目标跟踪的点跟踪技术(4)

来源：互联网发布：日本人二战知乎编辑：程序博客网时间：2024/05/16 08:29

获取特征点后，SIFT用方向梯度直方图描述每个特征点的特征。

首先计算每个特征点的梯度幅度和方向：

然后计算特征点周围16x16范围的每个点的梯度方向，对其中上下左右每4x4的区域，统计区域内各点的方向梯度直方图。

直方图把360度角划分为36个bin，计算前先用高斯平滑削弱边缘的影响。16x16点的梯度方向在进行直方图统计前先按特征点的“主方向”旋转，以获取旋转不变性特征。

以下是OpenCV中SIFT的简单应用：

#include <opencv2\opencv.hpp>#include <opencv2\nonfree\nonfree.hpp>using namespace std;using namespace cv;int main() {Mat img = imread("d:/dataset/book.jpg");Mat gray;cvtColor(img, gray, CV_RGB2GRAY);Mat rotImg;Mat affine = getRotationMatrix2D(Point2f((float)img.cols / 2, (float)img.rows / 2), 45, 0.5);warpAffine(img, rotImg, affine, Size(img.cols, img.rows));Mat rotGray;cvtColor(rotImg, rotGray, CV_RGB2GRAY);initModule_nonfree();Ptr<FeatureDetector> detector = FeatureDetector::create("SIFT");Ptr<DescriptorExtractor> extractor = DescriptorExtractor::create("SIFT");      Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce"); if (detector.empty() || extractor.empty() ) {cout << "create detector error" << endl;exit(-1);}vector<KeyPoint> pts1, pts2;Mat feats1, feats2;vector<DMatch> matches, goodMatches;detector->detect(gray, pts1);extractor->compute(gray, pts1, feats1);detector->detect(rotGray, pts2);extractor->compute(rotGray, pts2, feats2);matcher->match(feats1, feats2, matches);double maxDist = 0, minDist = 1000;for (int i = 0; i < matches.size(); i++) {if (minDist > matches[i].distance)minDist = matches[i].distance;if (maxDist < matches[i].distance)maxDist = matches[i].distance;}cout << minDist << " " << maxDist << endl;double thres = minDist + 0.1 * (maxDist - minDist);for (int i = 0; i < matches.size(); i++) {if (matches[i].distance <= thres)goodMatches.push_back(matches[i]);}Mat disp;drawMatches(img, pts1, rotImg, pts2, goodMatches, disp);namedWindow("disp");imshow("disp", disp);waitKey();}

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