How to load multiple images in a numpy array ?

Question

How to load pixels of multiple images in a directory in a numpy array . I have loaded a single image in a numpy array . But can not figure out how to load multiple images from a directory . Here what i have done so far

image = Image.open('bn4.bmp')
nparray=np.array(image)

This loads a 32*32 matrices . I want to load 100 of the images in a numpy array . I want to make 100*32*32 size numpy array . How can i do that ? I know that the structure would look something like this

for filename in listdir("BengaliBMPConvert"):
  if filename.endswith(".bmp"):
       -----------------
  else:
       continue

But can not find out how to load the images in numpy array

Answer 1

Getting a list of BMP files

To get a list of BMP files from the directory BengaliBMPConvert, use:

import glob
filelist = glob.glob('BengaliBMPConvert/*.bmp')

On the other hand, if you know the file names already, just put them in a sequence:

filelist = 'file1.bmp', 'file2.bmp', 'file3.bmp'

Combining all the images into one numpy array

To combine all the images into one array:

x = np.array([np.array(Image.open(fname)) for fname in filelist])

Pickling a numpy array

To save a numpy array to file using pickle:

import pickle
pickle.dump( x, filehandle, protocol=2 )

where x is the numpy array to be save, filehandle is the handle for the pickle file, such as open('filename.p', 'wb'), and protocol=2 tells pickle to use its current format rather than some ancient out-of-date format.

Alternatively, numpy arrays can be pickled using methods supplied by numpy (hat tip: tegan). To dump array x in file file.npy, use:

x.dump('file.npy')

To load array x back in from file:

x = np.load('file.npy')

For more information, see the numpy docs for dump and load.

Answer 2

Use OpenCV's imread() function together with os.listdir(), like

import numpy as np
import cv2
import os

instances = []

# Load in the images
for filepath in os.listdir('images/'):
    instances.append(cv2.imread('images/{0}'.format(filepath),0))

print(type(instances[0]))

class 'numpy.ndarray'

This returns you a list (==instances) in which all the greyscale values of the images are stored. For colour images simply set .format(filepath),1.

Answer 3

I just would like to share two sites where one can split a dataset into train, test and validation sets: split_folder and create numpy arrays out of images residing in respective folders code snippet from medium by muskulpesent