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编写高效的C#图像处理程序(3) Rgb=>Lab，图像缺陷检测的例子

2011-01-13 02:12 901 查看

最近项目需要检测图像是否存在偏色、过亮、模糊等缺陷。由于主要用在视频监控上，对性能要求比较高。有几项检测必须要在Lab彩色下进行，而众所周知Rgb => Lab 计算量较大，C#搞得定搞不定？测试表明，用纯C#编写的Rgb => Lab代码在性能上与C编写的Rgb => Lab代码极为接近。

1. Rgb24和Lab24

Rgb是电脑上使用较多的彩色空间，Lab是针对人的感知设计的均匀彩色空间，很多情况下进行彩色图像分析，需要在Rgb彩色空间和Lab彩色空间之间进行转化。关于Lab彩色空间的详细介绍和Rgb空间与Lab空间的转换公式见维基百科的对应词条 Lab色彩空间，本文不再叙述。

使用Rgb24和Lab24两个struct定义Rgb彩色空间的像素和Lab彩色空间的像素。

Rgb24 <=> Lab24 1 public sealed class UnmanagedImageConverter
2 {
3     /* 1024*(([0..511]./255)**(1./3)) */
4     static ushort[] icvLabCubeRootTab = new ushort[] {
5     0,161,203,232,256,276,293,308,322,335,347,359,369,379,389,398,
6     406,415,423,430,438,445,452,459,465,472,478,484,490,496,501,507,
7     512,517,523,528,533,538,542,547,552,556,561,565,570,574,578,582,
8     586,590,594,598,602,606,610,614,617,621,625,628,632,635,639,642,
9     645,649,652,655,659,662,665,668,671,674,677,680,684,686,689,692,
10     695,698,701,704,707,710,712,715,718,720,723,726,728,731,734,736,
11     739,741,744,747,749,752,754,756,759,761,764,766,769,771,773,776,
12     778,780,782,785,787,789,792,794,796,798,800,803,805,807,809,811,
13     813,815,818,820,822,824,826,828,830,832,834,836,838,840,842,844,
14     846,848,850,852,854,856,857,859,861,863,865,867,869,871,872,874,
15     876,878,880,882,883,885,887,889,891,892,894,896,898,899,901,903,
16     904,906,908,910,911,913,915,916,918,920,921,923,925,926,928,929,
17     931,933,934,936,938,939,941,942,944,945,947,949,950,952,953,955,
18     956,958,959,961,962,964,965,967,968,970,971,973,974,976,977,979,
19     980,982,983,985,986,987,989,990,992,993,995,996,997,999,1000,1002,
20     1003,1004,1006,1007,1009,1010,1011,1013,1014,1015,1017,1018,1019,1021,1022,1024,
21     1025,1026,1028,1029,1030,1031,1033,1034,1035,1037,1038,1039,1041,1042,1043,1044,
22     1046,1047,1048,1050,1051,1052,1053,1055,1056,1057,1058,1060,1061,1062,1063,1065,
23     1066,1067,1068,1070,1071,1072,1073,1074,1076,1077,1078,1079,1081,1082,1083,1084,
24     1085,1086,1088,1089,1090,1091,1092,1094,1095,1096,1097,1098,1099,1101,1102,1103,
25     1104,1105,1106,1107,1109,1110,1111,1112,1113,1114,1115,1117,1118,1119,1120,1121,
26     1122,1123,1124,1125,1127,1128,1129,1130,1131,1132,1133,1134,1135,1136,1138,1139,
27     1140,1141,1142,1143,1144,1145,1146,1147,1148,1149,1150,1151,1152,1154,1155,1156,
28     1157,1158,1159,1160,1161,1162,1163,1164,1165,1166,1167,1168,1169,1170,1171,1172,
29     1173,1174,1175,1176,1177,1178,1179,1180,1181,1182,1183,1184,1185,1186,1187,1188,
30     1189,1190,1191,1192,1193,1194,1195,1196,1197,1198,1199,1200,1201,1202,1203,1204,
31     1205,1206,1207,1208,1209,1210,1211,1212,1213,1214,1215,1215,1216,1217,1218,1219,
32     1220,1221,1222,1223,1224,1225,1226,1227,1228,1229,1230,1230,1231,1232,1233,1234,
33     1235,1236,1237,1238,1239,1240,1241,1242,1242,1243,1244,1245,1246,1247,1248,1249,
34     1250,1251,1251,1252,1253,1254,1255,1256,1257,1258,1259,1259,1260,1261,1262,1263,
35     1264,1265,1266,1266,1267,1268,1269,1270,1271,1272,1273,1273,1274,1275,1276,1277,
36     1278,1279,1279,1280,1281,1282,1283,1284,1285,1285,1286,1287,1288,1289,1290,1291
37     };
38
39     const float labXr_32f = 0.433953f /* = xyzXr_32f / 0.950456 */;
40     const float labXg_32f = 0.376219f /* = xyzXg_32f / 0.950456 */;
41     const float labXb_32f = 0.189828f /* = xyzXb_32f / 0.950456 */;
