1, Math.Min(value2, value3))), new ScalarArray(new MCvScalar(180, 255, Math.Max(value2, value3))), mask);
            Mat hue = new Mat();

            hue  = HSV.Mat.Split()[0];
            Mask = mask;
            int[]   Chn     = { 0 };
            int[]   size    = { 24 };
            float[] range   = { 0, 180 };
            var     vhue    = new VectorOfMat(hue);
            Mat     element = CvInvoke.GetStructuringElement(ElementShape.Rectangle, new Size(2, 2), new Point(1, 1));//  Mat element = getStructuringElement(MORPH_RECT, Size(3, 3));

            // CvInvoke.Erode(mask, mask, element, new Point(1, 1), 2, BorderType.Default, new MCvScalar(0, 0, 0));
            Mat element2 = CvInvoke.GetStructuringElement(ElementShape.Rectangle, new Size(2, 2), new Point(1, 1));

            CvInvoke.Dilate(mask, mask, element, new Point(-1, -1), 2, BorderType.Default, new MCvScalar(0, 0, 0));
            Mat hist = new Mat();

            //mask = MASK.Mat;
            CvInvoke.CalcHist(vhue, Chn, mask, hist, size, range, true);
            //  CvInvoke.EqualizeHist(hist, hist);

            CvInvoke.Normalize(hist, hist, 0, 200, NormType.MinMax);

            return(hist);
        }
예제 #13
0
    public static Array GetHistogramOfImage(Mat image, char channel = 'b', int size = 256, float range = 256)
    {
        if (image == null)
        {
            return(null);
        }

        Mat hist = new Mat();

        using (Emgu.CV.Util.VectorOfMat vm = new Emgu.CV.Util.VectorOfMat())
        {
            int[] histoChannel = { 0 };
            if (channel == 'b')
            {
                histoChannel = new int[] { 0 }
            }
            ;
            if (channel == 'g')
            {
                histoChannel = new int[] { 1 }
            }
            ;
            if (channel == 'r')
            {
                histoChannel = new int[] { 2 }
            }
            ;

            int[]   histoSize  = { size };
            float[] histoRange = { 0.0f, range };


            vm.Push(image);
            CvInvoke.CalcHist(vm, histoChannel, null, hist, histoSize, histoRange, false);

            //CvInvoke.Normalize(hist, hist, 0, image.Rows, NormType.MinMax);
        }

        return(hist.GetData().Cast <float>().ToArray());
    }
예제 #14
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        internal static double DetermineThreshold(Image <Gray, byte> img)
        {
            double threshhold = 0;
            float  smallest   = float.MaxValue;
            Mat    hist       = new Mat();

            using (VectorOfMat vm = new VectorOfMat())
            {
                vm.Push(img.Mat);
                float[] ranges = new float[] { 0.0f, 256.0f };
                CvInvoke.CalcHist(vm, new int[] { 0 }, null, hist, new int[] { 256 }, ranges, false);
            }
            for (int i = 5; i < 50; ++i)
            {
                if (hist.GetValue(0, i) < smallest)
                {
                    smallest   = hist.GetValue(0, i);
                    threshhold = i;
                }
            }
            return(threshhold);
        }
예제 #15
0
        public PageMetric(Mat image)
        {
            Mat hsv = new Mat();

            CvInvoke.CvtColor(image, hsv, ColorConversion.Rgb2Hsv);

            var  dim        = new int[] { 0 };
            var  histSize   = new int[] { 256 };
            var  range      = new float[] { 0, 255 };
            bool accumulate = false;

            Mat hist = new Mat();
            Mat rgb  = new Mat();

            using (VectorOfMat array = new VectorOfMat())
            {
                array.Push(hsv);
                CvInvoke.CalcHist(array, dim, new Mat(), hist, histSize, range, accumulate);
            }

            this.histogram = hist;
        }
예제 #16
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        private double Similar2(Bitmap bitmap1, Bitmap bitmap2)
        {
            //var fileName = @"C:\Users\Administrator\Pictures\A.png";
            //bitmap1 = new Bitmap(fileName);
            //bitmap2= new Bitmap(fileName);

            Mat mat1 = new Image <Gray, byte>(bitmap1).Mat;
            Mat mat2 = new Image <Gray, byte>(bitmap2).Mat;

            Mat hist1 = new Mat(), hist2 = new Mat();

            float[] range      = { 10, 50 };
            int[]   channels   = new int[] { 0 };
            int[]   histSize   = new int[] { 10, 10 };
            bool    uniform    = true;
            bool    accumulate = false;

            CvInvoke.CalcHist(mat1, channels, null, hist1, histSize, range, accumulate);
            CvInvoke.CalcHist(mat1, channels, null, hist2, histSize, range, accumulate);

            return(1);
        }
예제 #17
0
        public static double CompareHistograms(Mat img, Mat img2)
        {
            using (Mat hist = new Mat())
                using (Mat hist2 = new Mat())
                    using (VectorOfMat vm = new VectorOfMat())
                        using (VectorOfMat vm2 = new VectorOfMat()) {
                            vm.Push(img);
                            vm2.Push(img2);
                            var channels = new int[] { 0 };
                            var histSize = new int[] { 256 };
                            var ranges   = new float[] { 0, 256, };
                            CvInvoke.CalcHist(vm, channels, null, hist, histSize, ranges, false);
                            CvInvoke.CalcHist(vm2, channels, null, hist2, histSize, ranges, false);

                            //CvInvoke.Normalize(hist, hist, 0, 255, NormType.MinMax);
                            //CvInvoke.Normalize(hist2, hist2, 0, 255, NormType.MinMax);

                            //double res = CvInvoke.CompareHist(hist, hist2, HistogramCompMethod.Bhattacharyya);
                            //Debug.Log("Cards in Stock: " + (res > 0.5));

