OpenCV寻找正确的阈值来判断图像是否匹配

Question

我目前正在制作一个识别程序使用各种特征提取器和各种匹配器。使用matcher的分数，我想创建一个分数阈值，可以进一步确定它是正确的匹配还是错误的匹配。在

我试图从不同的匹配者那里理解匹配距离的含义，距离越小，匹配越好吗？如果是，我很困惑，因为位置不同的同一个图像返回的值比两个不同的图像大。在

我运行了两个测试用例：

1. 将同一幅图像与不同位置的同一幅图像进行比较等
2. 比较一个图像与一些位置完全不同的图像，等等

这是我的测试结果：

-----------------------------------------------

Positive image average distance
Total test number: 70
Comparing with SIFT
     Use BF with Ratio Test: 874.071456255
     Use FLANN             : 516.737270464

Comparing with SURF
     Use BF with Ratio Test: 2.92960552163
     Use FLANN             : 1.47225751158

Comparing with ORB
     Use BF                : 12222.1428571
     Use BF with Ratio Test: 271.638643755

Comparing with BRISK
     Use BF                : 31928.4285714
     Use BF with Ratio Test: 1537.63658578

Maximum positive image distance
Comparing with SIFT
     Use BF with Ratio Test: 2717.88008881
     Use FLANN             : 1775.63563538

Comparing with SURF
     Use BF with Ratio Test: 4.88817568123
     Use FLANN             : 2.81848525628

Comparing with ORB
     Use BF                : 14451.0
     Use BF with Ratio Test: 1174.47851562

Comparing with BRISK
     Use BF                : 41839.0
     Use BF with Ratio Test: 3846.39746094

-----------------------------------------

Negative image average distance
Total test number: 72
Comparing with SIFT
     Use BF with Ratio Test: 750.028228866
     Use FLANN             : 394.982576052

Comparing with SURF
     Use BF with Ratio Test: 2.89866939275
     Use FLANN             : 1.59815886725

Comparing with ORB
     Use BF                : 12098.9444444
     Use BF with Ratio Test: 261.874231339

Comparing with BRISK
     Use BF                : 31165.8472222
     Use BF with Ratio Test: 1140.46670034

Minimum negative image distance
Comparing with SIFT
     Use BF with Ratio Test: 0
     Use FLANN             : 0

Comparing with SURF
     Use BF with Ratio Test: 1.25826786458
     Use FLANN             : 0.316588282585

Comparing with ORB
     Use BF                : 10170.0
     Use BF with Ratio Test: 0

Comparing with BRISK
     Use BF                : 24774.0
     Use BF with Ratio Test: 0

此外，在某些情况下，当两个不同的图像相互测试而没有匹配时，匹配器也返回0分，当两个相同的图像一起比较时，该分数完全相同。在

经过进一步检查，主要有四起案件：

1. 两个相同的图像，很多匹配，距离=0
2. 两个相同的图像（不相同），很多匹配，距离=大 价值
3. 两个完全不同的图像，不匹配，距离=0
4. 两个不同的图像，一些匹配，距离=小值

根据这些情况找到正确的阈值似乎是一个问题，因为有些情况相互矛盾。 通常，图像越相同，距离值越低。在

在匹配器.py

^{pr2}$

在比较器.py

import matcher    

def check(img1, img2, kp1, kp2, des1, des2, matcherType, setDraw, ORB):
    if matcherType == 1:
        return matcher.useBruteForce(img1, img2, kp1, kp2, des1, des2, setDraw)
    elif matcherType == 2:
        return matcher.useBruteForceWithRatioTest(img1, img2, kp1, kp2, des1, des2, setDraw)
    elif matcherType == 3:
        return matcher.useFLANN(img1, img2, kp1, kp2, des1, des2, setDraw, ORB)
    else:
        print "Matcher not chosen correctly, use Brute Force matcher as default"
        return matcher.useBruteForce(img1, img2, kp1, kp2, des1, des2, matcherType, setDraw)


def useORB(filename1, filename2, matcherType, setDraw):
    img1 = cv2.imread(filename1,0) # queryImage
    img2 = cv2.imread(filename2,0) # trainImage

    # Initiate ORB detector
    orb = cv2.ORB_create()

    # find the keypoints and descriptors with ORB
    kp1, des1 = orb.detectAndCompute(img1,None)
    kp2, des2 = orb.detectAndCompute(img2,None)
    ORB = True
    return check(img1, img2, kp1, kp2, des1, des2, matcherType, setDraw, ORB)


def useSIFT(filename1, filename2, matcherType, setDraw):
    img1 = cv2.imread(filename1,0) # queryImage
    img2 = cv2.imread(filename2,0) # trainImage

    # 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)
    ORB = False
    return check(img1, img2, kp1, kp2, des1, des2, matcherType, setDraw, ORB)


def useSURF(filename1, filename2, matcherType, setDraw):
    img1 = cv2.imread(filename1, 0)
    img2 = cv2.imread(filename2, 0)

    # Here I set Hessian Threshold to 400
    surf = cv2.xfeatures2d.SURF_create(400)

    # Find keypoints and descriptors directly
    kp1, des1 = surf.detectAndCompute(img1, None)
    kp2, des2 = surf.detectAndCompute(img2, None)
    ORB = False
    return check(img1, img2, kp1, kp2, des1, des2, matcherType, setDraw, ORB)


def useBRISK(filename1, filename2, matcherType, setDraw):
    img1 = cv2.imread(filename1,0) # queryImage
    img2 = cv2.imread(filename2,0) # trainImage

    # Initiate BRISK detector
    brisk = cv2.BRISK_create()

    # find the keypoints and descriptors with BRISK
    kp1, des1 = brisk.detectAndCompute(img1,None)
    kp2, des2 = brisk.detectAndCompute(img2,None)
    ORB = True
    return check(img1, img2, kp1, kp2, des1, des2, matcherType, setDraw, ORB)

Answer 1

在OpenCV的教程中，据说

For BF matcher, first we have to create the BFMatcher object using cv.BFMatcher(). It takes two optional params. First one is normType. It specifies the distance measurement to be used. By default, it is cv.NORM_L2. It is good for SIFT, SURF etc (cv.NORM_L1 is also there). For binary string based descriptors like ORB, BRIEF, BRISK etc, cv.NORM_HAMMING should be used, which used Hamming distance as measurement. If ORB is using WTA_K == 3 or 4, cv.NORM_HAMMING2 should be used.

https://docs.opencv.org/3.4/dc/dc3/tutorial_py_matcher.html

所以你应该为SIFT和ORB创建不同的matcher对象。这可能就是为什么你计算的距离相差那么大的原因。