OpenCV4入门122：HOG特征计算

索引地址：系列索引

// 功能：代码  HOG特征计算
// 作者：朱伟 zhu1988wei@163.com
// 来源：《OpenCV图像处理编程实例》
// 博客：http://blog.csdn.net/zhuwei1988
// 更新：2016-8-1
// 说明：版权所有，引用或摘录请联系作者，并按照上面格式注明出处，谢谢。//
#include <iostream>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/opencv.hpp>
#include <opencv2/xfeatures2d/nonfree.hpp>
using namespace cv;
using namespace std;
#define NBINS 9
#define THETA 180 / NBINS
#define CELLSIZE 20
#define BLOCKSIZE 2
#define R (CELLSIZE * (BLOCKSIZE)*0.5)
// 计算积分图
std::vector<Mat> CalculateIntegralHOG(Mat &srcMat) {
    // sobel边缘检测
    Mat sobelMatX, sobelMatY;
    Sobel(srcMat, sobelMatX, CV_32F, 1, 0);
    Sobel(srcMat, sobelMatY, CV_32F, 0, 1);
    std::vector<Mat> bins(NBINS);
    for (int i = 0; i < NBINS; i++) {
        bins[ i ] = Mat::zeros(srcMat.size(), CV_32F);
    }
    Mat magnMat, angleMat;
    // 坐标转换
    cartToPolar(sobelMatX, sobelMatY, magnMat, angleMat, true);
    // 角度变换
    add(angleMat, Scalar(180), angleMat, angleMat < 0);
    add(angleMat, Scalar(-180), angleMat, angleMat >= 180);
    angleMat /= THETA;
    for (int y = 0; y < srcMat.rows; y++) {
        for (int x = 0; x < srcMat.cols; x++) {
            // 计算bins下幅值
            int ind = angleMat.at<float>(y, x);
            bins[ ind ].at<float>(y, x) += magnMat.at<float>(y, x);
        }
    }
    // 积分图像的生成
    std::vector<Mat> integrals(NBINS);
    for (int i = 0; i < NBINS; i++) {
        integral(bins[ i ], integrals[ i ]);
    }
    return integrals;
}
// 计算单个cell HOG特征
void cacHOGinCell(Mat &HOGCellMat, Rect roi, std::vector<Mat> &integrals) {
    // 快速积分HOG实现
    int x0 = roi.x, y0 = roi.y;
    int x1 = x0 + roi.width, y1 = y0 + roi.height;
    for (int i = 0; i < NBINS; i++) {
        // 根据矩阵的上下左右坐标
        Mat   integral             = integrals[ i ];
        float a                    = integral.at<double>(y0, x0);
        float b                    = integral.at<double>(y1, x1);
        float c                    = integral.at<double>(y0, x1);
        float d                    = integral.at<double>(y1, x0);
        HOGCellMat.at<float>(0, i) = (a + b) - (c + d);
    }
}
// HOG直方图获取
cv::Mat getHog(Point pt, std::vector<Mat> &integrals) {
    if (pt.x - R < 0 || pt.y - R < 0 || pt.x + R >= integrals[ 0 ].cols ||
        pt.y + R >= integrals[ 0 ].rows) {
        return Mat();
    }
    // 直方图
    Mat   hist(Size(NBINS * BLOCKSIZE * BLOCKSIZE, 1), CV_32F);
    Point tl(0, pt.y - R);
    int   c = 0;
    // 遍历块
    for (int i = 0; i < BLOCKSIZE; i++) {
        tl.x = pt.x - R;
        for (int j = 0; j < BLOCKSIZE; j++) {
            // 获取当前窗口进行局部直方图计算
            Rect roi(tl, tl + Point(CELLSIZE, CELLSIZE));
            Mat  hist_temp = hist.colRange(c, c + NBINS);
            cacHOGinCell(hist_temp, roi, integrals);
            tl.x += CELLSIZE;
            c += NBINS;
        }
        tl.y = CELLSIZE;
    }
    normalize(hist, hist, 1, 0, NORM_L2);
    return hist;
}
// 计算HOG特征
std::vector<Mat> cacHOGFeature(cv::Mat srcImage) {
    Mat              grayImage;
    std::vector<Mat> HOGMatVector;
    cv::cvtColor(srcImage, grayImage, CV_RGB2GRAY);
    grayImage.convertTo(grayImage, CV_8UC1);
    // 积分图像生成
    std::vector<Mat> integrals = CalculateIntegralHOG(grayImage);
    Mat              image     = grayImage.clone();
    image *= 0.5;
    // Block遍历
    cv::Mat HOGBlockMat(Size(NBINS, 1), CV_32F);
    for (int y = CELLSIZE / 2; y < grayImage.rows; y += CELLSIZE) {
        for (int x = CELLSIZE / 2; x < grayImage.cols; x += CELLSIZE) {
            // 获取当前窗口HOG
            cv::Mat hist = getHog(Point(x, y), integrals);
            if (hist.empty())
                continue;
            HOGBlockMat = Scalar(0);
            for (int i = 0; i < NBINS; i++) {
                for (int j = 0; j < BLOCKSIZE; j++) {
                    HOGBlockMat.at<float>(0, i) += hist.at<float>(0, i + j * NBINS);
                }
            }
            // L2范数归一化
            normalize(HOGBlockMat, HOGBlockMat, 1, 0, CV_L2);
            HOGMatVector.push_back(HOGBlockMat);
            Point center(x, y);
            // 绘制HOG特征图
            for (int i = 0; i < NBINS; i++) {
                double theta = (i * THETA + 90.0) * CV_PI / 180.0;
                Point  rd(CELLSIZE * 0.5 * cos(theta), CELLSIZE * 0.5 * sin(theta));
                Point  rp = center - rd;
                Point  lp = center - -rd;
                line(image, rp, lp, Scalar(255 * HOGBlockMat.at<float>(0, i), 255, 255));
            }
        }
    }
    imshow("out", image);
    return HOGMatVector;
}
int main() {
    cv::Mat srcImage = cv::imread("..\\images\\hand1.jpg");
    if (srcImage.empty())
        return -1;
    cv::imshow("srcImage ", srcImage);
    std::vector<Mat> HOGFeatureMat = cacHOGFeature(srcImage);
    cv::waitKey(0);
    return 0;
}