C#，OpenCv开发指南（02）——OpenCvSharp编程入门与矩阵Mat的基础知识

        在 Visual Studio 中很方便搭建与使用 OpenCV 的 C# 的开发环境，几乎不用键盘输入。

        使用 C# 开发 OpenCV 可以直接成为工业软件产品，而不是实验室程序。世界上几乎所有的视频厂家都提供 C# OpenCV 开发接口。

C#，人工智能，深度学习，OpenCV，C#开发环境OpenCvSharp的安装、搭建与可视化教程https://blog.csdn.net/beijinghorn/article/details/125528673

        OpenCV 学习了 Matlab 的设计思想，以矩阵Matrix为基础数据类型。因而，本文也以矩阵的知识为入门基础。我们跳过矩阵的最基础的部分开始。

1 认识 OpenCV 矩阵Mat的属性 Attributes

        学习一种开发组件，首先了解其属性、方法。

1.1 一段关于Mat属性的代码

using System;
using System.IO;
using System.Text;
using System.Collections.Generic;
using System.Windows.Forms;
using System.Drawing;
using System.Drawing.Imaging;
using System.Drawing.Drawing2D;
using System.Runtime.InteropServices;

using OpenCvSharp;
using OpenCvSharp.Extensions;

/// 

/// 部分 OpenCVSharp 拓展函数
/// 
public static partial class CVUtility
{
    public static string Attributes(Mat src)
    {
        StringBuilder sb = new StringBuilder();
        sb.AppendLine("");
        sb.AppendLine("");
        sb.AppendLine("");
        sb.AppendLine("");
        sb.AppendLine("");
        sb.AppendLine("");
        sb.AppendLine("");
        sb.AppendLine("");
        sb.AppendLine("");
        sb.AppendLine("");
        sb.AppendLine("");
        sb.AppendLine("");
        sb.AppendLine("");
        sb.AppendLine("");
        sb.AppendLine("");
        sb.AppendLine("
属
性
数据首地址Data(IntPtr): 
" + src.Data + "
行数Rows(=Height): 
" + src.Rows + "=" + src.Height + "
列数Cols(=Width): 
" + src.Cols + "=" + src.Width + "
尺寸Size(Width x Height): 
" + src.Size().Width + "x" + src.Size().Height + "
矩阵维度Dims: 
" + src.Dims + "
方
法
通道数Channels: 
" + src.Channels() + "
通道的深度Depth: 
" + src.Depth() + "
元素的数据大小ElemSize(bytes): 
" + src.ElemSize() + "
通道1元素的数据大小ElemSize1(bytes): 
" + src.ElemSize1() + "
每行步长Step(bytes): 
" + src.Step() + "
通道1每行步长Step1(bytes): 
" + src.Step1() + "
矩阵类型Type: 
" + src.Type() + "
");
        sb.AppendLine("");
        return sb.ToString();
    }
}

1.2 Mat属性的显示

using System;
using System.IO;
using System.Collections;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Windows.Forms;
using System.Drawing.Imaging;

using OpenCvSharp;

namespace Legalsoft.OpenCv.Train
{
    public partial class Form1 : Form
    {
        private void button1_Click(object sender, EventArgs e)
        {
            Mat src = new Mat(Path.Combine(Application.StartupPath, "101.jpg"), ImreadModes.AnyColor | ImreadModes.AnyDepth);
            webBrowser1.DocumentText = CVUtility.Attributes(src);
        }
    }
}