42
43     const float labYr_32f = 0.212671f /* = xyzYr_32f */;
44     const float labYg_32f = 0.715160f /* = xyzYg_32f */;
45     const float labYb_32f = 0.072169f /* = xyzYb_32f */;
46
47     const float labZr_32f = 0.017758f /* = xyzZr_32f / 1.088754 */;
48     const float labZg_32f = 0.109477f /* = xyzZg_32f / 1.088754 */;
49     const float labZb_32f = 0.872766f /* = xyzZb_32f / 1.088754 */;
50
51     const float labRx_32f = 3.0799327f /* = xyzRx_32f * 0.950456 */;
52     const float labRy_32f = (-1.53715f) /* = xyzRy_32f */;
53     const float labRz_32f = (-0.542782f)/* = xyzRz_32f * 1.088754 */;
54
55     const float labGx_32f = (-0.921235f)/* = xyzGx_32f * 0.950456 */;
56     const float labGy_32f = 1.875991f   /* = xyzGy_32f */ ;
57     const float labGz_32f = 0.04524426f /* = xyzGz_32f * 1.088754 */;
58
59     const float labBx_32f = 0.0528909755f /* = xyzBx_32f * 0.950456 */;
60     const float labBy_32f = (-0.204043f) /* = xyzBy_32f */;
61     const float labBz_32f = 1.15115158f   /* = xyzBz_32f * 1.088754 */;
62
63     const float labT_32f = 0.008856f;
64
65     const int lab_shift = 10;
66
67     const float labLScale2_32f = 903.3f;
68
69     const int labXr = (int)((labXr_32f) * (1 << (lab_shift)) + 0.5);
70     const int labXg = (int)((labXg_32f) * (1 << (lab_shift)) + 0.5);
71     const int labXb = (int)((labXb_32f) * (1 << (lab_shift)) + 0.5);
72
73     const int labYr = (int)((labYr_32f) * (1 << (lab_shift)) + 0.5);
74     const int labYg = (int)((labYg_32f) * (1 << (lab_shift)) + 0.5);
75     const int labYb = (int)((labYb_32f) * (1 << (lab_shift)) + 0.5);
76
77     const int labZr = (int)((labZr_32f) * (1 << (lab_shift)) + 0.5);
78     const int labZg = (int)((labZg_32f) * (1 << (lab_shift)) + 0.5);
79     const int labZb = (int)((labZb_32f) * (1 << (lab_shift)) + 0.5);
80
81     const float labLScale_32f = 116.0f;
82     const float labLShift_32f = 16.0f;
83
84     const int labSmallScale = (int)((31.27 /* labSmallScale_32f*(1<<lab_shift)/255 */ ) * (1 << (lab_shift)) + 0.5);
85
86     const int labSmallShift = (int)((141.24138 /* labSmallScale_32f*(1<<lab) */ ) * (1 << (lab_shift)) + 0.5);
87
88     const int labT = (int)((labT_32f * 255) * (1 << (lab_shift)) + 0.5);
89
90     const int labLScale = (int)((295.8) * (1 << (lab_shift)) + 0.5);
91     const int labLShift = (int)((41779.2) * (1 << (lab_shift)) + 0.5);
92     const int labLScale2 = (int)((labLScale2_32f * 0.01) * (1 << (lab_shift)) + 0.5);
93
94     public static unsafe void ToLab24(Rgb24* from, Lab24* to)
95     {
96         ToLab24(from,to,1);
97     }
98
99     public static unsafe void ToLab24(Rgb24* from, Lab24* to, int length)
100     {
101         // 使用 OpenCV 中的算法实现
102
103         if (length < 1) return;
104
105         Rgb24* end = from + length;
106
107         int x, y, z;
108         int l, a, b;
109         bool flag;
110
111         while (from != end)
112         {
113             Byte red = from->Red;
114             Byte green = from->Green;
115             Byte blue = from->Blue;
116
117             x = blue * labXb + green * labXg + red * labXr;
118             y = blue * labYb + green * labYg + red * labYr;
119             z = blue * labZb + green * labZg + red * labZr;
120
121             flag = x > labT;
122
123             x = (((x) + (1 << ((lab_shift) - 1))) >> (lab_shift));
124
125             if (flag)