                            return(CvInvoke.CompareHist(hist, hist2, HistogramCompMethod.Correl));
                        }
        }
예제 #18
0
        private (float SatAverage, float ValAverage) GetHistogramAverages(Image <Hsv, byte> image)
        {
            image._EqualizeHist();

            var hsvPlanes = new VectorOfMat();

            CvInvoke.Split(image, hsvPlanes);

            var histSize   = new[] { 256 };
            var range      = new[] { 0f, 255f };
            var accumulate = false;

            var sHist = new Mat();
            var vHist = new Mat();

            CvInvoke.CalcHist(hsvPlanes, new[] { 1 }, new Mat(), sHist, histSize, range, accumulate);
            CvInvoke.CalcHist(hsvPlanes, new[] { 2 }, new Mat(), vHist, histSize, range, accumulate);

            var sAvg = GetHistogramAverage(sHist);
            var vAvg = GetHistogramAverage(vHist);

            return(sAvg, vAvg);
        }
예제 #19
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        private double Similar2(Bitmap bitmap1, Bitmap bitmap2)
        {
            //FileStorage
            //var fileName = @"C:\Users\Administrator\Pictures\A.png";
            //bitmap1 = new Bitmap(fileName);
            //bitmap2 = new Bitmap(fileName);

            var mat1 = new Image <Gray, byte>(bitmap1);
            var mat2 = new Image <Gray, byte>(bitmap2);

            var hist1 = mat1.CopyBlank(); var hist2 = mat2.CopyBlank();

            float[] range      = { 0, 256 };
            int[]   channels   = new int[] { 0 };
            int[]   histSize   = new int[] { 256 };
            bool    uniform    = true;
            bool    accumulate = false;

            CvInvoke.CalcHist(mat1, channels, null, hist1, histSize, range, accumulate);
            CvInvoke.CalcHist(mat1, channels, null, hist2, histSize, range, accumulate);

            return(1);
        }
예제 #20
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        public Mat Tracker()
        {
            Mat hsv_roi = new Mat();

            CvInvoke.CvtColor(selection, hsv_roi, ColorConversion.Bgr2Hsv);
            // imageBox1.Image = selection;
            Mat mask_roi = new Mat();

            CvInvoke.InRange(hsv_roi, new ScalarArray(new MCvScalar(0, 60, 32)), new ScalarArray(new MCvScalar(180, 255, 255)), mask_roi);
            Mask            = mask_roi;
            imageBox1.Image = hsv_roi;
            Mat         hist_roi = new Mat();
            VectorOfMat vhsv_roi = new VectorOfMat(hsv_roi);

            //Mat hue = new Mat();
            // var vhue = new VectorOfMat(hue);
            int[]   Chn   = { 0 };
            int[]   size  = { 24 };
            float[] range = { 0, 180 };


            //imageBox1.Image = hsv_roi;
            //vInvoke.MixChannels(vhsv_roi,vhue);
            CvInvoke.CalcHist(vhsv_roi, Chn, mask_roi, hist_roi, size, range, true);
            // imageBox1.Image = hist_roi;
            CvInvoke.Normalize(hist_roi, hist_roi, 0, 255, NormType.MinMax);
            // double min = 0;double max = 0;Point minloc = new Point(0); Point maxloc = new Point(0);
            // CvInvoke.MinMaxLoc(hist_roi, ref min, ref max, ref minloc,ref maxloc);
            int    histSize = hist_roi.Rows;
            Mat    histimg  = new Mat(histSize, histSize, DepthType.Cv8U, 255);
            double hpt      = 0.9 * histSize;


            imageBox1.Image = hist_roi;

            return(hist_roi);
        }
        /// <summary>
        /// Calculate a mask for the pieces. The function calculates a histogram to find the piece background color.
        /// Everything within a specific HSV range around the piece background color is regarded as foreground. The rest is regarded as background.
        /// </summary>
        /// <param name="inputImg">Color input image</param>
        /// <returns>Mask image</returns>
        /// see: https://docs.opencv.org/2.4/modules/imgproc/doc/histograms.html?highlight=calchist
        public override Image <Gray, byte> GetMask(Image <Rgba, byte> inputImg)
        {
            Image <Gray, byte> mask;

            using (Image <Hsv, byte> hsvSourceImg = inputImg.Convert <Hsv, byte>())       //Convert input image to HSV color space
            {
                Mat hsvImgMat = new Mat();
                hsvSourceImg.Mat.ConvertTo(hsvImgMat, DepthType.Cv32F);
                VectorOfMat vm = new VectorOfMat(hsvImgMat);

                // Calculate histograms for each channel of the HSV image (H, S, V)
                Mat histOutH = new Mat(), histOutS = new Mat(), histOutV = new Mat();
                int hbins = 32, sbins = 32, vbins = 32;
                CvInvoke.CalcHist(vm, new int[] { 0 }, new Mat(), histOutH, new int[] { hbins }, new float[] { 0, 179 }, false);
                CvInvoke.CalcHist(vm, new int[] { 1 }, new Mat(), histOutS, new int[] { sbins }, new float[] { 0, 255 }, false);
                CvInvoke.CalcHist(vm, new int[] { 2 }, new Mat(), histOutV, new int[] { vbins }, new float[] { 0, 255 }, false);

                hsvImgMat.Dispose();
                vm.Dispose();

                // Draw the histograms for debugging purposes
                if (PluginFactory.GetGeneralSettingsPlugin().SolverShowDebugResults)
                {
                    PluginFactory.LogHandle?.Report(new LogEventImage("Hist H", Utils.DrawHist(histOutH, hbins, 30, 1024, new MCvScalar(255, 0, 0)).Bitmap));
                    PluginFactory.LogHandle?.Report(new LogEventImage("Hist S", Utils.DrawHist(histOutS, sbins, 30, 1024, new MCvScalar(0, 255, 0)).Bitmap));
                    PluginFactory.LogHandle?.Report(new LogEventImage("Hist V", Utils.DrawHist(histOutV, vbins, 30, 1024, new MCvScalar(0, 0, 255)).Bitmap));
                }