1.3 属性函数运行结果

1.4 属性和常量方法的解释

1.4.1 Data

       IntPtr类型的指针，指向Mat矩阵数据的首地址。一般不用。

1.4.2 Rows 或 Height

        Mat矩阵的行数，也是图片的高度（像素）。

1.4.3 Cols 或 Width

        Mat矩阵的列数，也是图片的宽度（像素）。

1.4.4 Size()

        Size() 返回 Width,Height 组成的结构。

1.4.5 Dims

        Mat矩阵的维度，若Mat是一个二维矩阵，则Dims=2，三维则Dims=3。

1.4.6 Channels()

        Mat矩阵元素的通道数。

        例如常见的RGB彩色图像，Channels =3；

        灰度图像只有一个灰度分量信息，Channels =1。

1.4.7 Depth()

        每一个像素中每一个通道的精度。

        在Opencv中，Mat.Depth()得到的是一个 0~6 的数字，分别代表不同的位数，

        对应关系如下：                            

                CV_8U=0

                CV_8S=1

                CV_16U=2

                CV_16S=3

                CV_32S=4

                CV_32F=5

                CV_64F=6     

        其中U是unsigned的意思，S表示signed，也就是有符号和无符号数。

1.4.8 ElemSize() and ElemSize1()

        矩阵中每一个元素的数据字节数(bytes)。

        如果Mat中的数据类型是CV_8UC1，那么ElemSize = 1；

        如果是CV_8UC3或CV_8SC3，那么 ElemSize = 3；

        如果是CV_16UC3或者CV_16SC3，那么 ElemSize = 6；

        可见，ElemSize是以字节为单位的；

        ElemSize1() 就是通道1的数据字节数。有：

        ElemSize1 = ElemSize / Channels

1.4.9 Step() and Step1()

        Mat矩阵中每一行的步长(字节)，即为每一行中所有元素的字节总量。

        Step1() 是通道1的步长。有：

        Step1 = Step / ElemSize1

1.4.10 Type()

        Mat矩阵的类型，包含有矩阵中元素的类型以及通道数信息。

1.5 Mat Type 的定义

/// 

/// typeof(T) -> MatType
/// 
protected static readonly IReadOnlyDictionary TypeMap = new Dictionary
{
        [typeof(byte)] = MatType.CV_8UC1,
        [typeof(sbyte)] = MatType.CV_8SC1,
        [typeof(short)] = MatType.CV_16SC1,
        [typeof(char)] = MatType.CV_16UC1,
        [typeof(ushort)] = MatType.CV_16UC1,
        [typeof(int)] = MatType.CV_32SC1,
        [typeof(float)] = MatType.CV_32FC1,
        [typeof(double)] = MatType.CV_64FC1,
 
        [typeof(Vec2b)] = MatType.CV_8UC2,
        [typeof(Vec3b)] = MatType.CV_8UC3,
        [typeof(Vec4b)] = MatType.CV_8UC4,
        [typeof(Vec6b)] = MatType.CV_8UC(6),
 
        [typeof(Vec2s)] = MatType.CV_16SC2,
        [typeof(Vec3s)] = MatType.CV_16SC3,
        [typeof(Vec4s)] = MatType.CV_16SC4,
        [typeof(Vec6s)] = MatType.CV_16SC(6),
 
        [typeof(Vec2w)] = MatType.CV_16UC2,
        [typeof(Vec3w)] = MatType.CV_16UC3,
        [typeof(Vec4w)] = MatType.CV_16UC4,
        [typeof(Vec6w)] = MatType.CV_16UC(6),
 
        [typeof(Vec2i)] = MatType.CV_32SC2,
        [typeof(Vec3i)] = MatType.CV_32SC3,
        [typeof(Vec4i)] = MatType.CV_32SC4,
        [typeof(Vec6i)] = MatType.CV_32SC(6),
 
        [typeof(Vec2f)] = MatType.CV_32FC2,
        [typeof(Vec3f)] = MatType.CV_32FC3,
        [typeof(Vec4f)] = MatType.CV_32FC4,
        [typeof(Vec6f)] = MatType.CV_32FC(6),
 
        [typeof(Vec2d)] = MatType.CV_64FC2,
        [typeof(Vec3d)] = MatType.CV_64FC3,
        [typeof(Vec4d)] = MatType.CV_64FC4,
        [typeof(Vec6d)] = MatType.CV_64FC(6),
 
        [typeof(Point)] = MatType.CV_32SC2,
        [typeof(Point2f)] = MatType.CV_32FC2,
        [typeof(Point2d)] = MatType.CV_64FC2,
 
        [typeof(Point3i)] = MatType.CV_32SC3,
        [typeof(Point3f)] = MatType.CV_32FC3,
        [typeof(Point3d)] = MatType.CV_64FC3,
 
