python OpenCV实现图像特征匹配示例详解

# create bfmatcher object
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
# match descriptors
matches = bf.match(des1, des2)
# sort them in the order of their distance
matches = sorted(matches, key=lambda x: x.distance)
# draw first 10 matches
img3 = cv2.drawMatches(img1, kp1, img2, kp2, matches[:10], None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
plt.imshow(img3)
plt.show()

import cv2
import numpy as np
from matplotlib import pyplot as plt
img1 = cv2.imread('box2.png', cv2.IMREAD_GRAYSCALE)
img2 = cv2.imread('box_in_scene.png', cv2.IMREAD_GRAYSCALE)
# initate sift detector
sift = cv2.xfeatures2d.SIFT_create()
# find teh keypoints and descriptors with sift
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)
# BFMatcher with default params
bf = cv2.BFMatcher()
matches = bf.knnMatch(des1, des2, k=2)
# apply ratio test
good = []
for m, n in matches:
    if m.distance < 0.75 * n.distance:
        good.append([m])
# cv.drawMatchsKnn expects list of lists as matches.
img3 = cv2.drawMatchesKnn(img1, kp1, img2, kp2, good, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
plt.imshow(img3)
plt.show()

FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)

 FLANN_INDEX_LSH = 6
 index_params = dict(
 	algorithm=FLANN_INDEX_LSH,
 	table_number=6,
 	key_size=12,
 	multi_probe_level=1)

import cv2
import numpy as np
from matplotlib import pyplot as plt
img1 = cv2.imread('box2.png', cv2.IMREAD_GRAYSCALE)
img2 = cv2.imread('box_in_scene.png', cv2.IMREAD_GRAYSCALE)
# initiate sift detector
sift = cv2.xfeatures2d.SIFT_create()
# find the keypoints and descriptors with sift
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)
# FLANN params
FLANN_INDEX_KDTREE = 1
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)
# need to draw only good matches, so create a mask
matchesMask = [[0,0] for i in range(len(matches))]
# ratio test as per low's papre
for i, (m, n) in enumerate(matches):
    if m.distance < 0.7*n.distance:
        matchesMask[i] = [1, 0]
# draw
darw_params = dict(
    matchColor=(0, 255, 0),
    singlePointColor=(255, 0, 0),
    matchesMask=matchesMask,
    flags=cv2.DrawMatchesFlags_DEFAULT)
img3 = cv2.drawMatchesKnn(img1, kp1, img2, kp2, matches, None, **darw_params)
plt.imshow(img3)
plt.show()