norm=cv.NORM_HAMMINGelifchunks[0] =='akaze': detector=cv.AKAZE_create() norm=cv.NORM_HAMMINGelifchunks[0] =='brisk': detector=cv.BRISK_create() norm=cv.NORM_HAMMINGelse:returnNone, Noneif'flann'inchunks:ifnorm ==cv.NORM_L2: flann_params= dict(algorithm=FLANN_INDEX_KDTREE, trees=5)...
self.sift = cv2.xfeatures2d.SIFT_create() self.img2 = cv2.imread(dir_path + 'stop.png',0) # trainImage1 self.kp2, self.des2 = self..detectAndCompute(self.img2,None) # FLANN: Fast Library for Approximate Nearest Neighbors FLANN_INDEX_KDTREE = 0 index_params = dict(algorithm = FL...
indexParams = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) searchParams = dict(checks=50) flann = cv2.FlannBasedMatcher(indexParams, searchParams) # 进行匹配 matches = flann.knnMatch(des1, des2, k=2) # 准备空的掩膜 画好的匹配项 matchesMask = [[0, 0] for i in range(len(matches))]...
flann::KDTreeIndexParams indexParams(2); //此处我将trees参数设置为2(这也就是kd-trees唯一需要设置的params) flann::Index kdtree(source, indexParams); //kd树索引建立完毕 1 2 3 4 5 6 7 8 KMeansIndexParams //K均值索引 struct KMeansIndexParams : public IndexParams { KMeansIndexParams( int...
FLANN_INDEX_KDTREE = 0 indexParams = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) searchParams = dict(checks=50) flann = cv2.FlannBasedMatcher(indexParams, searchParams) # 进行匹配 matches = flann.knnMatch(des1, des2, k=2) # 准备空的掩膜 画好的匹配项 ...
5 # FLANN 参数FLANN_INDEX_KDTREE = 0index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)search_params = dict(checks=50)# 使用FlannBasedMatcher 寻找最近邻近似匹配flann = cv.FlannBasedMatcher(index_params,search_params)# 使用knnMatch匹配处理，并返回匹配matchesmatches = flann.knnMatch...
index_params 字典类型,例如先选择算法:algorithm取值如下,在对该算法进行参数设置,例如选择KDTree算法,则可取dict(algorithm=FLANN_INDEX_KDTREE, trees=5) search_params 字典类型 SearchParams (int checks=32, float eps=0, bool sorted=true) checks为int类型,是遍历次数,一般只改变这个参数 ...
sift=cv2.xfeatures2d.SIFT_create()# 查找监测点和匹配符kp1,des1=sift.detectAndCompute(img1,None)kp2,des2=sift.detectAndCompute(img2,None)print(len(kp1),len(des1))# 1402, 1402FLANN_INDEX_KDTREE=0indexParams=dict(algorithm=FLANN_INDEX_KDTREE,trees=5)searchParams=dict(checks=50)flann=cv2...
然后，关键点找出来了，匹配就比较容易了，貌似就一句话：取一幅图像中的一个SIFT关键点，并找出其与...
//利用m_image构造 a set of randomized kd-trees 一系列随机多维检索树; cv::flann::Index flann_index(m_image, cv::flann::KDTreeIndexParams(4)); // using 4 randomized kdtrees //利用Knn近邻算法检索m_object;结果存入 m_indices, m_dists; flann_index.knnSearch(m_object, m_indices, ...