对比使用C# unsafe代码和OpenCV进行图像处理的效率(上)

        OpenCV是一套使用C/C++编写的开源计算机视觉库,全称Open Computer Vision,因其高效、全面,在计算机视觉领域应用极广。其在C#下的包装有多种,最常用的是Emgu。


        本人最近在写一套计算机视觉处理软件,用的就是C# + Emgu,因为用到的OpenCV方法就那么几个(大概10多个),为了这些为数不多的方法而带着数MB的Emgu DLL,心里很是不爽,于是乎萌生了将这些方法全部用C# unsafe代码重写的想法,反正OpenCV是开源的,算法可以写成一样的,效率上应该差不到哪去。


下面是我自己写的一个图像类:

    ///<summary>灰度图像处理类,作者:wmesci</summary>
    unsafe class Image :CriticalHandle,  IDisposable
    {
        [DllImport("kernel32.dll")]
        static extern IntPtr LocalAlloc(int flags, int size);

        [DllImport("kernel32.dll")]
        static extern IntPtr LocalFree(IntPtr memBlock);

        [DllImport("kernel32.dll", EntryPoint = "RtlMoveMemory")]
        static extern unsafe void CopyMemory(byte* dst, byte* src, int count);

        [DllImport("kernel32.dll", EntryPoint = "RtlMoveMemory")]
        static extern unsafe void CopyMemory(byte* dst, IntPtr src, int count);

        [DllImport("kernel32.dll", EntryPoint = "RtlMoveMemory")]
        static extern unsafe void CopyMemory(byte* dst, byte[] src, int count);

        const byte Max = 255;
        const byte Min = 0;

        public Image(int width, int height) 
            : base(IntPtr.Zero)
        {
            if (width <= 0 || height <= 0)
                throw new ArgumentOutOfRangeException();

            Width = width;
            Height = height;
            Length = Width * Height;
            base.SetHandle(LocalAlloc(0x40, width * height));

            Pointer = (byte*)handle.ToPointer();
        }

        public Image(int width, int height, byte[] dat) 
            : this(width, height)
        {
            if (dat != null)
            {
                CopyMemory(Pointer, dat, Length);
            }
        }

        public Image(int width, int height, byte* dat)
            : this(width, height)
        {
            CopyMemory(Pointer, dat, Length);
        }

        public Image(int width, int height, IntPtr dat)
            : this(width, height)
        {
            CopyMemory(Pointer, dat, Length);
        }

        public readonly int Width;

        public readonly int Height;

        public readonly int Length;

        public readonly byte* Pointer;

        public byte this[int x, int y] 
        {
            get
            {
                return *(Pointer + y * Width + x);
            }
            set
            {
                *(Pointer + y * Width + x) = value;
            }
        }

        public Image Clone()
        {
            return new Image(Width, Height, Pointer);
        }

        public void Add(Image img) 
        {
            Action<int> act = y =>
            {
                byte* p1 = Pointer + y * Width, p2 = (byte*)img.Pointer + y * img.Width;
                for (int x = 0; x < Width; x++, p1++, p2++)
                {
                    double d = *p1 + *p2;
                    if (d < 0)
                        *p1 = 0;
                    else if (d > 255)
                        *p1 = 255;
                    else
                        *p1 = (byte)d;
                }
            };
            Parallel.For(0, Height, act);
        }

        public void Sub(Image img) 
        {
            Action<int> act = y =>
            {
                byte* p1 = Pointer + y * Width, p2 = (byte*)img.Pointer + y * img.Width;
                for (int x = 0; x < Width; x++, p1++, p2++)
                {
                    double d = *p1 - *p2;
                    if (d < 0)
                        *p1 = 0;
                    else if (d > 255)
                        *p1 = 255;
                    else
                        *p1 = (byte)d;
                }
            };
            Parallel.For(0, Height, act);
        }

        public void Mul(Image img, double scale)
        {
            Action<int> act = y =>
            {
                byte* p1 = Pointer + y * Width, p2 = (byte*)img.Pointer + y * img.Width;
                for (int x = 0; x < Width; x++, p1++, p2++)
                {
                    double d = scale * *p1 * *p2;
                    if (d < 0)
                        *p1 = 0;
                    else if (d > 255)
                        *p1 = 255;
                    else
                        *p1 = (byte)d;
                }
            };
            Parallel.For(0, Height, act);
        }

        public void Threshold(byte threshold) 
        {
            Action<int> act = y => 
            {
                byte* p = Pointer + y * Width;
                for (int x = 0; x < Width; x++, p++)
                {
                    *p = *p > threshold ? Max : Min;
                }
            };
            Parallel.For(0, Height, act);
        }