        [typeof(Size)] = MatType.CV_32SC2,
        [typeof(Size2f)] = MatType.CV_32FC2,
        [typeof(Size2d)] = MatType.CV_64FC2,
 
        [typeof(Rect)] = MatType.CV_32SC4,
        [typeof(Rect2f)] = MatType.CV_32FC4,
        [typeof(Rect2d)] = MatType.CV_64FC4,
 
        [typeof(DMatch)] = MatType.CV_32FC4,
};

2 创建 Mat 实例

        有多达 15 种方法可以创建 Mat 的实例。选择常用的介绍一下。

2.1 从数据（数组）创建 Mat

        可以从数组创建一维、二维及更多为的矩阵。

using System;
using System.IO;
using System.Collections;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Windows.Forms;
using System.Drawing.Imaging;

using OpenCvSharp;

namespace Legalsoft.OpenCv.Train
{
    public partial class Form1 : Form
    {
        private void button1_Click(object sender, EventArgs e)
        {
            double[,] a = new double[4, 3] { 
                { 1, 2, 3 }, 
                { 4, 5, 6 }, 
                { 7, 8, 9 }, 
                { 10, 11, 12 } 
            };
            Mat src = new Mat(4, 3, MatType.CV_64F, a, 0);
            webBrowser1.DocumentText = CVUtility.ToHtmlTable(src);
        }
    }
}

        其中显示 矩阵 的方法 ToHtmlTable 源代码为：

using System;
using System.Text;
using System.Collections;
using System.Collections.Generic;

using OpenCvSharp;
using OpenCvSharp.Extensions;

/// 

/// 部分 OpenCVSharp 拓展函数
/// 
public static partial class CVUtility
{
    /// 

    /// 矩阵输出为HTML表格
    /// 
    /// 
    /// 
    public static string ToHtmlTable(Mat src)
    {
        StringBuilder sb = new StringBuilder();
        sb.AppendLine("");
        sb.AppendLine("");
        sb.AppendLine("");
        for (int y = 0; y < src.Height; y++)
        {
            if (y == 0)
            {
                // 标题行
                sb.AppendLine("");
                sb.Append("");
                for (int x = 0; x < src.Width; x++)
                {
                    sb.AppendFormat("", (x + 1));
                }
                sb.Append("");
                sb.AppendLine("");
            }
            sb.AppendLine("");
            sb.AppendFormat("", (y + 1));
            for (int x = 0; x < src.Width; x++)
            {
                sb.AppendFormat("", src.At(y, x));
            }
            sb.AppendFormat("", (y + 1));
            sb.AppendLine("");
            if (y == (src.Height - 1))
            {
                // 标题行
                sb.AppendLine("");
                sb.Append("");
                for (int x = 0; x < src.Width; x++)
                {
                    sb.AppendFormat("", (x + 1));
                }
                sb.Append("");
                sb.AppendLine("");
            }
        }
        sb.AppendLine("
{0:D}
{0:D}
{0:F6}
{0:D}
{0:D}
");
        sb.AppendLine("");
        return sb.ToString();
    }
}

矩阵数据显示：

2.2 从图片文件中读取并创建 Mat

        C# 代码很简单。

Mat src = Cv2.ImRead(imageFileName, ImreadModes.AnyColor | ImreadModes.AnyDepth);

或者：

Mat src = Cv2.ImRead(imageFileName);

     Cv2.ImRead 函数的定义：   

     Cv2.ImRead(string fileName, ImReadModes flags)

        Cv2.IMREAD_COLOR：默认参数，读入一副彩色图片，忽略alpha通道
	Cv2.IMREAD_GRAYSCALE：读入灰度图片
	Cv2.IMREAD_UNCHANGED：顾名思义，读入完整图片，包括alpha通道
        Cv2.AnyColor
        Cv2.AnyDepth

        Cv2.ImRead 默认将图片转换成了一个三维数组。最里面的一维代表的是一个像素的三个通道（BGR）的灰度值，第二个维度代表的是每一行所有像素的灰度值，第三个维度，也就是最外面的一个维度代表的是这一张图片。

