参考博客：LBP特征的实现及LBP+SVM分类
运行环境：ubuntu18.04+opencv3.2.0 识别速度3.87ms/张
LBP特征提取网上很多，包括其他Haar特征什么的，SVM级联分类器网上也有介绍训练和使用的博客，这里用的是简单的二分类，即是或不是。
直接复制粘贴代码吧，别的也没有用，原理我也不懂，我只是完成对摄像头误拍的非人图像进行去除，背景大概是这样的。
智能摄像头对场景进行智能人脸检测，但是会有概率误检，比如错误把某动物或者某植物检测为人脸，然后拍照并显示到大屏幕上，这样效果很不好。哪怕没拍到人脸，是个后脑勺或者半个身子，但是只要主体是人还能圆过去，弄个动物或者植物这种风马牛不相及的照片上屏根本无力解释，只能凸出算法效果不好，但是提升人脸检测算法难度较大，就想到在AI检测后做一个SVM分类器，判定拍摄图片是否是人或不是人，把不是人的去掉，是人的才显示到屏幕上。
基本流程：挑选正负样本图片集 >> 读取正负样本并贴标签 >>提取LBP特征 >> 训练xml模型 >> 加载模型识别图片
整体实现的基本流程就是这样很简单，但这里需要特别提醒，就是最开始的挑选正负样本图片集这一步才是最关键的，其他的LBP特征提取理不理解原理都不影响从网上直接贴源码调用，中间的函数调用也是常用的，也就是说如果你的代码编译或者运行不正确，会报错，如果代码没报错，能够正常运行，基本上就是对的，最终识别效果不好9.9成跟数据集有关系。
我解释一下原因，上面说过背景是把人和非人的图片分开，对AI摄像头误检照片进行二次筛选，那么我训练的xml分类器要分类的所有照片，其实本质上都是我的AI经过层层计算最终的评定为“是人”的结果，也就是我们看到的“非人”和“人”在AI那都是符合“人”的特征的，不然摄像头本身就不会拍摄；比如我用svm级联分类器提取haar特征去检测误检的“盆栽”，显示结果就是把叶子部分识别为人脸，甚至你对检测出来的叶子的区域做haar特征的人眼检测，依然能够检测到符合人眼的特征，所以结论就是，所有AI摄像头输出的图片，对AI来说，都是符合人的特征的。
这里讲既然都符合人的特征，我怎么判定区分是人或不是人？
首先是AI摄像头本身的误检率就只有10%不到，如果误检率太高，应该先提升算法精确度，也就是AI自身能够识别出绝大多数“非人”的情况，只有在某些特定条件下才会被误导拍摄，这些误检的图片都应该包含这些特殊信息，否则AI不会只拍摄这些图片，毕竟更多的时间场景是不变的，要真认为盆栽是人，会一直盯着盆栽一直拍，所以真相就是当光线变化或者有风等等特殊条件的存在时，误导了AI，那么误导AI的10%的图像都必然有同一特点，把这些特点提取出来，用xml分类器t贴上"非人"标签过滤出去就好。
上面说了一大堆，总结就是训练的正负样本集一定要从AI检测后拍摄的图像中来，私自添加其他数据集的，会导致xml二分类识别准确率下降，原因就是其他数据集的特征是你人眼看的，不是AI自己检测过滤的，私自添加的特征不符合我的AI对人的检测标准。比如我们通过人眼识别的"非人"照片里存在AI检测为人的“特征”，分类器把这个特征贴上“非人”的标签，在实际分类的时候，会将原来AI通过该特征检测为人的图片判定为“非人”，降低准确率。
至于用Haar还是LBP特征提取，或者其他什么的，都随便，因为二分类物体判定是或不是，本身就是特征差异特别大的才会这么简单暴力区分，要时刻谨记，检测的数据来源是AI，我们只处理AI误检的，影响AI检测的特殊条件特征都会集中在AI的误检照片里，不需要考虑更换场景后xml分类器是否试用，换个场景AI依然能够有90%以上的正确识别率，如果现在的xml效果差了，就逐步添加AI不同场景下的误检样本就可以。
LBP.h
//////////////////////////////////////////////////////////////////////////
// LBP.h (2.0)
// 2015-6-30，by QQ
//
// Please contact me if you find any bugs, or have any suggestions.
