opencv python 特征匹配

Feature Matching

Brute-Force匹配器

Brute-Force匹配非常简单，首先在第一幅图像中选取一个关键点然后依次与第二幅图像的每个关键点进行（描述符）距离测试，最后返回距离最近的关键点.

对于BF匹配器，首先我们必须使用cv2.BFMatcher（）创建BFMatcher对象。它需要两个可选的参数.

1. 第一个是normType,它指定要使用的距离测量,默认情况下，它是cv2.NORM_L2.它适用于SIFT，SURF等（cv2.NORM_L1也在那里）.对于基于二进制字符串的描述符，如ORB，BRIEF，BRISK等，应使用cv2.NORM_HAMMING,使用汉明距离作为度量,如果ORB使用WTA_K == 3or4，则应使用cv2.NORM_HAMMING2.
2. crossCheck：默认值为False。如果设置为True，匹配条件就会更加严格，只有到A中的第i个特征点与B中的第j个特征点距离最近，并且B中的第j个特征点到A中的第i个特征点也是最近时才会返回最佳匹配，即这两个特征点要互相匹配才行.

两个重要的方法是BFMatcher.match（）和BFMatcher.knnMatch（）, 第一个返回最佳匹配, 第二种方法返回k个最佳匹配，其中k由用户指定.

使用cv2.drawMatches()来绘制匹配的点，它会将两幅图像先水平排列，然后在最佳匹配的点之间绘制直线。如果前面使用的BFMatcher.knnMatch()，现在可以使用函数cv2.drawMatchsKnn为每个关键点和它的个最佳匹配点绘制匹配线。如果要选择性绘制就要给函数传入一个掩模.

Brute-Force Matching with ORB Descriptors

import numpy as np
import cv2
import matplotlib.pyplot as plt

img1 = cv2.imread('img.jpg',0)          # queryImage
img2 = cv2.imread('img1.jpg',0) # trainImage

# Initiate ORB detector
orb = cv2.ORB_create()

# find the keypoints and descriptors with ORB
kp1, des1 = orb.detectAndCompute(img1,None)
kp2, des2 = orb.detectAndCompute(img2,None)

# create BFMatcher object
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)

# Match descriptors.
matches = bf.match(des1,des2)

# Sort them in the order of their distance.
matches = sorted(matches, key = lambda x:x.distance)

# Draw first 10 matches.
img3 = cv2.drawMatches(img1,kp1,img2,kp2,matches[:10],None, flags=2)

plt.imshow(img3),plt.show()

Matcher对象

matches = bf.match（des1，des2）行的结果是DMatch对象的列表。 此DMatch对象具有以下属性：
•DMatch.distance - 描述符之间的距离。 越低越好。
•DMatch.trainIdx - 训练描述符中描述符的索引
•DMatch.queryIdx - 查询描述符中描述符的索引
•DMatch.imgIdx - 训练图像的索引

Brute-Force Matching with SIFT Descriptors and Ratio Test

import numpy as np
import cv2
import matplotlib.pyplot as plt

img1 = cv2.imread('img.jpg',0)          # queryImage
img2 = cv2.imread('img1.jpg',0) # trainImage

# Initiate SIFT detector
sift = cv2.xfeatures2d.SIFT_create()

# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1,None)
kp2, des2 = sift.detectAndCompute(img2,None)

# BFMatcher with default params
bf = cv2.BFMatcher()
matches = bf.knnMatch(des1,des2, k=2)

# Apply ratio test
good = []
for m,n in matches:
    if m.distance < 0.75*n.distance:
        good.append([m])

# cv.drawMatchesKnn expects list of lists as matches.
img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good,None,flags=2)

plt.imshow(img3),plt.show()

基于FLANN的Matcher

FLANN 代表 Fast Library for Approximate Nearest Neighbors. 它包含一组算法，这些算法针对大型数据集中的快速最近邻搜索和高维特征进行了优化.对于大型数据集，它比BFMatcher工作得更快.

import numpy as np
import cv2
import matplotlib.pyplot as plt

img1 = cv2.imread('img.jpg',0)          # queryImage
img2 = cv2.imread('img1.jpg',0) # trainImage

# Initiate SIFT detector
sift = cv2.xfeatures2d.SIFT_create()

# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1,None)
kp2, des2 = sift.detectAndCompute(img2,None)

# FLANN parameters
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks=50)   # or pass empty dictionary

flann = cv2.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 < 0.7*n.distance:
        matchesMask[i]=[1,0]

draw_params = dict(matchColor = (0,255,0),
                   singlePointColor = (255,0,0),
                   matchesMask = matchesMask,
                   flags = 0)

img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,matches,None,**draw_params)

plt.imshow(img3,),plt.show()