                //#warning Use border color
                //int borderHeight = 10;
                //Image<Hsv, byte> borderImg = hsvSourceImg.Copy(new Rectangle(0, hsvSourceImg.Height - borderHeight, hsvSourceImg.Width, borderHeight));
                //MCvScalar meanBorderColorScalar = CvInvoke.Mean(borderImg);
                //Hsv meanBorderColor = new Hsv(meanBorderColorScalar.V0, meanBorderColorScalar.V1, meanBorderColorScalar.V2);
                //if (PuzzleSolverParameters.Instance.SolverShowDebugResults)
                //{
                //    Image<Hsv, byte> borderColorImg = new Image<Hsv, byte>(12, 12);
                //    borderColorImg.SetValue(meanBorderColor);
                //    _logHandle.Report(new LogBox.LogEventImage("HSV Border Color (" + meanBorderColor.Hue + " ; " + meanBorderColor.Satuation + "; " + meanBorderColor.Value + ")", borderColorImg.Bitmap));
                //}


                // Find the peaks in the histograms and use them as piece background color. Black and white areas are ignored.
                Hsv pieceBackgroundColor = new Hsv
                {
                    Hue       = Utils.HighestBinValInRange(histOutH, MainHueSegment - HueDiffHist, MainHueSegment + HueDiffHist, 179), //25, 179, 179);
                    Satuation = Utils.HighestBinValInRange(histOutS, 50, 205, 255),                                                    //50, 255, 255);
                    Value     = Utils.HighestBinValInRange(histOutV, 75, 205, 255)                                                     //75, 255, 255);
                };

                histOutH.Dispose();
                histOutS.Dispose();
                histOutV.Dispose();

                // Show the found piece background color
                if (PluginFactory.GetGeneralSettingsPlugin().SolverShowDebugResults)
                {
                    Image <Hsv, byte> pieceBgColorImg     = new Image <Hsv, byte>(4, 12);
                    Image <Hsv, byte> lowPieceBgColorImg  = new Image <Hsv, byte>(4, 12);
                    Image <Hsv, byte> highPieceBgColorImg = new Image <Hsv, byte>(4, 12);
                    pieceBgColorImg.SetValue(pieceBackgroundColor);
                    lowPieceBgColorImg.SetValue(new Hsv(pieceBackgroundColor.Hue - HueDiff, pieceBackgroundColor.Satuation - SaturationDiff, pieceBackgroundColor.Value - ValueDiff));
                    highPieceBgColorImg.SetValue(new Hsv(pieceBackgroundColor.Hue + HueDiff, pieceBackgroundColor.Satuation + SaturationDiff, pieceBackgroundColor.Value + ValueDiff));

                    PluginFactory.LogHandle?.Report(new LogEventImage("HSV Piece Bg Color (" + pieceBackgroundColor.Hue + " ; " + pieceBackgroundColor.Satuation + "; " + pieceBackgroundColor.Value + ")", Utils.Combine2ImagesHorizontal(Utils.Combine2ImagesHorizontal(lowPieceBgColorImg.Convert <Rgb, byte>(), pieceBgColorImg.Convert <Rgb, byte>(), 0), highPieceBgColorImg.Convert <Rgb, byte>(), 0).Bitmap));

                    pieceBgColorImg.Dispose();
                    lowPieceBgColorImg.Dispose();
                    highPieceBgColorImg.Dispose();
                }

                // do HSV segmentation and keep only the meanColor areas with some hysteresis as pieces
                mask = hsvSourceImg.InRange(new Hsv(pieceBackgroundColor.Hue - HueDiff, pieceBackgroundColor.Satuation - SaturationDiff, pieceBackgroundColor.Value - ValueDiff), new Hsv(pieceBackgroundColor.Hue + HueDiff, pieceBackgroundColor.Satuation + SaturationDiff, pieceBackgroundColor.Value + ValueDiff));

                // close small black gaps with morphological closing operation
                Mat kernel = CvInvoke.GetStructuringElement(ElementShape.Rectangle, new Size(5, 5), new Point(-1, -1));
                CvInvoke.MorphologyEx(mask, mask, MorphOp.Close, kernel, new Point(-1, -1), 5, BorderType.Default, new MCvScalar(0));
            }
            return(mask);
        }
예제 #22
0
파일: Form1.cs 프로젝트: xiaodelea/Emgucv
        private void compareToolStripMenuItem_Click(object sender, EventArgs e)
        {
            try
            {
                if (pictureBox1.Image == null)
                {
                    return;
                }

                var img = new Bitmap(pictureBox1.Image)
                          .ToImage <Gray, byte>();

                Image <Gray, byte> img1   = null;
                OpenFileDialog     dialog = new OpenFileDialog();
                if (dialog.ShowDialog() == DialogResult.OK)
                {
                    img1 = new Image <Gray, byte>(dialog.FileName);
                }

                Mat hist  = new Mat();
                Mat hist1 = new Mat();

                float[] ranges   = new float[] { 0, 256 };
                int[]   channel  = { 0 };
                int[]   histSize = { 256 };

                VectorOfMat ms = new VectorOfMat();
                ms.Push(img);

                VectorOfMat ms1 = new VectorOfMat();
                ms1.Push(img1);


                CvInvoke.CalcHist(ms, channel, null, hist, histSize, ranges, false);
                CvInvoke.CalcHist(ms1, channel, null, hist1, histSize, ranges, false);

