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Opencv Python两幅图像匹配 Opencv Python实现两幅图像匹配

Scarlett2025   2021-06-22 我要评论
想了解Opencv Python实现两幅图像匹配的相关内容吗Scarlett2025在本文为您仔细讲解Opencv Python两幅图像匹配的相关知识和一些Code实例欢迎阅读和指正我们先划重点:opencv,python,图像匹配下面大家一起来学习吧

原图

import cv2

img1 = cv2.imread('SURF_2.jpg', cv2.IMREAD_GRAYSCALE)
img1 = cv2.resize(img1,dsize=(600,400))
img2 = cv2.imread('SURF_1.jpg', cv2.IMREAD_GRAYSCALE)
img2 = cv2.resize(img2,dsize=(600,400))
image1 = img1.copy()
image2 = img2.copy()


#创建一个SURF对象
surf = cv2.xfeatures2d.SURF_create(25000)
#SIFT对象会使用Hessian算法检测关键点并且对每个关键点周围的区域计算特征向量该函数返回关键点的信息和描述符
keypoints1,descriptor1 = surf.detectAndCompute(image1,None)
keypoints2,descriptor2 = surf.detectAndCompute(image2,None)
# print('descriptor1:',descriptor1.shape(),'descriptor2',descriptor2.shape())
#在图像上绘制关键点
image1 = cv2.drawKeypoints(image=image1,keypoints = keypoints1,outImage=image1,color=(255,0,255),flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
image2 = cv2.drawKeypoints(image=image2,keypoints = keypoints2,outImage=image2,color=(255,0,255),flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
#显示图像
cv2.imshow('surf_keypoints1',image1)
cv2.imshow('surf_keypoints2',image2)
cv2.waitKey(20)


matcher = cv2.FlannBasedMatcher()
matchePoints = matcher.match(descriptor1,descriptor2)
# print(type(matchePoints),len(matchePoints),matchePoints[0])

#提取强匹配特征点
minMatch = 1
maxMatch = 0
for i in range(len(matchePoints)):
    if minMatch > matchePoints[i].distance:
        minMatch = matchePoints[i].distance
    if maxMatch < matchePoints[i].distance:
        maxMatch = matchePoints[i].distance
    print('最佳匹配值是:',minMatch)
    print('最差匹配值是:',maxMatch)

#获取排雷在前边的几个最优匹配结果
goodMatchePoints = []
for i in range(len(matchePoints)):
    if matchePoints[i].distance < minMatch + (maxMatch-minMatch)/16:
        goodMatchePoints.append(matchePoints[i])

#绘制最优匹配点
outImg = None
outImg = cv2.drawMatches(img1,keypoints1,img2,keypoints2,goodMatchePoints,outImg,
                         matchColor=(0,255,0),flags=cv2.DRAW_MATCHES_FLAGS_DEFAULT)
cv2.imshow('matche',outImg)
cv2.waitKey(0)
cv2.destroyAllWindows()

原图

#coding=utf-8
import cv2
from matplotlib import pyplot as plt

img=cv2.imread('xfeatures2d.SURF_create2.jpg',0)
# surf=cv2.SURF(400)   #Hessian阈值400
# kp,des=surf.detectAndCompute(img,None)
# leng=len(kp)
# print(leng)
# 关键点太多重取阈值

surf=cv2.cv2.xfeatures2d.SURF_create(50000)   #Hessian阈值50000
kp,des=surf.detectAndCompute(img,None)
leng=len(kp)
print(leng)

img2=cv2.drawKeypoints(img,kp,None,(255,0,0),4)
plt.imshow(img2)
plt.show()

# 下面是U-SURF算法,关键点朝向一致运算速度加快
surf.upright=True
kp=surf.detect(img,None)
img3=cv2.drawKeypoints(img,kp,None,(255,0,0),4)

plt.imshow(img3)
plt.show()

#检测关键点描述符大小改64维成128维
surf.extended=True
kp,des=surf.detectAndCompute(img,None)
dem1=surf.descriptorSize()
print(dem1)
shp1=des.shape()
print(shp1)

效果图

import cv2
from matplotlib import pyplot as plt

leftImage = cv2.imread('xfeatures2d.SURF_create_1.jpg')
rightImage = cv2.imread('xfeatures2d.SURF_create_2.jpg')

# 创造sift
sift = cv2.xfeatures2d.SIFT_create()
kp1, des1 = sift.detectAndCompute(leftImage, None)
kp2, des2 = sift.detectAndCompute(rightImage, None)  # 返回关键点信息和描述符

FLANN_INDEX_KDTREE = 0
indexParams = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
searchParams = dict(checks=50)  # 指定索引树要被遍历的次数

flann = cv2.FlannBasedMatcher(indexParams, searchParams)
matches = flann.knnMatch(des1, des2, k=2)
matchesMask = [[0, 0] for i in range(len(matches))]
print("matches", matches[0])
for i, (m, n) in enumerate(matches):
    if m.distance < 0.07 * n.distance:
        matchesMask[i] = [1, 0]

drawParams = dict(matchColor=(0, 255, 0), singlePointColor=None,
                  matchesMask=matchesMask, flags=2)  # flag=2只画出匹配点flag=0把所有的点都画出
resultImage = cv2.drawMatchesKnn(leftImage, kp1, rightImage, kp2, matches, None, **drawParams)
plt.imshow(resultImage)
plt.show()


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