利用python學習OpenCV，個人感覺比較方便。函式的形式與C++基本相同，所以切換過來還是比較好的，對於像我這種對python不太熟練的人，使用python的整合開發環境PyCharm進行學習，可以設定斷點除錯，有助於我這類初學者理解掌握。

下面是利用python語言結合OpenCV進行SIFT特徵進行匹配的程式碼：

# -*- coding:utf-8 -*-
__author__ = 'Microcosm'

import cv2
#from find_obj import filter_matches,explore_match
import numpy as np

def filter_matches(kp1, kp2, matches, ratio = 0.75):
    mkp1, mkp2 = [], []
    for m in matches:
        if len(m) == 2 and m[0].distance < m[1].distance * ratio:
            m = m[0]
            mkp1.append( kp1[m.queryIdx] )
            mkp2.append( kp2[m.trainIdx] )
    p1 = np.float32([kp.pt for kp in mkp1])
    p2 = np.float32([kp.pt for kp in mkp2])
    kp_pairs = zip(mkp1, mkp2)
    return p1, p2, kp_pairs

def explore_match(win, img1, img2, kp_pairs, status = None, H = None):
    h1, w1 = img1.shape[:2]
    h2, w2 = img2.shape[:2]
    vis = np.zeros((max(h1, h2), w1+w2), np.uint8)
    vis[:h1, :w1] = img1
    vis[:h2, w1:w1+w2] = img2
    vis = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR)

    if H is not None:
        corners = np.float32([[0, 0], [w1, 0], [w1, h1], [0, h1]])
        corners = np.int32( cv2.perspectiveTransform(corners.reshape(1, -1, 2), H).reshape(-1, 2) + (w1, 0) )
        cv2.polylines(vis, [corners], True, (255, 255, 255))

    if status is None:
        status = np.ones(len(kp_pairs), np.bool)
    p1 = np.int32([kpp[0].pt for kpp in kp_pairs])
    p2 = np.int32([kpp[1].pt for kpp in kp_pairs]) + (w1, 0)

    green = (0, 255, 0)
    red = (0, 0, 255)
    white = (255, 255, 255)
    kp_color = (51, 103, 236)
    for (x1, y1), (x2, y2), inlier in zip(p1, p2, status):
        if inlier:
            col = green
            cv2.circle(vis, (x1, y1), 2, col, -1)
            cv2.circle(vis, (x2, y2), 2, col, -1)
        else:
            col = red
            r = 2
            thickness = 3
            cv2.line(vis, (x1-r, y1-r), (x1+r, y1+r), col, thickness)
            cv2.line(vis, (x1-r, y1+r), (x1+r, y1-r), col, thickness)
            cv2.line(vis, (x2-r, y2-r), (x2+r, y2+r), col, thickness)
            cv2.line(vis, (x2-r, y2+r), (x2+r, y2-r), col, thickness)
    vis0 = vis.copy()
    for (x1, y1), (x2, y2), inlier in zip(p1, p2, status):
        if inlier:
            cv2.line(vis, (x1, y1), (x2, y2), green)

    cv2.imshow(win, vis)

img1 = cv2.imread("E:\python\Python Project\opencv_showimage\images\multi_view_big.jpg")
img2 = cv2.imread("E:\python\Python Project\opencv_showimage\images\multi_view_small.jpg")

img1_gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
img2_gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)

sift = cv2.SIFT()

kp1,des1 = sift.detectAndCompute(img1_gray, None)
kp2,des2 = sift.detectAndCompute(img2_gray, None)

# BFmatcher with default parms
bf = cv2.BFMatcher(cv2.NORM_L2)
matches = bf.knnMatch(des1, des2, k=2)


p1,p2,kp_pairs = filter_matches(kp1,kp2,matches,ratio=0.5)
explore_match('matches', img1_gray,img2_gray,kp_pairs)
#img3 = cv2.drawMatchesKnn(img1_gray,kp1,img2_gray,kp2,good[:10],flag=2)

cv2.waitKey(0)
cv2.destroyAllWindows()