cv2.error: OpenCV(3.4.3) C:\projects\opencv-python\opencv_contrib\modules\xfeatures2d\src\sift.cpp:1207: error: (-213:The function/feature is not implemented) This algorithm is patented and is excluded in this configuration; Set OPENCV_ENABLE_NONFREE CMake option and rebuild the library in ...
FLANN_INDEX_KDTREE = 1 index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) search_params = dict(checks=50) flann = cv2.FlannBasedMatcher(index_params, search_params) matches = flann.knnMatch(des1, des2, k=2) # 应用比率测试 good_matches = [] for m, n in matches: if m.di...
第一个是 IndexParams。 index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) 。 这里使用的是KTreeIndex配置索引,指定待处理核密度树的数量(理想的数量在1-16)。 第二个字典是SearchParams。 search_params = dict(checks=100)用它来指定递归遍历的次数。值越高结果越准确,但是消耗的时间也越...
pipinstallopencv-python opencv-python-headless 1. 代码实现 importcv2importnumpyasnp# 读取待对齐的图像img1=cv2.imread('image1.jpg',cv2.IMREAD_GRAYSCALE)img2=cv2.imread('image2.jpg',cv2.IMREAD_GRAYSCALE)# 初始化SIFT检测器sift=cv2.SIFT_create()# 检测特征点和计算描述符keypoints1,descriptors1=s...
python # 使用FLANN匹配器 FLANN_INDEX_KDTREE = 1 index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) search_params = dict(checks=50) flann = cv2.FlannBasedMatcher(index_params, search_params) # 进行匹配 matches = flann.knnMatch(des1, des2, k=2) 4. 筛选和优化匹配结果 使用比率...
knnMatch方法是K=2的两个特征向量的k-NN匹配(k-nearest neighbors algorithm,K近邻算法),表明每个匹配的前两名作为特征向量返回。之所以我们要的是匹配的前两个而不是只有第一个,是因为我们需要用David Lowe’s ratio来测试假匹配然后做修剪。 之后,用第79行的rawMatches来计算每对描述子,但是这些描述子可能是错误...
SIFT算法好像专利到期了,根据opencv官方文档 Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe [145] . [145]David G. Lowe…
3 创建SIFT对象：sift = cv2.xfeatures2d.SIFT_create()创建Flann匹配：match = cv2.FlannBasedMatcher(dict(algorithm =2, trees =1), {})检测特征点，并描述特征点：kp1, de1 = sift.detectAndCompute(g1,None)kp2, de2 = sift.detectAndCompute(g2,None)4 用knn匹配，来提取de1和de2的靠前的数据...
FlannBasedMatcher(dict(algorithm =2, trees =1), {}) kp1, de1 = sift.detectAndCompute(g1,None) kp2, de2 = sift.detectAndCompute(g2,None) m = match.knnMatch(de1, de2, 2) m = sorted(m,key = lambda x:x[0].distance) ok = [m1 for (m1, m2) in m if m1.distance < 0.7...
(img1gray, None) kp2, des2 = sift.detectAndCompute(img2gray, None) # FLANN parameters FLANN_INDEX_KDTREE = 1 index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) search_params = dict(checks=50) flann = cv.FlannBasedMatcher(index_params, search_params) matches = flann.knnMatch(...