126                 x = icvLabCubeRootTab[x];
127             else
128                 x = (((x * labSmallScale + labSmallShift) + (1 << ((lab_shift) - 1))) >> (lab_shift));
129
130             flag = z > labT;
131             z = (((z) + (1 << ((lab_shift) - 1))) >> (lab_shift));
132
133             if (flag == true)
134                 z = icvLabCubeRootTab[z];
135             else
136                 z = (((z * labSmallScale + labSmallShift) + (1 << ((lab_shift) - 1))) >> (lab_shift));
137
138             flag = y > labT;
139             y = (((y) + (1 << ((lab_shift) - 1))) >> (lab_shift));
140
141             if (flag == true)
142             {
143                 y = icvLabCubeRootTab[y];
144                 l = (((y * labLScale - labLShift) + (1 << ((2 * lab_shift) - 1))) >> (2 * lab_shift));
145             }
146             else
147             {
148                 l = (((y * labLScale2) + (1 << ((lab_shift) - 1))) >> (lab_shift));
149                 y = (((y * labSmallScale + labSmallShift) + (1 << ((lab_shift) - 1))) >> (lab_shift));
150             }
151
152             a = (((500 * (x - y)) + (1 << ((lab_shift) - 1))) >> (lab_shift)) + 129;
153             b = (((200 * (y - z)) + (1 << ((lab_shift) - 1))) >> (lab_shift)) + 128;
154
155             l = l > 255 ? 255 : l < 0 ? 0 : l;
156             a = a > 255 ? 255 : a < 0 ? 0 : a;
157             b = b > 255 ? 255 : b < 0 ? 0 : b;
158
159             to->L = (byte)l;
160             to->A = (byte)a;
161             to->B = (byte)b;
162
163             from++;
164             to++;
165         }
166     }
167
168     public static unsafe void ToRgb24(Lab24* from, Rgb24* to)
169     {
170         ToRgb24(from,to,1);
171     }
172
173     public static unsafe void ToRgb24(Lab24* from, Rgb24* to, int length)
174     {
175         if (length < 1) return;
176
177         // 使用 OpenCV 中的算法实现
178         const float coeff0 = 0.39215686274509809f;
179         const float coeff1 = 0.0f;
180         const float coeff2 = 1.0f;
181         const float coeff3 = (-128.0f);
182         const float coeff4 = 1.0f;
183         const float coeff5 = (-128.0f);
184
185         if (length < 1) return;
186
187         Lab24* end = from + length;
188         float x, y, z,l,a,b;
189         int blue, green, red;
190
191         while (from != end)
192         {
193             l = from->L * coeff0 + coeff1;
194             a = from->A * coeff2 + coeff3;
195             b = from->B * coeff4 + coeff5;
196
197             l = (l + labLShift_32f) * (1.0f / labLScale_32f);
198             x = (l + a * 0.002f);
199             z = (l - b * 0.005f);
200
201             y = l * l * l;
202             x = x * x * x;
203             z = z * z * z;
204
205             blue = (int)((x * labBx_32f + y * labBy_32f + z * labBz_32f) * 255 + 0.5);
206             green = (int)((x * labGx_32f + y * labGy_32f + z * labGz_32f) * 255 + 0.5);
207             red = (int)((x * labRx_32f + y * labRy_32f + z * labRz_32f) * 255 + 0.5);
208
209             red = red < 0 ? 0 : red > 255 ? 255 : red;
210             green = green < 0 ? 0 : green > 255 ? 255 : green;
211             blue = blue < 0 ? 0 : blue > 255 ? 255 : blue;
212
213             to->Red = (byte)red;
214             to->Green = (byte)green;
215             to->Blue = (byte)blue;
216
217             from++;
218             to++;
219         }
220     }
221 }

