import cv2
def match_img_knn(queryImage, trainingImage, thread=0):
sift = cv2.xfeatures2d.SIFT_create() # 创建sift检测器
kp1, des1 = sift.detectAndCompute(queryImage, None)
kp2, des2 = sift.detectAndCompute(trainingImage, None)
#print(len(kp1))
# 设置Flannde参数
FLANN_INDEX_KDTREE = 1
indexParams = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
searchParams = dict(checks=50)
flann = cv2.FlannBasedMatcher(indexParams, searchParams)
matches = flann.knnMatch(des1, des2, k=2)
good = []
# 设置好初始匹配值
matchesMask = [[0, 0] for i in range(len(matches))]
for i, (m, n) in enumerate(matches):
if m.distance < 0.7*n.distance: # 舍弃小于0.7的匹配结果
matchesMask[i] = [1, 0]
good.append(m)
s = sorted(good, key=lambda x: x.distance)
'''
drawParams=dict(matchColor=(0,0,255),singlePointColor=(255,0,0),matchesMask=matchesMask,flags=0) #给特征点和匹配的线定义颜色
resultimage=cv2.drawMatchesKnn(queryImage,kp1,trainingImage,kp2,matches,None,**drawParams) #画出匹配的结果
cv2.imshow('res',resultimage)
cv2.waitKey(0)
'''
#print(len(good))
if len(good) > thread:
maxLoc = kp2[s[0].trainIdx].pt
#print(maxLoc)
return (int(maxLoc[0]), int(maxLoc[1]))
else:
return (0, 0)
if __name__ == "__main__":
queryImage = cv2.imread("img\\exp.png", 1)
trainingImage = cv2.imread("1.png", 1) # 读取要匹配的灰度照片
match_img_knn(queryImage, trainingImage)
