ICode9

精准搜索请尝试: 精确搜索
首页 > 编程语言> 文章详细

高效编写C#图像处理程序(3) Rgb=>Lab,图像缺陷检测的案例

2022-08-01 15:04:37  阅读:153  来源: 互联网

标签:32f img C# float Rgb24 Lab int Rgb const


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 [FieldOffset(0)]
10 public Byte Blue;
11 [FieldOffset(1)]
12 public Byte Green;
13 [FieldOffset(2)]
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. Rgb24 <=> Lab24 的实现

OpenCV中Bgr<=>Lab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*(([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)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。

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?

标签:32f,img,C#,float,Rgb24,Lab,int,Rgb,const
来源: https://www.cnblogs.com/valveszhishi/p/16540275.html

本站声明: 1. iCode9 技术分享网(下文简称本站)提供的所有内容,仅供技术学习、探讨和分享;
2. 关于本站的所有留言、评论、转载及引用,纯属内容发起人的个人观点,与本站观点和立场无关;
3. 关于本站的所有言论和文字,纯属内容发起人的个人观点,与本站观点和立场无关;
4. 本站文章均是网友提供,不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属;如您发现该文章侵犯了您的权益,可联系我们第一时间进行删除;
5. 本站为非盈利性的个人网站,所有内容不会用来进行牟利,也不会利用任何形式的广告来间接获益,纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。

专注分享技术,共同学习,共同进步。侵权联系[81616952@qq.com]

Copyright (C)ICode9.com, All Rights Reserved.

ICode9版权所有