与 ORB python opencv 匹配特性

Question

hi im working in Matching Features with ORB python opencv but when i run this code i get this error Traceback (most recent call last): File "ffl.py", line 27, in for m,n in matches: TypeError: 'cv2.DMatch' object is not iterable

i don't know how to fix it

import numpy as np
import cv2
import time

ESC=27   
camera = cv2.VideoCapture(0)
orb = cv2.ORB_create()
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)

imgTrainColor = cv2.imread('/home/shar/home.jpg')
imgTrainGray = cv2.cvtColor(imgTrainColor, cv2.COLOR_BGR2GRAY)

kpTrain = orb.detect(imgTrainGray,None)
kpTrain, desTrain = orb.compute(imgTrainGray, kpTrain)

firsttime = True

while True:

    ret, imgCamColor = camera.read()
    imgCamGray = cv2.cvtColor(imgCamColor, cv2.COLOR_BGR2GRAY)
    kpCam = orb.detect(imgCamGray,None)
    kpCam, desCam = orb.compute(imgCamGray, kpCam)
    bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
    matches = bf.match(desCam,desTrain)
    good = []
    for m,n in matches:
        if m.distance < 0.7*n.distance:
            good.append(m)

    if firsttime==True:
        h1, w1 = imgCamColor.shape[:2]
        h2, w2 = imgTrainColor.shape[:2]
        nWidth = w1+w2
        nHeight = max(h1, h2)
        hdif = (h1-h2)/2
        firsttime=False

    result = np.zeros((nHeight, nWidth, 3), np.uint8)
    result[hdif:hdif+h2, :w2] = imgTrainColor
    result[:h1, w2:w1+w2] = imgCamColor

    for i in range(len(matches)):
        pt_a=(int(kpTrain[matches[i].trainIdx].pt[0]), int(kpTrain[matches[i].trainIdx].pt[1]+hdif))
        pt_b=(int(kpCam[matches[i].queryIdx].pt[0]+w2), int(kpCam[matches[i].queryIdx].pt[1]))
        cv2.line(result, pt_a, pt_b, (255, 0, 0))

    cv2.imshow('Camara', result)

    key = cv2.waitKey(20)                                 
    if key == ESC:
        break

cv2.destroyAllWindows()
camera.release()

Answer 1

bf.match return only a list of single objects, you cannot iterate over it with m,n. Maybe you are confused with bf.knnMatch ?

You can just change your code to:

for m in matches:
    if m.distance < 0.7:
        good.append(m)

From the Python tutorials of OpenCV ( link ):

The result of matches = bf.match(des1,des2) line is a list of DMatch objects. This DMatch object has following attributes:

• DMatch.distance - Distance between descriptors. The lower, the better it is.
• DMatch.trainIdx - Index of the descriptor in train descriptors
• DMatch.queryIdx - Index of the descriptor in query descriptors
• DMatch.imgIdx - Index of the train image.

Answer 2

for m in matches:
    if m.distance < 0.7:
        good.append(m)

This block of code is good, but does not really have the same meaning as the original. I think that using ORB and something involving n and n+1 elements in the matches refers to the original intent of SIFT algorithm, which performs a ratio match.

So, a correct code would be (I guess) :

for i, m in enumerate(matches):
    if i < len(matches) - 1 and m.distance < 0.7 * matches[i+1].distance:
        good.append(m)

Less efficient, and there may be a workaround or better code. But my main point is to underline that the answered code is not doing the same as OP's code.

Original SIFT paper saying :

This test rejects poor matches by computing the ratio between the best and second-best match. If the ratio is below some threshold, the match is discarded as being low-quality.

Please also note that "0.7" is named "ratio" and is fixed to 0.75 (from memory) in the original paper.

Answer 3

The code attempts to use Lowe's ratio test (see original SIFT paper).

this requires, for every descriptor, the two closest matches.

the code should read:

    matches = bf.knnMatch(desCam, desTrain, k=2) # knnMatch is crucial
    good = []
    for (m1, m2) in matches: # for every descriptor, take closest two matches
        if m1.distance < 0.7 * m2.distance: # best match has to be this much closer than second best
            good.append(m1)

further, I would highly recommend the flann matcher. it's faster than the brute force matcher.

look at the OpenCV tutorials or the samples directory in OpenCV's source ( samples/python/find_obj.py ) for code that works.