FlANN.py

import numpy as np
import cv2
from matplotlib import pyplot as plt

MIN_MATCH_COUNT =20
template = cv2.imread('3.jpg')  # queryImage
target = cv2.imread('coke.jpg')  # trainImage

sift = cv2.xfeatures2d.SIFT_create()
# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(template, None)
kp2, des2 = sift.detectAndCompute(target, None)

FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)
flann = cv2.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1, des2, k=2)
# store all the good matches as per Lowe's ratio test.
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)

    M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
    matchesMask = mask.ravel().tolist()
    h, w = template.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)
    cv2.polylines(target, [np.int32(dst)], True, 0, 2, cv2.LINE_AA)
else:
    print("Not enough matches are found - %d/%d" % (len(good), MIN_MATCH_COUNT))
    matchesMask = None
draw_params = dict(matchColor=(0, 255, 0),
                   singlePointColor=None,
                   matchesMask=matchesMask,
                   flags=2)
result = cv2.drawMatches(template, kp1, target, kp2, good, None, **draw_params)
plt.imshow(result, 'gray')
plt.show()