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...
训练过程使用FlannBasedMatcher来优化,为 descriptor建立索引树,这种操作将在匹配大量数据时发挥巨大作用(比如在上百幅图像的数据集中查找匹配图像)。 FlannBasedMatcher接受两个参数:index_params和search_params 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di...
importnumpy as np#numpyimportcv2 as cv#opencv 库importitertools as it#迭代器frommultiprocessing.poolimportThreadPool#多进程frommatplotlibimportpyplot as plt#画图工具importos FLANN_INDEX_KDTREE=0 FLANN_INDEX_LSH= 6defaffine_skew(tilt, phi, img, mask=None): h, w= img.shape[:2]ifmaskisNone: ...
des1 = sift.detectAndCompute(template, None)kp2, des2 = sift.detectAndCompute(target, None)# 创建设置FLANN匹配FLANN_INDEX_KDTREE = 0index_params = dict(algorithm=FLANN_INDEX
(img2,None)# kdtree建立索引方式的常量参数FLANN_INDEX_KDTREE=0index_params=dict(algorithm=FLANN_INDEX_KDTREE,trees=5)# checks指定索引树要被遍历的次数search_params=dict(checks=50)flann=cv2.FlannBasedMatcher(index_params,search_params)matches=flann.knnMatch(des1,des2,k=2)# store all the good...
index_params=dict(algorithm = FLANN_INDEX_KDTREE,trees=5) 二 遍历次数 第二个字典是SearchParams。它用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多。如果想修改这个值,可以传入参数: search_params=dict( checks = 10) 具体python代码: import cv2 as cv import numpy as np from matplot...
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 ...
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
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) search_params = dict(checks = 50) self.flann = cv2.FlannBasedMatcher(index_params, search_params) kp1, des1 = self.sift.detectAndCompute(cv_image_input,None) # k-NearestNeighbor ...
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