// Contact:
// Telephone:17761745857
// Email:[email protected]
// Blog: http://blog.csdn.net/qianqing13579
//////////////////////////////////////////////////////////////////////////
// updated 2016-12-12 01:12:55 by QQ, LBP 1.1,GetMinBinary()函数修改为查找表，提高了计算速度
// updated 2016-12-13 14:41:58 by QQ, LBP 2.0,先计算整幅图像的LBP特征图，然后计算每个cell的LBP直方图
#ifndef __LBP_H__
#define __LBP_H__
#include "opencv2/opencv.hpp"
#include<vector>
using namespace std;
using namespace cv;
class LBP
{
public:
// 计算基本的256维LBP特征向量
void ComputeLBPFeatureVector_256(const Mat &srcImage, Size cellSize, Mat &featureVector);
void ComputeLBPImage_256(const Mat &srcImage, Mat &LBPImage);// 计算256维LBP特征图
// 计算灰度不变+等价模式LBP特征向量(58种模式)
void ComputeLBPFeatureVector_Uniform(const Mat &srcImage, Size cellSize, Mat &featureVector);
void ComputeLBPImage_Uniform(const Mat &srcImage, Mat &LBPImage);// 计算等价模式LBP特征图
// 计算灰度不变+旋转不变+等价模式LBP特征向量(9种模式)
void ComputeLBPFeatureVector_Rotation_Uniform(const Mat &srcImage, Size cellSize, Mat &featureVector);
void ComputeLBPImage_Rotation_Uniform(const Mat &srcImage, Mat &LBPImage); // 计算灰度不变+旋转不变+等价模式LBP特征图,使用查找表
// Test
void Test();// 测试灰度不变+旋转不变+等价模式LBP
void TestGetMinBinaryLUT();
private:
void BuildUniformPatternTable(int *table); // 计算等价模式查找表
int GetHopCount(int i);// 获取i中0,1的跳变次数
void ComputeLBPImage_Rotation_Uniform_2(const Mat &srcImage, Mat &LBPImage);// 计算灰度不变+旋转不变+等价模式LBP特征图,不使用查找表
int ComputeValue9(int value58);// 计算9种等价模式
int GetMinBinary(int binary);// 通过LUT计算最小二进制
uchar GetMinBinary(uchar *binary); // 计算得到最小二进制
};
#endif
LBP.cpp
#include"LBP.h"
//获取i中0,1的跳变次数
int LBP::GetHopCount(int i)
{
// 转换为二进制
int a[8] = { 0 };
int k = 7;
while (i)
{
// 除2取余
a[k] = i % 2;
i /= 2;
--k;
}
// 计算跳变次数
int count = 0;
for (int k = 0; k < 8; ++k)
{
// 注意，是循环二进制,所以需要判断是否为8
if (a[k] != a[k + 1 == 8 ? 0 : k + 1])
{
++count;
}
}
return count;
}
// 建立等价模式表
// 这里为了便于建立LBP特征图，58种等价模式序号从1开始:1~58,第59类混合模式映射为0
void LBP::BuildUniformPatternTable(int *table)
{
memset(table, 0, 256 * sizeof(int));
uchar temp = 1;
for (int i = 0; i < 256; ++i)
{
if (GetHopCount(i) <= 2)
{
table[i] = temp;
temp++;
}
}
}
void LBP::ComputeLBPFeatureVector_256(const Mat &srcImage, Size cellSize, Mat &featureVector)
{
// 参数检查，内存分配
//CV_Assert(srcImage.depth() == CV_8U&&srcImage.channels() == 1);
Mat LBPImage;
ComputeLBPImage_256(srcImage, LBPImage);
// 计算cell个数
int widthOfCell = cellSize.width;
int heightOfCell = cellSize.height;
int numberOfCell_X = srcImage.cols / widthOfCell;// X方向cell的个数
int numberOfCell_Y = srcImage.rows / heightOfCell;
// 特征向量的个数
int numberOfDimension = 256 * numberOfCell_X*numberOfCell_Y;
featureVector.create(1, numberOfDimension, CV_32FC1);
featureVector.setTo(Scalar(0));
// 计算LBP特征向量
int stepOfCell = srcImage.cols;
int pixelCount = cellSize.width*cellSize.height;
float *dataOfFeatureVector = (float *)featureVector.data;
// cell的特征向量在最终特征向量中的起始位置
int index = -256;
for (int y = 0; y <= numberOfCell_Y - 1; ++y)
{
for (int x = 0; x <= numberOfCell_X - 1; ++x)
{
index += 256;
// 计算每个cell的LBP直方图
Mat cell = LBPImage(Rect(x * widthOfCell, y * heightOfCell, widthOfCell, heightOfCell));
uchar *rowOfCell = cell.data;
for (int y_Cell = 0; y_Cell <= cell.rows - 1; ++y_Cell, rowOfCell += stepOfCell)
{
uchar *colOfCell = rowOfCell;
for (int x_Cell = 0; x_Cell <= cell.cols - 1; ++x_Cell, ++colOfCell)
{
++dataOfFeatureVector[index + colOfCell[0]];
}
}
// 一定要归一化！否则分类器计算误差很大
for (int i = 0; i <= 255; ++i)
dataOfFeatureVector[index + i] /= pixelCount;
}
}
}
//srcImage:灰度图
//LBPImage:LBP图
void LBP::ComputeLBPImage_256(const Mat &srcImage, Mat &LBPImage)
{