由于C代码中使用了宏，在改写成C#代码时需要手动内联，以提高性能。上面的代码已经实现手动内联。

3. (A)C#实现与(B)C实现的性能对比（C# vs. OpenCV/PInvoke）

C# 版本（ImageRgb24 代表一幅Rgb24图像，ImageLab24代表一幅Lab24图像，它们之间的变化是调用上文UnmanagedImageConverter中的方法实现的）：

Stopwatch sw = new Stopwatch();
sw.Start();
ImageLab24 imgLab = null;
imgLab = new ImageLab24(img); // img 是一个 ImageRgb24 对象
sw.Stop();
Message = sw.ElapsedMilliseconds.ToString();

OpenCV版本（使用EmguCV对OpenCV的PInvoke封装）

private Image<Lab,Byte> TestOpenCV()
{
    Image<Bgr, Byte> imgBgr = new Image<Bgr, byte>(imgMain.Image as Bitmap);
    Image<Lab,Byte> imgLab = new Image<Lab,byte>(new Size(imgBgr.Width, imgBgr.Height));
    Stopwatch sw = new Stopwatch();
    sw.Start();
    CvInvoke.cvCvtColor(imgBgr.Ptr,imgLab.Ptr, Emgu.CV.CvEnum.COLOR_CONVERSION.CV_BGR2Lab);
    sw.Stop();
    MessageBox.Show(sw.ElapsedMilliseconds.ToString() + "ms");
    return imgLab;
}

下面针对三副不同大小的图像进行测试，每张图像测试4次，每次测试将上面两种实现各跑一次，前2次，先跑OpenCV/PInvoke实现，后2次，先跑C#实现，单位皆为ms。

图像1，大小：485×342

A: 5 3 5 3
B: 41 5 6 2

图像2，大小：1845×611

A：25 23 23 23
B：23 34 20 21

图像3，大小：3888×2592

A：209 210 211 210
B：185 188 191 185

从测试结果可以看出，C# 和 OpenCV/PInvoke的性能极为接近。

4. 进一步改进性能

偏色、高光检测等不需要多么准确的Rgb=>Lab转换。如果把彩色图像的每个通道用4 bit来表示，则一共有 4096 种颜色，完全可以用查表方式来加速计算。用一个Lab24数组来表示Rgb24到Lab24空间的映射：

Lab24[] ColorMap

首先初始化ColorMap：

ColorMap = new Lab24[4096];
for (int r = 0; r < 16; r++)
{
    for (int g = 0; g < 16; g++)
    {
        for (int b = 0; b < 16; b++)
        {
            Rgb24 rgb = new Rgb24(r * 16, g * 16, b * 16);
            Lab24 lab = Lab24.CreateFrom(rgb);
            ColorMap[(r << 8) + (g << 4) + b] = lab;
        }
    }
}

然后，查表进行转换：

private unsafe ImageLab24 ConvertToImageLab24(ImageRgb24 img)
{
    ImageLab24 lab = new ImageLab24(img.Width, img.Height);
    Lab24* labStart = lab.Start;
    Rgb24* rgbStart = img.Start;
    Rgb24* rgbEnd = img.Start + img.Length;
    while (rgbStart != rgbEnd)
    {
        Rgb24 rgb = *rgbStart;
        *labStart = ColorMap[(((int)(rgb.Red) >> 4) << 8) + (((int)(rgb.Green) >> 4) << 4) + ((int)(rgb.Blue) >> 4) ];
        rgbStart++;
        labStart++;
    }
    return lab;
}

下面测试（C）查表计算的性能，结果和(A)C#实现与(B)C实现放在一起做对比。

图像1，大小：485×342

A: 5    3    5   3
B: 41   5    6   2
C: 3    2    2    2

图像2，大小：1845×611

A：25 23    23   23
B：23 34    20   21
C: 15   15   15   15

图像3，大小：3888×2592

A：209 210 211 210
B：185 188 191 185
C: 136 134 135 135

5. 原地进行变换

还可以进一步提高性能，因为Rgb24和Lab24大小一样，可以在原地进行Rgb24=>Lab24的变换。相应代码如下：

Rgb24[] ColorMapInSpace
...
ColorMap = new Lab24[4096];
ColorMapInSpace = new Rgb24[4096];
for (int r = 0; r < 16; r++)
{
    for (int g = 0; g < 16; g++)
    {
        for (int b = 0; b < 16; b++)
        {
            Rgb24 rgb = new Rgb24(r * 16, g * 16, b * 16);
            Lab24 lab = Lab24.CreateFrom(rgb);
            ColorMap[(r << 8) + (g << 4) + b] = lab;
            ColorMapInSpace[(r << 8) + (g << 4) + b] = new Rgb24(lab.L,lab.A,lab.B);
        }
    }
}

