1、ORB_create函数(初始化orb检测器) cv2.ORB_create(nfeatures = 500, scaleFactor = 1.2, nlevels = 8, edgeThreshold = 31, firstLevel = 0, WTA_K = 2, scoreType = HARRIS_SCORE, patchSize = 31, fastThreshold = 20) 1. 2. 3. 4. 5. 6. 7. 8. 9. 参数: nfeatures:特征点最大数...
当启用交叉检查时,只有在训练图像中的关键点A是查询图像中关键点B的最佳匹配(反之亦然)(即,如果查询图像中的关键点B是训练图像中的点A)。 # Set the default figure sizeplt.rcParams['figure.figsize']=[34.0,34.0]# Create a Brute Force Matcher object. We set crossCheck to True so that the BFMatche...
来看代码: def ORB(img):# Initiate ORB detectororb = cv2.ORB_create()# find the keypoints with ORBkp = orb.detect(img, None)# compute the descriptors with ORBkp, des = orb.compute(img, kp)# draw only keypoints location,not size and orientationimg2 = cv2.drawKeypoints(img, kp, Non...
1. Create a new repository Create a new, empty, GitHub repository. The name of your repository is not critical, but something similar to "myProject-orb" is recommended. Ensure that the repository is completely empty. Uncheck any options such as "Add a README.md" or "Choose a license"....
cv2.ORB_create(nfeatures = 500,scaleFactor=1.2,nlevels=8,edgeThreshold = 31,fistLevel =0,WTA_K=2, score_type = HARRIS_SCORE,patchSize = 31,fastThreshold=20) """ 参数解释: nfeatures : 确定想要定位的特征,即关键点的最大数量。(int) ...
Ptr<FeatureDetector> detector = ORB::create(); Ptr<DescriptorExtractor> descriptor = ORB::create(); Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming"); 通过定义FeatureDetector的对象可以使用多种特征检测方法。通过create()函数调用。 detector->detect(img_1, keypoints_1);...
# Create a Brute Force Matcher object. Set crossCheck to True so that the BFMatcher will only return consistent # pairs. Such technique usually produces best results with minimal number of outliers when there are enough matches. bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck = True) ...
# Create a Brute Force Matcher object. Set crossCheck to True so that the BFMatcher will only return consistent # pairs. Such technique usually produces best results with minimal number of outliers when there are enough matches. bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck = True) ...
Ptr<ORB>detector=ORB::create; detector->detectAndCompute(scene,Mat,keypoints_sence,deors_sence); detector->detectAndCompute(box,Mat,keypoints_obj,deors_box); vector<DMatch>matches; // 初始化flann匹配 // Ptr<FlannBasedMatcher> matcher = FlannBasedMatcher::create; // default is bad, using...
var descriptor = DescriptorExtractor.create(DescriptorExtractor.ORB);var matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);3. 计算图片特征 // 处理大图 var bigImg = Imgcodecs.imread(bigImgPath1);var descriptors1 = new Mat();var keyPoints1 = new MatOfKeyPoint();detector....