C#和C++哪个更高效?五方式图像计算实验-技术开发专区

C#和C++哪个更高效?五方式图像计算实验

作者：xiaotie博客编辑：胡铭娅 2010-03-09 11:51 来源：xiaotie

　　【IT168 技术】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<T> : 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<Argb32>
    {
        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;
        }
    }

第1页：五种方式进行图像灰度化计算第2页：测试结果第3页：一个测试程序(需要引用XNA)第4页：具体测试代码

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