Question

我正在尝试创建一个用于CBIR任务的代码簿。一切顺利，直到我尝试执行kmeans，然后我

Traceback (most recent call last):
File "path", line 36, in <module>
scipy.cluster.vq.kmeans(descriptors, k_or_guess=500, iter=20, thresh=1e-05)
File "path", line 513, in kmeans
No = obs.shape[0]
AttributeError: 'list' object has no attribute 'shape'

如果不使用kmeans的scipy函数，我会使用

cv2.kmeans(descriptors, K=500, criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_MAX_ITER, 1, 10), attempts=1, flags=cv2.KMEANS_RANDOM_CENTERS)

我有

Traceback (most recent call last):
File "path", line 35, in <module>
cv2.kmeans(descriptors, K=500, criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_MAX_ITER, 1,  10), attempts=1, flags=cv2.KMEANS_RANDOM_CENTERS)
TypeError: data is not a numpy array, neither a scalar

到目前为止我的代码是：

from scipy.cluster.vq import *
import numpy as np
import glob
import cv2


#CB

#creating a list of images 
images = []
for infile in glob.glob('path'):
    pic = cv2.imread(infile)
    images.append(pic)

np.random.shuffle(images)
my_set = images

#split set
train = my_set[:120]
test = my_set[120:]

#get train descriptors
descriptors = [cv2.SIFT().detectAndCompute(pic, None) for pic in train]


#kmeans
scipy.cluster.vq.kmeans(desc, k_or_guess=1000, iter=20, thresh=1e-05)

#then indexing 
#then implement retrieval

似乎问题在于对象＆＃34;描述符＆＃34;这是一个列表清单。我试图将其转换为np.array，但这种方法也不起作用。我做错了什么或错过了什么？

Answer 1

好的，显然问题是通过改变代码而不是我所做的列表理解来解决的：

descriptors = np.array([])
for pic in train:
    kp, des = cv2.SIFT().detectAndCompute(pic, None)
    descriptors = np.append(descriptors, des)

desc = np.reshape(descriptors, (len(descriptors)/128, 128))
desc = np.float32(desc)

与cv2 kmeans函数一起使用。

Answer 2

对于python 3，应为：

descriptors = np.array([])
for pic in train:
    kp, des = cv2.SIFT().detectAndCompute(pic, None)
    descriptors = np.append(descriptors, des)

desc = np.reshape(descriptors, (len(descriptors)//128, 128)) # (notice the // here)
desc = np.float32(desc)

在火车上设置图像和筛选描述符的kmeans

2 个答案: