编写高效的C#图像处理程序——我的实验

前些天阅读《各种图像处理类库的比较及选择(The Comparison of Image Processing Libraries)》，对后面的比较结果感觉怪异。对计算密集型运算，C#和C/C++的性能应该差别不大才是。为了探讨问题，做了以下实验。

本实验比较了五种方式进行图像灰度化计算：

（1）EmguCV实现，见 《各种图像处理类库的比较及选择(The Comparison of Image Processing Libraries)》 文中代码

（2）OpenCV/PInvoke实现，见 《各种图像处理类库的比较及选择(The Comparison of Image Processing Libraries)》 文中代码

（3）BitmapData实现，见 《各种图像处理类库的比较及选择(The Comparison of Image Processing Libraries)》 文中代码

（4）Array实现（ArgbImage8），核心代码如下:

（每一个）ImageChannel8 内含1个Byte数组Data。GrayscaleImage8  继承自 ImageChannel8 。

    public class ArgbImage8 : ImageChannelSet8
    {
        public ImageChannel8 A { get { return this.Channels[0]; } }
        public ImageChannel8 R { get { return this.Channels[0]; } }
        public ImageChannel8 G { get { return this.Channels[0]; } }
        public ImageChannel8 B { get { return this.Channels[0]; } }

        public ArgbImage8(int width, int height)
            : base(4, width, height)
        {
        }

        public GrayscaleImage8 ToGrayscaleImage()
        {
            return ToGrayscaleImage(0.299, 0.587, 0.114);
        }

        public GrayscaleImage8 ToGrayscaleImage(double rCoeff, double gCoeff, double bCoeff)
        {
            GrayscaleImage8 img = new GrayscaleImage8(this.Width, this.Height);
            Byte[] r = R.Data;
            Byte[] g = G.Data;
            Byte[] b = B.Data;
            Byte[] dst = img.Data;

            for (int i = 0; i < r.Length; i++)
            {
                dst[i] = (Byte)(r[i] * rCoeff + g[i] * gCoeff + b[i] * bCoeff);
            }

            return img;
        }

        //性能低下，先这样写了
        public static ArgbImage8 CreateFromBitmap(Bitmap map)
        {
            if (map == null) throw new ArgumentNullException("map");

            ArgbImage8 img = new ArgbImage8(map.Width, map.Height);
            Byte[] a = img.A.Data;
            Byte[] r = img.R.Data;
            Byte[] g = img.G.Data;
            Byte[] b = img.B.Data;

            for (int row = 0; row < img.Height; row++)
            {
                for (int col = 0; col < img.Width; col++)
                {
                    int index = row * img.Width + col;
                    Color c = map.GetPixel(col, row);
                    a[index] = c.A;
                    r[index] = c.R;
                    r[index] = c.R;
                    r[index] = c.R;
                }
            }

            return img;
        }
    }

（5）C# 指针/unsafe 实现（ArgbImage32 ），核心代码如下：

    public class UnmanagedMemory : IDisposable
        where T : struct
    {
        public Int32 ByteCount { get; private set; }
        public Int32 Length { get; private set; }
        public IntPtr Start { get; private set; }
        public Int32 SizeOfType { get; private set; }

        public UnmanagedMemory(Int32 length)
        {
            Length = length;
            SizeOfType = SizeOfT();
            ByteCount = SizeOfType * length;
            Start = Marshal.AllocHGlobal(ByteCount);
        }

        public void Dispose()
        {
            Dispose(true);
            GC.SuppressFinalize(this);
        }

        protected virtual void Dispose(bool disposing)
        {
            if (false == disposed)
            {
                 disposed = true;
                 Marshal.FreeHGlobal(Start);
            }
        }

        private bool disposed;

        ~UnmanagedMemory()
        {
            Dispose(false);
        }

        private Int32 SizeOfT()
        {
            return Marshal.SizeOf(typeof(T));
        }
    }

    public struct Argb32
    {
        public Byte Alpha;
        public Byte Red;
        public Byte Green;
        public Byte Blue;
    }

    public class Argb32Image : UnmanagedMemory
    {
        private unsafe Argb32* m_pointer;

        public unsafe Argb32* Pointer { get { return m_pointer; } }

        public unsafe Argb32Image(int length)
            : base(length)
        {
            m_pointer = (Argb32*)this.Start;
        }

        public unsafe Argb32 this[int index]
        {
            get { return *(m_pointer + index); }
            set { *(m_pointer + index) = value; }
        }

