LBP图像处理算法C++实现,基于opencv与VS,注释详细

#include<iostream> #include<opencv2\core\core.hpp> #include<opencv2\highgui\highgui.hpp> #include<opencv2\imgproc\imgproc.hpp> #include <bitset> //#include<opencv2\contrib\contrib.hpp> using namespace std; using namespace cv; //原始LBP特征计算 template <typename _tp> void getOriginLBPFeature(InputArray _src, OutputArray _dst) { Mat src = _src.getMat(); _dst.create(src.rows - 2, src.cols - 2, CV_8UC1); Mat dst = _dst.getMat(); dst.setTo(0); for (int i = 1; i<src.rows - 1; i++) { for (int j = 1; j<src.cols - 1; j++) { _tp center = src.at<_tp>(i, j); unsigned char lbpCode = 0; lbpCode |= (src.at<_tp>(i - 1, j - 1) > center) << 7; lbpCode |= (src.at<_tp>(i - 1, j) > center) << 6; lbpCode |= (src.at<_tp>(i - 1, j + 1) > center) << 5; lbpCode |= (src.at<_tp>(i, j + 1) > center) << 4; lbpCode |= (src.at<_tp>(i + 1, j + 1) > center) << 3; lbpCode |= (src.at<_tp>(i + 1, j) > center) << 2; lbpCode |= (src.at<_tp>(i + 1, j - 1) > center) << 1; lbpCode |= (src.at<_tp>(i, j - 1) > center) << 0; dst.at<uchar>(i - 1, j - 1) = lbpCode; } } } /* //圆形LBP特征计算，这种方法适于理解，但在效率上存在问题，声明时默认neighbors=8 template <typename _tp> void getCircularLBPFeature(InputArray _src, OutputArray _dst, int radius, int neighbors) { Mat src = _src.getMat(); //LBP特征图像的行数和列数的计算要准确 _dst.create(src.rows - 2 * radius, src.cols - 2 * radius, CV_8UC1); Mat dst = _dst.getMat(); dst.setTo(0); //循环处理每个像素 for (int i = radius; i<src.rows - radius; i++) { for (int j = radius; j<src.cols - radius; j++) { //获得中心像素点的灰度值 _tp center = src.at<_tp>(i, j); unsigned char lbpCode = 0; for (int k = 0; k<neighbors; k++) { //根据公式计算第k个采样点的坐标，这个地方可以优化，不必每次都进行计算radius*cos，radius*sin float x = i + static_cast<float>(radius * \ cos(2.0 * CV_PI * k / neighbors)); float y = j - static_cast<float>(radius * \ sin(2.0 * CV_PI * k / neighbors)); //根据取整结果进行双线性插值，得到第k个采样点的灰度值 //1.分别对x，y进行上下取整 int x1 = static_cast<int>(floor(x)); int x2 = static_cast<int>(ceil(x)); int y1 = static_cast<int>(floor(y)); int y2 = static_cast<int>(ceil(y)); //2.计算四个点(x1,y1),(x1,y2),(x2,y1),(x2,y2)的权重 //下面的权重计算方式有个问题，如果四个点都相等，则权重全为0，计算出来的插值为0 //float w1 = (x2-x)*(y2-y); //(x1,y1) //float w2 = (x2-x)*(y-y1); //(x1,y2) //float w3 = (x-x1)*(y2-y); //(x2,y1) //float w4 = (x-x1)*(y-y1); //(x2,y2) //将坐标映射到0-1之间 float tx = x - x1; float ty = y - y1; //根据0-1之间的x，y的权重计算公式计算权重 float w1 = (1 - tx) * (1 - ty); float w2 = tx * (1 - ty); float w3 = (1 - tx) * ty; float w4 = tx * ty; //3.根据双线性插值公式计算第k个采样点的灰度值 float neighbor = src.at<_tp>(x1, y1) * w1 + src.at<_tp>(x1, y2) *w2 \ + src.at<_tp>(x2, y1) * w3 + src.at<_tp>(x2, y2) *w4; //通过比较获得LBP值，并按顺序排列起来 lbpCode |= (neighbor>center) << (neighbors - k - 1); } dst.at<uchar>(i - radius, j - radius) = lbpCode; } } } */ //圆形LBP特征计算，效率优化版本，声明时默认neighbors=8 template <typename _tp> void getCircularLBPFeatureOptimization(InputArray _src, OutputArray _dst, int radius, int neighbors) { Mat src = _src.getMat(); //LBP特征图像的行数和列数的计算要准确 _dst.create(src.rows - 2 * radius, src.cols - 2 * radius, CV_8UC1); Mat dst = _dst.getMat(); dst.setTo(0); for (int k = 0; k<neighbors; k++) { //计算采样点对于中心点坐标的偏移量rx，ry float rx = static_cast<float>(radius * cos(2.0 * CV_PI * k / neighbors)); float ry = -static_cast<float>(radius * sin(2.0 * CV_PI * k / neighbors)); //为双线性插值做准备 //对采样点偏移量分别进行上下取整 int x1 = static_cast<int>(floor(rx)); int x2 = static_cast<int>(ceil(rx)); int y1 = static_cast<int>(floor(ry)); int y2 = static_cast<int>(ceil(ry)); //将坐标偏移量映射到0-1之间 float tx = rx - x1; float ty = ry - y1; //根据0-1之间的x，y的权重计算公式计算权重，权重与坐标具体位置无关，与坐标间的差值有关 float w1 = (1 - tx) * (1 - ty); float w2 = tx * (1 - ty); float w3 = (1 - tx) * ty; float w4 = tx * ty; //循环处理每个像素 for (int i = radius; i<src.rows - radius; i++) { for (int j = radius; j<src.cols - radius; j++) { //获得中心像素点的灰度值 _tp center = src.at<_tp>(i, j); //根据双线性插值公式计算第k个采样点的灰度值 float neighbor = src.at<_tp>(i + x1, j + y1) * w1 + src.at<_tp>(i + x1, j + y2) *w2 \ + src.at<_tp>(i + x2, j + y1) * w3 + src.at<_tp>(i + x2, j + y2) *w4; //LBP特征图像的每个邻居的LBP值累加，累加通过与操作完成，对应的LBP值通过移位取得 dst.at<uchar>(i - radius, j - radius) |= (neighbor>center) << (neighbors - k - 1); } } } } //旋转不变圆形LBP特征计算，声明时默认neighbors=8 template <typename _tp> void getRotationInvariantLBPFeature(InputArray _src, OutputArray _dst, int radius, int neighbors) { Mat src = _src.getMat(); //LBP特征图像的行数和列数的计算要准确 _dst.create(src.rows - 2 * radius, src.cols - 2 * radius, CV_8UC1); Mat dst = _dst.getMat(); dst.setTo(0); for (int k = 0; k<neighbors; k++) { //计算采样点对于中心点坐标的偏移量rx，ry float rx = static_cast<float>(radius * cos(2.0 * CV_PI * k / neighbors)); float ry = -static_cast<float>(radius * sin(2.0 * CV_PI * k / neighbors)); //为双线性插值做准备 //对采样点偏移量分别进行上下取整 int x1 = static_cast<int>(floor(rx)); int x2 = static_cast<int>(ceil(rx)); int y1 = static_cast<int>(floor(ry)); int y2 = static_cast<int>(ceil(ry)); //将坐标偏移量映射到0-1之间 float tx = rx - x1; float ty = ry - y1; //根据0-1之间的x，y的权重计算公式计算权重，权重与坐标具体位置无关，与坐标间的差值有关 float w1 = (1 - tx) * (1 - ty); float w2 = tx * (1 - ty); float w3 = (1 - tx) * ty; float w4 = tx * ty; //循环处理每个像素 for (int i = radius; i<src.rows - radius; i++) { for (int j = radius; j<src.cols - radius; j++) { //获得中心像素点的灰度值 _tp center = src.at<_tp>(i, j); //根据双线性插值公式计算第k个采样点的灰度值 float neighbor = src.at<_tp>(i + x1, j + y1) * w1 + src.at<_tp>(i + x1, j + y2) *w2 \ + src.at<_tp>(i + x2, j + y1) * w3 + src.at<_tp>(i + x2, j + y2) *w4; //LBP特征图像的每个邻居的LBP值累加，累加通过与操作完成，对应的LBP值通过移位取得 dst.at<uchar>(i - radius, j - radius) |= (neighbor>center) << (neighbors - k - 1); } } } //进行旋转不变处理 for (int i = 0; i<dst.rows; i++) { for (int j = 0; j<dst.cols; j++) { unsigned char currentValue = dst.at<uchar>(i, j); unsigned char minValue = currentValue; for (int k = 1; k<neighbors; k++) { //循环左移 unsigned char temp = (currentValue >> (neighbors - k)) | (currentValue << k); if (temp < minValue) { minValue = temp; } } dst.at<uchar>(i, j) = minValue; } } } //等价模式LBP特征计算 template <typename _tp> void getUniformPatternLBPFeature(InputArray _src, OutputArray _dst, int radius, int neighbors) { Mat src = _src.getMat(); //LBP特征图像的行数和列数的计算要准确 _dst.create(src.rows - 2 * radius, src.cols - 2 * radius, CV_8UC1); Mat dst = _dst.getMat(); dst.setTo(0); //LBP特征值对应图像灰度编码表，直接默认采样点为8位 uchar temp = 1; uchar table[256] = { 0 }; for (int i = 0; i<256; i++) { if (getHopTimes(i)<3) { table[i] = temp; temp++; } } //是否进行UniformPattern编码的标志 bool flag = false; //计算LBP特征图 for (int k = 0; k<neighbors; k++) { if (k == neighbors - 1) { flag = true; } //计算采样点对于中心点坐标的偏移量rx，ry float rx = static_cast<float>(radius * cos(2.0 * CV_PI * k / neighbors)); float ry = -static_cast<float>(rad