        读取之后的第二维长度为图片的宽（高）

        Cv2.ImRead 读取的是B、G、R(红、绿、蓝)一般取值范围0~255。

        Cv2.ImRead 函数，一定要注意读取的顺序是BGR。

2.3 获取图片的一部分（一般为矩形）创建 Mat

        图片处理往往是局部的。这个局部一般为矩形，也可以是圆形、椭圆、不规则形状及其他形状。

        用于处理的部分图片成为 ROI(region of interest)，感兴趣区域。

// 原图
Mat src = CVUtility.LoadImage("stars/roi/301.jpg");
// 定义 ROI 区域
int w = src.Width / 2;
int h = src.Height / 2;
int x = src.Width / 4;
int y = src.Height / 4;
Rect rect = new Rect(x, y, w, h);
// 提取 ROI
Mat dst = new Mat(src, rect);

2.4 一些特殊的矩阵

2.4.1 单位矩阵

Mat m1 = Mat.Eye(new OpenCvSharp.Size(5, 5), MatType.CV_64F);
webBrowser1.DocumentText = CVUtility.ToHtmlTable(m1);

2.4.2 全0矩阵

// 全为0的矩阵
Mat m2 = Mat.Zeros(new OpenCvSharp.Size(5, 5), MatType.CV_64F);
webBrowser1.DocumentText = CVUtility.ToHtmlTable(m2);

2.4.3 全1矩阵

// 全为1的矩阵
Mat m3 = Mat.Ones(new OpenCvSharp.Size(5, 5), MatType.CV_64F);
webBrowser1.DocumentText = CVUtility.ToHtmlTable(m3);

3 访问矩阵元素（图片像素）的多种方法

        下面列出 3 种访问图片像素的方法，并交换 Red Blue 通道的实例。

3.1 Get/Set (slow)

    /// 

    /// 普通访问方式
    /// Get/Set (slow)
    /// 
    /// 
    public static void Search_GetSet(Mat src)
    {
        for (int y = 0; y < src.Height; y++)
        {
            for (int x = 0; x < src.Width; x++)
            {
                Vec3b color = src.Get(y, x);
                byte temp = color.Item0;
                color.Item0 = color.Item2; // B <- R
                color.Item2 = temp;        // R <- B
                src.Set(y, x, color);
            }
        }
    }

3.2 GenericIndexer (reasonably fast)

    /// 

    /// 通用索引器方式访问像素
    /// GenericIndexer(reasonably fast)
    /// 
    /// 
    public static void Search_Indexer(Mat src)
    {
        Mat.Indexer indexer = src.GetGenericIndexer();
        for (int y = 0; y < src.Height; y++)
        {
            for (int x = 0; x < src.Width; x++)
            {
                Vec3b color = indexer[y, x];
                byte temp = color.Item0;
                color.Item0 = color.Item2; // B <- R
                color.Item2 = temp;        // R <- B
                indexer[y, x] = color;
            }
        }
    }

3.3 TypeSpecificMat (faster)

    /// 

    /// TypeSpecificMat(faster)
    /// 
    /// 
    public static void Search_TypeSpecific(Mat src)
    {
        Mat mat3 = new Mat(src);
        var indexer = mat3.GetIndexer();
        for (int y = 0; y < src.Height; y++)
        {
            for (int x = 0; x < src.Width; x++)
            {
                Vec3b color = indexer[y, x];
                byte temp = color.Item0;
                color.Item0 = color.Item2; // B <- R
                color.Item2 = temp;        // R <- B
                indexer[y, x] = color;
            }
        }
    }

4 矩阵与其他图片数据的转换

        利用 OpenCvSharp 进行计算或图片处理后，图片需要以各种方式予以体现，因而需要将 Mat 转为其他格式的图片信息，或反其道行之。

        

4.1 Mat -> System.Drawing.Bitmap

using OpenCvSharp;
using OpenCvSharp.Extensions;

Mat mat = new Mat("demo.jpg", ImreadModes.Color);
Bitmap bitmap = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(mat);

4.2 System.Drawing.Bitmap -> Mat

using OpenCvSharp;
using OpenCvSharp.Extensions;

Bitmap bitmap = new Bitmap("demo.png");
Mat mat = OpenCvSharp.Extensions.BitmapConverter.ToMat(bitmap);