                CvInvoke.Normalize(hist, hist);
                CvInvoke.Normalize(hist1, hist1);

                HistogramViewer viewer = new HistogramViewer();
                viewer.Text     = "Image Histogram";
                viewer.ShowIcon = false;
                viewer.HistogramCtrl.AddHistogram("Image1 Histogram", Color.Blue, hist, 256, ranges);
                viewer.HistogramCtrl.Refresh();
                viewer.Show();

                HistogramViewer viewer1 = new HistogramViewer();
                viewer1.Text     = "Image Histogram";
                viewer1.ShowIcon = false;
                viewer1.HistogramCtrl.AddHistogram("Image2 Histogram", Color.Blue, hist1, 256, ranges);
                viewer1.HistogramCtrl.Refresh();
                viewer1.Show();


                var result1 = CvInvoke.CompareHist(hist, hist, Emgu.CV.CvEnum.HistogramCompMethod.Correl);
                var result2 = CvInvoke.CompareHist(hist1, hist1, Emgu.CV.CvEnum.HistogramCompMethod.Correl);
                var result3 = CvInvoke.CompareHist(hist, hist1, Emgu.CV.CvEnum.HistogramCompMethod.Correl);

                lblBGR.Text = "Hist vs Hist = " + result1.ToString() + "\n" +
                              "Hist1 vs Hist1 = " + result2.ToString() + "\n" +
                              "Hist vs Hist1 = " + result3.ToString() + "\n";

                //pictureBox1.Image = CreateGraph(hist).GetImage();
            }
            catch (Exception ex)
            {
                MessageBox.Show(ex.Message);
            }
        }
예제 #23
0
        /// <summary>
        /// Generate histograms for the image. One histogram is generated for each color channel.
        /// You will need to call the Refresh function to do the painting afterward.
        /// </summary>
        /// <param name="image">The image to generate histogram from</param>
        /// <param name="numberOfBins">The number of bins for each histogram</param>
        public void GenerateHistograms(IInputArray image, int numberOfBins)
        {
            using (InputArray iaImage = image.GetInputArray())
            {
                int   channelCount = iaImage.GetChannels();
                Mat[] channels     = new Mat[channelCount];
                Type  imageType;
                if ((imageType = Toolbox.GetBaseType(image.GetType(), "Image`2")) != null ||
                    (imageType = Toolbox.GetBaseType(image.GetType(), "Mat")) != null ||
                    (imageType = Toolbox.GetBaseType(image.GetType(), "UMat")) != null)
                {
                    for (int i = 0; i < channelCount; i++)
                    {
                        Mat channel = new Mat();
                        CvInvoke.ExtractChannel(image, channel, i);
                        channels[i] = channel;
                    }
                }
                else if ((imageType = Toolbox.GetBaseType(image.GetType(), "CudaImage`2")) != null)
                {
                    using (Mat img = imageType.GetMethod("ToMat").Invoke(image, null) as Mat)
                        for (int i = 0; i < channelCount; i++)
                        {
                            Mat channel = new Mat();
                            CvInvoke.ExtractChannel(img, channel, i);
                            channels[i] = channel;
                        }
                }
                else
                {
                    throw new ArgumentException(String.Format("The input image type of {0} is not supported",
                                                              image.GetType().ToString()));
                }

                Type[]   genericArguments = imageType.GetGenericArguments();
                String[] channelNames;
                Color[]  colors;
                Type     typeOfDepth;
                if (genericArguments.Length > 0)
                {
                    IColor typeOfColor = Activator.CreateInstance(genericArguments[0]) as IColor;
                    channelNames = Reflection.ReflectColorType.GetNamesOfChannels(typeOfColor);
                    colors       = Reflection.ReflectColorType.GetDisplayColorOfChannels(typeOfColor);
                    typeOfDepth  = imageType.GetGenericArguments()[1];
                }
                else
                {
                    channelNames = new String[channelCount];
                    colors       = new Color[channelCount];
                    for (int i = 0; i < channelCount; i++)
                    {
                        channelNames[i] = String.Format("Channel {0}", i);
                        colors[i]       = Color.Red;
                    }

                    if (image is Mat)
                    {
                        typeOfDepth = CvInvoke.GetDepthType(((Mat)image).Depth);
                    }
                    else if (image is UMat)
                    {
                        typeOfDepth = CvInvoke.GetDepthType(((UMat)image).Depth);
                    }
                    else
                    {
                        throw new ArgumentException(String.Format(
                                                        "Unable to get the type of depth from image of type {0}", image.GetType().ToString()));
                    }
                }

                float minVal, maxVal;

                #region Get the maximum and minimum color intensity values

                if (typeOfDepth == typeof(Byte))
                {
                    minVal = 0.0f;
                    maxVal = 256.0f;
                }
                else
                {
                    #region obtain the maximum and minimum color value
                    double[] minValues, maxValues;
                    Point[]  minLocations, maxLocations;
                    using (InputArray ia = image.GetInputArray())
                        using (Mat m = ia.GetMat())
                        {
                            m.MinMax(out minValues, out maxValues, out minLocations, out maxLocations);

                            double min = minValues[0], max = maxValues[0];
                            for (int i = 1; i < minValues.Length; i++)
                            {
                                if (minValues[i] < min)
                                {
                                    min = minValues[i];
                                }
                                if (maxValues[i] > max)
                                {
                                    max = maxValues[i];
                                }
                            }

                            minVal = (float)min;
                            maxVal = (float)max;
                        }
                    #endregion
                }
                #endregion

                Mat[] histograms = new Mat[channels.Length];
                for (int i = 0; i < channels.Length; i++)
                {
                    //using (DenseHistogram hist = new DenseHistogram(numberOfBins, new RangeF(minVal, maxVal)))
                    using (Mat hist = new Mat())
                        using (Util.VectorOfMat vm = new Util.VectorOfMat())
                        {
                            vm.Push(channels[i]);

