lbp.cpp

#include <opencv2/opencv.hpp>
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <iostream>
#include <fstream>
#include <sstream>
#include <vector>

using namespace cv;
using namespace std;

template <typename _Tp> static
inline void elbp_(InputArray _src, OutputArray _dst, int radius, int neighbors) {
    //get matrices
    Mat src = _src.getMat();
    // allocate memory for result
    _dst.create(src.rows-2*radius, src.cols-2*radius, CV_32SC1);
    Mat dst = _dst.getMat();
    // zero
    dst.setTo(0);
    for(int n=0; n<neighbors; n++) {
        // sample points
        float x = static_cast<float>(radius * cos(2.0*CV_PI*n/static_cast<float>(neighbors)));
        float y = static_cast<float>(-radius * sin(2.0*CV_PI*n/static_cast<float>(neighbors)));
        // relative indices
        int fx = static_cast<int>(floor(x));
        int fy = static_cast<int>(floor(y));
        int cx = static_cast<int>(ceil(x));
        int cy = static_cast<int>(ceil(y));
        // fractional part
        float ty = y - fy;
        float tx = x - fx;
        // set interpolation weights
        float w1 = (1 - tx) * (1 - ty);
        float w2 =      tx  * (1 - ty);
        float w3 = (1 - tx) *      ty;
        float w4 =      tx  *      ty;
        // iterate through your data
        for(int i=radius; i < src.rows-radius;i++) {
            for(int j=radius;j < src.cols-radius;j++) {
                // calculate interpolated value
                float t = static_cast<float>(w1*src.at<_Tp>(i+fy,j+fx) + w2*src.at<_Tp>(i+fy,j+cx) + w3*src.at<_Tp>(i+cy,j+fx) + w4*src.at<_Tp>(i+cy,j+cx));
                // floating point precision, so check some machine-dependent epsilon
                dst.at<int>(i-radius,j-radius) += ((t > src.at<_Tp>(i,j)) || (std::abs(t-src.at<_Tp>(i,j)) < std::numeric_limits<float>::epsilon())) << n;
            }
        }
    }
}

static void elbp(InputArray src, OutputArray dst, int radius, int neighbors)
{
    int type = src.type();
    switch (type) {
    case CV_8SC1:   elbp_<char>(src,dst, radius, neighbors); break;
    case CV_8UC1:   elbp_<unsigned char>(src, dst, radius, neighbors); break;
    case CV_16SC1:  elbp_<short>(src,dst, radius, neighbors); break;
    case CV_16UC1:  elbp_<unsigned short>(src,dst, radius, neighbors); break;
    case CV_32SC1:  elbp_<int>(src,dst, radius, neighbors); break;
    case CV_32FC1:  elbp_<float>(src,dst, radius, neighbors); break;
    case CV_64FC1:  elbp_<double>(src,dst, radius, neighbors); break;
    default:
        string error_msg = format("Using Original Local Binary Patterns for feature extraction only works on single-channel images (given %d). Please pass the image data as a grayscale image!", type);
        CV_Error(CV_StsNotImplemented, error_msg);
        break;
    }
}

static Mat
histc_(const Mat& src, int minVal=0, int maxVal=255, bool normed=false)
{
    Mat result;
    // Establish the number of bins.
    int histSize = maxVal-minVal+1;
    // Set the ranges.
    float range[] = { static_cast<float>(minVal), static_cast<float>(maxVal+1) };
    const float* histRange = { range };
    // calc histogram
    calcHist(&src, 1, 0, Mat(), result, 1, &histSize, &histRange, true, false);
    // normalize
    if(normed) {
        result /= (int)src.total();
    }
    return result.reshape(1,1);
}