        public void AddWeighted(Image img, double a, double b)
        {
            Action<int> act = y =>
            {
                byte* p1 = this.Pointer + y * this.Width, p2 = (byte*)img.Pointer + y * img.Width;
                for (int x = 0; x < this.Width; x++, p1++, p2++)
                {
                    double d = a * *p1 + b * *p2;
                    if (d < 0)
                        *p1 = 0;
                    else if (d > 255)
                        *p1 = 255;
                    else
                        *p1 = (byte)d;
                }
            };
            Parallel.For(0, this.Height, act);
        }

        public static void Smooth(Image src, Image dst, int n)
        {
            int* tmp = (int*)Marshal.AllocHGlobal(src.Width * src.Height * 4).ToPointer();
            Action<int> act = y =>
            {
                byte* p = src.Pointer + y * src.Width;
                int d = 0;
                for (int i = -n; i <= n; i++)
                {
                    int xx = GetIndex(i, src.Width);

                    d += p[xx];
                }
                tmp[y * src.Width] = d;
            };
            Parallel.For(0, src.Height, act);

            act = y =>
            {
                int i = y * src.Width;
                byte* p = src.Pointer + y * src.Width;
                for (int x = 1; x < src.Width; x++)
                {
                    int d = tmp[i];

                    int x1 = GetIndex(x - n - 1, src.Width);
                    int x2 = GetIndex(x + n, src.Width);

                    d += (p[x2] - p[x1]);

                    tmp[++i] = d;
                }
            };
            Parallel.For(0, src.Height, act);

            double f = 1.0 / (2 * n + 1);
            f *= f;

            act = x =>
            {
                int d = 0;
                byte* p = dst.Pointer + x;
                for (int j = -n; j <= n; j++)
                {
                    int yy = GetIndex(j, src.Height);

                    d += tmp[x + yy * src.Width];
                }
                *p = (byte)(d * f);
                p += src.Width;

                for (int y = 1; y < src.Height; y++, p += src.Width)
                {
                    int y1 = GetIndex(y - n - 1, src.Height);
                    int y2 = GetIndex(y + n, src.Height);

                    d += (tmp[x + y2 * src.Width] - tmp[x + y1 * src.Width]);

                    *p = (byte)(d * f);
                }
            };

            Parallel.For(0, src.Width, act);
            Marshal.FreeHGlobal(new IntPtr(tmp));
        }

        private static int GetIndex(int i, int max)
        {
            if (i < 0) return 0;
            if (i >= max) return max - 1;
            return i;
        }

        public override bool IsInvalid
        {
            get { return handle == IntPtr.Zero; }
        }

        protected override bool ReleaseHandle()
        {
            LocalFree(handle);
            return true;
        }
    }

(如有可以优化的地方,烦请指正)


        用WPF写了个简单的测试程序,其中运行时间使用Stopwatch计算,取其ElapsedTicks值。

        先看下运行环境:


        OpenCV使用2.2版本。测试图像大小为600*896,预先进行了灰度化,然后再计算处理时间。


        下面直接上结果:

1、Add:

      Image:imgt.Add(img)

      OpenCV:CvInvoke.cvAdd(img, img, img, IntPtr.Zero)

      各执行50次,取平均数:


Image花费时间3246,OpenCV花费时间1514


2、Sub:

      Image:imgt.Sub(img)

      OpenCV:CvInvoke.cvSub(img, img, img, IntPtr.Zero)

      各执行50次,取平均数:


Image花费时间3378,OpenCV花费时间1370


3、Mul:

      Image:imgt.Mul(img, 1)

      OpenCV:CvInvoke.cvMul(img, img, img, 1)

      各执行50次,取平均数:


Image花费时间3817,OpenCV花费时间7480


4、Threshold:

      Image:imgt.Threshold(120)

      OpenCV:CvInvoke.cvcvThreshold(img, img, 120, 255, Emgu.CV.CvEnum.THRESH.CV_THRESH_BINARY)

      各执行50次,取平均数:


Image花费时间1645,OpenCV花费时间1361


5、Smooth:

      Image:Image.Smooth(img, dst, 3)

      OpenCV:CvInvoke.cvSmooth(img, dst, Emgu.CV.CvEnum.SMOOTH_TYPE.CV_BLUR, 7, 0, 0, 0)

      各执行50次,取平均数:


Image花费时间17589,OpenCV花费时间33574


6、AddWeighted:

      Image:dst.AddWeighted(img, 0.4, 0.4)

      OpenCV:CvInvoke.cvAddWeighted(img, 0.4, img, 0.4, 0, dst)

      各执行50次,取平均数:


Image花费时间3952,OpenCV花费时间9845


总结一下:


从上表可以看出,Image类和OpenCV基本上是胜率对半。至于为什么,且听下回分解~~~


更新在另一台电脑上运行的结果:



Image惨败!!


        源码及测试代码下载地址:http://download.csdn.net/detail/wmesci/3841089

        相关讨论帖:http://topic.csdn.net/u/20111124/23/1F236D07-420E-4E2E-83EE-C9C29E689477.html


posted @ 2011-11-24 22:49  RayTracer  阅读(3774)  评论(0编辑  收藏  举报