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OpenCV Python: sometimes segmentation error occurs when using FlannBasedMatcher

I am trying to classify objects using SURF and kNN. The code works well, but sometimes it crashes and shows "Segmentation Error". I'm not sure that I did something wrong, but I'm sure it is fixed. Here is the input file in case you want to reproduce the problem.

Link to download a dataset

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 paper
    for (m,n) in matches:
        if m.distance < 0.7 * n.distance:
            count += 1

    if count > maxCount:
        maxCount = count
        maxMatch = i

print maxMatch

Before I wrote this code, I tried to create a kNN model that contains all the training data and performs the match only once. However, it always fails and causes a segmentation error in "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)

Thank you very much for your help.

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