4.3 Mat -> byte[]

Mat mat = new Mat("demo.png", ImreadModes.Color);
byte[] bytes1 = mat.ToBytes(".png");

// or 

Cv2.ImEncode(".png", mat, out byte[] bytes2);

4.4 彩色图转灰度图或其他

        常用的函数是 

Cv2.CvtColor(Mat src, Mat dst, ColorConversionCodes code, int dstCn: 0);

        常见的实例：

/// 

/// 转为灰色图（8 bit）
/// 
/// 
/// 
public static Mat ToGray(Mat src)
{
    Mat dst = new Mat();
    // 转为灰度图 但通道 8 bit (必须)
    Cv2.CvtColor(src, dst, ColorConversionCodes.BGR2GRAY);
    return dst;
}

         其中的 ColorConversionCodes 枚举类型极多，记住几个常用的即可。

enum ColorConversionCodes {
    COLOR_BGR2BGRA     = 0, //!< add alpha channel to RGB or BGR image
    COLOR_RGB2RGBA     = COLOR_BGR2BGRA,
    COLOR_BGRA2BGR     = 1, //!< remove alpha channel from RGB or BGR image
    COLOR_RGBA2RGB     = COLOR_BGRA2BGR,
    COLOR_BGR2RGBA     = 2, //!< convert between RGB and BGR color spaces (with or without alpha channel)
    COLOR_RGB2BGRA     = COLOR_BGR2RGBA,
    COLOR_RGBA2BGR     = 3,
    COLOR_BGRA2RGB     = COLOR_RGBA2BGR,
    COLOR_BGR2RGB      = 4,
    COLOR_RGB2BGR      = COLOR_BGR2RGB,
    COLOR_BGRA2RGBA    = 5,
    COLOR_RGBA2BGRA    = COLOR_BGRA2RGBA,
    COLOR_BGR2GRAY     = 6, //!< convert between RGB/BGR and grayscale, @ref color_convert_rgb_gray "color conversions"
    COLOR_RGB2GRAY     = 7,
    COLOR_GRAY2BGR     = 8,
    COLOR_GRAY2RGB     = COLOR_GRAY2BGR,
    COLOR_GRAY2BGRA    = 9,
    COLOR_GRAY2RGBA    = COLOR_GRAY2BGRA,
    COLOR_BGRA2GRAY    = 10,
    COLOR_RGBA2GRAY    = 11,
    COLOR_BGR2BGR565   = 12, //!< convert between RGB/BGR and BGR565 (16-bit images)
    COLOR_RGB2BGR565   = 13,
    COLOR_BGR5652BGR   = 14,
    COLOR_BGR5652RGB   = 15,
    COLOR_BGRA2BGR565  = 16,
    COLOR_RGBA2BGR565  = 17,
    COLOR_BGR5652BGRA  = 18,
    COLOR_BGR5652RGBA  = 19,
    COLOR_GRAY2BGR565  = 20, //!< convert between grayscale to BGR565 (16-bit images)
    COLOR_BGR5652GRAY  = 21,
    COLOR_BGR2BGR555   = 22,  //!< convert between RGB/BGR and BGR555 (16-bit images)
    COLOR_RGB2BGR555   = 23,
    COLOR_BGR5552BGR   = 24,
    COLOR_BGR5552RGB   = 25,
    COLOR_BGRA2BGR555  = 26,
    COLOR_RGBA2BGR555  = 27,
    COLOR_BGR5552BGRA  = 28,
    COLOR_BGR5552RGBA  = 29,
    COLOR_GRAY2BGR555  = 30, //!< convert between grayscale and BGR555 (16-bit images)
    COLOR_BGR5552GRAY  = 31,
    COLOR_BGR2XYZ      = 32, //!< convert RGB/BGR to CIE XYZ, @ref color_convert_rgb_xyz "color conversions"
    COLOR_RGB2XYZ      = 33,
    COLOR_XYZ2BGR      = 34,
    COLOR_XYZ2RGB      = 35,
    COLOR_BGR2YCrCb    = 36, //!< convert RGB/BGR to luma-chroma (aka YCC), @ref color_convert_rgb_ycrcb "color conversions"
    COLOR_RGB2YCrCb    = 37,
    COLOR_YCrCb2BGR    = 38,
    COLOR_YCrCb2RGB    = 39,
    COLOR_BGR2HSV      = 40, //!< convert RGB/BGR to HSV (hue saturation value), @ref color_convert_rgb_hsv "color conversions"
