opencvsharp踩坑DAY2--图像增强illuminate

出差摸鱼做的一个用opencvsharp的东西，用于快速验证，水平极差，目前功能如下

 

 

今天搞的功能是复现halcon的图像增强算子illuminate，根据文档其运作过程为

1.输入均值（低通）滤波矩阵size，输入Factor，原图灰度集in

2.滤波in得图像m

3.然后out= round ( (val - m) * Factor + in )

4.其中val在halcon帮助中描述为For byte-images val equals 127, for int2-images and uint2-images val equals the median value. 而这个byte-images、int2-images、uint2-images区分则是其图像类型，参考大佬http://www.skcircle.com/?id=1547，在opencvsharp中则分别对应了遍历图像Mat.Get<byte>(x, y)、Mat.Get<int>(x, y)、Mat.Get<uint>(x, y)的值，而127也是0-255的中只所以选择该数值作为val。在本文中用到的图像类型默认是byte-images。

5.其中Factor与滤波器尺寸成正相关关系halcon说明中30x30到200x200的范围有以下几种组合

Height Width Factor

---------------------

40 40 0.55

100 100 0.7

150 150 0.8

6.综合上文，得知在低通滤波后图像将灰度中值比较，将其间差乘以因子Factor再加上原灰度值。让局部的灰度向灰度中值靠拢以达到增强图像的高频区域（边缘和拐角），使图像看起来更清晰的效果。原文：Very dark parts of the image are “illuminated” more strongly, very light ones are “darkened”. 

halcon效果如图

 

 opencvsharp实现效果如下

        private void illuminate()
        {
            int w, h;
            double factor;
            Cv2.CvtColor(dealing_object, dealing_object, ColorConversionCodes.BGR2GRAY);
            Mat mean = new Mat();
            w = int.Parse(InputBox("滤波器宽", "", ""));
            h = int.Parse(InputBox("滤波器高", "", ""));
            factor = double.Parse(InputBox("系数", "", ""));
            Cv2.Blur(dealing_object, mean, new OpenCvSharp.Size(w, h));

            Mat output = new Mat(dealing_object.Size(), dealing_object.Type());
            for (int i = 0; i < dealing_object.Height; i++)
            {
                for (int j = 0; j < dealing_object.Width; j++)
                {
                    int v = (int)Math.Round((172- mean.Get<byte>(i, j)) * factor) + dealing_object.Get<byte>(i, j);
                    v = v > 255 ? 255 : v;
                    v = v < 0 ? 0 : v;
                    output.Set(i, j, v);

                }
            }
            Cv2.ImShow("in", dealing_object);
            Cv2.ImShow("out", output);

        }