private unsafe void ConvertToImageLab24InSpace(ImageRgb24 img)
{
    Rgb24* rgbStart = img.Start;
    Rgb24* rgbEnd = img.Start + img.Length;
    while (rgbStart != rgbEnd)
    {
        Rgb24 rgb = *rgbStart;
        *rgbStart = ColorMapInSpace[(((int)(rgb.Red) >> 4) << 8) + (((int)(rgb.Green) >> 4) << 4) + ((int)(rgb.Blue) >> 4)];
        rgbStart++;
    }
}

下面测试D（原地查表变换）的性能，结果和(A)C#实现、(B)C实现、(C)查表计算进行比较：

图像1，大小：485×342

A: 5    3    5   3
B: 41   5    6   2
C: 3    2    2    2
D: 2    1    2    1

图像2，大小：1845×611

A：25 23    23   23
B：23 34    20   21
C: 15   15   15   15
D: 13   13   13   13

图像3，大小：3888×2592

A：209 210 211 210
B：185 188 191 185
C: 136 134 135 135
D: 117 118 122 117

6. 为什么用C#而不是C/C++

经常有人问，你为什么用C#而不用C/C++写图像处理程序。原因如下：

（1）C# 打开unsafe后，写的程序性能非常接近 C 程序的性能（当然，用不了SIMD是个缺陷。mono暂时不考虑。可通过挂接一个轻量级的C库来解决。）；

（2）写C#代码比写C代码爽多了快多了（命名空间、不用管头文件、快速编译、重构、生成API文档 ……）；

（3）庞大的.Net Framework是强有力的后盾。比如，客户想看演示，用Asp.Net写个页面，传个图片给后台，处理了显示出来。还有那些非性能攸关的地方，可以大量使用.Net Framework中的类，大幅度减少开发时间；

（4）结合强大的WPF，可以快速实现复杂的功能

（5）大量的时间在算法研究、实现和优化上，用C#可以把那些无关的惹人烦的事情给降到最小，所牺牲的只是一丁点儿性能。如果生产平台没有.net环境，将C#代码转换为C/C++代码也很快。

====

补充测试VC 9.0 版本

VC 实现与 C# 实现略有区别，C#版本RGB,Lab使用struct来表示，VC下直接用的三个Byte Channel来表示，然后以 redChannel, greenChannel, blueChannel 来代表不同的 Channel Offset。以 nChannel 代表 Channel 数量。VC下有Stride，C#下无Stride。查表实现也和C#版本有区别，直接使用的是静态的表。O2优化。

E: 非查表实现

void
::ImageQualityDetector::ConvertToLab(Orc::ImageInfo &img)
{
    static unsigned short icvLabCubeRootTab[] = {
        0,161,203……        };

    const float labXr_32f = 0.433953f /* = xyzXr_32f / 0.950456 */;
    const float labXg_32f = 0.376219f /* = xyzXg_32f / 0.950456 */;
    const float labXb_32f = 0.189828f /* = xyzXb_32f / 0.950456 */;

    const float labYr_32f = 0.212671f /* = xyzYr_32f */;
    const float labYg_32f = 0.715160f /* = xyzYg_32f */;
    const float labYb_32f = 0.072169f /* = xyzYb_32f */;

    const float labZr_32f = 0.017758f /* = xyzZr_32f / 1.088754 */;
    const float labZg_32f = 0.109477f /* = xyzZg_32f / 1.088754 */;
    const float labZb_32f = 0.872766f /* = xyzZb_32f / 1.088754 */;

    const float labRx_32f = 3.0799327f /* = xyzRx_32f * 0.950456 */;
    const float labRy_32f = (-1.53715f) /* = xyzRy_32f */;
    const float labRz_32f = (-0.542782f)/* = xyzRz_32f * 1.088754 */;

