OpenCV2 BatchDistance错误-215，在单个比较正常工作的情况下循环遍历图像时

问题描述

我无法确定我要去哪里.我有使用OpenCV2计算2张图像的特征的python代码，然后进行匹配并绘制最相似的点.如果我运行单独的代码，它可以工作，并且我可以更改第二个图像文件的名称，当我尝试使其自动化以遍历图像目录以与第一个图像进行比较时，在Ubuntu 14.04，OpenCV 3.10上出现以下错误

I having trouble seeing where I'm going wrong. I have python code to use OpenCV2 to calculate the features of 2 images, match then and plot the most similar points. If I run the individual code it works and I can change the name of the 2nd image file, when I try to automate it to loop through a directory of images to compare to the 1st image I get the following error on Ubuntu 14.04, OpenCV 3.10

除了我的代码的粗糙性之外，还有人看到为什么循环错误出了，但个人比较却没有吗?

Besides the roughness of my code, anyone see why the loop errors out but the individual comparisons don't?

opencv/modules/core/src/stat.cpp:3749:错误:(-215)type == src2.type()&& src1.cols == src2.cols&& (batchDistance函数中的(类型== CV_32F ||类型== CV_8U)

opencv/modules/core/src/stat.cpp:3749: error: (-215) type == src2.type() && src1.cols == src2.cols && (type == CV_32F || type == CV_8U) in function batchDistance

个人代码

import sys
import cv2
import os
import numpy as np
from matplotlib import pyplot as plt

#Load the crest and create a grey version
crest = cv2.imread('Crest.png')
greyCrest = cv2.cvtColor(crest,cv2.COLOR_BGR2GRAY)

#Create the ORB Object and BruteForce
orb=cv2.ORB_create()
#Use Hamming Distance as its ORD (other algorithms would use a different distance measure)
BruteForceMatch = cv2.BFMatcher(cv2.NORM_HAMMING,crossCheck=True)
#Calculate the keypoints and descriptors for the crest
Crestkeypoints,Crestdescriptor = orb.detectAndCompute(greyCrest,None)

#
testimage = cv2.imread('OtherImage.jpg')
testkeypoints, testdescriptors = orb.detectAndCompute(testimage,None)

#Basic Idea
matches = BruteForceMatch.match(Crestdescriptor,testdescriptors)
#Sort the matches so the strongest are at the top
matches = sorted(matches, key = lambda x:x.distance)
Title = "Total Matched " + str(len(matches)) +" Plotting Top 40"
#display the top 40 matches on the plot
MatchImage = cv2.drawMatches(greyCrest,Crestkeypoints,testimage,testkeypoints,matches[:40],testimage,flags=2)
#This gets rid of the blue tint to the end image when plotting OpenCV image in Matplotlib because of the RGB ,BGR difference
plt.imshow(cv2.cvtColor(MatchImage, cv2.COLOR_BGR2RGB))
plt.title(Title)
plt.show()
#Pause for 10 Seconds so you can see the plot
plt.pause(10)
#Have to close as for some reason the figures weren't plotting correctly when I ran the code the 2nd time
plt.close('all')

循环代码

下面是在目录中循环，将图像加载到OpenCV，计算图像的关键点和特征并将其与搜索图像进行比较的代码，然后获取匹配数量并绘制出前40个匹配项

Below is code to loop through a directory, load the image into OpenCV, calculate the keypoints and features of the image and compare it to a search image before getting the number of matches and plotting the top 40 matches

#Load the crest and create a grey version
import cv2
import os
import numpy as np
from matplotlib import pyplot as plt
    crest = cv2.imread('Crest.png')
    greyCrest = cv2.cvtColor(crest,cv2.COLOR_BGR2GRAY)

    #Create the ORB Object and BruteForce
    orb2=cv2.ORB_create()
    #Use Hamming Distance as its ORD (other algorithms would use a different distance measure)
    BruteForceMatch = cv2.BFMatcher(cv2.NORM_HAMMING,crossCheck=True)
    #Calculate the keypoints and descriptors for the crest
    Crestkeypoints,Crestdescriptor = orb2.detectAndCompute(greyCrest,None)


    #Get the folder of Images to be matched with ORB 1 by 1 against the Search Image
    for ImageToProcess in os.listdir('/MatchWithORB'):
        print "Processing Image", ImageToProcess
        #Load the current Image as greyscale and calculate the keypoints, descriptors
        CurrentImage = cv2.imread(ImageToProcess,cv2.IMREAD_GRAYSCALE)
        currImageKeyPoints, currImageDescriptors = orb2.detectAndCompute(CurrentImage,None)
        #Get the matches between the current image and the crest
        matches = BruteForceMatch.match(Crestdescriptor,currImageDescriptors)
        Title = "Crest Total Matched " + str(len(matches)) +" Plotting Top 40 " +ImageToProcess
        # Sort them so the top matches are higher
        matches = sorted(matches, key = lambda x:x.distance)
        MatchImage = cv2.drawMatches(greyCrest,Crestkeypoints,CurrentImage,currImageKeyPoints,matches[:40],CurrentImage,flags=2)
        plt.imshow(MatchImage)
        plt.draw()
        plt.pause(10)
        #tidyup
        BruteForceMatch.clear
    print"Looping"

推荐答案

旧问题，但是答案是，如果未检测到任何功能，detectAndCompute将返回"None"而不是空列表.然后，随着代码继续匹配，它将尝试将功能列表与None类型匹配，从而为您提供所描述的错误.简单的检查方法:

old question, but the answer is that if no features are detected, detectAndCompute returns "None" instead of an empty list. Then as the code continues to match, it tries to match a feature list with a None type giving you the described error.easy way to check this:

    if des1 is None or des2 is None:
    print(des1)
    print(des2)
    pdb.set_trace()

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