                            float[] ranges = new float[] { minVal, maxVal };
                            CvInvoke.CalcHist(vm, new int[] { 0 }, null, hist, new int[] { numberOfBins }, ranges, false);
                            //hist.Calculate(new IImage[1] { channels[i] }, true, null);
                            histograms[i] = GenerateHistogram(channelNames[i], colors[i], hist, numberOfBins, ranges);
                        }
                }

                if (histograms.Length == 1)
                {
                    this.Image = histograms[0];
                }
                else
                {
                    int maxWidth    = 0;
                    int totalHeight = 0;
                    for (int i = 0; i < histograms.Length; i++)
                    {
                        maxWidth     = Math.Max(maxWidth, histograms[i].Width);
                        totalHeight += histograms[i].Height;
                    }
                    Mat concated = new Mat(new Size(maxWidth, totalHeight), histograms[0].Depth, histograms[0].NumberOfChannels);

                    int currentY = 0;
                    for (int i = 0; i < histograms.Length; i++)
                    {
                        using (Mat roi = new Mat(concated, new Rectangle(new Point(0, currentY), histograms[i].Size)))
                        {
                            histograms[i].CopyTo(roi);
                        }
                        currentY += histograms[i].Height;
                        histograms[i].Dispose();
                    }

                    this.Image = concated;
                }
            }
        }
예제 #24
0
파일: Form1.cs 프로젝트: xiaodelea/Emgucv
        private void backProjectionToolStripMenuItem_Click(object sender, EventArgs e)
        {
            try
            {
                if (pictureBox1.Image == null)
                {
                    return;
                }

                var img = new Bitmap(pictureBox1.Image)
                          .ToImage <Gray, byte>();

                Image <Gray, byte> img1   = null;
                OpenFileDialog     dialog = new OpenFileDialog();
                if (dialog.ShowDialog() == DialogResult.OK)
                {
                    img1 = new Image <Gray, byte>(dialog.FileName);
                }

                Mat hist  = new Mat();
                Mat hist1 = new Mat();

                float[] ranges   = new float[] { 0, 256 };
                int[]   channel  = { 0 };
                int[]   histSize = { 256 };

                VectorOfMat ms = new VectorOfMat();
                ms.Push(img);

                VectorOfMat ms1 = new VectorOfMat();
                ms1.Push(img1);


                CvInvoke.CalcHist(ms, channel, null, hist, histSize, ranges, false);
                CvInvoke.CalcHist(ms1, channel, null, hist1, histSize, ranges, false);

                CvInvoke.Normalize(hist, hist);
                CvInvoke.Normalize(hist1, hist1);


                Mat proj = new Mat();
                CvInvoke.CalcBackProject(ms, channel, hist, proj, ranges);


                HistogramViewer viewer = new HistogramViewer();
                viewer.Text     = "Image Histogram";
                viewer.ShowIcon = false;
                viewer.HistogramCtrl.AddHistogram("Image1 Histogram", Color.Blue, hist, 256, ranges);
                viewer.HistogramCtrl.Refresh();
                viewer.Show();

                HistogramViewer viewer1 = new HistogramViewer();
                viewer1.Text     = "Image Histogram";
                viewer1.ShowIcon = false;
                viewer1.HistogramCtrl.AddHistogram("Image2 Histogram", Color.Blue, hist1, 256, ranges);
                viewer1.HistogramCtrl.Refresh();
                viewer1.Show();



                pictureBox1.Image = proj.ToBitmap();
            }
            catch (Exception ex)
            {
                MessageBox.Show(ex.Message);
            }
        }
        /// <summary>
        /// Generate histograms for the image. One histogram is generated for each color channel.
        /// You will need to call the Refresh function to do the painting afterward.
        /// </summary>
        /// <param name="image">The image to generate histogram from</param>
        /// <param name="numberOfBins">The number of bins for each histogram</param>
        public void GenerateHistograms(IImage image, int numberOfBins)
        {
            Mat[] channels = new Mat[image.NumberOfChannels];
            Type  imageType;

            if ((imageType = Toolbox.GetBaseType(image.GetType(), "Image`2")) != null ||
                (imageType = Toolbox.GetBaseType(image.GetType(), "Mat")) != null ||
                (imageType = Toolbox.GetBaseType(image.GetType(), "UMat")) != null)
            {
                for (int i = 0; i < image.NumberOfChannels; i++)
                {
                    Mat channel = new Mat();
                    CvInvoke.ExtractChannel(image, channel, i);
                    channels[i] = channel;
                }
            }
            else if ((imageType = Toolbox.GetBaseType(image.GetType(), "CudaImage`2")) != null)
            {
                IImage img = imageType.GetMethod("ToImage").Invoke(image, null) as IImage;
                for (int i = 0; i < img.NumberOfChannels; i++)
                {
                    Mat channel = new Mat();
                    CvInvoke.ExtractChannel(img, channel, i);
                    channels[i] = channel;
                }
            }
            else
            {
                throw new ArgumentException(String.Format("The input image type of {0} is not supported", image.GetType().ToString()));
            }

