图像算法之三：特征提取算子之SIFT

SIFT（Scale-invariant feature transform）是一种检测局部特征的算法，该算法通过求一幅图中的特征点（interest points,or corner points）及其有关scale 和 orientation 的描述子得到特征并进行图像特征点匹配，获得了良好效果，详细解析如下：

1、算法描述
SIFT特征不只具有尺度不变性，即使改变旋转角度，图像亮度或拍摄视角，仍然能够得到好的检测效果。整个算法分为以下几个部分：
（1）构造尺度空间：DoG尺度空间
（2）检测DoG尺度空间极值点
（3）去除不好的特征点
（4）给特征点赋值一个128维的方向参数。每个关键点都包含三个信息：位置、尺度和方向。
（5）关键点描述子的生成：
首先将坐标轴旋转为关键点的方向，以确保旋转不变性。以关键点为中心取8×8的窗口。
（6）最后进行特征匹配。当两幅图像的SIFT特征向量（128维）生成后，采用关键点特征向量的欧式聚类来作为相似性判定度量。
取图像1中的某个关键点，并找出其与图像2中欧式距离最近的前两个关键点，在这两个关键点中，如果最近的距离除以次近的距离少于某个比例阈值，则接受这一对匹配点。降低这个比例阈值，SIFT匹配点数目会减少，但更加稳定。

2、算法实现：
（一） SIFT的Matlab代码实现：

% [image, descriptors, locs] = sift(imageFile)%% This function reads an image and returns its SIFT keypoints.%   Input parameters:%     imageFile: the file name for the image.%%   Returned:%     image: the image array in double format%     descriptors: a K-by-128 matrix, where each row gives an invariant%         descriptor for one of the K keypoints.  The descriptor is a vector%         of 128 values normalized to unit length.%     locs: K-by-4 matrix, in which each row has the 4 values for a%         keypoint location (row, column, scale, orientation).  The%         orientation is in the range [-PI, PI] radians.%% Credits: Thanks for initial version of this program to D. Alvaro and%          J.J. Guerrero, Universidad de Zaragoza (modified by D. Lowe)function [image, descriptors, locs] = sift(imageFile)% Load imageimage = imread(imageFile);% If you have the Image Processing Toolbox, you can uncomment the following%   lines to allow input of color images, which will be converted to grayscale.% if isrgb(image)%    image = rgb2gray(image);% end[rows, cols] = size(image);% Convert into PGM imagefile, readable by "keypoints" executablef = fopen('tmp.pgm', 'w');if f == -1    error('Could not create file tmp.pgm.');endfprintf(f, 'P5\n%d\n%d\n255\n', cols, rows);fwrite(f, image', 'uint8');fclose(f);% Call keypoints executableif isunix    command = '!./sift ';else    command = '!siftWin32 ';endcommand = [command ' <tmp.pgm >tmp.key'];eval(command);% Open tmp.key and check its headerg = fopen('tmp.key', 'r');if g == -1    error('Could not open file tmp.key.');end[header, count] = fscanf(g, '%d %d', [1 2]);if count ~= 2    error('Invalid keypoint file beginning.');endnum = header(1);len = header(2);if len ~= 128    error('Keypoint descriptor length invalid (should be 128).');end% Creates the two output matrices (use known size for efficiency)locs = double(zeros(num, 4));descriptors = double(zeros(num, 128));% Parse tmp.keyfor i = 1:num    [vector, count] = fscanf(g, '%f %f %f %f', [1 4]); %row col scale ori    if count ~= 4        error('Invalid keypoint file format');    end    locs(i, :) = vector(1, :);    [descrip, count] = fscanf(g, '%d', [1 len]);    if (count ~= 128)        error('Invalid keypoint file value.');    end    % Normalize each input vector to unit length    descrip = descrip / sqrt(sum(descrip.^2));    descriptors(i, :) = descrip(1, :);endfclose(g);

使用方法：
1.寻找图像中的sift特征：

[image,descrips,locs] = sift('scene.pgm');showkeys(image,locs);

scene.pgm

book.pgm

2、对两幅图中的sift特征进行匹配

match(‘scene.pgm’,’book.pgm’);
由于scene和book两幅图中有相同的一本书，但是方向和尺度都不同，从匹配结果可以看出去sift特征匹配检测效果非常好滴！

关于SIFT的其他讲解：
http://blog.csdn.net/abcjennifer/article/details/7639681

http://blog.csdn.net/abcjennifer/article/details/7372880

http://blog.csdn.net/abcjennifer/article/details/7365882

（二）SIFT的Python实现：

import cv2import numpy as npimport pdbpdb.set_trace()#turn on the pdb prompt#read imageimg = cv2.imread('E:\OpenCV\Picture\sky.jpg',cv2.IMREAD_COLOR)gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)cv2.imshow('origin',img);#SIFTdetector = cv2.SIFT() #调用SIFT特征描述子keypoints = detector.detect(gray,None)#检测兴趣点img = cv2.drawKeypoints(gray,keypoints)#画出兴趣点#img = cv2.drawKeypoints(gray,keypoints,flags = cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)cv2.imshow('test',img);cv2.waitKey(0)cv2.destroyAllWindows()

测试：

实验结果：

（三）SIFT的OpenCV实现

#include <opencv2/opencv.hpp>#include <opencv2/core/core.hpp>#include <opencv2/imgproc/imgproc.hpp>#include <opencv2/highgui/highgui.hpp>#include <iostream>#include <opencv2/features2d/features2d.hpp>#include <opencv2/legacy/legacy.hpp>#include <opencv2/nonfree/nonfree.hpp>#include <vector>using namespace std;using namespace cv;int main(void){       cout << "当前使用的OpenCV版本：" << CV_VERSION << endl;       Mat image = imread("scene.pgm");       Mat image2 = imread("book.pgm");       if (image.empty())       {              fprintf(stderr, "Cannot load image %s \n", "scene.pgm");              return -1;       }       if (image2.empty())       {              fprintf(stderr, "Cannot load image %s \n", "book.pgm");              return -1;       }       //显示图像       imshow("image before", image);       waitKey(10);       imshow("image2 before", image2);       waitKey(10);       //sift特征点检测       SiftFeatureDetector siftdtc;       vector<KeyPoint> kp1, kp2;       siftdtc.detect(image, kp1);       Mat outimg1;       drawKeypoints(image, kp1, outimg1);       imshow("image1 keypoints", outimg1);       siftdtc.detect(image2, kp2);       Mat outimg2;       drawKeypoints(image2, kp2, outimg2);       imshow("image2 keypoints", outimg2);       //生成描述子       SiftDescriptorExtractor ext;       Mat descp1, descp2;       BruteForceMatcher<L2<float>> matcher;       vector<DMatch> matches;       Mat img_matches;       ext.compute(image, kp1, descp1);       ext.compute(image2, kp2, descp2);       imshow("desc", descp1);       waitKey(10);       //cout << endl << descp1 << endl;       matcher.match(descp1, descp2, matches);       drawMatches(image, kp1, image2, kp2, matches, img_matches);       imshow("matches", img_matches);       waitKey(10);       //namedWindow("My Image");       //imshow("My Image",image);       //waitKey(0);       //Mat result,image2,image3;       //result = image;       //image2 = result;       //result.copyTo(image3);       //flip(image, result, 1);//正数水平翻转，0表示垂直翻转，负数表示既有水平也有垂直翻转       //namedWindow("Output window");       //imshow("Output window", result);       //namedWindow("1");       //imshow("1", image2);       //namedWindow("2");       //imshow("2", image3);       waitKey(0);       return 0;}