// 参数检查，内存分配
//CV_Assert(srcImage.depth() == CV_8U&&srcImage.channels() == 1);
LBPImage.create(srcImage.size(), srcImage.type());
// 扩充原图像边界，便于边界处理
Mat extendedImage;
copyMakeBorder(srcImage, extendedImage, 1, 1, 1, 1, BORDER_DEFAULT);
// 计算LBP特征图
int heightOfExtendedImage = extendedImage.rows;
int widthOfExtendedImage = extendedImage.cols;
int widthOfLBP = LBPImage.cols;
uchar *rowOfExtendedImage = extendedImage.data + widthOfExtendedImage + 1;
uchar *rowOfLBPImage = LBPImage.data;
for (int y = 1; y <= heightOfExtendedImage - 2; ++y, rowOfExtendedImage += widthOfExtendedImage, rowOfLBPImage += widthOfLBP)
{
// 列
uchar *colOfExtendedImage = rowOfExtendedImage;
uchar *colOfLBPImage = rowOfLBPImage;
for (int x = 1; x <= widthOfExtendedImage - 2; ++x, ++colOfExtendedImage, ++colOfLBPImage)
{
// 计算LBP值
int LBPValue = 0;
if (colOfExtendedImage[0 - widthOfExtendedImage - 1] >= colOfExtendedImage[0])
LBPValue += 128;
if (colOfExtendedImage[0 - widthOfExtendedImage] >= colOfExtendedImage[0])
LBPValue += 64;
if (colOfExtendedImage[0 - widthOfExtendedImage + 1] >= colOfExtendedImage[0])
LBPValue += 32;
if (colOfExtendedImage[0 + 1] >= colOfExtendedImage[0])
LBPValue += 16;
if (colOfExtendedImage[0 + widthOfExtendedImage + 1] >= colOfExtendedImage[0])
LBPValue += 8;
if (colOfExtendedImage[0 + widthOfExtendedImage] >= colOfExtendedImage[0])
LBPValue += 4;
if (colOfExtendedImage[0 + widthOfExtendedImage - 1] >= colOfExtendedImage[0])
LBPValue += 2;
if (colOfExtendedImage[0 - 1] >= colOfExtendedImage[0])
LBPValue += 1;
colOfLBPImage[0] = LBPValue;
} // x
}// y
}
// cellSize:每个cell的大小,如16*16
void LBP::ComputeLBPFeatureVector_Uniform(const Mat &srcImage, Size cellSize, Mat &featureVector)
{
// 参数检查，内存分配
//CV_Assert(srcImage.depth() == CV_8U&&srcImage.channels() == 1);
Mat LBPImage;
ComputeLBPImage_Uniform(srcImage, LBPImage);
// 计算cell个数
int widthOfCell = cellSize.width;
int heightOfCell = cellSize.height;
int numberOfCell_X = srcImage.cols / widthOfCell;// X方向cell的个数
int numberOfCell_Y = srcImage.rows / heightOfCell;
// 特征向量的个数
int numberOfDimension = 58 * numberOfCell_X*numberOfCell_Y;
featureVector.create(1, numberOfDimension, CV_32FC1);
featureVector.setTo(Scalar(0));
// 计算LBP特征向量
int stepOfCell = srcImage.cols;
int index = -58;// cell的特征向量在最终特征向量中的起始位置
float *dataOfFeatureVector = (float *)featureVector.data;
for (int y = 0; y <= numberOfCell_Y - 1; ++y)
{
for (int x = 0; x <= numberOfCell_X - 1; ++x)
{
index += 58;
// 计算每个cell的LBP直方图
Mat cell = LBPImage(Rect(x * widthOfCell, y * heightOfCell, widthOfCell, heightOfCell));
uchar *rowOfCell = cell.data;
int sum = 0; // 每个cell的等价模式总数
for (int y_Cell = 0; y_Cell <= cell.rows - 1; ++y_Cell, rowOfCell += stepOfCell)
{
uchar *colOfCell = rowOfCell;
for (int x_Cell = 0; x_Cell <= cell.cols - 1; ++x_Cell, ++colOfCell)
{
if (colOfCell[0] != 0)
{
// 在直方图中转化为0~57，所以是colOfCell[0] - 1
++dataOfFeatureVector[index + colOfCell[0] - 1];
++sum;
}
}
}
// 一定要归一化！否则分类器计算误差很大
for (int i = 0; i <= 57; ++i)
dataOfFeatureVector[index + i] /= sum;
}
}
}
// 计算等价模式LBP特征图，为了方便表示特征图，58种等价模式表示为1~58,第59种混合模式表示为0
// 注：你可以将第59类混合模式映射为任意数值，因为要突出等价模式特征，所以非等价模式设置为0比较好
void LBP::ComputeLBPImage_Uniform(const Mat &srcImage, Mat &LBPImage)
{
// 参数检查，内存分配
//CV_Assert(srcImage.depth() == CV_8U&&srcImage.channels() == 1);
LBPImage.create(srcImage.size(), srcImage.type());
// 计算LBP图
// 扩充原图像边界，便于边界处理
Mat extendedImage;
copyMakeBorder(srcImage, extendedImage, 1, 1, 1, 1, BORDER_DEFAULT);
// 构建LBP 等价模式查找表
//int table[256];
//BuildUniformPatternTable(table);
// LUT(256种每一种模式对应的等价模式)
