Zi 字媒體

fanfuhan OpenCV 教學097 ~ opencv-097-基於描述子比對/匹配的已知對象定位/標記 資料來源: https://fanfuhan.github.io/ https://fanfuhan.github.io/2019/05/10/opencv-097/ GITHUB:https://github.com/jash-git/fanfuhan_ML_OpenCV 圖像特徵點檢測，描述子生成以後，就可以通過OpenCV提供的描述子匹配算法，得到描述子直接的距離，距離越小的說明是匹配越好的，設置一個距離閾值，通常是最大匹配距離的1/5〜1/4左右作為閾值，得到所有小於閾值的匹配點，作為輸入，通過單應性矩陣，獲得這兩個點所在平面的變換關係H，根據H使用透視變換就可以根據輸入的對像圖像獲得場景圖像中對象位置，最終放置位置即可。 C++ #include #include #include #define RATIO 0.4 using namespace cv; using namespace std; int main(int argc, char** argv) { Mat box = imread("D:/images/box.png"); Mat scene = imread("D:/images/box_in_scene.png"); if (scene.empty()) { printf("could not load image...\n"); return -1; } imshow("input image", scene); vector keypoints_obj, keypoints_sence; Mat descriptors_box, descriptors_sence; Ptr detector = ORB::create(); detector->detectAndCompute(scene, Mat(), keypoints_sence, descriptors_sence); detector->detectAndCompute(box, Mat(), keypoints_obj, descriptors_box); vector matches; // 初始化flann匹配 // Ptr matcher = FlannBasedMatcher::create(); // default is bad, using local sensitive hash(LSH) Ptr matcher = makePtr(makePtr(12, 20, 2)); matcher->match(descriptors_box, descriptors_sence, matches); // 发现匹配 vector goodMatches; printf("total match points : %d\n", matches.size()); float maxdist = 0; for (unsigned int i = 0; i < matches.size(); ++i) { printf("dist : %.2f \n", matches[i].distance); maxdist = max(maxdist, matches[i].distance); } for (unsigned int i = 0; i < matches.size(); ++i) { if (matches[i].distance < maxdist*RATIO) goodMatches.push_back(matches[i]); } Mat dst; drawMatches(box, keypoints_obj, scene, keypoints_sence, goodMatches, dst); imshow("output", dst); //-- Localize the object std::vector obj_pts; std::vector scene_pts; for (size_t i = 0; i < goodMatches.size(); i++) { //-- Get the keypoints from the good matches obj_pts.push_back(keypoints_obj[goodMatches[i].queryIdx].pt); scene_pts.push_back(keypoints_sence[goodMatches[i].trainIdx].pt); } Mat H = findHomography(obj_pts, scene_pts, RHO); // 无法配准 // Mat H = findHomography(obj, scene, RANSAC); //-- Get the corners from the image_1 ( the object to be "detected" ) std::vector obj_corners(4); obj_corners[0] = Point(0, 0); obj_corners[1] = Point(box.cols, 0); obj_corners[2] = Point(box.cols, box.rows); obj_corners[3] = Point(0, box.rows); std::vector scene_corners(4); perspectiveTransform(obj_corners, scene_corners, H); //-- Draw lines between the corners (the mapped object in the scene - image_2 ) line(dst, scene_corners[0] + Point2f(box.cols, 0), scene_corners[1] + Point2f(box.cols, 0), Scalar(0, 255, 0), 4); line(dst, scene_corners[1] + Point2f(box.cols, 0), scene_corners[2] + Point2f(box.cols, 0), Scalar(0, 255, 0), 4); line(dst, scene_corners[2] + Point2f(box.cols, 0), scene_corners[3] + Point2f(box.cols, 0), Scalar(0, 255, 0), 4); line(dst, scene_corners[3] + Point2f(box.cols, 0), scene_corners[0] + Point2f(box.cols, 0), Scalar(0, 255, 0), 4); //-- Show detected matches imshow("Good Matches & Object detection", dst); imwrite("D:/result.png", dst); waitKey(0); waitKey(0); return 0; } Python """ 基于描述子匹配的已知对象定位 """ import cv2 as cv import numpy as np box = cv.imread("images/box.png") box_in_scene = cv.imread("images/box_in_scene.png") cv.imshow("box", box) cv.imshow("box_in_scene", box_in_scene) # 创建ORB特征检测器 orb = cv.ORB_create() # 得到特征关键点和描述子 kp1, des1 = orb.detectAndCompute(box, None) kp2, des2 = orb.detectAndCompute(box_in_scene, None) # 暴力匹配 bf = cv.BFMatcher(cv.NORM_HAMMING, crossCheck=True) matchers = bf.match(des1, des2) # 发现匹配 maxdist = 0 goodMatches = [] for m in matchers: maxdist = max(maxdist, m.distance) for m in matchers: if m.distance < 0.4 * maxdist: goodMatches.append(m) # 找到本地化对象 obj_pts = np.float32([kp1[m.queryIdx].pt for m in goodMatches]).reshape(-1, 1, 2) scene_pts = np.float32([kp2[m.trainIdx].pt for m in goodMatches]).reshape(-1, 1, 2) # findHomography 函数是计算变换矩阵 # 参数cv.RANSAC / cv.RHO是使用RANSAC算法寻找一个最佳单应性矩阵H，即返回值M # 返回值：M 为变换矩阵，mask是掩模 M, mask = cv.findHomography(obj_pts, scene_pts, cv.RANSAC) # 获取box的图像尺寸 h, w, _ = box.shape # obj_corners是图像box的四个顶点 obj_corners = np.float32([[0, 0], [w, 0], [w, h], [0, h]]).reshape(-1, 1, 2) # 计算变换后的四个顶点坐标位置,透视变换 scene_corners = cv.perspectiveTransform(obj_corners, M) # 根据四个顶点坐标位置在img2图像画出变换后的边框 box_in_scene = cv.polylines(box_in_scene, [np.int32(scene_corners)], True, (0, 0, 255), 3, cv.LINE_AA) # 绘制 result = cv.drawMatches(box, kp1, box_in_scene, kp2, goodMatches, None) cv.imshow("orb-match", result) cv.waitKey(0) cv.destroyAllWindows()