CSharp + OpenCvSharp实现医学图像拼接

基于网络上很多都是C++ 、Python实现的图像拼接，或者直接调用的Stitch函数进行的拼接，想着手动实现一个C#版本，具体思路如下：

实际拼接效果如下：


实现起来不是很难，但还是有许多可以优化的点，这些放在以后把。
下面展示一些 内联代码片。

/// 

        /// 基于SIFT算法特征提取进行拼接
        /// 
        ///         
        /// 
        private void btnSift_Click(object sender, EventArgs e)
        {
            #region 读取图片
            //这一部分相对而言较为简单，读者按自己需求编写就可            
            #endregion
            //从这开始是正式流程
            /*
             *   图像二值化，加快特征点检测速度
             */
            Mat srcImgBgr2Gray = new Mat();
            Mat testImgBgr2Gray = new Mat();
            Cv2.CvtColor(srcImg, srcImgBgr2Gray,ColorConversionCodes.BGRA2GRAY);
            Cv2.CvtColor(testImg, testImgBgr2Gray, ColorConversionCodes.BGR2GRAY);

            #region 创建 SIFT  
            //var MySift = OpenCvSharp.Features2D.SIFT.Create(400);
            var MySift = OpenCvSharp.XFeatures2D.SIFT.Create(2000);

            /*
             *    特征点
             */
            KeyPoint[] srcKeyPoints = MySift.Detect(srcImgBgr2Gray);
            KeyPoint[] testKeyPoints = MySift.Detect(testImgBgr2Gray);

            /*
             *    描述子
             */
            Mat srcDescriptors = new Mat();
            Mat testDescriptors = new Mat();

            /*
             *    计算特征向量
             */
            srcKeyPoints = MySift.Detect(srcImg);
            testKeyPoints = MySift.Detect(testImg);
            MySift.Compute(srcImgBgr2Gray, ref srcKeyPoints, srcDescriptors);
            MySift.Compute(testImgBgr2Gray, ref testKeyPoints, testDescriptors);

            #endregion

            #region 创建FLANN匹配器
            FlannBasedMatcher matcher = new FlannBasedMatcher();
            DMatch[] matches = matcher.Match(srcDescriptors, testDescriptors);

            #endregion

            #region 比率测试
            double max_dist = 0;
            double min_dist = 100;
            for (int i = 0; i < srcDescriptors.Rows; i++)
            {
                double dist = matches[i].Distance;
                if (dist < 0.9 * min_dist)
                {
                    min_dist = dist;
                }
                if (dist > 0.9 * max_dist)
                {
                    max_dist = dist;
                }
            }            
            List<DMatch> good_matches = new List<DMatch>();
            for (int i = 0; i < srcDescriptors.Rows; i++)
            {
                if (matches[i].Distance < 3 * min_dist)
                {
                    good_matches.Add(matches[i]);   
                }
            }
            if (good_matches.Count <= 0)
            {
                MessageBox.Show("未匹配到特征点，无法进行拼接！\n请尝试手动拼接","提醒");
                return;
            }
            /*
             *    绘制匹配点
             */
            Mat img_matches = new Mat();
            Cv2.DrawMatches(srcImg, srcKeyPoints, testImg, testKeyPoints, good_matches, img_matches);
            
            /*
             *   创建模板
             */
            Mat srcImgTemp = new Mat();
            Cv2.CopyMakeBorder(srcImg, srcImgTemp, top, bottom, left, right, BorderTypes.Constant, 0);
            Mat testImgTemp = new Mat();            
            Cv2.CopyMakeBorder(testImg, testImgTemp, top, bottom, left, right, BorderTypes.Constant, 0); 
            #endregion

            #region 透视变换
            List<Point2f> srcImgTransf = new List<Point2f>();
            List<Point2f> testImgTransf = new List<Point2f>();
            for (int i = 0; i < good_matches.Count; i++)
            {
                srcImgTransf.Add(srcKeyPoints[good_matches[i].QueryIdx].Pt);
                testImgTransf.Add(testKeyPoints[good_matches[i].TrainIdx].Pt);
            }
            List<Point2d> srcImgPoints = srcImgTransf.ConvertAll(Point2fToPoint2d);
            List<Point2d> testImgPoints = testImgTransf.ConvertAll(Point2fToPoint2d);

            /*
             *   单应性矩阵
             */            
            Mat testImgWarp = new Mat();
            Mat M = Cv2.FindHomography(testImgPoints, srcImgPoints, HomographyMethods.Ransac, 5.0);
            OpenCvSharp.Size sizeTest = new OpenCvSharp.Size(testImg.Cols, testImg.Rows + 250);
            //CalcCorners(M, testImg);
            /*
             *   透视变换
             */                     
            Cv2.WarpPerspective(testImgTemp, testImgWarp, M, sizeTest, InterpolationFlags.WarpFillOutliers, BorderTypes.Constant, 0);           
            #endregion            

            #region 图像融合

            /*
             *     ①图像融合
             */
            int dstHeight = testImgWarp.Rows;
            int dstWidth = srcImg.Cols;
            Mat dst = new Mat(dstHeight, dstWidth, MatType.CV_8UC3);
            dst.SetTo(0);

            Rect rect = new Rect(0, 0, testImgWarp.Cols, testImgWarp.Rows);
            Mat dstTestImgWarp = new Mat(dst, rect);
            testImgWarp.CopyTo(dstTestImgWarp);

            Rect rectTestImg = new Rect(0, 0, srcImg.Cols, srcImg.Rows);
            Mat dstTestImg = new Mat(dst, rectTestImg);
            srcImg.CopyTo(dstTestImg);

            /*
             *     ②黑边处理
             */
            //OptimizeSeam(srcImg, dstTestImgWarp,dst);
            #endregion

            /*
             *   输出图像
             */            
            Bitmap map = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(dst);
            pbStitchImg.Image = map;
        }

以上便是本次实现过程demo，期待和大家共同探讨。