Opencv实战四 图像匹配

图像匹配是指通过一定的匹配算法在两幅或多幅图像之间识别同名点，如二维图像匹配中通过比较目标区和搜索区中相同大小的窗口的相关系数，取搜索区中相关系数最大所对应的窗口中心点作为同名点。其实质是在基元相似性的条件下，运用匹配准则的最佳搜索问题。

import cv2
import numpy as np
import matplotlib.pyplot as plt
img1 = cv2.imread('1.jpg', 0)
img2 = cv2.imread('2.jpg', 0)
def cv_show(name,img):
    cv2.imshow(name, img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
sift = cv2.xfeatures2d.SIFT_create()
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)
bf = cv2.BFMatcher(crossCheck=True)
matches = bf.match(des1, des2)
matches = sorted(matches, key=lambda x: x.distance)
img3 = cv2.drawMatches(img1, kp1, img2, kp2, matches[:10], None,flags=2)
cv_show('img3',img3)

结果显示

import cv2
import numpy as np
import matplotlib.pyplot as plt
img1 = cv2.imread('1.jpg', 0)
img2 = cv2.imread('2.jpg', 0)
def cv_show(name,img):
    cv2.imshow(name, img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
# cv_show('img1',img1)
# cv_show('img2',img2)
sift = cv2.xfeatures2d.SIFT_create()
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)
bf = cv2.BFMatcher()
matches = bf.knnMatch(des1, des2, k=2)
good = []
for m, n in matches:
    if m.distance < 0.75 * n.distance:
        good.append([m])
img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good,None,flags=2)
cv_show('img3',img3)

结果显示