K-最近邻匹配
所有机器学习算法中,KNN是最简单它也是在ORB框架下。但是它和之前ORB中的match的区别在于match返回最佳匹配,而KNN函数返回K个匹配,之后可以再用knnMatch进一步处理。
代码部分:
import numpy as np
import cv2
import matplotlib.pyplot as plt
img1 = cv2.imread('football.jpg', cv2.IMREAD_GRAYSCALE)
img2 = cv2.imread('shoot.jpg', cv2.IMREAD_GRAYSCALE)
orb = cv2.ORB_create()
kp1, des1 = orb.detectAndCompute(img1, None)
kp2, des2 = orb.detectAndCompute(img2, None)
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.knnMatch(des1, des2, k=1)
img3 = cv2.drawMatchesKnn(img1, kp1, img2, kp2, matches, img2, flags=2)
plt.imshow(img3)
plt.show()
FLANN匹配
FLANN(近似最近邻的快速库)具有一种内部机制,可以根据数据本身选择最合适的算法来处理数据集
效果图:
代码:
import numpy as np
import cv2
import matplotlib.pyplot as plt
queryImage = cv2.imread("chess.jpg")
trainImage = cv2.imread("chess1.jpg")
# create SIFT and detect/compute
sift = cv2.xfeatures2d.SIFT_create()
kp1, des1 = sift.detectAndCompute(queryImage, None)
kp2, des2 = sift.detectAndCompute(trainImage, None)
# FLANN match
FLANN_INDEX_KDTREE = 0
indexParams = dict(algorithm=FLANN_INDEX_KDTREE, tree=5)
searchParams = dict(checks=50)
flann = cv2.FlannBasedMatcher(indexParams, searchParams)
matches = flann.knnMatch(des1, des2, k=2)
# prepare an empty mask to draw good matches
matchesMask = [[0, 0]for i in range(len(matches))]
for i, (m,n) in enumerate(matches):
if m.distance < 0.5*n.distance: #0.5为灵敏度,越小舍弃的点越多
matchesMask[i] = [1,0]
drawParams = dict(matchColor=(0,0,255), singlePointColor=(255,0,0),
matchesMask=matchesMask, flags=0)
resultImg = cv2.drawMatchesKnn(queryImage, kp1, trainImage, kp2, matches, None, **drawParams)
plt.imshow(resultImg)
plt.show()
FLANN单应性匹配
单应性表示当两幅图中的一幅出现投影畸变时,他们还能够匹配。
相对于普通FLANN而言,其修改的部分主要在match部分
效果图:
代码:
import numpy as np
import cv2
import matplotlib.pyplot as plt
MIN_MATCH_COUNT = 10
img1 = cv2.imread("chess1.jpg",cv2.IMREAD_GRAYSCALE)
img2 = cv2.imread("chess.jpg",cv2.IMREAD_GRAYSCALE)
# create SIFT and detect/compute
sift = cv2.xfeatures2d.SIFT_create()
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)
# FLANN match
FLANN_INDEX_KDTREE = 0
indexParams = dict(algorithm=FLANN_INDEX_KDTREE, tree=5)
searchParams = dict(checks=50)
flann = cv2.FlannBasedMatcher(indexParams, searchParams)
matches = flann.knnMatch(des1, des2, k=2)
# record all good matches
good = []
for i, (m,n) in enumerate(matches):
if m.distance < 0.5*n.distance:
good.append(m)
if len(good)>MIN_MATCH_COUNT: # 良性匹配多于阈值
# 在原始图像里寻找关键点
SRC_PTS = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
# 在训练图像里寻找关键点
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)
matchmask = mask.ravel().tolist()
# 绘制边框
h,w = img1.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)
print(dst)
img2 = cv2.polylines(img2, [np.int32(dst)], True, (0,255,0), 3, cv2.LINE_AA)
else:
print("No match")
matchmask = None
drawParams = dict(matchColor=(0,0,255), singlePointColor=(255,0,0),
matchesMask=matchmask, flags=2)
resultImg = cv2.drawMatches(img1, kp1, img2, kp2, good, None, **drawParams)
plt.imshow(resultImg)
plt.show()