SIFT图像配准 python3.6 + opencv3.3代码

opencv3.x 中部分函数有改变：

1. SIFT：可以采用help(cv2.xfeatures2d)查询

2.drawKeypoints： 同样采用help()方法查询

opencv3 版本sift,surf 及其他不稳定的算法函数都放在opencv3.x的contrib版里。该模块下载地址 https://www.lfd.uci.edu/~gohlke/pythonlibs/

import cv2

import numpy as np

def sift_kp(image):

    gray_image = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)

    sift=cv2.xfeatures2d.SIFT_create()

    kp,des = sift.detectAndCompute(image,None)

    kp_image = cv2.drawKeypoints(gray_image,kp,None)

    return kp_image,kp,des

def get_good_match(des1,des2):

    bf = cv2.BFMatcher()

    matches = bf.knnMatch(des1, des2, k=2) #des1为模板图，des2为匹配图

    matches = sorted(matches,key=lambda x:x[0].distance/x[1].distance)

    good = []

    for m, n in matches:

        if m.distance < 0.75 * n.distance:

            good.append(m)

    return good

def siftImageAlignment(img1,img2):

   _,kp1,des1 = sift_kp(img1)

   _,kp2,des2 = sift_kp(img2)

   goodMatch = get_good_match(des1,des2)

   if len(goodMatch) > 4:

       ptsA= np.float32([kp1[m.queryIdx].pt for m in goodMatch]).reshape(-1, 1, 2)

       ptsB = np.float32([kp2[m.trainIdx].pt for m in goodMatch]).reshape(-1, 1, 2)

       ransacReprojThreshold = 4

       H, status =cv2.findHomography(ptsA,ptsB,cv2.RANSAC,ransacReprojThreshold);

       imgOut = cv2.warpPerspective(img2, H, (img1.shape[1],img1.shape[0]),flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP)

   return imgOut,H,status

img1 = cv2.imread(r'sift_img/8.png')

img2 = cv2.imread(r'sift_img/7.png')

_,kp1,des1 = sift_kp(img1)

_,kp2,des2 = sift_kp(img2)

goodMatch = get_good_match(des1,des2)

img3 = cv2.drawMatches(img1, kp1, img2, kp2, goodMatch[:5], None, flags=2)

#----or----

#goodMatch = np.expand_dims(goodMatch,1)

#img3 = cv2.drawMatchesKnn(img1, kp1, img2, kp2, goodMatch[:5], None, flags=2)

cv2.imshow('img',img3)

cv2.waitKey(0)

cv2.destroyAllWindows()

SIFT特征详解：

http://www.cnblogs.com/wangguchangqing/p/4853263.html

http://blog.csdn.net/abcjennifer/article/details/7639681