        public Grayscale8Image ToGrayscaleImage()
        {
            return ToGrayscaleImage(0.299, 0.587, 0.114);
        }

        public unsafe Grayscale8Image ToGrayscaleImage(double rCoeff, double gCoeff, double bCoeff)
        {
            Grayscale8Image img = new Grayscale8Image(this.Length);
            Argb32* p = Pointer;
            Byte* to = img.Pointer;
            Argb32* end = p + Length;

            while (p != end)
            {
                *to = (Byte)(p->Red * rCoeff + p->Green * gCoeff + p->Blue * bCoeff);
                p++;
                to++;
            }
            return img;
        }

        public unsafe static Argb32Image CreateFromBitmap(Bitmap map)
        {
            if (map == null) throw new ArgumentNullException("map");

            Argb32Image img = new Argb32Image(map.Width*map.Height);

            Argb32* p = img.Pointer;

            for (int row = 0; row < map.Height; row++)
            {
                for (int col = 0; col < map.Width; col++)
                {
                    Color c = map.GetPixel(col, row);
                    p->Alpha = c.A;
                    p->Red = c.R;
                    p->Green = c.G;
                    p->Blue = c.B;
                    p++;
                }
            }

            return img;
        }
    }

机器配置：

在每个方法测试前，均运行一段DoSomething（）清空高速缓存：

private static int[] DoSomething()
        {
            int[] data = new Int32[20000000];
            for (int i = 0; i < data.Length; i++)
            {
                data[i] = i;
            }
            return data;
        }

测试结果（每个执行5次，计算耗时总和。单位ms）:

图像1——

BitmapData:53
ArgbImage8:80
ArgbImage32:38
EmguCV:68
OpenCV:69

图像2——

BitmapData:25
ArgbImage8:45
ArgbImage32:19
EmguCV:42
OpenCV:45

图像3——

BitmapData:8
ArgbImage8:25
ArgbImage32:6
EmguCV:23
OpenCV:24

图像4——

BitmapData:48
ArgbImage8:76
ArgbImage32:39
EmguCV:67
OpenCV:69

图像5（大图：5000×6000）——

BitmapData:1584
ArgbImage8:1991
ArgbImage32:1229
EmguCV:1545
OpenCV:2817

下面删去ArgbImage8，仅比较剩下的4种（每个执行5次，计算耗时总和。单位ms）：

图像6——

BitmapData:17
ArgbImage32:10
EmguCV:25
OpenCV:25

图像7——

BitmapData:88
ArgbImage32:56
EmguCV:69
OpenCV:70

图像8——

BitmapData:41
ArgbImage32:25
EmguCV:40
OpenCV:43

图像5（大图：5000×6000）——

BitmapData:2855
ArgbImage32:1849
EmguCV:1578
OpenCV:2522

下面，把执行顺序颠倒一下，让EmguCV和OpenCV在前面。剩下的2个在后面：

图像8——

EmguCV:41
OpenCV:42
BitmapData:38
ArgbImage32:26

图像9——

EmguCV:32
OpenCV:34
BitmapData:28
ArgbImage32:18

好了，不做试验了。根据上面结果，再考虑到纯C/C++程序比P/Invoke程序性能高一些，可得出这样的结论（在我的机器上）：

（1）C#不直接用指针比P/Invoke 的 C/C++程序低效一些。

（2）C#直接用指针，可以写出非常高效的程序，至少比P/Invoke高效。从上面的代码可看出，C#下指针用很舒服，并且编译快。猜想：C#下玩指针+Struct，和C没啥区别。图像处理这样的基本类型简单的程序，非常适合用C#编写。大量用指针，大量用非托管内存，可以最大化性能，最小化内存占用，最小化GC对程序的影响，达到和C/C++所差无几的性能。

