OpenCV 特征匹配

1. 暴力匹配

暴力匹配使用一些距离计算两组特征描述之间的匹配度

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import numpy as np
import cv2 as cv
import matplotlib.pyplot as plt
 
img1 = cv.imread('images/box.png',cv.IMREAD_GRAYSCALE) # queryImage
img2 = cv.imread('images/box_in_scene.png',cv.IMREAD_GRAYSCALE) # trainImage

# 使用 ORB 
# Initiate ORB detector
orb = cv.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 = cv.BFMatcher(cv.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 = cv.drawMatches(img1,kp1,img2,kp2,matches[:15],None,flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
 
plt.imshow(img3),plt.title('ORB'),plt.show()

# 使用 SIFT
# Initiate SIFT detector
sift = cv.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 = cv.BFMatcher()
matches = bf.knnMatch(des1,des2,k=2)
 
# Apply ratio test
good = []
for m,n in matches:
 if m.distance < 0.35*n.distance:
    good.append([m])
 
# cv.drawMatchesKnn expects list of lists as matches.
img3 = cv.drawMatchesKnn(img1,kp1,img2,kp2,good,None,flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
 
plt.imshow(img3),plt.title('SIFT'),plt.show()

2. 基于FLANN的匹配

FLANN是近似最近邻的快速库.包含了针对大型高维特征快速最近邻搜索优化的算法集.在大数据集上比BFMatcher更好.
使用FLANN,需要传入索引字典IndexParams描述所用算法及其参数.如需要使用SIFT/SURF等,传入如下内容

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FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)

而使用ORB,使用如下,参数要自己调整,文档中建议的不一定最符合实际使用

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FLANN_INDEX_LSH = 6
index_params= dict(algorithm = FLANN_INDEX_LSH,
    table_number = 6, # 12
    key_size = 12, # 20
    multi_probe_level = 1) #2

第二个字典是SearchParams,定义应该递归遍历索引中的树的次数,这个值越高精度越高,但是同样会消耗更多的时间

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import numpy as np
import cv2 as cv
import matplotlib.pyplot as plt
 
img1 = cv.imread('images/box.png',cv.IMREAD_GRAYSCALE) # queryImage
img2 = cv.imread('images/box_in_scene.png',cv.IMREAD_GRAYSCALE) # trainImage
 
# Initiate SIFT detector
sift = cv.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 = 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 < 0.7*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()

3. 特征匹配与单应实现物体查找

前述的匹配是在另一张图片中寻找目标物体的一些部分.使用cv.findHomography(),传入两图片的点,可以找到物体在两个图片中的视角转换. 然后可以用cv.perspectiveTransform找到目标.至少需要4个正确的点来找到这个转换矩阵.

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import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt
 
MIN_MATCH_COUNT = 10
 
img1 = cv.imread('images/box.png', cv.IMREAD_GRAYSCALE) # queryImage
img2 = cv.imread('images/box_in_scene.png', cv.IMREAD_GRAYSCALE) # trainImage
 
# Initiate SIFT detector
sift = cv.SIFT_create()
 
# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1,None)
kp2, des2 = sift.detectAndCompute(img2,None)
 
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(des1,des2,k=2)
 
# store all the good matches as per Lowe's ratio test.
good = []
for m,n in matches:
 if m.distance < 0.7*n.distance:
    good.append(m)

if len(good)>MIN_MATCH_COUNT:
 src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
 dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)
 
 M, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC,5.0)
 matchesMask = mask.ravel().tolist()
 
 h,w = img1.shape
 pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
 dst = cv.perspectiveTransform(pts,M)
 
 img2 = cv.polylines(img2,[np.int32(dst)],True,255,3, cv.LINE_AA)
 
else:
 print( "Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT) )
 matchesMask = None

draw_params = dict(matchColor = (0,255,0), # draw matches in green color
 singlePointColor = None,
 matchesMask = matchesMask, # draw only inliers
 flags = 2)
 
img3 = cv.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)
 
plt.imshow(img3, 'gray'),plt.show()