ORB+FLANN
FLANN 代表 近似最近邻的快速库。它包含针对大型数据集中的快速最近邻搜索和高维特征优化的算法集合。对于大型数据集，它比BFMatcher工作得更快。
对于基于 FLANN 的匹配器，我们需要传递两个字典，指定要使用的算法、相关参数等。第一个是IndexParams。对于各种算法，要传递的信息在 FLANN 文档中进行了解释。作为总结，对于SIFT，SURF等算法。您可以通过以下内容：
# SIFT，SURF
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
使用 ORB 时，可以传递以下内容。根据文档，建议使用注释值，但在某些情况下，它没有提供所需的结果。其他值工作正常。
FLANN_INDEX_LSH = 6
index_params= dict(algorithm = FLANN_INDEX_LSH,
table_number = 6, # 12
key_size = 12, # 20
multi_probe_level = 1) #2
代码如下：
import numpy as np
import cv2 as cv
import matplotlib.pyplot as plt
img1 = cv.imread('./data/box.png',cv.IMREAD_GRAYSCALE) # queryImage
img2 = cv.imread('./data/box_in_scene.png',cv.IMREAD_GRAYSCALE) # trainImage
# Initiate SIFT detector
orb = cv.ORB_create()
# find the keypoints and descriptors with SIFT
kp1, des1 = orb.detectAndCompute(img1,None)
kp2, des2 = orb.detectAndCompute(img2,None)
# FLANN parameters
# FLANN_INDEX_KDTREE = 1
# index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
FLANN_INDEX_LSH = 6
index_params= dict(algorithm = FLANN_INDEX_LSH,
table_number = 6, # 12
key_size = 12, # 20
multi_probe_level = 1) #2
search_params = dict(checks=50) # or pass empty dictionary
flann = cv.FlannBasedMatcher(index_params,search_params)
matches = flann.knnMatch(des1,des2,k=2)
# Need to draw only good matches, so create a mask
matchesMask = [[0,0] for i in range(len(matches))]
# ratio test as per Lowe's paper
for i,(m,n) in enumerate(matches):
if m.distance < 0.8*n.distance:
matchesMask[i]=[1,0]
draw_params = dict(matchColor = (0,255,0),
singlePointColor = (255,0,0),
matchesMask = matchesMask,
flags = cv.DrawMatchesFlags_DEFAULT)
img3 = cv.drawMatchesKnn(img1,kp1,img2,kp2,matches,None,**draw_params)
plt.imshow(img3,),plt.show()
