OpenCV Python:使用FlannBasedMatcher时有时会出现分段错误 [英] OpenCV Python: Occasionally get segmentation fault when using FlannBasedMatcher

问题描述

我正在尝试使用SURF和kNN对​​对象进行分类.该代码运行良好，但是偶尔会崩溃并显示"Segmentation Fault".我不确定我是否做错了什么，但我很确定它已得到纠正.如果您想重现此问题，这是输入文件.

I'm trying to classify objects using SURF and kNN. The code work well however it occasionally crashes and shows 'Segmentation Fault'. I'm not sure whether I did something wrong but I'm pretty sure that it is corrected. Here is the input file in case that you want to reproduce the issue.

链接以下载数据集

import numpy as np
import cv2
import sys

trainfile = ['/home/nuntipat/Documents/Dataset/Bank/Training/15_20_front.jpg'
            , '/home/nuntipat/Documents/Dataset/Bank/Training/15_50_front.jpg'
            , '/home/nuntipat/Documents/Dataset/Bank/Training/15_100_front.jpg'
            , '/home/nuntipat/Documents/Dataset/Bank/Training/15_500_front.jpg'
            , '/home/nuntipat/Documents/Dataset/Bank/Training/15_1000_front.jpg'
            , '/home/nuntipat/Documents/Dataset/Bank/Training/16_20_front.jpg'
            , '/home/nuntipat/Documents/Dataset/Bank/Training/16_50_front.jpg'
            , '/home/nuntipat/Documents/Dataset/Bank/Training/16_500_front.jpg']
testfile = '/home/nuntipat/Documents/Dataset/Bank/20_1.jpg'

# FLANN parameters
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks=50)

# Initiate FLANN matcher
flann = cv2.FlannBasedMatcher(index_params, search_params)

# Initiate SURF detector
surf = cv2.xfeatures2d.SURF_create(500)

# Create list of describtor
descriptor = []
for file in trainfile:
    img = cv2.imread(file)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    kp, des = surf.detectAndCompute(gray, None)
    descriptor.append(des)

# Clasify using test file
img = cv2.imread(testfile)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
kp1, des = surf.detectAndCompute(gray, None)

maxCount = 0
for i, d in enumerate(descriptor):  
    matches = flann.knnMatch(d, des, k=2)

    count = 0

    # ratio test as per Lowe's paper
    for (m,n) in matches:
        if m.distance < 0.7 * n.distance:
            count += 1

    if count > maxCount:
        maxCount = count
        maxMatch = i

print maxMatch

在编写此代码之前，我曾尝试创建一个kNN模型，该模型包含每个训练数据，并且只进行一次匹配.但是，它总是失败，并在"flann.add(descriptors)"处引起分段错误.

Before I wrote this code, I have tried to create a kNN model which contain every training data and do the match only once. However it always fail and cause segmentation fault at "flann.add(descriptors)".

import numpy as np
import cv2

trainfile = ['/home/nuntipat/Documents/Dataset/Bank/100_1.jpg', '/home/nuntipat/Documents/Dataset/Bank/100_2.jpg', '/home/nuntipat/Documents/Dataset/Bank/100_3.jpg']
testfile = '/home/nuntipat/Documents/Dataset/Bank/100_1.jpg'

# FLANN parameters
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks=50)   # or pass empty dictionary

# Initiate FLANN matcher
flann = cv2.FlannBasedMatcher(index_params, search_params)

# Initiate SURF detector
surf = cv2.xfeatures2d.SURF_create()

# Train FLANN
for file in trainfile:
    img = cv2.imread(file)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    keypoints, descriptors = surf.detectAndCompute(gray, None)

    flann.add(descriptors)

非常感谢您的帮助.

推荐答案

在此链接中，其内容如下:

It says here in this link the following:

flann.add([descriptors])

http://answers .opencv.org/question/44592/flann-index-in-python-training-fails-with-segfault/

希望有帮助！

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