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fanfuhan OpenCV 教學107 ~ opencv-107-Brisk特徵提取與描述子匹配 資料來源: https://fanfuhan.github.io/ https://fanfuhan.github.io/2019/05/21/opencv-107/ GITHUB:https://github.com/jash-git/fanfuhan_ML_OpenCV BRISK（二進制魯棒不變可擴展關鍵點）是一種基於尺度空間不變性類似的ORB特徵描述子的特徵提取算法。BRISK主要步驟可以分為如下兩步：    ~建立尺度空間金字塔實現關鍵點定位    ~根據關鍵點生成描述子 C++ #include #include using namespace cv; using namespace std; int main(int argc, char** argv) { Mat box = imread("D:/images/box.png"); Mat box_in_sence = imread("D:/images/box_in_scene.png"); // 创建BRISK auto brisk_detector = BRISK::create(); vector kpts_01, kpts_02; Mat descriptors1, descriptors2; brisk_detector->detectAndCompute(box, Mat(), kpts_01, descriptors1); brisk_detector->detectAndCompute(box_in_sence, Mat(), kpts_02, descriptors2); // 定义描述子匹配 - 暴力匹配 Ptr matcher = DescriptorMatcher::create(DescriptorMatcher::BRUTEFORCE); std::vector< DMatch > matches; matcher->match(descriptors1, descriptors2, matches); // 绘制匹配 Mat img_matches; drawMatches(box, kpts_01, box_in_sence, kpts_02, matches, img_matches); imshow("AKAZE-Matches", img_matches); imwrite("D:/result.png", img_matches); waitKey(0); return 0; } Python import cv2 as cv box = cv.imread("D:/images/box.png"); box_in_sence = cv.imread("D:/images/box_in_scene.png"); cv.imshow("box", box) cv.imshow("box_in_sence", box_in_sence) # 创建BRISK特征检测器 brisk = cv.BRISK_create() kp1, des1 = brisk.detectAndCompute(box,None) kp2, des2 = brisk.detectAndCompute(box_in_sence,None) # 暴力匹配 bf = cv.BFMatcher(cv.NORM_HAMMING, crossCheck=True) matches = bf.match(des1,des2) # 绘制匹配 result = cv.drawMatches(box, kp1, box_in_sence, kp2, matches, None) cv.imshow("orb-match", result) cv.waitKey(0) cv.destroyAllWindows()