Cloud Studio代码运行 #include<opencv2/opencv.hpp>#include<iostream>#include<math.h>#defineRATIO0.4using namespace cv;using namespace std;intmain(int argc,char**argv){Mat box=imread("D:/vcprojects/images/box.png");Mat scene=imread("D:/vcprojects/images/box_in_scene.png");if(scene.empty...
*///所以这里的语句就是创建一个Ptr<ORB>类型的orb,用于接收ORB类中create()函数的返回值Ptr<ORB> orb = ORB::create();//第一步:检测Oriented FAST角点位置.//detect是Feature2D中的方法,orb是子类指针,可以调用//看一下detect()方法的原型参数:需要检测的图像,关键点数组,第三个参数为默认值/* CV_WRAP...
也就是，给定两张共享某些公共区域的图像，目标是“缝合”它们并创建一个全景图像场景。当然也可以是给定...
import copy plt.rcParams['figure.figsize'] = [14.0, 7.0] # 通过指定关键点最大数量和金字塔抽取率来设置ORB算法的参数 orb = cv2.ORB_create(200, 2.0) # 找到灰度图中的关键点,并计算它们的PRB描述符 # 设置None,确认我们没有使用mask keypoints, descriptor = orb.detectAndCompute(training_gray, Non...
(imgCat,None)kpSmallCat,desSmallCat=orb.detectAndCompute(imgSmallCat,None)bf=cv.BFMatcher_create(cv.NORM_HAMMING,crossCheck=True)matches=bf.match(desCat,desSmallCat)matchImg=cv.drawMatches(imgCat,kpCat,imgSmallCat,kpSmallCat,matches,None)cv.imshow("Cat",imgCat)cv.imshow("SmallCat",imgSmall...
进而,我们能通过刚定义的detector中的detect函数,将img中的像素进行分析处理,并将提取出的特征点存放于keypoints容器中;之后再使用刚定义的descriptor中的compute函数,对每张img中的keypoints进行描述子的计算,并存放于Mat类变量descriptor中。 DrawKeypoints函数 Mat outimg1; drawKeypoints( img_1, keypoints_1, ou...
(self.aim_area,cv2.COLOR_BGR2GRAY)self.aimkeypoints,self.aimdescriptor=self.orb.detectAndCompute(self.aim_area_gray,None)# Create copies of the training image to draw our keypoints onkeyp_without_size=copy.copy(self.aim_area)keyp_with_size=copy.copy(self.aim_area)cv2.drawKeypoints(self...
("hand1.jpg"); std::vector<cv::KeyPoint> keypoints; cv::FastFeatureDetector fast(15); // 检测的阈值为50 fast.detect(img, keypoints); cv::Mat img_keypoints; cv::drawKeypoints(img, keypoints, img_keypoints, Scalar::all(-1), DrawMatchesFlags::DEFAULT); cv::imshow("Keypoints", ...
importcv2 as cvdefORB_Feature(img1, img2):#初始化ORBorb =cv.ORB_create()#寻找关键点kp1 =orb.detect(img1) kp2=orb.detect(img2)#计算描述符kp1, des1 =orb.compute(img1, kp1) kp2, des2=orb.compute(img2, kp2)#画出关键点outimg1 = cv.drawKeypoints(img1, keypoints=kp1, outImage=...
keypoints_train, descriptors_train = orb.detectAndCompute(training_gray, None) keypoints_query, descriptors_query = orb.detectAndCompute(query_gray, None) # Create a Brute Force Matcher object. Set crossCheck to True so that the BFMatcher will only return consistent ...