fast特征点提取

 

using namespace cv;
//由图像得到FAST特征点（先得到候选点，进行非极大值抑制，计算Harris评分）
/*基本思想:
 * 1、遍历每个像素点，以像素点为中心，半径上的16个点，计算与中心点的灰度差，若灰度差小于阈值，则选为候选点集；
 *      使用小技巧加速该算法，这里取设定数目为12。

　　（1）判断点1和点9灰度值与点p差值绝对值是否都大于阈值，如果是则继续；否则pass该点

      （2）判断点1、点5、点9、点13四点与点p灰度值差值大于阈值的数目是否大于2，如果是则继续；否则pass该点

      （3）判断圆周上16点与点p灰度值差值大于阈值的数目是否不小于12，如果是则认为该点是候选点；否则pass该点

　　对图像中所有像素点进行上述操作后，得到候选点点集
 */
int sumDelta(uchar* delta, int size)
{
    int sum = 0;
    for (int i = 0; i < size; i++)
    {
	sum += *delta;
	delta++;
	
    }
    return sum;
}
void fastDector(Mat& image)
{
    Mat featureCandidate(image.size(), CV_8U, Scalar(0));
    Mat fastScore(image.size(), CV_32F, Scalar(0));
    int threshold = 20;
    vector points;
    int size = 16;
    for (int x = 3; x < image.rows - 3; x++)
    {
	for(int y = 3; y < image.cols - 3; y++)
	{
	    //定义数组存放偏差
	    uchar diff[16] = {0};
	    uchar delta[16] = {0};
	    
	    delta[0] = (diff[0] = abs(image.at(x, y) - image.at(x, y - 3)) > threshold);
	    delta[8] = (diff[8] = abs(image.at(x, y) - image.at(x, y + 3)) > threshold);	    
	    if (sumDelta(delta, size) ==0 )
		continue;
	    else
	    {
		delta[4] = (diff[4] = abs(image.at(x, y) - image.at(x - 3, y)) > threshold);
		delta[12] = (diff[12] = abs(image.at(x, y) - image.at(x + 3, y)) > threshold);
	    
		if (sumDelta(delta, size) < 3)
		    continue;
		else
		{
		    delta[1] = (diff[1] = abs(image.at(x,y) - image.at(x+1, y-3))) > threshold;
                    delta[2] = (diff[2] = abs(image.at(x,y) - image.at(x+2, y-2))) > threshold;
                    delta[3] = (diff[3] = abs(image.at(x,y) - image.at(x+3, y-1))) > threshold;
                                
                    delta[5] = (diff[5] = abs(image.at(x,y) - image.at(x+3, y+1))) > threshold;
                    delta[6] = (diff[6] = abs(image.at(x,y) - image.at(x+2, y+2))) > threshold;
                    delta[7] = (diff[7] = abs(image.at(x,y) - image.at(x+1, y+3))) > threshold;
 
                    delta[9] = (diff[9] = abs(image.at(x,y) - image.at(x-1, y+3))) > threshold;
                    delta[10] = (diff[10] = abs(image.at(x,y) - image.at(x-2, y+2))) > threshold;
                    delta[11] = (diff[11] = abs(image.at(x,y) - image.at(x-3, y+1))) > threshold;
 
                    delta[13] = (diff[13] = abs(image.at(x,y) - image.at(x-3, y-1))) > threshold;
                    delta[14] = (diff[14] = abs(image.at(x,y) - image.at(x-2, y-2))) > threshold;
                    delta[15] = (diff[15] = abs(image.at(x,y) - image.at(x-1, y-3))) > threshold;
		    if (sumDelta(delta, size) >= 12)
		    {
			
			points.push_back(Point(y, x));
			fastScore.at(x, y) = (sumDelta(delta, size));
			featureCandidate.at(x, y) = 255;   
		    }
		}
		
	    }
	}
    }
    vector::const_iterator itp = points.begin();
    Mat image1;
    image.copyTo(image1);
    while(itp != points.end())
    {
	circle(image1, *itp, 3, Scalar(255), 1);
	++itp;
    }
    imshow("筛选前", image1);
    waitKey(0);
    imwrite("筛选前.png", image1);
    //利用膨胀与比较的组合实现非极大值的抑制
    Mat image2;
    image.copyTo(image2);
    Mat dilated(fastScore.size(), CV_32F, Scalar(0));
    Mat localMax;
    dilate(fastScore, dilated, Mat());
    compare(fastScore, dilated, localMax, CMP_EQ);
    bitwise_and(featureCandidate, localMax, featureCandidate);
    
    for(int x = 0; x < featureCandidate.rows; x++)
    {
	for(int y = 0; y < featureCandidate.cols; y++)
	{
	    if (featureCandidate.at(x, y))
	    {
		circle(image2, Point(y, x), 3, Scalar(255), 1);
	    }
	}
    }
    imshow("极大值抑制", image2);
    imwrite("极大值抑制.png", image2);
    
}

#include "opencv2/opencv.hpp"
#include "../include/orb.h"
using namespace cv;

int main(int argc, char**argv)
{
    Mat image = imread("imgL.jpg", 0);
    fastDector(image);
    return 0;
}

参考链接：

https://blog.csdn.net/lwx309025167/article/details/80234307

包含fast的改进

https://www.cnblogs.com/Wiley-hiking/p/6901486.html?utm_source=itdadao&utm_medium=referral