    COLOR_RGB2HSV      = 41,
    COLOR_BGR2Lab      = 44, //!< convert RGB/BGR to CIE Lab, @ref color_convert_rgb_lab "color conversions"
    COLOR_RGB2Lab      = 45,
    COLOR_BGR2Luv      = 50, //!< convert RGB/BGR to CIE Luv, @ref color_convert_rgb_luv "color conversions"
    COLOR_RGB2Luv      = 51,
    COLOR_BGR2HLS      = 52, //!< convert RGB/BGR to HLS (hue lightness saturation), @ref color_convert_rgb_hls "color conversions"
    COLOR_RGB2HLS      = 53,
    COLOR_HSV2BGR      = 54, //!< backward conversions to RGB/BGR
    COLOR_HSV2RGB      = 55,
    COLOR_Lab2BGR      = 56,
    COLOR_Lab2RGB      = 57,
    COLOR_Luv2BGR      = 58,
    COLOR_Luv2RGB      = 59,
    COLOR_HLS2BGR      = 60,
    COLOR_HLS2RGB      = 61,
    COLOR_BGR2HSV_FULL = 66,
    COLOR_RGB2HSV_FULL = 67,
    COLOR_BGR2HLS_FULL = 68,
    COLOR_RGB2HLS_FULL = 69,
    COLOR_HSV2BGR_FULL = 70,
    COLOR_HSV2RGB_FULL = 71,
    COLOR_HLS2BGR_FULL = 72,
    COLOR_HLS2RGB_FULL = 73,
    COLOR_LBGR2Lab     = 74,
    COLOR_LRGB2Lab     = 75,
    COLOR_LBGR2Luv     = 76,
    COLOR_LRGB2Luv     = 77,
    COLOR_Lab2LBGR     = 78,
    COLOR_Lab2LRGB     = 79,
    COLOR_Luv2LBGR     = 80,
    COLOR_Luv2LRGB     = 81,
    COLOR_BGR2YUV      = 82, //!< convert between RGB/BGR and YUV
    COLOR_RGB2YUV      = 83,
    COLOR_YUV2BGR      = 84,
    COLOR_YUV2RGB      = 85,
    //! YUV 4:2:0 family to RGB
    COLOR_YUV2RGB_NV12  = 90,
    COLOR_YUV2BGR_NV12  = 91,
    COLOR_YUV2RGB_NV21  = 92,
    COLOR_YUV2BGR_NV21  = 93,
    COLOR_YUV420sp2RGB  = COLOR_YUV2RGB_NV21,
    COLOR_YUV420sp2BGR  = COLOR_YUV2BGR_NV21,
    COLOR_YUV2RGBA_NV12 = 94,
    COLOR_YUV2BGRA_NV12 = 95,
    COLOR_YUV2RGBA_NV21 = 96,
    COLOR_YUV2BGRA_NV21 = 97,
    COLOR_YUV420sp2RGBA = COLOR_YUV2RGBA_NV21,
    COLOR_YUV420sp2BGRA = COLOR_YUV2BGRA_NV21,
    COLOR_YUV2RGB_YV12  = 98,
    COLOR_YUV2BGR_YV12  = 99,
    COLOR_YUV2RGB_IYUV  = 100,
    COLOR_YUV2BGR_IYUV  = 101,
    COLOR_YUV2RGB_I420  = COLOR_YUV2RGB_IYUV,
    COLOR_YUV2BGR_I420  = COLOR_YUV2BGR_IYUV,
    COLOR_YUV420p2RGB   = COLOR_YUV2RGB_YV12,
    COLOR_YUV420p2BGR   = COLOR_YUV2BGR_YV12,
    COLOR_YUV2RGBA_YV12 = 102,
    COLOR_YUV2BGRA_YV12 = 103,
    COLOR_YUV2RGBA_IYUV = 104,
    COLOR_YUV2BGRA_IYUV = 105,
    COLOR_YUV2RGBA_I420 = COLOR_YUV2RGBA_IYUV,
    COLOR_YUV2BGRA_I420 = COLOR_YUV2BGRA_IYUV,
    COLOR_YUV420p2RGBA  = COLOR_YUV2RGBA_YV12,
    COLOR_YUV420p2BGRA  = COLOR_YUV2BGRA_YV12,
    COLOR_YUV2GRAY_420  = 106,
    COLOR_YUV2GRAY_NV21 = COLOR_YUV2GRAY_420,
    COLOR_YUV2GRAY_NV12 = COLOR_YUV2GRAY_420,
    COLOR_YUV2GRAY_YV12 = COLOR_YUV2GRAY_420,
    COLOR_YUV2GRAY_IYUV = COLOR_YUV2GRAY_420,
    COLOR_YUV2GRAY_I420 = COLOR_YUV2GRAY_420,
    COLOR_YUV420sp2GRAY = COLOR_YUV2GRAY_420,
