SURF算法python实现

-- coding: utf-8 --

‘’’
SURF算法
‘’’
import cv2
import numpy as np

‘’‘1、加载图片’’’
img1 = cv2.imread(’./pic/logo1.jpg’,cv2.IMREAD_GRAYSCALE)
img1 = cv2.resize(img1,dsize=(600,400))
img2 = cv2.imread(’./pic/logo2.jpg’,cv2.IMREAD_GRAYSCALE)
img2 = cv2.resize(img2,dsize=(600,400))
image1 = img1.copy()
image2 = img2.copy()

‘’‘2、提取特征点’’’
#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)

‘’‘3、特征点匹配’’’
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()