static const int table[256] = { 1, 2, 3, 4, 5, 0, 6, 7, 8, 0, 0, 0, 9, 0, 10, 11, 12, 0, 0, 0, 0, 0, 0, 0, 13, 0, 0, 0, 14, 0, 15, 16, 17, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 0, 0, 0, 0, 0, 0, 0, 19, 0, 0, 0, 20, 0, 21, 22, 23, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25,
0, 0, 0, 0, 0, 0, 0, 26, 0, 0, 0, 27, 0, 28, 29, 30, 31, 0, 32, 0, 0, 0, 33, 0, 0, 0, 0, 0, 0, 0, 34, 0, 0, 0, 0
, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 35, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 36, 37, 38, 0, 39, 0, 0, 0, 40, 0, 0, 0, 0, 0, 0, 0, 41, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 42
, 43, 44, 0, 45, 0, 0, 0, 46, 0, 0, 0, 0, 0, 0, 0, 47, 48, 49, 0, 50, 0, 0, 0, 51, 52, 53, 0, 54, 55, 56, 57, 58 };
// 计算LBP
int heightOfExtendedImage = extendedImage.rows;
int widthOfExtendedImage = extendedImage.cols;
int widthOfLBP = LBPImage.cols;
uchar *rowOfExtendedImage = extendedImage.data + widthOfExtendedImage + 1;
uchar *rowOfLBPImage = LBPImage.data;
for (int y = 1; y <= heightOfExtendedImage - 2; ++y, rowOfExtendedImage += widthOfExtendedImage, rowOfLBPImage += widthOfLBP)
{
// 列
uchar *colOfExtendedImage = rowOfExtendedImage;
uchar *colOfLBPImage = rowOfLBPImage;
for (int x = 1; x <= widthOfExtendedImage - 2; ++x, ++colOfExtendedImage, ++colOfLBPImage)
{
// 计算LBP值
int LBPValue = 0;
if (colOfExtendedImage[0 - widthOfExtendedImage - 1] >= colOfExtendedImage[0])
LBPValue += 128;
if (colOfExtendedImage[0 - widthOfExtendedImage] >= colOfExtendedImage[0])
LBPValue += 64;
if (colOfExtendedImage[0 - widthOfExtendedImage + 1] >= colOfExtendedImage[0])
LBPValue += 32;
if (colOfExtendedImage[0 + 1] >= colOfExtendedImage[0])
LBPValue += 16;
if (colOfExtendedImage[0 + widthOfExtendedImage + 1] >= colOfExtendedImage[0])
LBPValue += 8;
if (colOfExtendedImage[0 + widthOfExtendedImage] >= colOfExtendedImage[0])
LBPValue += 4;
if (colOfExtendedImage[0 + widthOfExtendedImage - 1] >= colOfExtendedImage[0])
LBPValue += 2;
if (colOfExtendedImage[0 - 1] >= colOfExtendedImage[0])
LBPValue += 1;
colOfLBPImage[0] = table[LBPValue];
} // x
}// y
}
// 计算9种等价模式，等价模式编号也是从1开始：1~9
int LBP::ComputeValue9(int value58)
{
int value9 = 0;
switch (value58)
{
case 1:
value9 = 1;
break;
case 2:
value9 = 2;
break;
case 4:
value9 = 3;
break;
case 7:
value9 = 4;
break;
case 11:
value9 = 5;
break;
case 16:
value9 = 6;
break;
case 22:
value9 = 7;
break;
case 29:
value9 = 8;
break;
case 58:
value9 = 9;
break;
}
return value9;
}
int LBP::GetMinBinary(int binary)
{
static const int miniBinaryLUT[256] = { 0, 1, 1, 3, 1, 5, 3, 7, 1, 9, 5, 11, 3, 13, 7, 15, 1, 17, 9, 19, 5,
21, 11, 23, 3, 25, 13, 27, 7, 29, 15, 31, 1, 9, 17, 25, 9, 37, 19, 39, 5, 37, 21, 43, 11, 45,
23, 47, 3, 19, 25, 51, 13, 53, 27, 55, 7, 39, 29, 59, 15, 61, 31, 63, 1, 5, 9, 13, 17, 21, 25,
29, 9, 37, 37, 45, 19, 53, 39, 61, 5, 21, 37, 53, 21, 85, 43, 87, 11, 43, 45, 91, 23, 87, 47, 95,
3, 11, 19, 27, 25, 43, 51, 59, 13, 45, 53, 91, 27, 91, 55, 111, 7, 23, 39, 55, 29, 87, 59, 119, 15,
47, 61, 111, 31, 95, 63, 127, 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 9, 25, 37,
39, 37, 43, 45, 47, 19, 51, 53, 55, 39, 59, 61, 63, 5, 13, 21, 29, 37, 45, 53, 61, 21, 53, 85,
87, 43, 91, 87, 95, 11, 27, 43, 59, 45, 91, 91, 111, 23, 55, 87, 119, 47, 111, 95, 127, 3,
7, 11, 15, 19, 23, 27, 31, 25, 39, 43, 47, 51, 55, 59, 63, 13, 29, 45, 61, 53, 87, 91, 95, 27, 59,
91, 111, 55, 119, 111, 127, 7, 15, 23, 31, 39, 47, 55, 63, 29, 61, 87, 95, 59, 111, 119, 127, 15, 31, 47, 63,
61, 95, 111, 127, 31, 63, 95, 127, 63, 127, 127, 255 };
return miniBinaryLUT[binary];
}
// 获取循环二进制的最小二进制模式
uchar LBP::GetMinBinary(uchar *binary)
