opencv-python 提取sift特征并匹配的实例

# -*- coding: utf-8 -*-
import cv2
import numpy as np
from find_obj import filter_matches,explore_match
from matplotlib import pyplot as plt
 
def getSift():
  '''
  得到并查看sift特征
  '''
  img_path1 = '../../data/home.jpg'
  #读取图像
  img = cv2.imread(img_path1)
  #转换为灰度图
  gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
  #创建sift的类
  sift = cv2.SIFT()
  #在图像中找到关键点 也可以一步计算#kp, des = sift.detectAndCompute
  kp = sift.detect(gray,None)
  print type(kp),type(kp[0])
  #Keypoint数据类型分析 http://www.cnblogs.com/cj695/p/4041399.html
  print kp[0].pt
  #计算每个点的sift
  des = sift.compute(gray,kp)
  print type(kp),type(des)
  #des[0]为关键点的list，des[1]为特征向量的矩阵
  print type(des[0]), type(des[1])
  print des[0],des[1]
  #可以看出共有885个sift特征，每个特征为128维
  print des[1].shape
  #在灰度图中画出这些点
  img=cv2.drawKeypoints(gray,kp)
  #cv2.imwrite('sift_keypoints.jpg',img)
  plt.imshow(img),plt.show()
 
def matchSift():
  '''
  匹配sift特征
  '''
  img1 = cv2.imread('../../data/box.png', 0) # queryImage
  img2 = cv2.imread('../../data/box_in_scene.png', 0) # trainImage
  sift = cv2.SIFT()
  kp1, des1 = sift.detectAndCompute(img1, None)
  kp2, des2 = sift.detectAndCompute(img2, None)
  # 蛮力匹配算法,有两个参数，距离度量(L2(default),L1)，是否交叉匹配(默认false)
  bf = cv2.BFMatcher()
  #返回k个最佳匹配
  matches = bf.knnMatch(des1, des2, k=2)
  # cv2.drawMatchesKnn expects list of lists as matches.
  #opencv2.4.13没有drawMatchesKnn函数，需要将opencv2.4.13\sources\samples\python2下的common.py和find_obj文件放入当前目录，并导入
  p1, p2, kp_pairs = filter_matches(kp1, kp2, matches)
  explore_match('find_obj', img1, img2, kp_pairs) # cv2 shows image
  cv2.waitKey()
  cv2.destroyAllWindows()
 
def matchSift3():
  '''
  匹配sift特征
  '''
  img1 = cv2.imread('../../data/box.png', 0) # queryImage
  img2 = cv2.imread('../../data/box_in_scene.png', 0) # trainImage
  sift = cv2.SIFT()
  kp1, des1 = sift.detectAndCompute(img1, None)
  kp2, des2 = sift.detectAndCompute(img2, None)
  # 蛮力匹配算法,有两个参数，距离度量(L2(default),L1)，是否交叉匹配(默认false)
  bf = cv2.BFMatcher()
  #返回k个最佳匹配
  matches = bf.knnMatch(des1, des2, k=2)
  # cv2.drawMatchesKnn expects list of lists as matches.
  #opencv3.0有drawMatchesKnn函数
  # Apply ratio test
  # 比值测试，首先获取与A 距离最近的点B（最近）和C（次近），只有当B/C
  # 小于阈值时（0.75）才被认为是匹配，因为假设匹配是一一对应的，真正的匹配的理想距离为0
  good = []
  for m, n in matches:
    if m.distance < 0.75 * n.distance:
      good.append([m])
  img3 = cv2.drawMatchesKnn(img1, kp1, img2, kp2, good[:10], None, flags=2)
  cv2.drawm
  plt.imshow(img3), plt.show()
 
matchSift()