下面尝试直接使用硬件。对图像处理加速最有效果的是GPU，好吧，下面就尝试调用GPU的功能。

如何在无界面的情况下调用GPU呢？

下面是我写的一个测试程序（需要引用XNA）：

using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.Xna.Framework;
using Microsoft.Xna.Framework.Audio;
using Microsoft.Xna.Framework.Content;
using Microsoft.Xna.Framework.GamerServices;
using Microsoft.Xna.Framework.Graphics;
using Microsoft.Xna.Framework.Input;
using Microsoft.Xna.Framework.Media;
using Microsoft.Xna.Framework.Net;
using Microsoft.Xna.Framework.Storage;

namespace Orc.SmartImage.Xna
{
    public class Shader
    {
        private class GameHelper : Game
        {
            public void Init()
            {
                this.Initialize();
                GraphicsDeviceManager m = new GraphicsDeviceManager(this);
                m.ApplyChanges();
            }
        }

        private GameHelper m_helper;

        public GraphicsDevice GraphicsDevice { get; set; }

        public Shader(IntPtr hwnd)
        {
            m_helper = new GameHelper();
            m_helper.Init();
            this.GraphicsDevice = m_helper.GraphicsDevice;
        }

        public void Test()
        {
            RenderTarget2D tar = new RenderTarget2D(this.GraphicsDevice, 100, 100, 1, SurfaceFormat.Color);
            this.GraphicsDevice.SetRenderTarget(0, tar);
            this.GraphicsDevice.Clear(Color.Yellow);
            this.GraphicsDevice.SetRenderTarget(0, null);
            Texture2D txt = tar.GetTexture();
            uint[] data = new uint[10000];
            txt.GetData(data);

            return;
        }
    }
}

 

进一步就是写HLSL了。

 

============================

离C/C++又远了一步。

附：具体测试代码

（注：那个Shader是我测试GPU计算能否通过的部分。IntPtr hwnd是因为GraphicsDevice构造函数中有这样一个参数，不过后来，我绕了过去，但测试程序这里我没删掉，还留在这里。）

using System;
using System.Collections.Generic;
using System.Runtime.InteropServices;
using System.Diagnostics;
using System.Linq;
using System.Text;
using System.Drawing;
using System.Drawing.Imaging;
using Orc.SmartImage;
using Emgu.CV;
using Emgu.CV.Structure;
using Emgu.CV.CvEnum;
using Orc.SmartImage.Gpu;
using Orc.SmartImage.UnmanagedObjects;

namespace Orc.SmartImage.PerformanceTest
{
    public class PerformanceTestCase0
    {
        public static String Test(IntPtr hwnd, Bitmap src, int count)
        {
            Shader sd = new Shader(hwnd);

//            ArgbImage8 img8 = ArgbImage8.CreateFromBitmap(src);
            Argb32Image img32 = Argb32Image.CreateFromBitmap(src);

            StringBuilder sb = new StringBuilder();
            Stopwatch sw = new Stopwatch();

            DoSomething();

            sw.Reset();
            sw.Start();
            for (int i = 0; i < count; i++)
                ProcessImageWithEmgucv(src);
            sw.Stop();
            sb.AppendLine("EmguCV:" + sw.ElapsedMilliseconds.ToString());

            DoSomething();
            sw.Reset();
            sw.Start();
            for (int i = 0; i < count; i++)
                ProcessImageWithOpencv(src);
            sw.Stop();
            sb.AppendLine("OpenCV:" + sw.ElapsedMilliseconds.ToString());

            DoSomething();

            sw.Reset();
            sw.Start();
            for (int i = 0; i < count; i++)
                Grayscale(src);
            sw.Stop();
            sb.AppendLine("BitmapData:" + sw.ElapsedMilliseconds.ToString());

            //DoSomething();
            //sw.Reset();
            //sw.Start();
            //for (int i = 0; i < count; i++)
            //    img8.ToGrayscaleImage();
            //sw.Stop();
            //sb.AppendLine("ArgbImage8:" + sw.ElapsedMilliseconds.ToString());

            DoSomething();
            sw.Reset();
            sw.Start();
            for (int i = 0; i < count; i++)
                img32.ToGrayscaleImage();
            sw.Stop();
            sb.AppendLine("ArgbImage32:" + sw.ElapsedMilliseconds.ToString());

           

            //sw.Reset();
            //sw.Start();
            //for (int i = 0; i < count; i++)
            //    img8.ToGrayscaleImage();
            //sw.Stop();
            //sb.AppendLine("ArgbImage8:" + sw.ElapsedMilliseconds.ToString());

            

            return sb.ToString();
        }

        private static int[] DoSomething()
        {
            int[] data = new Int32[20000000];
            for (int i = 0; i < data.Length; i++)
            {
                data[i] = i;
            }
            return data;
        }

        private static GrayscaleImage TestMyConvert(ArgbImage img)
        {
            return img.ToGrayscaleImage();
        }