{
// 计算8个二进制
uchar LBPValue[8] = { 0 };
for (int i = 0; i <= 7; ++i)
{
LBPValue[0] += binary[i] << (7 - i);
LBPValue[1] += binary[(i + 7) % 8] << (7 - i);
LBPValue[2] += binary[(i + 6) % 8] << (7 - i);
LBPValue[3] += binary[(i + 5) % 8] << (7 - i);
LBPValue[4] += binary[(i + 4) % 8] << (7 - i);
LBPValue[5] += binary[(i + 3) % 8] << (7 - i);
LBPValue[6] += binary[(i + 2) % 8] << (7 - i);
LBPValue[7] += binary[(i + 1) % 8] << (7 - i);
}
// 选择最小的
uchar minValue = LBPValue[0];
for (int i = 1; i <= 7; ++i)
{
if (LBPValue[i] < minValue)
{
minValue = LBPValue[i];
}
}
return minValue;
}
// cellSize:每个cell的大小,如16*16
void LBP::ComputeLBPFeatureVector_Rotation_Uniform(const Mat &srcImage, Size cellSize, Mat &featureVector)
{
// 参数检查，内存分配
//CV_Assert(srcImage.depth() == CV_8U&&srcImage.channels() == 1);
Mat LBPImage;
ComputeLBPImage_Rotation_Uniform(srcImage, LBPImage);
// 计算cell个数
int widthOfCell = cellSize.width;
int heightOfCell = cellSize.height;
int numberOfCell_X = srcImage.cols / widthOfCell;// X方向cell的个数
int numberOfCell_Y = srcImage.rows / heightOfCell;
// 特征向量的个数
int numberOfDimension = 9 * numberOfCell_X*numberOfCell_Y;
featureVector.create(1, numberOfDimension, CV_32FC1);
featureVector.setTo(Scalar(0));
// 计算LBP特征向量
int stepOfCell = srcImage.cols;
int index = -9;// cell的特征向量在最终特征向量中的起始位置
float *dataOfFeatureVector = (float *)featureVector.data;
for (int y = 0; y <= numberOfCell_Y - 1; ++y)
{
for (int x = 0; x <= numberOfCell_X - 1; ++x)
{
index += 9;
// 计算每个cell的LBP直方图
Mat cell = LBPImage(Rect(x * widthOfCell, y * heightOfCell, widthOfCell, heightOfCell));
uchar *rowOfCell = cell.data;
int sum = 0; // 每个cell的等价模式总数
for (int y_Cell = 0; y_Cell <= cell.rows - 1; ++y_Cell, rowOfCell += stepOfCell)
{
uchar *colOfCell = rowOfCell;
for (int x_Cell = 0; x_Cell <= cell.cols - 1; ++x_Cell, ++colOfCell)
{
if (colOfCell[0] != 0)
{
// 在直方图中转化为0~8，所以是colOfCell[0] - 1
++dataOfFeatureVector[index + colOfCell[0] - 1];
++sum;
}
}
}
// 直方图归一化
for (int i = 0; i <= 8; ++i)
dataOfFeatureVector[index + i] /= sum;
}
}
}
void LBP::ComputeLBPImage_Rotation_Uniform(const Mat &srcImage, Mat &LBPImage)
{
// 参数检查，内存分配
// CV_Assert(srcImage.depth() == CV_8U&&srcImage.channels() == 1);
LBPImage.create(srcImage.size(), srcImage.type());
// 扩充图像，处理边界情况
Mat extendedImage;
copyMakeBorder(srcImage, extendedImage, 1, 1, 1, 1, BORDER_DEFAULT);
// 构建LBP 等价模式查找表
//int table[256];
//BuildUniformPatternTable(table);
// 查找表
static const int table[256] = { 1, 2, 3, 4, 5, 0, 6, 7, 8, 0, 0, 0, 9, 0, 10, 11, 12, 0, 0, 0, 0, 0, 0, 0, 13, 0, 0, 0, 14, 0, 15, 16, 17, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 0, 0, 0, 0, 0, 0, 0, 19, 0, 0, 0, 20, 0, 21, 22, 23, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25,
0, 0, 0, 0, 0, 0, 0, 26, 0, 0, 0, 27, 0, 28, 29, 30, 31, 0, 32, 0, 0, 0, 33, 0, 0, 0, 0, 0, 0, 0, 34, 0, 0, 0, 0
, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 35, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 36, 37, 38, 0, 39, 0, 0, 0, 40, 0, 0, 0, 0, 0, 0, 0, 41, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 42
, 43, 44, 0, 45, 0, 0, 0, 46, 0, 0, 0, 0, 0, 0, 0, 47, 48, 49, 0, 50, 0, 0, 0, 51, 52, 53, 0, 54, 55, 56, 57, 58 };
int heigthOfExtendedImage = extendedImage.rows;
int widthOfExtendedImage = extendedImage.cols;
int widthOfLBPImage = LBPImage.cols;
uchar *rowOfExtendedImage = extendedImage.data + widthOfExtendedImage + 1;
uchar *rowOfLBPImage = LBPImage.data;
for (int y = 1; y <= heigthOfExtendedImage - 2; ++y, rowOfExtendedImage += widthOfExtendedImage, rowOfLBPImage += widthOfLBPImage)
{
// 列
