• 操作系统：ubuntu22.04
• OpenCV版本：OpenCV4.9
• IDE:Visual Studio Code
• 编程语言：C++11

算法描述

该函数用于在 GPU 上计算输入图像的直方图（histogram）。它将像素值区间均匀划分为若干个 bin（桶），并统计每个 bin 中像素的数量。

• 适用于 单通道图像（如灰度图或某个颜色通道）。
• 使用等间距的分箱方式（即“均匀直方图”）。
• 支持 8U 和 32S 类型的图像。

函数原型

void cv::cuda::histEven 
(
 	InputArray  	src,
	OutputArray  	hist,
	int  	histSize,
	int  	lowerLevel,
	int  	upperLevel,
	Stream &  	stream = Stream::Null() 
) 		

参数

• src：源图像。支持 CV_8U、CV_16U 或 CV_16S 类型的图像，并且通道数可以是 1 或 4。如果是 4 通道图像，则每个通道会分别进行处理。

• hist：目标直方图，是一个具有 1 行、histSize 列、数据类型为 CV_32S 的数组。

• histSize：直方图的大小（即 bin 的数量）。

• lowerLevel：最低级 bin 的下界值（包含此值）。

• upperLevel：最高级 bin 的上界值（不包含此值）。

• stream：用于异步操作的 CUDA 流对象。

代码示例

#include <opencv2/cudaimgproc.hpp>
#include <opencv2/opencv.hpp>

int main()
{
    // 读取图像
    cv::Mat h_src = cv::imread( "/media/dingxin/data/study/OpenCV/sources/images/Lenna.png", cv::IMREAD_GRAYSCALE );
    if ( h_src.empty() )
    {
        std::cerr << "无法加载图像！" << std::endl;
        return -1;
    }

    // 上传到 GPU
    cv::cuda::GpuMat d_src, d_hist;
    d_src.upload( h_src );

    // 设置直方图参数
    int histSize   = 256;  // 8-bit 图像有 256 个灰度级
    int lowerLevel = 0;
    int upperLevel = 256;

    // 创建输出直方图 Mat
    d_hist.create( 1, histSize, CV_32SC1 );

    // 计算直方图
    cv::cuda::histEven( d_src, d_hist, histSize, lowerLevel, upperLevel );

    // 下载结果
    cv::Mat h_hist;
    d_hist.download( h_hist );

    // 打印直方图内容（可选）
    for ( int i = 0; i < histSize; ++i )
    {
        std::cout << "Bin[" << i << "] = " << h_hist.at< int >( i ) << std::endl;
    }

