大家好,有没有朋友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。
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 与 Lab24
1 public partial struct Rgb24 { public static Rgb24 WHITE = new Rgb24 { Red = 255, Green = 255, Blue = 255 }; public static Rgb24 BLACK = new Rgb24(); public static Rgb24 RED = new Rgb24 { Red = 255 }; public static Rgb24 BLUE = new Rgb24 { Blue = 255 }; public static Rgb24 GREEN = new Rgb24 { Green = 255 }; [FieldOffset(0)] public Byte Blue; [FieldOffset(1)] public Byte Green; [FieldOffset(2)] public Byte Red; public Rgb24(int red, int green, int blue) { Red = (byte)red; Green = (byte)green; Blue = (byte)blue; } public Rgb24(byte red, byte green, byte blue) { Red = red; Green = green; Blue = blue; } } public partial struct Lab24 { public byte L; public byte A; public byte B; public Lab24(byte l,byte a,byte b) { L = l; A = a; B = b; } public Lab24(int l,int a,int b) { L = (byte)l; A = (byte)a; B = (byte)b; } }
Lab空间参照OpenCV,用一个byte来表示Lab空间的每个通道值,以求提高性能。由于标准的Lab空间中a和b通道是可付的,Lab24中的A、B值减去128,就是标准Lab空间的a,b通道值。
OpenCV中Bgr<=>Lab是用C语言实现的,下面将它转换为C#代码:
Rgb24 <=> Lab24 public sealed class UnmanagedImageConverter { /* 1024*(([0..511]./255)**(1./3)) */ static ushort[] icvLabCubeRootTab = new ushort[] { 0,161,203,232,256,276,293,308,322,335,347,359,369,379,389,398, 406,415,423,430,438,445,452,459,465,472,478,484,490,496,501,507, 512,517,523,528,533,538,542,547,552,556,561,565,570,574,578,582, 586,590,594,598,602,606,610,614,617,621,625,628,632,635,639,642, 645,649,652,655,659,662,665,668,671,674,677,680,684,686,689,692, 695,698,701,704,707,710,712,715,718,720,723,726,728,731,734,736, 739,741,744,747,749,752,754,756,759,761,764,766,769,771,773,776, 778,780,782,785,787,789,792,794,796,798,800,803,805,807,809,811, 813,815,818,820,822,824,826,828,830,832,834,836,838,840,842,844, 846,848,850,852,854,856,857,859,861,863,865,867,869,871,872,874, 876,878,880,882,883,885,887,889,891,892,894,896,898,899,901,903, 904,906,908,910,911,913,915,916,918,920,921,923,925,926,928,929, 931,933,934,936,938,939,941,942,944,945,947,949,950,952,953,955, 956,958,959,961,962,964,965,967,968,970,971,973,974,976,977,979, 980,982,983,985,986,987,989,990,992,993,995,996,997,999,1000,1002, 1003,1004,1006,1007,1009,1010,1011,1013,1014,1015,1017,1018,1019,1021,1022,1024, 1025,1026,1028,1029,1030,1031,1033,1034,1035,1037,1038,1039,1041,1042,1043,1044, 1046,1047,1048,1050,1051,1052,1053,1055,1056,1057,1058,1060,1061,1062,1063,1065, 1066,1067,1068,1070,1071,1072,1073,1074,1076,1077,1078,1079,1081,1082,1083,1084, 1085,1086,1088,1089,1090,1091,1092,1094,1095,1096,1097,1098,1099,1101,1102,1103, 1104,1105,1106,1107,1109,1110,1111,1112,1113,1114,1115,1117,1118,1119,1120,1121, 1122,1123,1124,1125,1127,1128,1129,1130,1131,1132,1133,1134,1135,1136,1138,1139, 1140,1141,1142,1143,1144,1145,1146,1147,1148,1149,1150,1151,1152,1154,1155,1156, 1157,1158,1159,1160,1161,1162,1163,1164,1165,1166,1167,1168,1169,1170,1171,1172, 1173,1174,1175,1176,1177,1178,1179,1180,1181,1182,1183,1184,1185,1186,1187,1188, 1189,1190,1191,1192,1193,1194,1195,1196,1197,1198,1199,1200,1201,1202,1203,1204, 1205,1206,1207,1208,1209,1210,1211,1212,1213,1214,1215,1215,1216,1217,1218,1219, 1220,1221,1222,1223,1224,1225,1226,1227,1228,1229,1230,1230,1231,1232,1233,1234, 1235,1236,1237,1238,1239,1240,1241,1242,1242,1243,1244,1245,1246,1247,1248,1249, 1250,1251,1251,1252,1253,1254,1255,1256,1257,1258,1259,1259,1260,1261,1262,1263, 1264,1265,1266,1266,1267,1268,1269,1270,1271,1272,1273,1273,1274,1275,1276,1277, 1278,1279,1279,1280,1281,1282,1283,1284,1285,1285,1286,1287,1288,1289,1290,1291 }; 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); public static unsafe void ToLab24(Rgb24* from, Lab24* to) { ToLab24(from,to,1); } public static unsafe void ToLab24(Rgb24* from, Lab24* to, int length) { // 使用 OpenCV 中的算法实现 if (length < 1) return; Rgb24* end = from + length; int x, y, z; int l, a, b; bool flag; while (from != end) { Byte red = from->Red; Byte green = from->Green; Byte blue = from->Blue; 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; to->L = (byte)l; to->A = (byte)a; to->B = (byte)b; from++; to++; } } public static unsafe void ToRgb24(Lab24* from, Rgb24* to) { ToRgb24(from,to,1); } public static unsafe void ToRgb24(Lab24* from, Rgb24* to, int length) { if (length < 1) return; // 使用 OpenCV 中的算法实现 const float coeff0 = 0.39215686274509809f; const float coeff1 = 0.0f; const float coeff2 = 1.0f; const float coeff3 = (-128.0f); const float coeff4 = 1.0f; const float coeff5 = (-128.0f); if (length < 1) return; Lab24* end = from + length; float x, y, z,l,a,b; int blue, green, red; while (from != end) { l = from->L * coeff0 + coeff1; a = from->A * coeff2 + coeff3; b = from->B * coeff4 + coeff5; l = (l + labLShift_32f) * (1.0f / labLScale_32f); x = (l + a * 0.002f); z = (l - b * 0.005f); y = l * l * l; x = x * x * x; z = z * z * z; blue = (int)((x * labBx_32f + y * labBy_32f + z * labBz_32f) * 255 + 0.5); green = (int)((x * labGx_32f + y * labGy_32f + z * labGz_32f) * 255 + 0.5); red = (int)((x * labRx_32f + y * labRy_32f + z * labRz_32f) * 255 + 0.5); red = red < 0 ? 0 : red > 255 ? 255 : red; green = green < 0 ? 0 : green > 255 ? 255 : green; blue = blue < 0 ? 0 : blue > 255 ? 255 : blue; to->Red = (byte)red; to->Green = (byte)green; to->Blue = (byte)blue; from++; to++; } } }
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。
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)例如:进口气动球阀
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。
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
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
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。
====
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
====
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
====
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
C#下图像开发是很给力的!还在犹豫什么呢?