    const float labGx_32f = (-0.921235f)/* = xyzGx_32f * 0.950456 */;
    const float labGy_32f = 1.875991f   /* = xyzGy_32f */ ;
    const float labGz_32f = 0.04524426f /* = xyzGz_32f * 1.088754 */;

    const float labBx_32f = 0.0528909755f /* = xyzBx_32f * 0.950456 */;
    const float labBy_32f = (-0.204043f) /* = xyzBy_32f */;
    const float labBz_32f = 1.15115158f   /* = xyzBz_32f * 1.088754 */;

    const float labT_32f = 0.008856f;

    const int lab_shift = 10;

    const float labLScale2_32f = 903.3f;

    const int labXr = (int)((labXr_32f) * (1 << (lab_shift)) + 0.5);
    const int labXg = (int)((labXg_32f) * (1 << (lab_shift)) + 0.5);
    const int labXb = (int)((labXb_32f) * (1 << (lab_shift)) + 0.5);

    const int labYr = (int)((labYr_32f) * (1 << (lab_shift)) + 0.5);
    const int labYg = (int)((labYg_32f) * (1 << (lab_shift)) + 0.5);
    const int labYb = (int)((labYb_32f) * (1 << (lab_shift)) + 0.5);

    const int labZr = (int)((labZr_32f) * (1 << (lab_shift)) + 0.5);
    const int labZg = (int)((labZg_32f) * (1 << (lab_shift)) + 0.5);
    const int labZb = (int)((labZb_32f) * (1 << (lab_shift)) + 0.5);

    const float labLScale_32f = 116.0f;
    const float labLShift_32f = 16.0f;

    const int labSmallScale = (int)((31.27 /* labSmallScale_32f*(1<<lab_shift)/255 */ ) * (1 << (lab_shift)) + 0.5);

    const int labSmallShift = (int)((141.24138 /* labSmallScale_32f*(1<<lab) */ ) * (1 << (lab_shift)) + 0.5);

    const int labT = (int)((labT_32f * 255) * (1 << (lab_shift)) + 0.5);

    const int labLScale = (int)((295.8) * (1 << (lab_shift)) + 0.5);
    const int labLShift = (int)((41779.2) * (1 << (lab_shift)) + 0.5);
    const int labLScale2 = (int)((labLScale2_32f * 0.01) * (1 << (lab_shift)) + 0.5);

    int width = img.Width;
    int height = img.Height;
    int nChannel = img.NChannel;
    int redChannel = img.RedChannel;
    int greenChannel = img.GreenChannel;
    int blueChannel = img.BlueChannel;
    int x, y, z;
    int l, a, b;
    bool flag;

    for(int h = 0; h < height; h++)
    {
        byte *line = img.GetLine(h);
        for(int w = 0; w < width; w++)
        {
            int red = line[redChannel];
            int green = line[greenChannel];
            int blue = line[blueChannel];

            x = blue * labXb + green * labXg + red * labXr;
            y = blue * labYb + green * labYg + red * labYr;
            z = blue * labZb + green * labZg + red * labZr;

            flag = x > labT;

            x = (((x) + (1 << ((lab_shift) - 1))) >> (lab_shift));

            if (flag)
                x = icvLabCubeRootTab[x];
            else
                x = (((x * labSmallScale + labSmallShift) + (1 << ((lab_shift) - 1))) >> (lab_shift));

            flag = z > labT;
            z = (((z) + (1 << ((lab_shift) - 1))) >> (lab_shift));

            if (flag == true)
                z = icvLabCubeRootTab[z];
            else
                z = (((z * labSmallScale + labSmallShift) + (1 << ((lab_shift) - 1))) >> (lab_shift));

            flag = y > labT;
            y = (((y) + (1 << ((lab_shift) - 1))) >> (lab_shift));

            if (flag == true)
            {
                y = icvLabCubeRootTab[y];
                l = (((y * labLScale - labLShift) + (1 << ((2 * lab_shift) - 1))) >> (2 * lab_shift));
            }
            else
            {
                l = (((y * labLScale2) + (1 << ((lab_shift) - 1))) >> (lab_shift));
                y = (((y * labSmallScale + labSmallShift) + (1 << ((lab_shift) - 1))) >> (lab_shift));
            }

