Q3Support.py 文件源码

def SIFTMATCHPOINTS(img1,img2):
    img1=img1.copy()
    img2=img2.copy()
    # find the 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
    matchesMask =np.array( [0 for i in range(len(matches))])
    good=[]
    for i,(m,n) in enumerate(matches):
        if 0.50*n.distance<m.distance < 0.85*n.distance:
            good.append(m)
            matchesMask[i]=1
    src_pts = [ tuple([int(pos) for pos in  kp1[m.queryIdx].pt]) for m in good ]
    dst_pts = [ tuple([int(pos) for pos in  kp2[m.trainIdx].pt]) for m in good ]
    return dict(zip(src_pts,dst_pts))
#    kp1=np.array(kp1)[matchesMask==1]
#    kp2=np.array(kp2)[matchesMask==1]
#    kp1pt=list(map(lambda x: tuple([int(posi) for posi in x.pt]),kp1))
#    kp2pt=list(map(lambda x: tuple([int(posi) for posi in x.pt]),kp2))
#    return dict(zip(kp1pt,kp2pt))