uchar *colOfExtendedImage = rowOfExtendedImage;
uchar *colOfLBPImage = rowOfLBPImage;
for (int x = 1; x <= widthOfExtendedImage - 2; ++x, ++colOfExtendedImage, ++colOfLBPImage)
{
// 计算LBP值
int LBPValue = 0;
if (colOfExtendedImage[0 - widthOfExtendedImage - 1] >= colOfExtendedImage[0])
LBPValue += 128;
if (colOfExtendedImage[0 - widthOfExtendedImage] >= colOfExtendedImage[0])
LBPValue += 64;
if (colOfExtendedImage[0 - widthOfExtendedImage + 1] >= colOfExtendedImage[0])
LBPValue += 32;
if (colOfExtendedImage[0 + 1] >= colOfExtendedImage[0])
LBPValue += 16;
if (colOfExtendedImage[0 + widthOfExtendedImage + 1] >= colOfExtendedImage[0])
LBPValue += 8;
if (colOfExtendedImage[0 + widthOfExtendedImage] >= colOfExtendedImage[0])
LBPValue += 4;
if (colOfExtendedImage[0 + widthOfExtendedImage - 1] >= colOfExtendedImage[0])
LBPValue += 2;
if (colOfExtendedImage[0 - 1] >= colOfExtendedImage[0])
LBPValue += 1;
int minValue = GetMinBinary(LBPValue);
// 计算58种等价模式LBP
int value58 = table[minValue];
// 计算9种等价模式
colOfLBPImage[0] = ComputeValue9(value58);
}
}
}
void LBP::ComputeLBPImage_Rotation_Uniform_2(const Mat &srcImage, Mat &LBPImage)
{
// 参数检查，内存分配
//CV_Assert(srcImage.depth() == CV_8U&&srcImage.channels() == 1);
LBPImage.create(srcImage.size(), srcImage.type());
// 扩充图像，处理边界情况
Mat extendedImage;
copyMakeBorder(srcImage, extendedImage, 1, 1, 1, 1, BORDER_DEFAULT);
// 构建LBP 等价模式查找表
//int table[256];
//BuildUniformPatternTable(table);
// 通过查找表
static const int table[256] = { 1, 2, 3, 4, 5, 0, 6, 7, 8, 0, 0, 0, 9, 0, 10, 11, 12, 0, 0, 0, 0, 0, 0, 0, 13, 0, 0, 0, 14, 0, 15, 16, 17, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 0, 0, 0, 0, 0, 0, 0, 19, 0, 0, 0, 20, 0, 21, 22, 23, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25,
0, 0, 0, 0, 0, 0, 0, 26, 0, 0, 0, 27, 0, 28, 29, 30, 31, 0, 32, 0, 0, 0, 33, 0, 0, 0, 0, 0, 0, 0, 34, 0, 0, 0, 0
, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 35, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 36, 37, 38, 0, 39, 0, 0, 0, 40, 0, 0, 0, 0, 0, 0, 0, 41, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 42
, 43, 44, 0, 45, 0, 0, 0, 46, 0, 0, 0, 0, 0, 0, 0, 47, 48, 49, 0, 50, 0, 0, 0, 51, 52, 53, 0, 54, 55, 56, 57, 58 };
uchar binary[8] = { 0 };// 记录每个像素的LBP值
int heigthOfExtendedImage = extendedImage.rows;
int widthOfExtendedImage = extendedImage.cols;
int widthOfLBPImage = LBPImage.cols;
uchar *rowOfExtendedImage = extendedImage.data + widthOfExtendedImage + 1;
uchar *rowOfLBPImage = LBPImage.data;
for (int y = 1; y <= heigthOfExtendedImage - 2; ++y, rowOfExtendedImage += widthOfExtendedImage, rowOfLBPImage += widthOfLBPImage)
{
// 列
uchar *colOfExtendedImage = rowOfExtendedImage;
uchar *colOfLBPImage = rowOfLBPImage;
for (int x = 1; x <= widthOfExtendedImage - 2; ++x, ++colOfExtendedImage, ++colOfLBPImage)
{
// 计算旋转不变LBP(最小的二进制模式)
binary[0] = colOfExtendedImage[0 - widthOfExtendedImage - 1] >= colOfExtendedImage[0] ? 1 : 0;
binary[1] = colOfExtendedImage[0 - widthOfExtendedImage] >= colOfExtendedImage[0] ? 1 : 0;
binary[2] = colOfExtendedImage[0 - widthOfExtendedImage + 1] >= colOfExtendedImage[0] ? 1 : 0;
binary[3] = colOfExtendedImage[0 + 1] >= colOfExtendedImage[0] ? 1 : 0;
binary[4] = colOfExtendedImage[0 + widthOfExtendedImage + 1] >= colOfExtendedImage[0] ? 1 : 0;
binary[5] = colOfExtendedImage[0 + widthOfExtendedImage] >= colOfExtendedImage[0] ? 1 : 0;
binary[6] = colOfExtendedImage[0 + widthOfExtendedImage - 1] >= colOfExtendedImage[0] ? 1 : 0;
binary[7] = colOfExtendedImage[0 - 1] >= colOfExtendedImage[0] ? 1 : 0;
int minValue = GetMinBinary(binary);