static Mat histc(InputArray _src, int minVal, int maxVal, bool normed)
{
    Mat src = _src.getMat();
    switch (src.type()) {
        case CV_8SC1:
            return histc_(Mat_<float>(src), minVal, maxVal, normed);
            break;
        case CV_8UC1:
            return histc_(src, minVal, maxVal, normed);
            break;
        case CV_16SC1:
            return histc_(Mat_<float>(src), minVal, maxVal, normed);
            break;
        case CV_16UC1:
            return histc_(src, minVal, maxVal, normed);
            break;
        case CV_32SC1:
            return histc_(Mat_<float>(src), minVal, maxVal, normed);
            break;
        case CV_32FC1:
            return histc_(src, minVal, maxVal, normed);
            break;
        default:
            CV_Error(CV_StsUnmatchedFormats, "This type is not implemented yet."); break;
    }
    return Mat();
}


static Mat spatial_histogram(InputArray _src, int numPatterns,
                             int grid_x, int grid_y, bool /*normed*/)
{
    Mat src = _src.getMat();
    // calculate LBP patch size
    int width = src.cols/grid_x;
    int height = src.rows/grid_y;
    // allocate memory for the spatial histogram
    Mat result = Mat::zeros(grid_x * grid_y, numPatterns, CV_32FC1);
    // return matrix with zeros if no data was given
    if(src.empty())
        return result.reshape(1,1);
    // initial result_row
    int resultRowIdx = 0;
    // iterate through grid
    for(int i = 0; i < grid_y; i++) {
        for(int j = 0; j < grid_x; j++) {
            Mat src_cell = Mat(src, Range(i*height,(i+1)*height), Range(j*width,(j+1)*width));
            Mat cell_hist = histc(src_cell, 0, (numPatterns-1), true);
            // copy to the result matrix
            Mat result_row = result.row(resultRowIdx);
            cell_hist.reshape(1,1).convertTo(result_row, CV_32FC1);
            // increase row count in result matrix
            resultRowIdx++;
        }
    }
    // return result as reshaped feature vector
    return result.reshape(1,1);
}

//------------------------------------------------------------------------------
// wrapper to cv::elbp (extended local binary patterns)
//------------------------------------------------------------------------------

static Mat elbp(InputArray src, int radius, int neighbors) {
    Mat dst;
    elbp(src, dst, radius, neighbors);
    return dst;
}

class LBPextractor
{
    public:
        LBPextractor() = default;
        //LBPextractor(string name): FileName(name) {}
        LBPextractor(string name);
        vector<double> getFeature();

    private:
        string FileName;
        vector<vector<int> > RandN;
        vector<vector<int> > xAndy;
};

LBPextractor::LBPextractor(string name)
{
    FileName = name;
    RandN.push_back({2,8});
    RandN.push_back({3, 16});
    RandN.push_back({4, 16});
    xAndy.push_back({5, 5});
    xAndy.push_back({4, 4});
    xAndy.push_back({3, 3});
    xAndy.push_back({2, 2});
}

vector<double> LBPextractor::getFeature()
{
    vector<double> f;
    Mat m_refFaceImg = imread(FileName, 0);
    for(int i = 0; i < RandN.size(); i++)
    {
        int radius = RandN[i][0];
        int neighbor = RandN[i][1];
        Mat lbp_image = elbp(m_refFaceImg, radius, neighbor);
        for(int j = 0; j < xAndy.size(); j++)
        {
            int m_grid_x = xAndy[j][0];
            int m_grid_y = xAndy[j][1];
            int m_numPatterns = neighbor * (neighbor - 1) + 3;
            Mat query = spatial_histogram(
            lbp_image, /* lbp_image */
            m_numPatterns, /* number of possible patterns可为static_cast<int>(std::pow(2.0, static_cast<double>(_neighbors)))*/
            m_grid_x, /* grid size x */
            m_grid_y, /* grid size y */
            true /* normed histograms */
            );
            vector<double> vec;
            query.row(0).copyTo(vec);
            f.insert(f.end(), vec.begin(), vec.end());
        }
    }
    return f;
}
/*
int main()
{
    string name = "a.jpg";
    LBPextractor A(name);
    vector<double> res = A.getFeature();
    cout<<res.size()<<endl;
    
}*/