    COLOR_YUV420p2GRAY  = COLOR_YUV2GRAY_420,
    //! YUV 4:2:2 family to RGB
    COLOR_YUV2RGB_UYVY = 107,
    COLOR_YUV2BGR_UYVY = 108,
    //COLOR_YUV2RGB_VYUY = 109,
    //COLOR_YUV2BGR_VYUY = 110,
    COLOR_YUV2RGB_Y422 = COLOR_YUV2RGB_UYVY,
    COLOR_YUV2BGR_Y422 = COLOR_YUV2BGR_UYVY,
    COLOR_YUV2RGB_UYNV = COLOR_YUV2RGB_UYVY,
    COLOR_YUV2BGR_UYNV = COLOR_YUV2BGR_UYVY,
    COLOR_YUV2RGBA_UYVY = 111,
    COLOR_YUV2BGRA_UYVY = 112,
    //COLOR_YUV2RGBA_VYUY = 113,
    //COLOR_YUV2BGRA_VYUY = 114,
    COLOR_YUV2RGBA_Y422 = COLOR_YUV2RGBA_UYVY,
    COLOR_YUV2BGRA_Y422 = COLOR_YUV2BGRA_UYVY,
    COLOR_YUV2RGBA_UYNV = COLOR_YUV2RGBA_UYVY,
    COLOR_YUV2BGRA_UYNV = COLOR_YUV2BGRA_UYVY,
    COLOR_YUV2RGB_YUY2 = 115,
    COLOR_YUV2BGR_YUY2 = 116,
    COLOR_YUV2RGB_YVYU = 117,
    COLOR_YUV2BGR_YVYU = 118,
    COLOR_YUV2RGB_YUYV = COLOR_YUV2RGB_YUY2,
    COLOR_YUV2BGR_YUYV = COLOR_YUV2BGR_YUY2,
    COLOR_YUV2RGB_YUNV = COLOR_YUV2RGB_YUY2,
    COLOR_YUV2BGR_YUNV = COLOR_YUV2BGR_YUY2,
    COLOR_YUV2RGBA_YUY2 = 119,
    COLOR_YUV2BGRA_YUY2 = 120,
    COLOR_YUV2RGBA_YVYU = 121,
    COLOR_YUV2BGRA_YVYU = 122,
    COLOR_YUV2RGBA_YUYV = COLOR_YUV2RGBA_YUY2,
    COLOR_YUV2BGRA_YUYV = COLOR_YUV2BGRA_YUY2,
    COLOR_YUV2RGBA_YUNV = COLOR_YUV2RGBA_YUY2,
    COLOR_YUV2BGRA_YUNV = COLOR_YUV2BGRA_YUY2,
    COLOR_YUV2GRAY_UYVY = 123,
    COLOR_YUV2GRAY_YUY2 = 124,
    //CV_YUV2GRAY_VYUY    = CV_YUV2GRAY_UYVY,
    COLOR_YUV2GRAY_Y422 = COLOR_YUV2GRAY_UYVY,
    COLOR_YUV2GRAY_UYNV = COLOR_YUV2GRAY_UYVY,
    COLOR_YUV2GRAY_YVYU = COLOR_YUV2GRAY_YUY2,
    COLOR_YUV2GRAY_YUYV = COLOR_YUV2GRAY_YUY2,
    COLOR_YUV2GRAY_YUNV = COLOR_YUV2GRAY_YUY2,
    //! alpha premultiplication
    COLOR_RGBA2mRGBA    = 125,
    COLOR_mRGBA2RGBA    = 126,
    //! RGB to YUV 4:2:0 family
    COLOR_RGB2YUV_I420  = 127,
    COLOR_BGR2YUV_I420  = 128,
    COLOR_RGB2YUV_IYUV  = COLOR_RGB2YUV_I420,
    COLOR_BGR2YUV_IYUV  = COLOR_BGR2YUV_I420,
    COLOR_RGBA2YUV_I420 = 129,
    COLOR_BGRA2YUV_I420 = 130,
    COLOR_RGBA2YUV_IYUV = COLOR_RGBA2YUV_I420,
    COLOR_BGRA2YUV_IYUV = COLOR_BGRA2YUV_I420,
    COLOR_RGB2YUV_YV12  = 131,
    COLOR_BGR2YUV_YV12  = 132,
    COLOR_RGBA2YUV_YV12 = 133,
    COLOR_BGRA2YUV_YV12 = 134,
    //! Demosaicing
    COLOR_BayerBG2BGR = 46,
    COLOR_BayerGB2BGR = 47,
    COLOR_BayerRG2BGR = 48,
    COLOR_BayerGR2BGR = 49,
    COLOR_BayerBG2RGB = COLOR_BayerRG2BGR,
    COLOR_BayerGB2RGB = COLOR_BayerGR2BGR,
    COLOR_BayerRG2RGB = COLOR_BayerBG2BGR,
    COLOR_BayerGR2RGB = COLOR_BayerGB2BGR,
    COLOR_BayerBG2GRAY = 86,
    COLOR_BayerGB2GRAY = 87,
    COLOR_BayerRG2GRAY = 88,
    COLOR_BayerGR2GRAY = 89,
    //! Demosaicing using Variable Number of Gradients
    COLOR_BayerBG2BGR_VNG = 62,
    COLOR_BayerGB2BGR_VNG = 63,
    COLOR_BayerRG2BGR_VNG = 64,
    COLOR_BayerGR2BGR_VNG = 65,

