在OpenCV里使用特征匹配和单映射变换的代码详解

#python 3.7.4,opencv4.1
#蔡军生 https://blog.csdn.net/caimouse/article/details/51749579
#
import numpy as np
import cv2
from matplotlib import pyplot as plt
 
MIN_MATCH_COUNT = 10
 
#读取文件
img1 = cv2.imread('rmb3.png')
img2 = cv2.imread('rmb4.png')
 
#初始化ORB检测器
orb = cv2.ORB_create()
 
#用ORB查找关键点
kp1, des1 = orb.detectAndCompute(img1,None)
kp2, des2 = orb.detectAndCompute(img2,None)
 
# FLANN参数
FLANN_INDEX_LSH = 6
index_params = dict(algorithm = FLANN_INDEX_LSH,
          table_number = 6, 
          key_size = 12,   
          multi_probe_level = 1)
search_params = dict(checks=50)  #或者使用一个空的字典
flann = cv2.FlannBasedMatcher(index_params,search_params)
 
matches = flann.knnMatch(des1,des2,k=2)
 
# 比率
good = []
for m,n in matches:
  if m.distance < 0.7*n.distance:
    good.append(m)
if len(good)>MIN_MATCH_COUNT:
  src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
  dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)
  #找到投影变换矩阵
  M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)
  matchesMask = mask.ravel().tolist()
  #进行投影变换
  h,w,d = img1.shape
  pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
  dst = cv2.perspectiveTransform(pts,M)
  #画变换后的外形
  img2 = cv2.polylines(img2,[np.int32(dst)],True,(255,255,255),3, cv2.LINE_AA)
 
else:
  print( "Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT) )
  matchesMask = Non
 
draw_params = dict(matchColor = (0,255,0), # draw matches in green color
          singlePointColor = None,
          matchesMask = matchesMask, # draw only inliers
          flags = 2)
 
img3 = cv2.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)
 
#显示图片
cv2.imshow('img3',img3)
      
cv2.waitKey(0)
cv2.destroyAllWindows()