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

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author@jason_ql（lql0716）
http://blog.csdn.net/lql0716

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• 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 cv2import numpy as npimport copydef 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 kpdef 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 kp0img = cv2.imread('D:/photo/06.jpg')gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)threshold = 20;window1 = 7pointMaxNum1 = 400kp = fastpoint(gray, threshold, window1, pointMaxNum1)img2 = copy.deepcopy(img)gray2 = copy.deepcopy(gray)window2 = 15pointMaxNum2 = 400kp2 = 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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