    COLOR_BayerBG2RGB_VNG = COLOR_BayerRG2BGR_VNG,
    COLOR_BayerGB2RGB_VNG = COLOR_BayerGR2BGR_VNG,
    COLOR_BayerRG2RGB_VNG = COLOR_BayerBG2BGR_VNG,
    COLOR_BayerGR2RGB_VNG = COLOR_BayerGB2BGR_VNG,
    //! Edge-Aware Demosaicing
    COLOR_BayerBG2BGR_EA  = 135,
    COLOR_BayerGB2BGR_EA  = 136,
    COLOR_BayerRG2BGR_EA  = 137,
    COLOR_BayerGR2BGR_EA  = 138,
    COLOR_BayerBG2RGB_EA  = COLOR_BayerRG2BGR_EA,
    COLOR_BayerGB2RGB_EA  = COLOR_BayerGR2BGR_EA,
    COLOR_BayerRG2RGB_EA  = COLOR_BayerBG2BGR_EA,
    COLOR_BayerGR2RGB_EA  = COLOR_BayerGB2BGR_EA,
    //! Demosaicing with alpha channel
    COLOR_BayerBG2BGRA = 139,
    COLOR_BayerGB2BGRA = 140,
    COLOR_BayerRG2BGRA = 141,
    COLOR_BayerGR2BGRA = 142,
    COLOR_BayerBG2RGBA = COLOR_BayerRG2BGRA,
    COLOR_BayerGB2RGBA = COLOR_BayerGR2BGRA,
    COLOR_BayerRG2RGBA = COLOR_BayerBG2BGRA,
    COLOR_BayerGR2RGBA = COLOR_BayerGB2BGRA,
    COLOR_COLORCVT_MAX  = 143
};

POWER BY 多可文档管理系统