本文实例为大家分享了python opencv实现图像配准与比较的具体代码，供大家参考，具体内容如下

代码

from skimage import io

import cv2 as cv

import numpy as np

import matplotlib.pyplot as plt

img_path1 = '2_HE_maxarea.png'

img_path2 = '2_IHC_maxarea.png'

img1 = io.imread(img_path1)

img2 = io.imread(img_path2)

img1 = np.uint8(img1)

img2 = np.uint8(img2)

# find the keypoints and descriptors with ORB

orb = cv.ORB_create()

kp1, des1 = orb.detectAndCompute(img1,None)

kp2, des2 = orb.detectAndCompute(img2,None)

# def get_good_match(des1,des2):

# bf = cv.BFMatcher()

# matches = bf.knnMatch(des1, des2, k=2)

# good = []

# for m, n in matches:

# if m.distance < 0.75 * n.distance:

# good.append(m)

# return good,matches

# goodMatch,matches = get_good_match(des1,des2)

# img3 = cv.drawMatchesKnn(img1,kp1,img2,kp2,matches[:20],None,flags=2)

# 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 20 matches.

img3 = cv.drawMatches(img1,kp1,img2,kp2,matches[:20],None, flags=2)

goodMatch = matches[:20]

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 =cv.findHomography(ptsA,ptsB,cv.RANSAC,ransacReprojThreshold);

#其中H为求得的单应性矩阵矩阵

#status则返回一个列表来表征匹配成功的特征点。

#ptsA,ptsB为关键点

#cv2.RANSAC, ransacReprojThreshold这两个参数与RANSAC有关

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

# 叠加配准变换图与基准图

rate = 0.5

overlapping = cv.addWeighted(img1, rate, imgOut, 1-rate, 0)

io.imsave('HE_2_IHC.png', overlapping)

err = cv.absdiff(img1,imgOut)

# 显示对比

plt.subplot(221)

plt.title('orb')

plt.imshow(img3)

plt.subplot(222)

plt.title('imgOut')

plt.imshow(imgOut)

plt.subplot(223)

plt.title('overlapping')

plt.imshow(overlapping)

plt.subplot(224)

plt.title('diff')

plt.imshow(err)

plt.show()

结果：

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持站圈网。