.Net高效编写C#图像处理程序(3) Rgb=>Lab，图像缺陷检测的案例

灌篮少年 发表于 2022-9-5 02:54:43

高效编写C#图像处理程序(3) Rgb=>Lab，图像缺陷检测的案例

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。
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 与 Lab24
1 public partial struct Rgb24 2 { 3 public static Rgb24 WHITE = new Rgb24 { Red = 255, Green = 255, Blue = 255 }; 4 public static Rgb24 BLACK = new Rgb24(); 5 public static Rgb24 RED = new Rgb24 { Red = 255 }; 6 public static Rgb24 BLUE = new Rgb24 { Blue = 255 }; 7 public static Rgb24 GREEN = new Rgb24 { Green = 255 }; 8 9 10 public Byte Blue; 11 12 public Byte Green; 13 14 public Byte Red; 15 16 public Rgb24(int red, int green, int blue) 17 { 18 Red = (byte)red; 19 Green = (byte)green; 20 Blue = (byte)blue; 21 } 22 23 public Rgb24(byte red, byte green, byte blue) 24 { 25 Red = red; 26 Green = green; 27 Blue = blue; 28 } 29 } 30 31 public partial struct Lab24 32 { 33 public byte L; 34 public byte A; 35 public byte B; 36 37 public Lab24(byte l,byte a,byte b) 38 { 39 L = l; 40 A = a; 41 B = b; 42 } 43 44 public Lab24(int l,int a,int b) 45 { 46 L = (byte)l; 47 A = (byte)a; 48 B = (byte)b; 49 } 50 }

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。
2. Rgb24Lab24 的实现

OpenCV中BgrLab是用C语言实现的，下面将它转换为C#代码：

Rgb24 <=> Lab24 1 public sealed class UnmanagedImageConverter 2 { 3 /* 1024*((./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; 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; 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; 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）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 TestOpenCV()
{
 Image imgBgr = new Image(imgMain.Image as Bitmap);
 Image imgLab = new Image(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;
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[(r4) > 4) > 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;
ColorMapInSpace = new Rgb24;
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[(r4)];
 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) * (14) > 4)) ;
 line = (byte)l;
 line = (byte)a;
 line = (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;
 int green = line;
 int blue = line;
 int index = 3 * (((red >> 4) > 4) > 4)) ;
 line = Rgb2LabSmallTable;
 line = Rgb2LabSmallTable;
 line = Rgb2LabSmallTable;
 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) > 4) > 4);
 rgbStart->Red = Rgb2LabSmallTable;
 rgbStart->Green = Rgb2LabSmallTable;
 rgbStart->Blue = Rgb2LabSmallTable;
 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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