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I am trying to write a generic Python script that do the following types of tasks:

  1. Load .npy file (the .npy file is of shape (m_samples, channels, row, column), which corresponds tom_samples images, and here the channels are always 1
  2. Check whether a given path exists
  3. Iterate against the .npy file, and save each image into the given path

I am not sure whether I am using the best practices to save an nd array into an image.

import numpy as npfrom scipy.misc import toimage, imsaveimport osimage_path = "raw"def ensure_directory_exist(image_path):    if not os.path.exists(image_path):        print("Allocating '{:}'",format(image_path))        os.mkdir(image_path)if __name__ == '__main__':    img_array = np.load('imgs_mask_test.npy')    ensure_directory_exist(image_path)    for i in range(img_array.shape[0]):       name = "img"+str(i)+".png"       imsave(os.path.join(image_path,name),img_array[i,0])
Jamal's user avatar
Jamal
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askedNov 29, 2016 at 20:19
user288609's user avatar
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1 Answer1

2
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  1. Instead of:

    ensure_directory_exist(image_path)

    use the built-inos.makedirs:

    os.makedirs(image_path, exist_ok=True)

  2. Iterate over the images themselves (rather than their indexes) and so avoid the lookupimg_array[i, 0] on each iteration:

    for i, img in enumerate(img_array[:, 0, :, :]):    name = "img{}.png".format(i)    imsave(os.path.join(image_path, name), img)
answeredJan 1, 2017 at 9:45
Gareth Rees's user avatar
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