        /// 

        /// 使用EmguCv处理图像
        /// 
        private static void ProcessImageWithEmgucv(Bitmap bitmapSource)
        {
            //灰度
            Image imageSource = new Imagebyte>(bitmapSource);
            Image imageGrayscale = imageSource.Convert();
        }

        /// 

        /// 使用Open Cv P/Invoke处理图像
        /// 
        unsafe private static void ProcessImageWithOpencv(Bitmap bitmapSource)
        {
            Image imageSource = new Imagebyte>(bitmapSource);
            IntPtr ptrSource = Marshal.AllocHGlobal(Marshal.SizeOf(typeof(MIplImage)));
            Marshal.StructureToPtr(imageSource.MIplImage, ptrSource, true);
            IntPtr ptrGrayscale = CvInvoke.cvCreateImage(imageSource.Size, IPL_DEPTH.IPL_DEPTH_8U, 1);
            CvInvoke.cvCvtColor(ptrSource, ptrGrayscale, COLOR_CONVERSION.CV_BGR2GRAY);
        }



        /// 

        /// 将指定图像转换成灰度图
        /// 
        /// 源图像支持3通道或者4通道图像，支持Format24bppRgb、Format32bppRgb和Format32bppArgb这3种像素格式
        /// 返回灰度图，如果转化失败，返回null。
        private static Bitmap Grayscale(Bitmap bitmapSource)
        {
            Bitmap bitmapGrayscale = null;
            if (bitmapSource != null && (bitmapSource.PixelFormat == PixelFormat.Format24bppRgb || bitmapSource.PixelFormat == PixelFormat.Format32bppArgb || bitmapSource.PixelFormat == PixelFormat.Format32bppRgb))
            {
                int width = bitmapSource.Width;
                int height = bitmapSource.Height;
                Rectangle rect = new Rectangle(0, 0, width, height);
                bitmapGrayscale = new Bitmap(width, height, PixelFormat.Format8bppIndexed);
                //设置调色板
                ColorPalette palette = bitmapGrayscale.Palette;
                for (int i = 0; i < palette.Entries.Length; i++)
                    palette.Entries[i] = Color.FromArgb(255, i, i, i);
                bitmapGrayscale.Palette = palette;
                BitmapData dataSource = bitmapSource.LockBits(rect, ImageLockMode.ReadOnly, bitmapSource.PixelFormat);
                BitmapData dataGrayscale = bitmapGrayscale.LockBits(rect, ImageLockMode.WriteOnly, PixelFormat.Format8bppIndexed);
                byte b, g, r;
                int strideSource = dataSource.Stride;
                int strideGrayscale = dataGrayscale.Stride;
                unsafe
                {
                    byte* ptrSource = (byte*)dataSource.Scan0.ToPointer();
                    byte* ptr1;
                    byte* ptrGrayscale = (byte*)dataGrayscale.Scan0.ToPointer();
                    byte* ptr2;
                    if (bitmapSource.PixelFormat == PixelFormat.Format24bppRgb)
                    {
                        for (int row = 0; row < height; row++)
                        {
                            ptr1 = ptrSource + strideSource * row;
                            ptr2 = ptrGrayscale + strideGrayscale * row;
                            for (int col = 0; col < width; col++)
                            {
                                b = *ptr1;
                                ptr1++;
                                g = *ptr1;
                                ptr1++;
                                r = *ptr1;
                                ptr1++;
                                *ptr2 = (byte)(0.114 * b + 0.587 * g + 0.299 * r);
                                ptr2++;
                            }
                        }
                    }
                    else    //bitmapSource.PixelFormat == PixelFormat.Format32bppArgb || bitmapSource.PixelFormat == PixelFormat.Format32bppRgb
                    {
                        for (int row = 0; row < height; row++)
                        {
                            ptr1 = ptrSource + strideGrayscale * row;
                            ptr2 = ptrGrayscale + strideGrayscale * row;
                            for (int col = 0; col < width; col++)
                            {
                                b = *ptr1;
                                ptr1++;
                                g = *ptr1;
                                ptr1++;
                                r = *ptr1;
                                ptr1 += 2;
                                *ptr2 = (byte)(0.114 * b + 0.587 * g + 0.299 * r);
                                ptr2++;
                            }
                        }
                    }
                }
                bitmapGrayscale.UnlockBits(dataGrayscale);
                bitmapSource.UnlockBits(dataSource);
            }
            return bitmapGrayscale;
        }

    }
}

转载于:https://www.cnblogs.com/xiaotie/archive/2010/03/08/1680662.html