            Type[]   genericArguments = imageType.GetGenericArguments();
            String[] channelNames;
            Color[]  colors;
            Type     typeOfDepth;

            if (genericArguments.Length > 0)
            {
                IColor typeOfColor = Activator.CreateInstance(genericArguments[0]) as IColor;
                channelNames = Emgu.CV.Reflection.ReflectColorType.GetNamesOfChannels(typeOfColor);
                colors       = Emgu.CV.Reflection.ReflectColorType.GetDisplayColorOfChannels(typeOfColor);
                typeOfDepth  = imageType.GetGenericArguments()[1];
            }
            else
            {
                channelNames = new String[image.NumberOfChannels];
                colors       = new Color[image.NumberOfChannels];
                for (int i = 0; i < image.NumberOfChannels; i++)
                {
                    channelNames[i] = String.Format("Channel {0}", i);
                    colors[i]       = Color.Red;
                }

                if (image is Mat)
                {
                    typeOfDepth = CvInvoke.GetDepthType(((Mat)image).Depth);
                }
                else if (image is UMat)
                {
                    typeOfDepth = CvInvoke.GetDepthType(((UMat)image).Depth);
                }
                else
                {
                    throw new ArgumentException(String.Format("Unable to get the type of depth from image of type {0}", image.GetType().ToString()));
                }
            }

            float minVal, maxVal;

            #region Get the maximum and minimum color intensity values

            if (typeOfDepth == typeof(Byte))
            {
                minVal = 0.0f;
                maxVal = 256.0f;
            }
            else
            {
                #region obtain the maximum and minimum color value
                double[] minValues, maxValues;
                Point[]  minLocations, maxLocations;
                image.MinMax(out minValues, out maxValues, out minLocations, out maxLocations);

                double min = minValues[0], max = maxValues[0];
                for (int i = 1; i < minValues.Length; i++)
                {
                    if (minValues[i] < min)
                    {
                        min = minValues[i];
                    }
                    if (maxValues[i] > max)
                    {
                        max = maxValues[i];
                    }
                }
                #endregion

                minVal = (float)min;
                maxVal = (float)max;
            }
            #endregion

            for (int i = 0; i < channels.Length; i++)
            {
                //using (DenseHistogram hist = new DenseHistogram(numberOfBins, new RangeF(minVal, maxVal)))
                using (Mat hist = new Mat())
                    using (Emgu.CV.Util.VectorOfMat vm = new Emgu.CV.Util.VectorOfMat())
                    {
                        vm.Push(channels[i]);

                        float[] ranges = new float[] { minVal, maxVal };
                        CvInvoke.CalcHist(vm, new int[] { 0 }, null, hist, new int[] { numberOfBins }, ranges, false);
                        //hist.Calculate(new IImage[1] { channels[i] }, true, null);
                        AddHistogram(channelNames[i], colors[i], hist, numberOfBins, ranges);
                    }
            }
        }
예제 #26
0
        private void StartMatching()
        {
            Image <Bgr, Byte>   target                       = Image_Target.Clone().Resize(scale, Inter.Linear);
            Image <Bgr, Byte>   texture                      = Image_Texture.Clone();
            Image <Gray, Byte>  target_gray                  = target.Convert <Gray, Byte>();
            Image <Gray, Byte>  texture_gray                 = texture.Convert <Gray, Byte>();
            Image <Gray, Byte>  target_hist_matched          = new Image <Gray, Byte>(target.Size);
            Image <Gray, Byte>  target_hist_matched_weighted = new Image <Gray, Byte>(target.Size);
            Image <Gray, float> target_sobel_x               = target_gray.Sobel(1, 0, 3);
            Image <Gray, float> target_sobel_y               = target_gray.Sobel(0, 1, 3);
            Image <Gray, float> texture_sobel_x              = texture_gray.Sobel(1, 0, 3);
            Image <Gray, float> texture_sobel_y              = texture_gray.Sobel(0, 1, 3);
            Image <Gray, float> target_sobel_mag             = new Image <Gray, float>(target_gray.Size);
            Image <Gray, float> texture_sobel_mag            = new Image <Gray, float>(texture_gray.Size);

            Image_Result          = new Image <Bgr, byte>(target.Width, target.Height, new Bgr(0, 0, 0));
            imageBox_Result.Image = Image_Result;

            this.Invoke(new MethodInvoker(() =>
            {
                progressBar_match.Value   = 0;
                progressBar_match.Maximum = (target.Width / size) * (target.Height / size);
            }));

            Matrix <byte> histLUT      = new Matrix <byte>(1, 256);
            Mat           hist_target  = new Mat();
            Mat           hist_texture = new Mat();

            VectorOfMat vm_target  = new VectorOfMat();
            VectorOfMat vm_texture = new VectorOfMat();

            vm_target.Push(target_gray);
            vm_texture.Push(texture_gray);

            CvInvoke.CalcHist(vm_target, new int[] { 0 }, null, hist_target, new int[] { 256 }, new float[] { 0, 255 }, false);
            CvInvoke.CalcHist(vm_texture, new int[] { 0 }, null, hist_texture, new int[] { 256 }, new float[] { 0, 255 }, false);

            float[] CDF_hist_target  = new float[256];
            float[] CDF_hist_texture = new float[256];
            Marshal.Copy(hist_target.DataPointer, CDF_hist_target, 0, 256);
            Marshal.Copy(hist_texture.DataPointer, CDF_hist_texture, 0, 256);

            for (int i = 1; i < 256; i++)
            {
                CDF_hist_target[i]  += CDF_hist_target[i - 1];
                CDF_hist_texture[i] += CDF_hist_texture[i - 1];
            }

            for (int i = 0; i < 256; i++)
            {
                histLUT.Data[0, i] = 0;
                for (int j = 0; j < 256; j++)
                {
                    if (CDF_hist_texture[j] >= CDF_hist_target[i])
                    {
                        histLUT.Data[0, i] = (byte)j;
                        break;
                    }
                }
            }
            CvInvoke.LUT(target_gray, histLUT, target_hist_matched);
            target_hist_matched_weighted = target_hist_matched * weight_hist + target_gray * (1.0 - weight_hist);

            CvInvoke.CartToPolar(target_sobel_x, target_sobel_y, target_sobel_mag, new Mat());
            CvInvoke.CartToPolar(texture_sobel_x, texture_sobel_y, texture_sobel_mag, new Mat());