// 计算58种等价模式LBP
int value58 = table[minValue];
// 计算9种等价模式
colOfLBPImage[0] = ComputeValue9(value58);
}
}
}
// 验证灰度不变+旋转不变+等价模式种类
void LBP::Test()
{
uchar LBPValue[8] = { 0 };
int k = 7, j;
int temp;
LBP lbp;
int number[256] = { 0 };
int numberOfMinBinary = 0;
// 旋转不变
for (int i = 0; i < 256; ++i)
{
k = 7;
temp = i;
while (k >= 0)
{
LBPValue[k] = temp & 1;
temp = temp >> 1;
--k;
}
int minBinary = lbp.GetMinBinary(LBPValue);
// 查找有无重复的
for (j = 0; j <= numberOfMinBinary - 1; ++j)
{
if (number[j] == minBinary)
break;
}
if (j == numberOfMinBinary)
{
number[numberOfMinBinary++] = minBinary;
}
}
cout << "旋转不变一共有：" << numberOfMinBinary << "种" << endl;
// LUT
static const int table[256] = { 1, 2, 3, 4, 5, 0, 6, 7, 8, 0, 0, 0, 9, 0, 10, 11, 12, 0, 0, 0, 0, 0, 0, 0, 13, 0, 0, 0, 14, 0, 15, 16, 17, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 0, 0, 0, 0, 0, 0, 0, 19, 0, 0, 0, 20, 0, 21, 22, 23, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25,
0, 0, 0, 0, 0, 0, 0, 26, 0, 0, 0, 27, 0, 28, 29, 30, 31, 0, 32, 0, 0, 0, 33, 0, 0, 0, 0, 0, 0, 0, 34, 0, 0, 0, 0
, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 35, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 36, 37, 38, 0, 39, 0, 0, 0, 40, 0, 0, 0, 0, 0, 0, 0, 41, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 42
, 43, 44, 0, 45, 0, 0, 0, 46, 0, 0, 0, 0, 0, 0, 0, 47, 48, 49, 0, 50, 0, 0, 0, 51, 52, 53, 0, 54, 55, 56, 57, 58 };
for (int i = 0; i <= numberOfMinBinary - 1; ++i)
{
cout << "旋转不变的LBP：" << number[i] << " " << "对应的等价模式：" << table[number[i]] << endl;
}
}
void LBP::TestGetMinBinaryLUT()
{
for (int i = 0; i <= 255; ++i)
{
uchar a[8] = { 0 };
int k = 7;
int j = i;
while (j)
{
// 除2取余
a[k] = j % 2;
j /= 2;
--k;
}
uchar minBinary = GetMinBinary(a);
printf("%d,", minBinary);
}
}
main.c
#include <stdio.h>
#include <time.h>
#include <opencv2/opencv.hpp>
#include <opencv/cv.h>
#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/ml/ml.hpp>
#include <sys/io.h>
#include <dirent.h>
#include <sys/stat.h>
#include <sys/time.h>
#include <iostream>
#include <fstream>
#include <unistd.h>
#include"LBP.h"
#define CELLSIZE_LBP 16 // LBP的窗口大小，4,8,16,32
#define TRUE_SWATCH_PATH "/home/qushy/share/OpenCV/SVM/1data/data/test/true" //训练正样本路径
#define FALSE_SWATCH_PATH "/home/qushy/share/OpenCV/SVM/1data/data/test/false" //训练负样本路径
#define TEST_SWATCH_PATH "/home/qushy/share/OpenCV/SVM/1data/data/test/src" //识别测试样本
#define SAVE_TRUE_BUBBLE "/home/qushy/share/OpenCV/SVM/1data/data/test/bubble" //识别正样本存储路径
#define SAVE_FALSE_BUBBLE "/home/qushy/share/OpenCV/SVM/1data/data/test/nobubble"//识别负样本存储路径
using namespace std;
using namespace cv;
using namespace cv::ml;
long what_time_is_it_now(void)
{
struct timeval t;
gettimeofday(&t, NULL);
return t.tv_sec * 1000 + t.tv_usec / 1000;
}
void cp_result_pic(const char* src, const char* dst)
{
char cmd[256] = { 0 };
sprintf(cmd, "cp %s %s", src, dst);
system(cmd);
}
void getFiles(string path, vector<string>& files)
{
DIR* dir = NULL;
struct dirent* pDir = NULL;
string p;
dir = opendir(path.c_str());
if (dir != NULL)
{
cout << path.c_str() << endl;
while (1)
{
pDir = readdir(dir);
if (pDir == NULL) break;
if (pDir->d_type == DT_REG)
{
files.push_back(p.assign(path).append("/").append(pDir->d_name));
}
}
}
closedir(dir);
}
//获取测试样本
void getTestBubble(vector<string>& imagePaths)
{
string filePath = TEST_SWATCH_PATH;
vector<string> files;
getFiles(filePath, files);
int number = files.size();
for (int i = 0; i < number; i++)
{
imagePaths.push_back(files[i].c_str());
}
cout << "getTestBubble " << number << endl;
}
//获取正样本
//并贴标签为1
