opencv-python 提取sift特征并匹配

# -*- 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()