Convert a list of matrices to only one 2D matrix in Python

I have a list of 3960 matrices, which is simply the SIFT descriptors of 3960 images. This is supposed to result in a list of matrices with an unknown number of lines (which of course will depend on the image) and 128 columns (from SIFT descriptors). I am trying to put this list in just one 2D matrix, which the number of lines is the sum of the number of lines of these matrices and 128 columns, however, I am not being able to do that. Here is my code:

sift_keypoints =

#read images from a text file
with open(file_images) as f:
images_names = f.readlines()
images_names = [a.strip() for a in images_names]

for line in images_names:
print(line)
#read image
image = cv2.imread(line,1)
#Convert to grayscale
image =cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
#SIFT extraction
sift = cv2.xfeatures2d.SIFT_create()
kp, descriptors = sift.detectAndCompute(image,None)
#appending sift keypoints to a list
sift_keypoints.append(descriptors)

#here is what I've tried
sift_keypoints = np.asmatrix(np.asarray(sift_keypoints))


The sift_keypoints shape is (1,3960) according to this code, which is, of course, not what I want. How to transform this list in a 2D numpy array?

EDIT one simple example that illustrates my problem is the one in the following code

#how to convert this list to a matrix with shape (412,128)?
import numpy as np
x=np.zeros((256,128))
y=np.zeros((156,128))
list=
list.append(x)
list.append(y)


2 Answers
2

Use np.concatenate:

np.concatenate

>>> from pprint import pprint
>>> import numpy as np
>>>
>>> a = [np.full((2, 3), i) for i in range(3)]
>>> pprint(a)
[array([[0, 0, 0],
[0, 0, 0]]),
array([[1, 1, 1],
[1, 1, 1]]),
array([[2, 2, 2],
[2, 2, 2]])]
>>> np.concatenate(a, axis=0)
array([[0, 0, 0],
[0, 0, 0],
[1, 1, 1],
[1, 1, 1],
[2, 2, 2],
[2, 2, 2]])


np.row_stack

Suppose l is your list of Numpy arrays of the shape (n, 128).

l

(n, 128)

Suppose that m is the total number of lines: the object is to stack all the objects and create a matrix of the shape (m, 128).

m

(m, 128)

We can proceed as follows, using Numpy's row_stack:

row_stack

result = np.row_stack(l)


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