void getBubble(vector<string>& imagePaths, vector<int>& imageClass)
{
string filePath = TRUE_SWATCH_PATH;
vector<string> files;
getFiles(filePath, files);
int number = files.size();
for (int i = 0; i < number; i++)
{
imagePaths.push_back(files[i].c_str());
imageClass.push_back(1); //该样本为数字1
}
cout << "getBubble " << number << endl;
}
//获取负样本
//并贴标签为0
void getNoBubble(vector<string>& imagePaths, vector<int>& imageClass)
{
string filePath = FALSE_SWATCH_PATH;
vector<string> files;
getFiles(filePath, files);
int number = files.size();
for (int i = 0; i < number; i++)
{
imagePaths.push_back(files[i].c_str());
imageClass.push_back(0); //该样本为数字0
}
cout << "getNoBubble " << number << endl;
}
void LBP_SVM_Rotation()
{
// 读入训练样本路径和类别
vector<string> imagePaths;
vector<int> imageClass;
getBubble(imagePaths, imageClass);
getNoBubble(imagePaths, imageClass);
// 计算样本LBP特征向量矩阵和类别矩阵
int lengthOfFeatureVector = (32 / CELLSIZE_LBP) * (64 / CELLSIZE_LBP) * 9; // 特征向量的维数
Mat featureVectorOfSample;
Mat classOfSample;
vector<string>::size_type numberOfSample = imagePaths.size();
Mat srcImage;
LBP lbp;
Mat featureVector;
cout << "computer lbp feature vector rotation" <<endl;
for (vector<string>::size_type i = 0; i <= numberOfSample - 1; ++i)
{
//cout << imagePaths[i].c_str() << endl;
// 读入图片
srcImage = imread(imagePaths[i].c_str(), 0);
resize(srcImage, srcImage, Size(256, 256), 0, 0, INTER_LINEAR);
// 计算样本LBP特征向量
lbp.ComputeLBPFeatureVector_Rotation_Uniform(srcImage, Size(CELLSIZE_LBP, CELLSIZE_LBP), featureVector);
if (featureVector.empty()) printf("ComputeLBPFeatureVector_Rotation_Uniform faild\n");
featureVectorOfSample.push_back(featureVector);
classOfSample.push_back(imageClass[i]);
}
// 使用SVM分类器训练
// 参数设置
Ptr<SVM> svm = SVM::create();
svm->setType(SVM::C_SVC);
svm->setKernel(SVM::RBF);//核函数
svm->setDegree(0);
svm->setGamma(1);
svm->setCoef0(0);
svm->setC(1);
svm->setNu(0);
svm->setP(0);
svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 100, 1e-6));
cout << "training svm ..." << endl;
svm->train(featureVectorOfSample, ROW_SAMPLE, classOfSample);
svm->save("Classifier.xml");
cout << "save svm Classifier.xml" << endl;
}
void LBP_SVM_Rotation_Test()
{
Ptr<ml::SVM>svm = ml::SVM::load("Classifier.xml");
// 使用训练好的分类器进行识别
vector<string> testImagePath;
getTestBubble(testImagePath);
// 识别
LBP lbp;
int lengthOfFeatureVector = (32 / CELLSIZE_LBP) * (64 / CELLSIZE_LBP) * 9;
vector<string>::size_type numberOfTestImage = testImagePath.size();
Mat featureVectorOfTestImage;
int result_true = 0;
int result_false = 0;
cout << "computer predict result:" << endl;
long t_start = what_time_is_it_now();
// 注意将循环体内的耗时变量和操作提取到循环体内
for (vector<string>::size_type i = 0; i <= numberOfTestImage - 1; ++i)
{
Mat testImage = imread(testImagePath[i].c_str(), 0);
resize(testImage, testImage, Size(256, 256), 0, 0, INTER_LINEAR);
//cout << testImagePath[i].c_str() << endl;
// 计算LBP特征向量
lbp.ComputeLBPFeatureVector_Rotation_Uniform(testImage, Size(CELLSIZE_LBP, CELLSIZE_LBP), featureVectorOfTestImage);
int predict = (int)svm->predict(featureVectorOfTestImage);
if (predict)
{
result_true++;
//cp_result_pic(testImagePath[i].c_str(), SAVE_TRUE_BUBBLE);
}
else
{
result_false++;
//cp_result_pic(testImagePath[i].c_str(), SAVE_FALSE_BUBBLE);
}
}
long t_end = what_time_is_it_now();
cout << "result true " << result_true << endl;
cout << "result false " << result_false << endl;
cout << "total " << t_end - t_start << " ms" << " average " << (t_end - t_start) * 1.0 / numberOfTestImage << " ms" << endl;
}
int main()
{
LBP_SVM_Rotation();
LBP_SVM_Rotation_Test();
return 0;
}