            List <Matrix <float> > transformation_matrixs        = new List <Matrix <float> >();
            List <Matrix <float> > transformation_matrixs_invert = new List <Matrix <float> >();
            List <RectangleF>      rotatedRects = new List <RectangleF>();

            for (int i = 1; i < rotations; i++)
            {
                double         angle                        = i * (360.0 / (float)rotations);
                RectangleF     rotatedRect                  = new RotatedRect(new PointF(), texture.Size, (float)angle).MinAreaRect();
                PointF         center                       = new PointF(0.5f * texture.Width, 0.5f * texture.Height);
                Matrix <float> transformation_matrix        = new Matrix <float>(2, 3);
                Matrix <float> transformation_matrix_invert = new Matrix <float>(2, 3);
                CvInvoke.GetRotationMatrix2D(center, angle, 1.0, transformation_matrix);

                transformation_matrix.Data[0, 2] += (rotatedRect.Width - texture.Width) / 2;
                transformation_matrix.Data[1, 2] += (rotatedRect.Height - texture.Height) / 2;
                CvInvoke.InvertAffineTransform(transformation_matrix, transformation_matrix_invert);
                transformation_matrixs.Add(transformation_matrix);
                transformation_matrixs_invert.Add(transformation_matrix_invert);
                rotatedRects.Add(rotatedRect);
            }

            List <Image <Bgr, byte> > texture_rotations = new List <Image <Bgr, byte> >(rotations)
            {
            };
            List <Image <Gray, byte> > texture_gray_rotations = new List <Image <Gray, byte> >(rotations)
            {
            };
            List <Image <Gray, float> > texture_sobel_rotations = new List <Image <Gray, float> >(rotations)
            {
            };
            List <Image <Gray, byte> > texture_mask_rotations = new List <Image <Gray, byte> >(rotations)
            {
            };

            texture_rotations.Add(texture);
            texture_gray_rotations.Add(texture_gray);
            texture_sobel_rotations.Add(texture_sobel_mag);
            texture_mask_rotations.Add(new Image <Gray, byte>(texture.Width, texture.Height, new Gray(255)));
            for (int i = 1; i < rotations; i++)
            {
                texture_mask_rotations.Add(new Image <Gray, byte>(rotatedRects[i - 1].Size.ToSize()));
                texture_rotations.Add(new Image <Bgr, byte>(rotatedRects[i - 1].Size.ToSize()));
                texture_gray_rotations.Add(new Image <Gray, byte>(rotatedRects[i - 1].Size.ToSize()));
                texture_sobel_rotations.Add(new Image <Gray, float>(rotatedRects[i - 1].Size.ToSize()));
            }
            for (int i = 1; i < rotations; i++)
            {
                CvInvoke.WarpAffine(texture, texture_rotations[i], transformation_matrixs[i - 1], rotatedRects[i - 1].Size.ToSize(), Inter.Nearest, Warp.Default, BorderType.Constant, new MCvScalar(0));
                CvInvoke.WarpAffine(texture_gray, texture_gray_rotations[i], transformation_matrixs[i - 1], rotatedRects[i - 1].Size.ToSize(), Inter.Nearest, Warp.Default, BorderType.Constant, new MCvScalar(0));
                CvInvoke.WarpAffine(texture_sobel_mag, texture_sobel_rotations[i], transformation_matrixs[i - 1], rotatedRects[i - 1].Size.ToSize(), Inter.Nearest, Warp.Default, BorderType.Constant, new MCvScalar(0));
                CvInvoke.WarpAffine(texture_mask_rotations[0], texture_mask_rotations[i], transformation_matrixs[i - 1], rotatedRects[i - 1].Size.ToSize(), Inter.Nearest, Warp.Default, BorderType.Constant, new MCvScalar(0));
            }

            // Directory.SetCurrentDirectory(path);
            String current_path = path + @"\matched_patches";

            if (Directory.Exists(current_path))
            {
                DirectoryInfo di = new DirectoryInfo(current_path);
                foreach (FileInfo file in di.GetFiles())
                {
                    file.Delete();
                }
                foreach (DirectoryInfo dir in di.GetDirectories())
                {
                    dir.Delete(true);
                }
            }
            else
            {
                DirectoryInfo di = Directory.CreateDirectory(current_path);
            }

            for (int y = 0; y < target_hist_matched_weighted.Height / size; y++)
            {
                for (int x = 0; x < target_hist_matched_weighted.Width / size; x++)
                {
                    Image <Bgr, byte>   template;
                    Image <Gray, byte>  template_gray;
                    Image <Gray, float> template_sobel;

                    template           = target.Clone();
                    template_gray      = target_hist_matched_weighted.Clone();
                    template_sobel     = target_sobel_mag.Clone();
                    template.ROI       = new Rectangle(x * size, y * size, size, size);
                    template_gray.ROI  = new Rectangle(x * size, y * size, size, size);
                    template_sobel.ROI = new Rectangle(x * size, y * size, size, size);
                    template           = template.Clone();
                    template_gray      = template_gray.Copy();
                    template_sobel     = template_sobel.Copy();

                    int    minMatchIndex = -1;
                    double minMatchValue = double.MaxValue;
                    Point  minMatchLoc   = new Point();
                    Object _lock         = new Object();
                    Parallel.For(0, rotations, i =>
                    {
                        Image <Gray, float> match_gray  = new Image <Gray, float>(texture_gray.Size);
                        Image <Gray, float> match_sobel = new Image <Gray, float>(texture_sobel_mag.Size);
                        Image <Gray, float> match_sum   = new Image <Gray, float>(texture.Size);
                        double minVal = 0, maxVal = 0;
                        Point minLoc  = new Point(), maxLoc = new Point();

