Arw*_*ego 8 python opencv matcher feature-extraction threshold
我目前正在使用各种功能提取器和各种匹配器进行识别程序.使用匹配器的分数,我想创建一个分数阈值,可以进一步确定它是正确匹配还是不正确匹配.
我试图理解来自各种匹配器的DMatch距离意义,距离值越小越好匹配吗？如果是,我很困惑,因为具有差异位置的相同图像返回比两个不同图像更大的值.
我运行了两个测试用例:
-----------------------------------------------
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
同样在某些情况下,当两个不同的图像彼此进行测试且没有匹配时,匹配器也返回0分,当两个相同的图像被比较在一起时,这些分数具有完全相同的分数.
经过进一步检查,有四个主要案例:
基于这些情况找到正确的阈值似乎是问题,因为某些情况相互矛盾.通常图像越相同,距离值越低.
def useBruteForce(img1, img2, kp1, kp2, des1, des2, setDraw):
# 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)
totalDistance = 0
for g in matches:
totalDistance += g.distance
if setDraw == True:
# Draw matches.
img3 = cv2.drawMatches(img1, kp1, img2, kp2, matches, None, flags=2)
plt.imshow(img3),plt.show()
return totalDistance
def useBruteForceWithRatioTest(img1, img2, kp1, kp2, des1, des2, setDraw):
# 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)
totalDistance = 0
for g in good:
totalDistance += g.distance
if setDraw == True:
# cv2.drawMatchesKnn expects list of lists as matches.
img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,[good],None,flags=2)
plt.imshow(img3),plt.show()
return totalDistance
def useFLANN(img1, img2, kp1, kp2, des1, des2, setDraw, type):
# Fast Library for Approximate Nearest Neighbors
MIN_MATCH_COUNT = 1
FLANN_INDEX_KDTREE = 0
FLANN_INDEX_LSH = 6
if type == True:
# Detect with ORB
index_params= dict(algorithm = FLANN_INDEX_LSH,
table_number = 6, # 12
key_size = 12, # 20
multi_probe_level = 1) #2
else:
# Detect with Others such as SURF, SIFT
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
# It specifies the number of times the trees in the index should be recursively traversed. Higher values gives better precision, but also takes more time
search_params = dict(checks = 90)
flann = cv2.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1, des2, k=2)
# store all the good matches as per Lowe's ratio test.
good = []
for m,n in matches:
if m.distance < 0.7*n.distance:
good.append(m)
totalDistance = 0
for g in good:
totalDistance += g.distance
if setDraw == True:
if len(good)>MIN_MATCH_COUNT:
src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
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)
matchesMask = mask.ravel().tolist()
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)
img2 = cv2.polylines(img2,[np.int32(dst)],True,255,3, cv2.LINE_AA)
else:
print "Not enough matches are found - %d/%d" % (len(good),MIN_MATCH_COUNT)
matchesMask = None
draw_params = dict(matchColor = (0,255,0), # draw matches in green color
singlePointColor = None,
matchesMask = matchesMask, # draw only inliers
flags = 2)
img3 = cv2.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)
plt.imshow(img3, 'gray'),plt.show()
return totalDistance
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)
在OpenCV的教程中，据说
对于 BF 匹配器，首先我们必须使用 cv.BFMatcher() 创建 BFMatcher 对象。它需要两个可选参数。第一个是normType。它指定要使用的距离测量。默认情况下，它是cv.NORM_L2。它适用于 SIFT、SURF 等（cv.NORM_L1 也在那里）。对于基于二进制字符串的描述符，如 ORB、BRIEF、BRISK 等，应使用 cv.NORM_HAMMING，它使用汉明距离作为测量。如果 ORB 使用 WTA_K == 3 或 4，则应使用 cv.NORM_HAMMING2。
https://docs.opencv.org/3.4/dc/dc3/tutorial_py_matcher.html
所以你应该为 SIFT 和 ORB 创建不同的匹配器对象（你明白了）。这可能就是您计算的距离差异如此之大的原因。
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