FAST角点检测算法（二）- 非极大值抑制筛选fast特征点

FAST角点检测算法（二）- 非极大值抑制筛选fast特征点

author@jason_ql（lql0716）

http://blog.csdn.net/lql0716

fast角点检测算法参考文章《fast角点检测算法》（涵盖fast角点检测原理及C++、python代码，以及效果图）

非极大值抑制，就是对于一个3*3（或5*5,7*7等奇数窗口）的窗口，如果存在多个特征点，则删除响应值较小的特征点，只保留响应值最大的特征点。

这里根据fast特征点的响应值的大小，做了非极大值抑制处理，对特征点进行了筛选。

1.1 C++代码

#include <opencv2\opencv.hpp>

using namespace cv;
using namespace std;

//局部极大值抑制，这里利用fast特征点的响应值做比较
void selectMax(int window, cv::Mat gray, std::vector<KeyPoint> & kp){

//window是局部极大值抑制的窗口大小，r为半径
int r = window / 2;
if (window != 0){
//对kp中的点进行局部极大值筛选
for (int i = 0; i < kp.size(); i++){
for (int j = i + 1; j < kp.size(); j++){
//如果两个点的距离小于半径r，则删除其中响应值较小的点
if (abs(kp[i].pt.x - kp[j].pt.x) + abs(kp[i].pt.y - kp[j].pt.y) <= 2 * r){
if (kp[i].response < kp[j].response){
std::vector<KeyPoint>::iterator it = kp.begin() + i;
kp.erase(it);
selectMax(window, gray, kp);
}
else{
std::vector<KeyPoint>::iterator it = kp.begin() + j;
kp.erase(it);
selectMax(window, gray, kp);
}
}
}
}
}

}

//
void fastpoint(cv::Mat gray, int threshold, int window, int pointNum, std::vector<KeyPoint> & kp){

std::vector<KeyPoint> keypoint;

cv::FastFeatureDetector fast(threshold, true);  //threshold 为阈值，越大，特征点越少
fast.detect(gray, keypoint);  //fast特征检测
if (keypoint.size() > pointNum){
threshold = threshold + 5;
fastpoint(gray, threshold, window, pointNum, keypoint);
}
selectMax(window, gray, keypoint);
kp.assign(keypoint.begin(), keypoint.end());    //复制可以point到kp

}

int main(){
cv::Mat img = cv::imread("D:/photo/06.jpg");
cv::Mat gray;
cv::cvtColor(img, gray, cv::COLOR_BGR2GRAY);
std::vector<KeyPoint> kp;
int threshold = 30;  //fast阈值
int window1 = 7;  //局部非极大值抑制窗口
int pointMaxNum1 = 400; //特征点最大个数
fastpoint(gray, threshold, window1, pointMaxNum1, kp);

cv::Mat img2, gray2;
std::vector<KeyPoint> kp2;
img.copyTo(img2);
gray.copyTo(gray2);
int window2 = 15;  //局部非极大值抑制窗口
int pointMaxNum2 = 400; //特征点最大个数
fastpoint(gray2, threshold, window2, pointMaxNum2, kp2);

cv::drawKeypoints(img, kp, img, Scalar(0, 0, 255));
cv::drawKeypoints(img2, kp2, img2, Scalar(255, 0, 0));

cv::imwrite("D:/photo/06_1.jpg", img);
cv::namedWindow("img", cv::WINDOW_NORMAL);
cv::imshow("img", img);

cv::imwrite("D:/photo/06_2.jpg", img2);
cv::namedWindow("img2", cv::WINDOW_NORMAL);
cv::imshow("img2", img2);

cv::waitKey(0);

system("pause");
return 0;
}

原图：



效果图img：



效果图img2：

1.2 python代码

# -*- coding: utf-8 -*-
"""
Created on Fri Jun 16 09:55:41 2017

@author: User
"""

import cv2
import numpy as np
import copy

def selectMax(window, gray, kp):
r = window / 2
a = 0
if window != 0:
for i in range(np.array(kp).shape[0]):
for j in range(i+1, np.array(kp).shape[0]):
if np.abs(kp[i].pt[0]-kp[j].pt[0]) + np.abs(kp[i].pt[1] - kp[j].pt[1]) <= 2*r:
if kp[i].response < kp[j].response:
kp.pop(i)
a = 1
break
else:
kp.pop(j)
a = 1
break
if a != 0:
kp = selectMax(window, gray, kp)

return kp

def fastpoint(gray, threshold, window, pointNum):
#fast = cv2.FeatureDetector_create('FAST')
#cv2.FAST_FEATURE_DETECTOR_TYPE_5_8
#cv2.FAST_FEATURE_DETECTOR_TYPE_7_12
#cv2.FAST_FEATURE_DETECTOR_TYPE_9_16

fasts = cv2.FastFeatureDetector(threshold)
kp = fasts.detect(gray, None)
if np.array(kp).shape[0] > pointNum:
threshold = threshold + 5
kp = fastpoint(gray, threshold, window, pointNum)

kp0 = selectMax(window, gray, kp)
return kp0

img = cv2.imread('D:/photo/06.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
threshold = 20;
window1 = 7
pointMaxNum1 = 400
kp = fastpoint(gray, threshold, window1, pointMaxNum1)

img2 = copy.deepcopy(img)
gray2 = copy.deepcopy(gray)
window2 = 15
pointMaxNum2 = 400
kp2 = fastpoint(gray2, threshold, window2, pointMaxNum2)

img = cv2.drawKeypoints(img, kp, color = (0,255,255))
img2 = cv2.drawKeypoints(img2, kp2, color = (255,255,0))

cv2.imwrite('D:/photo/06_1p.jpg', img)
cv2.namedWindow('img', cv2.WINDOW_NORMAL)
cv2.imshow('img',img)

cv2.imwrite('D:/photo/06_2p.jpg', img2)
cv2.namedWindow('img2', cv2.WINDOW_NORMAL)
cv2.imshow('img2',img2)

cv2.waitKey(0)

效果图img：



效果图img2：



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