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 loc
ORB::create() 报错: error: no matching function for call to ‘cv::ORB::create()’ Ptr<FeatureDetector> detector = ORB::create(); 源码里面的定义: // Create feature detector by detector name. CV_WRAP static Ptr<FeatureDetector> create( const string& detectorType ); 说明要指定类型(但书上的...
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...
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) ...
static Ptr<ORB> create(int nfeatures=500, float scaleFactor=1.2f, int nlevels=8, int edgeThreshold=31, int firstLevel=0, int WTA_K=2, int scoreType=ORB::HARRIS_SCORE, int patchSize=31, int fastThreshold=20); 参数一:int类型的nfeatures,用于ORB的,保留最大的关键点数,默认值500; 参数二...
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....
create();detector->detectAndCompute(scene,Mat(),keypoints_sence,descriptors_sence);detector->detectAndCompute(box,Mat(),keypoints_obj,descriptors_box);vector<DMatch>matches;// 初始化flann匹配// Ptr<FlannBasedMatcher> matcher = FlannBasedMatcher::create(); // default is bad, using local ...
# 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) ...
fast = =cv.FastFeatureDetector_create( threshold, nonmaxSuppression) 参数: threshold:阈值t,有默认值10 nonmaxSuppression:是否进行非极大值抑制,默认值True 返回: Fast:创建的FastFeatureDetector对象 2.利用fast.detect检测关键点,没有对应的关键点描述 ...