    return 0;
}

运行结果

Bin[0] = 0
Bin[1] = 0
Bin[2] = 0
Bin[3] = 0
Bin[4] = 0
Bin[5] = 0
Bin[6] = 0
Bin[7] = 0
Bin[8] = 0
Bin[9] = 0
Bin[10] = 0
Bin[11] = 0
Bin[12] = 0
Bin[13] = 0
Bin[14] = 0
Bin[15] = 0
Bin[16] = 0
Bin[17] = 0
Bin[18] = 0
Bin[19] = 0
Bin[20] = 0
Bin[21] = 0
Bin[22] = 0
Bin[23] = 0
Bin[24] = 0
Bin[25] = 0
Bin[26] = 0
Bin[27] = 0
Bin[28] = 0
Bin[29] = 0
Bin[30] = 0
Bin[31] = 0
Bin[32] = 0
Bin[33] = 0
Bin[34] = 0
Bin[35] = 0
Bin[36] = 0
Bin[37] = 0
Bin[38] = 0
Bin[39] = 45
Bin[40] = 0
Bin[41] = 0
Bin[42] = 0
Bin[43] = 388
Bin[44] = 0
Bin[45] = 0
Bin[46] = 1171
Bin[47] = 0
Bin[48] = 0
Bin[49] = 0
Bin[50] = 2211
Bin[51] = 0
Bin[52] = 0
Bin[53] = 3583
Bin[54] = 0
Bin[55] = 0
Bin[56] = 5472
Bin[57] = 0
Bin[58] = 0
Bin[59] = 6460
Bin[60] = 0
Bin[61] = 5922
Bin[62] = 0
Bin[63] = 0
Bin[64] = 5104
Bin[65] = 0
Bin[66] = 4325
Bin[67] = 0
Bin[68] = 3100
Bin[69] = 0
Bin[70] = 2479
Bin[71] = 0
Bin[72] = 2093
Bin[73] = 0
Bin[74] = 1730
Bin[75] = 0
Bin[76] = 1566
Bin[77] = 0
Bin[78] = 1469
Bin[79] = 0
Bin[80] = 1377
Bin[81] = 0
Bin[82] = 1345
Bin[83] = 0
Bin[84] = 1335
Bin[85] = 1367
Bin[86] = 0
Bin[87] = 1416
Bin[88] = 0
Bin[89] = 1479
Bin[90] = 1468
Bin[91] = 0
Bin[92] = 1465
Bin[93] = 1438
Bin[94] = 0
Bin[95] = 1328
Bin[96] = 1431
Bin[97] = 0
Bin[98] = 1465
Bin[99] = 1504
Bin[100] = 0
Bin[101] = 1522
Bin[102] = 1564
Bin[103] = 1605
Bin[104] = 0
Bin[105] = 1674
Bin[106] = 1604
Bin[107] = 1741
Bin[108] = 0
Bin[109] = 1774
Bin[110] = 2021
Bin[111] = 2398
Bin[112] = 2598
Bin[113] = 0
Bin[114] = 2673
Bin[115] = 2588
Bin[116] = 2403
Bin[117] = 2126
Bin[118] = 2027
Bin[119] = 1730
Bin[120] = 1676
Bin[121] = 0
Bin[122] = 1554
Bin[123] = 1522
Bin[124] = 1510
Bin[125] = 1529
Bin[126] = 1579
Bin[127] = 1536
Bin[128] = 1603
Bin[129] = 1542
Bin[130] = 1719
Bin[131] = 1795
Bin[132] = 1851
Bin[133] = 1796
Bin[134] = 1919
Bin[135] = 1916
Bin[136] = 2022
Bin[137] = 2198
Bin[138] = 2409
Bin[139] = 2650
Bin[140] = 2626
Bin[141] = 2629
Bin[142] = 2685
Bin[143] = 2411
Bin[144] = 4435
Bin[145] = 1980
Bin[146] = 1930
Bin[147] = 1925
Bin[148] = 1939
Bin[149] = 2058
Bin[150] = 2237
Bin[151] = 4821
Bin[152] = 2411
Bin[153] = 2506
Bin[154] = 2411
Bin[155] = 2507
Bin[156] = 4627
Bin[157] = 2240
Bin[158] = 2277
Bin[159] = 2346
Bin[160] = 4692
Bin[161] = 2352
Bin[162] = 2221
Bin[163] = 2278
Bin[164] = 4174
Bin[165] = 1798
Bin[166] = 1729
Bin[167] = 2888
Bin[168] = 1316
Bin[169] = 1285
Bin[170] = 2222
Bin[171] = 1066
Bin[172] = 1035
Bin[173] = 1955
Bin[174] = 1012
Bin[175] = 1883
Bin[176] = 941
Bin[177] = 998
Bin[178] = 1969
Bin[179] = 990
Bin[180] = 1979
Bin[181] = 971
Bin[182] = 1816
Bin[183] = 836
Bin[184] = 1502
Bin[185] = 651
Bin[186] = 1097
Bin[187] = 538
Bin[188] = 1020
Bin[189] = 487
Bin[190] = 886
Bin[191] = 458
Bin[192] = 1036
Bin[193] = 532
Bin[194] = 1093
Bin[195] = 1145
Bin[196] = 595
Bin[197] = 1227
Bin[198] = 628
Bin[199] = 1176
Bin[200] = 1129
Bin[201] = 595
Bin[202] = 1098
Bin[203] = 1009
Bin[204] = 527
Bin[205] = 999
Bin[206] = 1066
Bin[207] = 1124
Bin[208] = 616
Bin[209] = 1249
Bin[210] = 1236
Bin[211] = 598
Bin[212] = 1139
Bin[213] = 1145
Bin[214] = 947
Bin[215] = 835
Bin[216] = 319
Bin[217] = 567
Bin[218] = 452
Bin[219] = 411
Bin[220] = 364
Bin[221] = 296
Bin[222] = 142
Bin[223] = 225
Bin[224] = 213
Bin[225] = 133
Bin[226] = 94
Bin[227] = 59
Bin[228] = 56
Bin[229] = 41
Bin[230] = 28
Bin[231] = 14
Bin[232] = 10
Bin[233] = 6
Bin[234] = 8
Bin[235] = 0
Bin[236] = 1
Bin[237] = 0
Bin[238] = 1
Bin[239] = 0
Bin[240] = 0
Bin[241] = 0
Bin[242] = 2
Bin[243] = 0
Bin[244] = 0
Bin[245] = 2
Bin[246] = 0
Bin[247] = 0
Bin[248] = 0
Bin[249] = 0
Bin[250] = 0
Bin[251] = 0
Bin[252] = 0
Bin[253] = 0
Bin[254] = 0
Bin[255] = 0

• 直方图数据分析
  总体观察：
  • 图像中很多灰度级没有像素（如 Bin[0] 到 Bin[38] 都是 0），说明图像中没有非常暗的区域。
  • 从 Bin[39] 开始出现非零值，说明图像主要集中在 中高亮度区域（约40~255之间）。
  • 中间部分（如 Bin[85]、Bin[144]、Bin[160] 等）有一些明显高峰，说明图像中存在较多的中等亮度或高亮度区域。
  • 最后几个 Bin（235 及以后）又开始减少，说明图像中纯白区域不多。