                        CvInvoke.MatchTemplate(texture_gray_rotations[i], template_gray, match_gray, TemplateMatchingType.Sqdiff);
                        CvInvoke.MatchTemplate(texture_sobel_rotations[i], template_sobel, match_sobel, TemplateMatchingType.Sqdiff);
                        match_sum = match_gray + match_sobel;
                        //CvInvoke.MinMaxLoc(match_sum, ref minVal, ref maxVal, ref minLoc, ref maxLoc);
                        //CudaInvoke.MinMaxLoc(match_sum, ref minVal, ref maxVal, ref minLoc, ref maxLoc, GetMinMaxLocMask(texture_mask_rotations[i], size));
                        CvInvoke.MinMaxLoc(match_sum, ref minVal, ref maxVal, ref minLoc, ref maxLoc, GetMinMaxLocMask(texture_mask_rotations[i], size));
                        lock (_lock)
                        {
                            if (minVal < minMatchValue && minVal > 0)
                            {
                                minMatchValue = minVal;
                                minMatchIndex = i;
                                minMatchLoc   = minLoc;
                            }
                        }
                        match_gray.Dispose();
                        match_sobel.Dispose();
                        match_sum.Dispose();
                    });
                    Console.WriteLine($"minMatchValue = {minMatchValue}\r\nminMatchIndex = {minMatchIndex}\r\nminMatchLoc = {minMatchLoc}");
                    if (minMatchIndex < 0)
                    {
                        MessageBox.Show("Out of textures!!!!");
                        return;
                    }
                    texture_mask_rotations[minMatchIndex].Draw(new Rectangle(minMatchLoc.X, minMatchLoc.Y, size - 1, size - 1), new Gray(0), -1);
                    if (minMatchIndex > 0)
                    {
                        Image <Gray, byte> mask = new Image <Gray, byte>(texture_mask_rotations[0].Size);
                        CvInvoke.WarpAffine(texture_mask_rotations[minMatchIndex], mask, transformation_matrixs_invert[minMatchIndex - 1], mask.Size, Inter.Nearest, Warp.Default, BorderType.Constant, new MCvScalar(0));
                        texture_mask_rotations[0] = texture_mask_rotations[0] - (255 - mask);
                    }
                    for (int i = 1; i < rotations; i++)
                    {
                        Image <Gray, byte> mask_rot = new Image <Gray, byte>(texture_mask_rotations[i].Size);
                        CvInvoke.WarpAffine(texture_mask_rotations[0], mask_rot, transformation_matrixs[i - 1], mask_rot.Size, Inter.Nearest, Warp.Default, BorderType.Constant, new MCvScalar(0));
                        mask_rot.CopyTo(texture_mask_rotations[i]);
                    }

                    texture_rotations[minMatchIndex].ROI = new Rectangle(minMatchLoc, new Size(size, size));
                    CvInvoke.Imwrite($@"{current_path}\{x}_{y}.bmp", texture_rotations[minMatchIndex].Copy());
                    imageBox_match.Image    = texture_rotations[minMatchIndex].Copy();
                    imageBox_template.Image = template.Copy();
                    Image_Result.ROI        = new Rectangle(x * size, y * size, size, size);
                    texture_rotations[minMatchIndex].CopyTo(Image_Result);

                    Image_Result.ROI = Rectangle.Empty;
                    texture_rotations[minMatchIndex].ROI = Rectangle.Empty;

                    imageBox_Result.Image = Image_Result;


                    if (minMatchIndex > 0)
                    {
                        PointF[] maskPoints     = { minMatchLoc, new PointF(minMatchLoc.X + size - 1, minMatchLoc.Y), new PointF(minMatchLoc.X, minMatchLoc.Y + size - 1), new PointF(minMatchLoc.X + size - 1, minMatchLoc.Y + size - 1) };
                        PointF[] maskPoints_rot = new PointF[4];
                        for (int i = 0; i < 4; i++)
                        {
                            maskPoints_rot[i].X = maskPoints[i].X * transformation_matrixs_invert[minMatchIndex - 1].Data[0, 0] + maskPoints[i].Y * transformation_matrixs_invert[minMatchIndex - 1].Data[0, 1] + 1.0f * transformation_matrixs_invert[minMatchIndex - 1].Data[0, 2];
                            maskPoints_rot[i].Y = maskPoints[i].X * transformation_matrixs_invert[minMatchIndex - 1].Data[1, 0] + maskPoints[i].Y * transformation_matrixs_invert[minMatchIndex - 1].Data[1, 1] + 1.0f * transformation_matrixs_invert[minMatchIndex - 1].Data[1, 2];
                        }
                        Point[] maskPoints_rot_round = { Point.Round(maskPoints_rot[0]), Point.Round(maskPoints_rot[1]), Point.Round(maskPoints_rot[3]), Point.Round(maskPoints_rot[2]) };
                        texture.FillConvexPoly(maskPoints_rot_round, new Bgr(0, 0, 0));
                    }
                    else
                    {
                        //texture.DrawPolyline(new Point[] { minMatchLoc, new Point(minMatchLoc.X + size, minMatchLoc.Y), new Point(minMatchLoc.X + size, minMatchLoc.Y + size), new Point(minMatchLoc.X, minMatchLoc.Y + size) }, true, new Bgr(0, 0, 0), 0);
                        texture.Draw(new Rectangle(minMatchLoc, new Size(size - 1, size - 1)), new Bgr(0, 0, 0), -1);
                    }
                    imageBox_Texture.Image = texture;

                    template.Dispose();
                    template_gray.Dispose();
                    template_sobel.Dispose();
                    GC.Collect();

                    this.Invoke(new MethodInvoker(() =>
                    {
                        progressBar_match.Value++;
                    }));
                }
            }
        }