            a = (((500 * (x - y)) + (1 << ((lab_shift) - 1))) >> (lab_shift)) + 129;
            b = (((200 * (y - z)) + (1 << ((lab_shift) - 1))) >> (lab_shift)) + 128;

            l = l > 255 ? 255 : l < 0 ? 0 : l;
            a = a > 255 ? 255 : a < 0 ? 0 : a;
            b = b > 255 ? 255 : b < 0 ? 0 : b;

            int index = 3 * (((red >> 4) << 8) + ((green >> 4) << 4) + (blue >> 4)) ;
            line[0] = (byte)l;
            line[1] = (byte)a;
            line[2] = (byte)b;

            line += nChannel;
        }
    }
}

F: 查表实现

void
::ImageQualityDetector::FastConvertToLab(Orc::ImageInfo &img)
{
    static const byte Rgb2LabSmallTable[] = {
    0,    129,    128 ……
    };

    int width = img.Width;
    int height = img.Height;
    int nChannel = img.NChannel;
    int redChannel = img.RedChannel;
    int greenChannel = img.GreenChannel;
    int blueChannel = img.BlueChannel;
    for(int h = 0; h < height; h++)
    {
        byte *line = img.GetLine(h);
        for(int w = 0; w < width; w++)
        {
            int red = line[redChannel];
            int green = line[greenChannel];
            int blue = line[blueChannel];
            int index = 3 * (((red >> 4) << 8) + ((green >> 4) << 4) + (blue >> 4)) ;
            line[0] = Rgb2LabSmallTable[index];
            line[1] = Rgb2LabSmallTable[index + 1];
            line[2] = Rgb2LabSmallTable[index + 2];
            line += nChannel;
        }
    }
}

测试结果：

图像2，大小：1845×611

A：25 23    23   23
B：23 34    20   21
C: 15   15   15   15
D: 13   13   13   13
E: 32   30   37   37
F: 15    10   13 11

图像3，大小：3888×2592

A：209 210 211 210
B：185 188 191 185
C: 136 134 135 135
D: 117 118 122 117
E: 242 240 243 239
F: 70 69 67 67

====

补充测试：C# 下查表实现（Byte数组）

G: C#下直接查找Byte数组，相关代码

static byte[] Rgb2LabSmallTable = new byte[] {
0, 129, 128, … }

private unsafe void ConvertToImageLab24Fast(ImageRgb24 img)
{
    Rgb24* rgbStart = img.Start;
    Rgb24* rgbEnd = img.Start + img.Length;
    while (rgbStart != rgbEnd)
    {
        Rgb24 rgb = *rgbStart;
        int index = (((int)(rgb.Red) >> 4) << 8) + (((int)(rgb.Green) >> 4) << 4) + ((int)(rgb.Blue) >> 4);
        rgbStart->Red = Rgb2LabSmallTable[index];
        rgbStart->Green = Rgb2LabSmallTable[index+1];
        rgbStart->Blue = Rgb2LabSmallTable[index+2];
        rgbStart++;
    }
}

测试结果：

图像2，大小：1845×611

A：25 23    23   23
B：23 34    20   21
C: 15   15   15   15
D: 13   13   13   13
E: 32   30   37   37
F: 15    10   13 11
G: 12    11   13 11

图像3，大小：3888×2592

A：209 210 211 210
B：185 188 191 185
C: 136 134 135 135
D: 117 118 122 117
E: 242 240 243 239
F: 70 69 67 67
G: 64 64 65 64

====

补充测试：同一种实现下的C#和VC性能对比，附下载

下面消除两种语言的测试区别，C#版本查表时使用指针而非数组，VC下使用无Stride的Rgb24，相关测试代码见下载链接。

这又形成了4个测试用例：

H- C#，非查表；I-C#，查表; J-C++，非查表; K-C++，查表

C# 版为 .Net 4.0, VS2010 ，代码中选择快速一项为测试I，不选择为测试H。

C++版 - VS2008。选择快速一项为测试K，不选择为测试J。

测试结果：

图像2，大小：1845×611

H: 31 29 36 32
I: 10 10 10 10
J: 39 33 33 30
K: 9    8    8    8

图像3，大小：3888×2592

H: 195 194 194 195
I: 53    52    51    52
J: 220 218 218 222
K: 41   42    41   41

结论：

C#下图像开发是很给力的！还在犹豫什么呢？

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